Go to JCI Insight
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact
  • Clinical Research and Public Health
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Gastroenterology
    • Immunology
    • Metabolism
    • Nephrology
    • Neuroscience
    • Oncology
    • Pulmonology
    • Vascular biology
    • All ...
  • Videos
    • Conversations with Giants in Medicine
    • Video Abstracts
  • Reviews
    • View all reviews ...
    • Complement Biology and Therapeutics (May 2025)
    • Evolving insights into MASLD and MASH pathogenesis and treatment (Apr 2025)
    • Microbiome in Health and Disease (Feb 2025)
    • Substance Use Disorders (Oct 2024)
    • Clonal Hematopoiesis (Oct 2024)
    • Sex Differences in Medicine (Sep 2024)
    • Vascular Malformations (Apr 2024)
    • View all review series ...
  • Viewpoint
  • Collections
    • In-Press Preview
    • Clinical Research and Public Health
    • Research Letters
    • Letters to the Editor
    • Editorials
    • Commentaries
    • Editor's notes
    • Reviews
    • Viewpoints
    • 100th anniversary
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • Reviews
  • Review series
  • Conversations with Giants in Medicine
  • Video Abstracts
  • In-Press Preview
  • Clinical Research and Public Health
  • Research Letters
  • Letters to the Editor
  • Editorials
  • Commentaries
  • Editor's notes
  • Reviews
  • Viewpoints
  • 100th anniversary
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact

Usage Information

Identification of autoantibody clusters that best predict lupus disease activity using glomerular proteome arrays
Quan Li Zhen, … , Chaim Putterman, Chandra Mohan
Quan Li Zhen, … , Chaim Putterman, Chandra Mohan
Published December 1, 2005
Citation Information: J Clin Invest. 2005;115(12):3428-3439. https://doi.org/10.1172/JCI23587.
View: Text | PDF | Corrigendum
Research Article Immunology Article has an altmetric score of 6

Identification of autoantibody clusters that best predict lupus disease activity using glomerular proteome arrays

  • Text
  • PDF
Abstract

Nephrophilic autoantibodies dominate the seroprofile in lupus, but their fine specificities remain ill defined. We constructed a multiplexed proteome microarray bearing about 30 antigens known to be expressed in the glomerular milieu and used it to study serum autoantibodies in lupus. Compared with normal serum, serum from B6.Sle1.lpr lupus mice (C57BL/6 mice homozygous for the NZM2410/NZW allele of Sle1 as well as the FASlpr defect) exhibited high levels of IgG and IgM antiglomerular as well as anti–double-stranded DNA/chromatin Abs and variable levels of Abs to α-actinin, aggrecan, collagen, entactin, fibrinogen, hemocyanin, heparan sulphate, laminin, myosin, proteoglycans, and histones. The use of these glomerular proteome arrays also revealed 5 distinct clusters of IgG autoreactivity in the sera of lupus patients. Whereas 2 of these IgG reactivity clusters (DNA/chromatin/glomeruli and laminin/myosin/Matrigel/vimentin/heparan sulphate) showed association with disease activity, the other 3 reactivity clusters (histones, vitronectin/collagen/chondroitin sulphate, and entactin/fibrinogen/hyaluronic acid) did not. Human lupus sera also displayed 2 distinct IgM autoantibody clusters, one reactive to DNA and the other apparently polyreactive. Interestingly, the presence of IgM polyreactivity in patient sera was associated with reduced disease severity. Hence, the glomerular proteome array promises to be a powerful analytical tool for uncovering novel autoantibody disease associations and for distinguishing patients at high risk for end-organ disease.

Authors

Quan Li Zhen, Chun Xie, Tianfu Wu, Meggan Mackay, Cynthia Aranow, Chaim Putterman, Chandra Mohan

×

Usage data is cumulative from May 2024 through May 2025.

Usage JCI PMC
Text version 752 60
PDF 135 26
Figure 346 13
Table 55 0
Supplemental data 53 3
Citation downloads 71 0
Totals 1,412 102
Total Views 1,514
(Click and drag on plot area to zoom in. Click legend items above to toggle)

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

Advertisement

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

Sign up for email alerts

Referenced in 7 patents
81 readers on Mendeley
1 readers on CiteULike
See more details