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Multivariate data analysis of metabolomic multi-tissue samples
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Johan Trygg's group)ORCID iD: 0000-0002-6294-7844
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Multi-tissue metabolomics involves characterisation of the metabolome of several tissue types. The metabolome consists of small chemical entities of low molecular weight called metabolites, which are constantly produced and interchanged through a vast variety of biochemical reactions occurring throughout living organisms. Metabolome alterations can be attributed to genetics, environment, and diseases. We used gas chromatography timeof-flight mass spectrometry (GC TOF-MS) to characterise the metabolome of mouse organ samples: gut, kidney, liver, muscle, pancreas and plasma. Samples were obtained from wild-type mice and mice carrying a mutation in the hepatocyte nuclear factor 1b (HNF1b) gene, referred to as MODY5/RCAD (for maturity onset diabetes of the young 5/renal cysts and diabetes syndrome) mice. MODY is a class of hereditary diabetes mellitus, and MODY5 is caused by mutations in HNF1B, resulting in a wide range of manifestations, including renal diseases, kidney and genitourinary malformation, and elevation of liver enzymes. Today, MODY5 in humans is diagnosed using genetic tests, and varying referral rates and manifestations have resulted in misdiagnosis. Our main focus was therefore to increase understanding of the metabolism associated with MODY5/RCAD by studying the metabolic profiles of individual organs and plasma (Paper I) from MODY5/RCAD mutant and wildtype mice. The mouse model displayed an overall metabolic pattern consistent with the presumed outcome of the mutation in humans, making the MODY5/RCAD model suitable for studies of HNF1B-associated diseases. An understanding of metabolite origin would be beneficial for understanding the plasma profile associated with MODY5/RCAD. We used hierarchical modelling to provide an understanding of metabolite origin by detecting how metabolites from the organs contributed to the plasma metabolic profile (Paper II). Both specific and overall organ metabolite contributions to the plasma metabolic profile were studied. Further exploration of the dataset involved study of its innate variation using joint and unique multiblock analysis (JUMBA; Paper III). In addition, we explored the effects of improper sample handling for metabolomic multi-tissue data, and we studied the similarities and differences in the responses to thawing between organ tissues (Paper IV) and plasma samples (Paper V), thus identifying metabolic profiles that could indicate compromised samples. These profiles could be beneficial for large-scale collaborations that involve sample exposure to unsuitable conditions. Altogether, we have contributed to an increased understanding of the MODY5/RCAD multi-tissue metabolomic dataset and worked up protocols and strategies for how small datasets should be handled.

Abstract [sv]

Metabolomik är identifieringen och statistiska utvärderingen av halten av metaboliter i en mängd prover. Metaboliter är små kemiska strukturer som produceras av alla reaktioner som pågår i organismer. Genom att tolka halten av metaboliter får man en uppfattning om organismens status, vid provtagningstillfället. Den relativa mängden av metaboliter i ett prov kan identifieras genom olika metoder. Till dessa identifieringsmetoder räknas exempelvis kärnmagnetisk resonans (eng. Nuclear Magnetic Resonance (NMR)) och masspektrometri-plattformar så som gaskromatografi (eng. Gas Chromatography Mass Spectrometry (GC-MS)) och vätskekromatografi (eng. Liquid Chromatography Mass Spectrometry (LC-MS)). Valet av identifieringsplattform baseras på vad studien kräver.

Metabolomikstudier skiljer sig från sina föregångare. De tidigare studierna gick ut på att mäta en eller ett par variabler med hög precision. I metabolomikstudier hittas hundratals till tusentals potentiella metaboliter i varje prov. Därtill kan dess dataset innehålla en hel del brus. Metoder i klassiska, univariata, statistiken togs fram för att tillämpas på de typer av experiment där man mäter ett fåtal variabler, med hög precision. Metabolomikdata är inte av denna karaktär, utan består av många variabler (pikar/metaboliter) och färre observationer (försöksdjur/patienter). Med hjälp av multivariat dataanalys fokuserar vi på storleken hos olika variabler och deras variation för att identifiera metabolitmönster. För att finna dessa mönster används multivariata metoder som principalkomponentanalys (eng. Principal Component Analysis (PCA)) och diskriminantanalys (eng. (Orthogonal) Projections to Latent Structures Discriminant Analysis ((O)PLS-DA), där alla variabler analyseras simultant. De univariata metoderna används sedan som ett komplement till de multivariata metoderna i utvärderingen av metabolomikdata.

I denna avhandling har arbete i huvudsak kretsat kring Maturity Onset Diabetes of the Young (MODY). MODY utgör en grupp ärftliga diabetestyper som orsakas av mutation i en enda gen som leder till att individen får en rubbad insulinproduktion och diabetesliknande symptom. Patienter med MODY blir ofta missdiagnostiserade med diagnosen Diabetes Typ 1 (DT1) eller Diabetes Typ 2 (DT2). De flesta MODY patienter har en underproduktion av insulin, men det finns ingen insulinresistens som vid DT2. Behandlingen med insulin eller tabletter leder då till svår hypoglykemi (sockerkänning). Vi studerade MODY5, vilken orsakas av en mutation i genen som kodar för transkriptionsfaktorn Hnf1b. MODY5 (eller RCAD (Renal Cysts And Diabetes syndrome)) misstänks då patienten har en DT1 eller DT2 diagnos samt njurpåverkan. Idag diagnosticeras MODY5 genom genetiska tester, dock är dessa dyra och diagnosen relativt okänd bland kliniker. Genom att öka förståelsen för sjukdomen så finns förutsättningar för att förbättra både behandling och diagnostisering.

Proverna som analyserades i denna avhandling kom från möss av vildtyp samt möss med en mutation som gav ett MODY5-liknande tillstånd. Alla möss föddes upp i Paris, Frankrike (Cereghini et al.). Då mössen var åtta månader placerades de i varsin metabol bur och övervakades noggrant i fem dagar. Såväl mat- och vattenintag som mängden urin- och avförings samt blodsocker och kroppsvikt mättes dagligen och utgör metadata. På den femte dagen offrades mössen och deras lever, muskler, njurar, bukspottkörtel och magtarmsystem samt blodplasma placerades i -80 °C frysar. Dessa skickades i två försändelser till Umeå för analys, där proverna i den ena nådde Umeå tinade.

I Paper I fastställde vi de metabola profilerna för sjukdomstillståndet (MODY5/RCAD), i var och en av de undersökta organen och plasma. MODY5/RCAD mössen uppvisade tecken på nedsatt njurfunktion och förändrad fettsyra och lipidmetabolism i organvävnaderna. Vi fann även att tarmarna påverkats mindre av mutationen, jämfört med hur det såg ut för de övriga organen och plasma. Detta kan ha att göra med att Hnf1b är viktig vid bildandet av bukspottkörtel, lever och njurar, i det unga musembryot. Varför musklerna skulle vara mer påverkade än tarmen krävs det vidare studier för att fastställa.

I Paper II fokuserade vi på hur de olika organen bidrar till metaboliterna i blodplasma och undersökte vilka metaboliter som varje organ bidrar med till plasma. Målet med denna studie var att undersöka hur hierarkisk modellering kan användas för att identifiera detta bidrag. Vi visade att alla undersökta organ bidrog till metabolitnivåerna hos blodplasma, men att det var mag-tarmsystemet som hade största bidraget. Den hierarkiska modellen kunde även vis på de organspecifika metabolitbidraget till blodplasma. Då de identifierade flödena överensstämde med vad vi kunde förvänta oss, baserat på rådande forskning, skulle denna strategi kunna användas för att studera flödet av okända metaboliter.

Paper III behandlar hur man kan utföra dataintegrering av ett litet dataset. Genom att integrerar data från olika block (i detta fall representerade varje vävnadstyp ett block) kunde vi identifiera gemensamma mönster, gemensam variation. Den integrationsmetod vi använde var JUMBA (Joint and Unique MultiBlock Analysis) som kan identifiera både global variation (som återfinns i alla block), lokal variation (som återfinns i några av blocken) och unik variation (endast återfunnen i ett block). JUMBA extraherade två globala komponenter som vi kunde tolka som en annorlunda aminosyraprofil och fettsyraprofil hos en av mössen, samt att vissa skillnader berodde på mössens storlek. JUMBA fann även upp de två genotyperna, i alla block utom mag-tarmsystemet (alltså i en lokal komponent) vilket överensstämmer med fynden i Paper I. I en andra lokal komponent fann JUMBA hur de olika mössen befann sig i tre olika stadier av energimetabolism. Detta indikerar att JUMBA lämpar sig för att få fram information, även i fall med få prover.

I Paper IV undersökte vi hur de olika vävnadstyperna reagerat på upptining. De metabola profilerna hos prover som tinat under transporten jämfördes mot sådana som behandlats enligt standardprotokoll (SOP). Genom att identifiera metaboliter som är specifika för felbehandlade, tinade prover fann vi ett metabolitmönster som ska ses som alarmerande. De olika organproverna reagerade likartat på att ha tinat under transporten, med proteindegradering och cellsönderfall. Om denna typ av mönster observeras måste provens kvalité granskas. I en andra studie av tinade prover (Paper V) jämförde vi dessa resultat med de tinade plasmaproven och fann att organproverna var känsligare för upptining. Dock uppstod förändringar även i plasmaproverna.

Sammanfattningsvis har denna avhandling bidragit till en ökad förståelse för detta multivävnads dataset och upparbetat protokoll för hur små dataset (där små dataset avser sådana med färre än femton observationer per grupp) ska hanteras. Vi har bedömt hur väl MODY5/RCAD musmodellen skulle fungera som modell och dess potential i pre-kliniska studier av HNF1 B-associerade sjukdomar. Vi har studerat hur olika organ bidrar till de metabolitnivåer som återfinns i blodplasma. Vikten av att prover hanteras korrekt och på samma sätt samt vikten av randomisering har också diskuterats. Dessutom har vi diskuterat olika multivariata dataanalysmetoder och betydelsen av de metabola variationer vi identifierat.

Mycket har hänt inom metabolomik under mina år som doktorand, det är fortfarande ett relativt ungt fält men med mycket tydligare riktlinjer. Stora insatser har lagts på att standardisera namngivningen av metaboliter och hanteringen av prover. Mängden metaboliter som identifieras har ökat enormt och så även precisionen med vilken de mäts. Väl upparbetade standardprotokoll finns och kunskapen om olika metaboliter ökar för var dag som går. Fältet som sådant visar enorm potential vad gäller diagnostisering och monitorering av sjukdomar samt identifiering av nya behandlingsmål. Jag ser framemot att följa dess utveckling vidare.

Place, publisher, year, edition, pages
Umeå: Umeå universitet , 2020. , p. 63
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
URN: urn:nbn:se:umu:diva-170290ISBN: 978-91-7855-271-9 (print)ISBN: 978-91-7855-272-6 (electronic)OAI: oai:DiVA.org:umu-170290DiVA, id: diva2:1427657
Public defence
2020-05-29, Storahörsalen (KBE303), KBC-building, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2020-05-08 Created: 2020-04-30 Last updated: 2020-05-06Bibliographically approved
List of papers
1. Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice
Open this publication in new window or tab >>Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice
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2018 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 7, p. 2293-2306Article in journal (Refereed) Published
Abstract [en]

In the present study, we performed a metabolomics analysis to evaluate a MODY5/RCAD mouse mutant line as a potential model for HNF1B-associated diseases. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) of gut, kidney, liver, muscle, pancreas, and plasma samples uncovered the tissue specific metabolite distribution. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to identify the differences between MODY5/RCAD and wild-type mice in each of the tissues. The differences included, for example, increased levels of amino acids in the kidneys and reduced levels of fatty acids in the muscles of the MODY5/RCAD mice. Interestingly, campesterol was found in higher concentrations in the MODY5/RCAD mice, with a four-fold and three-fold increase in kidneys and pancreas, respectively. As expected, the MODY5/RCAD mice displayed signs of impaired renal function in addition to disturbed liver lipid metabolism, with increased lipid and fatty acid accumulation in the liver. From a metabolomics perspective, the MODY5/RCAD model was proven to display a metabolic pattern similar to what would be suspected in HNF1B-associated diseases. These findings were in line with the presumed outcome of the mutation based on the different anatomy and function of the tissues as well as the effect of the mutation on development.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2018
Keywords
HNF1B-associated diseases, metabolomics, OPLS-DA, multiorgan samples, MODY5, RCAD, mouse model
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:umu:diva-150378 (URN)10.1021/acs.jproteome.7b00821 (DOI)000438469900004 ()29873499 (PubMedID)2-s2.0-85048373012 (Scopus ID)
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2020-04-30Bibliographically approved
2. Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling
Open this publication in new window or tab >>Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling
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2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 6, article id e0129260Article in journal (Refereed) Published
Abstract [en]

Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS) at the Swedish Metabolomics centre, Umea University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs) and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.

National Category
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-106564 (URN)10.1371/journal.pone.0129260 (DOI)000356567500041 ()26086868 (PubMedID)
Funder
Swedish Research Council, 2011-6044
Available from: 2015-07-20 Created: 2015-07-20 Last updated: 2020-04-30Bibliographically approved
3. Multiblock analysis on a small metabolomic multi-tissue dataset
Open this publication in new window or tab >>Multiblock analysis on a small metabolomic multi-tissue dataset
(English)Manuscript (preprint) (Other (popular science, discussion, etc.))
Identifiers
urn:nbn:se:umu:diva-170456 (URN)
Available from: 2020-05-05 Created: 2020-05-05 Last updated: 2020-05-05
4. Tissue sample stability: thawing effect on multi-organ samples
Open this publication in new window or tab >>Tissue sample stability: thawing effect on multi-organ samples
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2016 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, no 2, article id 19Article in journal (Refereed) Published
Abstract [en]

Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeå University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples.

Place, publisher, year, edition, pages
Springer, 2016
Keywords
Thawing effect, Metabolomics, OPLS, Multivariate analysis, Multi-organ
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-117461 (URN)10.1007/s11306-015-0933-1 (DOI)000369343900010 ()
Note

Electronic supplementary material The online version of this article (doi:10.1007/s11306-015-0933-1) contains supplementary material, which is available to authorized users.

This research was supported by the Swedish Research Council Grant No. 2011-6044 (to JT), the Biology of Liver and Pancreatic Development and Disease (BOLD) Marie Curie Initial Training Network (MCITN) within EU’s FP7 programme (to TL, JT, KB, FT, SC, CH, TM) and the CNRS and Universite´ Pierre et Marie Curie (to SC, CH), the Institut National de la Sante´ et de la Recherche Me´dicale, INSERM (to SC), the Socie´te´ Francophone du Diabe`te and Emergence UPMC (to CH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest: JT, TM and TL are shareholders of AcureOmics AB. No financing has been received from this company.

Available from: 2016-03-01 Created: 2016-03-01 Last updated: 2020-04-30Bibliographically approved
5. The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples
Open this publication in new window or tab >>The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples
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2017 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 6, article id 66Article in journal (Refereed) Published
Abstract [en]

Introduction: Post-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze–thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome. Objectives: The aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types. Methods: In the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS). Results: The thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas. Conclusions: The comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Mouse, Metabolomics, Plasma, Multi-organ, Freeze-thaw cycle, OPLS-DA
National Category
Biochemistry and Molecular Biology Endocrinology and Diabetes
Identifiers
urn:nbn:se:umu:diva-135201 (URN)10.1007/s11306-017-1196-9 (DOI)000401711400001 ()28473743 (PubMedID)
Available from: 2017-05-22 Created: 2017-05-22 Last updated: 2020-04-30Bibliographically approved

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