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Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-4922-8415
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.
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.
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
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 9653Article in journal (Refereed) Published
Abstract [en]

Type 2 diabetes (T2D) mellitus is a complex metabolic disease commonly caused by insulin resistance in several tissues. We performed a matched two-dimensional metabolic screening in tissue samples from 43 multi-organ donors. The intra-individual analysis was assessed across five key metabolic tissues (serum, visceral adipose tissue, liver, pancreatic islets and skeletal muscle), and the inter-individual across three different groups reflecting T2D progression. We identified 92 metabolites differing significantly between non-diabetes and T2D subjects. In diabetes cases, carnitines were significantly higher in liver, while lysophosphatidylcholines were significantly lower in muscle and serum. We tracked the primary tissue of origin for multiple metabolites whose alterations were reflected in serum. An investigation of three major stages spanning from controls, to pre-diabetes and to overt T2D indicated that a subset of lysophosphatidylcholines was significantly lower in the muscle of pre-diabetes subjects. Moreover, glycodeoxycholic acid was significantly higher in liver of pre-diabetes subjects while additional increase in T2D was insignificant. We confirmed many previously reported findings and substantially expanded on them with altered markers for early and overt T2D. Overall, the analysis of this unique dataset can increase the understanding of the metabolic interplay between organs in the development of T2D.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2019. Vol. 9, article id 9653
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:uu:diva-391017DOI: 10.1038/s41598-019-45906-5ISI: 000474222900010PubMedID: 31273253OAI: oai:DiVA.org:uu-391017DiVA, id: diva2:1344467
Funder
AstraZenecaSwedish Research Council FormaseSSENCE - An eScience CollaborationSwedish Diabetes AssociationErnfors FoundationAvailable from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-09-22Bibliographically approved
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)
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Supervisors
Funder
AstraZeneca
Available from: 2019-10-18 Created: 2019-09-22 Last updated: 2019-10-18

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Diamanti, KlevCavalli, MarcoPan, GangPereira, Maria JGrabherr, ManfredRisérus, UlfEriksson, JanKomorowski, JanWadelius, Claes
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Computational Biology and BioinformaticsScience for Life Laboratory, SciLifeLabMedicinsk genetik och genomikClinical diabetology and metabolismDepartment of Medical Biochemistry and MicrobiologyClinical Nutrition and Metabolism
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