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Record W4403831483 · doi:10.1681/asn.2024p4xmck6v

IL-32 Is a Lipid Droplet-Associated Mediator of Tubular Injury in Diabetic Kidney Disease

2024· article· en· W4403831483 on OpenAlexaff
Hyunjae Chung, Sarthak Sinha, Mona Chappellaz, Arthur Lau, Waleed Rahmani, Sisay Getie Belay, Asha K. R. Swamy, Kevin R. Chapman, Graciela Andonegui, Hallgrímur Benediktsson, Peter K. Stys, Daniel A. Muruve, Justin Chun

Bibliographic record

VenueJournal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMediatorMedicineRenal injuryKidney diseaseDiseaseInternal medicineDiabetes mellitusEndocrinologyKidney

Abstract

fetched live from OpenAlex

Background: Diabetic kidney disease (DKD) is the leading cause of kidney failure worldwide. The mechanisms contributing to DKD progression remains poorly characterized. In biopsies of human DKD, lipid droplets (LD) accumulate primarily in tubules with advanced stages of DKD. The contribution of lipid dysregulation is not well understood for DKD. Here we use human kidney biopsies and DKD patient-derived kidney organoids to investigate how LDs contribute to the pathogenesis of DKD. Methods: Human kidney biopsies of the DKD as classified by the Renal Pathology Society classification of DKD were stained using Nile Red and analyzed to LD distribution and numbers. To model DKD, induced pluripotent stem cells (iPSC) were reprogrammed from healthy controls and DKD patients and differentiated to kidney organoids. Kidney organoids were treated with diabetic conditions in the absence of the SGLT2 inhibitor canagliflozin and analyzed by single cell RNA sequencing, in vitro assays, digital spatial imaging (CosMx), molecular spatial imaging (GeoMx) and live kidney organoid imaging. Results: High glucose uptake promoted prominent LD formation in proximal tubular cells (PTC) of human kidney organoids derived from the iPSC of DKD patients. Single cell RNA sequencing of kidney organoids identified IL32, a gene encoding a pro-inflammatory cytokine induced by high glucose and downregulated by the SGLT2 inhibitor canagliflozin. Analysis of human DKD biopsies by Nanostring's digital spatial transcriptomics and molecular spatial imaging confirmed enrichment of IL32 mRNA in injured proximal tubules. In human DKD organoids, IL-32 localized to tubular LD and its upregulation led to mitochondrial reactive oxygen species generation, mitochondrial fragmentation and tubular basement membrane thickening, attenuated by IL32 knockdown. Overexpression of the beta and gamma isoforms of IL-32 in primary human proximal tubular epithelial cells induced mitochondrial fragmentation, ROS, and caspase-3 and GSDME-mediated cell death. Conclusion: These findings identify IL-32 as a potential mediator linking metabolic dysfunction to chronic inflammation in DKD. IL-32 is a potentially targetable LD-associated cytokine that can be used to delay the progression of DKD. Funding: Government Support – Non-U.S.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.262
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueJournal of the American Society of NephrologySame topicLipid metabolism and disordersFrench-language works237,207