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Lamichhane, S., Ahonen, L., Dyrlund, T. S., Dickens, A. M., Siljander, H., Hyöty, H., . . . Oresic, M. (2019). Cord-Blood Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes. Biomolecules, 9(1), Article ID E33.
Open this publication in new window or tab >>Cord-Blood Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes
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2019 (English)In: Biomolecules, E-ISSN 2218-273X, Vol. 9, no 1, article id E33Article in journal (Refereed) Published
Abstract [en]

Previous studies suggest that children who progress to type 1 diabetes (T1D) later in life already have an altered serum lipid molecular profile at birth. Here, we compared cord blood lipidome across the three study groups: children who progressed to T1D (PT1D; n = 30), children who developed at least one islet autoantibody but did not progress to T1D during the follow-up (P1Ab; n = 33), and their age-matched controls (CTR; n = 38). We found that phospholipids, specifically sphingomyelins, were lower in T1D progressors when compared to P1Ab and the CTR. Cholesterol esters remained higher in PT1D when compared to other groups. A signature comprising five lipids was predictive of the risk of progression to T1D, with an area under the receiver operating characteristic curve (AUROC) of 0.83. Our findings provide further evidence that the lipidomic profiles of newborn infants who progress to T1D later in life are different from lipidomic profiles in P1Ab and CTR.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Autoimmunity, cord blood, lipidomics, metabolomics, type 1 diabetes
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:oru:diva-71853 (URN)10.3390/biom9010033 (DOI)000458051700033 ()30669674 (PubMedID)2-s2.0-85060365305 (Scopus ID)
Note

Funding Agencies:

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

Special research funds for Oulu, Tampere and Turku University Hospitals in Finland  

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 

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-03-04Bibliographically approved
Geng, D., Musse, A. A., Wigh, V., Carlsson, C., Engwall, M., Oresic, M., . . . Hyötyläinen, T. (2019). Effect of perfluorooctanesulfonic acid (PFOS) on the liver lipid metabolism of the developing chicken embryo. Ecotoxicology and Environmental Safety, 170, 691-698
Open this publication in new window or tab >>Effect of perfluorooctanesulfonic acid (PFOS) on the liver lipid metabolism of the developing chicken embryo
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2019 (English)In: Ecotoxicology and Environmental Safety, ISSN 0147-6513, E-ISSN 1090-2414, Vol. 170, p. 691-698Article in journal (Refereed) Published
Abstract [en]

Perfluorooctanesulfonate (PFOS) is a well-known contaminant in the environment and it has shown to disrupt multiple biological pathways, particularly those related with lipid metabolism. In this study, we have studied the impact of in ovo exposure to PFOS on lipid metabolism in livers in developing chicken embryos using lipidomics for detailed characterization of the liver lipidome. We used an avian model (Gallus gallus domesticus) for in ovo treatment at two levels of PFOS. The lipid profile of the liver of the embryo was investigated by ultra-high performance liquid chromatography combined with quadrupole-time-of-flight mass spectrometry and by gas chromatography mass spectrometry. Over 170 lipids were identified, covering phospholipids, ceramides, di- and triacylglycerols, cholesterol esters and fatty acid composition of the lipids. The PFOS exposure caused dose dependent changes in the lipid levels, which included upregulation of specific phospholipids associated with the phosphatidylethanolamine N-methyltransferase (PEMT) pathway, triacylglycerols with low carbon number and double bond count as well as of lipotoxic ceramides and diacylglycerols. Our data suggest that at lower levels of exposure, mitochondrial fatty acid β-oxidation is suppressed while the peroxisomal fatty acid β -oxidation is increased. At higher doses, however, both β -oxidation pathways are upregulated.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Avian model, Lipidomics, Liver metabolism, Mass spectrometry, Perfluorooctanesulfonate
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:oru:diva-71192 (URN)10.1016/j.ecoenv.2018.12.040 (DOI)000456890700083 ()30580163 (PubMedID)2-s2.0-85058940877 (Scopus ID)
Funder
Swedish Research Council, 2016-05176Swedish Research Council FormasKnowledge Foundation
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-03-04Bibliographically approved
Sen, P. & Oresic, M. (2019). Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview. Metabolites, 9(2), Article ID E22.
Open this publication in new window or tab >>Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview
2019 (English)In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 9, no 2, article id E22Article, review/survey (Refereed) Published
Abstract [en]

There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Constraint-based modeling, flux balance, genome-scale metabolic modeling, gut microbiome, host–microbiome, meta-omics, metabolic reconstructions, metabolism, metabolomics, metagenomics
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:oru:diva-72041 (URN)10.3390/metabo9020022 (DOI)000460288400006 ()30695998 (PubMedID)
Note

Funding Agencies:

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

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

European Union  634413 

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-03-19Bibliographically approved
Pedersen, H. K., Forslund, S. K., Gudmundsdottir, V., Østergaard Petersen, A., Hildebrand, F., Hyötyläinen, T., . . . Nielsen, H. B. (2018). A computational framework to integrate high-throughput '-omics' datasets for the identification of potential mechanistic links. Nature Protocols, 13(12), 2781-2800
Open this publication in new window or tab >>A computational framework to integrate high-throughput '-omics' datasets for the identification of potential mechanistic links
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2018 (English)In: Nature Protocols, ISSN 1754-2189, E-ISSN 1750-2799, Vol. 13, no 12, p. 2781-2800Article in journal (Refereed) Published
Abstract [en]

We recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various '-omics' readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome-microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in ~1 h on a standard PC.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Bioinformatics and Systems Biology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:oru:diva-69996 (URN)10.1038/s41596-018-0064-z (DOI)000451343400004 ()30382244 (PubMedID)2-s2.0-85055979835 (Scopus ID)
Funder
Novo Nordisk, NNF14CC0001
Note

Funding Agencies:

European Community  HEALTH-F4-2007-201052 

MetaCardis  HEALTH-2012-305312 

Innovative Medicines Initiative Joint Undertaking  115317 

Agence Nationale de la Recherche MetaGenoPolis grant 'Investissements d'avenir'  ANR-11-DPBS-0001 

Lundbeck Foundation  R218-2016-1367 

European Union  

EFPIA 

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2019-03-04Bibliographically approved
Lamichhane, S., Ahonen, L., Dyrlund, T. S., Siljander, H., Hyöty, H., Ilonen, J., . . . Oresic, M. (2018). A longitudinal plasma lipidomics dataset from children who developed islet autoimmunity and type 1 diabetes. Scientific Data, 5, Article ID 180250.
Open this publication in new window or tab >>A longitudinal plasma lipidomics dataset from children who developed islet autoimmunity and type 1 diabetes
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2018 (English)In: Scientific Data, E-ISSN 2052-4463, Vol. 5, article id 180250Article in journal (Refereed) Published
Abstract [en]

Early prediction and prevention of type 1 diabetes (T1D) are currently unmet medical needs. Previous metabolomics studies suggest that children who develop T1D are characterised by a distinct metabolic profile already detectable during infancy, prior to the onset of islet autoimmunity. However, the specificity of persistent metabolic disturbances in relation T1D development has not yet been established. Here, we report a longitudinal plasma lipidomics dataset from (1) 40 children who progressed to T1D during follow-up, (2) 40 children who developed single islet autoantibody but did not develop T1D and (3) 40 matched controls (6 time points: 3, 6, 12, 18, 24 and 36 months of age). This dataset may help other researchers in studying age-dependent progression of islet autoimmunity and T1D as well as of the age-dependence of lipidomic profiles in general. Alternatively, this dataset could more broadly used for the development of methods for the analysis of longitudinal multivariate data.

Place, publisher, year, edition, pages
Springer Nature, 2018
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:oru:diva-70175 (URN)10.1038/sdata.2018.250 (DOI)000449924000001 ()30422126 (PubMedID)2-s2.0-85056307525 (Scopus ID)
Note

Funding agencies:

JDRF (grants 4-1998-274, 4-1999-731 4-2001-435)

Oulu, Tampere and Turku University Hospitals

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

Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research – SyMMyS, Decision No. 250114)

Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2019-03-04Bibliographically approved
Lamichhane, S., Sen, P., Dickens, A. M., Hyötyläinen, T. & Orešič, M. (2018). An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives. In: Joaquim Jaumot; Carmen Bedia; Romà Tauler (Ed.), Data Analysis for Omic Sciences: Methods and Applications (pp. 387-413). Elsevier, 82
Open this publication in new window or tab >>An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives
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2018 (English)In: Data Analysis for Omic Sciences: Methods and Applications / [ed] Joaquim Jaumot; Carmen Bedia; Romà Tauler, Elsevier, 2018, Vol. 82, p. 387-413Chapter in book (Refereed)
Abstract [en]

Metabolomics is a study of small molecules in the body and the associated metabolic pathways and is considered to provide a close link between organism's genotype and phenotype. As with other ‘omics’ techniques, metabolomic analysis generates large-scale and complex datasets. Therefore, various data analysis tools are needed to extract biologically relevant information. The data analysis workflows in metabolomics studies are generally complex and involve several steps. In this chapter, we highlight the concept of metabolomics workflow and discuss the data analysis strategies for metabolomics experiments. We also discuss the available tools that can assist in biological interpretation of metabolomics data. We also present an emerging approach of developing genome-scale metabolic models to study cellular metabolism.

Place, publisher, year, edition, pages
Elsevier, 2018
Series
Comprehensive Analytical Chemistry, ISSN 0166-526X, E-ISSN 1875-788X
Keywords
Genome-scale metabolic models, Metabolic models, Metabolite set analysis, Metabolomics, Multivariate, Pathways, Univariate data analysis
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:oru:diva-71466 (URN)10.1016/bs.coac.2018.07.001 (DOI)2-s2.0-85051717444 (Scopus ID)978-0-444-64044-4 (ISBN)
Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-03-04Bibliographically approved
Schwarz, E., Maukonen, J., Hyytiäinen, T., Kieseppä, T., Oresic, M., Sabunciyan, S., . . . Suvisaari, J. (2018). 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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2018 (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, 2018
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: 2019-01-14Bibliographically approved
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: 2019-03-04Bibliographically 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, 149, 3-12
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 1095-9130, Vol. 149, p. 3-12Article in journal (Refereed) Published
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
Academic Press, 2018
Keywords
Faecal metabolites, Genome-scale metabolic models, Gut microbiota, Metabolomics, Microbiome
National Category
Microbiology Cell and Molecular Biology
Identifiers
urn:nbn:se:oru:diva-66868 (URN)10.1016/j.ymeth.2018.04.029 (DOI)000448632500002 ()29715508 (PubMedID)
Note

Funding Agencies:

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

Academy of Finland  292568 

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2018-11-13Bibliographically 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: 2019-03-04Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-2856-9165

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