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Lamichhane, S., Ahonen, L., Dyrlund, T. S., Kemppainen, E., Siljander, H., Hyöty, H., . . . Oresic, M. (2018). Dynamics of Plasma Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes - Type 1 Diabetes Prediction and Prevention Study (DIPP). Scientific Reports, 8, Article ID 10635.
Open this publication in new window or tab >>Dynamics of Plasma Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes - Type 1 Diabetes Prediction and Prevention Study (DIPP)
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2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 10635Article in journal (Refereed) Published
Abstract [en]

Type 1 diabetes (T1D) is one of the most prevalent autoimmune diseases among children in Western countries. Earlier metabolomics studies suggest that T1D is preceded by dysregulation of lipid metabolism. Here we used a lipidomics approach to analyze molecular lipids in a prospective series of 428 plasma samples from 40 children who progressed to T1D (PT1D), 40 children who developed at least a single islet autoantibody but did not progress to T1D during the follow-up (P1Ab) and 40 matched controls (CTR). Sphingomyelins were found to be persistently downregulated in PT1D when compared to the P1Ab and CTR groups. Triacylglycerols and phosphatidylcholines were mainly downregulated in PT1D as compared to P1Ab at the age of 3 months. Our study suggests that distinct lipidomic signatures characterize children who progressed to islet autoimmunity or overt T1D, which may be helpful in the identification of at-risk children before the initiation of autoimmunity.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:oru:diva-68333 (URN)10.1038/s41598-018-28907-8 (DOI)000438490500003 ()30006587 (PubMedID)2-s2.0-85049952074 (Scopus ID)
Note

Funding Agencies:

JDRF  4-1998-274  4-1999-731 4-2001-435 

Juvenile Diabetes Research Foundation  2-SRA-2014-159-Q-R 

Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research - SyMMyS)  250114 

Turku University Hospitals in Finland  

Special research funds for Oulu, Tampere 

Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2018-08-31Bibliographically approved
Lamichhane, S., Sen, P., Dickens, A. M., Oresic, M. & Bertram, H. C. (2018). Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe. Methods
Open this publication in new window or tab >>Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe
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2018 (English)In: Methods, ISSN 1046-2023, E-ISSN 1095-9130Article in journal (Refereed) Epub ahead of print
Abstract [en]

It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host-microbe interactions are highlighted.

Place, publisher, year, edition, pages
Maryland Heights, MO, United States: Academic Press, 2018
Keywords
Faecal metabolites, Genome-scale metabolic models, Gut microbiota, Metabolomics, Microbiome
National Category
Microbiology
Identifiers
urn:nbn:se:oru:diva-66868 (URN)10.1016/j.ymeth.2018.04.029 (DOI)29715508 (PubMedID)
Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2018-05-21Bibliographically approved
Suvitaival, T., Bondia-Pons, I., Yetukuri, L., Pöhö, P., Nolan, J. J., Hyötyläinen, T., . . . Orešič, M. (2018). Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men. Metabolism: Clinical and Experimental, 78(January), 1-12
Open this publication in new window or tab >>Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men
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2018 (English)In: Metabolism: Clinical and Experimental, ISSN 0026-0495, E-ISSN 1532-8600, Vol. 78, no January, p. 1-12Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: There is a need for early markers to track and predict the development of type 2 diabetes mellitus (T2DM) from the state of normal glucose tolerance through prediabetes. In this study we tested whether the plasma molecular lipidome has biomarker potential to predicting the onset of T2DM.

METHODS: We applied global lipidomic profiling on plasma samples from well-phenotyped men (107 cases, 216 controls) participating in the longitudinal METSIM study at baseline and at five-year follow-up. To validate the lipid markers, an additional study with a representative sample of adult male population (n = 631) was also conducted. A total of 277 plasma lipids were analyzed using the lipidomics platform based on ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry. Lipids with the highest predictive power for the development of T2DM were computationally selected, validated and compared to standard risk models without lipids.

RESULTS: A persistent lipid signature with higher levels of triacylglycerols and diacyl-phospholipids as well as lower levels of alkylacyl phosphatidylcholines was observed in progressors to T2DM. Lysophosphatidylcholine acyl C18:2 (LysoPC(18:2)), phosphatidylcholines PC(32:1), PC(34:2e) and PC(36:1), and triacylglycerol TG(17:1/18:1/18:2) were selected to the full model that included metabolic risk factors and FINDRISC variables. When further adjusting for BMI and age, these lipids had respective odds ratios of 0.32, 2.4, 0.50, 2.2 and 0.31 (all p < 0.05) for progression to T2DM. The independently-validated predictive power improved in all pairwise comparisons between the lipid model and the respective standard risk model without the lipids (integrated discrimination improvement IDI > 0; p < 0.05). Notably, the lipid models remained predictive of the development of T2DM in the fasting plasma glucose-matched subset of the validation study.

CONCLUSION: This study indicates that a lipid signature characteristic of T2DM is present years before the diagnosis and improves prediction of progression to T2DM. Molecular lipid biomarkers were shown to have predictive power also in a high-risk group, where standard risk factors are not helpful at distinguishing progressors from non-progressors.

Place, publisher, year, edition, pages
Saunders Elsevier, 2018
Keywords
Lipidomics, METSIM study, mass-spectrometry, metabolomics, plasma profiling, type 2 diabetes mellitus
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:oru:diva-62460 (URN)10.1016/j.metabol.2017.08.014 (DOI)000418631200002 ()28941595 (PubMedID)2-s2.0-85039561638 (Scopus ID)
Funder
Novo Nordisk
Note

Funding Agency:

EU programme project DEXLIFE 279228

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-09-03Bibliographically approved
Sen, P., Kempainen, E. & Oresic, M. (2018). Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells. Frontiers in Molecular Biosciences, 4, Article ID 96.
Open this publication in new window or tab >>Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells
2018 (English)In: Frontiers in Molecular Biosciences, E-ISSN 2296-889X, Vol. 4, article id 96Article, review/survey (Refereed) Published
Abstract [en]

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.

Place, publisher, year, edition, pages
Frontiers Research Foundation, 2018
Keywords
Systems biology, multi-omics, peripheral blood mononuclear cells, PBMCs, immune system, metabolomics, genome-scale metabolic models, pathways
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:oru:diva-64338 (URN)10.3389/fmolb.2017.00096 (DOI)
Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-01-19Bibliographically approved
Frank, E., Maier, D., Pajula, J., Suvitaival, T., Borgan, F., Butz-Ostendorf, M., . . . Oresic, M. (2018). Platform for systems medicine research and diagnostic applications in psychotic disorders - The METSY project. European psychiatry, 50, 40-46
Open this publication in new window or tab >>Platform for systems medicine research and diagnostic applications in psychotic disorders - The METSY project
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2018 (English)In: European psychiatry, ISSN 0924-9338, E-ISSN 1778-3585, Vol. 50, p. 40-46Article in journal (Refereed) Published
Abstract [en]

Psychotic disorders are associated with metabolic abnormalities including alterations in glucose and lipid metabolism. A major challenge in the treatment of psychosis is to identify patients with vulnerable metabolic profiles who may be at risk of developing cardiometabolic co-morbidities. It is established that both central and peripheral metabolic organs use lipids to control energy balance and regulate peripheral insulin sensitivity. The endocannabinoid system, implicated in the regulation of glucose and lipid metabolism, has been shown to be dysregulated in psychosis. It is currently unclear how these endocannabinoid abnormalities relate to metabolic changes in psychosis. Here we review recent research in the field of metabolic co-morbidities in psychotic disorders as well as the methods to study them and potential links to the endocannabinoid system. We also describe the bioinformatics platforms developed in the EU project METSY for the investigations of the biological etiology in patients at risk of psychosis and in first episode psychosis patients. The METSY project was established with the aim to identify and evaluate multi-modal peripheral and neuroimaging markers that may be able to predict the onset and prognosis of psychiatric and metabolic symptoms in patients at risk of developing psychosis and first episode psychosis patients. Given the intrinsic complexity and widespread role of lipid metabolism, a systems biology approach which combines molecular, structural and functional neuroimaging methods with detailed metabolic characterisation and multi-variate network analysis is essential in order to identify how lipid dysregulation may contribute to psychotic disorders. A decision support system, integrating clinical, neuropsychological and neuroimaging data, was also developed in order to aid clinical decision making in psychosis. Knowledge of common and specific mechanisms may aid the etiopathogenic understanding of psychotic and metabolic disorders, facilitate early disease detection, aid treatment selection and elucidate new targets for pharmacological treatments.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Bioinformatics, Biomarkers, Decision support systems, Endocannabinoid system, Lipid metabolism, Metabolomics, Psychoses, Schizophrenia
National Category
Psychiatry
Identifiers
urn:nbn:se:oru:diva-64483 (URN)10.1016/j.eurpsy.2017.12.001 (DOI)000430263900007 ()29361398 (PubMedID)2-s2.0-85040608354 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 602478
Available from: 2018-01-24 Created: 2018-01-24 Last updated: 2018-08-20Bibliographically approved
Luukkonen, P. K., Sädevirta, S., Zhou, Y., Kayser, B., Ali, A., Ahonen, L., . . . Yki-Järvinen, H. (2018). Saturated Fat Is More Metabolically Harmful for the Human Liver Than Unsaturated Fat or Simple Sugars. Diabetes Care, 41(8), 1732-1739
Open this publication in new window or tab >>Saturated Fat Is More Metabolically Harmful for the Human Liver Than Unsaturated Fat or Simple Sugars
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2018 (English)In: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 41, no 8, p. 1732-1739Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: Nonalcoholic fatty liver disease (i.e., increased intrahepatic triglyceride [IHTG] content), predisposes to type 2 diabetes and cardiovascular disease. Adipose tissue lipolysis and hepatic de novo lipogenesis (DNL) are the main pathways contributing to IHTG. We hypothesized that dietary macronutrient composition influences the pathways, mediators, and magnitude of weight gain-induced changes in IHTG.

RESEARCH DESIGN AND METHODS: O) basally and during euglycemic hyperinsulinemia, insulin resistance, endotoxemia, plasma ceramides, and adipose tissue gene expression at 0 and 3 weeks.

RESULTS: < 0.05). CARB increased IHTG (+33%) by stimulating DNL (+98%). SAT significantly increased while UNSAT decreased lipolysis. SAT induced insulin resistance and endotoxemia and significantly increased multiple plasma ceramides. The diets had distinct effects on adipose tissue gene expression.

CONCLUSIONS: Macronutrient composition of excess energy influences pathways of IHTG: CARB increases DNL, while SAT increases and UNSAT decreases lipolysis. SAT induced greatest increase in IHTG, insulin resistance, and harmful ceramides. Decreased intakes of SAT could be beneficial in reducing IHTG and the associated risk of diabetes.

Place, publisher, year, edition, pages
American Diabetes Association, 2018
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:oru:diva-67136 (URN)10.2337/dc18-0071 (DOI)000439288600025 ()29844096 (PubMedID)
Funder
Novo Nordisk
Note

Funding Agencies:

Suomen Laaketieteen Saatio  

Yrjo Jahnssonin Saatio  

Emil Aaltosen Saatio  

Diabetes Research Foundation  

Helsingin Yliopisto  

Elucidating Pathways of Steatohepatitis consortium - Horizon 2020 Framework Program of the European Union  EPoS 634413 

Ministero dell'Istruzione, dell'Universita e della Ricerca  

Consiglio Nazionale delle Ricerche  

PIA-F-Crin Force program  

British Heart Foundation Intermediate Fellowship in Basic Science  FS/11/18/28633 

Suomen Akatemia  

Sigrid Juselius Foundation  

Evo  

EU/EFPIA Innovative Medicines Initiative Joint Undertaking  EMIF 115372 

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-09-14Bibliographically approved
Dickens, A. M., Posti, J. P., Takala, R. S., Ala-Seppälä, H. M., Mattila, I., Coles, J. C., . . . Oresic, M. (2018). Serum metabolites associate with CT findings following TBI. Journal of Neurotrauma
Open this publication in new window or tab >>Serum metabolites associate with CT findings following TBI
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2018 (English)In: Journal of Neurotrauma, ISSN 0897-7151, E-ISSN 1557-9042Article in journal (Refereed) Epub ahead of print
Abstract [en]

There is a need to rapidly detect patients with traumatic brain injury (TBI) who require head computed tomography (CT). Given the energy crisis in the brain following TBI, we hypothesized that serum metabolomics would be a useful tool for developing a set of biomarkers to determine the need for CT and to distinguish between different types of injuries observed. Logistic regression models using metabolite data from the discovery cohort (n=144, Turku, Finland) were used to distinguish between patients with traumatic intracranial findings and negative findings on head CT. The resultant models were then tested in the validation cohort (n=66, Cambridge, UK). The levels of glial fibrillary acidic protein and ubiquitin C-terminal hydrolase-L1 were also quantified in the serum from the same patients. Despite there being significant differences in the protein biomarkers in patients with TBI, the model that determined the need for a CT scan validated poorly (AUC=0.64: Cambridge patients). However, using a combination of six metabolites (two amino acids, three sugar derivatives and one ketoacid) it was possible to discriminate patients with intracranial abnormalities on CT and patients with a normal CT (AUC=0.77 in Turku patients and AUC=0.73 in Cambridge patients). Furthermore, a combination of three metabolites could distinguish between diffuse brain injuries and mass lesions (AUC=0.87 in Turku patients and AUC=0.68 in Cambridge patients). This study identifies a set of validated serum polar metabolites, which associate with the need for a CT scan. Additionally, serum metabolites can also predict the nature of the brain injury. These metabolite markers may prevent unnecessary CT scans, thus reducing the cost of diagnostics and radiation load.

Place, publisher, year, edition, pages
Mary Ann Liebert, 2018
Keywords
Biomarkers, CT Scanning, Human Sstudies, Metabolism, Traumatic Brain Injury
National Category
Rheumatology and Autoimmunity
Identifiers
urn:nbn:se:oru:diva-67622 (URN)10.1089/neu.2017.5272 (DOI)29947291 (PubMedID)
Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-09-04Bibliographically approved
Oresic, M., Anderson, G., Mattila, I., Manoucheri, M., Soininen, H., Hyötyläinen, T. & Basignani, C. (2018). Targeted Serum Metabolite Profiling Identifies Metabolic Signatures in Patients with Alzheimer's Disease, Normal Pressure Hydrocephalus and Brain Tumor. Frontiers in Neuroscience, 11, Article ID 747.
Open this publication in new window or tab >>Targeted Serum Metabolite Profiling Identifies Metabolic Signatures in Patients with Alzheimer's Disease, Normal Pressure Hydrocephalus and Brain Tumor
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2018 (English)In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 11, article id 747Article in journal (Refereed) Published
Abstract [en]

Progression to AD is preceded by elevated levels of 2,4-dihydroxybutanoic acid (2,4-DHB), implicating hypoxia in early pathogenesis. Since hypoxia may play a role in multiple CNS disorders, we investigated serummetabolite profiles across three disorders, AD, Normal Pressure Hydrocephalus (NPH) and brain tumors (BT). Blood samples were collected from27 NPH and 20 BT patients. The profiles of 21metabolites were examined. Additionally, data from 37 AD patients and 46 controls from a previous study were analyzed together with the newly acquired data. No differences in 2,4-DHB were found across AD, NPH and BT samples. In the BT group, the fatty acids were increased as compared to HC and NPH groups, while the ketone body 3-hydroxybutyrate was increased as compared to AD. Glutamic acid was increased in AD as compared to the HC group. In the AD group, 3-hydroxybutyrate tended to be decreased with respect to all other groups (mean values −30% or more), but the differences were not statistically significant. Serine was increased in NPH as compared to BT. In conclusion, AD, NPH and BT have different metabolic profiles. This preliminary study may help in identifying the blood based markers that are specific to these three CNS diseases.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
Keywords
Alzheimer’s disease, brain tumor, hypoxia, metabolomics, normal pressure hydrocephalus
National Category
Neurosciences
Identifiers
urn:nbn:se:oru:diva-64339 (URN)10.3389/fnins.2017.00747 (DOI)000419589000001 ()2-s2.0-85040451494 (Scopus ID)
Note

Funding Agency:

Karen L. Wrenn Estate under the Florida Hospital Foundation

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-09-07Bibliographically approved
Schwarz, E., Maukonen, J., Hyytiäinen, T., Kieseppä, T., Oresic, M., Sabunciyan, S., . . . Suvisaari, J. (2017). Analysis of microbiota in first episode psychosis identifies preliminary associations with symptom severity and treatment response. Schizophrenia Research, 192, 398-403
Open this publication in new window or tab >>Analysis of microbiota in first episode psychosis identifies preliminary associations with symptom severity and treatment response
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2017 (English)In: Schizophrenia Research, ISSN 0920-9964, E-ISSN 1573-2509, Vol. 192, p. 398-403Article in journal (Refereed) Published
Abstract [en]

The effects of gut microbiota on the central nervous system, along its possible role in mental disorders, have received increasing attention. Here we investigated differences in fecal microbiota between 28 patients with first-episode psychosis (FEP) and 16 healthy matched controls and explored whether such differences were associated with response after up to 12months of treatment. Numbers of Lactobacillus group bacteria were elevated in FEP-patients and significantly correlated with severity along different symptom domains. A subgroup of FEP patients with the strongest microbiota differences also showed poorer response after up to 12months of treatment. The present findings support the involvement of microbiota alterations in psychotic illness and may provide the basis for exploring the benefit of their modulation on treatment response and remission.

Place, publisher, year, edition, pages
Amsterdam, Netherlands: Elsevier, 2017
Keywords
Microbiome, Psychosis, Response, Schizophrenia
National Category
Medical and Health Sciences Psychiatry
Identifiers
urn:nbn:se:oru:diva-59391 (URN)10.1016/j.schres.2017.04.017 (DOI)000426344800061 ()28442250 (PubMedID)2-s2.0-85018792632 (Scopus ID)
Available from: 2017-08-25 Created: 2017-08-25 Last updated: 2018-09-03Bibliographically approved
Bowden, J. A., Hyötyläinen, T., Oresic, M. & Zhou, S. (2017). Harmonizing Lipidomics: NIST Interlaboratory Comparison Exercise for Lipidomics using Standard Reference Material 1950 Metabolites in Frozen Human Plasma. Journal of Lipid Research, 58(12), 2275-2288
Open this publication in new window or tab >>Harmonizing Lipidomics: NIST Interlaboratory Comparison Exercise for Lipidomics using Standard Reference Material 1950 Metabolites in Frozen Human Plasma
2017 (English)In: Journal of Lipid Research, ISSN 0022-2275, E-ISSN 1539-7262, Vol. 58, no 12, p. 2275-2288Article in journal (Refereed) Published
Abstract [en]

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950 Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each lab using a different lipidomics workflow. A total of 1527 unique lipids were measured across all laboratories, and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and inter-laboratory quality control and method validation. These analyses were performed using non-standardized laboratory-independent workflows. The consensus locations were also compared to a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Place, publisher, year, edition, pages
American Society for Biochemistry and Molecular Biology, 2017
Keywords
Fatty Acyls, Glycerolipids, Lipidomics, Lipids, Phospholipids, Sphingolipids, Standard Reference Material 1950, Sterols, interlaboratory comparison exercise, quantitation
National Category
Clinical Laboratory Medicine Cell and Molecular Biology
Identifiers
urn:nbn:se:oru:diva-62436 (URN)10.1194/jlr.M079012 (DOI)000416963900004 ()28986437 (PubMedID)2-s2.0-85037028318 (Scopus ID)
Funder
Swedish Research Council, 2015-4870Swedish Heart Lung Foundation, HLF 20140469 HLF 20150640
Note

Funding Agencies:

University of Victoria U24 DK097209, UL1 TL001873

Kyushu University

Life Sciences Institute

Austrian Science Fund P26148-N19

National Research Foundation Singapore NRFI2015-05

Genome British Columbia 7203

Genome British Columbia MC3T

Genome British Columbia P01 HL034300

Genome British Columbia Spectrometry Core

Genome British Columbia 205MET

Genome British Columbia 215MET

McGill University DBI-1228622 P20-RR16475 MCB-0920663 MCB-413036 DBI-0521587 EPS-0236913

Natural Sciences and Engineering Research Council of Canada

Diabetes Research and Training Center

Japan Science and Technology Agency JPMJCR1395

McGill University

National University of Singapore

Advanced Low Carbon Technology Research and Development Program

Albert Einstein College of Medicine, Yeshiva University

Leading Edge Endowment Fund P60DK020541

National Institute of Standards and Technology

T.C. stanbul Kltr niversitesi

National Institute of General Medical Sciences PO1 GM095467

Genome Canada

Foundation for the National Institutes of Health 5P01CA120964

Foundation for the National Institutes of Health 5P30CA006516

National Institutes of Health R01 GM20501-41

National Institutes of Health P30 DK064391

National Institutes of Health U54 GM069338

Kansas State University

National Center for Research Resources S10RR027926

National Center for Research Resources U24 DK097154

National Center for Research Resources P20 HL113452, CFI 12156

School of Medicine

Jewish General Hospital

Canadian Institutes of Health Research FDN143309

National Center for Advancing Translational Sciences UL1 TR000040

Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-01-30Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-2856-9165

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