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Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis

Overview of attention for article published in Journal of Translational Medicine, September 2018
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Title
Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis
Published in
Journal of Translational Medicine, September 2018
DOI 10.1186/s12967-018-1625-1
Pubmed ID
Authors

Abdellah Tebani, Lenaig Abily-Donval, Isabelle Schmitz-Afonso, Bénédicte Héron, Monique Piraud, Jérôme Ausseil, Farid Zerimech, Bruno Gonzalez, Stéphane Marret, Carlos Afonso, Soumeya Bekri

Abstract

Metabolomics represent a valuable tool to recover biological information using body fluids and may help to characterize pathophysiological mechanisms of the studied disease. This approach has not been widely used to explore inherited metabolic diseases. This study investigates mucopolysaccharidosis type III (MPS III). A thorough and holistic understanding of metabolic remodeling in MPS III may allow the development, improvement and personalization of patient care. We applied both targeted and untargeted metabolomics to urine samples obtained from a French cohort of 49 patients, consisting of 13 MPS IIIA, 16 MPS IIIB, 13 MPS IIIC, and 7 MPS IIID, along with 66 controls. The analytical strategy is based on ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Twenty-four amino acids have been assessed using tandem mass spectrometry combined with liquid chromatography. Multivariate data modeling has been used for discriminant metabolite selection. Pathway analysis has been performed to retrieve metabolic pathways impairments. Data analysis revealed distinct biochemical profiles. These metabolic patterns, particularly those related to the amino acid metabolisms, allowed the different studied groups to be distinguished. Pathway analysis unveiled major amino acid pathways impairments in MPS III mainly arginine-proline metabolism and urea cycle metabolism. This represents one of the first metabolomics-based investigations of MPS III. These results may shed light on MPS III pathophysiology and could help to set more targeted studies to infer the biomarkers of the affected pathways, which is crucial for rare conditions such as MPS III.

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The data shown below were collected from the profiles of 4 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 21%
Student > Master 7 17%
Student > Bachelor 6 14%
Student > Ph. D. Student 5 12%
Student > Doctoral Student 3 7%
Other 6 14%
Unknown 6 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 24%
Agricultural and Biological Sciences 6 14%
Chemistry 5 12%
Medicine and Dentistry 3 7%
Unspecified 3 7%
Other 10 24%
Unknown 5 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 September 2018.
All research outputs
#15,018,183
of 23,102,082 outputs
Outputs from Journal of Translational Medicine
#2,010
of 4,055 outputs
Outputs of similar age
#200,539
of 335,392 outputs
Outputs of similar age from Journal of Translational Medicine
#27
of 79 outputs
Altmetric has tracked 23,102,082 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,055 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 335,392 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.