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Nuclear factor κB (NF-κB) activation is a deleterious molecular mechanism that drives acute kidney injury (AKI) and manifests in transplanted kidneys as delayed graft function. The TNFAIP3gene encodes A20, a cytoplasmic ubiquitin ligase and a master negative regulator of the NF- κB signaling pathway. Common population-specific TNFAIP3coding variants that reduce A20’s enzyme function and increase NF- κB activation have been linked to heightened protective immunity and autoimmune disease, but have not been investigated in AKI. Here, we functionally identified a series of unique human TNFAIP3coding variants linked to the autoimmune genome-wide association studies single nucleotide polymorphisms of F127C; namely F127C;R22Q, F127C;G281E, F127C;W448C and F127C;N449K that reduce A20’s anti-inflammatory function in an NF- κB reporter assay. To investigate the impact of TNFAIP3hypomorphic coding variants in AKI we tested a mouse Tnfaip3hypomorph in a model of ischemia reperfusion injury (IRI). The mouse Tnfaip3coding variant I325N increases NF- κB activation without overt inflammatory disease, providing an immune boost as I325N mice exhibit enhanced innate immunity to a bacterial challenge. Surprisingly, despite exhibiting increased intra-kidney NF- κB activation with inflammation in IRI, the kidney of I325N mice was protected. The I325N variant influenced the outcome of IRI by changing the dynamic expression of multiple cytoprotective mechanisms, particularly by increasing NF- κB-dependent anti-apoptotic factors BCL-2, BCL-XL, c-FLIP and A20, altering the active redox state of the kidney with a reduction of superoxide levels and the enzyme super oxide dismutase-1, and enhancing cellular protective mechanisms including increased Foxp3+T cells. Thus, TNFAIP3gene variants represent a kidney and population-specific molecular factor that can dictate the course of IRI.

Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte infiltrates. Periodic acid- Schiff stained slides of transplant biopsies (60 training and 33 testing) were used to quantify pathological lesions specific for interstitium, tubules and mononuclear leukocyte infiltration. The pipeline was applied to 789 whole slide images from baseline (478 pre-transplant and 311 post-transplant 12-month protocol biopsies) in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. Our model accurately recognized kidney tissue compartments and mononuclear leukocytes. The digital features significantly correlated with revised Banff 2007 scores, but were more sensitive to subtle pathological changes below the thresholds in the Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of one-year graft loss, while a Composite Damage Score in 12-month post-transplant protocol biopsies predicted later graft loss. ITASs and Composite Damage Scores outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITASs or Composite Damage Scores also demonstrated significantly higher incidence of estimated glomerular filtration rate decline and subsequent graft damage. Thus, our deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies, and demonstrated superior ability for prediction of post-transplant graft loss with potential application as a prevention, risk stratification or monitoring tool.