Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2015 May;16(5):439-60.
doi: 10.1111/tra.12280.

Targeting of the hydrophobic metabolome by pathogens

Affiliations
Review

Targeting of the hydrophobic metabolome by pathogens

J Bernd Helms et al. Traffic. 2015 May.

Abstract

The hydrophobic molecules of the metabolome - also named the lipidome - constitute a major part of the entire metabolome. Novel technologies show the existence of a staggering number of individual lipid species, the biological functions of which are, with the exception of only a few lipid species, unknown. Much can be learned from pathogens that have evolved to take advantage of the complexity of the lipidome to escape the immune system of the host organism and to allow their survival and replication. Different types of pathogens target different lipids as shown in interaction maps, allowing visualization of differences between different types of pathogens. Bacterial and viral pathogens target predominantly structural and signaling lipids to alter the cellular phenotype of the host cell. Fungal and parasitic pathogens have complex lipidomes themselves and target predominantly the release of polyunsaturated fatty acids from the host cell lipidome, resulting in the generation of eicosanoids by either the host cell or the pathogen. Thus, whereas viruses and bacteria induce predominantly alterations in lipid metabolites at the host cell level, eukaryotic pathogens focus on interference with lipid metabolites affecting systemic inflammatory reactions that are part of the immune system. A better understanding of the interplay between host-pathogen interactions will not only help elucidate the fundamental role of lipid species in cellular physiology, but will also aid in the generation of novel therapeutic drugs.

Keywords: bacteria; fungi; host-pathogen interactions; lipidome; lipids; metabolome; parasites; viruses.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Contribution of lipid metabolic pathways to the KEGG map of metabolism. The metabolic map was constructed based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (http://www.kegg.jp/) 245. The graphical presentation is based on the Genome‐Linked Application for Metabolic Maps (http://glamm.lbl.gov) 246 with a minor modification that allows visualization of elongation and desaturation of palmitic‐ to stearic‐ and oleic acid, respectively. Lipid classification into eight main categories (A–H) is according to the 2005 convention on lipid nomenclature 19: A, fatty acids; B, glycerolipids; C, glycerophospholipids; D, sphingolipids; E, sterols; F, prenol lipids; G, saccharolipids. Polyketides (lipid category H) are not commonly found in mammalian hosts and are not depicted. Saccharolipids (lipid category G) are shown as a dotted line and not discussed in this review as they are not constituents of the mammalian lipidome. In this graphical pathway representation, cholesterol esters, lyso‐phospholipids and bis(monoacylglycero)phosphate (BMP) species are lacking.
Figure 2
Figure 2
The involvement of lipids in host–pathogen interactions. Heat maps were generated for different types of pathogens (panels A–D) based on the weighted involvements of lipids in host–pathogen interactions (the list of pathogens and their lipid targets and weight factor is described in Table S1, Supporting Information). Heat maps show the frequency of involvements of specific host cell lipid (sub)classes (e.g. phosphatidylserine) and/or species (e.g. cholesterol) for viruses (A), bacteria (B), fungi (C), and parasites (D). Increased coloring indicates increased frequency. The heat maps were constructed with an algorithm using the R‐package for spatial statistics (spatstat) 247.

Similar articles

Cited by

References

    1. Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics – the apogee of the omics trilogy. Nat Rev Mol Cell Biol 2012;13:263–269. - PMC - PubMed
    1. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, et al. HMDB 3.0 – The Human Metabolome Database in 2013. Nucleic Acids Res 2013;41:D801–D807. - PMC - PubMed
    1. Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G. METLIN: a metabolite mass spectral database. Ther Drug Monit 2005;27:747–751. - PubMed
    1. Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 2012;30:826–828. - PMC - PubMed
    1. Fenn JB, Mann M, Meng CK, Wong SF, Whitehouse CM. Electrospray ionization for mass spectrometry of large biomolecules. Science 1989;246:64–71. - PubMed

Publication types

MeSH terms

LinkOut - more resources