Expression profiling in peripheral blood reveals signature for penetrance in DYT1 dystonia

Quantification of Behavioral Deficits in Developing Mice With Dystonic Behaviors

Converging evidence from structural imaging studies in patients, the function of dystonia-causing genes, and the comorbidity of neuronal and behavioral defects all suggest that pediatric-onset dystonia is a neurodevelopmental disorder. However, to fully appreciate the contribution of altered development to dystonia, a mechanistic understanding of how networks become dysfunctional is required for early-onset dystonia. One current hurdle is that many dystonia animal models are ideally suited for studying adult phenotypes, as the neurodevelopmental features can be subtle or are complicated by broad developmental deficits. Furthermore, most assays that are used to measure dystonia are not suited for developing postnatal mice. Here, we characterize the early-onset dystonia in Ptf1a ^Cre ;Vglut2 ^fl/fl mice, which is caused by the absence of neurotransmission from inferior olive neurons onto cerebellar Purkinje cells. We investigate motor control with two paradigms that examine how altered neural function impacts key neurodevelopmental milestones seen in postnatal pups (postnatal day 7-11). We find that Ptf1a ^Cre ;Vglut2 ^fl/fl mice have poor performance on the negative geotaxis assay and the surface righting reflex. Interestingly, we also find that Ptf1a ^Cre ;Vglut2 ^fl/fl mice make fewer ultrasonic calls when socially isolated from their nests. Ultrasonic calls are often impaired in rodent models of autism spectrum disorders, a condition that can be comorbid with dystonia. Together, we show that these assays can serve as useful quantitative tools for investigating how neural dysfunction during development influences neonatal behaviors in a dystonia mouse model. Our data implicate a shared cerebellar circuit mechanism underlying dystonia-related motor signs and social impairments in mice.

BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm

MOTIVATION

In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale.

RESULTS

BatchI algorithm is based on partitioning a series of high-throughput experiment samples into sub-series corresponding to estimated batches. The dynamic programming method is used for splitting data with maximal dispersion between batches, while maintaining minimal within batch dispersion. The procedure has been tested on a number of available datasets with and without prior information about batch partitioning. Datasets with a priori identified batches have been split accordingly, measured with weighted average Dice Index. Batch effect correction is justified by higher intra-group correlation. In the blank datasets, identified batch divisions lead to improvement of parameters and quality of biological information, shown by literature study and Information Content. The outcome of the algorithm serves as a starting point for correction methods. It has been demonstrated that omitting the essential step of batch effect control may lead to waste of valuable potential discoveries.

AVAILABILITY AND IMPLEMENTATION

The implementation is available within the BatchI R package at http://zaed.aei.polsl.pl/index.php/pl/111-software.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Optogenetic augmentation of the hypercholinergic endophenotype in DYT1 knock-in mice induced erratic hyperactive movements but not dystonia

BACKGROUND

The most prevalent inherited form of generalized dystonia is caused by a mutation in torsinA (DYT1, ∆GAG) with incomplete penetrance. Rodent models with mutated torsinA do not develop dystonic symptoms, but previous ex vivo studies indicated abnormal excitation of cholinergic interneurons (ChI) and increased striatal acetylcholine.

METHODS

We used in vivo optogenetics to exacerbate this endophenotype in order to determine its capacity to trigger dystonic symptoms in freely behaving mice. Tor1a^+/Δgag DYT1 mice and wildtype littermates expressing channelrhodopsin2 under the Chat promotor were implanted bilaterally with optical LED cannulae and stimulated with blue light pulses of varied durations.

FINDINGS

Six months old DYT1 KI mice but not wildtype controls responded with hyperactivity to blue light specifically at 25 ms pulse duration, 10 Hz frequency. Neuronal activity (c-Fos) in cholinergic interneurons was increased immediately after light stimulation and persisted only in DYT1 KI over 15 min. Substance P was increased specifically in striosome compartments in naïve DYT1 KI mice compared to wildtype. Under optogenetic stimulation substance P increased in wildtype to match levels in Dyt1 KI, and acetylcholinesterase was elevated in the striatum of stimulated DYT1 KI. No signs of dystonic movements were observed under stimulation of up to one hour in both genotypes and age groups, and the sensorimotor deficit previously observed in 6 months old DYT1 KI mice persisted under stimulation.

INTERPRETATION

Overall this supports an endophenotype of dysregulated cholinergic activity in DYT1 dystonia, but depolarizing cholinergic interneurons was not sufficient to induce overt dystonia in DYT1 KI mice.

Structure of the Golgi apparatus is not influenced by a GAG deletion mutation in the dystonia-associated gene Tor1a

Autosomal-dominant, early-onset DYT1 dystonia is associated with an in-frame deletion of a glutamic acid codon (ΔE) in the TOR1A gene. The gene product, torsinA, is an evolutionarily conserved AAA+ ATPase. The fact that constitutive secretion from patient fibroblasts is suppressed indicates that the ΔE-torsinA protein influences the cellular secretory machinery. However, which component is affected remains unclear. Prompted by recent reports that abnormal protein trafficking through the Golgi apparatus, the major protein-sorting center of the secretory pathway, is sometimes associated with a morphological change in the Golgi, we evaluated the influence of ΔE-torsinA on this organelle. Specifically, we examined its structure by confocal microscopy, in cultures of striatal, cerebral cortical and hippocampal neurons obtained from wild-type, heterozygous and homozygous ΔE-torsinA knock-in mice. In live neurons, the Golgi was assessed following uptake of a fluorescent ceramide analog, and in fixed neurons it was analyzed by immuno-fluorescence staining for the Golgi-marker GM130. Neither staining method indicated genotype-specific differences in the size, staining intensity, shape or localization of the Golgi. Moreover, no genotype-specific difference was observed as the neurons matured in vitro. These results were supported by a lack of genotype-specific differences in GM130 expression levels, as assessed by Western blotting. The Golgi was also disrupted by treatment with brefeldin A, but no genotype-specific differences were found in the immuno-fluorescence staining intensity of GM130. Overall, our results demonstrate that the ΔE-torsinA protein does not drastically influence Golgi morphology in neurons, irrespective of genotype, brain region (among those tested), or maturation stage in culture. While it remains possible that functional changes in the Golgi exist, our findings imply that any such changes are not severe enough to influence its morphology to a degree detectable by light microscopy.

Factors affecting the accuracy of a class prediction model in gene expression data

BACKGROUND

Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer.

RESULTS

Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation.

CONCLUSIONS

We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models.

The pathogenic human Torsin A in Drosophila activates the unfolded protein response and increases susceptibility to oxidative stress

Background

Dystonia1 (DYT1) dystonia is caused by a glutamic acid deletion (ΔE) mutation in the gene encoding Torsin A in humans (HTorA). To investigate the unknown molecular and cellular mechanisms underlying DYT1 dystonia, we performed an unbiased proteomic analysis.

Results

We found that the amount of proteins and transcripts of an Endoplasmic reticulum (ER) resident chaperone Heat shock protein cognate 3 (HSC3) and a mitochondria chaperone Heat Shock Protein 22 (HSP22) were significantly increased in the HTorA^ΔE– expressing brains compared to the normal HTorA (HTorA^WT) expressing brains. The physiological consequences included an increased susceptibility to oxidative and ER stress compared to normal HTorA^WT flies. The alteration of transcripts of Inositol-requiring enzyme-1 (IRE1)-dependent spliced X box binding protein 1(Xbp1), several ER chaperones, a nucleotide exchange factor, Autophagy related protein 8b (ATG8b) and components of the ER associated degradation (ERAD) pathway and increased expression of the Xbp1-enhanced Green Fluorescence Protein (eGFP) in HTorA^ΔE brains strongly indicated the activation of the unfolded protein response (UPR). In addition, perturbed expression of the UPR sensors and inducers in the HTorA^ΔEDrosophila brains resulted in a significantly reduced life span of the flies. Furthermore, the types and quantities of proteins present in the anti-HSC3 positive microsomes in the HTorA^ΔE brains were different from those of the HTorA^WT brains.

Conclusion

Taken together, these data show that HTorA^ΔE in Drosophila brains may activate the UPR and increase the expression of HSP22 to compensate for the toxic effects caused by HTorA^ΔE in the brains.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1518-0) contains supplementary material, which is available to authorized users.

Evaluation of gene expression classification studies: factors associated with classification performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Genetic issues in the diagnosis of dystonias

Dystonias are heterogeneous hyperkinetic movement disorders characterized by involuntary muscle contractions which result in twisting and repetitive movements and abnormal postures. Several causative genes have been identified, but their genetic bases still remain elusive. Primary Torsion Dystonias (PTDs), in which dystonia is the only clinical sign, can be inherited in a monogenic fashion, and many genes and loci have been identified for autosomal dominant (DYT1/TOR1A; DYT6/THAP1; DYT4/TUBB4a; DYT7; DYT13; DYT21; DYT23/CIZ1; DYT24/ANO3; DYT25/GNAL) and recessive (DYT2; DYT17) forms. However most sporadic cases, especially those with late-onset, are likely multifactorial, with genetic and environmental factors interplaying to reach a threshold of disease. At present, genetic counseling of dystonia patients remains a difficult task. Recently non-motor clinical findings in dystonias, new highlights in the pathophysiology of the disease, and the availability of high-throughput genome-wide techniques are proving useful tools to better understand the complexity of PTD genetics. We briefly review the genetic basis of the most common forms of hereditary PTDs, and discuss relevant issues related to molecular diagnosis and genetic counseling.

The c.-237_236GA>TT THAP1 sequence variant does not increase risk for primary dystonia

BACKGROUND

Sequence variants in coding and noncoding regions of THAP1 have been associated with primary dystonia.

METHODS

In this study, 1,446 Caucasian subjects with mainly adult-onset primary dystonia and 1,520 controls were genotyped for a variant located in the 5'-untranslated region of THAP1 (c.-237_236GA>TT).

RESULTS

Minor allele frequencies were 62/2892 (2.14%) and 55/3040 (1.81%) in subjects with dystonia and controls, respectively (P=0.202). Subgroup analyses by gender and anatomical distribution also failed to attain statistical significance. In addition, there was no effect of the TT variant on expression levels of THAP1 transcript or protein.

DISCUSSION

Our findings indicate that the c.-237_236GA>TT THAP1 sequence variant does not increase risk for adult-onset primary dystonia in Caucasians.

Molecular pathways in dystonia

The hereditary dystonias comprise a set of diseases defined by a common constellation of motor deficits. These disorders are most likely associated with different molecular etiologies, many of which have yet to be elucidated. Here we discuss recent advances in three forms of hereditary dystonia, DYT1, DYT6 and DYT16, which share a similar clinical picture: onset in childhood or adolescence, progressive spread of symptoms with generalized involvement of body regions and a steady state affliction without treatment. Unlike DYT1, the genes responsible for DYT6 and DYT16 have only recently been identified, with relatively little information about the function of the encoded proteins. Nevertheless, recent data suggest that these proteins may fit together within interacting pathways involved in dopaminergic signaling, transcriptional regulation, and cellular stress responses. This review focuses on these molecular pathways, highlighting potential common themes among these dystonias which may serve as areas for future research. This article is part of a Special Issue entitled "Advances in dystonia".

Advances in the genetics of primary torsion dystonia

Knowledge about the genetics of primary torsion dystonia (PTD) has been progressing at a very slow pace compared with other movement disorders. For many years, only one causative gene was known, DYT1/TOR1A, yet the recent identification of a second PTD causative gene (DYT6/THAP1), the detection of subclinical alterations caused by mutations in PTD genes in some healthy non-penetrant individuals, and functional studies on TOR1A and THAP1 protein products have significantly improved mutation detection, genotype-phenotype correlates, and our understanding of the cellular mechanisms underlying the development of dystonia.