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Record W4409949513 · doi:10.1002/art.43200

Aberrant Glycosylation of <scp>IgG</scp> in Children With Active Lupus Nephritis Alters Podocyte Metabolism and Causes Podocyte Injury

2025· article· en· W4409949513 on OpenAlexaff
Rhea Bhargava, Rohit Upadhyay, Prasad V. G. Katakam, Scott E. Wenderfer, Jing Chen, Hua He, Richard D. Cummings, Maria Tsokos, George C. Tsokos

Bibliographic record

VenueArthritis & Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical Sciences
KeywordsPodocyteLupus nephritisNephritisGlycosylationImmunologyCell biologyBiologyMedicineProteinuriaGeneticsKidneyInternal medicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Podocytes are integral to the maintenance of the glomerular filtration barrier. Their injury results in proteinuria and disease progression in lupus nephritis (LN). Aberrant IgG glycosylation drives podocyte injury in LN and leads to cytoskeletal rearrangement, motility changes, and decreased nephrin production. Based on these findings, we hypothesized that IgG glycosylation patterns differentiate systemic lupus erythematosus (SLE) with and without LN and that this aberrant glycosylation reprograms podocyte metabolism. METHODS: IgG was isolated from 40 pediatric SLE and from 7 healthy control samples. N-glycan analysis was performed using mass spectrometry. IgG deglycosylation was performed through enzymatic treatment by Peptide N-Glycosidase F for functional studies in podocytes. Untargeted metabolomics was performed in cultured podocytes after exposure to healthy IgG, LN-derived IgG, or deglycosylated LN-IgG and analyzed by metabolite set enrichment analysis. Digital droplet polymerase chain reaction was used to evaluate urine cells and podocytes in culture for pyruvate kinase expression. RESULTS: The glycosylation pattern of IgG from children with LN was different from that in children with SLE without kidney involvement. Successful treatment led to normalization of IgG glycosylation. Cultured podocytes treated with LN-derived IgG had a lower rate of glycolysis compared to podocytes incubated with deglycosylated LN-IgG or IgG from healthy volunteers. Untargeted metabolomics of podocytes revealed glycolysis as the most enriched pathway in LN and identified five key metabolites (pyruvic acid, phosphoenolpyruvic acid, 2-phosoglycerate, 3 phosphoglycerate, and fructose 1,6 bisphosphate) in which their levels significantly differed among podocytes exposed to LN-derived IgG (LN-IgG) compared to healthy IgG and deglycosylated LN-IgG. This analysis also revealed clustering around a rate limiting step of glycolysis catalyzed by PKM (Pyruvate Kinase M). Urine analyses revealed elevated pyruvic acid and greater expression of pyruvate kinase in podocytes shed in urine in patients with LN compared to levels in patients with SLE without kidney involvement. Podocytes in culture had elevated PKM levels when exposed to LN-IgG compared to IgG from patients with nonrenal SLE and LN in remission. CONCLUSION: Aberrant IgG glycosylation develops in children with LN and adversely alters podocyte metabolism, rendering these cells vulnerable to injury. Successful treatment reverses IgG glycosylation to patterns comparable to those in patients with nonrenal SLE. These data lay a strong foundation for larger translational studies evaluating the potential of IgG glycosylation as a predictive and pharmacodynamic biomarker for LN. This work also supports a need for the development of approaches to control the aberrant glycosylation of self-targeting IgG in patients with LN as a mechanism to minimize podocytopathy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.235
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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