Project description:Microsatellite DNA sequences display allele length alterations or microsatellite instability (MSI) in tumor tissues, and MSI is used diagnostically for tumor detection and classification. We discuss the known types of tumor-specific MSI patterns and the relevant mechanisms underlying each pattern. Mutation rates of individual microsatellites vary greatly, and the intrinsic DNA features of motif size, sequence, and length contribute to this variation. MSI is used for detecting mismatch repair (MMR)-deficient tumors, which display an MSI-high phenotype due to genome-wide microsatellite destabilization. Because several pathways maintain microsatellite stability, tumors that have undergone other events associated with moderate genome instability may display diagnostic MSI only at specific di- or tetranucleotide markers. We summarize evidence for such alternative MSI forms (A-MSI) in sporadic cancers, also referred to as MSI-low and EMAST. While the existence of A-MSI is not disputed, there is disagreement about the origin and pathologic significance of this phenomenon. Although ambiguities due to PCR methods may be a source, evidence exists for other mechanisms to explain tumor-specific A-MSI. Some portion of A-MSI tumors may result from random mutational events arising during neoplastic cell evolution. However, this mechanism fails to explain the specificity of A-MSI for di- and tetranucleotide instability. We present evidence supporting the alternative argument that some A-MSI tumors arise by a distinct genetic pathway, and give examples of DNA metabolic pathways that, when altered, may be responsible for instability at specific microsatellite motifs. Finally, we suggest that A-MSI in tumors could be molecular signatures of environmental influences and DNA damage. Importantly, A-MSI occurs in several pre-neoplastic inflammatory states, including inflammatory bowel diseases, consistent with a role of oxidative stress in A-MSI. Understanding the biochemical basis of A-MSI tumor phenotypes will advance the development of new diagnostic tools and positively impact the clinical management of individual cancers.

Project description:In Huntington’s disease (HD), an expanded CAG repeat produces characteristic striatal neurodegeneration. Interestingly, the HD CAG repeat, whose length determines age at onset, undergoes tissue-specific somatic instability, predominant in the striatum, suggesting that tissue-specific CAG length changes could modify the disease process. Therefore, understanding the mechanisms underlying the tissue specificity of somatic instability may provide novel routes to therapies. However progress in this area has been hampered by the lack of sensitive high-throughput instability quantification methods and global approaches to identify the underlying factors. Here we describe a novel approach to gain insight into the factors responsible for the tissue specificity of somatic instability. Using accurate genetic knock-in mouse models of HD, we developed a reliable, high-throughput method to quantify tissue HD CAG instability and integrated this with genome-wide bioinformatic approaches. Using tissue instability quantified in 16 tissues as a phenotype and tissue microarray gene expression as a predictor, we built a mathematical model and identified a gene expression signature that accurately predicted tissue instability. Using the predictive ability of this signature we found that somatic instability was not a consequence of pathogenesis. In support of this, genetic crosses with models of accelerated neuropathology failed to induce somatic instability. In addition, we searched for genes and pathways that correlated with tissue instability. We found that expression levels of DNA repair genes did not explain the tissue specificity of somatic instability. Instead, our data implicate other pathways, particularly cell cycle, metabolism and neurotransmitter pathways, acting in combination to generate tissue-specific patterns of instability. Our study clearly demonstrates that multiple tissue factors reflect the level of somatic instability in different tissues. In addition, our quantitative, genome-wide approach is readily applicable to high-throughput assays and opens the door to widespread applications with the potential to accelerate the discovery of drugs that alter tissue instability. Mouse striatum and cerebellum, 10 weeks old, Affymetrix MG430 2.0 arrays, gcRMA.

Project description:In Huntington’s disease (HD), an expanded CAG repeat produces characteristic striatal neurodegeneration. Interestingly, the HD CAG repeat, whose length determines age at onset, undergoes tissue-specific somatic instability, predominant in the striatum, suggesting that tissue-specific CAG length changes could modify the disease process. Therefore, understanding the mechanisms underlying the tissue specificity of somatic instability may provide novel routes to therapies. However progress in this area has been hampered by the lack of sensitive high-throughput instability quantification methods and global approaches to identify the underlying factors. Here we describe a novel approach to gain insight into the factors responsible for the tissue specificity of somatic instability. Using accurate genetic knock-in mouse models of HD, we developed a reliable, high-throughput method to quantify tissue HD CAG instability and integrated this with genome-wide bioinformatic approaches. Using tissue instability quantified in 16 tissues as a phenotype and tissue microarray gene expression as a predictor, we built a mathematical model and identified a gene expression signature that accurately predicted tissue instability. Using the predictive ability of this signature we found that somatic instability was not a consequence of pathogenesis. In support of this, genetic crosses with models of accelerated neuropathology failed to induce somatic instability. In addition, we searched for genes and pathways that correlated with tissue instability. We found that expression levels of DNA repair genes did not explain the tissue specificity of somatic instability. Instead, our data implicate other pathways, particularly cell cycle, metabolism and neurotransmitter pathways, acting in combination to generate tissue-specific patterns of instability. Our study clearly demonstrates that multiple tissue factors reflect the level of somatic instability in different tissues. In addition, our quantitative, genome-wide approach is readily applicable to high-throughput assays and opens the door to widespread applications with the potential to accelerate the discovery of drugs that alter tissue instability.

Project description:Background: Postural instability is a debilitating and largely treatment-resistant symptom of Parkinson's disease (PD). A better understanding of the neural substrates contributing to postural instability could lead to new targets for improved pharmacological and neurosurgical interventions. However, investigating these neural substrates necessitates the use of functional MRI scanners, which are almost exclusively horizontally-based. Objective: We aimed to develop, and validate the use of, an MRI compatible balance simulator to study static and dynamic balance control in PD patients and elderly controls. Methods: Our MRI compatible balance simulator allowed participants to actively balance an inverted pendulum by activating postural muscles around the ankle joint while supine. Two studies were performed to compare static and dynamic balance performance between upright stance and simulated stance in PD patients and controls. Study 1 (14 PD; 20 controls) required participants to maintain static balance during upright and simulated stance for 120 s with eyes open and closed. In study 2 (20 PD; 22 controls) participants repeated the static balance task (80 s, eyes closed only), and also completed a dynamic balance task which required maintaining balance while experiencing random anterior-posterior perturbations applied to the trunk/pendulum. Postural sway of the body/pendulum was measured using an angular velocity sensor (SwayStarTM, study 1) and Optotrak motion capture (study 2). Outcome measures were amplitude and frequency of center of mass sway for static balance, and peak and time-to-peak of center of mass displacement and velocity for dynamic balance. Results: PD patients had larger sway amplitude during both upright and simulated static balance compared to controls. PD patients had larger peak and time-to-peak sway, and larger time-to-peak sway velocity, during simulated, but not upright, dynamic balance compared to controls. Conclusions: Deficits in static and dynamic balance control can be detected in PD patients using a novel MRI compatible balance simulator. This technique allows for functional neuroimaging to be combined with balance-relevant tasks, and provides a new means to create insights into the neural substrates contributing to postural instability in PD.

Project description:BackgroundApproach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion function, we evaluated whether keypress responses toward pictures with potential motivational value produced any regular patterns, such as a trade-off between approach and avoidance, or recurrent lawful patterns as observed with prospect theory.Methodology/principal findingsThree sets of experiments employed this task with beautiful face images, a standardized set of affective photographs, and pictures of food during controlled states of hunger and satiety. An iterative modeling approach to data identified multiple law-like patterns, based on variables grounded in the individual. These patterns were consistent across stimulus types, robust to noise, describable by a simple power law, and scalable between individuals and groups. Patterns included: (i) a preference trade-off counterbalancing approach and avoidance, (ii) a value function linking preference intensity to uncertainty about preference, and (iii) a saturation function linking preference intensity to its standard deviation, thereby setting limits to both.Conclusions/significanceThese law-like patterns were compatible with critical features of prospect theory, the matching law, and alliesthesia. Furthermore, they appeared consistent with both mean-variance and expected utility approaches to the assessment of risk. Ordering of responses across categories of stimuli demonstrated three properties thought to be relevant for preference-based choice, suggesting these patterns might be grouped together as a relative preference theory. Since variables in these patterns have been associated with reward circuitry structure and function, they may provide a method for quantitative phenotyping of normative and pathological function (e.g., psychiatric illness).

Dataset Information

A novel multiplexed, image-based approach to detect phenotypes that underlie chromosome instability in human cells.

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A novel multiplexed, image-based approach to detect phenotypes that underlie chromosome instability in human cells.

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