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Integrating multi-omics for type 2 diabetes: Data science and big data towards personalized medicine
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. (Jan Komorowski)ORCID iD: 0000-0002-4922-8415
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 [en]
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: urn:nbn:se:uu:diva-393440ISBN: 978-91-513-0758-9 (print)OAI: oai:DiVA.org:uu-393440DiVA, id: diva2:1353269
Public defence
2019-11-11, C2:305, Biomedical Centrum (BMC), Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Funder
AstraZenecaAvailable from: 2019-10-18 Created: 2019-09-22 Last updated: 2019-11-12
List of papers
1. Maps of context-dependent putative regulatory regions and genomic signal interactions
Open this publication in new window or tab >>Maps of context-dependent putative regulatory regions and genomic signal interactions
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2016 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 44, no 19, p. 9110-9120Article in journal (Refereed) Published
Abstract [en]

Gene transcription is regulated mainly by transcription factors (TFs). ENCODE and Roadmap Epigenomics provide global binding profiles of TFs, which can be used to identify regulatory regions. To this end we implemented a method to systematically construct cell-type and species-specific maps of regulatory regions and TF-TF interactions. We illustrated the approach by developing maps for five human cell-lines and two other species. We detected similar to 144k putative regulatory regions among the human cell-lines, with the majority of them being similar to 300 bp. We found similar to 20k putative regulatory elements in the ENCODE heterochromatic domains suggesting a large regulatory potential in the regions presumed transcriptionally silent. Among the most significant TF interactions identified in the heterochromatic regions were CTCF and the cohesin complex, which is in agreement with previous reports. Finally, we investigated the enrichment of the obtained putative regulatory regions in the 3D chromatin domains. More than 90% of the regions were discovered in the 3D contacting domains. We found a significant enrichment of GWAS SNPs in the putative regulatory regions. These significant enrichments provide evidence that the regulatory regions play a crucial role in the genomic structural stability. Additionally, we generated maps of putative regulatory regions for prostate and colorectal cancer human cell-lines.

National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-310761 (URN)10.1093/nar/gkw800 (DOI)000388016900012 ()27625394 (PubMedID)
Funder
AstraZenecaSwedish Research CouncilSwedish Diabetes AssociationeSSENCE - An eScience Collaboration
Note

De två första författarna delar förstaförfattarskapet.

Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2019-09-22Bibliographically approved
2. Single Nuclei Transcriptome Analysis of Human Liver with Integration of Proteomics and Capture Hi-C Bulk Tissue Data
Open this publication in new window or tab >>Single Nuclei Transcriptome Analysis of Human Liver with Integration of Proteomics and Capture Hi-C Bulk Tissue Data
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(English)In: Article in journal (Refereed) Submitted
Abstract [en]

The liver is the largest solid organ and a primary metabolic hub. In recent years, intact cell nuclei were used to perform single-nuclei RNA-seq (snRNA-seq) for tissues difficult to dissociate and for flash-frozen archived tissue samples to discover unknown and rare cell sub-populations. In this study, we performed snRNA-seq of a liver sample to identify sub-populations of cells based on nuclear transcriptomics. In 4,282 single nuclei we detected on average 1,377 active genes and we identified seven major cell types. We integrated data from 94,286 distal interactions (p<0.05) for 7,682 promoters from a targeted chromosome conformation capture technique (HiCap) and mass spectrometry (MS) proteomics for the same liver sample. We observed a reasonable correlation between proteomics and in silico bulk snRNA-seq (r=0.47) using tissue-independent gene-specific protein abundancy estimation factors. We specifically looked at genes of medical importance. The DPYD gene is involved in the pharmacogenetics of fluoropyrimidines toxicity and some of its variants are analyzed for clinical purposes. We identified a new putative polymorphic regulatory element, which may contribute to variation in toxicity. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and we investigated all known risk genes. We found a complex regulatory network for the SLC2A2 gene with 16 candidate enhancers. Three of them harbor somatic motif breaking and other mutations in HCC in the Pan Cancer Analysis of Whole Genomes dataset and are candidates to contribute to malignancy. Our results highlight the potential of a multi-omics approach in the study of human diseases.

Keywords
multi-omics; snRNA-seq; MS proteomics; TAD; HiCap; liver; DPYD; SLC2A2
National Category
Medical Genetics
Identifiers
urn:nbn:se:uu:diva-393431 (URN)
Available from: 2019-09-21 Created: 2019-09-21 Last updated: 2019-09-22
3. Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes
Open this publication in new window or tab >>Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes
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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
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:uu:diva-391017 (URN)10.1038/s41598-019-45906-5 (DOI)000474222900010 ()31273253 (PubMedID)
Funder
AstraZenecaSwedish Research Council FormaseSSENCE - An eScience CollaborationSwedish Diabetes AssociationErnfors Foundation
Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-09-22Bibliographically approved
4. Integration of whole-body PET/MRI with non-targeted metabolomics provides new insights into insulin sensitivity of various tissues
Open this publication in new window or tab >>Integration of whole-body PET/MRI with non-targeted metabolomics provides new insights into insulin sensitivity of various tissues
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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
type 2 diabetes; metabolomics; imiomics; PET/MRI; insulin resistance;
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:uu:diva-393429 (URN)
Available from: 2019-09-21 Created: 2019-09-21 Last updated: 2019-09-22

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