| 1. |
- Tragante, Vinicius, et al.
(författare)
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Gene-centric Meta-analysis in 87,736 Individuals of European Ancestry Identifies Multiple Blood-Pressure-Related Loci.
- 2014
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Ingår i: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297. ; 94:3, s. 349-360
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Tidskriftsartikel (refereegranskat)abstract
- Blood pressure (BP) is a heritable risk factor for cardiovascular disease. To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), we genotyped ∼50,000 SNPs in up to 87,736 individuals of European ancestry and combined these in a meta-analysis. We replicated findings in an independent set of 68,368 individuals of European ancestry. Our analyses identified 11 previously undescribed associations in independent loci containing 31 genes including PDE1A, HLA-DQB1, CDK6, PRKAG2, VCL, H19, NUCB2, RELA, HOXC@ complex, FBN1, and NFAT5 at the Bonferroni-corrected array-wide significance threshold (p < 6 × 10(-7)) and confirmed 27 previously reported associations. Bioinformatic analysis of the 11 loci provided support for a putative role in hypertension of several genes, such as CDK6 and NUCB2. Analysis of potential pharmacological targets in databases of small molecules showed that ten of the genes are predicted to be a target for small molecules. In summary, we identified previously unknown loci associated with BP. Our findings extend our understanding of genes involved in BP regulation, which may provide new targets for therapeutic intervention or drug response stratification.
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| 2. |
- Holleboom, Adriaan G, et al.
(författare)
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Heterozygosity for a Loss-of-Function Mutation in GALNT2 Improves Plasma Triglyceride Clearance in Man
- 2011
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Ingår i: Cell Metabolism. - : Elsevier. - 1550-4131 .- 1932-7420. ; 14:6, s. 811-8
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies have identified GALNT2 as a candidate gene in lipid metabolism, but it is not known how the encoded enzyme ppGalNAc-T2, which contributes to the initiation of mucin-type O-linked glycosylation, mediates this effect. In two probands with elevated plasma high-density lipoprotein cholesterol and reduced triglycerides, we identified a mutation in GALNT2. It is shown that carriers have improved postprandial triglyceride clearance, which is likely attributable to attenuated glycosylation of apolipoprotein (apo) C-III, as observed in their plasma. This protein inhibits lipoprotein lipase (LPL), which hydrolyses plasma triglycerides. We show that an apoC-III-based peptide is a substrate for ppGalNAc-T2 while its glycosylation by the mutant enzyme is impaired. In addition, neuraminidase treatment of apoC-III which removes the sialic acids from its glycan chain decreases its potential to inhibit LPL. Combined, these data suggest that ppGalNAc-T2 can affect lipid metabolism through apoC-III glycosylation, thereby establishing GALNT2 as a lipid-modifying gene.
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| 3. |
- Helland, Ragnhild Holden, et al.
(författare)
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Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
- 2023
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Ingår i: Scientific reports. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
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