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Integration of whole-body PET/MRI with non-targeted metabolomics provides new insights into insulin sensitivity of various tissues
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical diabetology and metabolism.ORCID iD: 0000-0001-5498-3899
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik. Uppsala University, Science for Life Laboratory, SciLifeLab.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific alterations is challenging and requires a multi-omics approach. In this study, we aimed at discovering associations of metabolites from subcutaneous adipose tissue (SAT) and plasma with the volume, the fat fraction (FF) and the insulin sensitivity (Ki) of specific tissues using [18F]FDG PET/MRI.

Materials and Methods: In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body-mass-index (BMI) we calculated associations between parameters of whole-body FDG PET/MRI during clamp and non-targeted metabolomics profiling for SAT and blood plasma. We also used a rule-based classifier to identify a large collection of prevalent patterns of co-dependent metabolites that characterize non-diabetes (ND) and T2D.

Results: The plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Ki in visceral adipose tissue (VAT) and SAT, was positively associated with several species of lysophospholipids while the opposite applied to branched-chain amino acids (BCAA) and their intermediates. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. On the contrary, bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Finally, we presented a transparent machine-learning model that predicted ND or T2D in “unseen” data with an accuracy of 78%.

Conclusions: Novel associations of several metabolites from SAT and plasma with the FF, volume and insulin senstivity of various tissues throughout the body were discovered using PET/MRI and a new integrative multi-omics approach. A promising computational model that predicted ND and T2D with high certainty, suggested novel non-linear interdependencies of metabolites.

Keywords [en]
type 2 diabetes; metabolomics; imiomics; PET/MRI; insulin resistance;
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:uu:diva-393429OAI: oai:DiVA.org:uu-393429DiVA, id: diva2:1353235
Available from: 2019-09-21 Created: 2019-09-21 Last updated: 2019-09-22
In thesis
1. Integrating multi-omics for type 2 diabetes: Data science and big data towards personalized medicine
Open this publication in new window or tab >>Integrating multi-omics for type 2 diabetes: Data science and big data towards personalized medicine
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Type 2 diabetes (T2D) is a complex metabolic disease characterized by multi-tissue insulin resistance and failure of the pancreatic β-cells to secrete sufficient amounts of insulin. Cells recruit transcription factors (TF) to specific genomic loci to regulate gene expression that consequently affects the protein and metabolite abundancies. Here we investigated the interplay of transcriptional and translational regulation, and its impact on metabolome and phenome for several insulin-resistant tissues from T2D donors. We implemented computational tools and multi-omics integrative approaches that can facilitate the selection of candidate combinatorial markers for T2D.

We developed a data-driven approach to identify putative regulatory regions and TF-interaction complexes. The cell-specific sets of regulatory regions were enriched for disease-related single nucleotide polymorphisms (SNPs), highlighting the importance of such loci towards the genomic stability and the regulation of gene expression. We employed a similar principle in a second study where we integrated single nucleus ribonucleic acid sequencing (snRNA-seq) with bulk targeted chromosome-conformation-capture (HiCap) and mass spectrometry (MS) proteomics from liver. We identified a putatively polymorphic site that may contribute to variation in the pharmacogenetics of fluoropyrimidines toxicity for the DPYD gene. Additionally, we found a complex regulatory network between a group of 16 enhancers and the SLC2A2 gene that has been linked to increased risk for hepatocellular carcinoma (HCC). Moreover, three enhancers harbored motif-breaking mutations located in regulatory regions of a cohort of 314 HCC cases, and were candidate contributors to malignancy.

In a cohort of 43 multi-organ donors we explored the alternating pattern of metabolites among visceral adipose tissue (VAT), pancreatic islets, skeletal muscle, liver and blood serum samples. A large fraction of lysophosphatidylcholines (LPC) decreased in muscle and serum of T2D donors, while a large number of carnitines increased in liver and blood of T2D donors, confirming that changes in metabolites occur in primary tissues, while their alterations in serum consist a secondary event. Next, we associated metabolite abundancies from 42 subjects to glucose uptake, fat content and volume of various organs measured by positron emission tomography/magnetic resonance imaging (PET/MRI). The fat content of the liver was positively associated with the amino acid tyrosine, and negatively associated with LPC(P-16:0). The insulin sensitivity of VAT and subcutaneous adipose tissue was positively associated with several LPCs, while the opposite applied to branch-chained amino acids. Finally, we presented the network visualization of a rule-based machine learning model that predicted non-diabetes and T2D in an “unseen” dataset with 78% accuracy.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 65
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1860
Keywords
type 2 diabetes, multi-omics, genomics, metabolomics, data science, machine learning, personalized medicine
National Category
Bioinformatics (Computational Biology) Endocrinology and Diabetes
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-393440 (URN)978-91-513-0758-9 (ISBN)
Public defence
2019-11-11, C2:305, Biomedical Centrum (BMC), Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Funder
AstraZeneca
Available from: 2019-10-18 Created: 2019-09-22 Last updated: 2019-11-12

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Diamanti, KlevPereira, Maria JCavalli, MarcoPan, GangIngelsson, MartinFall, ToveLind, LarsRisérus, UlfEriksson, JanKullberg, JoelWadelius, ClaesAhlström, HåkanKomorowski, Jan

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Diamanti, KlevPereira, Maria JCavalli, MarcoPan, GangIngelsson, MartinFall, ToveLind, LarsRisérus, UlfEriksson, JanKullberg, JoelWadelius, ClaesAhlström, HåkanKomorowski, Jan
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Computational Biology and BioinformaticsDepartment of Surgical SciencesClinical diabetology and metabolismMedicinsk genetik och genomikScience for Life Laboratory, SciLifeLabGeriatricsMolecular epidemiologyCardiologyMolecular MedicineCentre for Research and Development, GävleborgUCR-Uppsala Clinical Research CenterClinical Nutrition and MetabolismRadiologyClinical EpidemiologyDepartment of Medical Sciences
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