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Longitudinal expression profiling of CD4+ and CD8+ cells in patients with active to quiescent giant cell arteritis

Overview of attention for article published in BMC Medical Genomics, July 2018
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Title
Longitudinal expression profiling of CD4+ and CD8+ cells in patients with active to quiescent giant cell arteritis
Published in
BMC Medical Genomics, July 2018
DOI 10.1186/s12920-018-0376-4
Pubmed ID
Authors

Elisabeth De Smit, Samuel W. Lukowski, Lisa Anderson, Anne Senabouth, Kaisar Dauyey, Sharon Song, Bruce Wyse, Lawrie Wheeler, Christine Y. Chen, Khoa Cao, Amy Wong Ten Yuen, Neil Shuey, Linda Clarke, Isabel Lopez Sanchez, Sandy S. C. Hung, Alice Pébay, David A. Mackey, Matthew A. Brown, Alex W. Hewitt, Joseph E. Powell

Abstract

Giant cell arteritis (GCA) is the most common form of vasculitis affecting elderly people. It is one of the few true ophthalmic emergencies but symptoms and signs are variable thereby making it a challenging disease to diagnose. A temporal artery biopsy is the gold standard to confirm GCA, but there are currently no specific biochemical markers to aid diagnosis. We aimed to identify a less invasive method to confirm the diagnosis of GCA, as well as to ascertain clinically relevant predictive biomarkers by studying the transcriptome of purified peripheral CD4+ and CD8+ T lymphocytes in patients with GCA. We recruited 16 patients with histological evidence of GCA at the Royal Victorian Eye and Ear Hospital, Melbourne, Australia, and aimed to collect blood samples at six time points: acute phase, 2-3 weeks, 6-8 weeks, 3 months, 6 months and 12 months after clinical diagnosis. CD4+ and CD8+ T-cells were positively selected at each time point through magnetic-assisted cell sorting. RNA was extracted from all 195 collected samples for subsequent RNA sequencing. The expression profiles of patients were compared to those of 16 age-matched controls. Over the 12-month study period, polynomial modelling analyses identified 179 and 4 statistically significant transcripts with altered expression profiles (FDR < 0.05) between cases and controls in CD4+ and CD8+ populations, respectively. In CD8+ cells, two transcripts remained differentially expressed after 12 months; SGTB, associated with neuronal apoptosis, and FCGR3A, associatied with Takayasu arteritis. We detected genes that correlate with both symptoms and biochemical markers used for predicting long-term prognosis. 15 genes were shared across 3 phenotypes in CD4 and 16 across CD8 cells. In CD8, IL32 was common to 5 phenotypes including Polymyalgia Rheumatica, bilateral blindness and death within 12 months. This is the first longitudinal gene expression study undertaken to identify robust transcriptomic biomarkers of GCA. Our results show cell type-specific transcript expression profiles, novel gene-phenotype associations, and uncover important biological pathways for this disease. In the acute phase, the gene-phenotype relationships we have identified could provide insight to potential disease severity and as such guide in initiating appropriate patient management.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 15%
Student > Master 4 15%
Professor 2 7%
Student > Ph. D. Student 2 7%
Student > Doctoral Student 2 7%
Other 3 11%
Unknown 10 37%
Readers by discipline Count As %
Medicine and Dentistry 8 30%
Biochemistry, Genetics and Molecular Biology 4 15%
Immunology and Microbiology 3 11%
Social Sciences 1 4%
Nursing and Health Professions 1 4%
Other 0 0%
Unknown 10 37%
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 25 July 2018.
All research outputs
#15,678,105
of 23,298,349 outputs
Outputs from BMC Medical Genomics
#692
of 1,253 outputs
Outputs of similar age
#210,234
of 330,263 outputs
Outputs of similar age from BMC Medical Genomics
#8
of 14 outputs
Altmetric has tracked 23,298,349 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 1,253 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 35th percentile – i.e., 35% 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 330,263 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 14 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 50% of its contemporaries.