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  • 1. Andorf, Sandra
    et al.
    Altmann, T
    Witucka-Wall, H
    Selbig, Joachim
    Repsilber, Dirk
    Institute of Genetics and Biometry, Bioinformatics and Biomathematics Unit, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
    Molecular network structures in heterozygotes: A systems-biology approach to heterosis2009Conference paper (Refereed)
  • 2.
    Andorf, Sandra
    et al.
    Bioinformatics and Biomathematics Group, Genetics and Biometry Unit, Research Institute for the Biology of Farm Animals (FBN), Dummersdorf, Germany.
    Gärtner, Tanja
    Institute for Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Steinfath, Matthias
    Institute for Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Witucka-Wall, Hanna
    Institute for Genetics, University of Potsdam, Potsdam-Golm, Germany.
    Altmann, Thomas
    Institute for Genetics, University of Potsdam, Potsdam-Golm, Germany.
    Repsilber, Dirk
    Bioinformatics and Biomathematics Group, Genetics and Biometry Unit, Research Institute for the Biology of Farm Animals (FBN), Dummersdorf, Germany.
    Towards systems biology of heterosis: a hypothesis about molecular network structure applied for the Arabidopsis metabolome2009In: EURASIP Journal on Bioinformatics and Systems Biology, ISSN 1687-4145, E-ISSN 1687-4153, article id 147157Article in journal (Refereed)
    Abstract [en]

    We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix) is used in our conceptual approach. It allows for directly relating structural network properties with biological function. Interpreting heterosis as increased adaptability, our model predicts that the biological networks involved show increasing connectivity of regulatory interactions. A detailed analysis of metabolite profile data reveals that the increasing-connectivity prediction is true for graphical Gaussian models in our data from early development. This mirrors properties of observed heterotic Arabidopsis phenotypes. Furthermore, the model predicts a limit for increasing hybrid vigor with increasing heterozygosity--a known phenomenon in the literature.

  • 3.
    Andorf, Sandra
    et al.
    Department Genetics and Biometry, Bioinformatics and Biomathematics Group, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany; Department of Medicine, Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany.
    Meyer, Rhonda C
    Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    Selbig, Joachim
    Bioinformatics Chair, Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
    Altmann, Thomas
    Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    Repsilber, Dirk
    Department Genetics and Biometry, Bioinformatics and Biomathematics Group, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
    Integration of a systems biological network analysis and QTL results for biomass heterosis in Arabidopsis thaliana2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 11, article id e49951Article in journal (Refereed)
    Abstract [en]

    To contribute to a further insight into heterosis we applied an integrative analysis to a systems biological network approach and a quantitative genetics analysis towards biomass heterosis in early Arabidopsis thaliana development. The study was performed on the parental accessions C24 and Col-0 and the reciprocal crosses. In an over-representation analysis it was tested if the overlap between the resulting gene lists of the two approaches is significantly larger than expected by chance. Top ranked genes in the results list of the systems biological analysis were significantly over-represented in the heterotic QTL candidate regions for either hybrid as well as regarding mid-parent and best-parent heterosis. This suggests that not only a few but rather several genes that influence biomass heterosis are located within each heterotic QTL region. Furthermore, the overlapping resulting genes of the two integrated approaches were particularly enriched in biomass related pathways. A chromosome-wise over-representation analysis gave rise to the hypothesis that chromosomes number 2 and 4 probably carry a majority of the genes involved in biomass heterosis in the early development of Arabidopsis thaliana.

  • 4. Andorf, Sandra
    et al.
    Repsilber, Dirk
    Institute of Genetics and Biometry, Bioinformatics and Biomathematics Unit, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
    Molecular network structures in heterozygotes: A systems biological approach to heterosis2009In: Neue Methoden der Biometrie: 55. Biometrisches Kolloquium / [ed] R. Foraita, T. Gerds, L. A. Hothorn, M. Kieser, O. Kuß, U. Munzel, R. Vonk, A. Ziegler, 2009Conference paper (Refereed)
  • 5.
    Andorf, Sandra
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Selbig, Joachim
    University of Potsdam, Potsdam-Golm, Germany.
    Altmann, T
    Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany.
    Witucka-Wall, H
    University of Potsdam, Potsdam-Golm, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Gremany.
    Heterosis in Arabidopsis thaliana: A metabolite network structure approach2010In: 11th Day of the Doktoral Student: abstract; 19 May 2010, Dummerstorf, Dummerstorf, Germany: FBN , 2010, p. 7-10Conference paper (Refereed)
  • 6.
    Andorf, Sandra
    et al.
    Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Selbig, Joachim
    Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Altmann, Thomas
    Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    Poos, Kathrin
    University of Applied Sciences Gelsenkirchen Site Recklinghausen, Recklinghausen, Germany .
    Witucka-Wall, Hanna
    Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Repsilber, Dirk
    Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Enriched partial correlations in genome-wide gene expression profiles of hybrids (A. thaliana): a systems biological approach towards the molecular basis of heterosis2010In: Theoretical and Applied Genetics, ISSN 0040-5752, E-ISSN 1432-2242, Vol. 120, no 2, p. 249-59Article in journal (Refereed)
    Abstract [en]

    Heterosis is a well-known phenomenon but the underlying molecular mechanisms are not yet established. To contribute to the understanding of heterosis at the molecular level, we analyzed genome-wide gene expression profile data of Arabidopsis thaliana in a systems biological approach. We used partial correlations to estimate the global interaction structure of regulatory networks. Our hypothesis states that heterosis comes with an increased number of partial correlations which we interpret as increased numbers of regulatory interactions leading to enlarged adaptability of the hybrids. This hypothesis is true for mid-parent heterosis for our dataset of gene expression in two homozygous parental lines and their reciprocal crosses. For the case of best-parent heterosis just one hybrid is significant regarding our hypothesis based on a resampling analysis. Summarizing, both metabolome and gene expression level of our illustrative dataset support our proposal of a systems biological approach towards a molecular basis of heterosis.

  • 7. Andorf, Sandra
    et al.
    Selbig, Joachim
    Meyer, Rhonda
    Altmann, Thomas
    Repsilber, Dirk
    Integrating a molecular network hypothesis and QTL results for heterosis in Arabidopsis thaliana2010In: Statistical Computings 2010: Abstracts der 42. Arbeitstagung, 2010, Vol. 5Conference paper (Refereed)
  • 8.
    Baxter, Charles J
    et al.
    Department of Plant Sciences, University of Oxford, Oxford, United Kingdom.
    Redestig, Henning
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Schauer, Nicolas
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Repsilber, Dirk
    ax-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Patil, Kiran R
    Center for Microbial Biotechnology, BioCentrum Technical University of Denmark, Kongens Lyngby, Denmark.
    Nielsen, Jens
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Selbig, Joachim
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Liu, Junli
    Genetics Programme, Scottish Crop Research Institute, Dundee, United Kingdom .
    Fernie, Alisdair R
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Sweetlove, Lee J
    Department of Plant Sciences, University of Oxford, Oxford, United Kingdom.
    The metabolic response of heterotrophic Arabidopsis cells to oxidative stress2007In: Plant Physiology, ISSN 0032-0889, E-ISSN 1532-2548, Vol. 143, no 1, p. 312-25Article in journal (Refereed)
    Abstract [en]

    To cope with oxidative stress, the metabolic network of plant cells must be reconfigured either to bypass damaged enzymes or to support adaptive responses. To characterize the dynamics of metabolic change during oxidative stress, heterotrophic Arabidopsis (Arabidopsis thaliana) cells were treated with menadione and changes in metabolite abundance and (13)C-labeling kinetics were quantified in a time series of samples taken over a 6 h period. Oxidative stress had a profound effect on the central metabolic pathways with extensive metabolic inhibition radiating from the tricarboxylic acid cycle and including large sectors of amino acid metabolism. Sequential accumulation of metabolites in specific pathways indicated a subsequent backing up of glycolysis and a diversion of carbon into the oxidative pentose phosphate pathway. Microarray analysis revealed a coordinated transcriptomic response that represents an emergency coping strategy allowing the cell to survive the metabolic hiatus. Rather than attempt to replace inhibited enzymes, transcripts encoding these enzymes are in fact down-regulated while an antioxidant defense response is mounted. In addition, a major switch from anabolic to catabolic metabolism is signaled. Metabolism is also reconfigured to bypass damaged steps (e.g. induction of an external NADH dehydrogenase of the mitochondrial respiratory chain). The overall metabolic response of Arabidopsis cells to oxidative stress is remarkably similar to the superoxide and hydrogen peroxide stimulons of bacteria and yeast (Saccharomyces cerevisiae), suggesting that the stress regulatory and signaling pathways of plants and microbes may share common elements.

  • 9.
    Beger, Richard D.
    et al.
    Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA.
    Dunn, Warwick
    School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Birmingham, UK.
    Schmidt, Michael A.
    Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, USA.
    Gross, Steven S.
    Department of Pharmacology, Weill Cornell Medical College, New York, USA.
    Kirwan, Jennifer A.
    School of Biosciences, University of Birmingham, Birmingham, UK.
    Cascante, Marta
    Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Brennan, Lorraine
    UCD Institute of Food and Health, UCD, Belfield, Ireland.
    Wishart, David S.
    Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, Canada.
    Oresic, Matej
    Turku Centre for Biotechnology, University of Turku, Turku, Finland.
    Hankemeier, Thomas
    Division of Analytical Biosciences and Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University & Netherlands Metabolomics Centre, Leiden, The Netherlands.
    Broadhurst, David I.
    School of Science, Edith Cowan University, Perth, Australia.
    Lane, Andrew N.
    Center for Environmental Systems Biochemistry, Department Toxicology and Cancer Biology, Markey Cancer Center, Lexington, USA.
    Suhre, Karsten
    Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar.
    Kastenmüller, Gabi
    Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, Oberschleißheim, Germany.
    Sumner, Susan J.
    Discovery Sciences, RTI International, Research Triangle Park, Durham, USA.
    Thiele, Ines
    University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Campus Belval, Esch-Sur-Alzette, Luxembourg.
    Fiehn, Oliver
    West Coast Metabolomics Center, UC Davis, Davis, USA; Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia.
    Kaddurah-Daouk, Rima
    Psychiatry and Behavioral Sciences, Duke Internal Medicine and Duke Institute for Brain Sciences and Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, USA.
    Metabolomics enables precision medicine: "A White Paper, Community Perspective"2016In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, no 10, article id 149Article in journal (Refereed)
    Abstract [en]

    INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.

    OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.

    CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

  • 10.
    Brand, Bodo
    et al.
    Research Group of Functional Genomics, Leibniz Institute of Farm Animal Biology, Dummerstorf, Germany .
    Hartmann, Anja
    Research Group of Functional Genomics, Leibniz Institute of Farm Animal Biology, Dummerstorf, Germany .
    Repsilber, Dirk
    Research Unit of Genetics and Biometry, Leibniz Institute of Farm Animal Biology, Dummerstorf, Germany .
    Griesbeck-Zilch, Bettina
    Institute of Physiology, Technical University Munich, Freising, Germany.
    Wellnitz, Olga
    Veterinary Physiology, Vetsuisse Faculty, University of Bern, Posieux, Switzerland .
    Kühn, Christa
    Research Unit of Molecular Biology, Leibniz Institute of Farm Animal Biology, Dummerstorf, Germany.
    Ponsuksili, Siriluck
    Research Group of Functional Genomics, Leibniz Institute of Farm Animal Biology, Dummerstorf, Germany .
    Meyer, Heinrich H D
    Institute of Physiology, Technical University Munich, Freising, Germany.
    Schwerin, Manfred
    Research Group of Functional Genomics, Leibniz Institute of Farm Animal Biology, Dummerstorf, Germany; Institute of Farm Animal Science and Technology, University of Rostock, Rostock, Germany .
    Comparative expression profiling of E. coli and S. aureus inoculated primary mammary gland cells sampled from cows with different genetic predispositions for somatic cell score2011In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 43, no 1, article id 24Article in journal (Refereed)
    Abstract [en]

    Background: During the past ten years many quantitative trait loci (QTL) affecting mastitis incidence and mastitis related traits like somatic cell score (SCS) were identified in cattle. However, little is known about the molecular architecture of QTL affecting mastitis susceptibility and the underlying physiological mechanisms and genes causing mastitis susceptibility. Here, a genome-wide expression analysis was conducted to analyze molecular mechanisms of mastitis susceptibility that are affected by a specific QTL for SCS on Bos taurus autosome 18 (BTA18). Thereby, some first insights were sought into the genetically determined mechanisms of mammary gland epithelial cells influencing the course of infection.

    Methods: Primary bovine mammary gland epithelial cells (pbMEC) were sampled from the udder parenchyma of cows selected for high and low mastitis susceptibility by applying a marker-assisted selection strategy considering QTL and molecular marker information of a confirmed QTL for SCS in the telomeric region of BTA18. The cells were cultured and subsequently inoculated with heat-inactivated mastitis pathogens Escherichia coli and Staphylococcus aureus, respectively. After 1, 6 and 24 h, the cells were harvested and analyzed using the microarray expression chip technology to identify differences in mRNA expression profiles attributed to genetic predisposition, inoculation and cell culture.

    Results: Comparative analysis of co-expression profiles clearly showed a faster and stronger response after pathogen challenge in pbMEC from less susceptible animals that inherited the favorable QTL allele 'Q' than in pbMEC from more susceptible animals that inherited the unfavorable QTL allele 'q'. Furthermore, the results highlighted RELB as a functional and positional candidate gene and related non-canonical Nf-kappaB signaling as a functional mechanism affected by the QTL. However, in both groups, inoculation resulted in up-regulation of genes associated with the Ingenuity pathways 'dendritic cell maturation' and 'acute phase response signaling', whereas cell culture affected biological processes involved in 'cellular development'.

    Conclusions: The results indicate that the complex expression profiling of pathogen challenged pbMEC sampled from cows inheriting alternative QTL alleles is suitable to study genetically determined molecular mechanisms of mastitis susceptibility in mammary epithelial cells in vitro and to highlight the most likely functional pathways and candidate genes underlying the QTL effect.

  • 11.
    Clish, Clary B.
    et al.
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Davidov, Eugene
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Oresic, Matej
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Plasterer, Thomas N.
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Lavine, Gary
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Londo, Tom
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Meys, Michael
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Snell, Philip
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Stochaj, Wayne
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Adourian, Aram
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Zhang, Xiang
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Morel, Nicole
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Neumann, Eric
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Verheij, Elwin
    TNO Pharma, Zeist, Netherlands.
    Vogels, Jack T. W. E.
    TNO Pharma, Zeist, Netherlands.
    Havekes, Louis M.
    TNO Prevention and Health, Gaubius Laboratorium, Leiden, Netherlands; Departments of Cardiology and Internal Medicine and Leiden Center for Cardiovascular Research, Leiden University Medical Center, Leiden, Netherlands.
    Afeyan, Noubar
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Regnier, Fred
    Department of Chemistry, Purdue University, Lafayette, Indiana, USA.
    van der Greef, Jan
    Beyond Genomics, Inc., Waltham, Massachusetts, USA; TNO Pharma, Zeist, Netherlands; Division of Analytical Biosciences, Leiden/Amsterdam Centre for Drug Research, Leiden University, Leiden, Netherlands.
    Naylor, Stephen
    Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Integrative biological analysis of the APOE*3-leiden transgenic mouse2004In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 8, no 1, p. 3-13Article in journal (Refereed)
    Abstract [en]

    Integrative (or systems biology) is a new approach to analyzing biological entities as integrated systems of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. Here, we demonstrate the application of intregrative biological analysis to a mammalian disease model, the apolipoprotein E3-Leiden (APO*E3) transgenic mouse. Mice selected for the study were fed a normal chow diet and sacrificed at 9 weeks of age-conditions under which they develop only mild type I and II atherosclerotic lesions. Hepatic mRNA expression analysis showed a 25% decrease in APO A1 and a 43% increase in liver fatty acid binding protein expression between transgenic and wild type control mice, while there was no change in PPAR-alpha expression. On-line high performance liquid chromatography-mass spectrometry quantitative profiling of tryptic digests of soluble liver proteins and liver lipids, coupled with principle component analysis, enabled rapid identification of early protein and metabolite markers of disease pathology. These included a 44% increase in L-FABP in transgenic animals compared to controls, as well as an increase in triglycerides and select bioactive lysophosphatidylcholine species. A correlation analysis of identified genes, proteins, and lipids was used to construct an interaction network. Taken together, these results indicate that integrative biology is a powerful tool for rapid identification of early markers and key components of pathophysiologic processes, and constitute the first application of this approach to a mammalian system.

  • 12.
    Curtis, R. Keira
    et al.
    University of Cambridge Department of Clinical Biochemistry, Box 232, Addenbrooke’s Hospital, Hills Road, Cambridge, UK.
    Oresic, Matej
    Technical Research Centre of Finland, VTT Biotechnology, Espoo, Finland.
    Vidal-Puig, Antonio
    University of Cambridge Department of Clinical Biochemistry, Box 232, Addenbrooke’s Hospital, Hills Road, Cambridge, UK.
    Pathways to the analysis of microarray data2005In: Trends in Biotechnology, ISSN 0167-7799, E-ISSN 1879-3096, Vol. 23, no 8, p. 429-435Article, review/survey (Refereed)
    Abstract [en]

    The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.

  • 13.
    Dalevi, Daniel A.
    et al.
    Department of Molecular Evolution, University of Uppsala, Uppsala .
    Eriksen, Niklas
    Department of Mathematics, Royal High School of Technology, Stockholm.
    Eriksson, Kimmo
    Department of Mathematics and Physics, Mälardalens högskola, Västerås.
    Andersson, Siv G.E.
    Department of Molecular Evolution, University of Uppsala, Uppsala .
    Measuring genome divergence in bacteria: A case study using Chlamydian data2002In: Journal of Molecular Evolution, ISSN 0022-2844, E-ISSN 1432-1432, Vol. 55, p. 24-36Article in journal (Refereed)
    Abstract [en]

    We have studied the relative contribution of inversions, transpositions, deletions, and nucleotide substitutions to the evolution of Chlamydia trachomatis and Chlamydia pneumoniae. The minimal number of rearrangement events required for converting the gene order structure of one genome into that of the other was estimated to 59 6 events, including 13% inversions, 38% short inversions, and 49% transpositions. In contrast to previous findings, no examples of horizontal gene transfer subsequent to species divergence were identified, nor any evidence for an excessive number of tandem gene duplications. A statistical model was used to compare nucleotide frequencies for a set of genes uniquely present in one species to a set of orthologous genes present in both species. The two data sets were not significantly different, which is indicative of a low frequency of horizontal gene transfer events. This is based on the assumption that a foreign gene of different nucleotide content will not have become completely ameliorated, as verified by simulations of the amelioration rate at twofold and fourfold degenerate codon sites. The frequencies of nucleotide substitutions at twofold and fourfold degenerate sites, deletions, inversions, and translocations were estimated to 1.42, 0.62, 0,18, 0.01, and 0.01 per site, respectively.

  • 14.
    Dalevi, Daniel
    et al.
    Department of Computing Science and Engineering, Chalmers University of Technology, Gothenburg.
    Eriksen, Niklas
    Department of Mathematical Sciences, Gothenburg University and Chalmers University of Technology, Gotenhburg.
    Expected Gene Order Distances and Model Selection in Bacteria2008In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 24, no 11, p. 1332-1338Article in journal (Refereed)
    Abstract [en]

    Motivation: The evolutionary distance inferred from gene order comparisons of related bacteria is dependent on the model. Therefore, it is highly important to establish reliable assumptions before inferring its magnitude.

    Results: We investigate the patterns of dotplots between species of bacteria with the purpose of model selection in gene order problems. We find several categories of data which can be explained by carefully weighing the contributions of reversals, transpositions, symmetrical reversals, single gene transpositions, and single gene reversals. We also derive method of moments distance estimates for some previously uncomputed cases, such as symmetrical reversals, single gene reversals and their combinations, as well as the single gene transpositions edit distance.

  • 15.
    Davidov, Eugene
    et al.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Clish, Clary B.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Oresic, Matej
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Meys, Michael
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Stochaj, Wayne
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Snell, Philip
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Lavine, Gary
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Londo, Thomas R.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Adourian, Aram
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Zhang, Xiang
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Johnston, Mark
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Morel, Nicole
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Marple, Edward W.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Plasterer, Thomas N.
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Neumann, Eric
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Verheij, Elwin
    TNO Pharma, Zeist, The Netherlands.
    Vogels, Jack T. W. E.
    TNO Pharma, Zeist, The Netherlands.
    Havekes, Louis M.
    TNO Prevention and Health, Gaubius Laboratorium, Leiden, The Netherlands; Departments of Cardiology and Internal Medicine and Leiden/Amsterdam Center for Drug Research, Leiden University Medical Center, Leiden University, Leiden, The Netherlands.
    van der Greef, Jan
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA; TNO Pharma, Zeist, The Netherlands; Departments of Cardiology and Internal Medicine and Leiden/Amsterdam Center for Drug Research, Leiden University Medical Center, Leiden University, Leiden, The Netherlands.
    Naylor, Stephen
    1Beyond Genomics, Inc., Waltham, Massachusetts, USA.
    Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model2004In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 8, no 4, p. 267-288Article in journal (Refereed)
    Abstract [en]

    Multitiered quantitative analysis of biological systems is rapidly becoming the desired approach to study hierarchical functional interactions between proteins and metabolites. We describe here a novel systematic approach to analyze organisms with complex metabolic regulatory networks. By using precise analytical methods to measure biochemical constituents and their relative abundance in whole plasma of transgenic ApoE*3-Leiden mice and an isogenic wild-type control group, simultaneous snapshots of metabolic and protein states were obtained. Novel data processing and multivariate analysis tools such as Impurity Resolution Software (IMPRESS) and Windows-based linear fit program (WINLIN) were used to compare protein and metabolic profiles in parallel. Canonical correlations of the resulting data show quantitative relationships between heterogeneous components in the TG animals. These results, obtained solely from whole plasma analysis allowed us, in a rapid manner, to corroborate previous findings as well as find new events pertaining to dominant and peripheral events in lipoprotein metabolism of a genetically modified mammalian organism in relation to ApoE3, a key mediator of lipoprotein metabolism.

  • 16.
    Degenkolbe, Thomas
    et al.
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany .
    Do, Phuc Thi
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany .
    Zuther, Ellen
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany .
    Repsilber, Dirk
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany; Forschungsinstitut für Die Biologie Landwirtschaftlicher Nutztiere (FBN), Dummerstorf, Germany.
    Walther, Dirk
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany .
    Hincha, Dirk K
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany .
    Köhl, Karin I
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany .
    Expression profiling of rice cultivars differing in their tolerance to long-term drought stress2009In: Plant Molecular Biology, ISSN 0167-4412, E-ISSN 1573-5028, Vol. 69, no 1-2, p. 133-53Article in journal (Refereed)
    Abstract [en]

    Understanding the molecular basis of plant performance under water-limiting conditions will help to breed crop plants with a lower water demand. We investigated the physiological and gene expression response of drought-tolerant (IR57311 and LC-93-4) and drought-sensitive (Nipponbare and Taipei 309) rice (Oryza sativa L.) cultivars to 18 days of drought stress in climate chamber experiments. Drought stressed plants grew significantly slower than the controls. Gene expression profiles were measured in leaf samples with the 20 K NSF oligonucleotide microarray. A linear model was fitted to the data to identify genes that were significantly regulated under drought stress. In all drought stressed cultivars, 245 genes were significantly repressed and 413 genes induced. Genes differing in their expression pattern under drought stress between tolerant and sensitive cultivars were identified by the genotype x environment (G x E) interaction term. More genes were significantly drought regulated in the sensitive than in the tolerant cultivars. Localizing all expressed genes on the rice genome map, we checked which genes with a significant G x E interaction co-localized with published quantitative trait loci regions for drought tolerance. These genes are more likely to be important for drought tolerance in an agricultural environment. To identify the metabolic processes with a significant G x E effect, we adapted the analysis software MapMan for rice. We found a drought stress induced shift toward senescence related degradation processes that was more pronounced in the sensitive than in the tolerant cultivars. In spite of higher growth rates and water use, more photosynthesis related genes were down-regulated in the tolerant than in the sensitive cultivars.

  • 17.
    Demczuk, Walter H.B.
    et al.
    National Microbiology Laboratory, Winnipeg, Canada.
    Sidhu, S.
    National Microbiology Laboratory, Winnipeg, Canada.
    Unemo, Magnus
    WHO Collaborating Centre for Gonorrhoea and Other STIs, Örebro University Hospital, Örebro, Sweden; School of Medical Sciences, Örebro University, Örebro, Sweden.
    Whiley, David M.
    Centre for Clinical Research, The University of Queensland, Brisbane, Australia.
    Allen, Vanessa G.
    Public Health Ontario Laboratories, Toronto , Canada.
    Dillon, Jeremiah R.
    Department of Microbiology and Immunology, University of Saskatchewan, Saskatoon, Canada.
    Cole, Michelle J.
    Public Health England, London, United Kingdom.
    Seah, Christine
    Public Health Ontario Laboratories, Toronto, Canada.
    Trembizki, Ella
    Centre for Clinical Research, The University of Queensland, Brisbane, Australia.
    Trees, David L.
    Centers for Disease Control and Prevention, Atlanta GA, United States.
    Kersh, Ellen N.
    Centers for Disease Control and Prevention, Atlanta GA, United States.
    Abrams, A. Jeanine
    Centers for Disease Control and Prevention, Atlanta GA, United States.
    de Vries, Henry J.C.
    STI Outpatient Clinic, Department of Infectious Diseases, Public Health Service Amsterdam, Amsterdam, the Netherlands; Department of Dermatology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Center for Infection and Immunity Amsterdam, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
    van Dam, Alje P.
    Public Health Laboratory, Public Health Service Amsterdam, Amsterdam, the Netherlands; Department of Medical Microbiology, OLVG General Hospital, Amsterdam, the Netherlands; .
    Medina, I.
    National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg MB, Canada.
    Bharat, Amrita
    National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg MB, Canada.
    Mulvey, Michael Richard
    National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg MB, Canada.
    Van Domselaar, Gary
    National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg MB, Canada.
    Martin, Irene E.
    National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg MB, Canada.
    Neisseria gonorrhoeae Sequence Typing for Antimicrobial Resistance: a Novel Antimicrobial Resistance Multilocus Typing Scheme for Tracking Global Dissemination of N. gonorrhoeae Strains2017In: Journal of Clinical Microbiology, ISSN 0095-1137, E-ISSN 1098-660X, Vol. 55, no 5, p. 1454-1468Article in journal (Refereed)
    Abstract [en]

    A curated Web-based user-friendly sequence typing tool based on antimicrobial resistance determinants in Neisseria gonorrhoeae was developed and is publicly accessible (https://ngstar.canada.ca). The N. gonorrhoeae Sequence Typing for Antimicrobial Resistance (NG-STAR) molecular typing scheme uses the DNA sequences of 7 genes (penA, mtrR, porB, ponA, gyrA, parC, and 23S rRNA) associated with resistance to β-lactam antimicrobials, macrolides, or fluoroquinolones. NG-STAR uses the entire penA sequence, combining the historical nomenclature for penA types I to XXXVIII with novel nucleotide sequence designations; the full mtrR sequence and a portion of its promoter region; portions of ponA, porB, gyrA, and parC; and 23S rRNA sequences. NG-STAR grouped 768 isolates into 139 sequence types (STs) (n = 660) consisting of 29 clonal complexes (CCs) having a maximum of a single-locus variation, and 76 NG-STAR STs (n = 109) were identified as unrelated singletons. NG-STAR had a high Simpson's diversity index value of 96.5% (95% confidence interval [CI] = 0.959 to 0.969). The most common STs were NG-STAR ST-90 (n = 100; 13.0%), ST-42 and ST-91 (n = 45; 5.9%), ST-64 (n = 44; 5.72%), and ST-139 (n = 42; 5.5%). Decreased susceptibility to azithromycin was associated with NG-STAR ST-58, ST-61, ST-64, ST-79, ST-91, and ST-139 (n = 156; 92.3%); decreased susceptibility to cephalosporins was associated with NG-STAR ST-90, ST-91, and ST-97 (n = 162; 94.2%); and ciprofloxacin resistance was associated with NG-STAR ST-26, ST-90, ST-91, ST-97, ST-150, and ST-158 (n = 196; 98.0%). All isolates of NG-STAR ST-42, ST-43, ST-63, ST-81, and ST-160 (n = 106) were susceptible to all four antimicrobials. The standardization of nomenclature associated with antimicrobial resistance determinants through an internationally available database will facilitate the monitoring of the global dissemination of antimicrobial-resistant N. gonorrhoeae strains.

  • 18.
    Elo, Laura L.
    et al.
    Department of Mathematics, University of Turku, Turku, Finland; Turku Centre for Biotechnology, Turku, Finland.
    Järvenpää, Henna
    Turku Centre for Biotechnology, Turku, Finland.
    Oresic, Matej
    Turku Centre for Biotechnology, Turku, Finland; VTT Biotechnology, Espoo, Finland.
    Lahesmaa, Riitta
    Turku Centre for Biotechnology, Turku, Finland.
    Aittokallio, Tero
    Department of Mathematics, University of Turku, Turku, Finland; Turku Centre for Biotechnology, Turku, Finland; Systems Biology Unit, Institut Pasteur, Paris, France.
    Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process2007In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 23, no 16, p. 2096-2103Article in journal (Refereed)
    Abstract [en]

    MOTIVATION: Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared.

    RESULTS: We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process.

    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

  • 19.
    Elo, Laura L.
    et al.
    Department of Mathematics, University of Turku, Turku, Finland; Turku Centre for Biotechnology, Turku, Finland.
    Katajamaa, Mikko
    Turku Centre for Biotechnology, Turku, Finland.
    Lund, Riikka
    Turku Centre for Biotechnology, Turku, Finland.
    Oresic, Matej
    Turku Centre for Biotechnology, Turku, Finland; VTT Biotechnology, Espoo, Finland.
    Lahesmaa, Riitta
    Turku Centre for Biotechnology, Turku, Finland.
    Aittokallio, Tero
    Department of Mathematics, University of Turku, Turku, Finland; Turku Centre for Biotechnology, Turku, Finland; Systems Biology Unit, Institut Pasteur, Paris, France.
    Improving identification of differentially expressed genes by integrative analysis of Affymetrix and Illumina arrays2006In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 10, no 3, p. 369-380Article in journal (Refereed)
    Abstract [en]

    Together with the widely used Affymetrix microarrays, the recently introduced Illumina platform has become a cost-effective alternative for genome-wide studies. To efficiently use data from both array platforms, there is a pressing need for methods that allow systematic integration of multiple datasets, especially when the number of samples is small. To address these needs, we introduce a meta-analytic procedure for combining Affymetrix and Illumina data in the context of detecting differentially expressed genes between the platforms. We first investigate the effect of different expression change estimation procedures within the platforms on the agreement of the most differentially expressed genes. Using the best estimation methods, we then show the benefits of the integrative analysis in producing reproducible results across bootstrap samples. In particular, we demonstrate its biological relevance in identifying small but consistent changes during T helper 2 cell differentiation.

  • 20.
    Fang, Wei
    et al.
    Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, Hubei, PR China.
    Santosh, Lamichhane
    Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
    Oresic, Matej
    Örebro University, School of Medical Sciences. Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, Hubei, PR China; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
    Hyötyläinen, Tuulia
    Örebro University, School of Science and Technology.
    Lipidomes in health and disease: Analytical strategies and considerations2019In: TrAC. Trends in analytical chemistry, ISSN 0165-9936, E-ISSN 1879-3142, Vol. 120, article id 115664Article, review/survey (Refereed)
    Abstract [en]

    Lipidomics is a rapidly-growing field which focuses on global characterization of lipids at molecular and systems levels. As small changes in the concentrations of lipids may have important physiological consequences, much attention in the field has recently been paid to more accurate quantitation and identification of lipids. Community-wide efforts have been initiated, aiming to develop best practices for lipidomic analyses and reporting of lipidomic data. Nevertheless, current approaches for comprehensive analysis of lipidomes have some inherent challenges and limitations. Additionally, there is, currently, limited knowledge concerning the impacts of various external and internal exposures on lipid levels. In this review, we discuss the recent progress in lipidomics analysis, with a primary focus on analytical approaches, as well as on the different sources of variation in quantifying lipid levels, both technical and biological.

  • 21.
    Golparian, Daniel
    et al.
    Örebro University, School of Medical Sciences. WHO Collaborating Centre for Gonorrhoea and other Sexually Transmitted Infections, Department of Laboratory Medicine, Clinical Microbiology.
    Donà, Valentina
    Institute for Infectious Diseases, University of Bern, Bern, Switzerland; Institute of Veterinary Bacteriology, Vetsuisse Faculty, University of Bern, Bern, Switzerland.
    Sánchez-Busó, Leonor
    Pathogen Genomics, The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom.
    Foerster, Sunniva
    WHO Collaborating Centre for Gonorrhoea and other Sexually Transmitted Infections, Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
    Harris, Simon
    Pathogen Genomics, The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom.
    Endimiani, Andrea
    Institute for Infectious Diseases, University of Bern, Bern, Switzerland.
    Low, Nicola
    Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
    Unemo, Magnus
    Örebro University, School of Medical Sciences. Örebro University Hospital. WHO Collaborating Centre for Gonorrhoea and other Sexually Transmitted Infections, Department of Laboratory Medicine, Clinical Microbiology.
    Antimicrobial resistance prediction and phylogenetic analysis of Neisseria gonorrhoeae isolates using the Oxford Nanopore MinION sequencer2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, no 1, article id 17596Article in journal (Refereed)
    Abstract [en]

    Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is common, compromising gonorrhoea treatment internationally. Rapid characterisation of AMR strains could ensure appropriate and personalised treatment, and support identification and investigation of gonorrhoea outbreaks in nearly real-time. Whole-genome sequencing is ideal for investigation of emergence and dissemination of AMR determinants, predicting AMR, in the gonococcal population and spread of AMR strains in the human population. The novel, rapid and revolutionary long-read sequencer MinION is a small hand-held device that generates bacterial genomes within one day. However, accuracy of MinION reads has been suboptimal for many objectives and the MinION has not been evaluated for gonococci. In this first MinION study for gonococci, we show that MinION-derived sequences analysed with existing open-access, web-based sequence analysis tools are not sufficiently accurate to identify key gonococcal AMR determinants. Nevertheless, using an in house-developed CLC Genomics Workbench including de novo assembly and optimised BLAST algorithms, we show that 2D ONT-derived sequences can be used for accurate prediction of decreased susceptibility or resistance to recommended antimicrobials in gonococcal isolates. We also show that the 2D ONT-derived sequences are useful for rapid phylogenomic-based molecular epidemiological investigations, and, in hybrid assemblies with Illumina sequences, for producing contiguous assemblies and finished reference genomes.

  • 22.
    Gopalacharyulu, Peddinti V.
    et al.
    VTT Biotechnology, Espoo, Finland.
    Lindfors, Erno
    VTT Biotechnology, Espoo, Finland.
    Bounsaythip, Catherine
    VTT Biotechnology, Espoo, Finland.
    Kivioja, Teemu
    VTT Biotechnology, Espoo, Finland.
    Yetukuri, Laxman
    VTT Biotechnology, Espoo, Finland.
    Hollmén, Jaakko
    Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland.
    Oresic, Matej
    VTT Biotechnology, Espoo, Finland.
    Data integration and visualization system for enabling conceptual biology2005In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 21 Suppl 1, p. i177-i185Article in journal (Refereed)
    Abstract [en]

    MOTIVATION: Integration of heterogeneous data in life sciences is a growing and recognized challenge. The problem is not only to enable the study of such data within the context of a biological question but also more fundamentally, how to represent the available knowledge and make it accessible for mining.

    RESULTS: Our integration approach is based on the premise that relationships between biological entities can be represented as a complex network. The context dependency is achieved by a judicious use of distance measures on these networks. The biological entities and the distances between them are mapped for the purpose of visualization into the lower dimensional space using the Sammon's mapping. The system implementation is based on a multi-tier architecture using a native XML database and a software tool for querying and visualizing complex biological networks. The functionality of our system is demonstrated with two examples: (1) A multiple pathway retrieval, in which, given a pathway name, the system finds all the relationships related to the query by checking available metabolic pathway, transcriptional, signaling, protein-protein interaction and ontology annotation resources and (2) A protein neighborhood search, in which given a protein name, the system finds all its connected entities within a specified depth. These two examples show that our system is able to conceptually traverse different databases to produce testable hypotheses and lead towards answers to complex biological questions.

  • 23.
    Gopalacharyulu, Peddinti V.
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Lindfors, Erno
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Miettinen, Jarkko
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Bounsaythip, Catherine K.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oresic, Matej
    VTT Technical Research Centre of Finland, Espoo, Finland.
    An integrative approach for biological data mining and visualisation2008In: International Journal of Data Mining and Bioinformatics, ISSN 1748-5673, E-ISSN 1748-5681, Vol. 2, no 1, p. 54-77Article in journal (Refereed)
    Abstract [en]

    The emergence of systems biology necessitates development of platforms to organise and interpret plentitude of biological data. We present a system to integrate data across multiple bioinformatics databases and enable mining across various conceptual levels of biological information. The results are represented as complex networks. Context dependent mining of these networks is achieved by use of distances. Our approach is demonstrated with three applications: full metabolic network retrieval with network topology study, exploration of properties and relationships of a set of selected proteins, and combined visualisation and exploration of gene expression data with related pathways and ontologies.

  • 24.
    Gopalacharyulu, Peddinti V.
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Velagapudi, Vidya R.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Lindfors, Erno
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Halperin, Eran
    International Computer Science Institute, Berkeley, California, USA .
    Oresic, Matej
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Dynamic network topology changes in functional modules predict responses to oxidative stress in yeast2009In: Molecular Biosystems, ISSN 1742-206X, E-ISSN 1742-2051, Vol. 5, no 3, p. 276-287Article in journal (Refereed)
    Abstract [en]

    In response to environmental challenges, biological systems respond with dynamic adaptive changes in order to maintain the functionality of the system. Such adaptations may lead to cumulative stress over time, possibly leading to global failure of the system. When studying such systems responses, it is therefore important to understand them in system-wide and dynamic context. Here we hypothesize that dynamic changes in the topology of functional modules of integrated biological networks reflect their activity under specific environmental challenges. We introduce topological enrichment analysis of functional subnetworks (TEAFS), a method for the analysis of integrated molecular profile and interactome data, which we validated by comprehensive metabolomic analysis of dynamic yeast response under oxidative stress. TEAFS identified activation of multiple stress response related mechanisms, such as lipid metabolism and phospholipid biosynthesis. We identified, among others, a fatty acid elongase IFA38 as a hub protein which was absent at all time points under oxidative stress conditions. The deletion mutant of the IFA38 encoding gene is known for the accumulation of ceramides. By applying a comprehensive metabolomic analysis, we confirmed the increased concentrations over time of ceramides and palmitic acid, a precursor of de novo ceramide biosynthesis. Our results imply that the connectivity of the system is being dynamically modulated in response to oxidative stress, progressively leading to the accumulation of (lipo)toxic lipids such as ceramides. Studies of local network topology dynamics can be used to investigate as well as predict the activity of biological processes and the system's responses to environmental challenges and interventions.

  • 25.
    Hartmann, Anja
    et al.
    Research Group Functional Genomics.
    Nürnberg, Gerd
    Research Unit Genetics and Biometrics.
    Repsilber, Dirk
    Research Unit Genetics and Biometrics.
    Janczyk, Pawel
    Institute of Veterinary Anatomy, Department of Veterinary Medicine, Free University of Berlin, Berlin, Germany.
    Walz, Chrsitina
    Research Group Functional Genomics.
    Ponsuksili, Siriluck
    Research Group Functional Genomics.
    Souffrant, Wolfgang-Bernhard
    Research Unit Nutritional Physiology, »Oskar Kellner«, Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Schwerin, Manfred
    Research Group Functional Genomics; Institute of Farm Animal Sciences and Technology, University of Rostock, Rostock, Germany.
    Effects of threshold choice on the results of gene expression profiling, using microarray analysis, in a model feeding experiment with rat2009In: Archiv für Tierzucht, ISSN 0003-9438, Vol. 52, no 1, p. 65-78Article in journal (Refereed)
    Abstract [en]

    Global gene expression studies using microarray technology are widely employed to identify biological processes which are influenced by a treatment e.g. a specific diet. Affected processes are characterized by a significant enrichment of differentially expressed genes (functional annotation analysis). However, different choices of statistical thresholds to select candidates for differential expression will alter the resulting candidates list. This study was conducted to investigate the effect of applying a False Discovery Rate (FDR) correction and different fold change thresholds in statistical analysis of microarray data on diet-affected biological processes based on a significantly increased proportion of differentially expressed genes. In a model feeding experiment with rats fed genetically modified food additives, animals received a supplement of either lyophilized inactivated recombinant VP60 baculovirus (rBV-VP60) or lyophilized inactivated wild type baculovirus (wtBV). Comparative expression profiling was done in spleen, liver and small intestine mucosa. We demonstrated the extent to which threshold choice can affect the biological processes identified as significantly regulated and thus the conclusion drawn from the microarray data. In our study, the combined application of a moderate fold change threshold (FC≥1.5) and a stringent FDR threshold (q≤0.05) exhibited high reliability of biological processes identified as differentially regulated. The application of a stringent FDR threshold of q≤0.05 seems to be an essential prerequisite to reduce considerably the number of false positives. Microarray results of selected differentially expressed molecules were validated successfully by using real-time RT-PCR.

  • 26.
    Hoefig, Kai P
    et al.
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Thorns, Christoph
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Roehle, Anja
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Kaehler, Christian
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Wesche, Kai O
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Repsilber, Dirk
    Biomathematics / Bioinformatics Group, Research Institute for the Biology of Farm Animals FBN, Dummerstorf, Germany.
    Branke, Biggi
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Thiere, Marlen
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Feller, Alfred C
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Merz, Hartmut
    Institute for Pathology, UKSH Campus Luebeck, Luebeck.
    Unlocking pathology archives for microRNA-profiling2008In: Anticancer Research, ISSN 0250-7005, E-ISSN 1791-7530, Vol. 28, no 1A, p. 119-23Article in journal (Refereed)
    Abstract [en]

    Background: MicroRNAs (miRNAs) are approximately 22 nucleotide long, non-coding RNAs that regulate gene expression by binding to the 3'-untranslated region of target mRNAs and also a variety of cellular processes. It has recently been established that dysregulation of miRNA expression can be detected in the majority of human cancers. A variety of high-throughput screening methods has been developed to identify dysregulated miRNA species in tumours. For retrospective clinical studies formalin-fixed, paraffin-embedded (FFPE) tissue is the most widely used material.

    Materials and methods: The miRNA expression profiles of freshly frozen (CRYO) and FFPE tissues of seven tonsil and four liver samples were compared, using a qPCR-based assay, profiling 157 miRNA species.

    Results: The significance of miRNA-profiles was barely influenced by FFPE treatment in both tissues and the variance induced by FFPE treatment was much smaller than the variance caused by biologically based differential expression.

    Conclusion: FFPE material is well suited for miRNA profiling.

  • 27.
    Jacobsen, Marc
    et al.
    Max Planck Institute for Infection Biology, Department of Immunology, Berlin, Germany.
    Mattow, Jens
    Max Planck Institute for Infection Biology, Department of Immunology, Berlin, Germany; Department of Immunology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany .
    Repsilber, Dirk
    Research Institute for the Biology of Farm Animals, Genetics and Biometry, Dummerstorf, Germany.
    Kaufmann, Stefan H E
    Max Planck Institute for Infection Biology, Department of Immunology, Berlin, Germany.
    Novel strategies to identify biomarkers in tuberculosis2008In: Biological chemistry (Print), ISSN 1431-6730, E-ISSN 1437-4315, Vol. 389, no 5, p. 487-95Article in journal (Refereed)
    Abstract [en]

    The more we learn about the immune response against tuberculosis (TB) and particularly about the features which distinguish protective immunity, disease susceptibility and pathology, the better we can define biomarkers which correlate with these different stages of infection. The most widely used biomarker in TB, which without a doubt is an important component of protective immunity, is IFNgamma secreted by antigen-specific CD4 T-cells. However, the complexity of the immune response against TB makes it more than likely that additional biomarkers are required for a reliable correlate of protection. As a corollary, we assume that a set of biomarkers will be required, termed a biosignature.

  • 28.
    Jacobsen, Marc
    et al.
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Repsilber, Dirk
    Institute of Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany; Institute for Biology and Biochemistry, University Potsdam, Potsdam-Golm, Germany.
    Gutschmidt, Andrea
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Neher, A
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Feldmann, K
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Mollenkopf, H J
    Microarray Core Facilities, Max Planck Institute for Infection Biology, Berlin, Germany.
    Kaufmann, S H E
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Ziegler, Andreas
    Institute of Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany.
    Deconfounding microarray analysis: independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias2006In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 45, no 5, p. 557-63Article in journal (Refereed)
    Abstract [en]

    Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type.

    Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes.

    Results: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results.

    Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.

  • 29.
    Jacobsen, Marc
    et al.
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Repsilber, Dirk
    Institute for Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany; Institute for Biochemistry and Biology, University Potsdam, Potsdam-Golm, Germany.
    Gutschmidt, Andrea
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Neher, Albert
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Feldmann, Knut
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Mollenkopf, Hans J
    Microarray Core Facilities, Max Planck Institute for Infection Biology, Berlin, Germany.
    Ziegler, Andreas
    Kaufmann, Stefan H E
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis2007In: Journal of Molecular Medicine, ISSN 0946-2716, E-ISSN 1432-1440, Vol. 85, no 6, p. 613-21Article in journal (Refereed)
    Abstract [en]

    Infection with Mycobacterium tuberculosis is controlled by an efficacious immune response in about 90% of infected individuals who do not develop disease. Although essential mediators of protection, e.g., interferon-gamma, have been identified, these factors are insufficient to predict the outcome of M. tuberculosis infection. As a first step to determine additional biomarkers, we compared gene expression profiles of peripheral blood mononuclear cells from tuberculosis patients and M. tuberculosis-infected healthy donors by microarray analysis. Differentially expressed candidate genes were predominantly derived from monocytes and comprised molecules involved in the antimicrobial defense, inflammation, chemotaxis, and intracellular trafficking. We verified differential expression for alpha-defensin 1, alpha-defensin 4, lactoferrin, Fcgamma receptor 1A (cluster of differentiation 64 [CD64]), bactericidal permeability-increasing protein, and formyl peptide receptor 1 by quantitative polymerase chain reaction analysis. Moreover, we identified increased protein expression of CD64 on monocytes from tuberculosis patients. Candidate biomarkers were then assessed for optimal study group discrimination. Using a linear discriminant analysis, a minimal group of genes comprising lactoferrin, CD64, and the Ras-associated GTPase 33A was sufficient for classification of (1) tuberculosis patients, (2) M. tuberculosis-infected healthy donors, and (3) noninfected healthy donors.

  • 30.
    Jacobsen, Marc
    et al.
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Repsilber, Dirk
    Institute for Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.
    Gutschmidt, Andrea
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Neher, Albert
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Feldmann, Knut
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Mollenkopf, Hans J
    Microarray Core Facilities, Max Planck Institute for Infection Biology, Berlin, Germany .
    Ziegler, Andreas
    Institute for Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.
    Kaufmann, Stefan H E
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Ras-associated small GTPase 33A, a novel T cell factor, is down-regulated in patients with tuberculosis2005In: Journal of Infectious Diseases, ISSN 0022-1899, E-ISSN 1537-6613, Vol. 192, no 7, p. 1211-8Article in journal (Refereed)
    Abstract [en]

    Ras-associated small GTPases (Rabs) are specific regulators of intracellular vesicle trafficking. Interference with host cell vesicular transport is a hallmark of many intracellular pathogens, including the notable example Mycobacterium tuberculosis. We performed, by quantitative polymerase chain reaction, gene-expression analyses for selected Rab molecules in peripheral-blood mononuclear cells from patients with tuberculosis (TB) and healthy control subjects, to identify candidate genes that are critically involved in the host immune response. Comparison revealed significant differences in the expression of genes for Rab13, Rab24, and Rab33A. Rab33A gene expression was down-regulated in patients with TB and was predominantly expressed in CD8+ T cells. We excluded possible influences of differences in T cell percentages between the 2 study groups, demonstrating that Rab33A gene expression changes on the single-cell level. In vitro, Rab33A RNA expression was induced in T cells on activation and by dendritic cells infected with M. tuberculosis. Our findings identify Rab33A as a T cell regulatory molecule in TB and suggest its involvement in disease processes.

  • 31.
    Jerby, Livnat
    et al.
    School of Computer Sciences, Tel Aviv University, Tel Aviv, Israel.
    Wolf, Lior
    School of Computer Sciences, Tel Aviv University, Tel Aviv, Israel.
    Denkert, Carsten
    Institute of Pathology, Charité Hospital, Berlin, Germany.
    Stein, Gideon Y.
    Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; Department of Internal Medicine B, Beilinson Hospital, Rabin Medical Center, Petah-Tikva, Israel.
    Hilvo, Mika
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oresic, Matej
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Geiger, Tamar
    Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
    Ruppin, Eytan
    School of Computer Sciences, Tel Aviv University, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
    Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer2012In: Cancer Research, ISSN 0008-5472, E-ISSN 1538-7445, Vol. 72, no 22, p. 5712-5720Article in journal (Refereed)
    Abstract [en]

    Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent "go or grow" dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER)(+) versus ER(-) tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer.

  • 32.
    Kankainen, Matti
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Gopalacharyulu, Peddinti
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Holm, Liisa
    Institute of Biotechnology, Department of Biological Sciences, University of Helsinki, Helsinki, Finland.
    Oresic, Matej
    Örebro University, School of Medical Sciences. VTT Technical Research Centre of Finland, Espoo, Finland.
    MPEA--metabolite pathway enrichment analysis2011In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 27, no 13, p. 1878-1879Article in journal (Refereed)
    Abstract [en]

    UNLABELLED: We present metabolite pathway enrichment analysis (MPEA) for the visualization and biological interpretation of metabolite data at the system level. Our tool follows the concept of gene set enrichment analysis (GSEA) and tests whether metabolites involved in some predefined pathway occur towards the top (or bottom) of a ranked query compound list. In particular, MPEA is designed to handle many-to-many relationships that may occur between the query compounds and metabolite annotations. For a demonstration, we analysed metabolite profiles of 14 twin pairs with differing body weights. MPEA found significant pathways from data that had no significant individual query compounds, its results were congruent with those discovered from transcriptomics data and it detected more pathways than the competing metabolic pathway method did.

    AVAILABILITY: The web server and source code of MPEA are available at http://ekhidna.biocenter.helsinki.fi/poxo/mpea/.

  • 33.
    Katajamaa, Mikko
    et al.
    Turku Centre for Biotechnology, Turku, Finland.
    Oresic, Matej
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Data processing for mass spectrometry-based metabolomics2007In: Journal of Chromatography A, ISSN 0021-9673, E-ISSN 1873-3778, Vol. 1158, no 1-2, p. 318-328Article, review/survey (Refereed)
    Abstract [en]

    Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.

  • 34.
    Katajamaa, Mikko
    et al.
    1Turku Centre for Biotechnology, Turku, Finland.
    Oresic, Matej
    2VTT Biotechnology, Espoo, Finland.
    Processing methods for differential analysis of LC/MS profile data2005In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 6, article id 179Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data.

    RESULTS: We present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures.

    CONCLUSION: The software is freely available under the GNU General Public License and it can be obtained from the project web page at: http://mzmine.sourceforge.net/.

  • 35.
    Leskinen, Tuija
    et al.
    Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
    Rinnankoski-Tuikka, Rita
    Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, Finland.
    Rintala, Mirva
    Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
    Seppänen-Laakso, Tuulikki
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Pöllänen, Eija
    Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
    Alen, Markku
    Department of Medical Rehabilitation, Oulu University Hospital, Oulu, Finland.
    Sipilä, Sarianna
    Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
    Kaprio, Jaakko
    Department of Public Health and Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland; National Institute for Health and Welfare, Helsinki, Finland.
    Kovanen, Vuokko
    Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
    Rahkila, Paavo
    Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
    Oresic, Matej
    Örebro University, School of Medical Sciences. VTT Technical Research Centre of Finland, Espoo, Finland.
    Kainulainen, Heikki
    Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, Finland.
    Kujala, Urho M.
    Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
    Differences in muscle and adipose tissue gene expression and cardio-metabolic risk factors in the members of physical activity discordant twin pairs2010In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 5, no 9, article id e12609Article in journal (Refereed)
    Abstract [en]

    High physical activity/aerobic fitness predicts low morbidity and mortality. Our aim was to identify the most up-regulated gene sets related to long-term physical activity vs. inactivity in skeletal muscle and adipose tissues and to obtain further information about their link with cardio-metabolic risk factors. We studied ten same-sex twin pairs (age range 50-74 years) who had been discordant for leisure-time physical activity for 30 years. The examinations included biopsies from m. vastus lateralis and abdominal subcutaneous adipose tissue. RNA was analyzed with the genome-wide Illumina Human WG-6 v3.0 Expression BeadChip. For pathway analysis we used Gene Set Enrichment Analysis utilizing active vs. inactive co-twin gene expression ratios. Our findings showed that among the physically active members of twin pairs, as compared to their inactive co-twins, gene expression in the muscle tissue samples was chronically up-regulated for the central pathways related to energy metabolism, including oxidative phosphorylation, lipid metabolism and supportive metabolic pathways. Up-regulation of these pathways was associated in particular with aerobic fitness and high HDL cholesterol levels. In fat tissue we found physical activity-associated increases in the expression of polyunsaturated fatty acid metabolism and branched-chain amino acid degradation gene sets both of which associated with decreased 'high-risk' ectopic body fat and plasma glucose levels. Consistent with other findings, plasma lipidomics analysis showed up-regulation of the triacylglycerols containing the polyunsaturated fatty acids. Our findings identified skeletal muscle and fat tissue pathways which are associated with the long-term physical activity and reduced cardio-metabolic disease risk, including increased aerobic fitness. In particular, improved skeletal muscle oxidative energy and lipid metabolism as well as changes in adipocyte function and redistribution of body fat are associated with reduced cardio-metabolic risk.

  • 36.
    Lindfors, Erno
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Gopalacharyulu, Peddinti V.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Halperin, Eran
    International Computer Science Institute, Berkeley, California, United States of America.
    Oresic, Matej
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse2009In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, no 10, article id e7323Article in journal (Refereed)
    Abstract [en]

    Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.

  • 37.
    Lindfors, Erno
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland; LifeGlimmer GmbH, Berlin, Germany; Chemistry Building, Wageningen, Netherlands.
    Jouhten, Paula
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oja, Merja
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Rintala, Eija
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oresic, Matej
    Örebro University, School of Medical Sciences. VTT Technical Research Centre of Finland, Espoo, Finland.
    Penttilä, Merja
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae2014In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 8, article id 16Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

    RESULTS: The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

    CONCLUSIONS: The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

  • 38.
    Lindfors, Erno
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Mattila, Jussi
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Gopalacharyulu, Peddinti V.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Pesonen, Antti
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Lötjönen, Jyrki
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Oresic, Matej
    Örebro University, School of Medical Sciences. VTT Technical Research Centre of Finland, Espoo, Finland.
    Heterogeneous biological network visualization system: case study in context of medical image data2012In: Advances in Experimental Medicine and Biology, ISSN 0065-2598, E-ISSN 2214-8019, Vol. 736, p. 95-118Article in journal (Refereed)
    Abstract [en]

    We have developed a system called megNet for integrating and visualizing heterogeneous biological data in order to enable modeling biological phenomena using a systems approach. Herein we describe megNet, including a recently developed user interface for visualizing biological networks in three dimensions and a web user interface for taking input parameters from the user, and an in-house text mining system that utilizes an existing knowledge base. We demonstrate the software with a case study in which we integrate lipidomics data acquired in-house with interaction data from external databases, and then find novel interactions that could possibly explain our previous associations between biological data and medical images. The flexibility of megNet assures that the tool can be applied in diverse applications, from target discovery in medical applications to metabolic engineering in industrial biotechnology.

  • 39.
    Lindroos, Hillevi L
    et al.
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Mira, Alex
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala; División de Microbiología, Universidad Miguel Hernández, Alicante, Spain.
    Repsilber, Dirk
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala; Institute for Medical Biometry and Statistics, University Lübeck, Lübeck, Germany .
    Vinnere, Olga
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Näslund, Kristina
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Dehio, Michaela
    Division of Molecular Microbiology, Biozentrum of the University of Basel, Basel, Switzerland .
    Dehio, Christoph
    Division of Molecular Microbiology, Biozentrum of the University of Basel, Basel, Switzerland .
    Andersson, Siv G E
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Characterization of the genome composition of Bartonella koehlerae by microarray comparative genomic hybridization profiling2005In: Journal of Bacteriology, ISSN 0021-9193, E-ISSN 1098-5530, Vol. 187, no 17, p. 6155-65Article in journal (Refereed)
    Abstract [en]

    Bartonella henselae is present in a wide range of wild and domestic feline hosts and causes cat-scratch disease and bacillary angiomatosis in humans. We have estimated here the gene content of Bartonella koehlerae, a novel species isolated from cats that was recently identified as an agent of human endocarditis. The investigation was accomplished by comparative genomic hybridization (CGH) to a microarray constructed from the sequenced 1.93-Mb genome of B. henselae. Control hybridizations of labeled DNA from the human pathogen Bartonella quintana with a reduced genome of 1.58 Mb were performed to evaluate the accuracy of the array for genes with known levels of sequence divergence. Genome size estimates of B. koehlerae by pulsed-field gel electrophoresis matched that calculated by the CGH, indicating a genome of 1.7 to 1.8 Mb with few unique genes. As in B. quintana, sequences in the prophage and the genomic islands were reported absent in B. koehlerae. In addition, sequence variability was recorded in the chromosome II-like region, where B. koehlerae showed an intermediate retention pattern of both coding and noncoding sequences. Although most of the genes missing in B. koehlerae are also absent from B. quintana, its phylogenetic placement near B. henselae suggests independent deletion events, indicating that host specificity is not solely attributed to genes in the genomic islands. Rather, the results underscore the instability of the genomic islands even within bacterial populations adapted to the same host-vector system, as in the case of B. henselae and B. koehlerae.

  • 40.
    Lindroos, Hillevi
    et al.
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala .
    Vinnere, Olga
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Mira, Alex
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Repsilber, Dirk
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Näslund, Kristina
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Andersson, Siv G E
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala.
    Genome rearrangements, deletions, and amplifications in the natural population of Bartonella henselae2006In: Journal of Bacteriology, ISSN 0021-9193, E-ISSN 1098-5530, Vol. 188, no 21, p. 7426-39Article in journal (Refereed)
    Abstract [en]

    Cats are the natural host for Bartonella henselae, an opportunistic human pathogen and the agent of cat scratch disease. Here, we have analyzed the natural variation in gene content and genome structure of 38 Bartonella henselae strains isolated from cats and humans by comparative genome hybridizations to microarrays and probe hybridizations to pulsed-field gel electrophoresis (PFGE) blots. The variation in gene content was modest and confined to the prophage and the genomic islands, whereas the PFGE analyses indicated extensive rearrangements across the terminus of replication with breakpoints in areas of the genomic islands. We observed no difference in gene content or structure between feline and human strains. Rather, the results suggest multiple sources of human infection from feline B. henselae strains of diverse genotypes. Additionally, the microarray hybridizations revealed DNA amplification in some strains in the so-called chromosome II-like region. The amplified segments were centered at a position corresponding to a putative phage replication initiation site and increased in size with the duration of cultivation. We hypothesize that the variable gene pool in the B. henselae population plays an important role in the establishment of long-term persistent infection in the natural host by promoting antigenic variation and escape from the host immune response.

  • 41.
    Ljungqvist, Olle
    et al.
    Örebro University, School of Medical Sciences. Dept of Surgery, Karolinska Institute and Hospital, Stockholm, Sweden.
    Hedenborg, Gunilla
    Dept of Clinical Chemistry, Karolinska Institute and Hospital, Stockholm, Sweden.
    Jacobsson, Stefan H.
    Dept of Medicine, Karolinska Institute and Hospital, Stockholm, Sweden.
    Lins, Lars Eric
    Dept of Medicine, Karolinska Institute and Hospital, Stockholm, Sweden.
    Samuelsson, Kickan
    Dept of Clinical Chemistry, Karolinska Institute and Hospital, Stockholm, Sweden.
    Tedner, Bo T.
    Dept of Baromedicine, Karolinska Institute and Hospital, Stockholm, Sweden.
    Zetterholm, Ulla Britt
    Dept of Medicine, Karolinska Institute and Hospital, Stockholm, Sweden.
    Whole body impedance measurements reflect total body water changes: A study in hemodialysis patients1990In: International Journal of Clinical Monitoring and Computing, ISSN 0167-9945, E-ISSN 2214-7314, Vol. 7, no 3, p. 163-169Article in journal (Refereed)
    Abstract [en]

    Fluid volume changes during hemodialysis was monitored by continuous whole body impedance measurements. The fluid changes recorded using this method was compared to fluid volume changes measured in plasma water (PV) using125I-albumin, and extracellular volume (ECV) using51Cr-EDTA before and after treatment, and total body water (TBW) changes reflected by continuous bed scale monitoring. Changes in impedance correlated to TBW changes, r=0.80, p<0.001, while correlations to changes in ECV and PV were: r=0.57 and r=0.55, respectively, p<0.05. Alterations in body fluid volumes recorded with whole body impedance is best correlated to total body water changes. The use of continuous whole body impedance monitoring has been shown to offer a simple non-invasive method for recording total body water changes during hemodialysis.

  • 42.
    Maertzdorf, Jeroen
    et al.
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Ota, Martin
    Medical Research Council Laboratories, Banjul, The Gambia.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology Genetics and Biometry, Dummersdorf, Germany.
    Mollenkopf, Hans J
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Weiner, January
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Hill, Philip C
    Medical Research Council Laboratories, Banjul, The Gambia.
    Kaufmann, Stefan H E
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Functional correlations of pathogenesis-driven gene expression signatures in tuberculosis2011In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 6, no 10, article id e26938Article in journal (Refereed)
    Abstract [en]

    Tuberculosis remains a major health threat and its control depends on improved measures of prevention, diagnosis and treatment. Biosignatures can play a significant role in the development of novel intervention measures against TB and blood transcriptional profiling is increasingly exploited for their rational design. Such profiles also reveal fundamental biological mechanisms associated with the pathology of the disease. We have compared whole blood gene expression in TB patients, as well as in healthy infected and uninfected individuals in a cohort in The Gambia, West Africa and validated previously identified signatures showing high similarities of expression profiles among different cohorts. In this study, we applied a unique combination of classical gene expression analysis with pathway and functional association analysis integrated with intra-individual expression correlations. These analyses were employed for identification of new disease-associated gene signatures, identifying a network of Fc gamma receptor 1 signaling with correlating transcriptional activity as hallmark of gene expression in TB. Remarkable similarities to characteristic signatures in the autoimmune disease systemic lupus erythematosus (SLE) were observed. Functional gene clusters of immunoregulatory interactions involving the JAK-STAT pathway; sensing of microbial patterns by Toll-like receptors and IFN-signaling provide detailed insights into the dysregulation of critical immune processes in TB, involving active expression of both pro-inflammatory and immunoregulatory systems. We conclude that transcriptomics (i) provides a robust system for identification and validation of biosignatures for TB and (ii) application of integrated analysis tools yields novel insights into functional networks underlying TB pathogenesis.

  • 43.
    Maertzdorf, Jeroen
    et al.
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Genetics and Biometry, Dummerstorf, Germany.
    Parida, S K
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Stanley, K
    Division of Molecular Biology and Human Genetics, MRC Centre for Molecular and Cellular Biology, Stellenbosch University, Cape Town, South Africa .
    Roberts, T
    Division of Molecular Biology and Human Genetics, MRC Centre for Molecular and Cellular Biology, Stellenbosch University, Cape Town, South Africa .
    Black, G
    Division of Molecular Biology and Human Genetics, MRC Centre for Molecular and Cellular Biology, Stellenbosch University, Cape Town, South Africa .
    Walzl, G
    Division of Molecular Biology and Human Genetics, MRC Centre for Molecular and Cellular Biology, Stellenbosch University, Cape Town, South Africa .
    Kaufmann, S H E
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Human gene expression profiles of susceptibility and resistance in tuberculosis2011In: Genes and Immunity, ISSN 1466-4879, E-ISSN 1476-5470, Vol. 12, no 1, p. 15-22Article in journal (Refereed)
    Abstract [en]

    Tuberculosis (TB) still poses a profound burden on global health, owing to significant morbidity and mortality worldwide. Although a fully functional immune system is essential for the control of Mycobacterium tuberculosis infection, the underlying mechanisms and reasons for failure in part of the infected population remain enigmatic. Here, whole-blood microarray gene expression analyses were performed in TB patients and in latently as well as uninfected healthy controls to define biomarkers predictive of susceptibility and resistance. Fc gamma receptor 1B (FCGRIB)was identified as the most differentially expressed gene, and, in combination with four other markers, produced a high degree of accuracy in discriminating TB patients and latently infected donors. We determined differentially expressed genes unique for active disease and identified profiles that correlated with susceptibility and resistance to TB. Elevated expression of innate immune-related genes in active TB and higher expression of particular gene clusters involved in apoptosis and natural killer cell activity in latently infected donors are likely to be the major distinctive factors determining failure or success in controlling M. tuberculosis infection. The gene expression profiles defined in this study provide valuable clues for better understanding of progression from latent infection to active disease and pave the way for defining predictive correlates of protection in TB.

  • 44.
    Mattila, Ismo
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Seppänen-Laakso, Tuulikki
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Suortti, Tapani
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oresic, Matej
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Application of lipidomics and metabolomics to the study of adipose tissue2008In: Methods in Molecular Biology, ISSN 1064-3745, E-ISSN 1940-6029, Vol. 456, p. 123-130Article in journal (Refereed)
    Abstract [en]

    Role of specific reactive lipids as well as amino acids in control of insulin signalling in adipose tissue is well recognized. Since it is practically impossible to measure the levels of all metabolites in the biological sample simultaneously with a single analytical platform, we utilize multiple platforms to study the lipids and metabolites of relevance to adipose tissue metabolism and insulin signalling. Two screening platforms cover a broad range of lipid molecular species (UPLC/MS based lipidomics platform) as well as organic acids and sterols (GCxGC-TOF platform). A targeted platform for amino acids (UPLC) is also applied.

  • 45.
    Melzer, Nina
    et al.
    FBN Dummerstorf, Dummerstorf, Germany.
    Jakubowski, S
    LKV, Güstrow, Germany.
    Hartwig, S
    LKV, Güstrow, Germany.
    Kesting, U
    LKV, Güstrow, Germany.
    Wolf, S
    LKV, Güstrow, Germany.
    Nürnberg, Gerd
    FBN Dummerstorf, Dummerstorf, Germany.
    Reinsch, Norbert
    FBN Dummerstorf, Dummerstorf, Germany.
    Repsilber, Dirk
    FBN Dummerstorf, Dummerstorf, Germany.
    Design, infrastructure and database structure for a study on predicting milk phenotypes from genome-wide SNP markers and metabolite profiles2010In: Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, German Society for Animal Science , 2010, p. 427-431Conference paper (Refereed)
  • 46.
    Melzer, Nina
    et al.
    Research Unit Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Research Unit Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Hartwig, S
    Landeskontrollverband für Leistungs-und Qualitatsprufung Mecklenburg-Vorpommern e.V. (LKV), Güstrow, Germany.
    Jakubowski, S
    Landeskontrollverband für Leistungs-und Qualitatsprufung Mecklenburg-Vorpommern e.V. (LKV), Güstrow, Germany.
    Kesting, U
    Landeskontrollverband für Leistungs-und Qualitatsprufung Mecklenburg-Vorpommern e.V. (LKV), Güstrow, Germany.
    Willmitzer, Lothar
    Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany .
    Lisec, Jan
    Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany .
    Reinsch, Norbert
    Research Unit Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Research Unit Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Investigating associations between milk metabolite profiles and milk traits of Holstein cows2013In: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198, Vol. 96, no 3, p. 1521-34Article in journal (Refereed)
    Abstract [en]

    In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.

  • 47. Melzer, Nina
    et al.
    Wittenburg, Dörte
    Repsilber, Dirk
    Accounting for a complex genotype-phenotype map in milk phenotypes from genome-wide data2010In: Statistical Computing 2010: Abstracts der 42. Arbeitstagung, 2010, Vol. 5Conference paper (Refereed)
  • 48.
    Melzer, Nina
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Including metabolomic profiles to improve genetic value prediction: an integrated bioinformatics approach using weighted genome-wide marker information2011In: 12th Day of the Doctoal Student: abstracts; 19 May 2011, Dummerstorf / [ed] Seyfert, H.-M., Viereck, G., Dummerstorf, Germany: FBN , 2011, p. 55-58Conference paper (Refereed)
  • 49.
    Melzer, Nina
    et al.
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows2013In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 8, article id e70256Article in journal (Refereed)
    Abstract [en]

    In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).

  • 50.
    Melzer, Nina
    et al.
    Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Investigating a complex genotype-phenotype map for the development of methods to predict genetic values based on genome-wide marker data – a simulation study for the livestock perspective2013In: Archiv für Tierzucht, ISSN 0003-9438, Vol. 56, no 38, p. 380-398Article in journal (Refereed)
    Abstract [en]

    Phenotypic variation can partly be explained by genetic variation, such as variation in single nucleotide polymorphism (SNP) genotypes. Genomic selection methods seek to predict genetic values (breeding values) based on SNP genotypes. To develop and to optimize these methods, simulated data are often used, which follow a rather simple genotype-phenotype map. Is the conventional approach for data simulation in this field an appropriate basis to optimize such methods in view of experimental data? Here, we present an alternative approach, striving to simulate more realistic data based on a genotype-phenotype map which includes a simulated metabolome level. This level was used to simulate genetic values, implicitly including additive and non-additive genetic effects, whereas in a conventional approach additive and dominance effects were explicitly simulated and assembled to genetic values. For both simulation approaches, different scenarios regarding numbers of quantitative trait loci (QTLs) and SNPs were analysed using fastBayesB as prediction method. We observed that our alternative map showed a smaller prediction precision (at least 3.75 %) compared to the conventional approach in all investigated scenarios. The observed degree of linearity is at least 94.12 % of the conventional approach or less. Additionally, we present results for different simulated data and experimental data to allow a comparison on a purely conceptual level. Concluding, simulating a more complex genotype-phenotype map including a molecular level, allows to study processing of variation from the genetic to the phenotype level in more detail and may prepare the ground for modern methods of genomic selection.

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