↓ 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

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
3 tweeters
patent
1 patent

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
13 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.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
South Africa 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Researcher 3 23%
Other 2 15%
Student > Master 2 15%
Student > Bachelor 1 8%
Other 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 46%
Biochemistry, Genetics and Molecular Biology 4 31%
Unspecified 2 15%
Computer Science 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 14 August 2018.
All research outputs
#3,628,525
of 13,366,062 outputs
Outputs from Journal of Proteome Research
#1,124
of 4,495 outputs
Outputs of similar age
#91,651
of 288,741 outputs
Outputs of similar age from Journal of Proteome Research
#31
of 108 outputs
Altmetric has tracked 13,366,062 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 4,495 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 74% 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 288,741 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 67% of its contemporaries.
We're also able to compare this research output to 108 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 71% of its contemporaries.