Showing papers by "Emmanuelle Masson published in 2019"

Common variants in glyoxalase I do not increase chronic pancreatitis risk.

Abstract: Chronic pancreatitis (CP) may be caused by oxidative stress. An important source of reactive oxygen species (ROS) is the methylglyoxal-derived formation of advanced glycation endproducts (AGE). Methylglyoxal is detoxified by Glyoxalase I (GLO1). A reduction in GLO1 activity results in increased ROS. Single nucleotide polymorphisms (SNPs) of GLO1 have been linked to various inflammatory diseases. Here, we analyzed whether common GLO1 variants are associated with alcoholic (ACP) and non-alcoholic CP (NACP).Using melting curve analysis, we genotyped a screening cohort of 223 ACP, 218 NACP patients, and 328 controls for 11 tagging SNPs defined by the SNPinfo LD TAG SNP Selection tool and the functionally relevant variant rs4746. For selected variants the cohorts were extended to up to 1,441 patient samples.In the ACP cohort, comparison of genotypes for rs1937780 between patients and controls displayed an ambiguous result in the screening cohort (p = 0.08). However, in the extended cohort of 1,441 patients no statistically significant association was found for the comparison of genotypes (p = 0.11), nor in logistic regression analysis (p = 0.214, OR 1.072, 95% CI 0.961-1.196). In the NACP screening cohort SNPs rs937662, rs1699012, and rs4746 displayed an ambiguous result when patients were compared to controls in the recessive or dominant model (p = 0.08, 0.08, and 0.07, respectively). Again, these associations were not confirmed in the extended cohorts (rs937662, dominant model: p = 0.07, logistic regression: p = 0.07, OR 1.207, 95% CI 0.985-1.480) or in the replication cohorts for rs4746 (Germany, p = 0.42, OR 1.080, 95% CI 0.673-1.124; France, p = 0.19, OR 0.90, 95% CI 0.76-1.06; China, p = 0.24, OR 1.18, 95% CI 0.90-1.54) and rs1699012 (Germany, Munich; p = 0.279, OR 0.903, 95% CI 0.750-1.087).Common GLO1 variants do not increase chronic pancreatitis risk.

First estimate of the scale of canonical 5' splice site GT>GC variants capable of generating wild-type transcripts.

Abstract: It has long been known that canonical 5' splice site (5'SS) GT>GC variants may be compatible with normal splicing. However, to date, the actual scale of canonical 5'SSs capable of generating wild-type transcripts in the case of GT>GC substitutions remains unknown. Herein, combining data derived from a meta-analysis of 45 human disease-causing 5'SS GT>GC variants and a cell culture-based full-length gene splicing assay of 103 5'SS GT>GC substitutions, we estimate that ~15-18% of canonical GT 5'SSs retain their capacity to generate between 1% and 84% normal transcripts when GT is substituted by GC. We further demonstrate that the canonical 5'SSs in which substitution of GT by GC-generated normal transcripts exhibit stronger complementarity to the 5' end of U1 snRNA than those sites whose substitutions of GT by GC did not lead to the generation of normal transcripts. We also observed a correlation between the generation of wild-type transcripts and a milder than expected clinical phenotype but found that none of the available splicing prediction tools were capable of reliably distinguishing 5'SS GT>GC variants that generated wild-type transcripts from those that did not. Our findings imply that 5'SS GT>GC variants in human disease genes may not invariably be pathogenic.

Toward a clinical diagnostic pipeline for SPINK1 intronic variants

Abstract: The clinical significance of SPINK1 intronic variants in chronic pancreatitis has been previously assessed by various approaches including a cell culture-based full-length gene assay. A close correlation between the results of this assay and in silico splicing prediction was apparent. However, until now, a clinical diagnostic pipeline specifically designed to classify SPINK1 intronic variants accurately and efficiently has been lacking. Herein, we present just such a pipeline and explore its efficacy and potential utility in potentiating the classification of newly described SPINK1 intronic variants. We confirm a close correlation between in silico splicing prediction and results from the cell culture-based full-length gene assay in the context of three recently reported pathogenic SPINK1 intronic variants. We then integrated in silico splicing prediction and the full-length gene assay into a stepwise approach and tested its utility in the classification of two novel datasets of SPINK1 intronic variants. The first dataset comprised 16 deep intronic variants identified in 52 genetically unexplained Chinese chronic pancreatitis patients by sequencing the entire intronic sequence of the SPINK1 gene. The second dataset comprised five novel rare proximal intronic variants identified through the routine analysis of the SPINK1 gene in French pancreatitis patients. Employing a minor allele frequency of > 5% as a population frequency filter, 6 of the 16 deep intronic variants were immediately classified as benign. In silico prediction of the remaining ten deep intronic variants and the five rare proximal intronic variants with respect to their likely impact on splice site selection suggested that only one proximal intronic variant, c.194 + 5G > A, was likely to be of functional significance. Employing the cell culture-based full-length gene assay, we functionally analyzed c.194 + 5G > A, together with seven predicted non-functional variants, thereby validating their predicted effects on splicing in all cases. We demonstrated the accuracy and efficiency of in silico prediction in combination with the cell culture-based full-length gene assay for the classification of SPINK1 intronic variants. Based upon these findings, we propose an operational pipeline for classifying SPINK1 intronic variants in the clinical diagnostic setting.

Genetic analysis of the STIM1 gene in chronic pancreatitis

Abstract: Chronic pancreatitis is a complex disease that involves many factors, both genetic and environmental. Over the past two decades, molecular genetic analysis of five genes that are highly expressed in human pancreatic acinar cells, namely PRSS1, PRSS2, SPINK1, CTRC and CTRB1/CTRB2 , has established that a trypsin-dependent pathway plays a key role in the etiology of chronic pancreatitis. Since Ca 2+ deregulation can lead to intracellular trypsin activation in experimental acute pancreatitis, we analyzed STIM1 (encoding stromal interaction molecule-1, the main regulator of Ca 2+ homeostasis in pancreatic acinar cells) as a candidate modifier gene in French, German and Chinese patients with chronic pancreatitis. The French and German subjects were analyzed by Sanger sequencing whereas the Chinese subjects were analyzed by targeted next-generation sequencing confirmed by Sanger sequencing. A total of 37 rare coding variants (35 missense and 2 nonsense) were identified, which were enriched in patients as compared with controls [2.28% (47/2,057) vs. 0.99% (33/3,322); odds ratio = 2.33, P = 0.0001]. This is the first large case-control study to demonstrate a putative association of rare STIM1 coding variants with chronic pancreatitis. Functional analysis will be required to clarify whether or not the rare STIM1 variants detected predispose to pancreatitis.

5’ splice site GC>GT variants differ from GT>GC variants in terms of their functionality and pathogenicity

Abstract: In the human genome, most 59 splice sites (~99%) employ the canonical GT dinucleotide whereas a small minority (~1%) use the non-canonical GC dinucleotide. The functionality and pathogenicity of 59 splice site GT>GC (i.e., +2T>C) variants have been extensively studied but we still know very little about 59 splice site GC>GT (+2C>T) variants. Herein, we sought to address this deficiency by performing a meta-analysis of identified +2C>T pathogenic variants together with a functional analysis of +2C>T substitutions using a cell culture-based full-length gene splicing assay. Our results establish a proof of concept that +2C>T variants are qualitatively different from +2T>C variants in terms of their functionality and pathogenicity and suggest that, in sharp contrast with +2T>C variants, most if not all +2C>T variants have no pathological relevance. Our findings have important implications for interpreting the clinical relevance of +2C>T variants but might also improve our understanding of the evolutionary basis of switching between GT and GC 59 splice sites in mammalian genomes.

The experimentally obtained functional impact assessments of GT>GC 5' splice site variants differ markedly from those predicted

Abstract: GT>GC 59 splice site (or +2T>C) variants have been frequently reported to cause human genetic disease. However, although we have demonstrated that GT>GC variants in human disease genes may not invariably be pathogenic, none of the currently available splicing prediction tools appear to be capable of reliably distinguishing those GT>GC variants that generate wild-type transcripts from those that do not. Recently, SpliceAI, a novel deep residual neural network tool, has been developed for splicing prediction. Methodologically distinct from previous approaches that either rely on human-engineered features and/or which focus on short nucleotide windows adjoining exon-intron boundaries, SpliceAI assesses splicing determinants by evaluating 10,000 nucleotides of flanking contextual sequence to predict the functional role in splicing of each position in the pre-mRNA transcript. Herein, we evaluated the performance of SpliceAI in the context of three datasets of GT>GC variants, all of which had been characterized functionally in terms of their impact on mRNA splicing. The first two datasets refer to our recently described "in vivo" dataset of 45 disease-causing GT>GC variants and the "in vitro" dataset of 103 GT>GC substitutions. The third dataset comprised 12 BRCA1 GT>GC variants that were recently analyzed by saturation genome editing. We processed all GT>GC variants using the default settings of SpliceAI. Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of the analyzed GT>GC variants revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue is that the impact of GT>GC (as well as GT>GA or +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts. Our findings highlight the challenges that we still face in attempting to accurately identify splice-altering variants.