Genetic deletion of transglutaminase 2 does not rescue the phenotypic deficits observed in R6/2 and zQ175 mouse models of Huntington's disease.

Huntington's disease (HD) is an autosomal dominant, progressive neurodegenerative disorder caused by expansion of CAG repeats in the huntingtin gene. Tissue transglutaminase 2 (TG2), a multi-functional enzyme, was found to be increased both in HD patients and in mouse models of the disease. Furthermore, beneficial effects have been reported from the genetic ablation of TG2 in R6/2 and R6/1 mouse lines. To further evaluate the validity of this target for the treatment of HD, we examined the effects of TG2 deletion in two genetic mouse models of HD: R6/2 CAG 240 and zQ175 knock in (KI). Contrary to previous reports, under rigorous experimental conditions we found that TG2 ablation had no effect on either motor or cognitive deficits, or on the weight loss. In addition, under optimal husbandry conditions, TG2 ablation did not extend R6/2 lifespan. Moreover, TG2 deletion did not change the huntingtin aggregate load in cortex or striatum and did not decrease the brain atrophy observed in either mouse line. Finally, no amelioration of the dysregulation of striatal and cortical gene markers was detected. We conclude that TG2 is not a valid therapeutic target for the treatment of HD.

Increased Body Weight of the BAC HD Transgenic Mouse Model of Huntington's Disease Accounts for Some but Not All of the Observed HD-like Motor Deficits.

The genome of the Bacterial Artificial Chromosome (BAC) transgenic mouse model of Huntington's Disease (BAC HD) contains the 170 kb human HTT locus modified by the addition of exon 1 with 97 mixed CAA-CAG repeats. BAC HD mice present robust behavioral deficits in both the open field and the accelerating rotarod tests, two standard behavioral assays of motor function. BAC HD mice, however, also typically present significantly increased body weights relative to wildtype littermate controls (WT) which potentially confounds the interpretation of any motor deficits associated directly with the effects of mutant huntingtin. In order to evaluate this possible confound of body weight, we directly compared the performance of BAC HD and WT female mice under food restricted versus free feeding conditions in both the open field and rotarod tasks to test the hypothesis that some of the motor deficits observed in this HTT-transgenic mouse line results solely from increased body weight. Our results suggest that the rotarod deficit exhibited by BAC HD mice is modulated by both body weight and non-body weight factors resulting from overexpression of full length mutant Htt. When body weights of WT and BAC HD transgenic mice were normalized using restricted feeding, the deficits exhibited by BAC HD mice on the rotarod task were less marked, but were still significant. Since the rotarod deficit between WT and BAC HD mice is attenuated when body weight is normalized by food restriction, utilization of this task in BAC HD mice during pre-clinical evaluation must be powered accordingly and results carefully considered as therapeutic benefit can result from decreased overall body weight and or motoric improvement that may not be related to body mass. Furthermore, after controlling for body weight differences, the hypoactive phenotype displayed by ad libitum fed BAC HD mice in the open field assay was not observed in the BAC HD mice undergoing food restriction. These findings suggest that assessment of spontaneous locomotor activity, as measured in the open field test, may not be the appropriate behavioral endpoint to evaluate the BAC HD mouse during preclinical evaluation since it appears that the apparent hypoactive phenotype in this model is driven primarily by body weight differences.

High-Throughput Automated Phenotyping of Two Genetic Mouse Models of Huntington's Disease.

Phenotyping with traditional behavioral assays constitutes a major bottleneck in the primary screening, characterization, and validation of genetic mouse models of disease, leading to downstream delays in drug discovery efforts. We present a novel and comprehensive one-stop approach to phenotyping, the PhenoCube™. This system simultaneously captures the cognitive performance, motor activity, and circadian patterns of group-housed mice by use of home-cage operant conditioning modules (IntelliCage) and custom-built computer vision software. We evaluated two different mouse models of Huntington's Disease (HD), the R6/2 and the BACHD in the PhenoCube™ system. Our results demonstrated that this system can efficiently capture and track alterations in both cognitive performance and locomotor activity patterns associated with these disease models. This work extends our prior demonstration that PhenoCube™ can characterize circadian dysfunction in BACHD mice and shows that this system, with the experimental protocols used, is a sensitive and efficient tool for a first pass high-throughput screening of mouse disease models in general and mouse models of neurodegeneration in particular.

Prediction of death in the SMNΔ7 mouse model of spinal muscular atrophy: insight into disease stage and progression.

Proximal Spinal Muscular Atrophy (SMA) is a debilitating neuromuscular disease and a leading inherited genetic cause of infant death. To date, there is no effective treatment for SMA. The SMNΔ7 neonatal mouse model of SMA recapitulates key features of the severe form of SMA and remains a valuable tool in preclinical drug discovery. At any particular postnatal age (P), the disease progression in the SMNΔ7 mouse model is not universal, as some animals die as early as the day of birth and others live for up to three weeks. Identification of the disease stage in SMNΔ7 mice, independent of age, would aid in the design and interpretation of preclinical studies. We developed a score (CD score), derived from body weight analysis, that allowed us to gain insight into the disease progression and predict death. Respiratory complication is a leading cause of mortality in the SMA patient and this phenotype has been reported in severe mouse models of SMA. We subsequently measured muscle and brain tissue lactate levels, an indirect measure of hypoxia, in SMNΔ7 mice at P10 and correlated these measures to respiratory rate. SMNΔ7 mice showed a significant increase in tissue lactate and a decrease in respiratory rate in comparison to control. The CD score correlates linearly with tissue lactate level and respiratory rate. The finding of lactate buildup in the SMNΔ7 mouse and the correlation with a score that is predictive of disease stage provide an interesting insight into the disease pathophysiology and a possible biomarker for SMA.

Comprehensive behavioral and molecular characterization of a new knock-in mouse model of Huntington's disease: zQ175.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder characterized by motor, cognitive and psychiatric manifestations. Since the mutation responsible for the disease was identified as an unstable expansion of CAG repeats in the gene encoding the huntingtin protein in 1993, numerous mouse models of HD have been generated to study disease pathogenesis and evaluate potential therapeutic approaches. Of these, knock-in models best mimic the human condition from a genetic perspective since they express the mutation in the appropriate genetic and protein context. Behaviorally, however, while some abnormal phenotypes have been detected in knock-in mouse models, a model with an earlier and more robust phenotype than the existing models is required. We describe here for the first time a new mouse line, the zQ175 knock-in mouse, derived from a spontaneous expansion of the CAG copy number in our CAG 140 knock-in colony [1]. Given the inverse relationship typically observed between age of HD onset and length of CAG repeat, since this new mouse line carries a significantly higher CAG repeat length it was expected to be more significantly impaired than the parent line. Using a battery of behavioral tests we evaluated both heterozygous and homozygous zQ175 mice. Homozygous mice showed motor and grip strength abnormalities with an early onset (8 and 4 weeks of age, respectively), which were followed by deficits in rotarod and climbing activity at 30 weeks of age and by cognitive deficits at around 1 year of age. Of particular interest for translational work, we also found clear behavioral deficits in heterozygous mice from around 4.5 months of age, especially in the dark phase of the diurnal cycle. Decreased body weight was observed in both heterozygotes and homozygotes, along with significantly reduced survival in the homozygotes. In addition, we detected an early and significant decrease of striatal gene markers from 12 weeks of age. These data suggest that the zQ175 knock-in line could be a suitable model for the evaluation of therapeutic approaches and early events in the pathogenesis of HD.

Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.

BACKGROUND: Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm). METHODOLOGY/PRINCIPAL FINDINGS: This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races. CONCLUSIONS: The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease.

Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival.

BACKGROUND: We have engaged in an international program designated the Bank On A Cure, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma. We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, n = 70) versus long term progression-free survivors (greater than 3 years, n = 73) in two phase III clinical trials. RESULTS: Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes. CONCLUSION: A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.

Genomic messaging system language including command extensions for clinical data categories.

This paper, in the area of clinical bioinformatics, highlights relatively efficient means of storing, exchanging, protecting, and searching human and other genomic data, so as to make the data securely accessible to researchers while respecting patient privacy. One important idea is that the GMSL language can be considered as an extension of the way DNA and protein sequences are written so as to carry with them the wishes of the patient in regard to fine-grained consent (as well as retaining the medical experts' cautions, instructions for use, and annotation), and this is carried, whatever environment (e.g., XML) that the data is from or whatever it is going to. At the deepest level, a stream of data expressed in GMSL resembles highly compressed stream of self-checking machine code. For the reader less familiar with the computational aspects, some simple examples illustrate how the raw language looks and works as a raw stream of (interpreted) bytes. The bioinformatics applications are not confined to the clinical domain. This paper completes the initial specification of the language as previously presented and reports on some important extensions including clinical data categories.

Genomic messaging system and DNA mark-up language for information-based personalized medicine with clinical and proteome research applications.

The convergence of clinical medicine and the Life Sciences, commencing with opportunities in clinical trials and clinically linked medical research, presents many novel challenges. The Genomic Messaging System (GMS) described here was originally developed as a tool for assembling clinical genomic records of individual and collective patients, and was then generalized to become a flexible workflow component that will link clinical records to a variety of computational biology research tools, for research and ultimately for a more personalized, focused, and preventative healthcare system. Prominent among the applications linked are protein science applications, including the rapid automated modeling of patient proteins with their individual structural polymorphisms. In an initial study, GMS formed the basis of a fully automated system for modeling patient proteins with structural polymorphisms as a basis for drug selection and ultimately design on an individual patient basis.

Clinical and pharmacogenomic data mining: 2. A simple method for the combination of information from associations and multivariances to facilitate analysis, decision, and design in clinical research and practice.

The physician and researcher must ultimately be able to combine qualitative and quantitative features from a variety of combinations of observations on data of many component items (i.e., many dimensions), and hence reach simple conclusions about interpretation, rational courses of action, and design. In the first paper of this series, it was noted that such needs are challenging the classical means of using statistics. Hence, the paper proposed the use of a Generalized Theory of Expected Information or "Zeta Theory". The conjoint event [a,b,c,..] is seen as a rule of association for a,b,c,.. associated with a rule strength I(a;b;c;...) = xi(s,o[a,b,c,..]) - xi (s,e[a,b,c,...]), where xi is the incomplete Zeta Function. Here, o[a,b,c,...] is the observed, and e[a,b,c,..] the expected, frequency of occurrence of conjoint event [a,b,c,...]. The present paper explores how output from this approach might be assembled in a form better suited for decision support. Related to this is the difficulty that the treatment of covariance and multivariance was previously rendered as a "fuzzy association" so that the output would fall into a similar form as the true associations, but this was a somewhat ad hoc approach in which only the final I( ) had any meaning. Users at clinical research sites had subsequently requested an alternative approach in which "effective frequencies" o[ ] and e[ ] calculated from the above variances and used to evaluate I( ) give some intuitive feeling analogous to the association treatment, and this is explored here. Though the present paper is theoretical, real examples are used to illustrate application. One clinical-genomic example illustrates experimental design by identifying data which is, or is not, statistically germane to the study. We also report on some impressions based on applying these techniques in studies of real, extensive patient record data which are now emerging, as well as on molecular design data originally studied in part to test the ability to deduce the effects of simple natural patient sequence variations ("SNPs") on patient protein activity. On the basis of these study experiences, methods of rationalizing and condensing the rules implied by associations and variances between data, as well as discussion of the difficulty of what is meant by "condensed", are presented in the Appendix.