Evidence map›Paper›PMID 26393792›Full record

ArticlePLoS computational biology2015

The LUX Score: A Metric for Lipidome Homology.

Chakravarthy Marella, Andrew E Torda, Dominik Schwudke

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.6field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Insect Lipidomics: Advances, Applications, and Physiological Insights.Advances in experimental medicine and biology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Chakravarthy MarellaDivision of Bioanalytical Chemistry, Research Center Borstel, Leibniz-Center for Medicine and Biosciences, Borstel, SH, Germany; National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, KA, India.
Andrew E TordaCentre for Bioinformatics, University of Hamburg, Hamburg, Germany.
Dominik SchwudkeDivision of Bioanalytical Chemistry, Research Center Borstel, Leibniz-Center for Medicine and Biosciences, Borstel, SH, Germany; National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, KA, India; Airway Research Center North, German Center for Lung Research, Grosshansdorf, SH, Germany; German Center for Infection Research, TTU-Tb, Borstel, SH, Germany.
German Center for Infection Research · DEResearch Center Borstel - Leibniz Lung Center · DEUniversität Hamburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A lipidome is the set of lipids in a given organism, cell or cell compartment and this set reflects the organism's synthetic pathways and interactions with its environment. Recently, lipidomes of biological model organisms and cell lines were published and the number of functional studies of lipids is increasing. In this study we propose a homology metric that can quantify systematic differences in the composition of a lipidome. Algorithms were developed to 1. consistently convert lipids structure into SMILES, 2. determine structural similarity between molecular species and 3. describe a lipidome in a chemical space model. We tested lipid structure conversion and structure similarity metrics, in detail, using sets of isomeric ceramide molecules and chemically related phosphatidylinositols. Template-based SMILES showed the best properties for representing lipid-specific structural diversity. We also show that sequence analysis algorithms are best suited to calculate distances between such template-based SMILES and we adjudged the Levenshtein distance as best choice for quantifying structural changes. When all lipid molecules of the LIPIDMAPS structure database were mapped in chemical space, they automatically formed clusters corresponding to conventional chemical families. Accordingly, we mapped a pair of lipidomes into the same chemical space and determined the degree of overlap by calculating the Hausdorff distance. We named this metric the 'Lipidome jUXtaposition (LUX) score'. First, we tested this approach for estimating the lipidome similarity on four yeast strains with known genetic alteration in fatty acid synthesis. We show that the LUX score reflects the genetic relationship and growth temperature better than conventional methods although the score is based solely on lipid structures. Next, we applied this metric to high-throughput data of larval tissue lipidomes of Drosophila. This showed that the LUX score is sufficient to cluster tissues and determine the impact of nutritional changes in an unbiased manner, despite the limited information on the underlying structural diversity of each lipidome. This study is the first effort to define a lipidome homology metric based on structures that will enrich functional association of lipids in a similar manner to measures used in genetics. Finally, we discuss the significance of the LUX score to perform comparative lipidome studies across species borders.

Indexed as

Models, BiologicalModels, MolecularAlgorithmsAnimalsComputational BiologyDrosophilaIsomerismLipidsOrgan SpecificityLipids

Identifiers

PMID26393792
PMCPMC4578897
OpenAlexW2949844824

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.