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Article

Differential Gene Expression Associated with Idiopathic Epilepsy in Belgian Shepherd Dogs

by
Nathan Kinsey
,
Janelle M. Belanger
and
Anita M. Oberbauer
*
Department of Animal Science, University of California, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Genes 2024, 15(11), 1474; https://doi.org/10.3390/genes15111474
Submission received: 26 October 2024 / Revised: 9 November 2024 / Accepted: 9 November 2024 / Published: 15 November 2024
(This article belongs to the Special Issue The Role of RNA Regulation in Development and Disease)
Browse Figures
Versions Notes

Abstract

:
Background: Idiopathic epilepsy (IE) disproportionately affects Belgian shepherd dogs and although genomic risk markers have been identified previously in the breed, causative variants have not been described. Methods: The current study analyzed differences in whole blood RNA expression associated with IE and with a previously identified IE risk haplotype on canine chromosome (CFA) 14 using a transcriptomics RNA-seq approach. Results: MFSD2A and a likely pseudogene of RPL19, both of which are genes implicated in seizure activity, were upregulated in dogs with IE. Genes in the interferon signaling pathway were downregulated in Belgian shepherds with IE. The CFA14 risk haplotype was associated with upregulation of CLIC1, ACE2, and PIGN and downregulation of EPDR1, all known to be involved with epilepsy or the Wnt/β-catenin signaling pathway. Conclusions: These results highlight the value of assessing gene expression in canine IE research to uncover genomic contributory factors.

1. Introduction

In the domestic dog (Canis lupus familiaris), one of the most common neurological disorders is epilepsy [1]. Epilepsy, defined as repeated seizures, is stressful for both dog and owner and, if symptoms are severe, can lead to euthanasia [2,3]. The expense of diagnosis places a large burden on dog owners and treatment options may cause dangerous side effects while frequently failing to control seizures [4]. Idiopathic epilepsy (IE), a form of epilepsy characterized by genetic inheritance, is observed in most, if not all, dog breeds with certain breeds having a higher incidence than average [5]. Relative to other dog breeds, the prevalence of IE is considered elevated in the Belgian Sheepdog (BS) and Belgian Tervuren (BT), with estimates of 7.5% in both [6,7].
Genome-wide association studies (GWAS) have been undertaken in various dog breeds, including the Dutch Partridge Dog [8], Petit Basset Griffon vendéen [9], Irish Wolfhound [10], and Belgian Shepherd [11,12], with the goal of identifying the underlying genetic cause of IE. The relatively high prevalence of IE in Belgian Shepherds has led investigators to focus efforts on BS and BT dogs. Those studies have revealed risk regions on canine chromosome (CFA) 14 [12] and CFA 37 [13]. On CFA 14, an ACTG risk haplotype was identified near RAPGEF5, a gene proposed to have neurological function and whose downregulation has been associated with focal seizures in rats [14,15]. The ACTG haplotype is associated with a 3-base pair insertion in exon 1 of the RAPGEF5 gene and has been shown to disrupt cellular localization [16,17]. The regions identified on CFA 37 are near ADAM23 [18,19] and KLF7 [20,21,22], genes implicated in canine epilepsy and neuronal development, respectively, although causative variant(s) have not been identified. Despite the identification of these risk loci, they only account for a small proportion of IE risk in dogs. A transcriptomics approach may reveal altered expression profiles reflecting underlying genetic changes leading to the discovery of additional mutations associated with canine IE. Using transcriptomics to profile the expression patterns of the genome has facilitated characterizing the impacts of many genetic variants simultaneously in other neurological conditions such as autism spectrum disorder [23]. Furthermore, functional impacts of genetic variants in intergenic regions and non-coding RNAs, including microRNA (miRNA), short interfering RNA (siRNA), and long non-coding RNA (lncRNA), can also be assessed through transcriptomic analyses [24,25].
The present study used transcriptomics to identify risk loci contributing to IE in the Belgian Shepherd. Gene expression analysis of Belgian Shepherds was undertaken to determine genes altered by IE status and if the risk haplotype on CFA 14, previously identified by GWAS, was associated with altered RNA expression thereby defining a functional impact of that risk variant. The findings provided insight into metabolic pathways that may underly IE in the dog.

2. Results

2.1. Differential RNA-Seq Expression Analysis

Gene abundance estimates plotted by PCA show the BS dogs clustering more closely than the BT dogs along the first two principal components (Figure 1). Sex did not appear to cluster in this way. To account for breed variety’s correlation with the principal components, it was included as a covariate in the differential expression models [26] while sex was not.
Ten genes had significant differential expression (FDR-corrected p-value < 0.10) in the blood samples of IE cases versus healthy controls, with two being upregulated and eight being downregulated in IE cases (Table 1). The upregulated genes were ENSCAFG00000004857 and MFSD2A with 7.6- and 1.6-fold increased expression in IE cases, respectively. Genes that were downregulated included the novel gene ENSCAFG00000002440, exhibiting a 20-fold reduction in expression, MAST4 with a 3.5-fold reduction, and six others with smaller observed reductions in dogs with IE. A pathway enrichment analysis of the differentially expressed genes revealed that the interferon (IFN) signaling pathway was overrepresented (FDR-corrected p-value < 0.01). Specifically, five of the downregulated genes, HERC5, HERC6, RSAD2, DDX58, and IFI44, all participate in IFN signaling [27], several of which, HERC5, HERC6, and IFI44, have been implicated in seizures and neurological development [28,29]. Genes associated with the previously identified risk loci on CFA 14 and CFA 37, that is RAPGEF5, ADAM23, and KLF7, did not demonstrate differential expression between IE cases and healthy controls (Table 1). The full gene expression data from both Kallisto and Salmon models are presented in an additional file (Table S1).
When only considering dogs with and without the risk genotype, five genes were upregulated and one was downregulated (FDR-corrected p-value < 0.10) in dogs with the ACTG risk haplotype when compared to dogs without the haplotype (Table 2). The full gene expression data from the haplotype analysis are presented in an additional file (Table S2). The expression of ADAM23, KLF7, and RAPGEF5, the three genes previously implicated in IE risk, was unchanged in dogs with and without the ACTG risk haplotype.

2.2. RT-qPCR Validation

To reinforce the RNA-seq results, a subset of the three differentially expressed genes, HERC5, IFI44, and MFSD2A, as well as RAPGEF5 were selected for RT-qPCR validation in a different cohort of dogs. Genes involved in the IFN pathway that were selected for confirmation were those specifically implicated as having a role in neurological function. HERC5 was selected because two indicated genes from the HERC family were found to be downregulated in the present study and large and small HERC proteins are directly associated with seizures and neurological development, respectively [29]. IFI44 was selected due to its association with febrile seizures in humans [28] and MFSD2A was selected for its role in brain development and blood-brain barrier function [30,31]. All three selected genes had a mean fold change expression that matched the direction of regulation indicated by the RNA-seq expression analysis (Figure S1). Specifically, HERC5 and IFI44 demonstrated downregulation, whereas MFSD2A demonstrated upregulation. The expression pattern of RAPGEF5 did not exhibit high amounts of differential expression (only a slight upregulation) between IE cases and healthy controls, again validating the RNA-seq observation.

2.3. Whole Genome Sequencing

Using the differentially expressed genes to drive the investigation of potential IE causal variants in the whole genome sequence, the 1 Mb regions surrounding the top ten differentially expressed genes were found to have a total of 305 different sequence variants (p < 0.01) between IE cases and healthy controls (Table 3). While a majority of those variants do not have a predicted impact on protein function or gene regulation (Table S3), twelve variants are purported to affect regions that control gene expression in humans. There were no impactful variants detected in the regions associated with genes differentially expressed in dogs having the ACTG risk haplotype.
In a splice region for MFSD2A (CFA15: 3226673), five of the controls were homozygous for a single nucleotide deletion, with the remaining two controls being heterozygous. The IE dogs, in contrast, had numerous homozygous reference genotypes (50%), a moderate number of heterozygotes (38%), and a single dog homozygous for the deletion. In the first intron of GAB2, a gene near ENSCAFG00000004857, all the IE cases were heterozygous or homozygous for a 5-bp deletion (CFA21: 20310516), while only two of the healthy controls were heterozygous for the variant, and the remainder were homozygous reference (75%). A single nucleotide deletion in an intergenic region near both HERC5 and HERC6 (CFA32: 30585125) was disproportionally prevalent in IE cases (66% homozygous and 33% heterozygous) and seen only as heterozygotes in healthy controls. In a 400,000 bp intergenic region upstream of RSAD2, nine variants were heterozygous in all but one IE case (89%) and homozygous reference in all healthy controls (Table S3). Additionally, two variants (CFA17: 4413952 and CFA17: 4413996) have a single-nucleotide substitution in the 5′ UTR of RSAD2, creating premature start codons; these were observed in all but one of the IE cases (22% homozygous for the substitution and 66% heterozygous), with only one of the healthy controls heterozygous (12.5%) and the remainder homozygous reference.

3. Discussion

In this study, transcriptomics expression analysis identified genes with significantly differential expression between healthy and IE dogs. Additionally, genes were differentially regulated in Belgian Shepherd dogs having the CFA 14 ACTG haplotype shown to be associated with IE risk [12]. As the present study is the first to investigate differential mRNA expression in IE dogs, these results suggest novel gene associations for canine epilepsy, including genes involved in immune signaling and neural development. Many of the genes exhibiting differential expression have been shown in previous human and mouse studies to be associated with seizures [31,32,33,34], epilepsy [35], and neurological development [30].
Two genes, ENSCAFG00000004857 and MFSD2A, exhibited increased expression in IE cases when compared to controls. A BLAST of the ENSCAFG00000004857 sequence suggests that it is likely a pseudogene of RPL19 [36], a ribosomal protein [37] that has elevated expression in epileptic rat models [37]. Additionally, ENSCAFG00000004857 lies within an intron of INTS4. No variants that could account for the differential expression were found when comparing IE cases and controls in INTS4 or in ENSCAFG00000004857, suggesting the existence of variants upstream of the genes themselves. The upregulation of MFSD2A in IE dogs warrants further investigation due to its established role in the development of the blood-brain barrier in mice [30]. While the increased expression was modest, with a log-fold change of 1.59, disruptions to proper molecular transport across this barrier have been associated with neurological disorders such as Alzheimer’s and Parkinson’s disease [38]. Furthermore, MFSD2A gene mutations in humans have been associated with microcephaly, a disease that alters brain size and can result in seizures [31,32]. The splice region variant in MFSD2A detected in the present study provides a potential explanation of its impact on the gene’s differential regulation, as splice region variants are known to affect gene expression [39], although the genotypic frequency of the splice variant between IE dogs and controls does not suggest that it is a major factor of IE.
Pathway analyses revealed that the IFN signaling pathway was significantly enriched by differentially expressed genes in IE dogs, suggesting a link between epilepsy and immune function. The elevated expression of genes associated with the immune system may reflect the direct risk for IE. For instance, dysfunctional HERC proteins that participate in IFN signaling have been shown to cause Purkinje cell death and deficits in neurotransmission [29]. Alternatively, the elevated expression may reflect the sequelae of seizure activity and the body’s adaptive response.
A great many studies have identified a role for the immune system in epileptogenesis [40,41] including canine IE [42]. This is exemplified by the fact that immunoglobulin therapy has been shown to have anticonvulsant effects for several seizure disorders [43]. The present study supports previous canine proteomic profiling which indicates that immunity proteins are the most impacted pathway in canine epilepsy [44]. Given that many of the downregulated genes identified in this study participate in the IFN pathway, this suggests that there may be a high-level inhibition of the pathway impacting multiple signaling steps. In addition to its activation in response to viral infection [45], the IFN signaling pathway is triggered by lipopolysaccharides, retinoic acid (RA), and genotoxic substances [46]. The RA trigger is of particular interest due to its antiepileptogenic properties [47]; if the retinoid metabolic pathway was disrupted, it may both reduce activation of the IFN signaling pathway and increase seizure activity (Figure 2). A BLAST of pseudogene, ENSCAFG00000002440 indicates a high synteny to AKR1B1 in multiple species [36]. AKR1B1 may contribute to the regulation of retinoic acid, and a downregulation of ENSCAFG00000002440 as observed in the present study could disrupt retinoid metabolism by altering the regulation of retinoid receptors [48]. Retinols are thought to lessen seizure activity through their inhibition of gap junction and synaptic signaling and voltage-gated calcium ion channels [47]. Importantly, AKR1B1 is duplicated in the dog genome relative to the wolf genome and has a suggested role in domestication, with the duplicated version assuming new functional roles in fatty acid synthesis and managing oxidation [49].
Retinoic acid increases extracellular IFN secretion [51,52], and IFN then binds to receptors to activate a complex of signal transducer and activator of transcription (STAT) proteins and interferon regulatory factor (IRF) [46]. This complex induces transcription at interferon-sensitive response element (ISRE) promoter regions that regulate the expression of interferon-stimulated genes (ISGs) [46]. Aside from participating in IFN signaling, RA has multiple mechanisms of controlling cellular communication in neurons that reduce epileptogenesis [47]. Specifically, RA interacts with neuronal G proteins, which can inhibit calcium flow across voltage-gated calcium ion channels, altering neurotransmitter release [53]. Additionally, RA binds with retinoic acid receptor (RAR)-like binding sites, signaling gap junction connexins to close [54] thereby decreasing seizure activity [55].
Pathway analysis did not detect a connection among the five genes upregulated in dogs with the ACTG risk haplotype. One upregulated gene, CLIC1, encodes a chloride ion channel. This is a promising candidate for IE as the misregulation of chloride homeostasis has been shown to affect the GABAergic neurotransmission signaling in the brain, which can lead to temporal lobe epilepsy in humans [56,57,58]. Additionally, the upregulated genes, CLIC1 and ACE2, and the downregulated gene, EPDR1, have been shown to participate in the Wnt/β-catenin signaling [59,60,61], a pathway that may help to control seizure activity [62] and is the pathway potentially affected by the RAPGEF5 insertion variant associated with the ACTG risk variant [17]. Notwithstanding that RAPGEF5 expression was unaltered in dogs having the ACTG risk haplotype, an alteration in the expression of other genes might be an attempt to compensate for a reduction in the functionality of the RAPGEF5 protein. Another gene upregulated, albeit minimally altered in expression, the role of PIGN is in anchoring proteins to blood cells [63], and mutations in the gene have been linked to various epilepsy phenotypes including generalized seizures in children [64]. The two remaining upregulated genes associated with the ACTG risk haplotype, RHEX and SLC45A3, do not have a clear connection to either epilepsy or the Wnt/β-catenin signaling pathway.
The majority of the differentially expressed genes associated with the IE phenotype were downregulated. Pathway analysis identified the IFN signaling pathway as enriched with the genes significantly downregulated in IE dogs. The genetic variants identified in the whole genome sequence found near the differentially expressed genes could affect the expression of genes with IFN and neurological function. Three of the five genes downregulated in this way, HERC5, HERC6, and IFI44, have been previously associated with neurological development and seizures [28,29]. The variants found near HERC5 and HERC6 lie in a region associated with a locus known in humans to regulate the expression of XBP1 [65]. Alterations in XBP1 expression are implicated in neurological disorders including epilepsy [66,67]. Additionally, XBP1 is involved in immune cell development and differentiation, including direct binding to IFN promoter regions [68]. The variant nearest the upregulated ENSCAFG00000004857 is an insertion in the first intron of GAB2 and is predicted to be a proximal enhancer in humans [65]. Disrupting a potential GAB2 enhancer may affect IFN signaling by modulating the expression of GAB2, a protein known to compete for binding sites on IFN receptors [69]. Additionally, GAB2 has known associations with seizures, and is downregulated in rats with temporal lobe epilepsy [70]. Although in the present study, ENSCAFG00000004857 expression was elevated, expression of GAB2 was not determined as being elevated or reduced in the IE dogs. The GAB2 insertion is near the adjacent gene NARS2, which has often been associated with epilepsies in human infants and children [71,72,73]. Of note is that the region adjacent to ENSCAFG00000004857 is annotated by NCBI as NARS2 and GAB2, although the Ensembl annotation of Dog10K Boxer Tasha represents it as one gene, NARS2 [36,74]. Both genes have been implicated in epilepsy and the variants identified coupled with the altered gene expression offer clues into the mechanism of IE development.
The nine variants identified upstream of RSAD2 occur in a region known to regulate human expression of KLF6, ELF3, and SPDEF [65] and thus may play a role in both IFN gene regulation and neurology by affecting these regulatory factors. KLF6 is a paralog of KLF7, a gene previously associated with canine IE and neural development [20,21,22]. ELF3 and SPDEF have been implicated in the upregulation of the IFN regulatory factor 6 [75] and inhibit gene expression in the IFN pathway [76], respectively. These variants may serve to connect IFN pathway regulation observed in the present study and the underlying neurology of IE.
The most downregulated gene in IE cases, ENSCAFG00000002440, is a pseudogene with high synteny to AKR1B1 [74], a gene known to regulate retinoid acid levels [48]. Additionally, AKR1B1 has been shown to induce prostaglandin PGF2α when signaled by COX1/COX2 [77]. It is possible that ENSCAFG00000002440 may share a similar function. While multiple studies have implicated the COX/prostaglandin pathway in epilepsies, they suggest that PGF2α protects against seizure activity [33] and deficient PGF2α increases seizure susceptibility [34]. Given this, the downregulation of a gene acting similarly to AKR1B1 could be expected to increase seizure risk, as suggested by the observed association in the present study, although there were no identified variants within ENSCAFG00000002440 that could account for the downregulation. Furthermore, despite ENSCAFG00000002440 being 3.9 MB away from the previously identified RAPGEF5 IE risk markers, there is no clear evidence that these loci interact; genomic regions have not been observed to influence expression if greater than 2–3 MB away [78]. Future investigation may seek to confirm if ENSCAFG00000002440 participates in the same pathway that AKR1B1 induces.
As this study used mRNA to assess genomic expression, future work may determine if there is differential expression of regulatory RNAs in IE dogs, including miRNA, siRNA, and lncRNA. This may help characterize genetic variants previously associated with IE, as well as describe the mechanisms for differential gene expression identified in this study. Furthermore, while the differentially expressed genes in whole blood significantly associate IE with immune function in the Belgian Shepherd, the present study may be limited by the sample source. In this study, RNA was extracted from blood due to the impracticality of collecting brain tissue samples including which neural tissue to use and temporal timing of collection. Despite blood effectively serving as a proxy tissue for identifying differentially expressed genes in human epilepsies [79,80], the results may not fully reflect the variety of expression differences between IE and healthy dogs in all tissue types. Similarly, genes previously associated with IE in Belgian Shepherds such as RAPGEF5, KLF7, and ADAM23 may have altered expression in IE dogs that can only be observed in neural tissues, despite their expression in canine blood [81], or these previously associated genes may not have altered transcription but their impacts may be due to changed localization [17] or protein structure. However, it is also possible that the observed IE-associated expression differences would not be detectable in many other tissues, reaffirming the utility of blood samples in epilepsy research.
The observed expression differences are valuable for understanding mechanisms associated with IE and reaffirm the role of the immune system in canine IE. The downregulation of particular genes in the immune pathway offer targets for further research to determine a causal relationship and if the altered expression pattern increases seizure risk or if these genes are a byproduct of past seizure activity. Future work may monitor gene regulation in IE individuals over time to differentiate changes in expression resulting from repeated seizures. This is especially relevant given the presence of variants that have the predicted role of altering gene expression with putative roles in epileptogenesis. Further research may also explore if the observed downregulation of IFN signaling is due to a link between IE and the pathway itself or one of the pathway precursors such as retinoic acid.

4. Materials and Methods

4.1. RNA Preparation and Sequencing

Dogs defined as having IE (n = 9, cases) had multiple generalized seizures with disease onset after one year of age (mean 4.63 years old). Sample phenotypes were classified based on previously described criteria [12] and in brief, IE diagnosis followed guidelines set by the International Veterinary Epilepsy Task Force for a Tier I confidence level diagnosis [82]. This includes an unremarkable veterinary examination of neurology, blood, and urine in IE dogs. Healthy control dogs (n = 7, controls) were at least 7 years of age (mean 10.47 years old) with no reported health issues. While dogs within phenotype groups (IE and healthy) were unrelated at the grandparent level, relatedness was allowed across phenotype groups. Dogs were genotyped for the CFA 14 risk haplotype to ensure a relative balance of breed variety across all haplotypes (Table 4). Two additional dogs with focal seizures were included in the risk haplotype analysis that were not present in the generalized IE analysis. Power calculations were performed using the Scotty web tool and default parameters [83] to determine that five replicates per treatment group were sufficient to detect differential gene expression between the affected and healthy groups. Moreover, recently published studies in dogs have successfully assessed differential gene expression using similar or fewer replicates per study group [84,85].
RNA-seq analyses of blood have been used to study genomic expression in human epilepsies revealing mutations associated with IE [79,80]. Whole blood was collected from 16 Belgian shepherds (5 BS = 4 F, 1 M; 11 BT = 6 F, 5 M) in DNA/RNA shield blood collection tubes, which can keep RNA stable for up to 5 days at room temperature (Zymo Research, Irvine, CA, USA), and stored at −80 °C. Unprocessed whole blood samples were then sent to Novogene (Beijing, China) in the collection tubes for mRNA extraction and sequencing. Globin mRNA was depleted from the blood using GLOBINclear (Invitrogen, Waltham, MA, USA) to improve exonic coverage. PolyA capture was used to isolate mRNA which was then randomly fragmented and primed. Second-strand cDNA was marked to enable directional sequencing following adenylation and adapter ligation [86]. Fragments with an insert size of 150 bases were selected for sequencing. The extracted mRNA was then sequenced using the NovaSeq PE150 platform (Illumina, San Diego, CA, USA). Sequencing quality was validated with MultiQC (1.10.1) and FastQC (0.11.9) software to assess base sequence quality scores, GC content distribution, and sequence duplication and overrepresentation [87,88]. Adapter sequences were trimmed from sequence reads and bases with a quality score below 2 were trimmed with a sliding window of four bases using Trimmomatic (0.39) [89].

4.2. Read Quantification and Differential Expression Analysis

Ensembl’s transcriptome of the Dog10K Boxer Tasha genome assembly was used as a reference for quantification [74,90]. Transcript quantification was performed on the trimmed reads using both Kallisto (0.46.2) and Salmon (1.3.0) [91,92]. Kallisto was run using default parameters and Salmon was run using the sequence and GC bias correcting options, which is recommended for improving accuracy in differential expression analyses [92]. Kallisto and Salmon yield concordant results with the advantage that Kallisto is more rapid and has better performance with correlation statistics [93]. The transcript quantification estimates were summarized to the gene level using the tximport (1.26.1) and GenomicFeatures (1.50.4) R packages [94,95,96] with default parameters. Principal component analysis (PCA) was performed on the gene abundance estimates using the DESeq2 R package to determine if breed variety or sex were responsible for any large variation in the data, and if so, these variables could be accounted for in the modeling step [94,97]. Two different differential expression models were built in DESeq2 (1.38.3) comparing expression differences between healthy controls and IE cases as well as dogs with the ACTG risk haplotype and dogs without the ACTG risk haplotype [98,99,100]. Models included covariates that were identified by PCA as contributing to a large amount of variation in the data. The models performed a Wald test with the null hypothesis assuming that the log fold change between treatment groups was equal to zero. A correction for false discovery rate (FDR) was performed using the Benjamini–Hochberg method as previously described [101] for all genes that have expression in blood, accounting for false positive genes that are not truly differentially expressed between groups. Significance was defined as having a corrected p-value less than 0.10 [102,103]; this permissive cutoff was chosen to maximize the genes used for pathway analysis, while avoiding a high FDR with the additional correction outlined below.
Genes with statistically significant gene expression between IE cases and controls were compiled for pathway analysis using the Reactome (90) web tool to identify enriched biological pathways [27]. For each pathway indicated by these genes, a binomial test was performed with the null hypothesis assuming a random distribution of genes across known pathways. A correction for FDR was performed for each known pathway using the Benjamini–Hochberg method [101], accounting for false positive pathways that are not truly enriched by differentially expressed genes. Significance for pathway analysis was defined as having a corrected p-value less than 0.05.

4.3. Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)

Genes that were expressed at significantly differential levels were chosen for RT-qPCR confirmation in a separate cohort of 10 Belgian shepherds (5 IE = 2 BS, 3 BT; 5 healthy = 2 BS, 3 BT). Dogs were classified as healthy controls or IE cases and whole blood was collected as described above. Whole blood samples were sent to the UC Davis Genome Center for RNA extraction and RT-qPCR as previously described [104]. Specifically, primers and TaqMan probes (Table S4) were designed for target genes and reference genes HPRT1 [105] and GAPDH [106] using Primer Express software (3.0.1) (Thermo Fisher Scientific, Carlsbad, CA, USA). The qPCR systems were validated in triplicate and standard curves were plotted against 10-fold dilutions of DNA positive for target genes. The slope of the curve was used to ensure that all target genes had amplification efficiencies greater than 90%.
RNA extraction was performed using a Quick-RNA whole blood kit, following manufacturer protocols (Zymo Research, Irvine, CA, USA). Each PCR reaction contained 400 nM of each primer, 80 nM TaqMan probe, TaqMan Universal PCR Mastermix (Thermo Fisher Scientific, Carlsbad, CA, USA), and 5 μL diluted cDNA. Reactions were run in triplicate using an ABI PRISM 7900 HTA FAST automated fluorometer, using standard conditions (Thermo Fisher Scientific, Carlsbad, CA, USA). Gene expression was quantified using the comparative CT method, averaging the two reference genes to normalize reference gene CT values [107]. Any concerns regarding the sample size of the RNA-seq analyses were minimized by the confirmation of expression profiles in a different cohort of dogs.

4.4. DNA Preparation and Whole Genome Sequencing

Whole blood was previously collected by our laboratory for 17 Belgian Tervuren (8 healthy = 3 F, 5 M; 9 IE = 4 F, 5 M) and DNA was extracted using the Qiagen DNA Blood Mini Kit, following manufacturer protocols (Qiagen Inc., Valencia, CA, USA). Extracted DNA was sent to Novogene (Beijing, China) for polymerase chain reaction (PCR)-free library preparation and sequencing using the NovaSeq PE150 platform (Illumina, San Diego, CA, USA). FASTQ files collected from different lanes of the same sample were merged using the “cat” BASH command. Sequence reads were assessed for quality and trimmed using MultiQC, FastQC, and Trimmomatic as described above [87,88,89].
Trimmed reads were mapped to the Dog10K Boxer Tasha genome assembly [90] using the Burrows–Wheeler Alignment tool (0.7.17) with default parameters [108]. The resulting mapped read files were then sorted and indexed using SAMtools software (1.14) [109]. While PCR duplicates are not a concern with PCR-free library preparation, optical duplicates were removed using the Picard toolkit (2.26.11) [110]. Deduplicated reads were then used for variant calling using the short haplotype caller FreeBayes (1.3.4) [111]. To comply with computational limits, regions with greater than 1000× coverage were skipped by the variant caller.
Variants within 1 Mb of differentially expressed genes were extracted using BCFtools (1.19) [109]. To identify variants that significantly differ in these regions between healthy and IE dogs, a Fisher’s exact test using a dominant model was performed by SnpSift (5.2c) and filtered with a p-value cutoff of 0.05 [112]. The functional effect of these variants were predicted with SnpEff software (5.2c) [113] using the Ensembl (112) annotation of the Dog10K Boxer Tasha reference assembly [74]. The variant coordinates were lifted over to the human GRCh38 reference assembly [114] using the UCSC liftOver web tool [115]. The ReMap database (4) was accessed through the UCSC genome browser and was used to determine if variants were located in known regulatory regions in humans [65,115].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes15111474/s1, Table S1: Genes differentially expressed in dogs with IE; Table S2: Genes differentially expressed in dogs with the ACTG risk haplotype; Table S3: Whole genome variants of genes differentially expressed in dogs with IE; Table S4: Primers and probes for target and reference genes; Figure S1: Box and whisker plot of RT-qPCR fold change expression for HERC5, IFI44, and MFSD2A genes in IE dogs when compared to controls.

Author Contributions

Conceptualization, A.M.O.; methodology, N.K.; software, N.K.; validation, all authors; formal analysis, N.K. and J.M.B.; investigation, N.K. and J.M.B.; resources, J.M.B.; data curation, N.K.; writing—original draft preparation, N.K. and A.M.O.; writing—review and editing, all authors; visualization, N.K.; supervision, A.M.O.; project administration, A.M.O.; funding acquisition, A.M.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The American Kennel Club Canine Health Foundation, grant number 02936.

Institutional Review Board Statement

This study was conducted with informed consent from dog owners and approved for ethical review by the University of California, Davis Institutional Animal Care and Use Committee (IACUC 23674 January 2024).

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.

Acknowledgments

The RT-qPCR was carried out at the DNA Technologies and Expression Analysis Cores at the UC Davis Genome Center, supported by NIH Shared Instrumentation Grant 1S10OD010786-01. The authors thank Samantha Barnum and Cara Wademan, UC Davis Genome Center, for their RT-qPCR technical expertise, and Barbara Nitta-Oda, UC Davis Department of Animal Science, for RT-qPCR data interpretation. The authors thank all owners who have actively participated in the research and every dog contributing to the study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Folkard, E.; Niel, L.; Gaitero, L.; James, F.M.K. Tools and techniques for classifying behaviours in canine epilepsy. Front. Vet. Sci. 2023, 10, 1211515. Available online: https://www.frontiersin.org/articles/10.3389/fvets.2023.1211515 (accessed on 26 December 2023). [CrossRef] [PubMed]
  2. Berendt, M.; Gredal, H.; Ersbøll, A.K.; Alving, J. Premature Death, Risk Factors, and Life Patterns in Dogs with Epilepsy. J. Vet. Intern. Med. 2007, 21, 754–759. [Google Scholar] [CrossRef] [PubMed]
  3. Arrol, L.; Penderis, J.; Garosi, L.; Cripps, P.; Gutierrez-Quintana, R.; Gonçalves, R. Aetiology and long-term outcome of juvenile epilepsy in 136 dogs. Vet. Rec. 2012, 170, 335. [Google Scholar] [CrossRef] [PubMed]
  4. Muñana, K.R. Management of Refractory Epilepsy. Top. Companion Anim. Med. 2013, 28, 67–71. [Google Scholar] [CrossRef]
  5. Hülsmeyer, V.-I.; Fischer, A.; Mandigers, P.J.J.; DeRisio, L.; Berendt, M.; Rusbridge, C.; Bhatti, S.F.M.; Pakozdy, A.; Patterson, E.E.; Platt, S.; et al. International Veterinary Epilepsy Task Force’s current understanding of idiopathic epilepsy of genetic or suspected genetic origin in purebred dogs. BMC Vet. Res. 2015, 11, 175. [Google Scholar] [CrossRef]
  6. Berendt, M.; Gulløv, C.H.; Christensen, S.L.K.; Gudmundsdottir, H.; Gredal, H.; Fredholm, M.; Alban, L. Prevalence and characteristics of epilepsy in the Belgian shepherd variants Groenendael and Tervueren born in Denmark 1995–2004. Acta Vet. Scand. 2008, 50, 51. [Google Scholar] [CrossRef]
  7. Gulløv, C.H.; Toft, N.; Berendt, M. A longitudinal study of survival in Belgian Shepherds with genetic epilepsy. J. Vet. Intern. Med. 2012, 26, 1115–1120. [Google Scholar] [CrossRef]
  8. Beckers, E.; Bhatti, S.F.M.; Van Poucke, M.; Polis, I.; Farnir, F.; Van Nieuwerburgh, F.; Mandigers, P.; Van Ham, L.; Peelman, L.; Broeckx, B.J.G. Identification of a Novel Idiopathic Epilepsy Risk Locus and a Variant in the CCDC85A Gene in the Dutch Partridge Dog. Animals 2023, 13, 810. [Google Scholar] [CrossRef]
  9. Deschain, T.; Fabricius, J.; Berendt, M.; Fredholm, M.; Karlskov-Mortensen, P. The first genome-wide association study concerning idiopathic epilepsy in Petit Basset Griffon Vendeen. Anim. Genet. 2021, 52, 762–766. [Google Scholar] [CrossRef]
  10. Hayward, J.J.; Castelhano, M.G.; Oliveira, K.C.; Corey, E.; Balkman, C.; Baxter, T.L.; Casal, M.L.; Center, S.A.; Fang, M.; Garrison, S.J.; et al. Complex disease and phenotype mapping in the domestic dog. Nat. Commun. 2016, 7, 10460. [Google Scholar] [CrossRef]
  11. Koskinen, L.L.E.; Seppälä, E.H.; Belanger, J.M.; Arumilli, M.; Hakosalo, O.; Jokinen, P.; Nevalainen, E.M.; Viitmaa, R.; Jokinen, T.S.; Oberbauer, A.M.; et al. Identification of a common risk haplotype for canine idiopathic epilepsy in the ADAM23 gene. BMC Genom. 2015, 16, 465. [Google Scholar] [CrossRef] [PubMed]
  12. Belanger, J.M.; Famula, T.R.; Gershony, L.C.; Palij, M.K.; Oberbauer, A.M. Genome-wide association analysis of idiopathic epilepsy in the Belgian shepherd. Canine Med. Genet. 2020, 7, 12. [Google Scholar] [CrossRef]
  13. Seppälä, E.H.; Koskinen, L.L.E.; Gulløv, C.H.; Jokinen, P.; Karlskov-Mortensen, P.; Bergamasco, L.; Körberg, I.B.; Cizinauskas, S.; Oberbauer, A.M.; Berendt, M.; et al. Identification of a Novel Idiopathic Epilepsy Locus in Belgian Shepherd Dogs. PLoS ONE 2012, 7, e33549. [Google Scholar] [CrossRef]
  14. Winden, K.D.; Bragin, A.; Engel, J.; Geschwind, D.H. Molecular alterations in areas generating fast ripples in an animal model of temporal lobe epilepsy. Neurobiol. Dis. 2015, 78, 35–44. [Google Scholar] [CrossRef] [PubMed]
  15. Bithell, A.; Alberta, J.; Hornby, F.; Stiles, C.D.; Williams, B.P. Expression of the guanine nucleotide exchange factor, mr-gef, is regulated during the differentiation of specific subsets of telencephalic neurons. Dev. Brain Res. 2003, 146, 107–118. [Google Scholar] [CrossRef]
  16. Belanger, J.M.; Heinonen, T.; Famula, T.R.; Mandigers, P.J.J.; Leegwater, P.A.; Hytönen, M.K.; Lohi, H.; Oberbauer, A.M. Validation of a Chromosome 14 Risk Haplotype for Idiopathic Epilepsy in the Belgian Shepherd Dog Found to Be Associated with an Insertion in the RAPGEF5 Gene. Genes 2022, 13, 1124. [Google Scholar] [CrossRef]
  17. Cayabyab, D.D.; Belanger, J.M.; Xu, C.; Maga, E.A.; Oberbauer, A.M. Cellular localization of a variant RAPGEF5 protein associated with idiopathic epilepsy risk in the Belgian shepherd. Canine Med. Genet. 2024, 11, 4. [Google Scholar] [CrossRef]
  18. Seppälä, E.H.; Jokinen, T.S.; Fukata, M.; Fukata, Y.; Webster, M.T.; Karlsson, E.K.; Kilpinen, S.K.; Steffen, F.; Dietschi, E.; Leeb, T.; et al. LGI2 Truncation Causes a Remitting Focal Epilepsy in Dogs. PLoS Genet. 2011, 7, e1002194. [Google Scholar] [CrossRef]
  19. Pakozdy, A.; Patzl, M.; Zimmermann, L.; Jokinen, T.S.; Glantschnigg, U.; Kelemen, A.; Hasegawa, D. LGI Proteins and Epilepsy in Human and Animals. J. Vet. Intern. Med. 2015, 29, 997–1005. [Google Scholar] [CrossRef]
  20. Laub, F.; Lei, L.; Sumiyoshi, H.; Kajimura, D.; Dragomir, C.; Smaldone, S.; Puche, A.C.; Petros, T.J.; Mason, C.; Parada, L.F.; et al. Transcription Factor KLF7 Is Important for Neuronal Morphogenesis in Selected Regions of the Nervous System. Mol. Cell Biol. 2005, 25, 5699–5711. [Google Scholar] [CrossRef]
  21. Lei, L.; Laub, F.; Lush, M.; Romero, M.; Zhou, J.; Luikart, B.; Klesse, L.; Ramirez, F.; Parada, L.F. The zinc finger transcription factor Klf7 is required for TrkA gene expression and development of nociceptive sensory neurons. Genes Dev. 2005, 19, 1354–1364. [Google Scholar] [CrossRef] [PubMed]
  22. Caiazzo, M.; Colucci-D’Amato, L.; Esposito, M.T.; Parisi, S.; Stifani, S.; Ramirez, F.; di Porzio, U. Transcription factor KLF7 regulates differentiation of neuroectodermal and mesodermal cell lineages. Exp. Cell Res. 2010, 316, 2365–2376. [Google Scholar] [CrossRef]
  23. Havdahl, A.; Niarchou, M.; Starnawska, A.; Uddin, M.; van der Merwe, C.; Warrier, V. Genetic contributions to autism spectrum disorder. Psychol. Med. 2021, 51, 2260–2273. [Google Scholar] [CrossRef]
  24. Byron, S.A.; Van Keuren-Jensen, K.R.; Engelthaler, D.M.; Carpten, J.D.; Craig, D.W. Translating RNA sequencing into clinical diagnostics: Opportunities and challenges. Nat. Rev. Genet. 2016, 17, 257–271. [Google Scholar] [CrossRef]
  25. Hrdlickova, R.; Toloue, M.; Tian, B. RNA-Seq methods for transcriptome analysis. WIREs RNA 2017, 8, e1364. [Google Scholar] [CrossRef]
  26. Reiter, T.; Pierce, N.T.; Charbonneau, A. RNA-Seq in the Cloud. GitHub. 2021. Available online: https://github.com/nih-cfde/rnaseq-in-the-cloud/blob/stable/rnaseq-env.yml (accessed on 7 April 2024).
  27. Rothfels, K.; Milacic, M.; Matthews, L.; Haw, R.; Sevilla, C.; Gillespie, M.; Stephan, R.; Gong, C.; Ragueneau, E.; May, B.; et al. Using the Reactome Database. Curr. Protoc. 2023, 3, e722. [Google Scholar] [CrossRef] [PubMed]
  28. Feenstra, B.; Pasternak, B.; Geller, F.; Carstensen, L.; Wang, T.; Huang, F.; Eitson, J.L.; Hollegaard, M.V.; Svanström, H.; Vestergaard, M.; et al. Common variants associated with general and MMR vaccine-related febrile seizures. Nat. Genet. 2014, 46, 1274–1282. [Google Scholar] [CrossRef] [PubMed]
  29. Pérez-Villegas, E.M.; Ruiz, R.; Bachiller, S.; Ventura, F.; Armengol, J.A.; Rosa, J.L. The HERC proteins and the nervous system. Semin. Cell Dev. Biol. 2022, 132, 5–15. [Google Scholar] [CrossRef]
  30. Ben-Zvi, A.; Lacoste, B.; Kur, E.; Andreone, B.J.; Mayshar, Y.; Yan, H.; Gu, C. Mfsd2a is critical for the formation and function of the blood–brain barrier. Nature 2014, 509, 507–511. [Google Scholar] [CrossRef]
  31. Khuller, K.; Yigit, G.; Martínez Grijalva, C.; Altmüller, J.; Thiele, H.; Nürnberg, P.; Elcioglu, N.H.; Yeter, B.; Hehr, U.; Stein, A.; et al. MFSD2A-associated primary microcephaly—Expanding the clinical and mutational spectrum of this ultra-rare disease. Eur. J. Med. Genet. 2021, 64, 104310. [Google Scholar] [CrossRef]
  32. Scala, M.; Chua, G.L.; Chin, C.F.; Alsaif, H.S.; Borovikov, A.; Riazuddin, S.; Riazuddin, S.; Chiara Manzini, M.; Severino, M.; Kuk, A.; et al. Biallelic MFSD2A variants associated with congenital microcephaly, developmental delay, and recognizable neuroimaging features. Eur. J. Hum. Genet. 2020, 28, 1509–1519. [Google Scholar] [CrossRef]
  33. Climax, J.; Sewell, R.D. Modification of convulsive behaviour and body temperature in mice by intracerebroventricular administration of prostaglandins, arachidonic acid and the soluble acetylsalicylic acid salt lysine acetylsalicylate. Arch. Int. Pharmacodyn. Ther. 1981, 250, 254–265. [Google Scholar]
  34. Chung, J.-I.; Kim, A.Y.; Lee, S.H.; Baik, E.J. Seizure susceptibility in immature brain due to lack of COX-2-induced PGF2α. Exp. Neurol. 2013, 249, 95–103. [Google Scholar] [CrossRef] [PubMed]
  35. Buono, R.J.; Bradfield, J.P.; Wei, Z.; Sperling, M.R.; Dlugos, D.J.; Privitera, M.D.; French, J.A.; Lo, W.; Cossette, P.; Schachter, S.C.; et al. Genetic Variation in PADI6-PADI4 on 1p36.13 Is Associated with Common Forms of Human Generalized Epilepsy. Genes 2021, 12, 1441. [Google Scholar] [CrossRef] [PubMed]
  36. NCBI. Genome Data Viewer. National Center for Biotechnology Information. 2024. Available online: https://www.ncbi.nlm.nih.gov/gdv/browser/genome/?id=GCF_000002285.5 (accessed on 25 July 2024).
  37. Bo, X.; Zhiguo, W.; Xiaosu, Y.; Guoliang, L.; Guangjie, X. Analysis of gene expression in genetic epilepsy-prone rat using a cDNA expression array. Seizure 2002, 11, 418–422. [Google Scholar] [CrossRef] [PubMed]
  38. Zlokovic, B.V. The Blood-Brain Barrier in Health and Chronic Neurodegenerative Disorders. Neuron 2008, 57, 178–201. [Google Scholar] [CrossRef]
  39. Hecker, M.; Rüge, A.; Putscher, E.; Boxberger, N.; Rommer, P.S.; Fitzner, B.; Zettl, U.K. Aberrant expression of alternative splicing variants in multiple sclerosis—A systematic review. Autoimmun. Rev. 2019, 18, 721–732. [Google Scholar] [CrossRef]
  40. Billiau, A.D.; Wouters, C.H.; Lagae, L.G. Epilepsy and the immune system: Is there a link? Eur. J. Paediatr. Neurol. 2005, 9, 29–42. [Google Scholar] [CrossRef]
  41. Chen, T.-S.; Lai, M.-C.; Huang, H.-Y.I.; Wu, S.-N.; Huang, C.-W. Immunity, Ion Channels and Epilepsy. Int. J. Mol. Sci. 2022, 23, 6446. [Google Scholar] [CrossRef]
  42. Knebel, A.; Kämpe, A.; Carlson, R.; Rohn, K.; Tipold, A. Th17 cell-mediated immune response in a subpopulation of dogs with idiopathic epilepsy. PLoS ONE 2022, 17, e0262285. [Google Scholar] [CrossRef]
  43. Falip, M.; Salas-Puig, X.; Cara, C. Causes of CNS Inflammation and Potential Targets for Anticonvulsants. CNS Drugs 2013, 27, 611–623. [Google Scholar] [CrossRef] [PubMed]
  44. Phochantachinda, S.; Chantong, B.; Reamtong, O.; Chatchaisak, D. Protein profiling and assessment of amyloid β levels in plasma in canine refractory epilepsy. Front. Vet. Sci. 2023, 10, 1258244. Available online: https://www.frontiersin.org/articles/10.3389/fvets.2023.1258244 (accessed on 9 February 2024). [CrossRef] [PubMed]
  45. Löscher, W.; Howe, C.L. Molecular Mechanisms in the Genesis of Seizures and Epilepsy Associated With Viral Infection. Front. Mol. Neurosci. 2022, 15, 870868. [Google Scholar] [CrossRef] [PubMed]
  46. Perng, Y.-C.; Lenschow, D.J. ISG15 in antiviral immunity and beyond. Nat. Rev. Microbiol. 2018, 16, 423–439. [Google Scholar] [CrossRef]
  47. Rosiles-Abonce, A.; Rubio, C.; Taddei, E.; Rosiles, D.; Rubio-Osornio, M. Antiepileptogenic Effect of Retinoic Acid. Curr. Neuropharmacol. 2021, 19, 383–391. [Google Scholar] [CrossRef]
  48. Ruiz, F.X.; Gallego, O.; Ardèvol, A.; Moro, A.; Domínguez, M.; Alvarez, S.; Alvarez, R.; de Lera, A.R.; Rovira, C.; Fita, I.; et al. Aldo-keto reductases from the AKR1B subfamily: Retinoid specificity and control of cellular retinoic acid levels. Chem. Biol. Interact. 2009, 178, 171–177. [Google Scholar] [CrossRef]
  49. Wang, G.-D.; Shao, X.-J.; Bai, B.; Wang, J.; Wang, X.; Cao, X.; Liu, Y.-H.; Wang, X.; Yin, T.-T.; Zhang, S.-J.; et al. Structural variation during dog domestication: Insights from gray wolf and dhole genomes. Natl. Sci. Rev. 2019, 6, 110–122. [Google Scholar] [CrossRef]
  50. Kinsey, N. The Impact of Retinoic Acid on Neuronal Activity and the Interferon Signaling Pathway. 2024. Created in BioRender. Available online: https://BioRender.com/s89o803 (accessed on 25 October 2024).
  51. Pelicano, L.; Li, F.; Schindler, C.; Chelbi-Alix, M.K. Retinoic acid enhances the expression of interferon-induced proteins: Evidence for multiple mechanisms of action. Oncogene 1997, 15, 2349–2359. [Google Scholar] [CrossRef]
  52. Dao, C.T.; Luo, J.-K.; Zhang, D.-E. Retinoic acid-induced protein ISGylation is dependent on interferon signal transduction. Blood Cells Mol. Dis. 2006, 36, 406–413. [Google Scholar] [CrossRef]
  53. de Hoog, E.; Lukewich, M.K.; Spencer, G.E. Retinoid receptor-based signaling plays a role in voltage-dependent inhibition of invertebrate voltage-gated Ca2+ channels. J. Biol. Chem. 2019, 294, 10076–10093. [Google Scholar] [CrossRef]
  54. Zhang, D.-Q.; McMahon, D.G. Direct gating by retinoic acid of retinal electrical synapses. Proc. Natl. Acad. Sci. USA 2000, 97, 14754–14759. [Google Scholar] [CrossRef] [PubMed]
  55. Li, Q.; Li, Q.-Q.; Jia, J.-N.; Liu, Z.-Q.; Zhou, H.-H.; Mao, X.-Y. Targeting gap junction in epilepsy: Perspectives and challenges. Biomed. Pharmacother. 2019, 109, 57–65. [Google Scholar] [CrossRef] [PubMed]
  56. Huberfeld, G.; Wittner, L.; Clemenceau, S.; Baulac, M.; Kaila, K.; Miles, R.; Rivera, C. Perturbed Chloride Homeostasis and GABAergic Signaling in Human Temporal Lobe Epilepsy. J. Neurosci. 2007, 27, 9866–9873. [Google Scholar] [CrossRef]
  57. Gururaja Rao, S.; Ponnalagu, D.; Patel, N.J.; Singh, H. Three Decades of Chloride Intracellular Channel Proteins: From Organelle to Organ Physiology. Curr. Protoc. Pharmacol. 2018, 80, 11.21.1–11.21.17. [Google Scholar] [CrossRef]
  58. Gururaja Rao, S.; Patel, N.J.; Singh, H. Intracellular Chloride Channels: Novel Biomarkers in Diseases. Front. Physiol. 2020, 11, 96. Available online: https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2020.00096/full (accessed on 8 October 2024). [CrossRef]
  59. Vallée, A.; Lecarpentier, Y.; Vallée, J.-N. Interplay of Opposing Effects of the WNT/β-Catenin Pathway and PPARγ and Implications for SARS-CoV2 Treatment. Front. Immunol. 2021, 12, 666693. Available online: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.666693/full (accessed on 16 September 2024). [CrossRef] [PubMed]
  60. Aissvarya, S.; Ling, K.-H.; Arumugam, M.; Thilakavathy, K. Molecular genetics of Dupuytren’s contracture. EFORT Open Rev. 2024, 9, 723–732. [Google Scholar] [CrossRef]
  61. Zheng, J.-H.; Zhu, Y.-H.; Yang, J.; Ji, P.-X.; Zhao, R.-K.; Duan, Z.-H.; Yao, H.-F.; Jia, Q.-Y.; Yin, Y.-F.; Hu, L.-P.; et al. A CLIC1 network coordinates matrix stiffness and the Warburg effect to promote tumor growth in pancreatic cancer. Cell Rep. 2024, 43, 114633. Available online: https://www.cell.com/cell-reports/abstract/S2211-1247(24)00983-5 (accessed on 8 October 2024). [CrossRef]
  62. Hodges, S.L.; Lugo, J.N. Wnt/β-catenin signaling as a potential target for novel epilepsy therapies. Epilepsy Res. 2018, 146, 9–16. [Google Scholar] [CrossRef]
  63. Ohba, C.; Okamoto, N.; Murakami, Y.; Suzuki, Y.; Tsurusaki, Y.; Nakashima, M.; Miyake, N.; Tanaka, F.; Kinoshita, T.; Matsumoto, N.; et al. PIGN mutations cause congenital anomalies, developmental delay, hypotonia, epilepsy, and progressive cerebellar atrophy. Neurogenetics 2014, 15, 85–92. [Google Scholar] [CrossRef]
  64. Bayat, A.; de Valles-Ibáñez, G.; Pendziwiat, M.; Knaus, A.; Alt, K.; Biamino, E.; Bley, A.; Calvert, S.; Carney, P.; Caro-Llopis, A.; et al. PIGN encephalopathy: Characterizing the epileptology. Epilepsia 2022, 63, 974–991. [Google Scholar] [CrossRef] [PubMed]
  65. Hammal, F.; de Langen, P.; Bergon, A.; Lopez, F.; Ballester, B. ReMap 2022: A database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022, 50, D316–D325. [Google Scholar] [CrossRef] [PubMed]
  66. Fu, J.; Tao, T.; Li, Z.; Chen, Y.; Li, J.; Peng, L. The roles of ER stress in epilepsy: Molecular mechanisms and therapeutic implications. Biomed. Pharmacother. 2020, 131, 110658. [Google Scholar] [CrossRef]
  67. Wang, Z.; Li, Q.; Kolls, B.J.; Mace, B.; Yu, S.; Li, X.; Liu, W.; Chaparro, E.; Shen, Y.; Dang, L.; et al. Sustained overexpression of spliced X-box-binding protein-1 in neurons leads to spontaneous seizures and sudden death in mice. Commun. Biol. 2023, 6, 252. [Google Scholar] [CrossRef]
  68. Luo, X.; Alfason, L.; Wei, M.; Wu, S.; Kasim, V. Spliced or Unspliced, That Is the Question: The Biological Roles of XBP1 Isoforms in Pathophysiology. Int. J. Mol. Sci. 2022, 23, 2746. [Google Scholar] [CrossRef] [PubMed]
  69. Baychelier, F.; Nardeux, P.-C.; Cajean-Feroldi, C.; Ermonval, M.; Guymarho, J.; Tovey, M.G.; Eid, P. Involvement of the Gab2 scaffolding adapter in type I interferon signalling. Cell. Signal. 2007, 19, 2080–2087. [Google Scholar] [CrossRef]
  70. Zhan, A.; Xu, X.; Chen, L.; Wang, X.; Yanfeng, X.; Dan, W.; Zhan, Y.; Shi, Q. Decreased expression of Gab2 in patients with temporal lobe epilepsy and pilocarpine-induced rat model. Synapse 2014, 68, 168–177. [Google Scholar] [CrossRef]
  71. Štěrbová, K.; Vlčková, M.; Hansíková, H.; Sebroňová, V.; Sedláčková, L.; Pavlíček, P.; Laššuthová, P. Novel variants in the NARS2 gene as a cause of infantile-onset severe epilepsy leading to fatal refractory status epilepticus: Case study and literature review. Neurogenetics 2021, 22, 359–364. [Google Scholar] [CrossRef]
  72. Hu, W.; Fang, H.; Peng, Y.; Li, L.; Guo, D.; Tang, J.; Yi, J.; Liu, Q.; Qin, W.; Wu, L.; et al. Clinical and genetic analyses of premature mitochondrial encephalopathy with epilepsia partialis continua caused by novel biallelic NARS2 mutations. Front. Neurosci. 2022, 16, 1076183. Available online: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.1076183/full (accessed on 23 August 2024). [CrossRef]
  73. Yang, N.; Chen, L.; Zhang, Y.; Wu, X.; Hao, Y.; Yang, F.; Yang, Z.; Liang, J. Novel NARS2 variants in a patient with early-onset status epilepticus: Case study and literature review. BMC Pediatr. 2024, 24, 96. [Google Scholar] [CrossRef]
  74. Howe, K.L.; Achuthan, P.; Allen, J.; Allen, J.; Alvarez-Jarreta, J.; Amode, M.R.; Armean, I.M.; Azov, A.G.; Bennett, R.; Bhai, J.; et al. Ensembl 2021. Nucleic Acids Res. 2021, 49, D884–D891. [Google Scholar] [CrossRef] [PubMed]
  75. Li, D.; Cheng, P.; Wang, J.; Qiu, X.; Zhang, X.; Xu, L.; Liu, Y.; Qin, S. IRF6 Is Directly Regulated by ZEB1 and ELF3, and Predicts a Favorable Prognosis in Gastric Cancer. Front. Oncol. 2019, 9, 220. [Google Scholar] [CrossRef] [PubMed]
  76. Korfhagen, T.R.; Kitzmiller, J.; Chen, G.; Sridharan, A.; Haitchi, H.-M.; Hegde, R.S.; Divanovic, S.; Karp, C.L.; Whitsett, J.A. SAM-pointed domain ETS factor mediates epithelial cell–intrinsic innate immune signaling during airway mucous metaplasia. Proc. Natl. Acad. Sci. USA 2012, 109, 16630–16635. [Google Scholar] [CrossRef]
  77. Yu, Y.; Nguyen, D.T.; Jiang, J. G protein-coupled receptors in acquired epilepsy: Druggability and translatability. Prog. Neurobiol. 2019, 183, 101682. [Google Scholar] [CrossRef]
  78. Krivega, I.; Dean, A. Enhancer and promoter interactions—Long distance calls. Curr. Opin. Genet. Dev. 2012, 22, 79–85. [Google Scholar] [CrossRef] [PubMed]
  79. Enright, N.; Simonato, M.; Henshall, D.C. Discovery and validation of blood microRNAs as molecular biomarkers of epilepsy: Ways to close current knowledge gaps. Epilepsia Open 2018, 3, 427–436. [Google Scholar] [CrossRef]
  80. Kernohan, K.D.; Frésard, L.; Zappala, Z.; Hartley, T.; Smith, K.S.; Wagner, J.; Xu, H.; McBride, A.; Bourque, P.R.; Bennett, S.A.L.; et al. Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy. Hum. Mutat. 2017, 38, 611–614. [Google Scholar] [CrossRef]
  81. Borchert, C.; Herman, A.; Roth, M.; Brooks, A.C.; Friedenberg, S.G. RNA sequencing of whole blood in dogs with primary immune-mediated hemolytic anemia (IMHA) reveals novel insights into disease pathogenesis. PLoS ONE 2020, 15, e0240975. [Google Scholar] [CrossRef] [PubMed]
  82. De Risio, L.; Bhatti, S.; Muñana, K.; Penderis, J.; Stein, V.; Tipold, A.; Berendt, M.; Farqhuar, R.; Fischer, A.; Long, S.; et al. International veterinary epilepsy task force consensus proposal: Diagnostic approach to epilepsy in dogs. BMC Vet. Res. 2015, 11, 148. [Google Scholar] [CrossRef]
  83. Busby, M.A.; Stewart, C.; Miller, C.A.; Grzeda, K.R.; Marth, G.T. Scotty: A web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics 2013, 29, 656–657. [Google Scholar] [CrossRef]
  84. Yang, X.; Zhang, H.; Shang, J.; Liu, G.; Xia, T.; Zhao, C.; Sun, G.; Dou, H. Comparative analysis of the blood transcriptomes between wolves and dogs. Anim. Genet. 2018, 49, 291–302. [Google Scholar] [CrossRef] [PubMed]
  85. Friedenberg, S.G.; Chdid, L.; Keene, B.; Sherry, B.; Motsinger-Reif, A.; Meurs, K.M. Use of RNA-seq to identify cardiac genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy. Am. J. Vet. Res. 2016, 77, 693–699. [Google Scholar] [CrossRef] [PubMed]
  86. Illumina. Illumina Stranded mRNA Prep|A Clear View of the Coding Transcriptome. 2023. Available online: https://www.illumina.com/products/by-type/sequencing-kits/library-prep-kits/stranded-mrna-prep.html (accessed on 8 April 2024).
  87. Ewels, P.; Magnusson, M.; Lundin, S.; Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016, 32, 3047–3048. [Google Scholar] [CrossRef] [PubMed]
  88. Andrews, S. Babraham Bioinformatics—FastQC A Quality Control Tool for High Throughput Sequence Data. 2010. Available online: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 23 May 2023).
  89. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed]
  90. Jagannathan, V.; Hitte, C.; Kidd, J.M.; Masterson, P.; Murphy, T.D.; Emery, S.; Davis, B.; Buckley, R.M.; Liu, Y.-H.; Zhang, X.-Q.; et al. Dog10k_Boxer_Tasha_1.0: A long-read assembly of the dog reference genome. Genes 2021, 12, 847. [Google Scholar] [CrossRef]
  91. Bray, N.L.; Pimentel, H.; Melsted, P.; Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 2016, 34, 525–527. [Google Scholar] [CrossRef]
  92. Patro, R.; Duggal, G.; Love, M.I.; Irizarry, R.A.; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 2017, 14, 417–419. [Google Scholar] [CrossRef]
  93. Zheng, H.; Brennan, K.; Hernaez, M.; Gevaert, O. Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples. GigaScience 2019, 8, giz145. [Google Scholar] [CrossRef]
  94. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022; Available online: https://www.R-project.org/ (accessed on 31 October 2022).
  95. Lawrence, M.; Huber, W.; Pagès, H.; Aboyoun, P.; Carlson, M.; Gentleman, R.; Morgan, M.T.; Carey, V.J. Software for Computing and Annotating Genomic Ranges. PLoS Comput. Biol. 2013, 9, e1003118. [Google Scholar] [CrossRef]
  96. Soneson, C.; Love, M.I.; Robinson, M.D. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research 2015, 4, 1521. [Google Scholar] [CrossRef]
  97. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [PubMed]
  98. Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139–140. [Google Scholar] [CrossRef] [PubMed]
  99. Chen, Y.; Lun, A.T.L.; Smyth, G.K. From reads to genes to pathways: Differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 2016, 5, 1438. Available online: https://f1000research.com/articles/5-1438 (accessed on 17 January 2024). [PubMed]
  100. McCarthy, D.J.; Chen, Y.; Smyth, G.K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012, 40, 4288–4297. [Google Scholar] [CrossRef]
  101. Benjamini, Y.; Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 2001, 29, 1165–1188. [Google Scholar] [CrossRef]
  102. Xu, J.; Sun, J.; Chen, J.; Wang, L.; Li, A.; Helm, M.; Dubovsky, S.L.; Bacanu, S.-A.; Zhao, Z.; Chen, X. RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia. BMC Genom. 2012, 13, S2. [Google Scholar] [CrossRef]
  103. Son, K.; Yu, S.; Shin, W.; Han, K.; Kang, K. A Simple Guideline to Assess the Characteristics of RNA-Seq Data. BioMed Res. Int. 2018, 2018, 2906292. [Google Scholar] [CrossRef]
  104. Wilcox, A.; Barnum, S.; Wademan, C.; Corbin, R.; Escobar, E.; Hodzic, E.; Schumacher, S.; Pusterla, N. Frequency of Detection of Respiratory Pathogens in Clinically Healthy Show Horses Following a Multi-County Outbreak of Equine Herpesvirus-1 Myeloencephalopathy in California. Pathogens 2022, 11, 1161. [Google Scholar] [CrossRef]
  105. Tanvetthayanont, P.; Yata, T.; Boonnil, J.; Temisak, S.; Ponglowhapan, S. Validation of droplet digital PCR for cytokeratin 19 mRNA detection in canine peripheral blood and mammary gland. Sci. Rep. 2022, 12, 13623. [Google Scholar] [CrossRef]
  106. Clements, D.N.; Carter, S.D.; Innes, J.F.; Ollier, W.E.; Day, P.J. Analysis of normal and osteoarthritic canine cartilage mRNA expression by quantitative polymerase chain reaction. Arthritis Res. Ther. 2006, 8, R158. [Google Scholar] [CrossRef]
  107. Schmittgen, T.D.; Livak, K.J. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 2008, 3, 1101–1108. [Google Scholar] [CrossRef] [PubMed]
  108. Li, H.; Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [PubMed]
  109. Danecek, P.; Bonfield, J.K.; Liddle, J.; Marshall, J.; Ohan, V.; Pollard, M.O.; Whitwham, A.; Keane, T.; McCarthy, S.A.; Davies, R.M.; et al. Twelve years of SAMtools and BCFtools. Gigascience 2021, 10, giab008. [Google Scholar] [CrossRef]
  110. Picard Toolkit. Broad Institute, GitHub Repository. 2019. Available online: https://broadinstitute.github.io/picard/ (accessed on 23 February 2022).
  111. Garrison, E.; Marth, G. Haplotype-based variant detection from short-read sequencing. arXiv 2012, arXiv:1207.3907. [Google Scholar] [CrossRef]
  112. Cingolani, P.; Patel, V.M.; Coon, M.; Nguyen, T.; Land, S.J.; Ruden, D.M.; Lu, X. Using Drosophila melanogaster as a Model for Genotoxic Chemical Mutational Studies with a New Program, SnpSift. Front. Genet. 2012, 3, 35. [Google Scholar] [CrossRef] [PubMed]
  113. Cingolani, P.; Platts, A.; Wang, L.L.; Coon, M.; Nguyen, T.; Wang, L.; Land, S.J.; Lu, X.; Ruden, D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 2012, 6, 80–92. [Google Scholar] [CrossRef]
  114. Schneider, V.A.; Graves-Lindsay, T.; Howe, K.; Bouk, N.; Chen, H.-C.; Kitts, P.A.; Murphy, T.D.; Pruitt, K.D.; Thibaud-Nissen, F.; Albracht, D.; et al. Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. Genome Res. 2017, 27, 849–864. [Google Scholar] [CrossRef]
  115. Nassar, L.R.; Barber, G.P.; Benet-Pagès, A.; Casper, J.; Clawson, H.; Diekhans, M.; Fischer, C.; Gonzalez, J.N.; Hinrichs, A.S.; Lee, B.T.; et al. The UCSC Genome Browser database: 2023 update. Nucleic Acids Res. 2023, 51, D1188–D1195. [Google Scholar] [CrossRef]
Genes 15 01474 g001
Figure 1. Principal component analysis of gene abundance comparing breed variety (A) and sex (B). Gene abundance estimates for 16 Belgian Shepherds were plotted along their first two principal components (PC1, horizontal axis, and PC2, vertical axis). Clustering was determined by visual assessment.
Figure 1. Principal component analysis of gene abundance comparing breed variety (A) and sex (B). Gene abundance estimates for 16 Belgian Shepherds were plotted along their first two principal components (PC1, horizontal axis, and PC2, vertical axis). Clustering was determined by visual assessment.
Genes 15 01474 g001
Genes 15 01474 g002
Figure 2. The impact of retinoic acid on neuronal activity and the IFN signaling pathway [50].
Figure 2. The impact of retinoic acid on neuronal activity and the IFN signaling pathway [50].
Genes 15 01474 g002
Table 1. Differentially expressed genes between IE cases and healthy controls. The top ten differentially expressed genes from the Kallisto and DESeq2 analysis and three genes previously associated with canine IE.
Table 1. Differentially expressed genes between IE cases and healthy controls. The top ten differentially expressed genes from the Kallisto and DESeq2 analysis and three genes previously associated with canine IE.
GeneLog2 Fold Change *FDR-Corrected p-Value
ENSCAFG000000048577.603.64 × 10−2
MFSD2A1.596.43 × 10−2
HERC6−1.335.71 × 10−2
EPSTI1−1.377.52 × 10−2
DDX58−1.459.60 × 10−2
HERC5−1.533.06 × 10−2
IFI44−1.582.93 × 10−2
RSAD2−2.136.63 × 10−2
MAST4−3.507.26 × 10−3
ENSCAFG00000002440−20.703.63 × 10−7
ADAM23−3.459.99 × 10−1
KLF70.219.99 × 10−1
RAPGEF50.109.99 × 10−1
* Negative log2 fold change values indicate downregulation, and positive values indicate upregulation in IE dogs.
Table 2. Differentially expressed genes between dogs with and without the ACTG risk haplotype. The top six differentially expressed genes from the Kallisto and DESeq2 analysis and the three genes previously associated with canine IE.
Table 2. Differentially expressed genes between dogs with and without the ACTG risk haplotype. The top six differentially expressed genes from the Kallisto and DESeq2 analysis and the three genes previously associated with canine IE.
GeneLog2 Fold Change *FDR-Corrected p-Value
CLIC118.851.70 × 10−3
RHEX3.511.80 × 10−3
ACE26.442.41 × 10−2
PIGN0.812.41 × 10−2
SLC45A34.624.57 × 10−2
EPDR1−1.379.55 × 10−2
ADAM23−3.019.98 × 10−1
KLF70.079.98 × 10−1
RAPGEF50.169.98 × 10−1
* Negative log2 fold change values indicate downregulation in ACTG dogs while positive values indicate upregulation.
Table 3. Whole genome sequence variants near differentially expressed genes comparing IE cases and healthy controls. The analyzed region is specified in coordinates from the Dog10K Boxer Tasha genome assembly. Of variants within 1 MB of a differentially expressed gene, Fisher’s exact test using a dominant model showed differences between IE case and control variant genotypes (p < 0.05).
Table 3. Whole genome sequence variants near differentially expressed genes comparing IE cases and healthy controls. The analyzed region is specified in coordinates from the Dog10K Boxer Tasha genome assembly. Of variants within 1 MB of a differentially expressed gene, Fisher’s exact test using a dominant model showed differences between IE case and control variant genotypes (p < 0.05).
CFAGenePosition (bp)Number of Significant Variants
p < 0.05		Number of Variants Within Gene
21ENSCAFG0000000485720,281,342–21,282,2392810
15MFSD2A2,723,565–3,736,071111
32HERC629,703,843–30,759,5591096
22EPSTI17,354,646–8,600,477220
11DDX5847,886,818–48,922,130330
32HERC529,653,835–30,697,247850
6IFI4470,521,268–71,559,185514
17RSAD23,913,918–4,934,440152484
2MAST448,559,935–50,095,3942317
14ENSCAFG0000000244030,665,380–31,666,3291340
Table 4. Sample size breakdown of CFA 14 haplotypes in the studied population. The IE status and breed varieties of all haplotypes at the CFA 14 risk locus. The ACTG haplotype confers the highest IE risk compared to the other two haplotypes.
Table 4. Sample size breakdown of CFA 14 haplotypes in the studied population. The IE status and breed varieties of all haplotypes at the CFA 14 risk locus. The ACTG haplotype confers the highest IE risk compared to the other two haplotypes.
CFA 14 HaplotypeEpilepticControlBSBT
ACTG6033
CTCT3526
CTCG2213
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Kinsey, N.; Belanger, J.M.; Oberbauer, A.M. Differential Gene Expression Associated with Idiopathic Epilepsy in Belgian Shepherd Dogs. Genes 2024, 15, 1474. https://doi.org/10.3390/genes15111474

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Kinsey N, Belanger JM, Oberbauer AM. Differential Gene Expression Associated with Idiopathic Epilepsy in Belgian Shepherd Dogs. Genes. 2024; 15(11):1474. https://doi.org/10.3390/genes15111474

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Kinsey, Nathan, Janelle M. Belanger, and Anita M. Oberbauer. 2024. "Differential Gene Expression Associated with Idiopathic Epilepsy in Belgian Shepherd Dogs" Genes 15, no. 11: 1474. https://doi.org/10.3390/genes15111474

APA Style

Kinsey, N., Belanger, J. M., & Oberbauer, A. M. (2024). Differential Gene Expression Associated with Idiopathic Epilepsy in Belgian Shepherd Dogs. Genes, 15(11), 1474. https://doi.org/10.3390/genes15111474

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