Common tissue-specific expressions and regulatory factors of c-KIT isoforms with and without GNNK and GNSK sequences across five mammals

Rikuto Goto; Naoaki Sakamoto; Akinori Awazu

doi:10.1371/journal.pone.0332294

Abstract

c-KIT is a transmembrane receptor tyrosine kinase involved in various signaling pathways. Alternative pre-mRNA splicing of KIT results in isoforms that differ in the presence or absence of four amino acid sequences in the extracellular juxtamembrane region, such as, isoforms with and without the GNNK sequence (GNNK+ and GNNK − , respectively) in humans and mice, and those with and without GNSK (GNSK+ and GNSK − , respectively) in domestic dogs, cats, and sheep. These isoforms have been extensively studied as disease-associated (particularly tumors or cancer) splice variants with differing kinase activities. However, the expression patterns and regulatory factors of each isoform in various animal species without tumors or cancer remain poorly understood. Studying these aspects can provide the basis for understanding the associations between c-KIT isoforms and disease. Therefore, in the present study, a comprehensive expression analysis of c-KIT isoforms was conducted using tissue-wide transcriptome data from humans, mice, dogs, cats, and sheep. We found that the expression ratio of c-KIT isoforms differs across tissues, and such features are conserved across animal species: GNNK+ and GNSK+ isoforms have high expression ratios in the central nervous system, while GNNK− and GNSK− predominate in other tissues. Furthermore, NOVA2, RBFOX1, RBFOX3, and DYRK1A were suggested to be candidate factors regulating the selection of the alternative 5′ splice donor site of KIT.

Citation: Goto R, Sakamoto N, Awazu A (2026) Common tissue-specific expressions and regulatory factors of c-KIT isoforms with and without GNNK and GNSK sequences across five mammals. PLoS One 21(1): e0332294. https://doi.org/10.1371/journal.pone.0332294

Editor: Tofazzal Md Rakib, Chattogram Veterinary and Animal Sciences University, BANGLADESH

Received: August 28, 2025; Accepted: December 30, 2025; Published: January 20, 2026

Copyright: © 2026 Goto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: A.A: Japan Science and Technology Agency (JST) Moonshot R&D (Grant Number JPMJMS2021), https://www.jst.go.jp/, No. A.A: Japan Society for the Promotion of Science (JSPS) KAKENHI grant (award number 24K09248), https://www.jsps.go.jp/, No.

Competing interests: The authors have declared that no competing interests exist.

Introduction

c-KIT is the transmembrane receptor tyrosine kinase encoded by the KIT gene, which is activated by its ligand, stem cell factor (SCF), to trigger multiple downstream signaling cascades, such as PI3K, Src, JAK/STAT, PLC-γ, and MAPK pathways, essential for hematopoiesis, stem cell maintenance, gametogenesis, melanogenesis, mast cell development, and cell migration [1,2]. c-KIT comprises five N-terminal immunoglobulin-like domains, a single transmembrane domain, and C-terminal intracellular tyrosine kinase domains. In humans and mice, the extracellular juxtamembrane region, which is located adjacent to the fifth immunoglobulin‑like domain (D5) that mediates dimer contacts [3], contains a four -amino-acid sequence Gly‑Asn‑Asn‑Lys (GNNK) (Fig 1A and 1B). In domestic dogs, cats, and sheep, this region contains another four-amino acid sequence, Gly-Asn-Ser-Lys (GNSK) [4–8]. KIT synthesizes isoforms that differ in the presence or absence of the GNNK/GNSK sequence (GN[N/S]K). Nevertheless, there are gaps in our current understanding of these isoforms among species. In the Reference Sequence Database (RefSeq) (https://www.ncbi.nlm.nih.gov/refseq/, [9]), the isoforms with and without GNNK (GNNK+ and GNNK − , respectively) in human and mouse have been curated and registered as Known RefSeq entries, with accession numbers beginning with “NP_” (Fig 1B). In contrast, in domestic dog and sheep, only GNSK− is included in the Known RefSeq, while the GNSK+ is represented as a predicted transcript in the Model RefSeq category and indicated by accession numbers beginning with “XP_”. Nonetheless, GNSK+ and GNSK− expression in various canine tissues has been reported [5,6,10]. Moreover, the co-expression of the two isoforms in the skin tissue of Merino sheep has been observed [8]. Additionally, in domestic cats, only GNSK+ has been annotated in the Known RefSeq. However, an GNSK− isoform has also been detected in feline samples [7].

Download:

Fig 1. Structural features and sequences of c-KIT across five mammals.

(A) Schematic representation of the structure of c-KIT protein. Blue indicates the functional domains of c-KIT, and orange indicates the location of the four amino acid sequence, GN[N/S]K. (B) Amino acid sequences around the juxtamembrane region of each species. Numbers in parentheses indicate RefSeq accession number, with prefix “NP_” representing curated Known RefSeq, and “XP_” representing computationally predicted Model RefSeq. (C) Nucleotide sequence of exon–intron boundaries around the region encoding the juxtamembrane region, arranged based on the genome assembly of each species. Blue squares indicate splice donor sites, and the yellow square indicates a splice acceptor site.

https://doi.org/10.1371/journal.pone.0332294.g001

Clinically, mutations in the KIT proto-oncogene have been frequently reported in various cancers, including gastrointestinal stromal tumors [11,12], mast cell tumors [13,14], mastocytosis [15,16], and acute myeloid leukemia (AML) [17]. Consequently, studies on c-KIT isoforms also have mainly focused on their involvement in various diseases. For instance, in humans, the GNNK − / + balance correlates with prognosis in AML [18]. The expression ratio of GNSK+ to GNSK− differs between healthy and tumorous canine mammary tissues [10]. Functionally, SCF-induced phosphorylation occurs via isoform-specific kinetics. GN[N/S]K− variants exhibit rapid and robust phosphorylation [7,19–23]. These differences in activity between the isoforms may be explained by differences in the orientation and flexibility of each protomer within the dimer, based on differences in the length of the juxtamembrane domain [3,23,24]. These findings suggest that isoform-dependent differences in phosphorylation and downstream signaling play critical roles in tumor initiation and progression.

Previous studies have predominantly focused on disease-specific contexts, particularly tumors or cancer, thereby limiting the range of tissues examined. Thus, studies on the comprehensive tissue-dependent characterization of the expression and regulatory mechanisms of c-KIT isoforms with GN[N/S]K presence or absence are lacking, even though KIT is expressed throughout the animal body. Therefore, in the present study, we performed a comprehensive expression analysis of c-KIT isoforms using tissue-wide transcriptome data from humans, mice, and domestic dogs, cats, and sheep, which should provide fundamental knowledge for clinical research.

Materials and methods

Ethics statement

This study utilized publicly available data from the ENCODE database and National Center for Biotechnology Information Sequence Read Archive (NCBI SRA). As this research involved the analysis of only deidentified, publicly accessible data, it did not require ethical approval. No additional informed consent was necessary as the data were already collected and made available for research purposes. We adhered to all relevant ethical guidelines for the use of public data in research.

Processing of transcriptome data

For the analysis of human and mouse tissue-wide transcriptome data, we first downloaded the aligned RNA sequencing (RNA-seq) data files (BAM format, aligned to GRCh38.p12 and GRCm38.p6, respectively) with their corresponding transcriptome-level gene expression quantification files (TSV format) from the ENCODE Project (https://www.encodeproject.org [25,26], S1 Table). Reference genome files and gene transfer format (GTF) files for humans and mice were obtained from GENCODE, matching the versions obtained from ENCODE: Homo sapiens GRCh38.p12 (GENCODE Release 29; https://www.gencodegenes.org/human/release_29.html) and Mus musculus GRCm38.p6 (GENCODE Release M21; https://www.gencodegenes.org/mouse/release_M21.html). To obtain splice junction data (SJ.out.tab format file) for KIT, we extracted reads (human: chr4:54652918–54745715, mouse: chr5:75569987–75661722) from the downloaded BAM files using the samtools view command (options -h -b) of samtools (version 1.21), and then converted them to FASTQ format using the samtools fastq command (no options for single-end sequence, and options −1, −2 to split FASTQ for pair-end sequence), and the alignment of the pairs for pair-end sequence was facilitated using repair.sh from BBMap.

RNA-seq data (FASTQ format) from samples in various developmental stages in the central nervous system (CNS) (PRJEB26969 for human and PRJEB26869 for mice) and from the knockout (KO) or knockdown (KD) experiments of alternative splicing factor [27–29] (S3 Table) were downloaded from the NCBI SRA. For the analysis of tissue-wide transcriptome data, reference genomes for domestic dogs (Canis lupus familiaris; GCF_011100685.1), cats (Felis catus; GCF_018350175.1, AnAmus 1.0 [30]) and sheep (Ovis aries; GCF_016772045.2) were downloaded from the NCBI Datasets portal (https://www.ncbi.nlm.nih.gov/datasets/genome/). RNA-seq data from various tissues of these mammals were obtained from NCBI SRA (S1 Table). Quality control of each raw read of the RNA-seq data was performed using fastp (version 0.22.0) [31] with default parameters. Filtered reads were aligned to the reference genome using STAR (version 2.5.2b for human and mouse and 2.7.4a for dog, cat, and sheep, using the options -outSAMtype BAM SortedByCoordinate, -quantMode TranscriptomeSAM, -outSAMattributes All, -outFilterMultimapNmax 1000, -outSAMunmapped Within KeepPairs, -outSAMstrandField intronMotif) [32]. Transcript quantification was performed using RSEM (version 1.3.1, options: -estimate-rspd, -strandedness reverse, -no-bam-output) [33].

Tissue categories were assigned based on the ENCODE “human and mouse body maps.” Samples that did not conform to any predefined category were excluded. Additionally, we introduced “adipose tissue” and “embryo” as separate categories.

Phylogenetic analysis

ClustalW alignments were performed for the full-length KIT gene, including introns, and for the region encompassing the alternative exon, downstream intron, and exon of all five species. A neighbor-joining tree was then constructed in MEGA11 using the Maximum Composite Likelihood model. This model was used to compute the distances, which are expressed as the number of base substitutions per site. This analysis involved five nucleotide sequences, with all ambiguous positions removed for each sequence pair (pairwise deletion option) [34].

Quantification of splicing variants

To distinguish between the long and the short exon of KIT, we focused on RNA-seq reads spanning splice junctions generated by the selective use of alternative donor sites (5′ splice sites). Based on the genomic coordinates of splice sites, reads were classified as either longer or shorter exons (for c-KIT, inclusion or exclusion of GN[N/S]K), and the number of reads supporting each splice variant was counted (S1 Fig). Samples were filtered to include only those with KIT expression levels of ≥1 transcript per million (TPM) and a combined total of at least three junction-spanning reads, supporting either the inclusion or exclusion of the relevant domains. Using this filtering method, we obtained 334 human samples across 14 tissue types, 184 mouse samples across 13 tissue types, 34 dog samples across seven tissue types, 76 cat samples across 12 tissue types, and 197 sheep samples across 11 tissue types for subsequent analysis (S1 Table). For data from these samples, the inclusion-exclusion ratio (in-ex ratio) for the spliced domains was calculated using the following formula:

where [SDS1&SAS] indicates the number of reads aligned to the genome region containing only splice donor site 1 (SDS1) and the splice acceptor site (SAS), and [SDS2&SAS] indicates those aligned to the genome region with SDS1, SDS2, and SAS (S1 Fig). For c-KIT, [SDS2&SAS] corresponds to the count of GN[N/S]K+ and [SDS1&SAS] corresponds to that of GN[N/S]K − .

Statistical analysis

Statistical analyses were performed using R software (version 4.4.1). Wasserstein distance was computed using the wasserstein1d function (parameter p = 1) from the “transport” package. Hierarchical clustering was computed using pheatmap function (parameter clustering_method = “complete,” scale = “none,” cluster_rows = TRUE, cluster_cols = TRUE, treeheight_row = 0) from the “pheatmap” package. Wilcoxon rank-sum test was computed using the wilcox_test function (parameter distribution = “exact”).

Gene set enrichment analysis

The gene set files (GAF format) for humans and mice were downloaded from the Gene Ontology (GO) Consortium (https://current.geneontology.org/products/pages/downloads.html) [35,36], and those for dogs, cats, and sheep were downloaded from the NCBI portal and then converted to the GMT format. The Pearson correlation coefficient between the in-ex ratio and the expression level of all genes (TPM) was calculated using the cor function from the “StatsBase” package in Julia software (version 1.11.3). Gene set enrichment analysis (GSEA) was performed by setting the seed value with set.seed(1111) and using the GSEA function (parameter TERM2GENE={gmt files for each species}, minGSSize = 20, maxGSSize = 500, pvalueCutoff = 0.01, seed = TRUE, eps = 0) from the “clusterProfiler” package in R. Genes were ranked based on their correlation coefficient, and the top and bottom genes were used as input.

Analysis of HITS-CLIP and IP-seq data

High-throughput sequencing-crosslinking immunoprecipitation (HITS-CLIP) datasets for NOVA2 (PRJNA286234 [37]) and RBFOX1/3 (PRJNA234443 [38]), as well as immunoprecipitation sequencing (IP-seq) data for RBFOX1 (PRJNA1134607 [39]), were downloaded from the NCBI. These FASTQ files were quality-controlled using fastp with default parameters and aligned to reference genomes using STAR (version 2.5.2b, options -outFilterMultimapNmax 1000, -outSAMattributes All, -outSAMtype BAM SortedByCoordinate). Subsequently, BAM files corresponding to biological replicates under the same condition were merged into a single BAM file using the samtools merge command with default parameters. PCR duplicate reads were removed from the merged BAM files using samtools markdup with the -r option.

Scoring of NOVA2 binding motif

The NOVA2 binding motif (YCAY cluster) in the gene sequence was scored following Ule et al. [40]. A sliding-window analysis was performed with a 45-nt window moving by 1 nt, in which each window was examined to identify and score the following motifs.

YCAY followed by the next YCAY at ≥7 nt distance (YCAY-N_{_≧7}-YCAY) → score = 0
YCAY followed by the next YCAY within 3–6 nt (YCAY-N_3-6-YCAY-N_{_≧7}-YCAY) → score = 1
YCAY followed by the next YCAY within 2 nt (YCAY-N_≤2-YCAY-N_{_≧7}-YCAY) → score = 2
Three YCAY motifs with mixed distances (≤2 nt and 3–6 nt) being consecutive (YCAY-N_≤2-YCAY-N_3-6-YCAY or YCAY-N_3-6-YCAY-N_≤2-YCAY) → Score = 4
Three YCAY motifs occurring consecutively with spacing of ≤2 nt between each (YCAY-N_≤2-YCAY-N_≤2-YCAY) → score = 8

The total score for each window was then calculated and transformed using the common logarithm. Cross-species integrated scores were not computed; instead, species-specific scores were calculated separately.

Results

Two selective splicing 5′ donor sites were closely positioned in KIT

For human, mouse, dog, cat, and sheep, we confirmed that GN[N/S]K − / + isoforms of c-KIT were generated via alternative 5′ donor site splicing that follows the canonical GU-AG rule [41,42] (Fig 1C). Within the pre-mRNA, two closely spaced GU donor sequences were present at the 5′ end of the intron immediately upstream of the exon encoding the transmembrane domain. Selection of the upstream donor site resulted in the exclusion of GN[N/S]K, whereas selection of the downstream donor site, located 12 nt downstream, led to GN[N/S]K inclusion.

Animal species-independent tissue-specific expression of c-KIT isoforms

To clarify the whole-body expression of the isoforms, we aligned the transcriptome reads of the five species to their reference genomes to quantify KIT expression (S1 Table). The metadata associated with the analyzed datasets did not explicitly indicate that any samples were derived from patients with tumors or cancer. Next, referring to the splice junction coordinates, we estimated the expression of GN[N/S]K+ and GN[N/S]K − . Subsequently, the in-ex ratio was calculated for each dataset, where −1 ≤ ‘in-ex ratio’ ≤ 1 was satisfied, and ‘in-ex ratio’ = 1 when all c-KIT proteins contained GN[N/S]K, and ‘in-ex ratio’ = −1 vice versa (see Materials and Methods for details). The whole-body expression of both GN[N/S]K+ and GN[N/S]K − was identified in human (Fig 2A) and the other mammals (S2 Fig). The results revealed the following species-independent characteristics in the in-ex ratio across tissue types: Most tissues exhibited a shift toward negative in-ex ratio values, predominantly expressing GN[N/S]K− isoform. Conversely, the CNS showed a shift toward positive values, with a higher proportion of GN[N/S]K+ isoform (Fig 2B and S3 Fig).

Download:

Fig 2. Histogram of in-ex ratio for human.

(A) Histogram of in-ex ratio across 14 tissue types based on ENCODE tissue classification. “n” indicates the number of samples for each tissue. (B) Histogram of in-ex ratio colored for two categories: CNS and other tissues. Red and blue bars indicate normalized numbers of CNS samples and other tissue samples exhibiting in-ex ratio values in each bin, respectively; the total areas of red and grey bars were each assigned 1. The wavy line at x = 0 is where the expression of GNNK+ and GNNK− is equal.

https://doi.org/10.1371/journal.pone.0332294.g002

To quantify the differences in in-ex ratio distributions across tissues, we calculated Wasserstein distances and performed hierarchical clustering (S4 Fig). In all species analyzed, CNS samples were consistently distinct from other tissue types in the first branch of the dendrogram, indicating that the in-ex ratio distribution in the CNS differed specifically from that in other tissues. A Wilcoxon rank-sum test revealed significant differences in the in-ex ratio values between CNS and non-CNS tissue samples: human (p < 2.22 × 10⁻¹⁶, r = 0.652), mouse (p < 2.22 × 10⁻¹⁶, r = 0.568), dog (p = 1.94 × 10⁻¹², r = 0.851), cat (p = 2.95 × 10⁻¹⁶, r = 0.726), and sheep (p = 6.32 × 10⁻¹², r = 0.399). For both human and mouse, the p-values reached the minimum value represented in R.

Enrichment analysis of genes correlated with in-ex ratio

The previous section revealed the tissue-specific expression ratios of GN[N/S]K+ and GN[N/S]K− at the metadata level. To validate the tissue specificity at the gene expression level, we identified GO terms associated with changes in the in-ex ratio, using RNA-seq gene expression data. GSEA was performed on a ranked list of genes based on the correlation coefficient between the in-ex ratio and gene expression (TPM). In all five species, the top-ranked gene groups whose expression positively correlated with GN[N/S]K+ expression were enriched in categories related to neural development, neural function, ion transport, and cell adhesion. In contrast, the lower-ranked gene groups whose expression positively correlated with GN[N/S]K− expression were enriched in categories related to DNA repair and replication, immune response, and cell matrix (Fig 3). Consistent with these findings, and as expected, the expression of the GN[N/S]K+ isoform was strongly associated with the CNS at the gene expression level.

Download:

Fig 3. Enriched GO terms related to the ratio of c-KIT isoforms.

Enrichment analysis was performed based on a list of genes sorted by the correlation coefficient between the in-ex ratio and gene expression level (TPM). Distribution of NES for GO terms and their descriptions that are shared across all five species (top). Distribution of NES for GO terms and descriptions that are common to at least three species (bottom). GO, gene ontology; TPM, transcripts per million; NES, normalized enrichment score.

https://doi.org/10.1371/journal.pone.0332294.g003

Developmental stage-dependent expression of c-KIT isoforms in CNS

Alternative splicing frequently occurs in the CNS [43] and exhibits dynamic regulation during developmental stages [44]. To capture the expression dynamics of c-KIT isoforms during CNS development, we calculated the in-ex ratio from RNA-seq data of the CNS in various developmental stages, ranging from 4 weeks post conception to adulthood in humans and from embryonic day 10.5 to adulthood in mice. Consequently, a temporal fluctuation in the in-ex ratios was identified. The GNNK− isoform predominated at early stages, whereas the relative abundance of the GNNK+ isoform increased as development proceeds (Fig 4).

Download:

Fig 4. Distribution of in-ex ratio across CNS developmental time series.

In-ex ratios were calculated for CNS samples from developmental time series RNA-seq datasets (human: PRJEB26969; mice: PRJEB26869). Point plots of (A) human and (B) mouse were created to depict in-ex ratios across developmental stages, with the shape of each point indicating the CNS region (human: forebrain and hindbrain; mouse: brain, forebrain, and hindbrain). wpc, week post conception; dpb, day post birth; mpb, month post birth; ypb, year post birth; E, Embryonic day; P, Postnatal day; CNS, central nervous system.

https://doi.org/10.1371/journal.pone.0332294.g004

Splice site switching is regulated by splicing factors

The expression of c-KIT isoforms was regulated in both tissue- and temporal-specific manners. We focused on genes annotated with the GO term “regulation of alternative mRNA splicing via the spliceosome” (GO:0000381). In a mouse tissue-wide dataset, 10 genes exhibited a correlation coefficient >0.3 (p < 0.01, FDR < 0.08) between the in–ex ratio and gene expression (S3 Table). Seven of these genes had publicly available RNA-seq datasets of KO or KD experiments, and c-KIT in–ex ratio was compared between KO/KD and wild-type samples. The datasets for NOVA2, RBFOX1 & RBFOX3, and DYRK1A exhibited effect sizes that were comparable to or greater than those observed in the tissue-wide comparison analysis (Fig 5A and S3 Table). During the development of CNS, NOVA2 expression raised sharply at embryonic day 12.5 and then declined to a constant level after postnatal development (Fig 5B). The expression of both RBFOX1 and RBFOX3 was activated on embryonic day 12.5 and increased monotonically. DYRK1A, meanwhile, exhibited little fluctuation and was consistently expressed in the CNS.

Download:

Fig 5. In–ex ratio distribution in NOVA2, RBFOX1, RBFOX3, and DYRK1A KO/KD experiments and gene expression across CNS developmental stages.

(A) Comparison of in–ex ratio between KO/KD and WT samples: NOVA2 KO vs. WT, RBFOX1/RBFOX3 dKD vs. WT, and DYRK1A hKO vs. WT. “p” and “r” indicate the p-value and effect size from the Wilcoxon rank-sum test. (B) Gene expression levels of NOVA2, RBFOX1, RBFOX3, and DYRK1A across CNS developmental stages. The x-axis represents gene expression levels (TPM), and the y-axis corresponds to that in Fig 4. KO, knockout; hKO, heterozygous knockout; KD, knockdown; dKD, double knockdown; WT, wild-type; CNS, central nervous system.

https://doi.org/10.1371/journal.pone.0332294.g005

RNA-binding protein interactions and binding motifs

NOVA2, RBFOX1, and RBFOX3 are RNA-binding proteins that regulate alternative splicing through their binding to target gene RNAs [40, 45–50]. RBFOX1 and RBFOX3 are associated with target RNAs both directly and indirectly via the large assembly of splicing regulators (LASR) [39,51]. HITS-CLIP and IP-seq datasets were analyzed to investigate the binding of splicing regulators to KIT pre-mRNA.

(i) NOVA2

In mice, NOVA2 exhibited binding to the intronic region immediately downstream of the alternative 5′ spliced donor site (Fig 6A). We assessed the evolutionary conservation of the NOVA2-binding motif sequence (YCAY cluster) in the regions surrounding the alternative 5′ spliced donor site across species. In all species analyzed, enrichment of YCAY cluster motif was observed in the regions surrounding the alternatively spliced donor sites, where NOVA2 binding was detected in mice (Fig 6B). The RNA-binding domain of NOVA2, which consists of three KH domains, showed a high degree of amino acid sequence identity among the five species (S5 Fig). This conservation suggests that NOVA2 is likely to bind near the alternative 5′ spliced donor site in other mammals, as observed in mice.

Download:

Fig 6. RNA binding protein interactions and motifs.

(A) HITS-CLIP tags mapping for RBFOX1 (PRJNA234443), RBFOX3 (PRJNA234443), and NOVA2 (PRJNA286234) around the mouse KIT alternative 5′ spliced donor site. The figure shows sequencing read coverage (data range: 0–10) and the locations of aligned reads. The red arrow indicates the position of the alternative spliced donor site. (B) Distribution of YCAY cluster scores around the KIT GN[N/S]K-coding regions in reference genomes of human, mouse, dog, cat, and sheep. The horizontal dashed line represents a score of 0.6. According to Ule et al. [40], values < 0.6 represent poor conservation, or only two conserved YCAY motifs. The red arrow indicates the position of the alternative spliced donor site. (C) IP-seq tags mapping of RNAs co-purified with Rbfox1/LASR in HEK293 cells overexpressing RBFOX1. The figure shows the coverage distribution of aligned reads around the human KIT alternative exon (data range: 0–40). The red arrow indicates the position of the alternative spliced donor site. (D) Full-length KIT sequences from five species were aligned using Clustal W, and the nucleotide sequence corresponding to the region showing high coverage in (C) +FLAG-RBFOX1 IP-seq in human is shown. Purple highlights indicate GU-rich stretches (runs of four or more consecutive G or U nucleotides). Hyphens (–) denote deletions.

https://doi.org/10.1371/journal.pone.0332294.g006

(ii) RBFOX1 and RBFOX3

HITS-CLIP analyses of RBFOX1 and RBFOX3 in mouse revealed no enrichment of CLIP tags around the alternative 5′ spliced donor site (Fig 6A). Although a small number of single-read alignments were detected, these likely represented background noise rather than specific binding events. However, analysis of IP-seq data obtained from purified RBFOX1/LASR complexes in HEK293 cell line revealed a clear enrichment of RBFOX1 tags (+FLAG-RBFOX1) within the intron approximately 0.5 kilobases downstream of the alternative 5′ spliced donor site (Fig 6C). Conversely, no such enrichment was observed in the control sample lacking RBFOX1 expression (−FLAG-RBFOX1), suggesting that the signal was unlikely to represent background noise. IP-seq is capable of detecting not only direct RNA–protein interactions but also RNA regions that are protected by the LASR complex [39]. These results indicate that RBFOX1 associates indirectly with the intronic region downstream of the KIT alternative 5′ spliced donor site through the LASR complex rather than via direct RNA binding. In human, the sequences of the CLIP-enriched regions were prominently GU-rich, a motif recognized by the LASR subunit HNRNPM [39,52] (Fig. 6D). GU-rich elements were also observed in the corresponding regions of the other four species. This observation suggests that the LASR subunit HNRNPM may mediate direct binding to KIT pre-mRNA.

Discussion

In this study, we performed a comprehensive tissue-wide analysis of the expression of the c-KIT isoforms GN[N/S]K+ and GN[N/S]K− in human, mouse, dog, cat, and sheep. Our analysis revealed that both isoforms were co-expressed in many tissues, with the GN[N/S]K− isoform generally predominant. In the CNS, the GN[N/S]K+ isoform showed a higher expression ratio, and in human and mouse, the ratio varied according to developmental stage, indicating tissue- and stage-specific expression patterns. These patterns were observed in non-tumor samples, suggesting that the expression of these isoforms exhibits tissue-specific variation independently of tumorigenesis. These observations of the dynamics of c-KIT isoforms expression are consistent with those of previous studies [4,6,7,10,53–56].

Although c-KIT is highly expressed in the brain during development and adulthood, no overt phenotypic changes were observed in KIT mutant mice, suggesting that c-KIT expression in the brain is functionally redundant [57]. However, subsequent studies reported that a brain-specific KIT mutation induced during development suppressed neural crest cell proliferation [58,59]. In addition, signaling activity differed between the GN[N/S]K+ and GN[N/S]K− isoforms through differences in phosphorylation activity following SCF stimulation [20,22]. Therefore, one potential role of the GN[N/S]K+ isoform of c-KIT in the CNS may be to fine-tune signaling activity and pathway intensity during CNS development and morphogenesis by regulating the relative expression of GN[N/S]K+ and GN[N/S]K− isoforms.

NOVA2, RBFOX1, RBFOX3, and DYRK1A were identified as candidate regulators contributing to the tissue-specific alternative splicing of KIT. Previous studies on NOVA2 (and its paralog NOVA1) have mainly focused on the regulation of cassette exons, proposing an RNA regulatory map in which NOVA1/2 binding downstream of a target exon promotes exon inclusion, whereas binding upstream of the exon induces exon skipping [40]. Although minor alternative splicing events involving alternative 5′ donor sites, such as those observed in KIT, remain less well characterized, previous reports have shown that Ccdc9 [40] and Ncdn [60] are regulated by NOVA1/2 and exhibit selective modulation of the alternative 5′ donor site. These findings suggest a common regulatory principle underlying NOVA1/2-mediated control, encompassing not only cassette exons but also alternative 5′ donor sites and other minor forms of alternative splicing events. Although RBFOX1 and RBFOX3 did not exhibit direct pre-mRNA binding around the alternative 5′ spliced donor site of KIT, RBFOX1 was indirectly associated with KIT intronic RNA, suggesting that the RBFOX/LASR complex may contribute to the regulation of KIT alternative splicing. DYRK1A participates in the regulation of alternative splicing by phosphorylating proline residues in the splicing factor SRSF6 (SRP55), thereby modulating its activity in splice site selection [61].

The findings of this study indicate that, in both human clinical and veterinary research, it is crucial to consider tissue-specific variations in c-KIT isoform ratios when evaluating the efficacy of c-KIT-targeting agents (such as imatinib). In addition to the isoforms considered in the present study, c-KIT in sheep and cats was predicted to synthesize another type of isoform lacking the domain corresponding to exon 1 (called d1KIT in domestic cats), which might contribute to the development of the neural crest and regulation of skin coat color [8,62]. Therefore, identifying potential tissue-specific, particularly CNS-specific, functions and regulatory mechanisms of different c-KIT isoforms using in vivo and in vitro studies, as well as in silico predictions, should be a crucial focus of future research.

The present study has some limitations. The analysis of alternative splicing regulatory factors was performed primarily using data from mice (with one dataset from a human cell line included) due to the abundance of publicly available experimental resources. The correlation coefficients between the in–ex ratio and expression of regulatory factors varied among species (S3 Table), and tended to be lower in mice. This variation was likely influenced by differences in the types and numbers of tissue samples included in the datasets across species. However, in the neighbor-joining trees constructed from nucleotide alignments of the entire KIT locus (including introns) and from the sequence spanning the exon containing the GN[N/S]K-encoding region, the mouse KIT was positioned as the earliest-branching lineage among the five species examined (S6 Fig). This suggests that interspecies differences in the KIT gene sequence, as well as the potential presence of species-specific regulatory factors involved in KIT alternative splicing, may also contribute to this variation. Furthermore, owing to data availability, variations in isoform expression ratios across developmental stages of the CNS were examined only in mice and humans. Similar analyses in dogs, cats, and sheep will be necessary to clarify whether the same developmental patterns are conserved. For RBFOX1 and RBFOX3, only double KD data were analyzed; therefore, it remains uncertain whether both or only one of these proteins contributes to KIT alternative splicing. In addition, RNA binding of RBFOX1 mediated through the LASR has been demonstrated only in one of the human cultured cell lines, indicating that identification in vivo in humans and other species will also be required.

Supporting information

S1 Fig. Identification of isoforms.

Refer to the positions of the splice donor site (SDS) and splice acceptor site (SAS) in aligned RNA-seq sequence reads to distinguish between the shorter exon and the longer exon (A). Genomic locus of SDS1, SDS2, and SAS of KIT on the reference genome for each species (B).

https://doi.org/10.1371/journal.pone.0332294.s001

(PDF)

S2 Fig. Histograms of the in–ex ratio.

Histograms of in–ex ratios across tissue types based on ENCODE tissue classification. (A) Mouse (Mus musculus), (B) dog (Canis lupus familiaris), (C) cat (Felis catus), and (D) sheep (Ovis aries). “n” indicates the number of samples for each histogram. The wavy line at x = 0 is where the expression of GN[N/S]K+ and GN[N/S]K − is equal.

https://doi.org/10.1371/journal.pone.0332294.s002

(PDF)

S3 Fig. Histogram of in–ex ratios colored by two categories: CNS and other tissues.

The x-axis represents in–ex ratio values, and the y-axis represents the density calculated separately for CNS and other tissues. (A) Mouse, (B) dog, (C) cat, and (D) sheep. Red and blue bars indicate normalized numbers of CNS samples and other tissue samples exhibiting in–ex ratio values in each bin, respectively; the total areas of red and grey bars were each assigned 1. The wavy line at x = 0 is where the expression of GNNK+ and GNNK− is equal.

https://doi.org/10.1371/journal.pone.0332294.s003

(PDF)

S4 Fig. Matrix of Wasserstein distance.

Matrix of Wasserstein distance among histograms of in–ex ratios in different tissues was estimated for human (A), mouse (B), dog (C), cat (D), and sheep (E). The heatmap represents the Wasserstein distances, where each color bar range is independently scaled per species. A dendrogram was obtained by hierarchical clustering applied to a series of Wasserstein distances of each tissue.

https://doi.org/10.1371/journal.pone.0332294.s004

(PDF)

S5 Fig. Cross-species comparison of NOVA2 amino acid sequences.

Comparison of amino acid sequences of NOVA2 among human (NP_002507.1), mouse (NP_001025048.2), dog (XP_038278991.1), cat (XP_023100626.2), and sheep (XP_027834310.1). Sequence regions with black bars indicate KH domains. The red squares indicate positions with amino acid differences, and the numbers indicate their positions. Positions with asterisks represent identical amino acids among all five species. The amino acid sequences exhibited complete conservation among four mammals (human, dog, cat, and sheep), and mutations at seven amino acids were identified within the non-functional region of the mouse, situated between the second and third KH domains.

https://doi.org/10.1371/journal.pone.0332294.s005

(PDF)

S6 Fig. Phylogenetic analysis of the KIT in five species.

Sequence phylogenetic comparison of (A) the full-length KIT gene including introns and (B) the region encompassing the alternative exon, downstream intron and exon, using neighbor-joining tree method. Branch lengths above the branches represent evolutionary distances used to infer the phylogeny.

https://doi.org/10.1371/journal.pone.0332294.s006

(PDF)

S1 Table. Details of analyzed datasets.

(SampleInfo_targetGeneExpression.xlsx). Human and mouse: IDs of ENCODE database (experiment ID, Aligned BAM file ID, Gene expression file ID), summaries of sample, tissue type, tissue category, read count of GNNK− and GNNK + , in–ex ratio, and TPM of KIT. Dogs, cats, and sheep: RNA-seq accession ID, tissue type, tissue category, read count of GNSK– and GNSK + , in–ex ratio, and TPM of KIT.

https://doi.org/10.1371/journal.pone.0332294.s007

(XLSX)

S2 Table. Details of GSEA results.

(GSEA_result.xlsx). Results of GSEA using a gene list ranked based on the correlation coefficient between the in–ex ratio of GN[N/S]K of the KIT and expression of all genes. A positive normalized enrichment score (NES) indicates that a given Gene Ontology (GO) Biological Process term is enriched among genes at the top of the ranked list, whereas a negative NES indicates enrichment among genes at the bottom. GSEA, gene set enrichment analysis.

https://doi.org/10.1371/journal.pone.0332294.s008

(XLSX)

S3 Table. Alternative splicing factors.

(AlternativeSplicingFactor.xlsx). [corr tab] Correlation coefficients between the in–ex ratio and genes encoding alternative splicing regulators (GO:0000381) in tissue-wide RNA-seq datasets. The genes included in GO:0000381 differ among species, and if a gene is not included for a given species, its value is recorded as NA. [Mouse_experiments tab] Association between KIT isoform expression and alternative splicing regulators in mouse, based on the analysis of available experimental data for genes with correlation coefficients ≥ 0.3. The in–ex ratios of knockout or knockdown samples were compared with those of wild-type samples using the Wilcoxon rank-sum test, and p-values and effect sizes were calculated.

https://doi.org/10.1371/journal.pone.0332294.s009

(XLSX)

References

1. Sheikh E, Tran T, Vranic S, Levy A, Bonfil RD. Role and significance of c-KIT receptor tyrosine kinase in cancer: A review. Bosn J Basic Med Sci. 2022;22(5):683–98. pmid:35490363
- View Article
- PubMed/NCBI
- Google Scholar
2. Roskoski R Jr. Structure and regulation of Kit protein-tyrosine kinase--the stem cell factor receptor. Biochem Biophys Res Commun. 2005;338(3):1307–15. pmid:16226710
- View Article
- PubMed/NCBI
- Google Scholar
3. Krimmer SG, Bertoletti N, Suzuki Y, Katic L, Mohanty J, Shu S, et al. Cryo-EM analyses of KIT and oncogenic mutants reveal structural oncogenic plasticity and a target for therapeutic intervention. Proc Natl Acad Sci U S A. 2023;120(13):e2300054120. pmid:36943885
- View Article
- PubMed/NCBI
- Google Scholar
4. London CA, Galli SJ, Yuuki T, Hu ZQ, Helfand SC, Geissler EN. Spontaneous canine mast cell tumors express tandem duplications in the proto-oncogene c-kit. Exp Hematol. 1999;27(4):689–97. pmid:10210327
- View Article
- PubMed/NCBI
- Google Scholar
5. Gramer I, Kessler M, Geyer J. Detection of novel polymorphisms in the ckit gene of canine patients with lymphoma, melanoma, haemangiosarcoma, and osteosarcoma. Vet Res Commun. 2016;40(2):89–95. pmid:26971271
- View Article
- PubMed/NCBI
- Google Scholar
6. Chen Y-C, Liao J-W, Hsu W-L, Chang S-C. Identification of the two KIT isoforms and their expression status in canine hemangiosarcomas. BMC Vet Res. 2016;12(1):142. pmid:27422008
- View Article
- PubMed/NCBI
- Google Scholar
7. Hasegawa Y, Shosu K, Tsuji K, Shimoyama Y, Miyama TS, Baba K, et al. Intratumoral heterogeneity of c-KIT mutations in a feline splenic mast cell tumor and their functional effects on cell proliferation. Sci Rep. 2022;12(1):15791. pmid:36138037
- View Article
- PubMed/NCBI
- Google Scholar
8. Saravanaperumal SA, Pallotti S, Pediconi D, Renieri C, La Terza A. Exon-1 skipping and intron-1 retaining by alternative splicing of the c-KIT gene encodes a novel splice variant in the skin of Merino sheep (Ovis aries). Mol Biol Rep. 2021;48(5):4987–94. pmid:34148208
- View Article
- PubMed/NCBI
- Google Scholar
9. Goldfarb T, Kodali VK, Pujar S, Brover V, Robbertse B, Farrell CM, et al. NCBI RefSeq: Reference sequence standards through 25 years of curation and annotation. Nucleic Acids Research. 2025;53: D243–D257.
- View Article
- Google Scholar
10. Chen Y-C, Liao J-W, Chang S-C, Hsu W-L. Expression frequency of c-kit isoforms and its prognostic potential in canine mammary tumours. Vet Comp Oncol. 2020;18(3):303–14. pmid:31652393
- View Article
- PubMed/NCBI
- Google Scholar
11. Hirota S, Isozaki K, Moriyama Y, Hashimoto K, Nishida T, Ishiguro S, et al. Gain-of-function mutations of c-kit in human gastrointestinal stromal tumors. Science. 1998;279(5350):577–80. pmid:9438854
- View Article
- PubMed/NCBI
- Google Scholar
12. Gregory-Bryson E, Bartlett E, Kiupel M, Hayes S, Yuzbasiyan-Gurkan V. Canine and human gastrointestinal stromal tumors display similar mutations in c-KIT exon 11. BMC Cancer. 2010;10:559. pmid:20950418
- View Article
- PubMed/NCBI
- Google Scholar
13. Isotani M, Yamada O, Lachowicz JL, Tamura K, Yagihara H, Fujino Y, et al. Mutations in the fifth immunoglobulin-like domain of kit are common and potentially sensitive to imatinib mesylate in feline mast cell tumours. Br J Haematol. 2010;148(1):144–53. pmid:19804453
- View Article
- PubMed/NCBI
- Google Scholar
14. Webster JD, Yuzbasiyan-Gurkan V, Kaneene JB, Miller R, Resau JH, Kiupel M. The role of c-KIT in tumorigenesis: evaluation in canine cutaneous mast cell tumors. Neoplasia. 2006;8(2):104–11. pmid:16611403
- View Article
- PubMed/NCBI
- Google Scholar
15. Nagata H, Worobec AS, Oh CK, Chowdhury BA, Tannenbaum S, Suzuki Y, et al. Identification of a point mutation in the catalytic domain of the protooncogene c-kit in peripheral blood mononuclear cells of patients who have mastocytosis with an associated hematologic disorder. Proc Natl Acad Sci U S A. 1995;92(23):10560–4. pmid:7479840
- View Article
- PubMed/NCBI
- Google Scholar
16. Garcia-Montero AC, Jara-Acevedo M, Teodosio C, Sanchez ML, Nunez R, Prados A, et al. KIT mutation in mast cells and other bone marrow hematopoietic cell lineages in systemic mast cell disorders: a prospective study of the Spanish Network on Mastocytosis (REMA) in a series of 113 patients. Blood. 2006;108(7):2366–72. pmid:16741248
- View Article
- PubMed/NCBI
- Google Scholar
17. Beghini A, Ripamonti CB, Cairoli R, Cazzaniga G, Colapietro P, Elice F, et al. KIT activating mutations: incidence in adult and pediatric acute myeloid leukemia, and identification of an internal tandem duplication. Haematologica. 2004;89(8):920–5. pmid:15339674
- View Article
- PubMed/NCBI
- Google Scholar
18. Guerrini F, Galimberti S, Ciabatti E, Brizzi S, Testi R, Pollastrini A, et al. Molecular detection of GNNK- and GNNK+ c-kit isoforms: a new tool for risk stratification in adult acute myeloid leukaemia. Leukemia. 2007;21(9):2056–8. pmid:17554388
- View Article
- PubMed/NCBI
- Google Scholar
19. Caruana G, Cambareri AC, Ashman LK. Isoforms of c-KIT differ in activation of signalling pathways and transformation of NIH3T3 fibroblasts. Oncogene. 1999;18(40):5573–81. pmid:10523834
- View Article
- PubMed/NCBI
- Google Scholar
20. Voytyuk O, Lennartsson J, Mogi A, Caruana G, Courtneidge S, Ashman LK, et al. Src family kinases are involved in the differential signaling from two splice forms of c-Kit. J Biol Chem. 2003;278(11):9159–66. pmid:12511554
- View Article
- PubMed/NCBI
- Google Scholar
21. Young SM, Cambareri AC, Odell A, Geary SM, Ashman LK. Early myeloid cells expressing c-KIT isoforms differ in signal transduction, survival and chemotactic responses to Stem Cell Factor. Cell Signal. 2007;19(12):2572–81. pmid:17855052
- View Article
- PubMed/NCBI
- Google Scholar
22. Sun J, Pedersen M, Rönnstrand L. Gab2 is involved in differential phosphoinositide 3-kinase signaling by two splice forms of c-Kit. J Biol Chem. 2008;283(41):27444–51. pmid:18697750
- View Article
- PubMed/NCBI
- Google Scholar
23. Phung B, Steingrímsson E, Rönnstrand L. Differential activity of c-KIT splice forms is controlled by extracellular peptide insert length. Cell Signal. 2013;25(11):2231–8. pmid:23880320
- View Article
- PubMed/NCBI
- Google Scholar
24. Bell CA, Tynan JA, Hart KC, Meyer AN, Robertson SC, Donoghue DJ. Rotational coupling of the transmembrane and kinase domains of the Neu receptor tyrosine kinase. Mol Biol Cell. 2000;11(10):3589–99. pmid:11029057
- View Article
- PubMed/NCBI
- Google Scholar
25. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489: 57–74. pmid:22955616
- View Article
- PubMed/NCBI
- Google Scholar
26. Luo Y, Hitz BC, Gabdank I, Hilton JA, Kagda MS, Lam B, et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 2020;48(D1):D882–9. pmid:31713622
- View Article
- PubMed/NCBI
- Google Scholar
27. Lee J-A, Damianov A, Lin C-H, Fontes M, Parikshak NN, Anderson ES, et al. Cytoplasmic Rbfox1 Regulates the Expression of Synaptic and Autism-Related Genes. Neuron. 2016;89(1):113–28. pmid:26687839
- View Article
- PubMed/NCBI
- Google Scholar
28. Traunmüller L, Schulz J, Ortiz R, Feng H, Furlanis E, Gomez AM, et al. A cell-type-specific alternative splicing regulator shapes synapse properties in a trans-synaptic manner. Cell Rep. 2023;42(3):112173. pmid:36862556
- View Article
- PubMed/NCBI
- Google Scholar
29. Saito Y, Yuan Y, Zucker-Scharff I, Fak JJ, Jereb S, Tajima Y, et al. Differential NOVA2-Mediated Splicing in Excitatory and Inhibitory Neurons Regulates Cortical Development and Cerebellar Function. Neuron. 2019;101(4):707-720.e5. pmid:30638744
- View Article
- PubMed/NCBI
- Google Scholar
30. Matsumoto Y, Yik-Lok Chung C, Isobe S, Sakamoto M, Lin X, Chan T-F, et al. Chromosome-scale assembly with improved annotation provides insights into breed-wide genomic structure and diversity in domestic cats. J Adv Res. 2025;75:863–74. pmid:39490737
- View Article
- PubMed/NCBI
- Google Scholar
31. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90. pmid:30423086
- View Article
- PubMed/NCBI
- Google Scholar
32. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1)15–21. pmid:23104886
- View Article
- PubMed/NCBI
- Google Scholar
33. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323. pmid:21816040
- View Article
- PubMed/NCBI
- Google Scholar
34. Tamura K, Stecher G, Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol Biol Evol. 2021;38(7):3022–7. pmid:33892491
- View Article
- PubMed/NCBI
- Google Scholar
35. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. pmid:10802651
- View Article
- PubMed/NCBI
- Google Scholar
36. Gene Ontology Consortium, Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, et al. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224(1):iyad031. pmid:36866529
- View Article
- PubMed/NCBI
- Google Scholar
37. Saito Y, Miranda-Rottmann S, Ruggiu M, Park CY, Fak JJ, Zhong R, et al. NOVA2-mediated RNA regulation is required for axonal pathfinding during development. Elife. 2016;5:e14371. pmid:27223325
- View Article
- PubMed/NCBI
- Google Scholar
38. Weyn-Vanhentenryck SM, Mele A, Yan Q, Sun S, Farny N, Zhang Z, et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 2014;6(6):1139–52. pmid:24613350
- View Article
- PubMed/NCBI
- Google Scholar
39. Peyda P, Lin C-H, Onwuzurike K, Black DL. The Rbfox1/LASR complex controls alternative pre-mRNA splicing by recognition of multipart RNA regulatory modules. Genes Dev. 2025;39(5–6):364–83. pmid:39880658
- View Article
- PubMed/NCBI
- Google Scholar
40. Ule J, Stefani G, Mele A, Ruggiu M, Wang X, Taneri B, et al. An RNA map predicting Nova-dependent splicing regulation. Nature. 2006;444(7119):580–6. pmid:17065982
- View Article
- PubMed/NCBI
- Google Scholar
41. Breathnach R, Benoist C, O’Hare K, Gannon F, Chambon P. Ovalbumin gene: evidence for a leader sequence in mRNA and DNA sequences at the exon-intron boundaries. Proc Natl Acad Sci U S A. 1978;75(10):4853–7. pmid:283395
- View Article
- PubMed/NCBI
- Google Scholar
42. McManus CJ, Graveley BR. RNA structure and the mechanisms of alternative splicing. Curr Opin Genet Dev. 2011;21(4):373–9. pmid:21530232
- View Article
- PubMed/NCBI
- Google Scholar
43. Raj B, Blencowe BJ. Alternative Splicing in the Mammalian Nervous System: Recent Insights into Mechanisms and Functional Roles. Neuron. 2015;87(1):14–27. pmid:26139367
- View Article
- PubMed/NCBI
- Google Scholar
44. Dillman AA, Hauser DN, Gibbs JR, Nalls MA, McCoy MK, Rudenko IN, et al. mRNA expression, splicing and editing in the embryonic and adult mouse cerebral cortex. Nat Neurosci. 2013;16(4):499–506. pmid:23416452
- View Article
- PubMed/NCBI
- Google Scholar
45. Ule J, Jensen KB, Ruggiu M, Mele A, Ule A, Darnell RB. CLIP identifies Nova-regulated RNA networks in the brain. Science. 2003;302(5648):1212–5. pmid:14615540
- View Article
- PubMed/NCBI
- Google Scholar
46. Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M, Chi SW, et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature. 2008;456(7221):464–9. pmid:18978773
- View Article
- PubMed/NCBI
- Google Scholar
47. Zhang C, Frias MA, Mele A, Ruggiu M, Eom T, Marney CB, et al. Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls. Science. 2010;329(5990):439–43. pmid:20558669
- View Article
- PubMed/NCBI
- Google Scholar
48. Fisher E, Feng J. RNA splicing regulators play critical roles in neurogenesis. Wiley Interdiscip Rev RNA. 2022;13(6):e1728. pmid:35388651
- View Article
- PubMed/NCBI
- Google Scholar
49. Conboy JG. Developmental regulation of RNA processing by Rbfox proteins. Wiley Interdiscip Rev RNA. 2017;8(2):10.1002/wrna.1398. pmid:27748060
- View Article
- PubMed/NCBI
- Google Scholar
50. Ponthier JL, Schluepen C, Chen W, Lersch RA, Gee SL, Hou VC, et al. Fox-2 splicing factor binds to a conserved intron motif to promote inclusion of protein 4.1R alternative exon 16. J Biol Chem. 2006;281(18):12468–74. pmid:16537540
- View Article
- PubMed/NCBI
- Google Scholar
51. Damianov A, Ying Y, Lin C-H, Lee J-A, Tran D, Vashisht AA, et al. Rbfox Proteins Regulate Splicing as Part of a Large Multiprotein Complex LASR. Cell. 2016;165(3):606–19. pmid:27104978
- View Article
- PubMed/NCBI
- Google Scholar
52. Ho JS, Di Tullio F, Schwarz M, Low D, Incarnato D, Gay F, et al. HNRNPM controls circRNA biogenesis and splicing fidelity to sustain cancer cell fitness. Elife. 2021;10:e59654. pmid:34075878
- View Article
- PubMed/NCBI
- Google Scholar
53. Crosier P, Ricciardi S, Hall L, Vitas M, Clark S, Crosier K. Expression of isoforms of the human receptor tyrosine kinase c-kit in leukemic cell lines and acute myeloid leukemia. Blood. 1993;82(4):1151–8.
- View Article
- Google Scholar
54. Zhu WM, Dong WF, Minden M. Alternate splicing creates two forms of the human kit protein. Leuk Lymphoma. 1994;12(5–6):441–7. pmid:7514064
- View Article
- PubMed/NCBI
- Google Scholar
55. Sakuma Y, Sakurai S, Oguni S, Hironaka M, Saito K. Alterations of the c-kit gene in testicular germ cell tumors. Cancer Sci. 2003;94(6):486–91. pmid:12824871
- View Article
- PubMed/NCBI
- Google Scholar
56. Tuck AR, Robker RL, Norman RJ, Tilley WD, Hickey TE. Expression and localisation of c-kit and KITL in the adult human ovary. J Ovarian Res. 2015;8:31. pmid:26008799
- View Article
- PubMed/NCBI
- Google Scholar
57. Motro B, van der Kooy D, Rossant J, Reith A, Bernstein A. Contiguous patterns of c-kit and steel expression: analysis of mutations at the W and Sl loci. Development. 1991;113(4):1207–21. pmid:1811937
- View Article
- PubMed/NCBI
- Google Scholar
58. Aoki H, Hara A, Kunisada T. Induced haploinsufficiency of Kit receptor tyrosine kinase impairs brain development. JCI Insight. 2017;2(19):e94385. pmid:28978807
- View Article
- PubMed/NCBI
- Google Scholar
59. Minei R, Aoki H, Ogura A, Kunisada T. Compensatory gene expression potentially rescues impaired brain development in Kit mutant mice. Sci Rep. 2023;13(1):4166. pmid:36914660
- View Article
- PubMed/NCBI
- Google Scholar
60. Ule J, Ule A, Spencer J, Williams A, Hu J-S, Cline M, et al. Nova regulates brain-specific splicing to shape the synapse. Nat Genet. 2005;37(8):844–52. pmid:16041372
- View Article
- PubMed/NCBI
- Google Scholar
61. Yin X, Jin N, Gu J, Shi J, Zhou J, Gong C-X, et al. Dual-specificity tyrosine phosphorylation-regulated kinase 1A (Dyrk1A) modulates serine/arginine-rich protein 55 (SRp55)-promoted Tau exon 10 inclusion. J Biol Chem. 2012;287(36):30497–506. pmid:22767602
- View Article
- PubMed/NCBI
- Google Scholar
62. Awazu A, Takemoto D, Watanabe K, Sakamoto N. Possibilities of skin coat color-dependent risks and risk factors of squamous cell carcinoma and deafness of domestic cats inferred via RNA-seq data. Genes Cells. 2023;28(12):893–905. pmid:37864512
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Sheikh E, Tran T, Vranic S, Levy A, Bonfil RD. Role and significance of c-KIT receptor tyrosine kinase in cancer: A review. Bosn J Basic Med Sci. 2022;22(5):683–98. pmid:35490363
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Roskoski R Jr. Structure and regulation of Kit protein-tyrosine kinase--the stem cell factor receptor. Biochem Biophys Res Commun. 2005;338(3):1307–15. pmid:16226710
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Krimmer SG, Bertoletti N, Suzuki Y, Katic L, Mohanty J, Shu S, et al. Cryo-EM analyses of KIT and oncogenic mutants reveal structural oncogenic plasticity and a target for therapeutic intervention. Proc Natl Acad Sci U S A. 2023;120(13):e2300054120. pmid:36943885
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. London CA, Galli SJ, Yuuki T, Hu ZQ, Helfand SC, Geissler EN. Spontaneous canine mast cell tumors express tandem duplications in the proto-oncogene c-kit. Exp Hematol. 1999;27(4):689–97. pmid:10210327
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Gramer I, Kessler M, Geyer J. Detection of novel polymorphisms in the ckit gene of canine patients with lymphoma, melanoma, haemangiosarcoma, and osteosarcoma. Vet Res Commun. 2016;40(2):89–95. pmid:26971271
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Chen Y-C, Liao J-W, Hsu W-L, Chang S-C. Identification of the two KIT isoforms and their expression status in canine hemangiosarcomas. BMC Vet Res. 2016;12(1):142. pmid:27422008
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Hasegawa Y, Shosu K, Tsuji K, Shimoyama Y, Miyama TS, Baba K, et al. Intratumoral heterogeneity of c-KIT mutations in a feline splenic mast cell tumor and their functional effects on cell proliferation. Sci Rep. 2022;12(1):15791. pmid:36138037
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Saravanaperumal SA, Pallotti S, Pediconi D, Renieri C, La Terza A. Exon-1 skipping and intron-1 retaining by alternative splicing of the c-KIT gene encodes a novel splice variant in the skin of Merino sheep (Ovis aries). Mol Biol Rep. 2021;48(5):4987–94. pmid:34148208
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Goldfarb T, Kodali VK, Pujar S, Brover V, Robbertse B, Farrell CM, et al. NCBI RefSeq: Reference sequence standards through 25 years of curation and annotation. Nucleic Acids Research. 2025;53: D243–D257.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref10] 10. Chen Y-C, Liao J-W, Chang S-C, Hsu W-L. Expression frequency of c-kit isoforms and its prognostic potential in canine mammary tumours. Vet Comp Oncol. 2020;18(3):303–14. pmid:31652393
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Hirota S, Isozaki K, Moriyama Y, Hashimoto K, Nishida T, Ishiguro S, et al. Gain-of-function mutations of c-kit in human gastrointestinal stromal tumors. Science. 1998;279(5350):577–80. pmid:9438854
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Gregory-Bryson E, Bartlett E, Kiupel M, Hayes S, Yuzbasiyan-Gurkan V. Canine and human gastrointestinal stromal tumors display similar mutations in c-KIT exon 11. BMC Cancer. 2010;10:559. pmid:20950418
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Isotani M, Yamada O, Lachowicz JL, Tamura K, Yagihara H, Fujino Y, et al. Mutations in the fifth immunoglobulin-like domain of kit are common and potentially sensitive to imatinib mesylate in feline mast cell tumours. Br J Haematol. 2010;148(1):144–53. pmid:19804453
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. Webster JD, Yuzbasiyan-Gurkan V, Kaneene JB, Miller R, Resau JH, Kiupel M. The role of c-KIT in tumorigenesis: evaluation in canine cutaneous mast cell tumors. Neoplasia. 2006;8(2):104–11. pmid:16611403
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref15] 15. Nagata H, Worobec AS, Oh CK, Chowdhury BA, Tannenbaum S, Suzuki Y, et al. Identification of a point mutation in the catalytic domain of the protooncogene c-kit in peripheral blood mononuclear cells of patients who have mastocytosis with an associated hematologic disorder. Proc Natl Acad Sci U S A. 1995;92(23):10560–4. pmid:7479840
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref16] 16. Garcia-Montero AC, Jara-Acevedo M, Teodosio C, Sanchez ML, Nunez R, Prados A, et al. KIT mutation in mast cells and other bone marrow hematopoietic cell lineages in systemic mast cell disorders: a prospective study of the Spanish Network on Mastocytosis (REMA) in a series of 113 patients. Blood. 2006;108(7):2366–72. pmid:16741248
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref17] 17. Beghini A, Ripamonti CB, Cairoli R, Cazzaniga G, Colapietro P, Elice F, et al. KIT activating mutations: incidence in adult and pediatric acute myeloid leukemia, and identification of an internal tandem duplication. Haematologica. 2004;89(8):920–5. pmid:15339674
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref18] 18. Guerrini F, Galimberti S, Ciabatti E, Brizzi S, Testi R, Pollastrini A, et al. Molecular detection of GNNK- and GNNK+ c-kit isoforms: a new tool for risk stratification in adult acute myeloid leukaemia. Leukemia. 2007;21(9):2056–8. pmid:17554388
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref19] 19. Caruana G, Cambareri AC, Ashman LK. Isoforms of c-KIT differ in activation of signalling pathways and transformation of NIH3T3 fibroblasts. Oncogene. 1999;18(40):5573–81. pmid:10523834
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref20] 20. Voytyuk O, Lennartsson J, Mogi A, Caruana G, Courtneidge S, Ashman LK, et al. Src family kinases are involved in the differential signaling from two splice forms of c-Kit. J Biol Chem. 2003;278(11):9159–66. pmid:12511554
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref21] 21. Young SM, Cambareri AC, Odell A, Geary SM, Ashman LK. Early myeloid cells expressing c-KIT isoforms differ in signal transduction, survival and chemotactic responses to Stem Cell Factor. Cell Signal. 2007;19(12):2572–81. pmid:17855052
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref22] 22. Sun J, Pedersen M, Rönnstrand L. Gab2 is involved in differential phosphoinositide 3-kinase signaling by two splice forms of c-Kit. J Biol Chem. 2008;283(41):27444–51. pmid:18697750
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref23] 23. Phung B, Steingrímsson E, Rönnstrand L. Differential activity of c-KIT splice forms is controlled by extracellular peptide insert length. Cell Signal. 2013;25(11):2231–8. pmid:23880320
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref24] 24. Bell CA, Tynan JA, Hart KC, Meyer AN, Robertson SC, Donoghue DJ. Rotational coupling of the transmembrane and kinase domains of the Neu receptor tyrosine kinase. Mol Biol Cell. 2000;11(10):3589–99. pmid:11029057
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref25] 25. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489: 57–74. pmid:22955616
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref26] 26. Luo Y, Hitz BC, Gabdank I, Hilton JA, Kagda MS, Lam B, et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 2020;48(D1):D882–9. pmid:31713622
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref27] 27. Lee J-A, Damianov A, Lin C-H, Fontes M, Parikshak NN, Anderson ES, et al. Cytoplasmic Rbfox1 Regulates the Expression of Synaptic and Autism-Related Genes. Neuron. 2016;89(1):113–28. pmid:26687839
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref28] 28. Traunmüller L, Schulz J, Ortiz R, Feng H, Furlanis E, Gomez AM, et al. A cell-type-specific alternative splicing regulator shapes synapse properties in a trans-synaptic manner. Cell Rep. 2023;42(3):112173. pmid:36862556
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref29] 29. Saito Y, Yuan Y, Zucker-Scharff I, Fak JJ, Jereb S, Tajima Y, et al. Differential NOVA2-Mediated Splicing in Excitatory and Inhibitory Neurons Regulates Cortical Development and Cerebellar Function. Neuron. 2019;101(4):707-720.e5. pmid:30638744
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref30] 30. Matsumoto Y, Yik-Lok Chung C, Isobe S, Sakamoto M, Lin X, Chan T-F, et al. Chromosome-scale assembly with improved annotation provides insights into breed-wide genomic structure and diversity in domestic cats. J Adv Res. 2025;75:863–74. pmid:39490737
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref31] 31. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90. pmid:30423086
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref32] 32. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1)15–21. pmid:23104886
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref33] 33. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323. pmid:21816040
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref34] 34. Tamura K, Stecher G, Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol Biol Evol. 2021;38(7):3022–7. pmid:33892491
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref35] 35. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. pmid:10802651
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref36] 36. Gene Ontology Consortium, Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, et al. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224(1):iyad031. pmid:36866529
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref37] 37. Saito Y, Miranda-Rottmann S, Ruggiu M, Park CY, Fak JJ, Zhong R, et al. NOVA2-mediated RNA regulation is required for axonal pathfinding during development. Elife. 2016;5:e14371. pmid:27223325
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref38] 38. Weyn-Vanhentenryck SM, Mele A, Yan Q, Sun S, Farny N, Zhang Z, et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 2014;6(6):1139–52. pmid:24613350
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref39] 39. Peyda P, Lin C-H, Onwuzurike K, Black DL. The Rbfox1/LASR complex controls alternative pre-mRNA splicing by recognition of multipart RNA regulatory modules. Genes Dev. 2025;39(5–6):364–83. pmid:39880658
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref40] 40. Ule J, Stefani G, Mele A, Ruggiu M, Wang X, Taneri B, et al. An RNA map predicting Nova-dependent splicing regulation. Nature. 2006;444(7119):580–6. pmid:17065982
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref41] 41. Breathnach R, Benoist C, O’Hare K, Gannon F, Chambon P. Ovalbumin gene: evidence for a leader sequence in mRNA and DNA sequences at the exon-intron boundaries. Proc Natl Acad Sci U S A. 1978;75(10):4853–7. pmid:283395
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref42] 42. McManus CJ, Graveley BR. RNA structure and the mechanisms of alternative splicing. Curr Opin Genet Dev. 2011;21(4):373–9. pmid:21530232
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref43] 43. Raj B, Blencowe BJ. Alternative Splicing in the Mammalian Nervous System: Recent Insights into Mechanisms and Functional Roles. Neuron. 2015;87(1):14–27. pmid:26139367
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref44] 44. Dillman AA, Hauser DN, Gibbs JR, Nalls MA, McCoy MK, Rudenko IN, et al. mRNA expression, splicing and editing in the embryonic and adult mouse cerebral cortex. Nat Neurosci. 2013;16(4):499–506. pmid:23416452
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref45] 45. Ule J, Jensen KB, Ruggiu M, Mele A, Ule A, Darnell RB. CLIP identifies Nova-regulated RNA networks in the brain. Science. 2003;302(5648):1212–5. pmid:14615540
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref46] 46. Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M, Chi SW, et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature. 2008;456(7221):464–9. pmid:18978773
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref47] 47. Zhang C, Frias MA, Mele A, Ruggiu M, Eom T, Marney CB, et al. Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls. Science. 2010;329(5990):439–43. pmid:20558669
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref48] 48. Fisher E, Feng J. RNA splicing regulators play critical roles in neurogenesis. Wiley Interdiscip Rev RNA. 2022;13(6):e1728. pmid:35388651
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref49] 49. Conboy JG. Developmental regulation of RNA processing by Rbfox proteins. Wiley Interdiscip Rev RNA. 2017;8(2):10.1002/wrna.1398. pmid:27748060
View Article
PubMed/NCBI
Google Scholar

[193] View Article

[194] PubMed/NCBI

[195] Google Scholar

[ref50] 50. Ponthier JL, Schluepen C, Chen W, Lersch RA, Gee SL, Hou VC, et al. Fox-2 splicing factor binds to a conserved intron motif to promote inclusion of protein 4.1R alternative exon 16. J Biol Chem. 2006;281(18):12468–74. pmid:16537540
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

[ref51] 51. Damianov A, Ying Y, Lin C-H, Lee J-A, Tran D, Vashisht AA, et al. Rbfox Proteins Regulate Splicing as Part of a Large Multiprotein Complex LASR. Cell. 2016;165(3):606–19. pmid:27104978
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref52] 52. Ho JS, Di Tullio F, Schwarz M, Low D, Incarnato D, Gay F, et al. HNRNPM controls circRNA biogenesis and splicing fidelity to sustain cancer cell fitness. Elife. 2021;10:e59654. pmid:34075878
View Article
PubMed/NCBI
Google Scholar

[205] View Article

[206] PubMed/NCBI

[207] Google Scholar

[ref53] 53. Crosier P, Ricciardi S, Hall L, Vitas M, Clark S, Crosier K. Expression of isoforms of the human receptor tyrosine kinase c-kit in leukemic cell lines and acute myeloid leukemia. Blood. 1993;82(4):1151–8.
View Article
Google Scholar

[209] View Article

[210] Google Scholar

[ref54] 54. Zhu WM, Dong WF, Minden M. Alternate splicing creates two forms of the human kit protein. Leuk Lymphoma. 1994;12(5–6):441–7. pmid:7514064
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref55] 55. Sakuma Y, Sakurai S, Oguni S, Hironaka M, Saito K. Alterations of the c-kit gene in testicular germ cell tumors. Cancer Sci. 2003;94(6):486–91. pmid:12824871
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref56] 56. Tuck AR, Robker RL, Norman RJ, Tilley WD, Hickey TE. Expression and localisation of c-kit and KITL in the adult human ovary. J Ovarian Res. 2015;8:31. pmid:26008799
View Article
PubMed/NCBI
Google Scholar

[220] View Article

[221] PubMed/NCBI

[222] Google Scholar

[ref57] 57. Motro B, van der Kooy D, Rossant J, Reith A, Bernstein A. Contiguous patterns of c-kit and steel expression: analysis of mutations at the W and Sl loci. Development. 1991;113(4):1207–21. pmid:1811937
View Article
PubMed/NCBI
Google Scholar

[224] View Article

[225] PubMed/NCBI

[226] Google Scholar

[ref58] 58. Aoki H, Hara A, Kunisada T. Induced haploinsufficiency of Kit receptor tyrosine kinase impairs brain development. JCI Insight. 2017;2(19):e94385. pmid:28978807
View Article
PubMed/NCBI
Google Scholar

[228] View Article

[229] PubMed/NCBI

[230] Google Scholar

[ref59] 59. Minei R, Aoki H, Ogura A, Kunisada T. Compensatory gene expression potentially rescues impaired brain development in Kit mutant mice. Sci Rep. 2023;13(1):4166. pmid:36914660
View Article
PubMed/NCBI
Google Scholar

[232] View Article

[233] PubMed/NCBI

[234] Google Scholar

[ref60] 60. Ule J, Ule A, Spencer J, Williams A, Hu J-S, Cline M, et al. Nova regulates brain-specific splicing to shape the synapse. Nat Genet. 2005;37(8):844–52. pmid:16041372
View Article
PubMed/NCBI
Google Scholar

[236] View Article

[237] PubMed/NCBI

[238] Google Scholar

[ref61] 61. Yin X, Jin N, Gu J, Shi J, Zhou J, Gong C-X, et al. Dual-specificity tyrosine phosphorylation-regulated kinase 1A (Dyrk1A) modulates serine/arginine-rich protein 55 (SRp55)-promoted Tau exon 10 inclusion. J Biol Chem. 2012;287(36):30497–506. pmid:22767602
View Article
PubMed/NCBI
Google Scholar

[240] View Article

[241] PubMed/NCBI

[242] Google Scholar

[ref62] 62. Awazu A, Takemoto D, Watanabe K, Sakamoto N. Possibilities of skin coat color-dependent risks and risk factors of squamous cell carcinoma and deafness of domestic cats inferred via RNA-seq data. Genes Cells. 2023;28(12):893–905. pmid:37864512
View Article
PubMed/NCBI
Google Scholar

[244] View Article

[245] PubMed/NCBI

[246] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Ethics statement

Processing of transcriptome data

Phylogenetic analysis

Quantification of splicing variants

Statistical analysis

Gene set enrichment analysis

Analysis of HITS-CLIP and IP-seq data

Scoring of NOVA2 binding motif

Results

Two selective splicing 5′ donor sites were closely positioned in KIT

Animal species-independent tissue-specific expression of c-KIT isoforms

Enrichment analysis of genes correlated with in-ex ratio

Developmental stage-dependent expression of c-KIT isoforms in CNS

Splice site switching is regulated by splicing factors

RNA-binding protein interactions and binding motifs

(i) NOVA2

(ii) RBFOX1 and RBFOX3

Discussion

Supporting information

S1 Fig. Identification of isoforms.

S2 Fig. Histograms of the in–ex ratio.

S3 Fig. Histogram of in–ex ratios colored by two categories: CNS and other tissues.

S4 Fig. Matrix of Wasserstein distance.

S5 Fig. Cross-species comparison of NOVA2 amino acid sequences.

S6 Fig. Phylogenetic analysis of the KIT in five species.

S1 Table. Details of analyzed datasets.

S2 Table. Details of GSEA results.

S3 Table. Alternative splicing factors.

References