Pathologic inflammation is a major driver of kidney damage in lupus nephritis (LN), but the immune mechanisms of disease progression and risk factors for end organ damage are poorly understood.
To ...characterize molecular profiles through the development of LN, we carried out gene expression analysis of microdissected kidneys from lupus-prone NZM2328 mice. We examined male mice and the congenic NZM2328.R27 strain as a means to define mechanisms associated with resistance to chronic nephritis. Gene expression profiles in lupus mice were compared with those in human LN.
NZM2328 mice exhibited progress from acute to transitional and then to chronic glomerulonephritis (GN). Each stage manifested a unique molecular profile. Neither male mice nor R27 mice progressed past the acute GN stage, with the former exhibiting minimal immune infiltration and the latter enrichment of immunoregulatory gene signatures in conjunction with robust kidney tubule cell profiles indicative of resistance to cellular damage. The gene expression profiles of human LN were similar to those noted in the NZM2328 mouse suggesting comparable stages of LN progression.
Overall, this work provides a comprehensive examination of the immune processes involved in progression of murine LN and thus contributes to our understanding of the risk factors for end-stage renal disease. In addition, this work presents a foundation for improved classification of LN and illustrates the applicability of murine models to identify the stages of human disease.
Background/PurposeSystemic lupus erythematosus (SLE) is a chronic autoimmune disease in which clinical symptoms and laboratory measurements are heterogeneous across patients.1 2 Such clinical ...heterogeneity complicates diagnosis and management. To characterize the phenotype of lupus patients before and after diagnosis and also determine the most common clinical management paradigms for lupus patients, we investigated claims data with particular attention to the year before and after diagnosis, focusing on patients with general SLE, SLE patients with renal involvement lupus nephritis (LN) and patients with only cutaneous manifestations cutaneous lupus erythematosus (CLE).MethodsData were acquired using both adjudicated (Closed) and non-adjudicated (Open) commercial databases (from EVERSANA) of patients across the United States. Both databases include claims for diagnoses, procedures, prescriptions, and physician specialties. Analyses were conducted between April 2022 and March 2023. To increase stringency in the identification of lupus patients, cohorts created for LN, SLE, and CLE required two of the specified diagnoses within a six-month period. Altogether, over 100,000 lupus patients were identified by our specifications in the Closed Claims database, and ~38,000 lupus patients in the Open Claims database.ResultsThe cumulative percentages of claims for diagnoses, laboratory testing, procedures and medication generally increased in the year before diagnosis and over the subsequent year. Although we observed differences among the cohorts with respect to concomitant diagnoses and laboratory testing, the basis for diagnosis of patients in each cohort was not always apparent. For example, at index date only 53.4% of SLE patients had received an ANA test and only 43.4% had received an anti-dsDNA test, with comparably low frequencies in LN and CLE. Moreover, at diagnosis, only 8.9% of LN patients had received a kidney biopsy and 23.3% of CLE patients had received a skin biopsy. Subspecialty care by rheumatologists, nephrologists, and dermatologists was associated with increased testing in many instances. Anti-dsDNA and complement testing were increased in patients who had encountered a rheumatologist, kidney biopsies were increased in patients who had encountered a nephrologist, and skin biopsies were increased in patients who had encountered a dermatologist. In addition, there were also differences among cohorts with regard to drug management and emergency department (ED) visits. Of the drug prescriptions examined, at index opioids had the greatest cumulative frequency in LN and SLE, whereas hydroxychloroquine had the highest cumulative frequency in CLE. Among other standard of care drugs, cyclophosphamide was prescribed minimally, mycophenolate mofetil/mycophenolic acid was prescribed more in LN, and methotrexate was prescribed more in SLE. Moreover, when a matched control population was examined, opioid prescription was higher among all lupus cohorts than controls. Notably, LN patients had a greater frequency of ED visits; LN patients with an encounter with a rheumatologist had fewer ED visits, whereas an encounter with a nephrologist was associated with more ED visits. Finally, cost of care was increased in lupus cohorts in the year before diagnosis and the year subsequently, and was highest in LN.ConclusionThe steadily increasing frequency of laboratory tests, emergency department visits, and cost in the year before diagnosis demonstrates the complexity of lupus diagnosis and management. At diagnosis and thereafter, there are major differences between the evaluation and management of lupus patients observed in the general care community reflected within claims databases than those set forth by professional society guidelines.ReferencesAlarcón GS, McGwin G, Petri M, Reveille JD, Ramsey-Goldman R, Kimberly RP, et al. Baseline characteristics of a multiethnic lupus cohort: PROFILE. Lupus 2002;11:95–101.Weckerle CE, Franek BS, Kelly JA, Kumabe M, Mikolaitis RA, Green SL, et al. Network analysis of associations between serum interferon-α activity, autoantibodies, and clinical features in systemic lupus erythematosus. Arthritis Rheum 2011;63:1044–1053.AcknowledgmentsWe thank Mark Delsesto, Ryan Rumantir, David Kauffman, and Shailendra Singh for their assistance with cohort generation/design and organization of the database. We thank C. Neil Lyons for providing the data to convert the cost to 2022 dollars.
Glioblastoma (GBM) prognosis remains dismal due in part to the invasiveness of GBM cells. Interstitial fluid flow (IFF) has been shown to increase invasion of glioma cells in vitro through the CXCR4 ...receptor interacting with autologous, pericellular gradients of CXCL12 (autologous chemotaxis) or through the CD44 receptor interactions with the extracellular matrix (hyaluronan-mediated mechanotransduction). These mechanisms have not been examined together and thus we hypothesized that both mechanisms contribute to invasion in populations of cancer cells. Therefore, we examined IFF-stimulated CXCR4-, CXCL12-, and CD44-dependent invasion in patient-derived glioblastoma stem cells (GSCs). Using our 3D in vitro assay and correlative in vivo studies we demonstrated GSC lines show increased invasion with flow. This flow-stimulated invasion was reduced by blockade of CXCR4, CXCL12, and/or CD44, revealing that GSC invasion may be mediated simultaneously by both mechanisms. Characterization of CXCR4
, CXCL12
, and CD44
populations in four GSC lines revealed different percentages of protein positive subpopulations for each line. We developed an agent-based model to identify the contributions of each subpopulation to flow-stimulated invasion and validated the model through comparisons with experimental blocking studies. Clinically relevant radiation therapy increased flow-stimulated invasion in one GSC line. Our agent-based model predicted that IFF-stimulated invasion is driven primarily by CXCR4
CXCL12
populations, and, indeed our irradiated cells had an increase in this subpopulation. Together, these data indicate that different mechanisms govern the flow response across GSCs, but that within a single patient, there are subpopulations of GSCs that respond to flow via either CD44- or CXCR4-CXCL12 mechanisms.
Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the ...spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion in vivo. Here, we develop a technique to non-invasively measure interstitial flow velocities in the glioma microenvironment of mice using dynamic contrast-enhanced magnetic resonance imaging (MRI), a common clinical technique. Using our in vitro model as a phantom “tumor” system and in silico models of velocity vector fields, we show we can measure average velocities and accurately reconstruct velocity directions. With our combined MR and analysis method, we show that velocity magnitudes are similar across four human GBM cell line xenograft models and the direction of fluid flow is heterogeneous within and around the tumors, and not always in the outward direction. These values were not linked to the tumor size. Finally, we compare our flow velocity magnitudes and the direction of flow to a classical marker of vessel leakage and bulk fluid drainage, Evans blue. With these data, we validate its use as a marker of high and low IFF rates and IFF in the outward direction from the tumor border in implanted glioma models. These methods show, for the first time, the nature of interstitial fluid flow in models of glioma using a technique that is translatable to clinical and preclinical models currently using contrast-enhanced MRI.
BackgroundWe previously developed a novel machine learning (ML) pipeline leveraging analysis of gene expression data to identify subsets of SLE patients with common molecular patterns of disease or ...endotypes.1 These molecular subsets exhibited significant differences in clinical characteristics, frequency of subsequent flares, and clinical responsiveness to a lupus biologic, tabalumab. The current study makes use of the ML classifier to determine endotype membership in an independent validation cohort of SLE patients.MethodsGene expression by RNA-sequencing of whole blood and clinical metadata were collected from 91 SLE patients from two clinical trials (NCT03626311 and NCT03180021). Patients met ACR classification criteria of SLE and patients from one trial had renal biopsies at the time gene expression was measured. Endotype membership of the 91 patients was identified using a random forest classifier trained on 2183 lupus patient transcriptomes, employing 26 modules of genes reflecting immune cell types and inflammatory processes. Lupus Cell and Immune Score (LuCIS), a continuous score measuring the extent of immune perturbations determined by ridge-penalized logistic regression, was also calculated for each patient.ResultsThe ML prediction of independent SLE patients into endotypes yielded eight subsets with molecular patterns mirroring those found previously in a development and testing cohort of 3166 patients (figure 1). Endotypes were designated A-H, with A representing the group with the least number of transcriptional lupus-related aberrancies and H representing the group with the greatest immunologic perturbations. Groups H, A, C, and E contained the greatest number of patients whereas B and G were small and underrepresented in this cohort. Endotype H was comprised of the greatest number of patients with proliferative lupus nephritis (LN) whereas no patient with LN was found in subset A or B. Serum complement differed among the subsets, with lower levels reflected in the more immunologically active subsets. LuCIS values reflected the immunologic activity of the subsets, but did not correlate with SLEDAI, although they were moderately, inversely correlated with serum C3 and C4 levels (figure 2). Eight patients had moderate/severe flares during the six months of the trials, all of whom had elevated LuCIS scores at baseline (figure 3).ConclusionA novel endotyping pipeline based on gene expression profiles and ML identified previously observed patient endotypes in new datasets. Patients in the endotypes with the least immunologic activity did not have proliferative nephritis and also experienced no lupus flares during the subsequent six months. Endotyping SLE patients based on transcriptional profiles can provide important prognostic information and provide novel molecular insights in support of personalized management.ReferenceKim YH, Park MR, Kim SY, Kim MY, Kim KW, Sohn MH. Respiratory microbiome profiles are associated with distinct inflammatory phenotype and lung function in children with asthma. J Investig Allergol Clin Immunol. 2023 Jun 1:0. doi: 10.18176/jiaci.0918. Epub ahead of print. PMID: 37260034.Abstract 1104 Figure 1Identification of Endotypes Among 91 SLE Patients. Molecular subsets identified by a random forest algorithm using gene set variation analysis (GSVA) enrichment scores of 26 immune/inflammatory modules. Clinical metadata for each patient (x-axis) was annotated as shown. Heatmap constructed in R using the ComplexHeatmap package.Abstract 1104 Figure 2LuCIS Correlations with Clinical Data. Pearson correlations of LuCIS values with baseline clinical characteristics. Each data point is colored by endotype membership. Plots were constructed in R using the ggplot2 package.Abstract 1104 Figure 3LuCIS of Eight Patients with Flares. Visualization of the LuCIS values at baseline in eight patients from NCT03626311 who experienced a flare over the six months of the trial. The solid black line refers to the mean +1 standard deviation of the LuCIS values of all 91 patients. Plot constructed in R using the ggplot2 package and edited in Microsoft PowerPoint.Lay SummaryLupus patients present with arrays of symptoms that are highly variable, a phenomenon called heterogeneity. Heterogeneity is also observed in the biological mechanisms that underlie lupus disease activity. To address this issue, we identified endotypes, or subsets of patients with commonalities in these underlying mechanisms. We previously developed computational algorithms to accurately predict endotype membership of any given lupus patient. Here, we are validating one such algorithm in a new, independent set of patients. We were able to identify the same subsets (endotypes) in the new data sets with differences in clinical characteristics similar to what was previously observed in the development and testing data sets. These results validate the use of gene expression profiles to provide information that could support lupus patients clinically.
Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each ...condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in "prelesional" skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.
Background/PurposeMolecular kidney or blood-based biomarkers of lupus nephritis (LN) would provide an advance over standard renal biopsy analysis. Therefore, we analyzed bulk RNA from renal biopsies ...and paired blood to determine molecular biomarkers of LN, associated with gene expression-determined lupus nephritis endotypes.MethodsUsing Gene Set Variation Analysis (GSVA), we analyzed the enrichment of informative modules of co-expressed genes in the biopsies of 76 kidneys derived from patients with LN and matched blood for 71 patients. Gene modules identifying immune/inflammatory cells, resident kidney cells, and metabolic and inflammatory processes were employed where appropriate.ResultsGSVA analysis of LN gene expression elucidated four endotypes of LN (figure 1), which were characterized by minimal disease abnormalities (coral); inflammatory disease with minimal kidney cell damage and minimal metabolic dysfunction (yellow); inflammatory disease with marked kidney cell and marked metabolic dysfunction (purple), and little inflammation with markedly decreased kidney cell and markedly decreased metabolic function (black). GSVA analysis of the same LN-derived clusters in the blood of paired patients revealed the two clusters with marked kidney damage (purple and black) had significant enrichment of the LDG signature (figure 2a). The purple cluster was consistently characterized by decreased blood expression of T cell and T cell receptor chain signatures (figure 2b-f), whereas the black cluster exhibited decreased blood expression of the dendritic cell signature (figure 2g). Although production of erythropoietin is known to decrease with chronic kidney disease,1 expression of EPO was unchanged in the blood across subsets (figure 2h).ConclusionsTranscriptomic analyses support the existence of LN endotypes that progress from acute inflammatory to chronic kidney disease with little inflammation and marked kidney damage. Analysis of the blood of this small cohort of patients with LN suggest that the two most severe molecular endotypes of LN have different profiles than patients with minimal disease. Although the LDG signature can relate to glucocorticoid treatment2 and T cell lymphopenia is a marker of severe lupus in general,3 the combination suggests greater suspicion of progression to LN.Abstract 2103 Figure 1Clustering of GSVA enrichment scores in lupus kidneys reveals four distinct endotypes of patients with LN. (a) Row and column hierarchical clustering of 76 patients with LN into four groups based upon gene expression of cellular and pathway gene modules. (b) Reordered clustering of LN patients in order of molecular disease severity from least to greatest. The columns represent individual patients that are grouped into four clusters (black, coral, yellow, and purple). The rows represent gene modules indicative of immune/inflammatory cells, non- hematopoietic cells, and cellular metabolism.Abstract 2103 Figure 2Analysis of paired blood of patients with LN demonstrates cluster-specific enrichment of inflammatory signatures. GSVA of (a) LDG, (b) T cell, (c) TCRA, (d) TCRAJ, (e) TCRB, (f) anergic/activated T cell, and (g) dendritic cell signatures in the blood of patients with LN. (h) Log2 expression of EPO in the blood of patients with LN. X-axis clusters denote the cluster to which the sample belongs based upon analysis of paired kidney gene expression. Significant differences in enrichment of gene signatures or log2 expression between each cluster and Coral was assessed by Brown-Forsythe and Welch ANOVA with Dunnett’s T3 multiple comparisons. *, p < 0.05, **, p < 0.01, ***, p < 0.001.ReferencesPortolés J, Martín L, Broseta JJ, Cases A. Anemia in chronic kidney disease: from pathophysiology and current treatments, to future agents. Front Med. 2021;8:642296.Dale DC, Fauci AS, Guerry D IV, Wolff SM. Comparison of agents producing a neutrophilic leukocytosis in man. Hydrocortisone, prednisone, endotoxin, and etiocholanolone. J Clin Invest. 1975;56(4):808–813.Martin M, Guffroy A, Argemi X, Martin T. Systemic lupus erythematosus and lymphopenia: Clinical and pathophysiological features. Rev Med Interne. 2017;38(9):603–613.
BackgroundSLE patients exhibit considerable clinical and molecular heterogeneity. A robust patient stratification approach can help to characterize individual lupus patients more effectively and aid ...patient care.MethodsWe employed gene set variation analysis (GSVA) of informative gene modules and k-means clustering to identify molecular endotypes of SLE patients based on dysregulation of specific biologic pathways and interrogated them for clinical utility. We utilized machine learning (ML) of these molecular profiles to classify individual lupus patients into singular molecular subsets and used logistic regression with ridge penalization to develop a novel, composite metric estimating the severity of disease based on lupus-related immunologic activity. Shapley Additive Explanation (SHAP) was employed to understand the impact of specific molecular features on the patient sub-setting.ResultsSix molecular endotypes were identified in a proof-of-concept cohort from the Illuminate trials (GSE88884) using baseline gene expression profiles. Significant differences in clinical characteristics were associated with different endotypes, with the least perturbed transcriptional profile manifesting the lowest disease activity, and endotypes with more perturbed transcriptional profiles exhibiting more severe disease activity. The more abnormal endotypes were also identified as more likely to have a severe flare over the 52 weeks of the trial and specific endotypes were more likely to be clinical responders to the investigational product (tabalumab). GSVA and k-means clustering of 3166 samples in 17 datasets revealed a total of eight SLE molecular endotypes, each with unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms were trained and validated on 2183 patients from GSE88884 (ILLUMINATE-1 and ILLUMINATE-2) and three additional datasets (GSE116006, GSE65391, and GSE45291) and demonstrated high degrees of accuracy (98%), precision (94%), sensitivity, and specificity in classifying patients into one of the eight endotypes. A composite molecular score, which comprised aggregate molecular scores of each GSVA gene module, was calculated for each lupus patient. A subset of patients was identified whose molecular scores were not different than those found in normal subjects, whereas other subsets of lupus patients had progressively higher scores indicative of the aggregation of molecular abnormalities. The composite molecular scores were significantly correlated with both anti-DNA titers and SLEDAI. Finally, SHAP analysis of the impact of input GSVA scores indicated that a unique array of features was influential in sorting individual samples into each of the molecular endotypes.ConclusionsTranscriptomic profiling and ML allowed for reproducible separation of lupus patients into molecular endotypes with significant differences in clinical outcomes and responsiveness to therapy.Gene expression profiles were reduced to a score to assess lupus-related immune activity that correlated with clinical features, the implementation of which may provide a means to categorize lupus patients numerically based on the nature of each individual’s underlying molecular abnormalities.Lay SummaryLupus patients present with arrays of symptoms that are highly variable, which we describe as heterogeneity. This heterogeneity is also present at a molecular level which means the biological mechanisms underlying disease differ from patient to patient at a given moment in time. We have addressed the clinical challenges presented by this heterogeneity by developing a new way to identify endotypes, or subsets of patients with commonalities in these underlying mechanisms. We used data from thousands of patients in multiple datasets to ensure we are representing the likely universe of lupus patients and used computational algorithms to not only subset the patients but also develop machine learning models that can accurately predict subset (endotype) membership. Finally, the underlying molecular commonalities among these subsets were simplified to the calculation of a single score reflecting an individual patient’s current status of immunologic perturbation. Together, these analyses should provide a new way to categorize lupus patients based on information not currently captured in clinical settings.
Vitamin A (VA) deficiency (VAD) is observed in both humans and mice with lupus nephritis. However, whether VAD is a driving factor for accelerated progression of lupus nephritis is unclear. In this ...study, we investigated the effect of VAD on the progression of lupus nephritis in a lupus-prone mouse model, MRL/lpr. We initiated VAD either during gestation or after weaning to reveal a potential time-dependent effect. We found exacerbated lupus nephritis at ∼15 wk of age with both types of VAD that provoked tubulointerstitial nephritis leading to renal failure. This was concomitant with significantly higher mortality in all VAD mice. Importantly, restoration of VA levels after weaning reversed VAD-induced mortality. These results suggest VAD-driven acceleration of tubulointerstitial lupus nephritis. Mechanistically, at the earlier time point of 7 wk of age and before the onset of clinical lupus nephritis, continued VAD (from gestation until postweaning) enhanced plasma cell activation and augmented their autoantibody production, while also increasing the expansion of T lymphocytes that could promote plasma cell autoreactivity. Moreover, continued VAD increased the renal infiltration of plasmacytoid dendritic cells. VAD initiated after weaning, in contrast, showed modest effects on autoantibodies and renal plasmacytoid dendritic cells that were not statistically significant. Remarkably, analysis of gene expression in human kidney revealed that the retinoic acid pathway was decreased in the tubulointerstitial region of lupus nephritis, supporting our findings in MRL/lpr mice. Future studies will elucidate the underlying mechanisms of how VAD modulates cellular functions to exacerbate tubulointerstitial lupus nephritis.
BackgroundPathologic inflammation is a major driver of kidney damage in lupus nephritis (LN), but the immune mechanisms of disease progression and risk factors for end organ damage are poorly ...understood. Previous studies established the NZM2328 lupus-prone mouse strain as a model for human proliferative glomerulonephritis (GN). These studies determined that disease in female NZM2328 mice presents in acute (AGN) and chronic (CGN) stages, each of which was associated with genetic loci (Agnz1 and Cgnz1). In addition, male mice and the congenic NZM2328.R27 strain were found to be resistant to the development of chronic nephritis. To characterize molecular profiles through the development of LN, we carried out gene expression analysis of micro-dissected kidneys from lupus-prone NZM2328 mice at different stages of disease severity and examined male and R27 mice as a means to define pathogenic processes associated with disease progression. Gene expression analysis of human LN patients was carried out to determine whether similar molecular profiles could be identified in human LN kidneys.MethodsKidneys from NZM2328 and R27 mice were harvested and the stage of GN was confirmed by histological classification at regular intervals of disease progression. Tissues from young mice, before disease development, were used as a control for diseased mice. Laser capture microdissection was used to isolate glomeruli and tubulointerstitial tissue from control and diseased mice. Total RNA was extracted and hybridized to Affymetrix Mouse Clariom D (NZM2328 and R27 female) or Mouse 430 v2.0 (NZM2328 male) arrays. Differential expression (DE) analysis, gene set variation analysis (GSVA), and linear regression were utilized to define the stages of GN in NZM2328 mice and identify immune populations and processes associated with disease progression. Human orthologs of selected murine gene signatures were utilized for GSVA of two gene expression datasets from kidneys of human LN patients.ResultsGene expression profiling identified a continuum of inflammatory processes associated with progression from acute inflammatory to chronic destructive disease initiated in the glomeruli and progressing to the tubules. AGN mice exhibited evidence of immune cell infiltration including enrichment of inflammatory M1-like macrophages and activated lymphocytes (figure 1). We also uncovered a newly recognized transitional (TGN) stage in which we observed the greatest level of immune activity and that served as a critical checkpoint driving progression to the CGN stage and de-enrichment of kidney tissue cells. Male mice exhibited minimal immune infiltration in the glomeruli resulting in non-progressive renal pathology. Immune infiltrates in the glomeruli of R27 mice expressed a regulatory gene signature and especially a dominance of M2-like macrophages. Moreover, R27 mice manifested an enhanced kidney tubule signature, with evidence of increased mitochondrial and metabolic activity consistent with a functional resistance to cellular damage. The robust tubule signature was associated with the absence of an immune/inflammatory gene signature. Numerous genes in the R27 genetic region were upregulated in NZM2328 nephritic kidneys and could contribute to the protective effect of this interval on the evolution of LN. The gene expression profiles of human LN were similar to those noted in the NZM2328 mouse suggesting comparable stages of LN progression.ConclusionTranscriptome analysis revealed distinct immune profiles for AGN, after initial IC deposition in the kidney glomerulus, TGN in which inflammatory cell and pathway enrichment is at its peak, and CGN in which the accumulated insults result in irreversible damage to the kidney tissue. In addition, we identified distinct mechanisms of resistance to chronic disease based on differences in gender and genetics. Using a gene expression-based clustering approach, we identified a core set of gene signatures able to classify disease stages of murine GN into molecular endotypes that effectively translate to human LN patients. Therefore, this work provides a foundation for improved classification of LN based on molecular endotypes and illustrates the applicability of murine models to better understanding human disease.Lay SummarySystemic lupus erythematosus (SLE) is an autoimmune disorder that can affect a variety of tissues, including the kidney. Lupus nephritis (LN) is one of the most severe organ manifestations of SLE and affects approximately 40% of adult lupus patients with 10-20% of patients developing end-stage renal disease (ESRD). Therefore, there remains a need to understand the risk factors for chronic disease and the stages of inflammation leading to ESRD in greater detail. Mice that develop spontaneous lupus-like disease serve as important tools for understanding lupus pathology and testing potential therapies for lupus patients. Previous studies have established the NZM2328 lupus-prone mouse strain as a model for human LN. We have used gene expression analysis of NZM2328 mice at different stages of disease to understand the pathogenesis of lupus nephritis and, in particular, immune populations and processes associated with the progression from acute to chronic disease. We characterized molecular profiles associated with acute inflammatory and chronic, destructive disease as well as a transitional stage with the highest degree of immune activity ultimately leading to kidney damage and end stage disease. We also identified mechanisms of resistance to chronic disease based on differences in gender and genetics, which altered the nature of inflammation in the kidneys of diseased mice. In addition, we demonstrated similarities in gene expression profiles between human lupus and lupus-prone mice that provide evidence for the applicability of mouse models to better understanding disease progression in human lupus patients.Abstract 1113 Figure 1Graphical model of AGN in NZM2328 and R27 mice. Model summarizing differences in immunologic gene signature enrichment between female NZM2328 and R27 mice with AGN and their proposed impact on the development of chronic disease.
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