↓ Skip to main content

Tracking the Antibody Immunome in Type 1 Diabetes Using Protein Arrays

Overview of attention for article published in Journal of Proteome Research, October 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
3 X users
patent
4 patents

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
29 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Tracking the Antibody Immunome in Type 1 Diabetes Using Protein Arrays
Published in
Journal of Proteome Research, October 2016
DOI 10.1021/acs.jproteome.6b00354
Pubmed ID
Authors

Xiaofang Bian, Clive Wasserfall, Garrick Wallstrom, Jie Wang, Haoyu Wang, Kristi Barker, Desmond Schatz, Mark Atkinson, Ji Qiu, Joshua LaBaer

Abstract

We performed an unbiased proteome-scale profiling of humoral autoimmunity in recent-onset type 1 diabetes (T1D) patients and non-diabetic controls against ~10,000 human proteins using a Nucleic Acid Programmable Protein Array (NAPPA) platform, complemented by a knowledge-based selection of proteins from genes enriched in human pancreas. Although the global response was similar between cases and controls, we identified and then validated six specific novel T1D-associated autoantibodies (AAbs) with sensitivities that ranged from 16% to 27% at 95% specificity. These included AAbs against PTPRN2, MLH1, MTIF3, PPIL2, NUP50 (from NAPPA screening) and QRFPR (by targeted ELISA). Immunohistochemistry demonstrated that NUP50 protein behaved differently in islet cells, where it stained both nucleus and cytoplasm, compared with only nuclear staining in exocrine pancreas. Conversely, PPIL2 staining was absent in islet cells, despite its presence in exocrine cells. The combination of anti-PTPRN2, -MLH1, -PPIL2 and -QRFPR had an AUC of 0.74 and 37.5% sensitivity at 95% specificity. These data indicates that these markers behave independently and support the use of unbiased screening to find biomarkers, because the majority was not predicted based on predicted abundance. Our study enriches the knowledge of the "autoantibody-ome" in unprecedented breadth and width.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 %
South Africa 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 38%
Researcher 7 24%
Student > Master 2 7%
Student > Bachelor 2 7%
Lecturer > Senior Lecturer 1 3%
Other 3 10%
Unknown 3 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 31%
Agricultural and Biological Sciences 7 24%
Medicine and Dentistry 3 10%
Linguistics 1 3%
Nursing and Health Professions 1 3%
Other 4 14%
Unknown 4 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 09 August 2022.
All research outputs
#4,233,281
of 24,229,740 outputs
Outputs from Journal of Proteome Research
#1,232
of 6,241 outputs
Outputs of similar age
#65,700
of 320,179 outputs
Outputs of similar age from Journal of Proteome Research
#25
of 131 outputs
Altmetric has tracked 24,229,740 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,241 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done well, scoring higher than 80% 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 320,179 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.