Alternative splicing (AS) is an important post-transcriptional regulatory mechanism to generate transcription diversity. However, the functional roles of AS in multiple cell types from one organ have not been reported. Here, we provide the most comprehensive profile for cell-type- resolved AS patterns in mouse liver. A total of 13,637 AS events are detected, representing 81.5% of all known AS events in the database. About 46.2% of multi-exon genes undergo AS from the four cell types of mouse liver: hepatocyte, liver sinusoidal endothelial cell, Kupffer cell and hepatic stellate cell, which regulates cell-specific functions and maintains cell characteris- tics. We also present a cell-type-specific splicing factors network in these four cell types of mouse liver, allowing data mining and generating knowledge to elucidate the roles of splicing factors in sustaining the cell-type-specialized AS profiles and functions. The splicing switching of Tak1 gene between different cell types is firstly discovered and the specific Tak1 isoform reg- ulates hepatic cell-type-specific functions is verified. Thus, our work constructs a hepatic cell- specific splicing landscape and reveals the considerable contribution of AS to the cell type con- stitution and organ features. Key words: alternative splicing, cell specificity, hepatic cell types, splicing factor, isoform function 1. Introduction AS patterns signiﬁcantly contribute to shaping species-speciﬁc differ- 8–10 Alternative splicing (AS) plays a major role in increasing transcrip- ences. Furthermore, it was suggested that proteins with tissue- 1,2 tome variability and proteome diversity. It is estimated that 95% speciﬁc isoforms were likely to occupy key hubs in protein interac- 4,6,7,11 of multi-exon genes in human undergo AS, and the resulting iso- tion networks. Although the roles of AS in tissue speciﬁcity forms from one gene could present identical, similar or even opposite have been well-studied, splicing differences at the cellular level and in 4,5 protein functions. AS variants are variably expressed between dif- multiple cell types from one single organ were almost neglected to ferent tissues and cell types, and most of the recent studies focused date. 4,6,7 on the varying effects of AS in different species or tissue types. Thus, we expected to establish splicing proﬁling of four cell types AS is highly prevalent across distinct species, and the divergences in from the mouse liver as a paradigm of exploring cell-type-speciﬁc V C The Author(s) 2018. Published by Oxford University Press on behalf of Kazusa DNA Research Institute. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com 265 Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 266 The alternative splicing in mouse liver cells splicing patterns. The liver is the largest metabolic organ of the mam- 2.4. Feature analysis of CEEs mals and participates in multiple biological functions, the majority of Cell-type-enriched exons (CEEs) required statistically signiﬁcant dif- which are performed by parenchymal cells, also named hepatocytes ferences (DPSI> 0.25 and FDR< 0.05) between at least two cell (HCs), occupying approximately 80% of the liver volume. The types. All exons were mapped to annotated proteins (Ensembl remainders are nonparenchymal cells (NPCs), including the liver release 78) using the method of Ensembl Perl API. Protein domains sinusoidal endothelial cells (LSECs), the Kupffer cells (KCs), the hep- of Pfam were extracted from Ensembl annotations for mapped atic stellate cells (HSCs) and several rare cell types. Despite the exons. IUPred was applied to predict protein disorder information. small fraction in total liver volume, NPCs also perform signiﬁcant The fraction of segments classiﬁed as disordered by the IUPred cutoff 13–15 functions. of 0.4. ANCHOR was used for predicting protein binding regions. Recently, although some cell-type-speciﬁc proteomic analyses had PTM sites were collected from the UniProt database after mapping 16,17 performed in mouse liver that globally investigated the proteome the Ensembl protein ID to UniProt accessions. The statistically signif- proﬁles of individual cell types and possible mechanisms of intercel- icant differences were assessed by v test. lular crosstalk, we still want to know how this proteomic complexity was generated and how AS contributed in such processes. Given that 2.5. Splicing validation by RT-PCR tissue-speciﬁc exons play crucial roles in determining tissue identity, TM Total RNA was reverse transcribed using the PrimeScript RT we wondered whether this feature also expands to the cellular level Master Mix (TaKaRa Bio Technology), and PCR was performed using in the liver and how AS regulates the biological functions of isoforms TM TM Premix Taq (TaKaRa Taq Version 2.0 plus dye, TaKaRa in different cell types. Thus, in our study, we obtained highly puriﬁed BioTechnology). The PCR products were separated by electrophoresis liver cells and applied RNA-Seq to build the ﬁrst dataset of cell-type- in 2% agarose gels and detected using the Gel Doc EZ System (Bio- speciﬁc transcriptomic proﬁling. Next, we comprehensively com- Rad, USA). Sequencing of the PCR products was performed by pared the splicing patterns between the four hepatic cell types and BGI.tech (Beijing, China). All the primers were listed in Supplementary analyzed their biological characteristics. Furthermore, we identiﬁed File S1. the cell-type-speciﬁc splicing factors that dominated the splicing pat- terns and functional characteristics in respective cell types. Last, we 2.6. GO and pathway enrichment analysis took HCs-speciﬁc Tak1 isoform as an example to demonstrate the All enrichment analysis was performed by the DAVID online tool effect of splicing switching on cell-type speciﬁcities. Overall, our (http://david.abcc.ncifcrf.gov/). Enriched functional terms mainly results revealed a hepatic cell-type-speciﬁc splicing proﬁle that played included three ontology terms (BP_FAT, CC_FAT and MF_FAT) and a considerable role in determining cellular-speciﬁc functions. the KEGG pathway. Signiﬁcantly enriched terms were determined under FDR< 0.05. The GO and pathway networks for splicing factor- 2. Materials and methods regulated target genes were built using Enrichment Map in Cytoscape 3.2.0 with the parameters of P< 0.001 and FDR< 0.1. 2.1. Isolation of four hepatic cell types C57BL/6 male mice (8–10 weeks old) were killed, and their livers 2.7. Oligonucleotide synthesis were excised by operation according to the ethics committee guide- The splice switching oligonucleotides (SSOs) were synthesised by lines of the Beijing Institute of Radiation Medicine. To reduce the Sangon Biotech (Shanghai) Co., Ltd. The sequences were listed in biological variation among individuals, the liver samples from three Supplementary File S1. mice were pooled for the sequencing of each cell type. All procedures were reported before. 2.8. Cell culture The AML12 (alpha mouse liver 12) cell line was purchased from 2.2. RNA-seq data generation and processing ATCC (CRL-2254), which was established from HCs from a mouse Total RNA of four hepatic cell types was isolated with Trizol (CD1 strain, line MT42) transgenic for human TGFa. These cells Reagent (Invitrogen) according to the manufacturer’s protocol. RNA exhibit typical HC features, being a suitable alternative option for quality was assessed using Agilent 2100 Bioanalyzer. RNA-seq the mouse primary HCs. The cells were cultured in DMEM (GIBCO, library was prepared using Illumina’s reagent and protocol. Paried- USA) with 10% of fetal bovine serum (GIBCO, USA) and supplied end 90 bp sequencing was performed on an Illumina HiSeq 2000 with 1% of insulin-transferrin-selenium (GIBCO, USA) and 40 ng/ml genome analyzer. Clean reads were mapped to the mouse dexamethasone (Sigma, USA). GENCODE M4 (GRCm38) using Tophat2 (version 2.0.10) with a mate inner size of 20. FPKM were then calculated as relative abun- dance value for each gene and transcript by Cufﬂinks (version 2.9. The knockdown of the splicing factors 2.2.1) supplied with a protein-coding gene model annotation ﬁle in The siRNAs used for the knockdown of the indicated splicing factors GTF format (GENCODE M4). were designed and synthesised by Sangon Biotech (Shanghai) Co., Ltd. After 72-hs transfection, the total RNA of AML12 cells was extracted and the AS of Tak1 gene was analyzed. The siRNA sequen- 2.3. Differential analysis of AS between four cell types 20 ces were listed in Supplementary File S1. MATS was applied to identify the differential alternative splicing between four cell types with options ‘-t paired –len 90 –a 8 –c 0.0001 2.10. Enrichment analysis of RNA-binding sites –expressionChange 20’. Signiﬁcant different events were ﬁltered under the threshold of FDR< 0.05 for ﬁve common splicing classes. RNA-binding sites of splicing factors were mapped to pre-mRNA Percent spliced-in (PSI) values for each alternative exon were using the RBPmap online tool (http://rbpmap.technion.ac.il/) with extracted from MATS outputs. the parameters of a high stringency level and conservation ﬁlter. The Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 P. Wu et al. 267 input sequences included alternative exon regions and their neigh- 4.2. Association of gene functions with AS boring intron regions (300 bp). Enrichment of RNA-binding sites We classiﬁed the AS-regulated genes detected in all cell types into was performed using the hypergeometric test, and FDR< 0.05 was several function categories using MetaCore software. In general, the used to assess signiﬁcance. genes that most frequently regulated by AS were those encoding ‘regulators (GDI, GAP and GEF)’ (62.2%), ‘transcription factor’ (56.7%) and ‘generic kinase’ (56.2%). While for ‘receptor ligand’, 2.11. Data presentation ‘GPCR’ and ‘ligand-gated ion channel’, only 33.2, 29.7 and 24.1% Unsupervised hierarchical clustering was performed by Perseus soft- of genes were regulated by AS (Fig. 2A), indicating a putative mecha- ware with default options (distance ¼ ‘Euclidean’). Cumulative distri- nism that the distinct regulation of AS on genes of different function bution curves were visualized using the ggplot2 package in R. Other categories. Besides, AS did not happen equally across different cell statistical tests were also performed by R software. types even for the genes in the same function categories. We found that the genes of ligand-gated ion channels did not undergo AS equally across cell types, the genes were more often regulated by AS 3. Data availability in LSECs (45.8%) than in HCs (16.7%) (Fig. 2A). The statistical sig- niﬁcance of spliced genes proportion for each function category The RNA-Seq data from this study have been deposited into the between different cell types was shown in Supplementary Fig. S2A. NCBI Short Read Archive database under study accession number In the liver, several diseases were caused by ion channel dys- SRP033468. function. Different ion channels might have specialized biological signiﬁcances in one particular cell type, yet no relevant investigation has been reported so far. 4. Results We next analyzed the enriched functional terms of AS-regulated 4.1. A global view of gene expression and genes in distinct cell contexts. The AS-regulated genes in HCs were splicing patterns highly enriched in multiple metabolic processes, such as the organic substance metabolic process and the primary metabolic process. In total, we identiﬁed 12,947 genes with FPKM1(Fig. 1A and Similarly, in NPCs, the AS-regulated genes also tended to play roles in Supplementary Table S1), each cell type had a series of cell-speciﬁc cell-type-speciﬁc functions, just as cytoskeleton organization and genes, while the majority (65.6%) were detected in all. The detected immune system process (Fig. 2B and Supplementary Fig. S2B). To our gene number of HCs, LSECs, KCs and HSCs was 9,967, 10,573, surprise, plenty of AS-regulated genes functioning in regulating biolog- 10,964 and 11,767, respectively. The parenchymal cells, which par- ical processes, such as ‘positive regulation of metabolic process’, were ticipate in the liver’s main biological functions, had the least number signiﬁcantly enriched in NPCs instead of in HCs (Fig. 2B). To elimi- of total genes but more high-abundance (FPKM 100) genes than nate the potential effects of low abundance genes on enrichment analy- NPCs (Fig. 1A). RT-qPCR of 20 randomly selected genes was per- sis, we separately chose the 1000 and 2000 most abundant AS genes formed in additional biological samples, the expression patterns of in each cell type to reanalyze the enriched biological processes and which were similar to the patterns obtained from the RNA-Seq, indi- obtained the consistent trend (Supplementary Fig. S2C and D). cating the high correlation and good reproducibility between multi- Besides, all the four cell types showed an almost uniform cumulative ple samples (Supplementary Fig. S1A). distribution and the bulk of transcription was dominated by a few The genome-wide extent of AS was analyzed by searching against high abundance genes (Supplementary Fig. S3A), demonstrating they known splicing junctions. We focused on ﬁve common types of AS would be identically affected by detection sensitivity for low abun- (Supplementary Fig. S1B). The AS events identiﬁed in our data dance genes. These ﬁndings conﬁrmed the important roles involving achieved an average coverage of 81.5% of all known events in the regulation of metabolic process in NPCs and provided new evidence of Ensembl database (Supplementary Fig. S1C), consistent with the fact the cooperation between HCs and NPCs on the isoform level. of that the liver was frequently regulated by AS (Supplementary Fig. Furthermore, we explored the association of AS with gene functions S1D and Table S2). Most of the AS events existed in all four cell using the quantitative data of splicing in different cell types. We ﬁrstly types (Fig. 1B), and in our data, 46.2% of multi-exon genes under- deﬁned the CEEs as described in the Materials and Methods section. went AS, half of which had three or more AS variants, indicating the The unsupervised hierarchical clustering revealed that these exons distinguishing contribution of AS to the proteome diversity in mouse exhibited obvious splicing switches between different cell types liver cells (Fig. 1C). Consistent with previous studies in mammals, (Fig. 2C). Functionally, the genes with HC-enriched exons were linked the skipped exon (SE) was the main AS type in mouse liver, followed to ‘regulation of kinase cascade’ and ‘phosphoprotein’, largely reﬂect- 0 0 by A3SS (Alternative 3 splice site) and A5SS (Alternative 5 splice ing the identities of the HCs and differing them from NPC-enriched site), and a low frequency was observed for retained intron and exons. In NPCs, each cluster also revealed signiﬁcant enrichment in speciﬁc GO terms, including ‘cell surface receptor linked signal trans- mutually exclusive exons (Fig. 1B and Supplementary Fig. S1C). All duction’ (LSEC-enriched), ‘regulation of immune effector process’ types tended to occur equally in different cell types, with a rank from (KC-enriched) and ‘membrane organization’ (HSC-enriched). These 69.8% to 72.5% for SE (Fig. 1B). Although LSECs didn’t have the CEE-contained genes reﬂected the core functions of respective cell largest gene number, it had more AS events than the other three cell types, as the visualized global diversity of all genes with FPKM 1 types, which indicated a precise regulation on the splicing level and the functional enrichment analysis (Supplementary Fig. S3B and C). beyond the transcriptional regulation in LSECs. To conﬁrm the identiﬁcation of AS from RNA-Seq data, ran- domly selected AS events were validated by RT-PCR (Fig. 1D and 4.3. Functional properties of CEEs Supplementary Fig. S1E), and the results displayed a high Pearson Tissue-enriched exons had exhibited multiple properties that distin- 2 6,7 correlation (R ¼ 0.808) of PSI values from RT-PCR and RNA-Seq. guished them from constitutive exons. Thus, we wondered if the Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 268 The alternative splicing in mouse liver cells Figure 1. Cell-type-resolved transcriptome expression and AS profiling in the mouse liver. (A) The overlap of identified genes and the number of detected genes at different FPKM levels. (B) The overlap of AS events and the number of five splicing types. (C) Left: Percentage of alternatively spliced genes in all identified genes. Right: Percentage of alternatively spliced genes with a different number of splicing variants. (D) The correlation analysis of PSI values calculated from RNA-Seq and RT-PCR. CEEs in mouse liver also had similar properties and how AS affecting liver, our results suggested that the CEEs might regulate isoform protein functions. In our data, although some of the CEEs destroyed functions through affecting PPIs and modifying PTM process, consis- 6,7 the functional protein domain (e.g. the immunoglobulin domain 1 of tent with the previous reports on the tissue-speciﬁc exons. Fgfr1) when excluded from the pre-mRNA, the CEEs had a signiﬁ- A scatter diagram illustrated all the alternative exons that exhib- cantly smaller fraction of overlapping to protein domains compared ited markedly different PSIs between HCs and NPCs (Fig. 3E). The with other alternative exons and constitutive exons (Fig. 3A; circles shown in Fig. 3E highlighted the ﬂexibility of the splicing reg- P<2.07E-15). In contrast, an increased fraction overlapping to pre- ulatory machinery, with a large number of exons predominantly dicted disordered regions was observed (Fig. 3B; P<4.56E-11). The included in one cell type but excluded in others (just like exon 12 of disordered regions had important regulatory effects on protein– Tak1, with a PSI of 0.886 in HCs, but 0.051, 0.059 and 0.117 in 6,7 protein interaction (PPI) networks, and consistently, the CEEs LSECs, KCs and HSCs, respectively; Supplementary File S3). We were signiﬁcantly enriched in regions that involved in PPI regulation used RT-PCR assays to validate individual AS events detected by RNA-Seq (Fig. 3F). The validated examples included (but not limited (Fig. 3C; P<7.16E-04). In addition, these CEEs contained more post-translational modiﬁcation (PTM) sites than others (Fig. 3D, to) the genes that functioning in regulating the MAPK pathway adjusted to the length of the exons; P<0.022). Together, in mouse (Tak1), splicing regulation (Mbnl1), glucose homeostasis (Sidt2) and Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 P. Wu et al. 269 Figure 2. Functional categories of spliced genes across cell types. (A) Altered percentages of alternatively spliced genes for different functional categories. ‘General’ column represents the combination of spliced genes in all four hepatic cell types. (B) Differentially enriched function terms revealed by enrichment analysis in four hepatic cell types. (C) Unsupervised hierarchical clustering for PSI values of AS events. Enrichment analysis shows the corresponding functional annotation for spliced gene clusters. a gene with putative immune functions (Lrch3). Most of the exons and NPCs, and each cell type had a set of highly expressed splicing correlated well with our RNA-Seq data, exhibiting a strong cell-type- factors, as listed in Supplementary File S4. In both mRNA and protein levels, we identiﬁed the high- regulated AS pattern. expressed splicing factors in each cell type, such as Esrp2 in HCs, Rbfox2 in LSECs, Srsf9 in KCs and Rbms3 in HSCs (Fig. 4B, proteo- 4.4. Regulation of as by splicing factors mics data came from the literature 17). We used RBPmap to search We were interested in how these CEEs generated and which particu- the target genes of these splicing factors, followed by the functional lar splicing factor (SF) might dominate the splicing patterns. First, we enrichment analysis. These splicing factors controlled a large propor- collected 94 splicing factors from RBPmap, 78 of which (with 114 tion of the cell-type-speciﬁc AS events, and the target genes were experimentally conﬁrmed binding motifs) were expressed in our data highly enriched in cell specialized functions. For example, Esrp2 was with FPKM 1 and displayed variable cell-type speciﬁcities highly expressed in HCs but not in three other NPCs, and the targets (Fig. 4A). The most signiﬁcant diversity was observed between HCs of Esrp2 were signiﬁcantly enriched in terms of liver specialized Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 270 The alternative splicing in mouse liver cells Figure 3. Characterization of CEEs. (A–D) Percentages of three groups of exons encoding (A) protein domains, (B) disordered regions, (C) binding regions and (D) PTM sites. Significance was calculated using the v test. (E) Different PSI values of alternative exons between HCs and NPCs. Exons in circles are highlighted as examples. (F) Variants-switching validation between HCs and NPCs for Tak1, Sidt2, Lrch3 and Mbnl1. functions, such as lipoprotein and coenzyme metabolism (Fig. 5A). data showed that Rbms3 were exclusively high-expressed in HSCs, The target genes of the LSEC-speciﬁc splicing factor Rbfox2 were regulating a range of biological processes, especially those HSC- enriched in cytoskeleton organization and cell adhesion (Fig. 5B). speciﬁc functions like cell-matrix adhesion (Fig. 5D). Rbfox2 was reported to splice the Tak1 pre-mRNA to the short var- Besides analyzing the differential expressed splicing factors, we iant Tak1-A instead of the full-length Tak1-B, consistent with the combined the trans-acting splicing factors and cis-element (binding motifs) surrounding CEEs and built a splicing regulatory map, in distribution of Tak1 variants in HCs and LSECs. Similarly, in Kupffer Cells, we found that the target genes of Srsf9 were highly order to further identify the important splicing factors in each cell enriched in multiple immunity-related biological processes and path- type (Supplementary Fig. S4B). Speciﬁcally, LSECs exhibited the ways (Fig. 5C and Supplementary Fig. S4A). Furthermore, although most signiﬁcant enrichment of RNA-binding motifs of splicing fac- little was reported about the functions of Rbms3 in the liver, our tors across all cell types, consistent with the largest AS event number Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 P. Wu et al. 271 Figure 4. Identification of cell-type-specific splicing factors. (A) Differential expression patterns of splicing factors revealed by unsupervised hierarchical cluster- ing. (B) Differential expression abundances of four cell-type-specific splicing factors in both mRNA and protein level. in LSECs (Supplementary Fig. S4B and 1B), suggesting the functional between HCs and NPCs, thus, the intronic area might be more diversity of LSECs and potential interplay with other cell types in important in the AS regulation of Tak1. Several splicing factors were 30–32 various aspects. proved to be involved in this regulation, but single splicing factor did not play a decisive role (Supplementary Fig. S5C), which indicated that the speciﬁc splicing pattern of Tak1 resulted from the possible 4.5. Specific Tak1 isoform regulates hepatic cell-type- cooperation and competition of multiple splicing regulators in mouse specific functions liver cells. Growing evidence suggested that protein isoforms generated from 33,34 We collected 561 public RNA-Seq datasets from the Sequence AS can perform distinct or even opposite functions. We Read Archive database with RPBmap tools to obtain a comprehen- attempted to reveal the functions of cell-speciﬁc isoforms of Tak1 in sive expression proﬁle of Tak1 variants. Tak1-B was highly distinct cell types. Tak1 has four main variants generated by AS expressed in tissues with strong proliferation and metabolism prop- (Supplementary Fig. S5A), depending on the spliced-in or -out of erties, whereas Tak1-A was usually existed in tissues with immune exon 12 and exon 16. We provided a detailed splicing map of exon functions, such as the spleen and several immune cells 12 of Tak1 (TGF-activated kinase 1) gene. Rbfox2, which highly (Supplementary Fig. S5D). Coordinately, a multiple instance learning expressed in LSECs, was reported to splice the Tak1 pre-mRNA to based analysis revealed that Tak1-B was more concentrated in the short variant Tak1-A, consistent with the distribution of Tak1 some basic metabolic processes, while Tak1-A was primarily respon- variants in HCs and LSECs. Besides, we used RBPmap to predict the sible for immune functions (Fig. 6A), consistent with the distribution splicing factors that considered to be involved in the formation of of Tak1 variants in HCs and NPCs. Tak1’s splicing pattern, and several candidates with distinct expres- We downloaded the PDB ﬁles of the two isoforms to predict the sion patterns were selected, as shown in Supplementary Fig. S5B.We considered that splicing factors with different expression patterns protein structure differences between Tak1-A and Tak1-B by search- ing Protein Data Bank (PDB) archive. Although the serine-threonine/ were more likely to contribute in the formation of the cell-type- tyrosine protein kinase catalytic domain of the two isoforms were speciﬁc splicing patterns, while in the exonic regions, the predicted splicing factors did not exhibit signiﬁcant expression differences unaffected by AS (from Pfam database), they had differences in Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 272 The alternative splicing in mouse liver cells Figure 5. Functional regulation by splicing factors. Enriched biological processes of spliced genes regulated by (A) Esrp2 in HCs, (B) Rbfox2 in LSECs, (C) Srsf9 in KCs and (D) Rbms3 in HSCs. spatial structures around the peptides that translated from exon 11 type speciﬁc functions such as lipid metabolism. While in NPCs, to exon 13 (Fig. 6B). The peptide segment altered by the spliced exon Rbfox2, as well as some other splicing factors, made Tak1-A the 12 fell in the protein-binding region (marked in Fig. 6B), suggesting main isoform. Tak1-A bound to Tab2 more easily than Tak1-B did, that gain or loss of Tak1 isoform function might be caused by the activated the downstream JNK and p38 pathways. Thus, Tak1-A affected interaction protein partner. exhibited the immune process regulation properties in NPCs. These We then used SSOs to explore the exact functional differences observations indicated that the regulation of CEEs by AS was closely between Tak1 isoforms. We converted Tak1-B to Tak1-A in AML12 associated with the cell-type-speciﬁc functions and had important cells, in which Tak1-B was the main variant, just like in the HCs roles in the formation of liver cell-type speciﬁcities. (Fig. 6C). As we analyzed previously, the CEEs could change the PPIs by affecting the protein binding domains, here, Co-IP showed that Tak1-A bound to Tab2 (but not to Tab1) more easily than 5. Discussion Tak1-B did (Fig. 6D). It was reported that the binding to Tab1 and Although the cell-type-speciﬁc proteomic research in the mouse liver Tab2 was necessary for the activation of Tak1 and the downstream provided a better understanding of the identities of the individual cell pathways. Our results showed that as the conversion of Tak1-B to type and how these cell types worked together, we still wanted to Tak1-A, the downstream JNK, p38 and NF-jB pathways were sig- know how this complexity of proteomics was generated and how AS niﬁcantly activated (Fig. 6E). Activated JNK and p38 pathways had variants distributed and functioned across different cell types. In this important roles in cellular inﬂammatory response, which consis- article, we identiﬁed 13,637 AS events from all four cell types, with tent with the distribution and the functionality of Tak1-A in NPCs. an average coverage of 81.5% of all known events. We revealed the We also found that the conversion of Tak1-B to Tak1-A triggered ﬁrst proﬁle of cell-type-speciﬁc AS events deﬁned in mouse liver, and a signiﬁcant up-regulation of genes related with lipids and cholesterol this proﬁle was associated with a broad range of functional processes synthesis in AML12 cells, whereas lipolysis-related genes (Lipc) were of the liver. down-regulated (Fig. 6F). These results were also consistent with the AS variants had differential distribution patterns across various cell potential metabolic regulation function of Tak1-B in HCs (Fig. 6A). types. In HCs, the AS-regulated genes were highly enriched in func- In addition, Lkb1, the gene associated with maintenance of cell tions that include ‘regulation of kinase cascade’, ‘phosphoprotein’ and polarity in HCs, was signiﬁcantly down-regulated (Fig. 6F), suggest- ‘metabolism’, whereas the genes involved in ’regulation of immune ing that Tak1-B also played important roles in maintaining cell effector process’ in KCs were most frequently regulated by AS. This polarity (Fig. 6A). ﬁnding indicated that the genes with distinct cell-type-speciﬁc splicing Thus, for the Tak1 gene, isoforms distributed in a cell-type- manners were relevant to the core biological functions of respective speciﬁc manner adequately fulﬁlled its functional plasticity. The full- length Tak1-B was the dominant isoform in HCs, regulating the cell- cell types. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/265/4793385 by Ed 'DeepDyve' Gillespie user on 26 June 2018 P. Wu et al. 273 Figure 6. The functional differences between Tak1 isoforms. (A) Two variants of Tak1 were responsible for diverse biological processes. (B) The differences in protein structure between Tak1-A and Tak1-B isoforms. (C) SSOs converted Tak1-B to Tak1-A in AML12 cells. (D) Tak1-A bound to Tab2 (not Tab1) more easily than Tak1-B did. The ratio of Tak1/Tab1 or Tak1/Tab2 was calculated from image intensity. (E) The MAPK and NF-jB pathways were activated when Tak1-B con- verted to Tak1-A. (F) Several lipids and cholesterol synthesis related genes were up-regulated after the conversion. Significance was calculated using the t-test. *P<0.05. Increasing studies reported that some AS events had crucial AS is regulated by complicated interplays of cis-elements and impacts on multiple biological processes and developmental stages, trans-acting factors (splicing factors). The splicing factors recognize 33,38 especially in the nervous system. In our research, the genes cod- the ‘binding motifs’ to promote or suppress the splicing of the ing the kinase and transcription factors were more likely to be regu- exon. According to Professor Phillip A. Sharp, some splicing fac- lated by AS, which might be the important mechanism that AS tors are required for dominating the specialized splicing patterns and caused wide impacts on multiple biological processes. More widely, have critical roles in maintaining cellular homeostasis, namely the the CEEs were signiﬁcantly enriched in regions that involved in PPI ‘master splicing factors’. Here, we found several relatively high- regulation, which might also be the approach that AS affecting iso- expressed splicing factors in each cell type, like Esrp2 in HCs and form functions. Rbfox2 in LSECs. 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DNA Research – Oxford University Press
Published: Jan 8, 2018
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