| 1. |
- 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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| 2. |
- Atabaki Pasdar, Naeimeh, et al.
(författare)
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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
- 2020
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Ingår i: PLoS Medicine. - San Francisco : Public Library of Science (PLoS). - 1549-1676 .- 1549-1277. ; 17:6, s. 1003149-1003149
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (
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| 3. |
- 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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| 4. |
- Bennet, Anna M, et al.
(författare)
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Genetic association of sequence variants near AGER/NOTCH4 and dementia.
- 2011
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Ingår i: Journal of Alzheimer's disease : JAD. - 1875-8908. ; 24:3, s. 475-84
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Tidskriftsartikel (refereegranskat)abstract
- We performed a survey of sequence variation in a series of 20 genes involved in inflammation-related pathways for association with dementia risk in twin and unrelated case-control samples consisting in total of 1462 Swedish dementia casesand 1929 controls. For a total of 218 tested genetic markers, strong evidence was obtained implicating a region near AGER and NOTCH4 on chromosome 6p with replication across both samples and maximum combined significance at marker rs1800625 (OR = 1.37, 95% CI 1.19–1.56, p = 1.36×10(–6)). Imputation of the associated genomic interval provided an improved signal atrs8365, near the 3UTR of AGER (p = 7.34×10(–7)). The associated region extends 120 kb encompassing 11 candidate genes.While AGER encodes a key receptor for amyloid-β protein, an analysis of network context based upon genes now confirmed to contribute to dementia risk (AβPP, PSEN1, PSEN2, CR1, CLU, PICALM, and APOE) suggested strong functional coupling to NOTCH4, with no significant coupling to the remaining candidates. The implicated region occurs in the broad HLA locus on chromosome 6p, but associated markers were not in strong LD with known variants that regulate HLA gene function, suggesting that this may represent a signal distinct from immune-system pathways.
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| 5. |
- Brown, A.A., et al.
(författare)
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Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits
- 2023
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue. © 2023, Springer Nature Limited.
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| 6. |
- Bruzelius, Maria, et al.
(författare)
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PDGFB, a new candidate plasma biomarker for venous thromboembolism : results from the VEREMA affinity proteomics study
- 2016
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Ingår i: Blood. - : American Society of Hematology. - 0006-4971 .- 1528-0020. ; 128:23, s. E59-E66
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Tidskriftsartikel (refereegranskat)abstract
- There is a clear clinical need for high-specificity plasma biomarkers for predicting risk of venous thromboembolism (VTE), but thus far, such markers have remained elusive. Utilizing affinity reagents from the Human Protein Atlas project and multiplexed immuoassays, we extensively analyzed plasma samples from 2 individual studies to identify candidate protein markers associated with VTE risk. We screened plasma samples from 88 VTE cases and 85 matched controls, collected as part of the Swedish Venous Thromboembolism Biomarker Study, using suspension bead arrays composed of 755 antibodies targeting 408 candidate proteins. We identified significant associations between VTE occurrence and plasma levels of human immunodeficiency virus type I enhancer binding protein 1 (HIVEP1), von Willebrand factor (VWF), glutathione peroxidase 3 (GPX3), and platelet-derived growth factor beta (PDGFB). For replication, we profiled plasma samples of 580 cases and 589 controls from the French FARIVE study. These results confirmed the association of VWF and PDGFB with VTE after correction for multiple testing, whereas only weak trends were observed for HIVEP1 and GPX3. Although plasma levels of VWF and PDGFB correlated modestly (rho similar to 0.30) with each other, they were independently associated with VTE risk in a joint model in FARIVE (VWF P < .001; PDGFB P = .002). PDGF. was verified as the target of the capture antibody by immunocapture mass spectrometry and sandwich enzyme-linked immunosorbent assay. In conclusion, we demonstrate that high-throughput affinity plasma proteomic profiling is a valuable research strategy to identify potential candidate biomarkers for thrombosis-related disorders, and our study suggests a novel association of PDGFB plasma levels with VTE.
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| 7. |
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| 8. |
- Byström, Sanna, et al.
(författare)
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Affinity Proteomic Profiling of Plasma, Cerebrospinal Fluid, and Brain Tissue within Multiple Sclerosis
- 2014
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 13:11, s. 4607-4619
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Tidskriftsartikel (refereegranskat)abstract
- The brain is a vital organ and because it is well shielded from the outside environment, possibilities for noninvasive analysis are often limited. Instead, fluids taken from the spinal cord or circulatory system are preferred sources for the discovery of candidate markers within neurological diseases. In the context of multiple sclerosis (MS), we applied an affinity proteomic strategy and screened 22 plasma samples with 4595 antibodies (3450 genes) on bead arrays, then defined 375 antibodies (334 genes) for targeted analysis in a set of 172 samples and finally used 101 antibodies (43 genes) on 443 plasma as well as 573 cerebrospinal spinal fluid (CSF) samples. This revealed alteration of protein profiles in relation to MS subtypes for IRF8, IL7, METTL14, SLC30A7, and GAP43. Respective antibodies were subsequently used for immunofluorescence on human post-mortem brain tissue with MS pathology for expression and association analysis. There, antibodies for IRF8, IL7, and METTL14 stained neurons in proximity of lesions, which highlighted these candidate protein targets for further studies within MS and brain tissue. The affinity proteomic translation of profiles discovered by profiling human body fluids and tissue provides a powerful strategy to suggest additional candidates to studies of neurological disorders.
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| 9. |
- Byström, Sanna, et al.
(författare)
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Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density
- 2018
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Ingår i: Breast Cancer Research. - : BIOMED CENTRAL LTD. - 1465-5411 .- 1465-542X. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- Background: Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density. Methods: Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI). Results: Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD. Conclusions: Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.
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| 10. |
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