| 1. |
- 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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| 2. |
- 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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| 3. |
- Dodig-Crnkovic, Tea, et al.
(författare)
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Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling
- 2020
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Ingår i: Ebiomedicine. - : Elsevier BV. - 2352-3964. ; 57
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Tidskriftsartikel (refereegranskat)abstract
- Background: Precision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate. Methods: To provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals' short-term health trajectories. Findings: We found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed that 49% of the protein profiles were stable over one year. We also identified eight networks of proteins in which 11-242 proteins covaried over time. For each participant, there were unique protein profiles of which some could be explained by associations to genetic variants. Interpretation: This observational and non-interventional study identifyed noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. To enable more personal hence precise assessments of health states, longitudinal profiling of circulating proteomes can provide a valuable component for precision medicine approaches.
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| 4. |
- Dodig-Crnković, Tea
(författare)
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On the application and validation of multiplexed affinity assays
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Proteins are essential macromolecules that carry out complex functions in human cells, tissues, and organs. They regulate a diverse set of biological processes and protect against pathogens. However, dysregulation or malformation of proteins can cause disease. By characterizing proteins in health and disease, we can gain insights into disease aetiology and identify druggable targets to treat disorders. By bringing protein discoveries from the research lab into clinical practice, protein assays have been and will continue to be important tools for enabling and improving medical decision-making. The work presented in this thesis concerns both exploratory and targeted affinity-based assays applied for the study of proteins. High-throughput and multiplexed suspension bead arrays have been the primary technology for measuring proteins with antibodies in samples such as human blood. Identification and validation of protein-protein interactions that may provide novel insights into the druggable proteome have also been carried out. Throughout the projects, methods for validating the observations have been pursued and include replication in independent sample sets, as well as the assessment of antibody selectivity via other proteomics assays or orthogonal methods such as genetic associations. In Paper I, we used multiplexed exploratory antibody arrays comprising almost 1.500 affinity binders to study proteins that circulate in plasma. Here, the focus was to determine the longitudinal variability of proteins. We analysed samples from 101 clinically healthy individuals, collected each third month for one year. The protein data provided insights into inter-individual diversity and the unique molecular fingerprint of each participant. We found that 49% of the studied proteins were stable across one year, as these had low variability in each individual. Eight modules, each containing 11-242 proteins, were found to co-vary across one year. We also found genetic variations to influence 15 of the detected protein profiles and confirmed selected indications in an independent set of 3.000 subjects. In summary, we observed the existence of individual-specific protein profiles and found that short-term and continuous changes occurred in almost every participant. In Paper II, we investigated blood-derived serum and plasma to identify age-associated proteins. We started from a large set of exploratory antibody bead arrays to screen 156 individuals aged 50-92 years. We found protein profiles of the histidine-rich glycoprotein (HRG) to be significantly associated with age. This association was further corroborated by the analysis of >4.000 individuals from eight additional and independent sets of blood samples. We further validated the HRG protein profiles by sandwich assays and protein microarrays developed in-house. Comparing genetic data and HRG profiles obtained by two independent antibodies, we observed strong but inverse associations to the genetic variants for two anti-HRG antibodies. In Paper III, we applied multiplexed assays for the detection of autoantibodies against cancer-testis antigens (CTAs) in 133 non-small cell lung cancer (NSCLC) patients. We found reactivity against 29 unique CTAs exclusively in cases, compared to 57 matched controls with benign lung diseases. The presence of six CTAs was further confirmed in an independent set of 34 NSCLC cases. Analysis of longitudinal samples from seven patients demonstrated that the presence of CTA autoantibodies was stable over time for each of the individuals. In Paper IV, we developed a novel multiplexed sandwich-immunoassay for the detection of interaction partners to G-protein coupled receptors (GPCRs). This pharmaceutically important family of membrane proteins is believed to be regulated by another group of receptor activity-modulating proteins (RAMPs) by the formation of protein complexes. We studied cell lysates expressing combinations of 23 GPCRs with three RAMPs. We confirmed most of the previously reported interaction pairs and additionally found evidence for 15 new GPCR-RAMP complexes. All interactions were validated using epitope tags that were engineered onto the proteins. Selected complexes were further validated by in situ proximity ligation assays performed in cell membranes. In summary, the work included in this thesis describes the use of multiplexed affinity-based assays for research within plasma proteomics and the interrogation of protein complexes. The work highlights the method’s potential for the identification of circulating proteins that may aid and add to the current knowledge about human health and disease.
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| 5. |
- Drobin, Kimi, et al.
(författare)
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Molecular Profiling for Predictors of Radiosensitivity in Patients with Breast or Head-and-Neck Cancer
- 2020
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 12:3
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Tidskriftsartikel (refereegranskat)abstract
- Nearly half of all cancers are treated with radiotherapy alone or in combination with other treatments, where damage to normal tissues is a limiting factor for the treatment. Radiotherapy-induced adverse health effects, mostly of importance for cancer patients with long-term survival, may appear during or long time after finishing radiotherapy and depending on the patient's radiosensitivity. Currently, there is no assay available that can reliably predict the individual's response to radiotherapy. We profiled two study sets from breast (n = 29) and head-and-neck cancer patients (n = 74) that included radiosensitive patients and matched radioresistant controls. We studied 55 single nucleotide polymorphisms (SNPs) in 33 genes by DNA genotyping and 130 circulating proteins by affinity-based plasma proteomics. In both study sets, we discovered several plasma proteins with the predictive power to find radiosensitive patients (adjusted p < 0.05) and validated the two most predictive proteins (THPO and STIM1) by sandwich immunoassays. By integrating genotypic and proteomic data into an analysis model, it was found that the proteins CHIT1, PDGFB, PNKD, RP2, SERPINC1, SLC4A, STIM1, and THPO, as well as the VEGFA gene variant rs69947, predicted radiosensitivity of our breast cancer (AUC = 0.76) and head-and-neck cancer (AUC = 0.89) patients. In conclusion, circulating proteins and a SNP variant of VEGFA suggest that processes such as vascular growth capacity, immune response, DNA repair and oxidative stress/hypoxia may be involved in an individual's risk of experiencing radiation-induced toxicity.
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| 6. |
- Gudmundsdottir, Valborg, et al.
(författare)
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Whole blood co-expression modules associate with metabolic traits and type 2 diabetes : an IMI-DIRECT study
- 2020
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Ingår i: Genome Medicine. - : BioMed Central. - 1756-994X. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D.Methods: Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts.Results: We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling.Conclusions: Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.
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| 7. |
- Hong, Mun-Gwan, et al.
(författare)
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Profiles of histidine-rich glycoprotein associate with age and risk of all-cause mortality
- 2020
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Ingår i: Life Science Alliance. - : Life Science Alliance, LLC. - 2575-1077. ; 3:10, s. e202000817-
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Tidskriftsartikel (refereegranskat)abstract
- Despite recognizing aging as a common risk factor of many human diseases, little is known about its molecular traits. To identify age-associated proteins circulating in human blood, we screened 156 individuals aged 50–92 using exploratory and multiplexed affinity proteomics assays. Profiling eight additional study sets (N = 3,987), performing antibody validation, and conducting a meta-analysis revealed a consistent age association (P = 6.61 × 10−6) for circulating histidine-rich glycoprotein (HRG). Sequence variants of HRG influenced how the protein was recognized in the immunoassays. Indeed, only the HRG profiles affected by rs9898 were associated with age and predicted the risk of mortality (HR = 1.25 per SD; 95% CI = 1.12–1.39; P = 6.45 × 10−5) during a follow-up period of 8.5 yr after blood sampling (IQR = 7.7–9.3 yr). Our affinity proteomics analysis found associations between the particular molecular traits of circulating HRG with age and all-cause mortality. The distinct profiles of this multipurpose protein could serve as an accessible and informative indicator of the physiological processes related to biological aging.
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| 8. |
- Zhong, Wen, et al.
(författare)
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Whole-genome sequence association analysis of blood proteins in a longitudinal wellness cohort
- 2020
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Ingår i: Genome Medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Background The human plasma proteome is important for many biological processes and targets for diagnostics and therapy. It is therefore of great interest to understand the interplay of genetic and environmental factors to determine the specific protein levels in individuals and to gain a deeper insight of the importance of genetic architecture related to the individual variability of plasma levels of proteins during adult life. Methods We have combined whole-genome sequencing, multiplex plasma protein profiling, and extensive clinical phenotyping in a longitudinal 2-year wellness study of 101 healthy individuals with repeated sampling. Analyses of genetic and non-genetic associations related to the variability of blood levels of proteins in these individuals were performed. Results The analyses showed that each individual has a unique protein profile, and we report on the intra-individual as well as inter-individual variation for 794 plasma proteins. A genome-wide association study (GWAS) using 7.3 million genetic variants identified by whole-genome sequencing revealed 144 independent variants across 107 proteins that showed strong association (P < 6 x 10(-11)) between genetics and the inter-individual variability on protein levels. Many proteins not reported before were identified (67 out of 107) with individual plasma level affected by genetics. Our longitudinal analysis further demonstrates that these levels are stable during the 2-year study period. The variability of protein profiles as a consequence of environmental factors was also analyzed with focus on the effects of weight loss and infections. Conclusions We show that the adult blood levels of many proteins are determined at birth by genetics, which is important for efforts aimed to understand the relationship between plasma proteome profiles and human biology and disease.
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