Functional integration of transcriptional and RNA processing machineries

Pandit, Shatakshi; Wang, Dong; Fu, Xiang‐Dong

doi:10.1016/j.ceb.2008.03.001

Cited by 153 publications

(141 citation statements)

References 72 publications

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“…Nevertheless, increasing evidence has established that a reciprocal and mutually beneficial coupling exists between transcription and RNA processing. Promoter strength may influence alternative splicing ("forward coupling") and splicing can stimulate transcription ("reverse coupling") (Damgaard et al, 2008;Pandit et al, 2008). The influence that the splicing process exerts on transcription seems not to be restricted only to transcription initiation, but also involves the elongation phase (Pandit et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Identification and characterization of novel collagen VI non-canonical splicing mutations causing ullrich congenital muscular dystrophy

Martoni

Urciuolo

Sabatelli³

et al. 2009

Hum. Mutat.

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Splicing mutations occurring outside the invariant GT and AG dinucleotides are frequent in disease genes and the definition of their pathogenic potential is often challenging. We have identified four patients affected by Ullrich congenital muscular dystrophy and carrying unusual mutations of COL6 genes affecting RNA splicing. In three cases the mutations occurred in the COL6A2 gene and consisted of nucleotide substitutions within the degenerated sequences flanking the canonical dinucleotides. In the fourth case, a genomic deletion occurred which removed the exon8-intron8 junction of the COL6A1 gene. These mutations induced variable splicing phenotypes, consisting of exon skipping, intron retention and cryptic splice site activation/usage. A quantitative RNA assay revealed a reduced level of transcription of the mutated in-frame mRNA originating from a COL6A2 point mutation at intronic position +3. At variance, the transcription level of the mutated in-frame mRNA originating from a genomic deletion which removed the splicing sequences of COL6A1 exon 8 was normal. These findings suggest a different transcriptional efficiency of a regulatory splicing mutation compared to a genomic deletion causing a splicing defect.

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Section: Discussionmentioning

confidence: 99%

Identification and characterization of novel collagen VI non-canonical splicing mutations causing ullrich congenital muscular dystrophy

Martoni

Urciuolo

Sabatelli³

et al. 2009

Hum. Mutat.

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“…There is a need for finding approaches to identify biologically variable loci in nonreplicable systems. Mechanistically, a mutation (e.g., the allele "a" or "A") may alter the regulation of gene expression at any stage of the coupled processes of transcription, cotranscription, and post-transcription (Pandit et al 2008;Majewski and Pastinen 2011;Chalancon et al 2012). Also a vast array of biophysical parameters governing these processes may be involved, including the efficiency of initiation, the speed of transcription, the frequency of transcriptional bursting, mRNA stability, the presence of antisense RNA-mediated degradation, nonsense-mediated decay, the status of regulatory network, and so on (Raser and Certainly, there are also many nongenetic factors introduced in the path from the human donor to the study of gene expression of an LCL in vitro .…”

Section: Formation Of Evqtlmentioning

confidence: 99%

Genetic Variants Contribute to Gene Expression Variability in Humans

2013

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Expression quantitative trait loci (eQTL) studies have established convincing relationships between genetic variants and gene expression. Most of these studies focused on the mean of gene expression level, but not the variance of gene expression level (i.e., gene expression variability). In the present study, we systematically explore genome-wide association between genetic variants and gene expression variability in humans. We adapt the double generalized linear model (dglm) to simultaneously fit the means and the variances of gene expression among the three possible genotypes of a biallelic SNP. The genomic loci showing significant association between the variances of gene expression and the genotypes are termed expression variability QTL (evQTL). Using a data set of gene expression in lymphoblastoid cell lines (LCLs) derived from 210 HapMap individuals, we identify cis-acting evQTL involving 218 distinct genes, among which 8 genes, ADCY1, CTNNA2, DAAM2, FERMT2, IL6, PLOD2, SNX7, and TNFRSF11B, are cross-validated using an extra expression data set of the same LCLs. We also identify $300 trans-acting evQTL between .13,000 common SNPs and 500 randomly selected representative genes. We employ two distinct scenarios, emphasizing single-SNP and multiple-SNP effects on expression variability, to explain the formation of evQTL. We argue that detecting evQTL may represent a novel method for effectively screening for genetic interactions, especially when the multiple-SNP influence on expression variability is implied. The implication of our results for revealing genetic mechanisms of gene expression variability is discussed.Q UANTITATIVE genetic analysis has long focused on detecting genetic variants that affect organismal phenotypes. This is often done by contrasting mean differences in phenotypes among genotypes. Despite increasing evidence across several species for genetic control of phenotypic variance (Ansel et al. 2008;Hill and Mulder 2010; JimenezGomez et al. 2011), variance differences in phenotypes have been largely ignored. Recently, however, the fact that variance of phenotypes is genotype dependent has inspired the detection of genetic variants associated with phenotypic variability (Pare et al. 2010;Sudmant et al. 2010;Yang et al. 2012).When gene expression level is considered as a heritable, quantitative trait, statistical associations between mean gene expression and genotype can be established to identify those genomic loci associated with or linked to gene expression level (i.e., expression QTL, eQTL) (Montgomery and Dermitzakis 2011). The difference in mean gene expression and its genetic control have been extensively examined in humans (Stranger et al. 2005(Stranger et al. , 2007bPickrell et al. 2010). The difference in variance of gene expression (i.e., gene expression variability) is genetically controlled and likely to be selectable (Raser and O'Shea 2005;Blake et al. 2006;Maheshri and O'Shea 2007;Cheung and Spielman 2009;Zhang et al. 2009). A small number of initial efforts have been m...

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“…Nearby lies the C terminus of Rpb1, from which extends a flexible linker, followed by Ͼ20 repeats of a 7-aa sequence [the C-terminal domain (CTD)], which interact not only with Mediator but also with capping, splicing, and cleavage/polyadenylation factors during transcription (17).…”

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confidence: 99%

Schizosacharomyces pombe RNA polymerase II at 3.6-Å resolution

Spåhr

Calero

Bushnell

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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The second structure of a eukaryotic RNA polymerase II so far determined, that of the enzyme from the fission yeast Schizosaccharomyces pombe, is reported here. Comparison with the previous structure of the enzyme from the budding yeast Saccharomyces cerevisiae reveals differences in regions implicated in start site selection and transcription factor interaction. These aspects of the transcription mechanism differ between S. pombe and S. cerevisiae, but are conserved between S. pombe and humans. Amino acid changes apparently responsible for the structural differences are also conserved between S. pombe and humans, suggesting that the S. pombe structure may be a good surrogate for that of the human enzyme. molecular replacement ͉ X-ray crystallography ͉ yeast X -ray crystal structures of RNA polymerases from bacteria and Archaea, and of RNA polymerase II (pol II) from the budding yeast Saccharomyces cerevisiae, have given insight into the fundamental mechanism of transcription (1-7). The structures are closely similar in the catalytic core of the enzyme, where mobile elements interact with nucleic acids during transcription. The bacterial and S. cerevisiae structures differ in the periphery, where the enzymes interact with accessory factors, most notably the general transcription factors and Mediator of eukaryotes. One of the general factors, transcription factor II B (TFIIB), exhibits a degree of conservation with bacterial sigma factor and has a counterpart in Archaea, and a subunit of another general factor, the TATA-binding protein, occurs in Archaea as well, but the remaining general factors and Mediator are unique to eukaryotes.Conserved elements of the catalytic core include ''switch regions'' at the base of a swinging clamp, which interact with the DNA-RNA hybrid and downstream DNA during transcription (2, 3) and may be involved in transcription start site determination (8, 9). A so-called ''trigger loop'' interacts with the substrate nucleoside triphosphate to achieve the high fidelity of transcription (10)(11)(12)(13)(14). A ''bridge helix'' interacts with the coding base in the template strand of the DNA and is believed to play a role in the translocation step of transcription.Peripheral, more divergent elements of pol II include subunits Rpb4 and Rpb7, which form a dimer protruding from the enzyme surface (15, 16) and may be involved in RNA binding. Nearby lies the C terminus of Rpb1, from which extends a flexible linker, followed by Ͼ20 repeats of a 7-aa sequence [the C-terminal domain (CTD)], which interact not only with Mediator but also with capping, splicing, and cleavage/polyadenylation factors during transcription (17).Pol II is 53% identical in amino acid sequence between S. cerevisiae and humans, assuring a high degree of structural similarity. Notable differences in transcription have nevertheless emerged, including differences in promoter structure and interactions with accessory factors. In these respects, the fission yeast Schizosaccharomyces pombe more closely resembles the human system....

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Functional integration of transcriptional and RNA processing machineries

Cited by 153 publications

References 72 publications

Identification and characterization of novel collagen VI non-canonical splicing mutations causing ullrich congenital muscular dystrophy

Identification and characterization of novel collagen VI non-canonical splicing mutations causing ullrich congenital muscular dystrophy

Genetic Variants Contribute to Gene Expression Variability in Humans

Schizosacharomyces pombe RNA polymerase II at 3.6-Å resolution

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