Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data

被引:9
|
作者
Tsouka, Sofia [1 ]
Masoodi, Mojgan [1 ]
机构
[1] Bern Univ Hosp, Inselspital, Inst Clin Chem, CH-3010 Bern, Switzerland
基金
瑞士国家科学基金会;
关键词
metabolomics; metabolism; pathway analysis; over-representation analysis; network topology; NETWORKS; PHENOTYPES; CENTRALITY; GENES; WORLD;
D O I
10.3390/biom13020244
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions (e.g., from microbiota) and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Global metabolic profiling (metabonomics/metabolomics) using dried blood spots: advantages and pitfalls
    Wilson, Ian
    BIOANALYSIS, 2011, 3 (20) : 2255 - 2257
  • [2] Comprehensive investigation of pathway enrichment methods for functional interpretation of LC -MS global metabolomics data
    Lu, Yao
    Pang, Zhiqiang
    Xia, Jianguo
    BRIEFINGS IN BIOINFORMATICS, 2023, 24 (01)
  • [3] Challenges in clinical interpretation of next-generation sequencing data: Advantages and Pitfalls
    Karakoyun, Hilal Keskin
    Sayar, Ceyhan
    Yararbas, Kanay
    RESULTS IN ENGINEERING, 2023, 20
  • [4] PITFALLS IN DATA ANALYSIS AND INTERPRETATION - A REPLY TO ROSENTHAL
    BARBER, TX
    SILVER, MJ
    PSYCHOLOGICAL BULLETIN, 1968, 70 (6P2) : 48 - &
  • [5] Sphingolipid Metabolic Pathway Impacts Thiazide Diuretics Blood Pressure Response: Insights From Genomics, Metabolomics, and Lipidomics
    Shahin, Mohamed H.
    Gong, Yan
    Frye, Reginald F.
    Rotroff, Daniel M.
    Beitelshees, Amber L.
    Baillie, Rebecca A.
    Chapman, Arlene B.
    Gums, John G.
    Turner, Stephen T.
    Boerwinkle, Eric
    Motsinger-Reif, Alison
    Fiehn, Oliver
    Cooper-DeHoff, Rhonda M.
    Han, Xianlin
    Kaddurah-Daouk, Rima
    Johnson, Julie A.
    JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2018, 7 (01):
  • [6] Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation
    Amara, Adam
    Frainay, Clement
    Jourdan, Fabien
    Naake, Thomas
    Neumann, Steffen
    Novoa-del-Toro, Elva Maria
    Salek, Reza M.
    Salzer, Liesa
    Scharfenberg, Sarah
    Witting, Michael
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2022, 9
  • [7] Analysis of plasma metabolic profiling and evaluation of the effect of the intake of Angelica keiskei using metabolomics and lipidomics
    Oh, Hyun-A
    Lee, Hyunbeom
    Park, Soo-yeon
    Lim, Yeni
    Kwon, Oran
    Kim, Ji Yeon
    Kim, Donghak
    Jung, Byung Hwa
    JOURNAL OF ETHNOPHARMACOLOGY, 2019, 243
  • [8] Elementary Flux Modes Analysis of Functional Domain Networks Allows a Better Metabolic Pathway Interpretation
    Peres, Sabine
    Felicori, Liza
    Molina, Franck
    PLOS ONE, 2013, 8 (10):
  • [9] Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis
    Volkova, Svetlana
    Matos, Marta R. A.
    Mattanovich, Matthias
    de Mas, Igor Marin
    METABOLITES, 2020, 10 (08) : 1 - 27
  • [10] Multiorigination of Chromatographic Peaks in Derivatized GC/MS Metabolomics: A Confounder That Influences Metabolic Pathway Interpretation
    Xu, Fengguo
    Zou, Li
    Ong, Choon Nam
    JOURNAL OF PROTEOME RESEARCH, 2009, 8 (12) : 5657 - 5665