Evidence map›Paper›PMID 30844279›Full record

ArticleJournal of natural products2019

Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures.

Lindsay K Caesar, Joshua J Kellogg, Olav M Kvalheim, Nadja B Cech

Abstract read
In one paragraph

Article in Journal of natural products, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
6.0field-weighted citation impact, top 3% 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

47 citing papers in PubMed, 95 citations in OpenAlex.

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  12. Modulation ofJournal of natural products · 2023
    Article
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  15. Synergistic Inhibiting Effect of Phytochemicals inMolecules (Basel, Switzerland) · 2023
    Article
  16. Discovering New Natural Products Using Metabolomics-Based Approaches.Advances in experimental medicine and biology · 2023
    Article
  17. Review
  18. Review
  19. Review
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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

4 authors at 2 institutions in 2 countries.

Lindsay K CaesarDepartment of Chemistry & Biochemistry , University of North Carolina Greensboro , Greensboro , North Carolina 27402 , United States.
Joshua J KelloggDepartment of Chemistry & Biochemistry , University of North Carolina Greensboro , Greensboro , North Carolina 27402 , United States.ORCID 0000-0001-8685-0353
Olav M KvalheimDepartment of Chemistry , University of Bergen , 5020 Bergen , Norway.
Nadja B CechDepartment of Chemistry & Biochemistry , University of North Carolina Greensboro , Greensboro , North Carolina 27402 , United States.ORCID 0000-0001-6773-746X
University of North Carolina at Greensboro · USUniversity of Bergen · NO

Funding

Strategies to Investigate Synergy in Botanical MedicinesR01AT006860 · NCCIH · UNIVERSITY OF NORTH CAROLINA GREENSBORO · PI CECH, NADJA B · 2013 to 2017
$1.8M
Predoctoral Training: Innovative Technologies for Natural Products and Integrative Medicine ResearchT32AT008938 · NCCIH · UNIVERSITY OF NORTH CAROLINA GREENSBORO · PI Nadja B Cech, Jonathan Rodi Chekan · 2016 to 2026
$1.6M
NCCIH NIH HHS R01 AT006860NCCIH NIH HHS T32 AT008938
6 · The paper itself

Abstract

Compounds derived from natural sources represent the majority of small-molecule drugs utilized today. Plants, owing to their complex biosynthetic pathways, are poised to synthesize diverse secondary metabolites that selectively target biological macromolecules. Despite the vast chemical landscape of botanicals, drug discovery programs from these sources have diminished due to the costly and time-consuming nature of standard practices and high rates of compound rediscovery. Untargeted metabolomics approaches that integrate biological and chemical data sets potentially enable the prediction of active constituents early in the fractionation process. However, data acquisition and data processing parameters may have major impacts on the success of models produced. Using an inactive botanical mixture spiked with known antimicrobial compounds, untargeted mass spectrometry-based metabolomics data were combined with bioactivity data to produce selectivity ratio models subjected to a variety of data acquisition and data processing parameters. Selectivity ratio models were used to identify active constituents that were intentionally added to the mixture, along with an additional antimicrobial compound, randainal (5), which was masked by the presence of antagonists in the mixture. These studies found that data-processing approaches, particularly data transformation and model simplification tools using a variance cutoff, had significant impacts on the models produced, either masking or enhancing the ability to detect active constituents in samples. The current study highlights the importance of the data processing step for obtaining reliable information from metabolomics models and demonstrates the strengths and limitations of selectivity ratio analysis to comprehensively assess complex botanical mixtures.

Indexed as

MetabolomicsAngelicaAnti-Infective AgentsBiological ProductsChromatography, LiquidComplex MixturesMass SpectrometryMicrobial Sensitivity TestsPlant RootsAnti-Infective AgentsBiological ProductsComplex Mixtures

Identifiers

PMID30844279
PMCPMC6837904
OpenAlexW2920984608

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.