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- Dekkers, Bas J W, et al.
(författare)
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The Arabidopsis DELAY OF GERMINATION 1 gene affects ABSCISIC ACID INSENSITIVE 5 (ABI5) expression and genetically interacts with ABI3 during Arabidopsis seed development
- 2016
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Ingår i: The Plant Journal. - : Wiley-Blackwell. - 0960-7412 .- 1365-313X. ; 85:4, s. 451-465
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Tidskriftsartikel (refereegranskat)abstract
- The seed expressed gene DELAY OF GERMINATION (DOG) 1 is absolutely required for the induction of dormancy. Next to a non-dormant phenotype, the dog1-1 mutant is also characterized by a reduced seed longevity suggesting that DOG1 may affect additional seed processes as well. This aspect however, has been hardly studied and is poorly understood. To uncover additional roles of DOG1 in seeds we performed a detailed analysis of the dog1 mutant using both transcriptomics and metabolomics to investigate the molecular consequences of a dysfunctional DOG1 gene. Further, we used a genetic approach taking advantage of the weak aba insensitive (abi) 3-1 allele as a sensitized genetic background in a cross with dog1-1. DOG1 affects the expression of hundreds of genes including LATE EMBRYOGENESIS ABUNDANT and HEAT SHOCK PROTEIN genes which are affected by DOG1 partly via control of ABI5 expression. Furthermore, the content of a subset of primary metabolites, which normally accumulate during seed maturation, was found to be affected in the dog1-1 mutant. Surprisingly, the abi3-1 dog1-1 double mutant produced green seeds which are highly ABA insensitive, phenocopying severe abi3 mutants, indicating that dog1-1 acts as an enhancer of the weak abi3-1 allele and thus revealing a genetic interaction between both genes. Analysis of the dog1 and dog1 abi3 mutants revealed additional seed phenotypes and therefore we hypothesize that DOG1 function is not limited to dormancy but that it is required for multiple aspects of seed maturation, in part by interfering with ABA signalling components.
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- Allesøe, Rosa Lundbye, et al.
(författare)
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Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
- 2023
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Ingår i: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 41:3, s. 399-408
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Tidskriftsartikel (refereegranskat)abstract
- The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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- Bar, N., et al.
(författare)
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A reference map of potential determinants for the human serum metabolome
- 2020
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Ingår i: Nature. - : Nature Research. - 0028-0836 .- 1476-4687. ; 588:7836, s. 135-140
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Tidskriftsartikel (refereegranskat)abstract
- The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.
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- Dekkers, W, et al.
(författare)
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Good Death or Good Life as a Goal of Palliative Care
- 2002
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Ingår i: The ethics of palliative care: European perspectives. - : Buckingham: Open University Press. - 0335211410 ; , s. 106-125
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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- Kuilenburg, André B P van, et al.
(författare)
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Phenotypic and clinical implications of variants in the dihydropyrimidine dehydrogenase gene.
- 2016
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Ingår i: Biochimica et Biophysica Acta. - : Elsevier BV. - 0006-3002 .- 1878-2434. ; 1862:4, s. 754-762
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Tidskriftsartikel (refereegranskat)abstract
- Dihydropyrimidine dehydrogenase (DPD) is the initial and rate-limiting enzyme in the catabolism of the pyrimidine bases uracil, thymine and the antineoplastic agent 5-fluorouracil. Genetic variations in the gene encoding DPD (DPYD) have emerged as predictive risk alleles for 5FU-associated toxicity. Here we report an in-depth analysis of genetic variants in DPYD and their consequences for DPD activity and pyrimidine metabolites in 100 Dutch healthy volunteers. 34 SNPs were detected in DPYD and 15 SNPs were associated with altered plasma concentrations of pyrimidine metabolites. DPD activity was significantly associated with the plasma concentrations of uracil, the presence of a specific DPYD mutation (c.1905+1G>A) and the combined presence of three risk variants in DPYD (c.1905+1G>A, c.1129-5923C>G, c.2846A>T), but not with an altered uracil/dihydrouracil (U/UH2) ratio. Various haplotypes were associated with different DPD activities (haplotype D3, a decreased DPD activity; haplotype F2, an increased DPD activity). Functional analysis of eight recombinant mutant DPD enzymes showed a reduced DPD activity, ranging from 35% to 84% of the wild-type enzyme. Analysis of a DPD homology model indicated that the structural effect of the novel p.G401R mutation is most likely minor. The clinical relevance of the p.D949V mutation was demonstrated in a cancer patient heterozygous for the c.2846A>T mutation and a novel nonsense mutation c.1681C>T (p.R561X), experiencing severe grade IV toxicity. Our studies showed that the endogenous levels of uracil and the U/UH2 ratio are poor predictors of an impaired DPD activity. Loading studies with uracil to identify patients with a DPD deficiency warrants further investigation.
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- Wilman, H. R., et al.
(författare)
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Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration
- 2019
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 71:3, s. 594-602
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Tidskriftsartikel (refereegranskat)abstract
- Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10−8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart disease. We identified 3 genetic variants that are linked to an increased risk of developing higher liver iron content. We show that the same genetic variants are linked to higher risk of many diseases, but they may also be associated with some health advantages. Finally, we use genetic variants associated with waist-to-hip ratio as a tool to show that central obesity is causally associated with increased liver iron content.
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