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Identification of Discriminating Metabolic Pathways and Metabolites in Human PBMCs Stimulated by Various Pathogenic Agents

Overview of attention for article published in Frontiers in Physiology, February 2018
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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7 X users
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1 Google+ user

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Title
Identification of Discriminating Metabolic Pathways and Metabolites in Human PBMCs Stimulated by Various Pathogenic Agents
Published in
Frontiers in Physiology, February 2018
DOI 10.3389/fphys.2018.00139
Pubmed ID
Authors

Xiang Zhang, Adil Mardinoglu, Leo A. B. Joosten, Jan A. Kuivenhoven, Yang Li, Mihai G. Netea, Albert K. Groen

Abstract

Immunity and cellular metabolism are tightly interconnected but it is not clear whether different pathogens elicit specific metabolic responses. To address this issue, we studied differential metabolic regulation in peripheral blood mononuclear cells (PBMCs) of healthy volunteers challenged byCandida albicans, Borrelia burgdorferi, lipopolysaccharide, andMycobacterium tuberculosis in vitro. By integrating gene expression data of stimulated PBMCs of healthy individuals with the KEGG pathways, we identified both common and pathogen-specific regulated pathways depending on the time of incubation. At 4 h of incubation, pathogenic agents inhibited expression of genes involved in both the glycolysis and oxidative phosphorylation pathways. In contrast, at 24 h of incubation, particularly glycolysis was enhanced while genes involved in oxidative phosphorylation remained unaltered in the PBMCs. In general, differential gene expression was less pronounced at 4 h compared to 24 h of incubation. KEGG pathway analysis allowed differentiation between effects induced byCandidaand bacterial stimuli. Application of genome-scale metabolic model further generated aCandida-specific set of 103 reporter metabolites (e.g., desmosterol) that might serve as biomarkers discriminatingCandida-stimulated PBMCs from bacteria-stimuated PBMCs. Our analysis also identified a set of 49 metabolites that allowed discrimination between the effects ofBorrelia burgdorferi, lipopolysaccharide andMycobacterium tuberculosis. We conclude that analysis of pathogen-induced effects on PBMCs by a combination of KEGG pathways and genome-scale metabolic model provides deep insight in the metabolic changes coupled to host defense.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 28%
Student > Master 3 10%
Professor 2 7%
Student > Doctoral Student 2 7%
Professor > Associate Professor 2 7%
Other 4 14%
Unknown 8 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 17%
Immunology and Microbiology 5 17%
Biochemistry, Genetics and Molecular Biology 3 10%
Psychology 2 7%
Medicine and Dentistry 2 7%
Other 2 7%
Unknown 10 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 April 2018.
All research outputs
#6,932,312
of 25,088,711 outputs
Outputs from Frontiers in Physiology
#3,225
of 15,396 outputs
Outputs of similar age
#112,366
of 335,728 outputs
Outputs of similar age from Frontiers in Physiology
#89
of 377 outputs
Altmetric has tracked 25,088,711 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 15,396 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 335,728 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 377 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.