- Open Access
Lipopolysaccharide treatment induces genome-wide pre-mRNA splicing pattern changes in mouse bone marrow stromal stem cells
© The Author(s). 2016
- Published: 22 August 2016
Lipopolysaccharide (LPS) is a gram-negative bacterial antigen that triggers a series of cellular responses. LPS pre-conditioning was previously shown to improve the therapeutic efficacy of bone marrow stromal cells/bone-marrow derived mesenchymal stem cells (BMSCs) for repairing ischemic, injured tissue.
In this study, we systematically evaluated the effects of LPS treatment on genome-wide splicing pattern changes in mouse BMSCs by comparing transcriptome sequencing data from control vs. LPS-treated samples, revealing 197 exons whose BMSC splicing patterns were altered by LPS. Functional analysis of these alternatively spliced genes demonstrated significant enrichment of phosphoproteins, zinc finger proteins, and proteins undergoing acetylation. Additional bioinformatics analysis strongly suggest that LPS-induced alternatively spliced exons could have major effects on protein functions by disrupting key protein functional domains, protein-protein interactions, and post-translational modifications.
Although it is still to be determined whether such proteome modifications improve BMSC therapeutic efficacy, our comprehensive splicing characterizations provide greater understanding of the intracellular mechanisms that underlie the therapeutic potential of BMSCs.
- Alternative splicing
- Mesenchymal stem cells
Alternative splicing (AS) is important for gene regulation and is a major source of proteome diversity in mammals  through altering the composition of mRNA transcripts by including or excluding specific exons . AS can further modulate organism complexity not only by effectively increasing regulatory and signaling network complexity, but also by doing so in a temporal- and spatial-specific manner, supporting cell differentiation, developmental pathways, and other processes associated with multicellular organisms. Indeed, AS shows a strong relationship with organism complexity, as estimated by the organism’s number of different cell types . The recent ENCODE Project concluded that at least 90 % of human genes express multiple mRNAs through alternative splicing of exons or exon segments . As might be expected, deregulation of this process is associated with numerous diseases [5–10].
Bone marrow-derived mesenchymal stem cells (BMSCs) are adult stem cells capable of self-renewal and differentiation into numerous cell lineages, including osteocytes, adipocytes, and chondrocytes . One promising use of BMSCs is repair of ischemia-damaged cardiac tissue. BMSCs are easy to expand in vitro, can be genetically modified and exhibit significant immunotolerance properties [12–14], making BMSCs an attractive candidate for tissue repair/regeneration therapy. Intramyocardial injection of BMSCs reduces inflammation, fibrosis, infarct size, ventricular remodeling, and therefore, improves cardiac function following tissue insult [15–18].
Because the majority of BMSCs are soon lost during after injection, the observed therapeutic effects likely derive from paracrine effects of bioactive molecules released from these cells [15, 16]. Indeed, BMSC-mediated release of cytoprotective protein factors or transfer of intracellular components (e.g.,mRNAs, microRNAs, and proteins) via cell membrane exosomes, represents a novel mechanism of cell-to-cell communication . To date, however, clinical trials have demonstrated that while effective, delivery of BMSCs to ischemic myocardium results in only modest and short-lived benefits [20, 21]. Therefore, there is a critical need to elucidate the mechanisms by which BMSCs mediate their therapeutic benefits, including identification of their specific paracrine factor(s), and conditions under which their functions can be optimized.
Upon injection into damaged heart tissue, BMSCs face a hypoxic, ischemic environment that severely limits their therapeutic efficacy. Thus, preconditioning BMSCs with various growth factors and endogenous or exogenous molecules has been used to improve BMSC therapeutic efficacy [22–24]. Indeed, it has been reported previously that bacterial endotoxin (lipopolysaccharide, LPS) could stimulate BMSCs to release paracrine factors, including angiogenic growth factors, cytokines, and chemokines that facilitate tissue repair [13, 14]. In addition, our previous study suggested that BMSC expression of the LPS receptor, toll-like receptor 4 (TLR4), regulates BMSC paracrine properties and intracellular STAT3 signaling cascades . Moreover, preconditioning of BMSCs with LPS improves their therapeutic efficacy in rodent models of ischemia/reperfusion injury . However, BMSC transcriptomic changes (in particular, alterations in mRNA transcript processing and splicing) that occur following LPS stimulation have been little studied.
Besides use as an attractive therapeutic tool for repairing ischemic heart, BMSCs have been used for numerous other diseases, including graft-versus-host disease, Crohn’s disease, stroke, cartilage defects, diabetes and many others [26–31]. With the growing incidence of bacterial endotoxin LPS detected in older or immunocompromised patients with multiple-drug resistant bacteria, diabetes, cancer, indwelling IV catheters, and on complex chemotherapy regimens [32, 33], it is of great importance to study whether the stimulation of these implanted BMSCs by endogenous LPS would alter their therapeutic efficacy. Moreover, because MSCs are present in bone marrow and many other tissues, it merits extensive investigation whether LPS stimulation of these endogenous MSCs would influence the clinical outcomes of complex therapeutic regimens.
Despite BMSC’s strong clinical potential, the role(s) of alternative splicing in LPS response has not been fully explored. The recent development of high-throughput sequencing technology has now made transcriptome-wide profiling of splicing isoforms possible. In this study, we used RNA-seq analysis of BMSCs to identify and characterize gene transcripts whose splicing patterns were altered by LPS treatment.
To investigate LPS-induced transcriptomic changes in BMSCs due to alternative splicing, RNA-seq analysis was conducted on BMSCs before and after LPS treatment, in triplicate. A strand-directed single-end RNA-seq protocol (75 bp reads) was used with the SOLiD 5500xl instrument.
The total analysis resulted in 326 million reads, with each of the six samples ranging from 43 to 59 million reads. After removing the reads with low sequencing quality (see Methods) and filtering reads mapped to ribosomal RNAs and repeats, the remaining reads were mapped to the standard mouse reference genome (mm9). The total number of mappable reads in each sample ranged from 29 to 36 million, with an average mapping percentage of 59 %. Among the mappable reads in each sample, 3.8 to 5.0 million are mapped to protein coding exons, and 2.8 to 4.0 million are mapped to splice junctions. Detailed mapping statistics for the six samples are listed in Additional file 1.
LPS-induced alternative splicing
We applied a MISO (Mixture of Isoform) algorithm  to identify alternative splicing events elicited by LPS treatment. Based on a Bayesian inference framework, MISO is a probabilistic framework that quantitates the expression levels of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated exons across samples. MISO computes Percent Spliced In (PSI, or Ψ) values for each alternative splicing event, representing the fraction of a gene’s mRNA that includes the exon. For each event, MISO also calculates a Bayesian Factor (BF) that quantifies the likelihood of the changes. For instance, [BF] = 5 indicates it is five times more likely that a specific alternative splicing event occurred than did not occur.
Among the 197 LPS-induced alternative splicing events, 103 were located in the coding regions of transcripts, and 94 were either in the 5′- or 3′- untranslated regions (UTRs). Among the 103 alternatively spliced coding events, 65 were composed of multiples of three nucleotides, leading to the inclusion or exclusion of specific amino-acid residues in the final protein products. These events could potentially generate multiple viable protein products having the same translation frame. Thirty-eight of the 103 coding exons contained either a premature stop codon, and/or a shift in their translation frames. Such events trigger either nonsense-mediated decay (NMD) mechanisms , or a translated protein having a complete different amino acid sequence downstream of the alternatively spliced exon.
To understand the biological functions of genes whose splicing patterns were altered by LPS treatment, we conducted functional annotation analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 . Three functional terms in the SP_PIR (Swiss-Prot and Protein Information Resources) category showed significant enrichment in our gene list. Among the 161 genes that could be mapped to DAVID gene annotations, 97 categorized as phosphoproteins (p-value = 7.2×10−12, FDR = 6.6×10−10). In addition, 26 genes contained zinc finger domain proteins (p-value = 3.6×10−5, FDR = 2.2×10−3) whose functions range from DNA or RNA binding to protein-protein interactions and membrane association . Furthermore, 35 genes were involved in protein acetylation (p-value = 1.3 ×10−3, FDR = 3.2×10−2). Together, these results suggest that LPS treatment has major effects on the splicing patterns of signaling proteins.
Both gene expression levels and splicing patterns may be altered by BMSC responses to LPS treatment. While differential gene expression may lead to changes in the abundance of the entire gene product, alternative spicing modifies the structural composition of a specific protein. To evaluate to what extent the two mechanisms interact, we examined the number of genes present in both differentially expressed and alternatively spliced gene sets. We utilized edgeR  to identify genes differentially expressed between LPS-treated and control samples. In total, 416 differentially expressed genes were identified using a false discovery rate ≤ 0.05. Surprisingly, only one gene, Plscr2 (Phospholipid Scramblase 2) was both differentially expressed and alternatively spliced. The expression level of Plscr2 increased 1.77-fold in LPS-induced samples with FDR = 0.01, while the percentage of inclusion of one cassette exon in the 3′-untranslated region (3′-UTR) increased by 0.16.
Known protein domains are alternatively spliced in LPS-induced transcripts
Alternatively spliced genes containing known protein domains
Protein kinase domain
alternative 5′ splice site
Ligand-binding domain of nuclear hormone receptor
alternative 5′ splice site
Adenosine-deaminase (editase) domain
alternative 3′ splice site
Predicted coiled-coil protein
alternative 3′ splice site
Histidine phosphatase superfamily (branch 2)
Alternative splicing in known protein domains may affect protein-protein interactions
The differences in the percentage of inclusion for these three events ranged from 14 to 31 %. The potential protein partners included Rabep1, Gga1 (Golgi-associated, gamma adaptin ear containing, ARF-binding protein 1), Gga2 (Golgi-associated, gamma adaptin ear containing, ARF binding protein 2), Gga3 (Golgi-associated, gamma adaptin ear containing, ARF binding protein 3), Camkk1 (calcium/calmodulin-dependent protein kinase kinase 1, alpha), Nr0b2 (nuclear receptor subfamily 0, group B, member 2), Rxra (retinoid X receptor, alpha), and Rxrb (retinoid X receptor, beta). LPS-induced splicing changes could significantly impact these proteins’ interactions with their partners. Among these putative protein interaction partners, only one protein, Nr0b2 (nuclear receptor subfamily 0, group B, member 2), was not expressed.
Intrinsic disorder and molecular recognition features in LPS-induced alternative spliced regions
A molecular recognition feature (MoRF) is a region in an RNA that undergoes a disorder–order transformation while bound by another protein. We predicted MoRF regions within the alternative regions using the software tool MoRF2 . As a result, among the 55 alternatively spliced exons in the partial or totally disordered regions, 22 contained regions predicted to be MoRFs (Fig. 6, Additional file 4); these regions could thus be regarded as potential protein-protein interaction sites.
Post-translational modification sites within alternatively spliced regions
Characterization of potential splicing regulators
Lipopolysaccharide (LPS, endotoxin) is a complex associated with the outer membrane of Gram-negative bacteria, capable of triggering a series of cellular responses in many cell types. One promising advance is to use LPS as a pre-conditioning agent to improve BMSC therapeutic efficacy for repairing ischemic, injured tissues [23, 50]. For such application, because LPS is a potent stimulant for the host immune system, BMSCs should be washed using PBS to completely remove any residual endotoxin before administration. We reported previously that BMSCs treated with LPS produced more angiogenic factors VEGF, IGF-1 and HGF [51, 52] which can spur the formation of new blood vessels in ischemic tissue and survival and differentiation of implanted BMSCs. By contrast, with the growing incidence of sepsis, in which free LPS can bind to and activate Toll-like receptor 4 on many cell types, the roles of LPS on endogenous BMSCs and other cell types are worth detailed investigation.
Microarray studies have reported that expression levels of hundreds of genes can be altered after LPS treatment in different tissues. In recent years, high-throughput RNA sequencing technology has provided a more accurate and comprehensive measurement of RNA transcript levels and their isoforms than historic array-based methods. This technological advance has enabled measuring not only gene expression level alterations amongst different conditions, but also complicated splicing pattern changes in response to specific cellular perturbations. In this study, we systematically identified alternative splicing changes in mouse bone marrow-derived mesenchymal stem cells (BMSCs) in response to LPS treatment, using RNA-seq technology. We further implemented a series of bioinformatics tools to evaluate the biological functions of alternatively spliced exons and their host genes.
We observed strong enrichment in three functional categories amongst the gene products whose splicing patterns were altered by LPS treatment, phosphoproteins, zinc finger proteins, and proteins subject to acetylation. Most of these proteins were signaling proteins, and the subtle differences in their splicing isoforms could affect their function.
Among 161 gene products containing AS exons, 97 belonged to phosphoprotein families, five of which contained documented phosphorylation sites in their AS regions found in the UniProt database. These proteins included Kansl2 (KAT8 regulatory NSL complex subunit 2), Depdc1a (DEP domain-containing 1), Abi1 (abl-interactor 1), Ybx3 (Y box-binding protein 3), and UBl4a (Slc10a3-Ubl4 readthrough). The functions of these proteins strongly associate with the functions of BMSCs. For instance, Abi1 contains one cassette of exons whose percentage of inclusion increased by 14 % after LPS induction (ΔΨ = 0.14), with one phosphorylation site in the AS region documented in the UniProt database. Widely expressed with highest levels in bone marrow, spleen, brain, testes, and embryonic brain, Abi1 may negatively regulate cell growth and transformation by interacting with the nonreceptor tyrosine kinases ABL1 and/or ABL2, thus regulating EGF-induced Erk pathway activation and EGFR signaling. In addition to these five proteins, eight other AS regions were predicted to have phosphorylation sites, based on their amino acid contents. These proteins included Usp45 (ubiquitin-specific peptidase 45), Mark3 (MAP/microtubule affinity-regulating kinase 3), Ncor1 (nuclear receptor corepressor 1), Ctnnd1 (cadherin-associated protein, beta 1), Ambra1 (autophagy/beclin-1 regulator 1), Ddx6 (DEAD (Asp-Glu-Ala-Asp) box helicase 6), Ehbp1l1 (EH domain binding protein 1-like 1), and Akt1s1 (AKT1 substrate 1). Overall, LPS may affect the functions of these proteins by including/excluding specific domains amenable to phosphorylation.
Among the proteins containing LPS-induced alternative splicing events, 25 contained multiple types of zinc finger domains, including PHD (Plant Homeo Domain), RING (Really Interesting New Gene), and C2H2-type zinc-finger domains. Four proteins, Phf7 (PHD finger protein 7), Phf20 (PHD finger protein 20), Phf20l1 (PHD finger protein 20-like 1), and Phrf1 (PHD and ring finger domains 1), contained PHD-type zinc finger domains known to recognize trimethylated histone lysines (thus possibly influencing chromatin structure). Four other proteins, Rnf14 (ring finger protein 14), Rad18 (RAD18 homolog), Trim28 (tripartite motif-containing 28), and Trim2 (tripartite motif-containing 2), all contain RING-type zinc fingers, known ligases for ubiquitination enzymes and their substrates. It is well documented that both PHD and RING-type domains are usually involved in protein-protein binding [53, 54], and such binding could possibly be disrupted by splicing variations.
We further evaluated how differences in splicing patterns in transcriptional regulators affected their regulatory activity by assessing gene expression changes of their downstream target genes. NFYA (nuclear transcriptional factor Y) contains an alternative acceptor site whose splicing pattern in BMSCs is altered by LPS treatment; the overall percentage of inclusion of the alternative acceptor site decreased by 31 % (Sashimi plot for NFYA shown in Additional file 5). Moreover, the expression of five downstream target genes of NFYA were enriched for genes found differentially expressed (p-value ≤ 0.01) by LPS treatment (FDR ≤ 0.05), including COL11A1 (collagen, type XI, alpha 1), COL5A3 (collagen, type V, alpha 3), FGFR2 (Fibroblast Growth Factor Receptor 2), PGK1 (phosphoglycerate kinase 1) and RGS4 (regulator of G-protein signaling 4). It was previously reported that NFYA activates transcription levels of COL11A1 and FGFR2 ; these two genes were both downregulated by LPS, suggesting inhibition of NFYA function by the removal of 18 nt (or 6 amino acids) after LPS treatment, thus impacting NFYA downstream effectors.
In summary, we used RNA sequencing to analyze LPS-induced alternative splicing changes in BMSCs. LPS modified the alternative splicing pattern of phosphoproteins, zinc finger proteins, and proteins subject to acetylation. Most of the affected proteins were signaling proteins that could change BMSC biological function. Although it is still to be determined whether such modifications underlie BMSC therapeutic efficacy, our characterizations provide greater understanding of the mechanisms and clinical usage of promising BMSC therapies.
Preparation of mouse BMSCs
A single-step stem cell purification method was employed as previously described . Briefly, BMSCs were collected from the bilateral femurs and tibias of sacrificed mice by removing the epiphyses and flushing the shaft with complete media, Iscove’s Modified Dulbecco’s Medium (IMDM; Life Technologies) and 10 % fetal bovine serum (Life Technologies), using a syringe with a 26G needle. Cells were disaggregated by vigorous pipetting and passed through a 30-μm nylon mesh to remove any remaining clumps of tissue. Cells were then centrifuged for 5 min at 500 g at 24 °C. The cell pellet was then resuspended and cultured in 75 cm2 culture flasks in complete media at 37 °C with 5 % CO2. Since BMSCs preferentially attach to polystyrene , after 48 h, floating non-adherent cells were discarded. Fresh complete media was added and replaced every three or four days thereafter. When the cells reached 90 % confluence, MSC cultures were recovered by the addition of a solution of 0.25 % trypsin-EDTA (Invitrogen) and passaged. Cell passage was restricted to passages 6–10 for the experiments. To purify BMSCs, the cells were subject to fluorescence-activated cell sorting (FACS) analysis, with collection of cells positive for Sca-1 and CD44 , but negative for the hematopoietic stem cell and macrophage marker CD45 .
RNA sample preparation and RNA-seq assay
BMSCs were plated at 1 × 105 cells/well/ml for 24 h and further treated with LPS (200 ng/ml) for another 24 h, and total RNA was extracted before and after LPS treatment, following a standard protocol . Experiments were conducted in triplicate.
Standard methods were used for RNA-seq library construction, EZBead preparation, and Next-Gen sequencing, based on the Life Technologies SOLiD 5500xl system. Briefly, 2 μg of total RNA per sample was used for library preparation. The rRNA was first depleted using the standard protocol of RiboMinus Eukaryote Kit for RNA-Seq (Ambion), and rRNA-depleted RNA was concentrated using a PureLink RNA Micro Kit (Invitrogen) with 1 volume of lysis buffer and 2.5 volumes of 100 % ethanol. After rRNA depletion, a whole transcriptome library was prepared and barcoded per sample using the standard protocol of SOLiD Total RNA-seq Kit (Life Technologies). Each barcoded library was quantified by quantitative polymerase chain reaction (qPCR) using SOLiD Library Taqman qPCR Module (Life Technologies) and pooled in equal molarity. EZBead preparation, bead library amplification, and bead enrichment were then conducted using the Life Technologies EZ Bead E80 System. Finally sequencing by ligation was performed using a standard single-read, 5′-3′ strand-specific sequencing procedure (75 nt-read) on SOLiD 5500xl.
Bioinformatics analysis for RNA-seq data
RNA-seq data analysis included the following steps: quality assessment, sequence alignment, and alternative splicing analysis. The RNA-seq data can be accessed through the Gene Expression Omnibus (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/geo/) with accession number GSE64568).
Data processing and quality assessment
We used SOLiD Instrument Control Software and SOLiD Experiment Tracking System software for read quality recalibration. Each sequence read was scanned for low-quality regions, and if a 5-base sliding window had an average quality score less than 20, the read was truncated at that position. Any read < 35 bases was discarded. Our experience suggests that this strategy effectively eliminates low-quality reads, while retaining high-quality regions [63–65].
We used BFAST (http://sourceforge.net/projects/bfast/)  as our primary alignment algorithm due to its high sensitivity for aligning reads on loci containing small insertions and deletions, as compared to the reference genome (mm9). We then used a TopHat-like strategy  to align the sequencing reads containing cross-splicing junctions using NGSUtils (http://ngsutils.org/) . After aligning the reads to a filtering index including repeats, ribosome RNA, and other sequences that were not of interest, we conducted a sequence alignment at three levels: genome, known junctions (University of California Santa Cruz Genome Browser), and novel junctions (based on the enriched regions identified in the genomic alignment). We restricted our analysis to uniquely aligned sequences with no more than two mismatches.
Alternative splicing analysis
We used MISO (mixture of isoforms)  to identify alternatively spliced exons whose splicing patterns were altered after LPS treatment. We first used Samtools (v0.1.19) to merge six RNA-seq samples into two BAM files according to their biological conditions, i.e., control vs. LPS-treated samples. We then estimated Percent Spliced In (PSI or Ψ), which indicates the proportion of RNA isoforms containing the alternatively spliced exon (inclusive isoforms) among all isoforms (inclusive plus exclusive isoforms). We also computed a Bayes factor (BF) to describe the likelihood of an AS event between the LPS-treated and control conditions. A BF of 5 means that an AS event is 5 times more likely to be differentially spliced than not. Both Ψ and BF values were computed by the software package MISO . The difference between Ψ s across the two conditions was defined as ΔΨ. We required each AS event to have a BF > 5 and |ΔΨ| > 0.05 to be considered differentially spliced.
The functions and cellular locations of AS genes were annotated by the pathway analysis tool Ingenuity Pathway Analysis (IPA), and the functional and biochemical properties of these genes were further annotated based on SwissProt and PIR keywords with DAVID v6.7 .
Protein domains overlapping AS regions
Protein domain information was predicted based on the RNA nucleotide sequences of the alternatively spliced exon, and 30-base flanking sequencings of both upstream and downstream exons. These RNA sequences were then translated into peptides, based on open reading frames (ORFs) documented by Ensembl and Refseq, which were then input into Pfam  for identification of protein domains overlapping AS regions.
Identification of protein-protein interactions (PPI)
We also examined whether alternatively spliced exons overlapped with potential protein-protein interaction domains. Based on the protein domains identified in or overlapping AS regions, we retrieved their binding partner domains with iPfam , which documents domain-domain interactions in the Protein Data Bank (PDB). We further used Pfam to search for genes encoding partner domains (i.e., potential protein interaction partners). The identified protein interaction partners were verified by two protein-protein interaction databases derived from high-throughput experiments.
Protein disorder was predicted with VSL2B , a highly regarded protein disorder prediction tool, especially for long regions of disorder . We required the peptides flanking the AS regions to be at least 9 amino acids long for accurate prediction. Potential binding sites were predicted with MoRF2, a software tool that predicts protein-binding sites that undergo a disorder–order transformation while binding another protein molecule . Known post-translational modification (PTM) sites were derived from UniProt, and novel PTM sites were predicted by ModPred . The upstream gene regulator NFYA (Nuclear transcription Factor Y subunit Alpha)  was predicted by Ingenuity Pathway Analysis (IPA), based on gene expression data and known regulatory gene interactions.
AS, Alternative splicing; BF, Bayesian factor; BMSCs, Bone marrow stromal cells; LPS, Lipopolysaccharide; NMD, Nonsense-mediated decay; PSI, or Ψ, Percent spliced in; PTM, Post-translational modification; PPIs, Protein-protein interactions; UTRs, Untranslated regions
The RNA-seq was conducted in the Center for Medical Genomics at Indiana University School of Medicine. The data was analyzed in the Bioinformatics Core at Indiana University School of Medicine. This work was supported in part by the Medical and Molecular Genetics, Indiana University School of Medicine Startup Funds, Showalter Trust Award and by the Indiana Clinical and Translational Sciences Institute, funded in part by grant # TR 000006 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award.
The publication costs for this article were funded by the corresponding author.
This article has been published as part of BMC Genomics Volume 17 Supplement 7, 2016: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM) 2015: genomics. The full contents of the supplement are available online at http://0-bmcgenomics.biomedcentral.com.brum.beds.ac.uk/articles/supplements/volume-17-supplement-7.
Availability of data and materials
The RNA-seq data can be accessed through the Gene Expression Omnibus (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/geo/) with accession number GSE64568).
AZ, YL and YW designed the study, conducted the analysis, and drafted the manuscript. ML contributed in functional analysis of alternative splicing events. BH, FH and AKD assisted in identification of MoRF in alternatively spliced regions. WF, BX, CB, and BL participated design of the study, and drafting of the manuscript. All the authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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- Chen L, Bush SJ, Tovar-Corona JM, Castillo-Morales A, Urrutia AO. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol Biol Evol. 2014;31(6):1402–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Stamm S, Ben-Ari S, Rafalska I, Tang Y, Zhang Z, Toiber D, Thanaraj TA, Soreq H. Function of alternative splicing. Gene. 2005;344:1–20.View ArticlePubMedGoogle Scholar
- Chen L, Bush SJ, Tovar-Corona JM, Castillo-Morales A, Urrutia AO. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol Biol Evol. 2014;31:1402–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74.View ArticleGoogle Scholar
- Diamond RH, Du K, Lee VM, Mohn KL, Haber BA, Tewari DS, Taub R. Novel delayed-early and highly insulin-induced growth response genes. Identification of HRS, a potential regulator of alternative pre-mRNA splicing. J Biol Chem. 1993;268(20):15185–92.PubMedGoogle Scholar
- Du K, Peng Y, Greenbaum LE, Haber BA, Taub R. HRS/SRp40-mediated inclusion of the fibronectin EIIIB exon, a possible cause of increased EIIIB expression in proliferating liver. Mol Cell Biol. 1997;17(7):4096–104.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang R, Xing Z, Luan Z, Wu T, Wu X, Hu G. A specific splicing variant of SVH, a novel human armadillo repeat protein, is up-regulated in hepatocellular carcinomas. Cancer Res. 2003;63(13):3775–82.PubMedGoogle Scholar
- Kanai Y, Saito Y, Ushijima S, Hirohashi S. Alterations in gene expression associated with the overexpression of a splice variant of DNA methyltransferase 3b, DNMT3b4, during human hepatocarcinogenesis. J Cancer Res Clin Oncol. 2004;130(11):636–44.View ArticlePubMedGoogle Scholar
- Kurahashi H, Takami K, Oue T, Kusafuka T, Okada A, Tawa A, Okada S, Nishisho I. Biallelic inactivation of the APC gene in hepatoblastoma. Cancer Res. 1995;55(21):5007–11.PubMedGoogle Scholar
- Saito Y, Kanai Y, Sakamoto M, Saito H, Ishii H, Hirohashi S. Overexpression of a splice variant of DNA methyltransferase 3b, DNMT3b4, associated with DNA hypomethylation on pericentromeric satellite regions during human hepatocarcinogenesis. Proc Natl Acad Sci U S A. 2002;99(15):10060–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Pevsner-Fischer M, Morad V, Cohen-Sfady M, Rousso-Noori L, Zanin-Zhorov A, Cohen S, Cohen IR, Zipori D. Toll-like receptors and their ligands control mesenchymal stem cell functions. Blood. 2007;109(4):1422–32.View ArticlePubMedGoogle Scholar
- Gnecchi M, Danieli P, Cervio E. Mesenchymal stem cell therapy for heart disease. Vasc Pharmacol. 2012;57(1):48–55.View ArticleGoogle Scholar
- van den Akker F, de Jager SC, Sluijter JP. Mesenchymal stem cell therapy for cardiac inflammation: immunomodulatory properties and the influence of toll-like receptors. Mediat Inflamm. 2013;2013:181020.Google Scholar
- Lu X, Liu T, Gu L, Huang C, Zhu H, Meng W, Xi Y, Li S, Liu Y. Immunomodulatory effects of mesenchymal stem cells involved in favoring type 2 T cell subsets. Transpl Immunol. 2009;22(1–2):55–61.View ArticlePubMedGoogle Scholar
- Gnecchi M, He H, Liang OD, Melo LG, Morello F, Mu H, Noiseux N, Zhang L, Pratt RE, Ingwall JS, et al. Paracrine action accounts for marked protection of ischemic heart by Akt-modified mesenchymal stem cells. Nat Med. 2005;11(4):367–8.View ArticlePubMedGoogle Scholar
- Gnecchi M, He H, Noiseux N, Liang OD, Zhang L, Morello F, Mu H, Melo LG, Pratt RE, Ingwall JS, et al. Evidence supporting paracrine hypothesis for Akt-modified mesenchymal stem cell-mediated cardiac protection and functional improvement. Faseb J. 2006;20(6):661–9.View ArticlePubMedGoogle Scholar
- Kocher AA, Schuster MD, Szabolcs MJ, Takuma S, Burkhoff D, Wang J, Homma S, Edwards NM, Itescu S. Neovascularization of ischemic myocardium by human bone-marrow-derived angioblasts prevents cardiomyocyte apoptosis, reduces remodeling and improves cardiac function. Nat Med. 2001;7(4):430–6.View ArticlePubMedGoogle Scholar
- Steele A, Steele P. Stem cells for repair of the heart. Curr Opin Pediatr. 2006;18(5):518–23.View ArticlePubMedGoogle Scholar
- Tomasoni S, Longaretti L, Rota C, Morigi M, Conti S, Gotti E, Capelli C, Introna M, Remuzzi G, Benigni A. Transfer of growth factor receptor mRNA via exosomes unravels the regenerative effect of mesenchymal stem cells. Stem Cells Dev. 2013;22(5):772–80.View ArticlePubMedGoogle Scholar
- Abdel-Latif A, Bolli R, Tleyjeh IM, Montori VM, Perin EC, Hornung CA, Zuba-Surma EK, Al-Mallah M, Dawn B. Adult bone marrow-derived cells for cardiac repair: a systematic review and meta-analysis. Arch Intern Med. 2007;167(10):989–97.View ArticlePubMedGoogle Scholar
- Dai W, Hale SL, Martin BJ, Kuang JQ, Dow JS, Wold LE, Kloner RA. Allogeneic mesenchymal stem cell transplantation in postinfarcted rat myocardium: short- and long-term effects. Circulation. 2005;112(2):214–23.View ArticlePubMedGoogle Scholar
- Gnecchi M, Zhang Z, Ni A, Dzau VJ. Paracrine mechanisms in adult stem cell signaling and therapy. Circ Res. 2008;103(11):1204–19.View ArticlePubMedPubMed CentralGoogle Scholar
- Yao Y, Zhang F, Wang L, Zhang G, Wang Z, Chen J, Gao X. Lipopolysaccharide preconditioning enhances the efficacy of mesenchymal stem cells transplantation in a rat model of acute myocardial infarction. J Biomed Sci. 2009;16:74.View ArticlePubMedPubMed CentralGoogle Scholar
- Herrmann JL, Wang Y, Abarbanell AM, Weil BR, Tan J, Meldrum DR. Preconditioning mesenchymal stem cells with transforming growth factor-alpha improves mesenchymal stem cell-mediated cardioprotection. Shock. 2010;33(1):24–30.View ArticlePubMedGoogle Scholar
- Wang Y, Abarbanell AM, Herrmann JL, Weil BR, Manukyan MC, Poynter JA, Meldrum DR. TLR4 inhibits mesenchymal stem cell (MSC) STAT3 activation and thereby exerts deleterious effects on MSC-mediated cardioprotection. PLoS One. 2010;5(12):e14206.View ArticlePubMedPubMed CentralGoogle Scholar
- Ma A, Jiang L, Song L, Hu Y, Dun H, Daloze P, Yu Y, Jiang J, Zafarullah M, Chen H. Reconstruction of cartilage with clonal mesenchymal stem cell-acellular dermal matrix in cartilage defect model in nonhuman primates. Int Immunopharmacol. 2013;16(3):399–408.View ArticlePubMedGoogle Scholar
- El-Badri N, Ghoneim MA. Mesenchymal stem cell therapy in diabetes mellitus: progress and challenges. J Nucleic Acids. 2013;2013:194858.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang LQ, Lin ZZ, Zhang HX, Shao B, Xiao L, Jiang HG, Zhuge QC, Xie LK, Wang B, Su DM, et al. Timing and dose regimens of marrow mesenchymal stem cell transplantation affect the outcomes and neuroinflammatory response after ischemic stroke. CNS Neurosci Ther. 2014;20(4):317–26.View ArticlePubMedGoogle Scholar
- Uccelli A, Moretta L, Pistoia V. Mesenchymal stem cells in health and disease. Nat Rev Immunol. 2008;8(9):726–36.View ArticlePubMedGoogle Scholar
- Kurtzberg J, Prockop S, Teira P, Bittencourt H, Lewis V, Chan KW, Horn B, Yu L, Talano JA, Nemecek E, et al. Allogeneic human mesenchymal stem cell therapy (remestemcel-L, Prochymal) as a rescue agent for severe refractory acute graft-versus-host disease in pediatric patients. Biol Blood Marrow Transplant. 2014;20(2):229–35.View ArticlePubMedGoogle Scholar
- Dalal J, Gandy K, Domen J. Role of mesenchymal stem cell therapy in Crohn’s disease. Pediatr Res. 2012;71(4 Pt 2):445–51.View ArticlePubMedGoogle Scholar
- Martin GS. Sepsis, severe sepsis and septic shock: changes in incidence, pathogens and outcomes. Expert Rev Anti-Infect Ther. 2012;10(6):701–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Balk RA. Severe sepsis and septic shock. Definitions, epidemiology, and clinical manifestations. Crit Care Clin. 2000;16(2):179–92.View ArticlePubMedGoogle Scholar
- Katz Y, Wang ET, Airoldi EM, Burge CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010;7(12):1009–15.View ArticlePubMedPubMed CentralGoogle Scholar
- Rhee SH, Hwang D. Murine TOLL-like receptor 4 confers lipopolysaccharide responsiveness as determined by activation of NF kappa B and expression of the inducible cyclooxygenase. J Biol Chem. 2000;275(44):34035–40.View ArticlePubMedGoogle Scholar
- Fitzgerald KA, Rowe DC, Barnes BJ, Caffrey DR, Visintin A, Latz E, Monks B, Pitha PM, Golenbock DT. LPS-TLR4 signaling to IRF-3/7 and NF-kappaB involves the toll adapters TRAM and TRIF. J Exp Med. 2003;198(7):1043–55.View ArticlePubMedPubMed CentralGoogle Scholar
- Chang YF, Imam JS, Wilkinson MF. The nonsense-mediated decay RNA surveillance pathway. Annu Rev Biochem. 2007;76:51–74.View ArticlePubMedGoogle Scholar
- da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.View ArticleGoogle Scholar
- Laity JH, Lee BM, Wright PE. Zinc finger proteins: new insights into structural and functional diversity. Curr Opin Struct Biol. 2001;11(1):39–46.View ArticlePubMedGoogle Scholar
- Robinson MD, McCarthy DJ, Smyth GK. EdgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.View ArticlePubMedGoogle Scholar
- Finn RD, Miller BL, Clements J, Bateman A. IPfam: a database of protein family and domain interactions found in the protein data bank. Nucleic Acids Res. 2014;42(Database issue):D364–373.View ArticlePubMedGoogle Scholar
- Bossi A, Lehner B. Tissue specificity and the human protein interaction network. Mol Syst Biol. 2009;5:260.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang X, Wei X, Thijssen B, Das J, Lipkin SM, Yu H. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol. 2012;30(2):159–64.View ArticlePubMedGoogle Scholar
- Romero PR, Zaidi S, Fang YY, Uversky VN, Radivojac P, Oldfield CJ, Cortese MS, Sickmeier M, LeGall T, Obradovic Z, et al. Alternative splicing in concert with protein intrinsic disorder enables increased functional diversity in multicellular organisms. Proc Natl Acad Sci U S A. 2006;103(22):8390–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z. Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics. 2006;7:208.View ArticlePubMedPubMed CentralGoogle Scholar
- Buljan M, Chalancon G, Dunker AK, Bateman A, Balaji S, Fuxreiter M, Babu MM. Alternative splicing of intrinsically disordered regions and rewiring of protein interactions. Curr Opin Struct Biol. 2013;23(3):443–50.View ArticlePubMedGoogle Scholar
- Pejaver V, Hsu WL, Xin FX, Dunker AK, Uversky VN, Radivojac P. The structural and functional signatures of proteins that undergo multiple events of post-translational modification. Protein Sci. 2014;23(8):1077–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics. 2011;27(7):1017–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Ray D, Kazan H, Cook KB, Weirauch MT, Najafabadi HS, Li X, Gueroussov S, Albu M, Zheng H, Yang A, et al. A compendium of RNA-binding motifs for decoding gene regulation. Nature. 2013;499(7457):172–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang ZJ, Zhang FM, Wang LS, Yao YW, Zhao Q, Gao X. Lipopolysaccharides can protect mesenchymal stem cells (MSCs) from oxidative stress-induced apoptosis and enhance proliferation of MSCs via Toll-like receptor (TLR)-4 and PI3K/Akt. Cell Biol Int. 2009;33(6):665–74.View ArticlePubMedGoogle Scholar
- Crisostomo PR, Wang Y, Markel TA, Wang M, Lahm T, Meldrum DR. Human mesenchymal stem cells stimulated by TNF-alpha, LPS, or hypoxia produce growth factors by an NF kappa B- but not JNK-dependent mechanism. Am J Physiol Cell Physiol. 2008;294(3):C675–682.View ArticlePubMedGoogle Scholar
- Wang M, Tan J, Wang Y, Meldrum KK, Dinarello CA, Meldrum DR. IL-18 binding protein-expressing mesenchymal stem cells improve myocardial protection after ischemia or infarction. Proc Natl Acad Sci U S A. 2009;106(41):17499–504.View ArticlePubMedPubMed CentralGoogle Scholar
- Pawson T, Nash P. Assembly of cell regulatory systems through protein interaction domains. Science. 2003;300(5618):445–52.View ArticlePubMedGoogle Scholar
- Pena PV, Davrazou F, Shi X, Walter KL, Verkhusha VV, Gozani O, Zhao R, Kutateladze TG. Molecular mechanism of histone H3K4me3 recognition by plant homeodomain of ING2. Nature. 2006;442(7098):100–3.PubMedPubMed CentralGoogle Scholar
- Stenmark H, Vitale G, Ullrich O, Zerial M. Rabaptin-5 is a direct effector of the small GTPase Rab5 in endocytic membrane fusion. Cell. 1995;83(3):423–32.View ArticlePubMedGoogle Scholar
- Harding C, Heuser J, Stahl P. Receptor-mediated endocytosis of transferrin and recycling of the transferrin receptor in rat reticulocytes. J Cell Biol. 1983;97(2):329–39.View ArticlePubMedGoogle Scholar
- Johnstone RM, Adam M, Hammond JR, Orr L, Turbide C. Vesicle formation during reticulocyte maturation. Association of plasma membrane activities with released vesicles (exosomes). J Biol Chem. 1987;262(19):9412–20.PubMedGoogle Scholar
- Hirst J, Lui WW, Bright NA, Totty N, Seaman MN, Robinson MS. A family of proteins with gamma-adaptin and VHS domains that facilitate trafficking between the trans-Golgi network and the vacuole/lysosome. J Cell Biol. 2000;149(1):67–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Valsdottir R, Hashimoto H, Ashman K, Koda T, Storrie B, Nilsson T. Identification of rabaptin-5, rabex-5, and GM130 as putative effectors of rab33b, a regulator of retrograde traffic between the Golgi apparatus and ER. FEBS Lett. 2001;508(2):201–9.View ArticlePubMedGoogle Scholar
- Pallai R, Simpkins H, Chen J, Parekh HK. The CCAAT box binding transcription factor, nuclear factor-Y (NF-Y) regulates transcription of human aldo-keto reductase 1C1 (AKR1C1) gene. Gene. 2010;459(1–2):11–23.View ArticlePubMedPubMed CentralGoogle Scholar
- Peister A, Mellad JA, Larson BL, Hall BM, Gibson LF, Prockop DJ. Adult stem cells from bone marrow (MSCs) isolated from different strains of inbred mice vary in surface epitopes, rates of proliferation, and differentiation potential. Blood. 2004;103(5):1662–8.View ArticlePubMedGoogle Scholar
- Secunda R, Vennila R, Mohanashankar AM, Rajasundari M, Jeswanth S, Surendran R. Isolation, expansion and characterisation of mesenchymal stem cells from human bone marrow, adipose tissue, umbilical cord blood and matrix: a comparative study. Cytotechnology. 2014;67:793–807.View ArticlePubMedGoogle Scholar
- Breese MR, Liu Y. NGSUtils: a software suite for analyzing and manipulating next-generation sequencing datasets. Bioinformatics. 2013;29(4):494–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Juan L, Wang G, Radovich M, Schneider BP, Clare SE, Wang Y, Liu Y. Potential roles of microRNAs in regulating long intergenic noncoding RNAs. BMC Med Genomics 2013;6 Suppl 1:S7. https://0-bmcmedgenomics-biomedcentral-com.brum.beds.ac.uk/articles/10.1186/1755-8794-6-S1-S7.
- Todd AG, Lin H, Ebert AD, Liu Y, Androphy EJ. COPI transport complexes bind to specific RNAs in neuronal cells. Hum Mol Genet. 2013;22(4):729–36.View ArticlePubMedGoogle Scholar
- Homer N, Merriman B, Nelson SF. BFAST: an alignment tool for large scale genome resequencing. PLoS One. 2009;4(11), e7767.View ArticlePubMedPubMed CentralGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Peng ZL, Kurgan L. Comprehensive comparative assessment of in-silico predictors of disordered regions. Curr Protein Pept Sci. 2012;13(1):6–18.View ArticlePubMedGoogle Scholar
- Cheng YG, Oldfield CJ, Meng JW, Romero P, Uversky VN, Dunker AK. Mining alpha-helix-forming molecular recognition features with cross species sequence alignments. Biochemistry. 2007;46(47):13468–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhu J, Zhang Y, Joe GJ, Pompetti R, Emerson SG. NF-Ya activates multiple hematopoietic stem cell (HSC) regulatory genes and promotes HSC self-renewal. Proc Natl Acad Sci U S A. 2005;102(33):11728–33.View ArticlePubMedPubMed CentralGoogle Scholar