A molecular morphometric approach to diabetic kidney disease can link structure to function and outcome

Diabetic kidney disease is the leading cause of kidney failure. However, studies of molecular mechanisms of early kidney damage are lacking. Here we examined for possible linkage between transcriptional regulation and quantitative structural damage in early diabetic kidney disease in Pima Indians with type 2 diabetes. Tissue obtained from protocol kidney biopsies underwent genome-wide compartment-specific gene expression profiling and quantitative morphometric analysis. The ultrastructural lesion most strongly associated with transcriptional regulation was cortical interstitial fractional volume (VvInt), an index of tubule-interstitial damage. Transcriptional co-expression network analysis identified 1843 transcripts that correlated significantly with VvInt. These transcripts were enriched for pathways associated with mitochondrial dysfunction, inflammation, migratory mechanisms, and tubular metabolic functions. Pathway network analysis identified IL-1β as a key upstream regulator of the inflammatory response and five transcription factors cooperating with p53 to regulate metabolic functions. VvInt-associated transcripts showed significant correlation with the urine albumin to creatinine ratio and measured glomerular filtration rate 10 years after biopsy, establishing a link between the early molecular events and long-term disease progression. Thus, molecular mechanisms active early in diabetic kidney disease were revealed by correlating intrarenal transcripts with quantitative morphometry and long-term outcomes. This provides a starting point for identification of urgently needed therapeutic targets and non-invasive biomarkers of early diabetic kidney disease. Diabetic kidney disease is the leading cause of kidney failure. However, studies of molecular mechanisms of early kidney damage are lacking. Here we examined for possible linkage between transcriptional regulation and quantitative structural damage in early diabetic kidney disease in Pima Indians with type 2 diabetes. Tissue obtained from protocol kidney biopsies underwent genome-wide compartment-specific gene expression profiling and quantitative morphometric analysis. The ultrastructural lesion most strongly associated with transcriptional regulation was cortical interstitial fractional volume (VvInt), an index of tubule-interstitial damage. Transcriptional co-expression network analysis identified 1843 transcripts that correlated significantly with VvInt. These transcripts were enriched for pathways associated with mitochondrial dysfunction, inflammation, migratory mechanisms, and tubular metabolic functions. Pathway network analysis identified IL-1β as a key upstream regulator of the inflammatory response and five transcription factors cooperating with p53 to regulate metabolic functions. VvInt-associated transcripts showed significant correlation with the urine albumin to creatinine ratio and measured glomerular filtration rate 10 years after biopsy, establishing a link between the early molecular events and long-term disease progression. Thus, molecular mechanisms active early in diabetic kidney disease were revealed by correlating intrarenal transcripts with quantitative morphometry and long-term outcomes. This provides a starting point for identification of urgently needed therapeutic targets and non-invasive biomarkers of early diabetic kidney disease. Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) globally, increasing health care costs and straining health care resources.1Collins A.J. Foley R.N. Herzog C. et al.US Renal Data System 2012 annual data report.AmJ Kidney Dis. 2013; 61 (e1-476): A7Abstract Full Text Full Text PDF PubMed Scopus (391) Google Scholar, 2Saran R, Li Y, Robinson B, et al. US Renal Data System 2014 annual data report: Epidemiology of kidney disease in the United States. Am J Kidney Dis. 2015;66(suppl 1):Svii, S1-305. Erratum in Am J Kidney Dis. 2015;66:545.Google Scholar, 3Zhuo X. Zhang P. Barker L. et al.The lifetime cost of diabetes and its implications for diabetes prevention.Diabetes Care. 2014; 37: 2557-2564Crossref PubMed Scopus (125) Google Scholar The current diagnostic approach to DKD, using estimated glomerular filtration rate (eGFR) and urinary albumin:creatinine ratio (ACR), identifies individuals susceptible to progressive kidney disease and cardiovascular morbidity and mortality.4de Boer I.H. Rue T.C. Cleary P.A. et al.Long-term renal outcomes of patients with type 1 diabetes mellitus and microalbuminuria: an analysis of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications cohort.Arch Intern Med. 2011; 171: 412-420Crossref PubMed Scopus (267) Google Scholar, 5de Boer I.H. Rue T.C. 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In the absence of renal disease, 20 year mortality risk in type 1 diabetes is comparable to that of the general population: a report from the Pittsburgh Epidemiology of Diabetes Complications Study.Diabetologia. 2010; 53: 2312-2319Crossref PubMed Scopus (236) Google Scholar The trajectory of DKD, however, is often variable at the early stages,9Lemley K.V. Boothroyd D.B. Blouch K.L. et al.Modeling GFR trajectories in diabetic nephropathy.Am J Physiol Renal Physiol. 2005; 289: F863-F870Crossref PubMed Scopus (23) Google Scholar and it exposes the substantial limitations of our current approach to identifying the at-risk population in early clinical disease stages.10Levey A.S. Cattran D. Friedman A. et al.Proteinuria as a surrogate outcome in CKD: report of a scientific workshop sponsored by the National Kidney Foundation and the US Food and Drug Administration.Am J Kidney Dis. 2009; 54: 205-226Abstract Full Text Full Text PDF PubMed Scopus (245) Google Scholar Moreover, progression to ESRD in the absence of albuminuria,11Kramer H.J. Nguyen Q.D. Curhan G. Hsu C.Y. Renal insufficiency in the absence of albuminuria and retinopathy among adults with type 2 diabetes mellitus.JAMA. 2003; 289: 3273-3277Crossref PubMed Scopus (477) Google Scholar and the frequent regression of microalbuminuria, further aggravate the diagnostic and therapeutic challenges relating to early-stage DKD.12Lemley K.V. Abdullah I. Myers B.D. et al.Evolution of incipient nephropathy in type 2 diabetes mellitus.Kidney int. 2000; 58: 1228-1237Abstract Full Text Full Text PDF PubMed Scopus (81) Google Scholar, 13Pavkov M.E. Knowler W.C. Hanson R.L. et al.Predictive power of sequential measures of albuminuria for progression to ESRD or death in Pima Indians with type 2 diabetes.Amj Kidney Dis. 2008; 51: 759-766Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar, 14Pavkov M.E. Mason C.C. Bennett P.H. et al.Change in the distribution of albuminuria according to estimated glomerular filtration rate in Pima Indians with type 2 diabetes.Diabetes Care. 2009; 32: 1845-1850Crossref PubMed Scopus (21) Google Scholar, 15Perkins B.A. Ficociello L.H. Silva K.H. et al.Regression of microalbuminuria in type 1 diabetes.New Engl J Med. 2003; 348: 2285-2293Crossref PubMed Scopus (652) Google Scholar The advent of high-throughput molecular profiling approaches provides a unique opportunity to advance our understanding of diabetic complications. The most commonly used methodology, genome-wide transcriptomic profiling, has been applied successfully in various kidney diseases in humans and model organisms, highlighting its potential for detecting and exploring relevant pathways in DKD.16Berthier C.C. Zhang H. Schin M. et al.Enhanced expression of Janus kinase-signal transducer and activator of transcription pathway members in human diabetic nephropathy.Diabetes. 2009; 58: 469-477Crossref PubMed Scopus (234) Google Scholar, 17Schmid H. Boucherot A. Yasuda Y. et al.European Renal cDNA Bank (ERCB) ConsortiumModular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy.Diabetes. 2006; 55: 2993-3003Crossref PubMed Scopus (335) Google Scholar, 18Wiggins J.E. Patel S.R. Shedden K.A. et al.NFkappaB promotes inflammation, coagulation, and fibrosis in the aging glomerulus.J Am Soc Nephrol. 2010; 21: 587-597Crossref PubMed Scopus (75) Google Scholar, 19Woroniecka K.I. Park A.S. Mohtat D. et al.Transcriptome analysis of human diabetic kidney disease.Diabetes. 2011; 60: 2354-2369Crossref PubMed Scopus (364) Google Scholar A complementary approach to understanding pathophysiologic mechanisms leading to DKD is through quantitative morphometric studies of tissue from kidney biopsies. Combining these 2 approaches allows for exploration of the molecular pathways associated with structural damage in DKD. An overarching goal of our studies is to define the molecular correlates of early-stage structural damage in DKD and their association with long-term outcomes. Given that tubulointerstitial changes are major determinants of progressive kidney damage,20Nath K.A. Tubulointerstitial changes as a major determinant in the progression of renal damage.Am J Kidney Dis. 1992; 20: 1-17Abstract Full Text PDF PubMed Scopus (860) Google Scholar the present study identified the transcriptional profiles associated with the degree of tubulointerstitial ultrastructural damage in protocol kidney biopsies from 49 Pima Indians who had early-stage DKD. The resulting morphogenomic signatures were then compared with transcriptional profiles active in established DKD, thereby linking early-stage alterations to the long-term course of DKD. An overview of the analytical strategy is shown in Figure 1. Baseline clinical, demographic, and morphometric characteristics at time of kidney biopsy are presented in Table 1. The mean measured iothalamate glomerular filtration rate (iGFR) was 147 ml/min, and the median urine ACR was 35 mg/g. Of the 49 participants included in this study, 23 had normal ACR (<30 mg/g) at the time of biopsy; 19 had microalbuminuria (30–299 mg/g); and 7 had macroalbuminuria (>300 mg/g). Cortical interstitial fractional volume (VvInt), expressed as a percentage of renal cortex, was 29.5% ± 9.6%, compared with 11.9% ± 2.8% in nondiabetic living kidney donor biopsies (P < 0.0001). The time courses of iGFR and ACR over a median of 10 years of post-biopsy follow-up (median of 15.9 years of observation from enrollment) are provided in Supplementary Figure S1, A and B.Table 1Demographic, clinical, and morphometric characteristics at the time of kidney biopsy in 49 Pima Indians with type 2 diabetesBaseline characteristicsDKD (n = 49)Male/female (% male)15/34 (30.6)Age (yr)46 ± 9.8Diabetes duration (yr)15.7 ± 6.8Body mass index (kg/m2)35.2 ± 8.2Glycated hemoglobin A1c (%)9.20 ± 2Systolic blood pressure (mm/Hg)125 ± 14Diastolic blood pressure (mm/Hg)78 ± 8Urinary albumin-to-creatinine ratio (mg/g)35.46 (90.21)Iothalamate glomerular filtration rate (ml/min)147 ± 45Serum creatinine (mg/dl)0.7 ± 0.2Cortical interstitial fractional volume (%)29.5 ± 9.6Follow-up length post-biopsy (yr)10.1 (2.0)Data are presented as mean (±SD) or median (interquartile range), unless otherwise indicated. Open table in a new tab Data are presented as mean (±SD) or median (interquartile range), unless otherwise indicated. Protocol kidney biopsy specimens were used for Affymetrix-based gene expression profiling of the tubulointerstitial compartment. Weighted gene co-expression network analysis21Langfelder P. Horvath S. WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics. 2008; 9: 559Crossref PubMed Scopus (10265) Google Scholar was used to define co-expressed gene sets (modules) and their relationships with VvInt, iGFR, and ACR (Figure 1). The protocol first identifies modules or groups of genes that preserve their correlational structure within each module, and then merges these modules based on their module eigengene (ME) profile (Step 1). This approach reduces the dimensions of the dataset and helps to define relevant functional associations. Using this method, we identified 11 functional modules in the tubulointerstitial expression dataset. The module sizes ranged from 129 to 2378 transcripts. The MEs were then tested for their association with clinical and morphometric traits measured at the time of biopsy (Step 2), minimizing multiple-testing penalties (Figure 2). The MEs that were significantly associated with the structural parameter VvInt (P ≤ 0.05) are highlighted in Figure 2. Some MEs that showed significant correlation with VvInt were also associated with iGFR and ACR at the time of biopsy, though to a lesser extent. Four modules in Figure 2 (black, blue, brown, and green) showed the strongest eigenvector associations with VvInt (P ≤ 0.05). These modules contained 484, 1664, 1617, and 1118 co-expressed genes, respectively. Of the 1843 genes significantly correlated with VvInt (q-value ≤ 0.05) in these 4 modules, 913 co-expressed transcripts in the brown and black modules correlated negatively with VvInt, and 930 transcripts found in the blue and green modules correlated positively with VvInt (Supplementary Table S1). A total of 21 genes from the black module that correlated negatively with VvInt showed positive correlation with iGFR at the time of biopsy; 123 genes from the green module that correlated positively with VvInt showed positive correlations with ACR (Supplementary Table S2). VvInt-associated transcripts from the 4 modules were then tested for known interactions using a co-citation network approach, implemented in the Genomatix Pathway System (Genomatix, Munich, Germany). Genes were scanned on PubMed indexed publications for association with each other on a sentence level through a functional attribute (e.g., "A induces B"). The 100 transcripts with the highest co-citation connectivity were displayed separately for positive and negative correlation with VvInt in co-citation networks (Figure 3, a and b). Major network subdomains included inflammatory signaling mediators (e.g., CCL2 and ICAM1), cell cycle control, and proliferation mediators (e.g., TP53), and growth factor–related signaling mechanisms (e.g., epidermal growth factor [EGF] and vascular endothelial growth factor [VEGF]), suggesting activation of these transcriptional programs in the early stages of DKD. The VvInt-associated gene sets were then analyzed for the gene ontology traits "cellular compartment" and "biological processes." Transcripts that were correlated positively with VvInt were enriched with immune regulatory functions, cell activation, and cellular compartments, including focal adhesion, extracellular matrix and space, and plasma membrane. Transcripts that were correlated negatively with VvInt were enriched with cellular pathways localized within specialized cellular organelles, such as mitochondria and peroxisomes. Pathway analysis was performed to define the functional context of the VvInt-associated transcripts. Significant enrichment with migratory, inflammatory, and cell–cell/cell–matrix interaction pathways was found in the transcripts that correlated positively with VvInt. In contrast, transcripts that correlated negatively with VvInt showed significant enrichment for pregnane X receptor/retinoid X receptor activation, farnesoid X receptor/retinoid X receptor activation pathways and metabolic pathways, turnover of amino acids, sugars, and lipids. The complex interaction structure among disease-associated pathways was visualized by constructing a VvInt-associated pathway network from the 53 significantly enriched pathways, using the 1843 VvInt-related transcripts as the analysis input (Figure 4). In this representation, highly interconnected pathways (i.e., multiple shared genes) were aggregated into subclusters/domains. A clear bowtie structure of the VvInt-associated pathways emerged. Enzymatic and metabolic pathways co-aggregated, implying that their expression changes are associated with greater tubulointerstitial damage in early-stage DKD. Additionally, amino acid and lipid metabolism pathways intersected with detoxification pathways. In the second subdomain, signaling mechanisms, including extracellular matrix and growth factor signaling such as "Inhibition of Angiogenesis by TSP1," co-aggregated, also implicating the interaction of these pathways in early-stage disease manifestation. In the VvInt pathway network, a number of pathways were densely interconnected throughout the entire network (Figure 4). This trend was also reflected in the hierarchy of genes shared across multiple pathways. Most of the genes were either private or shared between only 2 or 3 pathways. A small subgroup, however, showed high connectivity among the VvInt-associated pathways, including PRKCQ, NFKB1, MAPK8, ALDH2, and RAC1. The capacity of these genes to connect multiple processes and signaling mechanisms suggests that they play a more central role in coordinating phenotypic features and clinical traits of potential interest. The presence of a complex interconnected network suggests the possibility of a causal upstream mechanism activated in this dense network that affects the observed expression patterns in these downstream functions. We extracted 229 genes that were shared among more than one pathway (Supplementary Table S3). Using a causal network inference approach22Krämer A. Green J. Pollard Jr., J. Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis.Bioinformatics. 2014; 30: 523-530Crossref PubMed Scopus (2767) Google Scholar IL1β was identified as the master regulator of inflammation in our dataset, affecting the expression of more than 50% of these shared gene sets via intermediate regulators. A separate set of 5 transcriptional regulators (PHF1, SOX2, NFAT5, TRIM29, HEY1), linked with the common intermediate transcriptional regulator TP53, can modulate downstream targets associated with differentiated tubule function and oxidative stress. To further elucidate the relationships between early-stage structural damage, associated gene expression, and disease progression, VvInt expression modules were tested for their association with iGFR and ACR over time. The median (interquartile range) observation period for study participants was 15.9 (2.5) years, with a median follow-up of 10.1 (2.0) years after kidney biopsy (Figure 5). From the 1843 VvInt-related transcripts within the 4 identified modules described earlier, 787 and 466 genes correlated with at least 3 iGFR and ACR measurements, respectively, over a median 10-year follow up after biopsy (P ≤ 0.05). We also evaluated the association of the VvInt modules with DKD progression, defined by the slopes of iGFR and ACR. A total of 33% of the VvInt-associated genes in the black and brown modules were associated significantly with iGFR slope (Supplementary Table S1). None of the 4 modules showed a significant association with ACR slope. To test the ability of this approach to identify non-invasive surrogates of VvInt linked to long-term clinical outcomes, a pilot study measured urinary EGF (uEGF) as a candidate non-invasive biomarker of renal function in the subset of patients who had urine samples available from the time of biopsy (n = 46 of 49 samples). Intrarenal EGF mRNA correlated positively (r = 0.43; P < 0.0001) with the urinary protein levels, suggesting that the intrarenal level of EGF is captured by the urinary protein. Levels of uEGF were strongly correlated with baseline kidney function, iGFR (r = 0.47; P < 0.0001), and iGFR slope (r = 0.25; P = 0.01), indicating its potential utility as a non-invasive biomarker of disease progression. To determine how gene expression patterns observed in the Pima Indian early-stage DKD biopsies compared with those in more-advanced DKD, differential gene expression patterns of VvInt-associated transcripts from the Pima Indian protocol biopsies were compared with tubulointerstitial expression patterns in advanced clinically indicated biopsies (n = 17) from a European (European Renal cDNA Bank) DKD cohort. A total of 1302 of the 1843 (71%) VvInt-related transcripts were significantly differentially expressed (q-value ≤ 0.05) in advanced DKD, compared with those from the living kidney donors (N = 31). Strikingly, 1287 of the 1302 differentially expressed transcripts in advanced DKD showed concordant changes with disease. Figure 6 highlights the preserved regulatory nodes from the VvInt-transcriptional network in advanced DKD. In this study, we developed a strategy combining genome-wide intrarenal gene expression profiling, quantitative morphometric analysis, and clinical outcome data analysis to explore pathways underlying early-stage "morphogenomic" changes in DKD. The protocol biopsies obtained from a relatively homogenous Pima Indian population permitted detection of signaling and metabolic interactions activated in early-stage DKD. These interactions were then linked with both structural lesions in the cortical interstitium and long-term disease outcomes. Transcripts positively correlated with VvInt showed enrichment for inflammatory mechanisms, whereas those negatively correlated with VvInt showed enrichment for metabolic processes. Our study links structure and function by combining molecular and morphometric data in early-stage DKD, with a goal of identifying potential targets for therapeutic intervention before irreversible loss of kidney function has occurred. The resulting gene expression sets were further assessed for disease relevance by testing the VvInt-linked transcripts for their associations with long-term functional outcomes (iGFR/ACR trajectories) over the next decade of follow-up post-biopsy. The extended follow-up of the Pima protocol biopsy cohort made a direct replication of the findings in a study with parallel design difficult. However, a significant proportion of the implicated transcripts were also differentially expressed in a European cohort with more-advanced DKD. The concordant regulation of a significant segment of the VvInt-associated transcripts in a different ethnic and environmental background supports the relevance of the identified molecular mechanism beyond the Pima Indian cohort. This mechanism is particularly relevant because Pima Indians develop diabetes at earlier ages than do typical type 2 diabetes mellitus Caucasian patients with DKD, and they lack many of the comorbidities seen in other populations. In addition, the Pima Indian study cohort had significant exposure to renin–angiotensin system blockade. These findings suggest that molecular–morphometric approaches can better capture relevant regulatory events at an early stage of disease, when intervention may be more effective, than at later stages of disease progression. Combining differentially expressed genes into functional categories of coordinated regulation, referred to as pathway analysis, facilitates the discovery of individual system components in a given tissue. This approach has been applied in previous gene expression studies in human and murine DKD, which focused primarily on differential expression of DKD in indication biopsies compared with normal kidneys.16Berthier C.C. Zhang H. Schin M. et al.Enhanced expression of Janus kinase-signal transducer and activator of transcription pathway members in human diabetic nephropathy.Diabetes. 2009; 58: 469-477Crossref PubMed Scopus (234) Google Scholar, 17Schmid H. Boucherot A. Yasuda Y. et al.European Renal cDNA Bank (ERCB) ConsortiumModular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy.Diabetes. 2006; 55: 2993-3003Crossref PubMed Scopus (335) Google Scholar, 19Woroniecka K.I. Park A.S. Mohtat D. et al.Transcriptome analysis of human diabetic kidney disease.Diabetes. 2011; 60: 2354-2369Crossref PubMed Scopus (364) Google Scholar, 23Hodgin J.B. Nair V. Zhang H. et al.Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli.Diabetes. 2013; 62: 299-308Crossref PubMed Scopus (133) Google Scholar These prior studies elucidated the role of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and Janus kinase/signal transducers and activators of transcription (Jak/STAT) signaling, which was established as a pathogenetic contributor to DKD progression.16Berthier C.C. Zhang H. Schin M. et al.Enhanced expression of Janus kinase-signal transducer and activator of transcription pathway members in human diabetic nephropathy.Diabetes. 2009; 58: 469-477Crossref PubMed Scopus (234) Google Scholar, 17Schmid H. Boucherot A. Yasuda Y. et al.European Renal cDNA Bank (ERCB) ConsortiumModular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy.Diabetes. 2006; 55: 2993-3003Crossref PubMed Scopus (335) Google Scholar, 23Hodgin J.B. Nair V. Zhang H. et al.Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli.Diabetes. 2013; 62: 299-308Crossref PubMed Scopus (133) Google Scholar, 24Navarro-González J.F. Mora-Fernández C. Muros de Fuentes M. García-Pérez J. Inflammatory molecules and pathways in the pathogenesis of diabetic nephropathy.Nat Rev Nephrol. 2011; 7: 327-340Crossref PubMed Scopus (767) Google Scholar, 25Ortiz-Muñoz G. Lopez-Parra V. Lopez-Franco O. et al.Suppressors of cytokine signaling abrogate diabetic nephropathy.J Am Soc Nephrol. 2010; 21: 763-772Crossref PubMed Scopus (147) Google Scholar Generating DKD pathway maps from gene expression studies provided part of the rationale for testing the JAK1 and JAK2 inhibitor baricitinib as a potential therapy in advanced DKD (ClinicalTrials.gov number ). Baricitinib significantly reduced the level of urine albumin excretion in a dose-dependent manner, compared with placebo, after 6 months of treatment, with a sustained benefit noted 4 weeks after discontinuation of baricitinib.26Brosius F.C. Tuttle K.R. Kretzler M. JAK inhibition in the treatment of diabetic kidney disease.Diabetologia. 2016; 59: 1624-1627Crossref PubMed Scopus (91) Google Scholar In addition, reduction in blood and urinary markers of JAK-STAT activation predicted from the gene expression profiling studies preceded the albuminuria reduction, demonstrating the power of this approach to identify targets together with their engagement biomarkers. A single process or pathway is rarely the sole determinant of disease progression. Through the simultaneous assessment and integration of information across multiple pathways, relevant pathway interactions in complex disease processes can be described. This approach facilitates the identification of regulatory bottlenecks that could be evaluated as potential drug targets or as markers of integrated disease activity. In our study, the individual structure–function pathways were mapped to each other using a matrix of shared genes among the pathways, establishing a pathway network present in early-stage DKD. This network shows a bowtie-shaped structure tying together 2 distinct clusters of gene activity, linking tubular dedifferentiation and inflammation processes with 2 pathways—"mitochondrial dysfunction" and "lipopolysaccharide/IL-1–mediated inhibition of retinoid X receptor function" (Figure 4). In the inflammation cluster, IL1β was identified as the dominant upstream regulator affecting multiple pathways. IL1β-dependent mechanisms are well characterized in DKD model systems of advanced tubular cell dysfunction,27Lorenz G. Darisipudi M.N. Anders H.J. Canonical and non-canonical effects of the NLRP3 inflammasome in kidney inflammation and fibrosis.Nephrol Dial Transplantat. 2014; 29: 41-48Crossref PubMed Scopus (144) Google Scholar, 28Wada J. Makino H. Innate immunity in diabetes and diabetic nephropathy.Nat RevNephrol. 2016; 12: 13-26Google Scholar where they link apoptosis and innate immune activation. IL1β-dependent transcripts are well established downstream signaling elements of the inflammasome, which is activated in progressive loss of tubule function.29Anders H.J. Of inflammasomes and alarmins: IL-1β and IL-1α in kidney disease.J Am Soc Nephrol. 2016; 27: 2564-2575Crossref PubMed Scopus (148) Google Scholar In the dedifferentiation cluster, a set of 5 transcription factors were identified as potential upstream regulators of the tubular differentiation pathways. The interplay between the 5 master regulators (PHF1, SOX2, NFAT5, TRIM29, and HEY1) and TP53 could affect downstream targets involved in oxidative phosphorylation, mitochondrial dysfunction, and production of nitric oxide and reactive oxygen species in macrophages. Indeed, proximal tubule cell-specific TP53 deletion resulted in decreased oxidative stress, reduced macrophage infiltration, and tubule structural damage.30Ying Y. Kim J. Westphal S.N. et al.Targeted deletion of p53 in the proximal tubule prevents ischemic renal injury.J Am Soc Nephrol. 2014; 25: 2707-2716Crossref PubMed Scopus (85) Google Scholar An additional speculative association involves NFAT5, a transcription factor that has both tonicity-dependent and -independent functions.31Neuhofer W. Role of NFAT5 in inflammatory disorders associated with osmotic stress.CurrGenomics. 2010; 11: