Cross-species transcriptome analysis for early detection and specific therapeutic targeting of human lupus nephritis.
Autoimmune Diseases
Autoimmunity
Lupus Nephritis
Systemic Lupus Erythematosus
Therapeutics
Journal
Annals of the rheumatic diseases
ISSN: 1468-2060
Titre abrégé: Ann Rheum Dis
Pays: England
ID NLM: 0372355
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
received:
31
12
2021
accepted:
08
04
2022
pubmed:
30
7
2022
medline:
15
9
2022
entrez:
29
7
2022
Statut:
ppublish
Résumé
Patients with lupus nephritis (LN) are in urgent need for early diagnosis and therapeutic interventions targeting aberrant molecular pathways enriched in affected kidneys. We used mRNA-sequencing in effector (spleen) and target (kidneys, brain) tissues from lupus and control mice at sequential time points, and in the blood from 367 individuals (261 systemic lupus erythematosus (SLE) patients and 106 healthy individuals). Comparative cross-tissue and cross-species analyses were performed. The human dataset was split into training and validation sets and machine learning was applied to build LN predictive models. In murine SLE, we defined a kidney-specific molecular signature, as well as a molecular signature that underlies transition from preclinical to overt disease and encompasses pathways linked to metabolism, innate immune system and neutrophil degranulation. The murine kidney transcriptome partially mirrors the blood transcriptome of patients with LN with 11 key transcription factors regulating the cross-species active LN molecular signature. Integrated protein-to-protein interaction and drug prediction analyses identified the kinases TRRAP, AKT2, CDK16 and SCYL1 as putative targets of these factors and capable of reversing the LN signature. Using murine kidney-specific genes as disease predictors and machine-learning training of the human RNA-sequencing dataset, we developed and validated a peripheral blood-based algorithm that discriminates LN patients from normal individuals (based on 18 genes) and non-LN SLE patients (based on 20 genes) with excellent sensitivity and specificity (area under the curve range from 0.80 to 0.99). Machine-learning analysis of a large whole blood RNA-sequencing dataset of SLE patients using human orthologs of mouse kidney-specific genes can be used for early, non-invasive diagnosis and therapeutic targeting of LN. The kidney-specific gene predictors may facilitate prevention and early intervention trials.
Identifiants
pubmed: 35906002
pii: annrheumdis-2021-222069
doi: 10.1136/annrheumdis-2021-222069
pmc: PMC9484391
doi:
Substances chimiques
Adaptor Proteins, Vesicular Transport
0
DNA-Binding Proteins
0
SCYL1 protein, human
0
RNA
63231-63-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1409-1419Informations de copyright
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: ED is an employee of GSK. His contribution was performed before he joined GSK.
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