Alternative splicing and disease. Biochim Biophys Acta

University of Montpellier II, Institute of Molecular Genetics, Centre Nationale de Recherche Scientifique, 1919 Route de Mende, France.
Biochimica et Biophysica Acta (Impact Factor: 4.66). 11/2008; 1792(1):14-26. DOI: 10.1016/j.bbadis.2008.09.017
Source: PubMed


Almost all protein-coding genes are spliced and their majority is alternatively spliced. Alternative splicing is a key element in eukaryotic gene expression that increases the coding capacity of the human genome and an increasing number of examples illustrates that the selection of wrong splice sites causes human disease. A fine-tuned balance of factors regulates splice site selection. Here, we discuss well-studied examples that show how a disturbance of this balance can cause human disease. The rapidly emerging knowledge of splicing regulation now allows the development of treatment options.

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    • "The process of intron removal from mRNA precursors (premRNA splicing) is mediated by the spliceosome, one of the most complex ribonucleoprotein machineries within the cell (Wahl et al. 2009), and represents an essential and versatile step for the regulation of gene expression in multicellular organisms (Jangi and Sharp 2014). Alternative patterns of intron removal (alternative splicing, AS) expand the coding potential of the genome (Nilsen and Graveley 2010) and alterations of cis and trans-acting elements involved in splice site recognition are frequently associated with the establishment and progression of disease states (Wang and Cooper 2007; Cooper et al. 2009, Tazi et al. 2009). A major hallmark of the initiation of atherosclerotic lesions is the uptake and accumulation of low-density lipoproteins (LDLs and Ox-LDLs), resulting in foam cell formation in atheroma plaques (Vohra et al. 2006). "
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    ABSTRACT: The OLR1 gene encodes the oxidized low-density lipoprotein receptor (LOX-1), which is responsible for the cellular uptake of oxidized LDL (Ox-LDL), foam cell formation in atheroma plaques and atherosclerotic plaque rupture. Alternative splicing (AS) of OLR1 exon 5 generates two protein isoforms with antagonistic functions in Ox-LDL uptake. Previous work identified six single nucleotide polymorphisms (SNPs) in linkage disequilibrium that influence the inclusion levels of OLR1 exon 5 and correlate with the risk of cardiovascular disease. Here we use minigenes to recapitulate the effects of two allelic series (Low- and High-Risk) on OLR1 AS and identify one SNP in intron 4 (rs3736234) as the main contributor to the differences in exon 5 inclusion, while the other SNPs in the allelic series attenuate the drastic effects of this key SNP. Bioinformatic, proteomic, mutational and functional high-throughput analyses allowed us to define regulatory sequence motifs and identify SR protein family members (SRSF1, SRSF2) and HMGA1 as factors involved in the regulation of OLR1 AS. Our results suggest that antagonism between SRSF1 and SRSF2/HMGA1, and differential recognition of their regulatory motifs depending on the identity of the rs3736234 polymorphism, influence OLR1 exon 5 inclusion and the efficiency of Ox-LDL uptake, with potential implications for atherosclerosis and coronary disease. © 2015 Tejedor et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.
    RNA 04/2015; 21(6). DOI:10.1261/rna.049890.115 · 4.94 Impact Factor
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    • "Although no statistically significant association was found between this polymorphism and blood cadmium levels (p 40.05), it was detected that individuals with the CC (A À ) genotype had higher blood cadmium levels than those with AA and CA genotypes (Aþ) (Table 1). Although the functional effect of this genetic variability on DMT1 is not known, it is speculated that IVS4 þ 44 C/A polymorphism that is located in the intron may affect either constitutive splicing or alternative splicing by the corruption of splicing regulatory cis-elements, which may give incorrect isoforms of the protein (Tazi et al., 2009; Ward and Cooper, 2010; Przybyłkowski et al., 2014). Furthermore, non-translated regions (promoter and introns) can affect transcriptional and post-transcriptional events and ultimately the translation of mRNA into protein although they do not appear in proteins (Kayaalti et al., 2011b). "
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    ABSTRACT: Divalent metal transporter 1 (DMT1), a member of the proton-coupled metal ion transporter family, mediates transport of ferrous iron from the lumen of the intestine into the enterocyte and export of iron from endocytic vesicles. It has an affinity not only for iron but also for other divalent cations including manganese, cobalt, nickel, cadmium, lead, copper, and zinc. DMT1 is encoded by the SLC11a2 gene that is located on chromosome 12q13 in humans and express four major mammalian isoforms (1A/+IRE, 1A/-IRE, 2/+IRE and 2/-IRE). Mutations or polymorphisms of DMT1 gene may have an impact on human health by disturbing metal trafficking. To study the possible association of DMT1 gene with the blood levels of some divalent cations such as iron, lead and cadmium, a single nucleotide polymorphism (SNP) (IVS4+44C/A) in DMT1 gene was investigated in 486 unrelated and healthy individuals in a Turkish population by method of polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The genotype frequencies were found as 49.8% homozygote typical (CC), 38.3% heterozygote (CA) and 11.9% homozygote atypical (AA). Metal levels were analyzed by dual atomic absorption spectrometer system and the average levels of iron, lead and cadmium in the blood samples were 446.01±81.87ppm, 35.59±17.72ppb and 1.25±0.87ppb, respectively. Individuals with the CC genotype had higher blood iron, lead and cadmium levels than those with AA and CA genotypes. Highly statistically significant associations were detected between IVS4+44 C/A polymorphism in the DMT1 gene and iron and lead levels (p=0.001 and p=0.036, respectively), but no association was found with cadmium level (p=0.344). This study suggested that DMT1 IVS4+44 C/A polymorphism is associated with inter-individual variations in blood iron, lead and cadmium levels. Copyright © 2014 Elsevier Inc. All rights reserved.
    Environmental Research 12/2014; 137C:8-13. DOI:10.1016/j.envres.2014.11.008 · 4.37 Impact Factor
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    • "functional relationship; AS, alternative splicing; HCIs, highest connected isoforms; NCIs, nonhighest connected isoforms different, or even opposite biological functions [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]. The resulting splice variants greatly increase the repertoire of gene products and therefore their functional complexity. "
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    ABSTRACT: Canonical isoforms in different databases have been defined as the most prevalent, most conserved, most expressed, longest, or the one with the clearest description of domains or post-translational modifications. In this article, we revisit these definitions of canonical isoforms based on functional genomics and proteomics evidence, focusing on mouse data. We report a novel functional relationship network-based approach for identifying the Highest Connected Isoforms (HCIs). We show that 46% of these HCIs are not the longest transcripts. In addition, this approach revealed many genes that have more than one highly connected isoforms. Averaged across 175 RNA-seq datasets covering diverse tissues and conditions, 65% of the HCIs show higher expression levels than non-highest connected isoforms (NCIs) at the transcript level. At the protein level, these HCIs highly overlap with the expressed splice variants, based on proteomic data from eight different normal tissues. These results suggest that a more confident definition of canonical isoforms can be made through integration of multiple lines of evidence, including highest connected isoforms defined by biological processes and pathways, expression prevalence at the transcript level, and relative or absolute abundance at the protein level. This integrative proteogenomics approach can successfully identify principal isoforms that are responsible for the canonical functions of genes.This article is protected by copyright. All rights reserved
    Proteomics 12/2014; 14(23-24). DOI:10.1002/pmic.201400170 · 3.81 Impact Factor
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