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Immunological profiling of patients with ulcerative colitis leads to identification of two inflammatory conditions and CD1a as a disease marker

Overview of attention for article published in Journal of Translational Medicine, November 2016
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Title
Immunological profiling of patients with ulcerative colitis leads to identification of two inflammatory conditions and CD1a as a disease marker
Published in
Journal of Translational Medicine, November 2016
DOI 10.1186/s12967-016-1048-9
Pubmed ID
Authors

M. Föhlinger, P. Palamides, U. Mansmann, F. Beigel, M. Siebeck, R. Gropp

Abstract

Conventional approaches to understand mechanisms underlying the development of pathological manifestations in ulcerative colitis (UC) mostly rely on identification of certain cell types and cytokines followed by verification of their roles in vitro and in vivo. In light of the highly dynamic processes in UC, requiring the cross talk of immune cells, epithelial-, endothelial-, muscle cells and fibrocytes, this approach might neglect temporal and spatial connectivity of individually differing inflammatory responses. We undertook a more holistic approach whereby we designed a flow cytometric analysis- and ELISA panel and determined the immunological profiles of UC patients in comparison to Non UC donors. This panel consisted of B-cells, T-cells, macrophages, monocytes, NK- and NK T-cells and subtypes thereof, the cytokines TGFß1 and HGF, the chemokine TARC and periostin. Blood was collected from 41 UC patients and 30 non-UC donors. Isolated PBMC were subjected to flow cytometric analysis and sera were analyzed by ELISA. Data were analysed by cluster- and correlation analysis. To corroborate that the identified cells reflected the inflammatory condition in the colon of UC patients, leucocytes were isolated from colons of UC patients and subjected to the same flow cytometric analysis. Immunological profiling followed by cluster- and correlation analysis led to the identification of two inflammatory conditions: An 'acute' condition characterized by adaptive immune cells as plasma cells,  TSLPR expressing CD11b+ macrophages, CD64 and CCR2 expressing CD14+ monocytes, HGF and TARC and a 'remodeling' condition signified by NK T-cells and TLSPR expressing CD14+ monocytes, TGFß1 and periostin. ROC analysis identified TARC and TGFß1 as biological markers with high potential to discriminate between these two conditions (Δ = -6687.72 ng/ml; p = 1E-04; AUC = 0.87). In addition, CD1a+ CD11b+ macrophages (Δ = 17.73% CD1a+ CD11b+; p = 5E-04; AUC = 0.86) and CD1a+ CD14+ monocytes (Δ = 20.35; p = 0.02, AUC = 0.75) were identified as markers with high potential to discriminate between UC and Non UC donors. CD1a+ CD11b+ macrophages and NK T-cells were found to be significantly increased in inflamed colons of UC patients as compared to non-UC control samples (p = 0.02). Immunological profiling of UC patients might improve our understanding of the pathology underlying individual manifestations and phases of the disease. This might lead to the development of novel diagnostics and therapeutic interventions adapted to individual needs and different phases of the disease. In addition, it might result in stratification of patients for clinical trials.

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The data shown below were collected from the profile of 1 X user 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 16%
Student > Master 6 16%
Student > Ph. D. Student 5 13%
Student > Bachelor 3 8%
Professor 3 8%
Other 6 16%
Unknown 9 24%
Readers by discipline Count As %
Immunology and Microbiology 7 18%
Medicine and Dentistry 6 16%
Agricultural and Biological Sciences 5 13%
Biochemistry, Genetics and Molecular Biology 4 11%
Computer Science 2 5%
Other 3 8%
Unknown 11 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 November 2016.
All research outputs
#15,392,529
of 22,899,952 outputs
Outputs from Journal of Translational Medicine
#2,240
of 4,010 outputs
Outputs of similar age
#195,604
of 311,569 outputs
Outputs of similar age from Journal of Translational Medicine
#39
of 68 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,010 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 31st percentile – i.e., 31% 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 311,569 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.