FGF4 retrogene on CFA12 is responsible for chondrodystrophy and intervertebral disc disease in dogs.

Chondrodystrophy in dogs is defined by dysplastic, shortened long bones and premature degeneration and calcification of intervertebral discs. Independent genome-wide association analyses for skeletal dysplasia (short limbs) within a single breed (PBonferroni = 0.01) and intervertebral disc disease (IVDD) across breeds (PBonferroni = 4.0 × 10-10) both identified a significant association to the same region on CFA12. Whole genome sequencing identified a highly expressed FGF4 retrogene within this shared region. The FGF4 retrogene segregated with limb length and had an odds ratio of 51.23 (95% CI = 46.69, 56.20) for IVDD. Long bone length in dogs is a unique example of multiple disease-causing retrocopies of the same parental gene in a mammalian species. FGF signaling abnormalities have been associated with skeletal dysplasia in humans, and our findings present opportunities for both selective elimination of a medically and financially devastating disease in dogs and further understanding of the ever-growing complexity of retrogene biology.

Chronic progressive lymphoedema in Friesian horses: suggestive phenotype of affected horses and genome-wide association study.

BACKGROUND: Chronic progressive lymphoedema (CPL) is a disabling condition affecting various draft horse breeds, including Friesian horses. HYPOTHESIS: The high incidence of CPL in Friesian horses suggests a genetic component and a predisposing phenotype. ANIMALS: For the genomic study, 26 affected and 19 control horses were evaluated. Body measurements were taken from 28 affected and nine control Friesian horses. METHODS AND MATERIALS: Axiom® Equine Genotyping Array with a total of 307,474 single nucleotide polymorphism (SNPs) was used for the case/control genome-wide association study (GWAS). Height, weight and leg measurements (seven different body measurements) were obtained and compared between groups. RESULTS: No significant associations were identified with the GWAS. There were significant differences (P < 0.05) in gaskin length measurements between CPL-affected horses and controls. CONCLUSION: The current study suggests that the potential genetic contribution to CPL in Friesian horses is complex and may include other SNP-associated factors. Significantly increased gaskin length of affected horses suggests a phenotype prone to developing CPL.

Evaluation of the major histocompatibility complex (MHC) class II as a candidate for sudden acquired retinal degeneration syndrome (SARDS) in Dachshunds.

OBJECTIVE: Sudden acquired retinal degeneration syndrome (SARDS) is one of the leading causes of acute blindness in dogs, with an unknown etiology and no effective treatment. Certain breeds such as Dachshunds are overrepresented among SARDS patients, and therefore, the syndrome is suspected to have a genetic component. The objective of this study was to determine if a genetic locus associated with SARDS in Dachshunds could be identified using a genome-wide association study (GWAS). PROCEDURES: Genome-wide association mapping was performed in 15 SARDS-affected and 16 unaffected Dachshunds. Genotyping of three classical DLA class II genes (DLA-DRB1, DLA-DQA1, and DLA-DQB1) was performed in 34 SARDS-affected and 66 unaffected Dachshunds to evaluate for an association in this region. RESULTS: Although no single nucleotide polymorphisms (SNPs) were of genome-wide statistical significance (PBonferroni  < 0.05), 5 of the top 9 SNPs were in the major histocompatibility complex (MHC). Using DLA typing, the allele DLA-DRB1*09401 was identified as a risk factor for the development of SARDS (P = 0.0032, OR = 4.0). The alleles DLA-DQB1*00101 (P = 0.0050, OR = 0.31), DLA-DQA1*00901 (P = 0.0087, OR = 0.33), and a previously identified DLA-DRB1allele described as "DRB1-T" (P = 0.0284, OR = 0.37) were identified as protective factors. CONCLUSIONS: Although far from definitive, association of SARDS with alleles of immunologic importance further supports the hypothesis that autoimmunity may play a role in the pathogenesis of SARDS.

Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network.

Cells utilize dynamic, network-level rearrangements in highly interconnected protein interaction networks to transmit and integrate information from distinct signaling inputs. Despite the importance of protein interaction network dynamics, the organizational logic underlying information flow through these networks is not well understood. Previously, we developed the quantitative multiplex co-immunoprecipitation platform, which allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins in shared complexes. Here, we adapt quantitative multiplex co-immunoprecipitation to define the activity-dependent dynamics of an 18-member protein interaction network in order to better understand the underlying principles governing glutamatergic signal transduction. We first establish that immunoprecipitation detected by flow cytometry can detect activity-dependent changes in two known protein-protein interactions (Homer1-mGluR5 and PSD-95-SynGAP). We next demonstrate that neuronal stimulation elicits a coordinated change in our targeted protein interaction network, characterized by the initial dissociation of Homer1 and SynGAP-containing complexes followed by increased associations among glutamate receptors and PSD-95. Finally, we show that stimulation of distinct glutamate receptor types results in different modular sets of protein interaction network rearrangements, and that cells activate both modules in order to integrate complex inputs. This analysis demonstrates that cells respond to distinct types of glutamatergic input by modulating different combinations of protein co-associations among a targeted network of proteins. Our data support a model of synaptic plasticity in which synaptic stimulation elicits dissociation of pre-existing multiprotein complexes, opening binding slots in scaffold proteins and allowing for the recruitment of additional glutamatergic receptors. Open Science: This manuscript was awarded with the Open Materials Badge. For more information see: https://cos.io/our-services/open-science-badges/.

The impact of resilience and subsequent stressful life events on MDD and GAD.

BACKGROUND: There remains a dearth of research examining the "buffering" effect of resilience, wherein resilience at one point in time would be expected to protect an individual against development of psychopathology following future adverse life events. METHODS: Using longitudinal data from an epidemiological twin sample (N = 7463), this study tested whether resilience would act as a buffer for stressful life events (SLEs) against risk for major depressive disorder (MDD) and generalized anxiety disorder (GAD). Resilience, demographics, and psychopathology were measured at Time 1 and recent SLEs and current MDD and GAD were measured at Time 2. RESULTS: Final models, controlling for demographic covariates and Time 1 diagnosis, examined the impact of Time 1 resilience, recent SLEs, their interaction, and a three-way interaction adding sex on MDD and GAD. CONCLUSIONS: The pattern of findings was the same for MDD and GAD, wherein main effects and two-way interactions of resilience and SLEs were significant, such that greater resilience was protective even in the context of high numbers of past-year SLEs. The three-way interaction was not significant, suggesting that the relationship between SLEs and resilience on psychopathology was the same for both men and women. Findings support the conceptualization of resilience as a buffer against the impact of future life stressors on common internalizing psychopathology. Longitudinal designs and trajectory-based studies that include recurring measures of SLEs could inform conceptualizations of resilience in the context of ongoing adversity and aid in developing interventions aimed at fostering healthy adaptation in the face of stressors.

Genetic analysis of optic nerve head coloboma in the Nova Scotia Duck Tolling Retriever identifies discordance with the NHEJ1 intronic deletion (collie eye anomaly mutation).

Collie eye anomaly (CEA) encompasses a spectrum of different ophthalmic phenotypes from clinically inconsequential choroidal hypoplasia to blindness from coloboma of the optic nerve head (ONH). A previous study found a 7.8-kb deletion in intron 4 of the NHEJ1 gene to be associated with CEA. A genetic test based on this association is recommended for many breeds, including the Nova Scotia Duck Tolling Retriever (NSDTR). Collection of ONH coloboma-affected NSDTR showed lack of concordance of the NHEJ1 intronic deletion with ONH coloboma. Using genomewide single nucleotide polymorphism (SNP) genotyping in 7 ONH coloboma-affected NSDTR cases and 47 unaffected NSDTR controls with no ophthalmic signs, one SNP, located on chromosome 7, demonstrated genomewide significance. However, high genomic inflation may have confounded the results. Therefore, the genomewide association study was repeated using EMMAX to control for population structure in the cohort of 7 cases and 47 controls. However, no regions of the genome were significantly associated with ONH coloboma. These results failed to document significant association with the CEA locus. Due to the complex genetic etiology of ONH coloboma, the NHEJ1 intronic deletion test results should be carefully considered when making breeding decisions. If the goal is to select for visually competent dogs, our data suggest that eye examinations of puppies would be more effective as a guide in selection of breeding pairs than relying solely on currently available genetic tests.

Early detection monitoring for aquatic non-indigenous species: Optimizing surveillance, incorporating advanced technologies, and identifying research needs.

Following decades of ecologic and economic impacts from a growing list of nonindigenous and invasive species, government and management entities are committing to systematic early- detection monitoring (EDM). This has reinvigorated investment in the science underpinning such monitoring, as well as the need to convey that science in practical terms to those tasked with EDM implementation. Using the context of nonindigenous species in the North American Great Lakes, this article summarizes the current scientific tools and knowledge - including limitations, research needs, and likely future developments - relevant to various aspects of planning and conducting comprehensive EDM. We begin with the scope of the effort, contrasting target-species with broad-spectrum monitoring, reviewing information to support prioritization based on species and locations, and exploring the challenge of moving beyond individual surveys towards a coordinated monitoring network. Next, we discuss survey design, including effort to expend and its allocation over space and time. A section on sample collection and analysis overviews the merits of collecting actual organisms versus shed DNA, reviews the capabilities and limitations of identification by morphology, DNA target markers, or DNA barcoding, and examines best practices for sample handling and data verification. We end with a section addressing the analysis of monitoring data, including methods to evaluate survey performance and characterize and communicate uncertainty. Although the body of science supporting EDM implementation is already substantial, research and information needs (many already actively being addressed) include: better data to support risk assessments that guide choice of taxa and locations to monitor; improved understanding of spatiotemporal scales for sample collection; further development of DNA target markers, reference barcodes, genomic workflows, and synergies between DNA-based and morphology-based taxonomy; and tools and information management systems for better evaluating and communicating survey outcomes and uncertainty.

Genome mapping in intensively studied wild vertebrate populations.

Over the past decade, long-term studies of vertebrate populations have been the focus of many quantitative genetic studies. As a result, we have a clearer understanding of why some fitness-related traits are heritable and under selection, but are apparently not evolving. An exciting extension of this work is to identify the genes underlying phenotypic variation in natural populations. The advent of next-generation sequencing and high-throughput single nucleotide polymorphism (SNP) genotyping platforms means that mapping studies are set to become widespread in those wild populations for whom appropriate phenotypic data and DNA samples are available. Here, we highlight the progress made in this area and define evolutionary genetic questions that have become tractable with the arrival of these new genomics technologies.

High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging.

Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified >18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand-receptor interactions. Data on >800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset .

A Missense Variant in ALDH5A1 Associated with Canine Succinic Semialdehyde Dehydrogenase Deficiency (SSADHD) in the Saluki Dog.

Dogs provide highly valuable models of human disease due to the similarity in phenotype presentation and the ease of genetic analysis. Seven Saluki puppies were investigated for neurological abnormalities including seizures and altered behavior. Magnetic resonance imaging showed a diffuse, marked reduction in cerebral cortical thickness, and symmetrical T2 hyperintensity in specific brain regions. Cerebral cortical atrophy with vacuolation (status spongiosus) was noted on necropsy. Genome-wide association study of 7 affected and 28 normal Salukis revealed a genome-wide significantly associated region on CFA 35. Whole-genome sequencing of three confirmed cases from three different litters revealed a homozygous missense variant within the aldehyde dehydrogenase 5 family member A1 (ALDH5A1) gene (XM_014110599.2: c.866G>A; XP_013966074.2: p.(Gly288Asp). ALDH5A1 encodes a succinic semialdehyde dehydrogenase (SSADH) enzyme critical in the gamma-aminobutyric acid neurotransmitter (GABA) metabolic pathway. Metabolic screening of affected dogs showed markedly elevated gamma-hydroxybutyric acid in serum, cerebrospinal fluid (CSF) and brain, and elevated succinate semialdehyde in urine, CSF and brain. SSADH activity in the brain of affected dogs was low. Affected Saluki dogs had striking similarities to SSADH deficiency in humans although hydroxybutyric aciduria was absent in affected dogs. ALDH5A1-related SSADH deficiency in Salukis provides a unique translational large animal model for the development of novel therapeutic strategies.

Muscle-Specific Sensitivity to Voluntary Physical Activity and Detraining.

Aerobic physical activity triggers adaptations in skeletal muscle including a fast-to-slow shift in myosin heavy chain (MHC) isoforms, an enhanced capillary network, and mitochondrial biogenesis to meet increased demands placed upon this tissue. Although the magnitude of these responses appears to be dependent on muscle phenotype as well as training volume and/or intensity, the whole-muscle response to detraining remains mostly unexplored. Here, we hypothesized that the shifts toward slower MHC phentotype and the increased capillarity and mitochondrial oxidative markers induced with training would return toward sedentary (SED) control levels sooner in the fast plantaris than in the slow soleus muscle as a result of detraining. Soleus and plantaris muscles from 8-week (TR 8wk) voluntarily running adult female Sprague-Dawley rats were compared to muscles from SED and detrained rats (DETR) (4 weeks voluntary running followed by 4 weeks of reduced activity), which were subdivided into low- (DETR Lo) and high-running-distance (DETR Hi) groups. We show that maintaining the fast-to-slow MHC isoform shift required consistent aerobic training in the soleus and plantaris muscles: detraining clearly abolished any fast-to-slow gains in the plantaris, whereas the training volume in DETR Hi rats appeared to influence the MHC return to basal levels in the soleus. Total capillary number (per mm2) in the plantaris increased in all groups compared to SED levels, but, in the soleus, this enhancement was observed only in the TR 8wk rats. Generally, increased mitochondrial markers for aerobicitiy were observed in TR 8wk plantaris, but not soleus, muscles. In a second experiment, we show that the muscle-specific adaptations were similar after 4 weeks of voluntary exercise (TR 4wk) as in 4 weeks (TR 8wk). Taken together, our findings suggest that the plantaris muscle is more sensitive to voluntary physical activity and detraining than the soleus muscle; these results also demonstrate that the soleus muscle requires a greater aerobic challenge (i.e., intensity, duration) to trigger phenotypic, angiogenic, or aerobic enzyme adaptations. Our findings generally suggest that muscular aerobic fitness to voluntary running, or its loss during detraining, manifests as changes occurring primarily within fast, rather than slow, muscle phenotypes.

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Activity-dependent changes in synaptic protein complex composition are consistent in different detergents despite differential solubility.

At the post-synaptic density (PSD), large protein complexes dynamically form and dissociate in response to synaptic activity, comprising the biophysical basis for learning and memory. The use of detergents to isolate the PSD and release its membrane-associated proteins complicates studies of these activity-dependent protein interaction networks, because detergents can simultaneously disrupt the very interactions under study. Despite widespread recognition that different detergents yield different experimental results, the effect of detergent on activity-dependent synaptic protein complexes has not been rigorously examined. Here, we characterize the effect of three detergents commonly used to study synaptic proteins on activity-dependent protein interactions. We first demonstrate that SynGAP-containing interactions are more abundant in 1% Deoxycholate (DOC), while Shank-, Homer- and mGluR5-containing interactions are more abundant in 1% NP-40 or Triton. All interactions were detected preferentially in high molecular weight complexes generated by size exclusion chromatography, although the detergent-specific abundance of proteins in high molecular weight fractions did not correlate with the abundance of detected interactions. Activity-dependent changes in protein complexes were consistent across detergent types, suggesting that detergents do not isolate distinct protein pools with unique behaviors. However, detection of activity-dependent changes is more or less feasible in different detergents due to baseline solubility. Collectively, our results demonstrate that detergents affect the solubility of individual proteins, but activity-dependent changes in protein interactions, when detectable, are consistent across detergent types.

Optimization and performance testing of a sequence processing pipeline applied to detection of nonindigenous species.

Genetic taxonomic assignment can be more sensitive than morphological taxonomic assignment, particularly for small, cryptic or rare species. Sequence processing is essential to taxonomic assignment, but can also produce errors because optimal parameters are not known a priori. Here, we explored how sequence processing parameters influence taxonomic assignment of 18S sequences from bulk zooplankton samples produced by 454 pyrosequencing. We optimized a sequence processing pipeline for two common research goals, estimation of species richness and early detection of aquatic invasive species (AIS), and then tested most optimal models' performances through simulations. We tested 1,050 parameter sets on 18S sequences from 20 AIS to determine optimal parameters for each research goal. We tested optimized pipelines' performances (detectability and sensitivity) by computationally inoculating sequences of 20 AIS into ten bulk zooplankton samples from ports across Canada. We found that optimal parameter selection generally depends on the research goal. However, regardless of research goal, we found that metazoan 18S sequences produced by 454 pyrosequencing should be trimmed to 375-400 bp and sequence quality filtering should be relaxed (1.5 ≤ maximum expected error ≤ 3.0, Phred score = 10). Clustering and denoising were only viable for estimating species richness, because these processing steps made some species undetectable at low sequence abundances which would not be useful for early detection of AIS. With parameter sets optimized for early detection of AIS, 90% of AIS were detected with fewer than 11 target sequences, regardless of whether clustering or denoising was used. Despite developments in next-generation sequencing, sequence processing remains an important issue owing to difficulties in balancing false-positive and false-negative errors in metabarcoding data.

Clustering the autisms using glutamate synapse protein interaction networks from cortical and hippocampal tissue of seven mouse models.

Background: Autism spectrum disorders (ASDs) are a heterogeneous group of behaviorally defined disorders and are associated with hundreds of rare genetic mutations and several environmental risk factors. Mouse models of specific risk factors have been successful in identifying molecular mechanisms associated with a given factor. However, comparisons among different models to elucidate underlying common pathways or to define clusters of biologically relevant disease subtypes have been complicated by different methodological approaches or different brain regions examined by the labs that developed each model. Here, we use a novel proteomic technique, quantitative multiplex co-immunoprecipitation or QMI, to make a series of identical measurements of a synaptic protein interaction network in seven different animal models. We aim to identify molecular disruptions that are common to multiple models. Methods: QMI was performed on 92 hippocampal and cortical samples taken from seven mouse models of ASD: Shank3B, Shank3Δex4-9, Ube3a2xTG, TSC2, FMR1, and CNTNAP2 mutants, as well as E12.5 VPA (maternal valproic acid injection on day 12.5 post-conception). The QMI panel targeted a network of 16 interacting, ASD-linked, synaptic proteins, probing 240 potential co-associations. A custom non-parametric statistical test was used to call significant differences between ASD models and littermate controls, and Hierarchical Clustering by Principal Components was used to cluster the models using mean log2 fold change values. Results: Each model displayed a unique set of disrupted interactions, but some interactions were disrupted in multiple models. These tended to be interactions that are known to change with synaptic activity. Clustering revealed potential relationships among models and suggested deficits in AKT signaling in Ube3a2xTG mice, which were confirmed by phospho-western blots. Conclusions: These data highlight the great heterogeneity among models, but suggest that high-dimensional measures of a synaptic protein network may allow differentiation of subtypes of ASD with shared molecular pathology.

Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?

DNA metabarcoding is a promising method for describing communities and estimating biodiversity. This approach uses high-throughput sequencing of targeted markers to identify species in a complex sample. By convention, sequences are clustered at a predefined sequence divergence threshold (often 3%) into operational taxonomic units (OTUs) that serve as a proxy for species. However, variable levels of interspecific marker variation across taxonomic groups make clustering sequences from a phylogenetically diverse dataset into OTUs at a uniform threshold problematic. In this study, we use mock zooplankton communities to evaluate the accuracy of species richness estimates when following conventional protocols to cluster hypervariable sequences of the V4 region of the small subunit ribosomal RNA gene (18S) into OTUs. By including individually tagged single specimens and "populations" of various species in our communities, we examine the impact of intra- and interspecific diversity on OTU clustering. Communities consisting of single individuals per species generated a correspondence of 59-84% between OTU number and species richness at a 3% divergence threshold. However, when multiple individuals per species were included, the correspondence between OTU number and species richness dropped to 31-63%. Our results suggest that intraspecific variation in this marker can often exceed 3%, such that a single species does not always correspond to one OTU. We advocate the need to apply group-specific divergence thresholds when analyzing complex and taxonomically diverse communities, but also encourage the development of additional filtering steps that allow identification of artifactual rRNA gene sequences or pseudogenes that may generate spurious OTUs.

Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods.

Metabarcoding has the potential to become a rapid, sensitive, and effective approach for identifying species in complex environmental samples. Accurate molecular identification of species depends on the ability to generate operational taxonomic units (OTUs) that correspond to biological species. Due to the sometimes enormous estimates of biodiversity using this method, there is a great need to test the efficacy of data analysis methods used to derive OTUs. Here, we evaluate the performance of various methods for clustering length variable 18S amplicons from complex samples into OTUs using a mock community and a natural community of zooplankton species. We compare analytic procedures consisting of a combination of (1) stringent and relaxed data filtering, (2) singleton sequences included and removed, (3) three commonly used clustering algorithms (mothur, UCLUST, and UPARSE), and (4) three methods of treating alignment gaps when calculating sequence divergence. Depending on the combination of methods used, the number of OTUs varied by nearly two orders of magnitude for the mock community (60-5068 OTUs) and three orders of magnitude for the natural community (22-22191 OTUs). The use of relaxed filtering and the inclusion of singletons greatly inflated OTU numbers without increasing the ability to recover species. Our results also suggest that the method used to treat gaps when calculating sequence divergence can have a great impact on the number of OTUs. Our findings are particularly relevant to studies that cover taxonomically diverse species and employ markers such as rRNA genes in which length variation is extensive.

Genome-wide association analysis identifies a mutation in the thiamine transporter 2 (SLC19A3) gene associated with Alaskan Husky encephalopathy.

Alaskan Husky Encephalopathy (AHE) has been previously proposed as a mitochondrial encephalopathy based on neuropathological similarities with human Leigh Syndrome (LS). We studied 11 Alaskan Husky dogs with AHE, but found no abnormalities in respiratory chain enzyme activities in muscle and liver, or mutations in mitochondrial or nuclear genes that cause LS in people. A genome wide association study was performed using eight of the affected dogs and 20 related but unaffected control AHs using the Illumina canine HD array. SLC19A3 was identified as a positional candidate gene. This gene controls the uptake of thiamine in the CNS via expression of the thiamine transporter protein THTR2. Dogs have two copies of this gene located within the candidate interval (SLC19A3.2 - 43.36-43.38 Mb and SLC19A3.1 - 43.411-43.419 Mb) on chromosome 25. Expression analysis in a normal dog revealed that one of the paralogs, SLC19A3.1, was expressed in the brain and spinal cord while the other was not. Subsequent exon sequencing of SLC19A3.1 revealed a 4bp insertion and SNP in the second exon that is predicted to result in a functional protein truncation of 279 amino acids (c.624 insTTGC, c.625 C>A). All dogs with AHE were homozygous for this mutation, 15/41 healthy AH control dogs were heterozygous carriers while 26/41 normal healthy AH dogs were wild type. Furthermore, this mutation was not detected in another 187 dogs of different breeds. These results suggest that this mutation in SLC19A3.1, encoding a thiamine transporter protein, plays a critical role in the pathogenesis of AHE.