Cross-Species Transcriptional Network Analysis Defines Shared Inflammatory Responses in Murine and Human Lupus Nephritis This information is current as of October 2, 2021. Celine C. Berthier, Ramalingam Bethunaickan, Tania Gonzalez-Rivera, Viji Nair, Meera Ramanujam, Weijia Zhang, Erwin P. Bottinger, Stephan Segerer, Maja Lindenmeyer, Clemens D. Cohen, Anne Davidson and Matthias Kretzler Downloaded from J Immunol 2012; 189:988-1001; Prepublished online 20 June 2012; doi: 10.4049/jimmunol.1103031 http://www.jimmunol.org/content/189/2/988 http://www.jimmunol.org/

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The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology

Cross-Species Transcriptional Network Analysis Defines Shared Inflammatory Responses in Murine and Human Lupus Nephritis

Celine C. Berthier,*,1 Ramalingam Bethunaickan,†,1 Tania Gonzalez-Rivera,‡,1 Viji Nair,* Meera Ramanujam,† Weijia Zhang,x Erwin P. Bottinger,x Stephan Segerer,{ Maja Lindenmeyer,{ Clemens D. Cohen,{ Anne Davidson,† and Matthias Kretzler*

Lupus nephritis (LN) is a serious manifestation of systemic lupus erythematosus. Therapeutic studies in mouse LN models do not always predict outcomes of human therapeutic trials, raising concerns about the human relevance of these preclinical models. In this study, we used an unbiased transcriptional network approach to define, in molecular terms, similarities and differences among three lupus models and human LN. Genome-wide -expression networks were generated using natural language processing Downloaded from and automated promoter analysis and compared across species via suboptimal graph matching. The three murine models and human LN share both common and unique features. The 20 commonly shared network nodes reflect the key pathologic processes of immune cell infiltration/activation, endothelial cell activation/injury, and tissue remodeling/fibrosis, with macrophage/dendritic cell activation as a dominant cross-species shared transcriptional pathway. The unique nodes reflect differences in numbers and types of infiltrating cells and degree of remodeling among the three mouse strains. To define mononuclear phagocyte-derived pathways in human LN, gene sets activated in isolated NZB/W renal mononuclear cells were compared with human LN kidney http://www.jimmunol.org/ profiles. A tissue compartment-specific macrophage-activation pattern was seen, with NF-kB1 and PPARg as major regulatory nodes in the tubulointerstitial and glomerular networks, respectively. Our study defines which pathologic processes in murine models of LN recapitulate the key transcriptional processes active in human LN and suggests that there are functional differences between mononuclear phagocytes infiltrating different renal microenvironments. The Journal of Immunology, 2012, 189: 988–1001.

ystemic lupus erythematosus (SLE) is an autoimmune and the adaptive immune systems (8, 9); disappointingly, trans- disorder characterized by loss of tolerance to nucleic acids lating the therapeutic successes in animal models to successful and their binding , resulting in the production of clinical interventions for nephritis has been challenging. There-

S by guest on October 2, 2021 autoantibodies that initiate inflammation or injury. Lupus nephritis fore, there is a pressing need for a scientific approach that allows (LN) is a major cause of end-organ damage in SLE and is a risk the exploration of the similarities and differences among species. factor for mortality (1, 2). Despite many advances in the diagnosis To define shared pathogenetic mechanisms in the development of and management of LN, the incidence of end-stage renal disease LN in mice and humans and to determine which mouse model most secondary to SLE has not decreased in the last 20 y (3, 4). The accurately reflects specific molecular pathways occurring in human complex and heterogeneous nature of SLE has represented disease, we compared gene-expression profiles from micro- a challenge for defining pathogenesis and developing effective dissected human LN kidney biopsies and whole kidneys from three therapeutics. SLE-prone murine models: NZB/W, NZM2410, and NZW/BXSB. Murine models that spontaneously develop lupus (5, 6) have The NZB/W female mouse (10) is characterized by hypercellular played a key role in our understanding of human disease (7) and renal lesions and fibrinoid necrosis, similar to the lesions seen in have been used extensively for the identification of therapeutic class IV human LN kidney specimens (11). NZM2410 mice are targets. However, there are fundamental differences in the genetic characterized by high levels of IL-4 and the production of auto- composition of mice and humans that extend to both the innate antibodies of the IgG1 and IgE isotypes; they develop a rapidly

*Division of Nephrology, Department of Internal Medicine, University of Michigan, The microarray data presented in this article have been submitted to Ann Arbor, MI 48109; †Center for Autoimmunity and Musculoskeletal Diseases, Fein- Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE27045, stein Institute for Medical Research, Manhasset, NY 11030; ‡Division of Rheumatol- GSE32583, GSE32591, GSE35488, and GSE37463. ogy, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109; x Address correspondence and reprint requests to Dr. Matthias Kretzler or Dr. Anne Department of Medicine, Mount Sinai School of Medicine, New York, NY 10029; and { Davidson, Division of Nephrology, Department of Internal Medicine, University of Division of Nephrology, University Hospital Zurich, Zurich, 8091 Switzerland Michigan, 1150 W. Medical Center Drive, 1570A MSRB II, Ann Arbor, MI 48109 1C.C.B., R.B., and T.G.-R. contributed equally to this work and share first authorship. (M.K.) or Feinstein Institute for Medical Research, 350 Community Drive, Manhas- set, NY 11030 (A.D.). E-mail addresses: [email protected] (M.K.) and Received for publication October 24, 2011. Accepted for publication May 14, 2012. [email protected] (A.D.) This work was supported by National Institutes of Health Grants R01 DK085241 (to The online version of this article contains supplemental material. A.D.) and R01 DK079912 and P30 DK081943 (to M.K.) and by the Alliance for Lupus Research (to A.D. and M.K.). C.C.B. was supported by research fellowship Abbreviations used in this article: DC, dendritic cell; DC-SIGN, dendritic cell- FLB1245 from the National Kidney Foundation. R.B. was supported by a Kirkland specific intercellular adhesion molecule-3-grabbing nonintegrin; ERCB, European Scholar Award (Rheuminations). S.S. is supported by a grant from the University of Renal cDNA Bank; GEO, Gene Expression Omnibus; HT, hypertensive nephropathy; Zurich, a Clinical Evidence Council grant from Baxter Healthcare Corporation, and IgAN, IgA nephropathy; LD, living donor; LN, lupus nephritis; SLE, systemic lupus a grant from the Swiss National Science Foundation (SNF 32003B_129710). erythematosus; TALE, Tool for Approximate LargE graph matching.

Copyright Ó 2012 by The American Association of Immunologists, Inc. 0022-1767/12/$16.00 www.jimmunol.org/cgi/doi/10.4049/jimmunol.1103031 The Journal of Immunology 989 progressive glomerulosclerosis with scant lymphocytic infiltrate in NZW/BXSB mice (14) were 8 wk old (control group, without serum the kidneys (12). Males of the NZW/BXSB strain carry the Yaa (Y- autoantibodies or proteinuria n = 4), 17 wk old (prenephritic control group, linked autoimmune acceleration) that contains a reduplication with serum autoantibodies, proteinuria # 100 mg/dl, and histologic glomer- ular score # 2, n = 6), and 18–21 wk old (with .7-d proteinuria . 300 mg/dl of the Tlr7 gene (13). These mice develop an acute proliferative and histologic glomerular score . 2, n =6). glomerulonephritis with severe tubulointerstitial inflammation (14). The three strains have different autoantibody profiles, with Human renal biopsy samples the production of anti-dsDNA Abs in the NZB/W mouse, anti- Human renal biopsies were collected after informed consent was obtained, nucleosome Abs in the NZM2410 mouse, and anti-Sm Ag/ according to the guidelines of the respective local ethics committees. A total ribonucleoprotein and anti-phospholipid Abs in the NZW/BXSB of 47 samples from the European Renal cDNA Bank (ERCB) (21) was processed and used for microarray analysis: pretransplant healthy living mouse. They also respond differently to therapeutic intervention donors (LDs) (n = 15) and LN patients (n = 32). For real-time PCR, 11 LD (15); this underscores the effect of genetic heterogeneity not only and 9 LN samples were used from an independent cohort (of the ERCB). on disease phenotype but also on responses to therapies. Anal- Demographic, clinical, and histologic characteristics of these patients are ysis of comprehensive renal-expression profiles with unbiased provided in Supplemental Table I. There was no statistical difference in natural language-processing tools (Genomatix BiblioSphere) and any parameters between the LN cohorts used in arrays and RT-PCR. a suboptimal matching algorithm based-approach (Tool for Ap- Murine and human RNA extraction, microarray preparation, proximate LargE graph matching [TALE]) identified multiple and processing shared key conserved regulatory network hubs (nodes) among Kidneys were removed from perfused mice and immediately snap frozen. the three mouse strains and the human LN samples. These Tissues were homogenized in 3 ml TRIzol reagent, and RNA was isolated pathway maps allow selection of the mouse model with the according to the manufacturer’s instructions. cDNA was generated from 5 mg highest degree of similarity to the human disease or with acti- total RNA and labeled with biotin using the Ovation Biotin system (NuGEN Downloaded from vation of a pathway of interest for further therapeutic inter- Technologies, San Carlos, CA). Biotin-labeled cDNA was fragmented and hybridized to Affymetrix Mouse Genome 430 2.0 GeneChip arrays (Santa ventions or mechanistic studies in the model system. We found Clara, CA). After hybridization, GeneChip arrays were washed, stained, that the sclerotic kidneys of the NZM2410 mice shared the most and scanned with a GeneChip Scanner 3000 7G, according to the Affymetrix renal transcriptional events with human LN kidneys, followed by Expression Analysis Technical Manual (http://media.affymetrix.com/ support/downloads/manuals/expression_analysis_technical_manual.pdf). the NZB/W and the NZW/BXSB mice. hi int hi We previously showed that the onset of proteinuria in all three CD11b /CD11c /F4/80 mononuclear cells from perfused NZB/W mouse http://www.jimmunol.org/ kidneys were isolated by fluorescence cell sorting, as previously described strains of mice is associated with the expansion and activation of (17), using Abs to CD11b, CD11c, and F4/80. RNA was extracted, amplified, a dominant population of resident renal mononuclear phagocytes and analyzed using gene-expression profiling, as previously described (17). that have a resting phenotype of CD11b+/CD11cint/F4/80hi/Gr1lo/ Normalized data are available at the Gene Expression Omnibus (GEO) Web MHChi and have variably been referred to as intrinsic renal site (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE27045. Cortical tissue segments from human biopsy samples were manually macrophages or resident renal dendritic cells (DCs), because of microdissected into glomerular and tubulointerstitial compartments, as their mixed function as both APCs and phagocytic cells (16). At previously described (22–24). Total RNA was isolated from the micro- nephritis onset, these cells markedly upregulate their expression of dissected human tissues using the RNeasy mini kit (QIAGEN), according CD11b, and they accumulate both in the interstitium and in the to the manufacturer’s instructions. Gene-expression profiling from micro- by guest on October 2, 2021 periglomerular space (14, 17, 18). In NZB/W mice, we demon- dissected human kidney biopsies was performed, as previously described, using the U133A Affymetrix GeneChip arrays (Santa strated a marked change in the gene-expression profile of isolated Clara, CA) (22, 23). F4/80hi renal macrophages at proteinuria onset, with the expres- Murine and human gene-expression data processing and sion of proinflammatory, anti-inflammatory, and tissue repair analysis (17). We show in this article that this macrophage profile is shared by the human LN samples but that there are significant The mouse and human CEL files were processed using the GenePattern differences in the gene-expression profiles of the glomerular and analysis pipeline (http://www.genepattern.com). CEL file normalization was performed with the Robust Multichip Average method using the interstitial compartments, suggesting functional differences in this mouse and human -Gene custom CDF annotation from Brain Array cell type in the two intrarenal microenvironments. version 10 (http://brainarray.mbni.med.umich.edu/Brainarray/default.asp). The normalized files were log2 transformed, and batch correction was performed for the NZB/W and NZW/BXSB murine data (25). Materials and Methods The Poly-A RNA Control Kit was used for processing the mouse NZB/W, NZM2410, and NZW/BXSB SLE mouse strains microarray data. The expression baseline was defined by calculating the NZB/W F1 mice were purchased from The Jackson Laboratory (Bar Harbor, gene-expression median of each gene and adding 1 SD to the minimum ME), NZM2410 mice were purchased from Taconic (Hudson, NY), and value obtained. Of the 16,539 mouse genes represented on the Affymetrix NZW/BXSB F1 male mice were bred in our facility from the respective GeneChip, 13,425, 13,600, and 14,252 were expressed above the defined parents (The Jackson Laboratory). All mice were housed in a specific expression baseline in NZB/W, NZM2410, and NZW/BXSB mice, re- pathogen-free facility with 12-h light/dark cycles and unlimited access to spectively. Of the 12,029 human genes, 11,285 and 11,429 were expressed above the 27 Poly-A Affymetrix control expression baseline (negative food and water. All animal experiments were approved by the Institute Animal Care and Use Committee of the Feinstein Institute. Supplemental controls) in the glomerular and tubulointerstitial compartments, respec- Table I provides details of the subgroups of mice analyzed from each mouse tively, and were used for further analyses. Mouse and human normalized strain, as described in previous publications (14, 18, 19). Briefly, the NZB/ data files were uploaded to the GEO Web site (http://www.ncbi.nlm.nih.gov/ W F1 strain included groups of the following ages: 16 wk old (control geo/) under accession numbers GSE32583 and GSE32591. group without any serum autoantibodies, immune complex deposition, or IgA nephropathy (IgAN) and hypertensive nephropathy (HT) gene- expression profiles from ERCB cohorts were available to the investigators proteinuria, n = 8), 23 wk old (prenephritic control group, with serum autoantibodies, minimal immune complex deposition, and proteinuria , as part of an independent study (W. Ju, C.S. Greene, F. Eichinger, V. Nair, J.F. 100 mg/dl, n = 11), 23 wk old (with abundant immune complex deposition Hodgin, M. Bitzer, Y.S. Lee, Q. Zhu, M. Kehata, M. Li, et al., submitted for and new onset of proteinuria . 300 mg/dl, n = 6), and 36 wk old (with publication) and were compared with the presented LN data. IgAN and HT established proteinuria . 300 mg/dl for .2 wk, n = 10) (20). data will be available on the GEO Web site upon acceptance of the W. Ju NZM2410 mice were 7 wk old (control group without autoantibodies, et al. manuscript under accession numbers GSE35488 and GSE37463. renal immune complex deposition, or proteinuria, n = 5) and 22–30 wk old Real-time PCR analysis of human samples (with proteinuria . 300 mg/dl for 7–10 d, n = 5) (18). Mice of this strain were harvested 7–10 d after proteinuria onset, because most NZM2410 RT and real-time PCR were performed, as reported previously (22). Pre- mice die within 14 d of proteinuria onset. developed TaqMan reagents (ABI Assays-on-demand) were used for all 990 MACROPHAGES IN CROSS-SPECIES ANALYSIS OF LUPUS NEPHRITIS genes analyzed. The expression of each gene was normalized to the geo- Statistical analysis metric mean of the housekeeping genes 18S rRNA, PGK1, and GAPDH (26). Those three reference genes were selected as described (27), as well For the array study, statistical unpaired analyses for each comparison be- as based on their low variance and absence of regulation in LN compared tween the relevant study groups were performed using the Significance with LD in arrays. mRNA expression was analyzed by the DD-Ct method, Analysis of Microarrays method implemented in the MultiExperiment following the manufacturer’s instructions (User Bulletin #2, ABI Prism Viewer application (31, 32). Genes regulated between two groups with , 7700 Sequence Detection System). a q-value (false discovery rate) 0.05 were considered significant and used for further transcriptional and pathway analyses. A Student t test was Cross-species comparison used for the RT-PCR study; a p value , 0.05 was considered significant. To avoid ambiguity, the mouse genes were converted to the corresponding human orthologs using the National Center for Integrative Biomedical Results Informatics homolog (Build 64) and Genomatix annotated ortholog data- Renal LN cross-species functional analysis bases. Differential gene expression was defined by a q value , 0.05, with a fold change $ 1.2 for the upregulated genes and #0.8 for the down- Differentially regulated genes were identified from the kidneys regulated genes. of mice with LN compared with prediseased controls and from Network and pathway analyses humans with LN compared with healthy kidneys. Human LN kidneys demonstrated 3159 glomerular and 2261 tubulointerstitial Significantly regulated genes were analyzed by creating biological genes with mRNA expression significantly modified compared literature-based networks using Genomatix BiblioSphere software (http:// www.genomatix.de). Canonical pathways were extracted using Ingenuity with LD kidneys. Lupus mice were compared with their pre- Pathway Analysis software (http://www.ingenuity.com). nephritic counterparts, using the same significance criteria and fold change as in the human study. A total of 2642, 4015, and 2900

TALE analysis Downloaded from genes was differentially regulated in NZB/W mice (36-wk old with TALE was used to compare the resulting large transcriptional networks established proteinuria compared with 23-wk-old prenephritic), from humans and mice (see Ref. 28 for technical implementation). This NZM2410 mice (22–30-wk nephritic compared with 7-wk pre- algorithm allows comparison of large networks (often with 1000s of nodes nephritic controls), and NZW/BXSB mice (18–21-wk nephritic and edges) and extraction of meaningful relationships between the query and database networks. TALE allows definition of the degree of mismatch compared with 17-wk prenephritic), respectively. Real-time PCR tolerance in the resulting overlapping network. In addition to the query validation of 120 genes confirmed .85% of the genes identified in network and the database networks, TALE requires three fields of user the microarray analysis (R. Bethunaickan, C. Berthier, M. Kretzler, http://www.jimmunol.org/ inputs: a similarity assignment for the genes in the networks, the per- and A. Davidson, manuscript in preparation). Table I displays the centage of important nodes, and the percentage of mismatch. The first parameter is required to group the nodes in the networks. For cross-species number of genes significantly regulated in the murine disease comparison, the orthologous information was used to group the human and groups available for analyses, as well as the number of regulated mouse genes. The second parameter defines the set of seed genes in the genes overlapping with the human data in each renal compart- query network to generate the overlapping network. In this setting, the ment. Murine data sets with the highest degree of overlap in the importance of a node (or a gene) is defined by the number of connections network-level comparison were used for further studies (see that it has with the remaining genes in the human network. In the current study, the top 10% of nodes was used to build the core network elements. Materials and Methods for more details) (Table I). The third parameter defines the mismatch allowed while generating the Gene-expression profiles from whole mouse kidneys were by guest on October 2, 2021 neighborhood of the seed genes, as well as extending the network; a mis- compared with profiles from human microdissected kidneys. This match tolerance of 10% was set in the current study. The transcriptional study compares the murine whole-kidney expression data sets with networks (human and mice) generated from the literature-based Genomatix BiblioSphere software (gene/function word/gene [B3] filter) were used as the human tubulointerstitial compartment, because the tubu- input. The resulting mouse networks were populated into the database, and lointerstitial compartment constitutes .95% of total kidney mass, the human network was used as the query network. The resulting TALE driving the majority of the signatures obtained from the mouse networks were visualized using Cytoscape version 2.7 (29). tissue. In addition, tubulointerstitial inflammatory lesions and fi- Prior to the comparison of each murine model with human LN, a pre- brosis correlate with the decrease in renal function in LN and are liminary TALE analysis was performed within the NZB/Wand NZW/BXSB models to define which group comparison (corresponding to different markers of poor outcome (33–36). disease stages, Table I) most closely resembled the human data sets. The The strategy used for cross-species shared transcriptional net- NZB/W analysis included the following comparisons: 36-wk old with work analysis is depicted in Fig. 1. A sequential knowledge- established nephritis compared with 16- or 23-wk-old prenephritic, as well extraction approach was applied first to generate a transcrip- as 23-wk old at onset of proteinuria compared with 16- or 23-wk-old prenephritic. The 36-wk-old mice with established proteinuria, compared tional network that integrates differential gene expression with with the 23-wk-old prenephritic mice, shared the highest number of public knowledge of gene interactions and automated promoter transcriptional network nodes with the human tubulointerstitium LN ver- analysis to define transcriptional dependencies in each data set. sus control tubulointerstitium; in comparison with the human glomerular Next, conservation of regulatory network elements was tested LN dataset, the 36- versus 16-wk-old mice had the greatest overlap. These using a suboptimal graph matching approach (TALE) developed datasets were subsequently used for further analysis. Similarly, we com- pared the results for either 8- or 17-wk prenephritic NZW/BXSB with 18–21- by Tian and Patel (28). TALE identifies core network elements wk nephritic NZW/BXSB mice and found that they were similar (Table I). shared between two data sets. TALE tolerates a predefined degree Therefore, we used the larger 17-wk prenephritic control group for the of mismatches in network substructure during consensus network comparison analyses. generation, a critical feature shown to be robust for identifying the Immunohistochemistry cross-species differences seen in many regulatory pathways be- tween mouse and man. In the human–mouse comparison of LN, To localize macrophages and DCs in human LN, archival sections from 10 renal biopsies with LN class IV (International Society of Nephrology/Renal the NZM2410 glomerulosclerotic mouse model showed the Pathology Society 2003 classification) and 5 preimplantation biopsies were highest number of cross-species conserved regulatory hubs studied. Immunohistochemistry was performed, as previously described (nodes) identified by TALE, followed by the NZB/W model and (30). The monoclonal mouse anti-CD68 (clone PG-M1; Dako, Germany, the NZW/BXSB model. The number of shared nodes was 125 Hamburg), mouse anti-DC-specific intercellular adhesion molecule-3- (Fig. 2A), 86 (Fig. 2B), and 67 (Fig. 2C), respectively. Of these, grabbing nonintegrin SIGN (DC-SIGN; CD209, clone DCN46; BD Phar- mingen, Heidelberg, Germany), and polyclonal rabbit anti-S100 (Dako) 81 for the NZM2410 model, 62 for the NZB/W model, and 52 for were used. the NZW/BXSB model were regulated in the same direction as in The Journal of Immunology 991

the human samples. The lists of nodes and their regulation in each species are provided in Supplemental Table II. Twenty nodes (19 induced and 1 repressed) were common to the

b three LN murine models and humans and regulated in the same direction (Fig. 2D, 2E, Supplemental Table II). These nodes 29 48 mainly reflect transcripts associated with cellular infiltration and 242 251 268 177 200 244 246 activation and included genes involved in signal transduction

Compartments (STAT1, LYN, ANXA2), cytokines/chemokines (CCL5, CXCL10),

Human LN in Both Renal cellular reorganization/traffic (RAB8B, VIM), cell surface markers

Genes Regulated in Murine and (PTPRC, TLR2, CD44), and Ag-presentation/processing (B2M and inducible immunoproteasome subunits PSMB8, PSMB9). Three nodes were related to extracellular matrix/glomerular a

b basement membrane/fibrosis (FN1, COL18A1, TIMP1). An in- crease in lysozyme (LYZ) is most likely reflective of proximal tubular damage. Finally, four nodes reflected endothelial cell ac- 56 79 tivation (VCAM1), fibrinolysis (ANXA2), coagulation (F2R - 379 467 447 242 309 385 384 thrombin receptor PAR1), and decreased angiogenesis (VEGF-A) (Fig. 2A). A decrease in VEGF-A expression was confirmed at the Regulated in Human Tubulointerstitial LN 2).

Overlap with 2261 Genes level in the glomerular compartment of patients with LN Downloaded from # compared with control biopsies or biopsies from patients with vasculitis and a similar degree of kidney function (37). F2R a (thrombin receptor PAR1), FN1, LYN, and VEGFA represented the

b main nodes of the transcriptional network built based on the liter- ature knowledge, identifying these molecules as core elements in 58 95 the shared regulatory network across models and species (Fig. 2E). 631 699 622 315 375 674 568 Pathway analysis of the genes regulated in the same direction http://www.jimmunol.org/

Glomerular LN from each cross-species conserved network further confirmed these Regulated in Human findings, because the top pathways that were shared among the three Overlap with 3159 Genes

100 mg/dl, histologic nephritis score models and human LN interstitium reflected innate and adaptive

# immune activation, immune cell infiltration, and tissue remodeling (Table II, Supplemental Table III). In addition, upregulation of Ig genes was observed in all three models (particularly the NZB/W model) and in human LN, reflecting B cell and plasma cell infil- 826 203 302 tration. These genes did not appear in the TALE or ingenuity by guest on October 2, 2021 2442 3727 2652 1227 2972 1757 analyses because of species differences in the Ig repertoire.

Orthologous Genes TALE consensus networks generated from the comparison of Corresponding Human the three mouse models and the glomerular human LN data set are provided in Supplemental Fig. 1. Similar to the results for the

a tubulointerstitial compartment, NZM2410 mice shared the most c c c nodes with human LN, and 24 nodes, representing a prominent 919 1382 2642 4015 Genes macrophage/DC signature, were shared among all three models Regulated and LN. Of the three models, the sclerotic kidneys of NZM2410 mice

0.8 for the downregulated genes. shared the most renal transcriptional events with human LN #

2); Pre-N, prenephritic (serum autoantibodies, proteinuria tubulointerstitium. Of the 125 shared nodes between NZM2410

. mice and human LN kidneys and excluding the 20 common genes described above, 61 were regulated in the same direction (22 were downregulated and 39 were upregulated) (Supplemental Table II). The downregulated nodes included many genes involved in cho- lesterol metabolism, as well as genes involved in solute transport, consistent with the loss of differentiated tubular cell function. The upregulated nodes reflected tissue-injury processes (apopto-

Groups Compared sis, fibrosis, coagulation, tissue remodeling); expression of pro- 1.2 for the upregulated genes and teases, kinases, and protease inhibitors; and IFN-induced genes $ (Supplemental Table II). 300 mg/dl, histologic nephritis score 30-wk N compared with 7-wk control 23-wk N compared with36-wk 23-wk N Pre-N compared with 23-wk Pre-N Each of the other two models shared unique nodes with human 23-wk N compared with 16-wk control 36-wk N compared with 16-wk control 3201 . 18–21-wk N compared with 8-wk control 1877 18–21-wk N compared with 17-wk Pre-N 2900 23-wk Pre-N compared with 16-wk control 234 LN interstitium. The NZB/W mouse, with diffuse proliferative glomerulonephritis, shared 86 nodes with the human LN kidneys. Excluding the 20 shared genes described above, 42 of them were

0.05 and fold change regulated in the same direction in both NZB/W mice and human

, tubulointerstitium (27 upregulated and 15 downregulated). Eigh- teen nodes were shared only by the NZB/W mice and human LN value Mouse Model q Genes regulated in same directionality in regulation. Groups with highest level of shared nodes in network analysis used for further studies. NZB/W NZW/BXSB 17-wk Pre-N compared with 8-wk control 319 NZM2410 N, Nephritic (proteinuria a b c and included IFI30, CXCR4, and CCL9 (Supplemental Table IID).

Table I. Number of genes regulated among the groups compared in mouse models and human and overlapping with human data sets Many of the nodes unique to this human–NZB/W comparison 992 MACROPHAGES IN CROSS-SPECIES ANALYSIS OF LUPUS NEPHRITIS Downloaded from http://www.jimmunol.org/

FIGURE 1. Analytical strategy of cross-species shared tubulointerstitial transcriptional networks using TALE. Individual transcriptional networks were generated using the literature-based Genomatix BiblioSphere software and were overlapped using TALE to define cross-species shared transcriptional networks. reflected lymphocyte infiltration and activation, a feature of hu- did the IgAN or HT biopsies (Fig. 3A), suggesting a greater de- by guest on October 2, 2021 man LN not found in NZM2410 mice. gree of oxidative stress in the LN biopsies. The LN interstitium The NZW/BXSB mouse, with severe proliferative glomerulo- had a more prominent type I IFN signature (CXCL10 node) and nephritis, shared the least number of nodes with human LN more evidence of procoagulant activity (THBS1 node) than did the tubulointerstitium (67 nodes). Excluding the 20 genes in common IgAN or HT biopsies (Fig. 3B, data not shown). with the two other models, as described above, 32 nodes were regulated in the same direction as in human disease (26 upregulated Renal LN macrophage functional analysis and 6 downregulated). Seventeen genes were shared exclusively by A key feature of the cross-species comparison in LN was the the NZW/BXSB mice and human LN (Supplemental Table IID). concordant regulation of macrophage/DC transcripts, with a prom- Unique nodes in this mouse included Muc1, Nid2, and Matn1, inent Ag-presentation and pattern-recognition receptor signature which are involved in extracellular matrix formation; the DC- shared between mouse and man (Table II, Supplemental Table IIIA). derived chemokine CXCL3; and IRF7, likely reflecting the in- Mononuclear phagocytic cells have been extensively described as crease in TLR signaling that occurs both in this mouse and in key players in both acute and chronic kidney diseases (38–40); human LN. however, to our knowledge, no study has analyzed the macrophage The gene-expression pattern that we describe in this study in transcriptome within the kidneys of LN patients. Purifying small patients with LN might also be shared with other inflammatory subpopulations of cells from human LN kidney biopsies is techni- renal diseases. To address this point, microarray data from ERCB cally challenging because of the limited amount of kidney tissue cohorts available to the investigators as part of an independent available from renal biopsy cores. Therefore, we applied a cross- study (W. Ju et al., submitted for publication) were compared with species integration strategy of defining the transcriptome of the the LN data. Gene-expression data from glomeruli and tubu- renal mononuclear phagocytes from nephritic mouse kidneys and lointerstitial compartments of the inflammatory disease IgAN and using these data to define potential macrophage/DC-derived genes the noninflammatory disease HT were compared with the human/ from SLE biopsy samples. For an overview of the strategy used, see mouse overlapping gene-expression profiles shown in Table I. For Fig. 4. these comparisons, we focused on NZB/W and NZM2410 mouse We previously showed in all three mouse models that activation models, because they share most features with the human disease. of mononuclear phagocytes associated with upregulation of CD11b As shown in Fig. 3, a significant proportion of transcripts was on CD11b/F4/80hi/CD11cint intrinsic renal “macrophages” (F4/ found to be regulated in an LN-specific manner (26–39% of the 80hi cells) is a cardinal feature of new-onset proteinuria (14, 17, regulated genes in LN). As expected, more inflammatory genes 18). Furthermore, in all three models, F4/80hi cells are the dom- were shared with the IgAN biopsies than with the HT biopsies. inant mononuclear phagocyte population both in prenephritic and Interestingly, the LN glomeruli had more downregulation of nephritic kidneys (14, 17, 18). Although cells from the NZM2410 VEGFA and of mitochondrial genes (citrate synthase node) than mouse kidneys, which share the most number of nodes with the The Journal of Immunology 993 Downloaded from http://www.jimmunol.org/ by guest on October 2, 2021

FIGURE 2. Human–mouse shared tubulointerstitial transcriptional networks. Networks sharing the most connections between human LN and NZM2410 (nephritic versus young control; 125 nodes) (A), NZB/W (36 wk with established nephritis versus 23 wk prenephritic; 86 nodes) (B), and NZW/BXSB (nephritic versus prenephritic; 67 nodes) (C). (D) Overlap of the nodes between the three comparisons, using only the nodes regulated in the same direction in both species in each network (81/125 nodes in the NZM2410 model, 62/86 nodes in the NZB/W model, and 52/67 nodes in the NZW/BXSB model). Each node represents a gene; each edge (blue line) represents a connection between two nodes. The nodes having .20 connections, 2–19 connections, and only 1 connection are displayed in the inner layer, the middle layer, and the outside layer, respectively. The nodes upregulated in both species, down- regulated in both species, or discordantly regulated among species are shown in red, green, and white, respectively. (E) Transcriptional Genomatix Bib- lioSphere network from the 20 overlapping nodes of the TALE results. human LN tubulointerstitium, might provide the most informa- from NZB/W kidneys as a probe for the human samples. Gene- tion, these mice have far fewer F4/80hi cells than do the other two expression array data from these cells were reported previously strains and die almost as soon as they become proteinuric (18). (17). A total of 2506 genes was differentially regulated in F4/80hi Therefore, we used the transcriptome from isolated F4/80hi cells cells isolated from kidneys from nephritic mice compared with 994 MACROPHAGES IN CROSS-SPECIES ANALYSIS OF LUPUS NEPHRITIS

Table II. Top canonical pathways significantly regulated (p , 0.05) from the genes shared and regulated in the same direction in human LN (LN versus LD) tubulointerstitium and in each mouse model (nephritic versus prenephritic), as assessed by Ingenuity Pathway Analysis

NZM2410 (467 Genes) NZB/W (379 Genes) NZW/BXSB (447 Genes)

Regulated Regulated Regulated Canonical Pathways Genes in Genes in Genes in (No. of Genes in Pathway) Rank p Value Pathway (No.) Rank p Value Pathway (No.) Rank p Value Pathway (No.) Ag-presentation pathway (43) 1 2.0E209 12 1 1.2E211 13 1 1.2E209 12 Role of pattern recognition receptors 2 8.5E208 15 5 6.8E209 15 2 6.6E209 16 in recognition of bacteria and viruses (87) OX40-signaling pathway (90) 3 2.6E207 12 8 3.3E208 12 3 1.7E207 12 CTL-mediated apoptosis of target 4 6.2E207 11 2 6.3E210 13 4 4.2E207 11 cells (81) Th cell differentiation (72) 5 7.9E207 13 13 7.1E207 12 5 5.1E207 13 Allograft rejection signaling (91) 6 1.9E206 10 3 2.2E209 12 6 1.4E206 10 DC maturation (188) 7 2.8E206 19 4 5.6E209 21 8 1.5E206 19 Type I diabetes mellitus signaling (121) 8 2.9E206 16 9 2.4E207 16 9 1.7E206 16 Cdc42 signaling (174) 9 3.4E206 17 10 2.5E207 17 12 8.8E206 16 Autoimmune thyroid disease 10 7.9E206 9 6 1.1E208 11 11 5.7E206 9 signaling (61)

Graft-versus-host disease signaling (50) 11 1.6E205 9 7 3.0E208 11 13 1.2E205 9 Downloaded from Complement system (35) 13 2.5E205 8 15 6.2E206 8 10 1.9E206 9 Hepatic fibrosis/hepatic stellate cell 16 3.7E205 17 17 1.3E205 16 7 1.4E206 19 activation (147)

those from #16-wk-old NZB/W mice without any renal disease (Trem2, MERTK, and TGM2), as well as tissue repair genes http://www.jimmunol.org/ (q value , 0.05, fold change $ 1.2 for the upregulated genes and (MMP2, Heparanase, the anti-oxidant HMOX1, and SMAD7) and #0.8 for the downregulated genes) (Fig. 4A). proinflammatory genes (Trem1, FPR1, CD40). Induction of mul- To analyze whether this murine renal F4/80hi intrinsic macro- tiple elements of the proteasome resulted in “protein ubiq- phage signature is present in human LN kidneys, we compared the uitination” being scored as the top ranking pathway (Supple- regulated murine renal F4/80hi genes with the genes that were mental Table IIIB) found in the shared transcriptome induced in differentially expressed in the tubulointerstitium and glomeruli both murine LN macrophages and glomeruli from human LN. of patients with LN compared with LD controls. A total of 213 One hundred and three “macrophage” genes were shared with murine macrophage genes was regulated in the same direction as the genes differentially regulated in the LN tubulointerstitium and in the human tubulointerstitium, and 334 macrophage genes were included molecules from the coagulation and fibrinolytic systems: by guest on October 2, 2021 concordantly regulated in human glomeruli (Fig. 4B). Literature- F2R, Serpine 2 (PAI-1), TNC, THBS1,andANXA5 (Fig. 4B). based networks were generated from these 213 “tubulointerstitial Transcription factor-binding analysis indicated a significant en- LN macrophage/DC genes” and 334 “glomerular LN macrophage/ richment of NF-kB binding sites in promoter regions of the shared DC genes” for further interpretation. regulated transcripts (Table III). A total of 110 macrophage/DC genes was expressed in both In sum, the comparative analysis of murine-derived tran- tubulointerstitium and glomeruli of human LN biopsies (Fig. 4B) scriptome of renal macrophages with human LN signatures allowed and were dominated by a type I IFN signature with both MyD88 us to define transcriptional networks operating ubiquitously in and IRF7 nodes (Fig. 4B, lower panel). This signature included LN, including IRF7-dependent transcripts, as well as those oper- MDA-5 (IFIH1) and Viperin (RSAD2), genes involved in nucleic ating in distinct microenvironments. Signatures of glomerular- acid sensing and the IFN pathway. Fourteen of the “macrophage” resident macrophages showed a specific enrichment for PPARg- genes found in both human renal compartments had a binding site dependent regulation, whereas tubulointerstitial macrophages for the transcription factor IRF7 in their promoter, as assessed by showed a predominance of NF-kB–dependent transcripts. Genomatix (Table III), suggesting a role for TLR activation in Much of the data from the macrophages of NZB/W mice was tissue injury (30). Other concordantly induced genes included the validated previously using real-time RT-PCR (17), and real-time src family kinases Hck and Lyn, the chemokine receptor CXCR4, RT-PCR data for whole kidneys from each of the three mouse complement factor C3, and Factor B, an important amplifier of strains will be reported separately (R. Sahu, R. Bethunaickan, complement activation through the alternative complement path- O. Edegbe, and A. Davidson, manuscript in preparation). Human way. Myc transcription factor consensus binding sites were signif- microarray data were validated by processing TaqMan real-time icantly enriched in the promoter regions of macrophage genes; PCR on tubulointerstitial samples from an independent cohort for concordant with this observation, the transcriptional repressor Myc several of the specific genes of interest described above (Table IV; was found to be downregulated in the isolated murine F4/80hi cells. Supplemental Table I). Because glomerular material is very lim- A total of 224 unique “macrophage” genes was expressed in the ited, we focused validation on the most relevant gene (ITGAM) in glomerular profile from human LN biopsies. In this compartment, these samples (Table IV). All genes evaluated by RT-PCR were PPARg, MMP2, and the CD11b a-chain ITGAM were key nodes significantly regulated, with high fold change in LN compared in the transcriptional network (Fig. 4B). PPARg binding sites were with healthy pretransplant LDs (fold change between 1.48 and enriched in promoter regions of 25 glomerular macrophage genes, 22.21, p , 0.05), with the exception of CD14, CXCR4,and further supporting a role for this transcriptional activator in GPNMB, for which there was a trend toward upregulation, as ob- macrophage transcript regulation in LN (Table III). Multiple served in the arrays. TaqMan real-time PCR was also performed on phagocytic receptors were upregulated in the glomerular profile a subset of 9 LN from the 32 LN used on arrays and 6 LDs. All The Journal of Immunology 995 Downloaded from http://www.jimmunol.org/ by guest on October 2, 2021

FIGURE 3. Human–NZB/W mouse LN comparison with IgAN and HT. (A) Glomerular compartment. (B) Tubulointerstitial compartment. The figures display the transcriptional networks (Genomatix BiblioSphere) obtained from the genes that were co-cited in PubMed abstracts in the same sentence linked to a function word (B2 filter) (145 of 166 genes [(A), left panel]; 220 of 243 genes [(A), right panel]; 104 of 112 genes [(B), left panel]; 138 of 147 genes [(B), right panel]). LN-regulated transcripts were mapped into the transcriptional networks and included in the comparison. *q value , 0.05 and fold change $ 1.2 for the upregulated genes and #0.8 for the downregulated genes. genes were significantly upregulated in this technical replicate performed in human LN (Fig. 5). As predicted by the transcriptional (data not shown). analysis, SLE patients had an increase in cells staining with all three markers. CD68+ cells were found both within the glomeruli and in Immunohistochemical staining of kidneys the interstitium, whereas DC-SIGN+ cells were restricted to the To illustrate differences between mononuclear cells in micro- interstitium. S100+ cells were loosely scattered in both glomeruli compartments of the human kidney, a series of 10 biopsies with LN (circulating cells within glomerular capillaries) and the interstitium. was compared with 5 biopsies of pretransplant controls (Fig. 5). These findings confirm our hypothesis generated from the tran- Immunohistochemistry for DC-SIGN (CD209), the myeloid DC scriptional data that distinct renal mononuclear phagocytes infiltrate marker S100, and the macrophage scavenger receptor CD68 was the different intrarenal compartments in human LN. 996 MACROPHAGES IN CROSS-SPECIES ANALYSIS OF LUPUS NEPHRITIS Downloaded from http://www.jimmunol.org/ by guest on October 2, 2021

FIGURE 4. Renal LN F4/80hi intrinsic functional analysis. (A) Analytical strategy of renal LN “macrophage” functional analysis (q value , 0.05, fold change $ 1.2 for the upregulated genes and #0.8 for the downregulated genes). (B) Overlap of the defined tubulointerstitial and glomerular “macrophage genes.” Transcriptional networks were generated using the literature-based Genomatix BiblioSphere software from the 103 tubulointerstitial-restricted genes, the 224 glomerular-restricted genes, and the 110 genes shared by both compartments and mouse F4/80hi intrinsic macrophages (q value , 0.05, fold change $ 1.2 for the upregulated genes and #0.8 for the downregulated genes). Respectively, the pictures display the 42 of 103, 78 of 110, and 120 of 224 genes that were co-cited in PubMed abstracts in the same sentence linked to a function word (B2 filter).

In sum, our data suggest that there are different functional the effluent blood flow from the glomerulus provides the sole blood features of various mononuclear phagocytic populations that in- supply for peritubular capillaries, glomerular hypertension and hy- filtrate different renal microenvironments in LN. pertrophy compromise peritubular blood flow, resulting in hypoxia, tubular activation, and tubular epithelial cell death. This induces Discussion resident macrophage activation and peritubular inflammation and Although much of the focus in the classification of human lupus has is associated with tissue remodeling, fibrosis (33), and finally, ir- been on the glomerulus, there is an increasing understanding that reversible renal damage. Release of damaged tissue, cytokines, and renal outcomes correlate best with the degree of tubulointerstitial other inflammatory mediators amplify the inflammatory process. inflammation and damage (33–35). After the deposition of immune These effector inflammatory processes are likely to be shared be- complexes in the glomeruli, periglomerular macrophages may be tween individuals and may be addressed therapeutically. attracted by locally produced chemokines or may be activated by Therefore, our study had two major goals. The first was to inflammatory mediators to proliferate in situ. In addition, because identify common expression profiles that reflect renal events shared The Journal of Immunology 997

between the three murine models and human LN. Defining such common pathways between mouse models and human diseases will most likely result in therapeutic targets with broad specificity. Our second goal was to define which molecular pathways found in the three disparate murine models with proliferative (NZB/W and NZW/BXSB) or sclerotic (NZM2410) glomerulonephritis are shared with human LN. This will facilitate experimental studies in murine LN models by allowing the a priori selection of the model system most closely mimicking the human LN situation for the specific pathway under interrogation. Our study showed that the sclerotic kidneys of NZM2410 mice shared most renal transcrip- tional events with human LN kidneys, but it also identified unique features of each of the murine models that were shared with human LN, stressing the need to have multiple model systems with matching molecular data available for further experimental vali- dation of human LN pathways. Transcriptional analyses of human and mouse kidneys with LN and proteinuria using TALE allowed us to extend our analysis beyond a simple gene–gene comparison by overlapping tran- scriptional networks from the two different species and then de- Downloaded from fining shared functional relationships. This analysis showed that macrophages/DCs are key players in the development of LN disease. The small amount of tissue obtained from kidney biopsies is a limitation for studying the role of isolated cell populations in the pathogenesis of human LN disease. The strategy of analyzing gene-expression profiles from infiltrating macrophage populations http://www.jimmunol.org/ obtained from mouse kidneys allowed us to attribute mRNA signature to those cells in the human samples, even when such

LYN ZBTB16 COL1A2 RRM2 NME1information CCNB1 HCK might not be derived from the human tissue homog- MGP MYC DOCK2 IFI35 LGALS3BP ISG20 OASL PCK2 TGM2 SMARCD3 LMO4 NRP1 NCK1 S100A4 FAS SERPING1 CD44 LGALS1 ANXA1 DKK3 VCAM1 LTF TIMP1 CXCR4 PLAC8 CD38 MMP2 MAP4K4 CD8A OLR1 SMAD7 CFLAR PLA2G7 THBS1 SERPINE2 TRAF3IP2 TXNDC5 GPR126 PDGFB TNC HMMR ENO2 VAV1 BATF PLIN2 CD40 AURKB S100A6 NPPA C1R ANXA2 APOBEC2 XBP1 DPP4 MYD88 IGFBP6 IFIT1 IFIH1 RSAD2 IFI44 MYD88 SATB1 IRF7 RBP1 LGALS3BP HTATIP2 L1CAM CD9 ANXA3 HMOX1 CCR2 SOAT1 ID2 ALAS1 ITGAM ACSL5 MAP4K2 VIMenate TSPO initially. ADAM9 ANXA5 These DYNLL1 studies CLU suggest differences in functional activities between mononuclear phagocytes in different renal subcompartments. The shared transcriptional profile by guest on October 2, 2021 Using TALE analysis, we identified 20 common transcriptional network nodes that were shared by all three LN murine models and the tubulointerstitium of human LN and regulated in the same direction. These nodes reflect key processes in chronic renal injury in SLE nephritis (i.e., immune cell infiltration and activation, macrophage and DC activation, endothelial cell activation, and

(V$IRFF) in their promoter damage and tissue remodeling/fibrosis), which were confirmed by (V$EBOX) in their promoter pathway analysis. 14 genes having a binding site for IRF7 27 genes having a binding site for MYC Of the three models, the sclerotic kidneys of NZM2410 mice shared most renal transcriptional events with human LN kidneys. Histologically, the kidneys of NZM2410 mice have a limited degree of inflammation and more glomerulosclerosis than do those of the other two strains (12). The greatest similarity of this model to human LN may reflect the possibility that patients, in contrast to the murine models, accumulate significant chronic end-organ damage before they are referred to a nephrologist and undergo renal biopsy. In addition, most patients have already been treated, to some extent, by the time biopsies are performed, thus repressing the severity of acute inflammation compared with the untreated mouse models. Thus, the NZM2410 mouse may be an appropriate mouse model for testing add-on therapies directed at chronic progression of LN. Most of the transcriptional network nodes unique to the NZB/W mouse, with diffuse proliferative glomerulonephritis, reflected lymphocyte infiltration and activation, a feature found in many SLE biopsies; this feature of nephritis is not found in NZM2410 mice. In particular, the expression of CXCR4 was elevated; this chemokine receptor is expressed on plasma cells in mice, it is overexpressed on macrophage genes” having a(V$PERO) binding in site their for promoter PPARG macrophage genes” genes” having a bindingpromoter site for NFKB1 (V$NFKB) in their 25 genes from the 224 “glomerular-restricted From the 110 “shared tubulointerstitial and glomerular 24 genes from the 103 “tubulointerstitial-restricted macrophage multiple leukocyte subsets in active lupus patients, and its ligand

Table III. Transcription factor analysis from the defined “tubulointerstitial and glomerular macrophage genes,” as assessed by Genomatix BiblioSphere software CXCL12 is overexpressed in human LN kidneys (41, 42). Tubu- 998 MACROPHAGES IN CROSS-SPECIES ANALYSIS OF LUPUS NEPHRITIS

Table IV. TaqMan real-time PCR of selected genes of interest as validation of microarrays data

Microarrays Real-Time PCR

Compartment Gene Fold Change q Value Fold Change p Value Tubulointerstitium CCR1 1.29 0.004 2.18 0.047 CD14 1.81 0.001 1.48 0.332 CCL5 1.81 0.001 12.81 0.049 CTSS 1.86 0.000 10.57 0.001 CXCL10 3.01 0.000 16.20 0.003 STAT1 3.62 0.000 9.28 0.000 CXCR4 1.86S 0.001 1.95 0.237 IRF7 1.66 0.000 6.99 0.002 HCK 1.37 0.000 3.35 0.003 LYN 1.63 0.000 2.54 0.009 CFB 2.74 0.000 5.22 0.000 IFI44 10.16 0.000 22.21 ,0.0001 GPNMB 2.10 0.000 3.36 0.058 Glomeruli ITGAM 3.21 0.000 3.82 0.009 The comparison of LN versus LD represents the fold change. lointerstitial inflammation with lymphoid aggregates that may in- 44). Indeed, a prominent Ig signature was found both in mouse Downloaded from clude germinal centers and abundant plasma cells is commonly kidneys and in the human tubulointerstitium. Compared with found in human LN and is associated with worse renal outcome (43, NZM2410 mice, there was less expression of nodes that included proteases, proapoptotic molecules, and extracellular matrix. Thus, this model appears to have more inflammation and less tissue re- modeling than does the NZM2410 model. The NZW/BXSB mouse model shared the least number of nodes http://www.jimmunol.org/ with the human LN kidneys; surprisingly, given that both NZB/W and NZW/BXSB mice have severe proliferative disease, it over- lapped less with the NZB/W mouse than it did with the NZM2410 mouse. This may reflect, as discussed above, the fact that human disease has most likely been treated to some extent. Unique nodes in this mouse were associated with extracellular matrix formation and myeloid cell activation, consistent with the large numbers of infiltrating myeloid cells in this model (14). by guest on October 2, 2021 Several nodes were discordantly regulated in the murine models and human samples. These included nodes indicating a response to stress or hypoxia (SERPINE1, EGR1, ATF3, MYC, CDKN1A, Fos, and Jun) and genes that downregulate acute inflammatory cytokines (SOCS3, ATF3, LIF, NFKBIA). These findings suggest a greater degree of hypoxia in the mouse models than in human disease, perhaps reflecting the aggressive nature of untreated murine disease. In summary, the three regulatory networks representing the transcriptional events shared among the human LN tubulointer- stitium and kidneys of three LN mouse models showed significant overlaps, as well as characteristics specific to each model, each of which is shared with the human disease. These findings underscore the heterogeneity of LN and the pitfalls of extrapolating from a single mouse model to human disease. Our study illustrates the FIGURE 5. Localization of macrophage/DC markers in human LN. necessity of studying different mouse models, because specific Immunohistochemistry for CD68 (A, D, G), S100 (B, E), and DC-SIGN (C, questions about human disease might best be answered using F, H) was performed on consecutive sections from a pretransplant biopsy a particular model. Importantly, the transcriptional profile high- (A–C) and biopsies of LN class IV (International Society of Nephrology/ lighted a large number of genes associated with processes involved Renal Pathology Society 2003 classification) [original magnification 3200 in chronic renal injury and disease progression that may be resistant (A–F), 3400 (G, H)]. Scattered CD68+ cells (A) and lower numbers of to systemic immune suppression. Furthermore, comparison of the S100+ cells (B) were found in the tubulointerstitium (arrows) and occa- LN profiles with those of two other human renal diseases showed sionally in glomerular capillaries (arrowhead) in pretransplant biopsies. (C) both similarities and differences. In particular, the differences + A low number of DC-SIGN cells was present in the tubulointerstitium between LN and IgAN reflected more prominent tissue hypoxia and (arrow). (D–G) In contrast, prominent numbers of CD68+ and DC-SIGN+ tissue remodeling in the LN biopsies, features that might require cells were present in biopsies from patients with LN. Consecutive sections demonstrate a prominent number of CD68+ cells in glomerular capillaries a different type of therapeutic intervention to prevent a poor long- (arrowheads), as well as a prominent accumulation of CD68+ cells in the term outcome seen in LN. Our findings stress the need for including tubulointerstitium (arrows). (H) DC-SIGN+ cells were restricted to the management strategies in SLE nephritis directed at slowing the tubulointerstitium (arrows); no DC-SIGN was expressed on glomerular progression of the renal impairment that may continue even after CD68+ cells. systemic inflammation has resolved. The Journal of Immunology 999

This approach has a number of intrinsic limitations. First, TALE In humans, only limited phenotypic analyses of macrophages was designed to provide the similarities between large networks and DC have been performed (30, 48, 49), mostly using immu- (from two species in this study). Indeed, it aligns networks to define nohistochemistry. Human tubulointerstitial CD68+ mononuclear the conserved network. By definition, “approximate large graph phagocytes have a mixed phenotype and include CX3CR1+ cells matching” is useful because it has a defined mismatch tolerance and cells variably positive for the C-type lectins DC-SIGN and and, thereby, can build a consensus signature; however, this fea- BDCA-1. As we show in this study, in SLE biopsies, CD68+ cells ture is not optimized to define differences between networks. infiltrate both the glomerular and the tubulointerstitial compart- Second, because our transcriptional network generation integrates ment; interestingly, however, the tubulointerstitial cells tend to differential gene expression with automated promoter analysis and be positive for DC-SIGN, whereas cells that accumulate in and natural language processing using PubMed abstracts, it is biased around glomeruli are negative for DC-SIGN (49), suggesting toward the current literature, thereby enriching for known bio- potential functional differences between the two compartments. logical interactions between transcripts. Third, because most of A previous study of gene expression of microdissected glomeruli the patients had experienced some type of treatment at the time of from SLE patients revealed that nearly all lupus biopsies have biopsy, leading to treatment-induced alterations of the expression a myelomonocytic-expression signature (44) that includes genes profiles, they may not display the natural disease history observed that we also detected in F4/80hi cells from nephritic kidneys. in the murine models, masking important aspects of disease However, functional studies in humans are still lacking. pathogenesis. Despite these constraints, our study identified a Our strategy allows us to separately assess the glomerular/ prominent macrophage/DC-activation signature as a key feature periglomerular and tubulointerstitial compartments of human LN of all three lupus models and of human LN. Indeed, our study biopsy samples for genes that are regulated in activated renal confirms a previous report (44) of a prominent myelomonocytic mononuclear phagocytes from nephritic NZB/W mice. This Downloaded from signature in the glomeruli of most LN patients, with expression strategy allows us to extract gene-expression patterns that reflect of a set of genes similar to that identified in this study. Therefore, differences in expression rather than changes in cell number. The our findings suggest that further study of these cell types, their genes shared by murine activated F4/80hi macrophages and both origins, and the mechanisms by which they become activated human intrarenal compartments are dominated by a prominent in the kidneys may yield new therapeutic strategies, as well as type I IFN signature, activation of the alternative complement that mouse models, such as NZB/W, are suitable for performing pathway through Factor B, and upregulation of the src family http://www.jimmunol.org/ these studies. kinases Hck and Lyn, which are important effectors of integrin- mediated macrophage adhesion and FcR-mediated phagocytosis The macrophage profile (47, 50–52). The chemokine receptor CXCR4, which is expressed Pathway analysis of the shared nodes identified mononuclear by multiple leukocyte subsets in SLE kidneys (41, 42), is also phagocyte activation as a key player in LN. Therefore, we in- highly upregulated. CXCR4 blockade was reported to reduce renal vestigated the contribution of these cells to the total mRNA ex- leukocyte infiltration in murine LN (41, 53). Transcription factor pression profile by comparing the human compartment-specific analysis revealed a large number of genes regulated by the tran- gene-expression profiles with the profile of the dominant F4/80hi scriptional repressor Myc, which, itself, is downregulated in the by guest on October 2, 2021 -intrinsic macrophage population isolated from control and ne- murine cells. Downregulation of Myc is associated with monocyte phritic NZB/W kidneys. This strategy allowed us to detect tran- to macrophage differentiation, although the functional target scripts of the macrophage signature in the heterogenous human genes are not well defined (54, 55). renal tissue. Unique profiles of the “macrophage signature” in the glomerular Resident renal mononuclear phagocytes have variably been and interstitial compartments also suggest functional differences. called resident or intrinsic renal macrophages or resident renal DCs The 224 unique genes shared between the NZB/W macrophage (16). In mice, there are at least five subpopulations of these cells in signature and human LN glomeruli are dominated by a PPARg the kidneys (R. Sahu and A. Davidson, manuscript in preparation) transcriptional profile. PPARg is expressed by alternatively acti- (17), but the major population that forms a network throughout the vated macrophages, and it suppresses the production of inflam- interstitium expresses high levels of F4/80, CD11b, intermediate matory cytokines while inducing the production of IL-10 (56, 57); levels of CD11c, and low/intermediate levels of Ly6C; it is pos- it also mediates phagocytosis of apoptotic material (58). PPARg itive for MHC class II but expresses low levels of costimulatory agonists decrease extracellular matrix accumulation and prevent molecules (17). The generation of CX3CR1-GFP–labeled mice renal macrophage accumulation in response to injury. PPARg- has allowed visualization of analogous CX3CR1+ cells in a net- deficient macrophages also fail to acquire an anti-inflammatory work surrounding glomerular tubules (45). These cells are capable phenotype upon engulfment of apoptotic cells, suggesting a role of phagocytosis and constantly retract and extend dendritic pro- for PPARg in immune clearance. Several recent studies showed cesses into the interstitium (46, 47). Their role is probably a sen- shown remarkably beneficial effects of PPARg agonists in murine tinel one under physiologic circumstances, but they can contribute models of SLE nephritis, suggesting that these drugs could be to renal injury once activated. We recently showed that F4/80hi appropriate therapies for preventing chronic renal damage in SLE -resident renal cells increase in number in the periglomerular area (59, 60); our study suggests that these drugs may enhance a pro- and/or throughout the interstitium in all three lupus models during tective function of glomerular macrophages in humans as well. the course of the disease; they become activated with disease onset Other phagocytic receptors were also highly upregulated in the (17–19) and revert to their physiologic state upon induction of glomerular profile, suggesting that the glomerular macrophages remission. The expression profile of these cells reveals a mixed are actively involved in phagocytosis as are the mouse F4/80hi functional signature, with both M1- and M2-like characteristics, cells (17). Pathway analysis revealed a significant enrichment of which likely reflects exposure of SLE kidney-resident macro- protein ubiquitination transcript regulation in glomeruli. We pre- phages to multiple stimuli, including immune complexes, multiple viously showed that F4/80hi cells from proteinuric NZB/W mice cytokines, TLR signals, fibrinogen, dead and dying cells, hypoxia, accumulate large numbers of autophagocytic vesicles (17). An and other danger signals. This aberrant activation profile is asso- increase in both protein ubiquitination and autophagy pathways, ciated with chronic and progressive renal injury (17). together with the increase in cell surface molecules involved in 1000 MACROPHAGES IN CROSS-SPECIES ANALYSIS OF LUPUS NEPHRITIS phagocytosis, suggests an important role for these cells in intra- further experimentation in our mouse models. This bidirectional cellular proteolysis, presumably of oxidatively modified and flow of information should allow us to discover new therapeutic ubiquitinated proteins. The glomerular “macrophage” profile also opportunities, as well as to identify genes that are biomarkers for uniquely expressed a tissue repair signature, suggesting a role for clinical disease stage or outcome. these cells in tissue remodeling. Together, these data suggest that the glomerular mononuclear phagocyte is an actively phagocytic Acknowledgments cell with potentially protective functions but that it may also We thank the University of Michigan Microarray Core Facility (Cancer contribute to aberrant tissue remodeling. Center) for human DNA chip hybridizations and Greg Khitrov for mouse The unique tubulointerstitial-restricted “macrophage” signature DNA chip hybridizations; Dr. Nicolas Kozakowski (Department of Surgical was characterized by the expression of genes involved in the co- Pathology, University of Vienna, Vienna, Austria) for providing archival agulation and fibrinolytic systems. Therefore, these genes might biopsies from LN; Jignesh M. Patel for tool development (TALE); and Stefanie Gaiser (Division of Nephrology, University Hospital) for help be useful as markers for the presence of tubulointerstitial infil- with TaqMan real-time PCR. We are grateful to all of the members of trates and possibly a risk for fibrosis in LN. Another gene highly the ERCB-Kro¨ner-Fresenius biopsy bank at the time of the study: C.D. expressed in the interstitium is GPNMB (osteoactivin), which is Cohen, M. Fischereder, H. Schmid, P.J. Nelson, M. Kretzler, D. Schloen- upregulated during macrophage differentiation from monocytes dorff, W. Samtleben (Mu¨nich); J.D. Sraer, P. Ronco (Paris); M.P. Rastaldi, and appears to function as a negative regulator of inflammation G. D’Amico (Milan); F. Mampaso (Madrid); P. Doran, H.R. Brady (Dub- (61). Upregulation of osteoactivin was observed in monocytes lin); D. Moenks (Goettingen); P. Mertens, J. Floege (Aachen); N. Braun, obtained from uremic patients or in normal monocytes cultured in T. Risler (Tu¨bingen); L. Gesualdo, F.P. Schena (Bari); J. Gerth, G. Wolf uremic serum (61), as well as in CD11b+ cells within inflamma- (Jena); R. Oberbauer, D. Kerjaschki (Vienna); B. Banas, B.K. Kraemer tory myocardial infiltrates in a model of adverse myocardial (Regensburg); H. Peters, H.H. Neumayer (Berlin); K. Ivens, B. Grabensee Downloaded from remodeling (62). In addition, this compartment was characterized (Duesseldorf); R.P. Wuethrich (Zurich); and V. Tesar (Prague). by a striking NF-kB transcriptional profile that was absent in the glomerular “macrophage” signature. In sum, these findings sug- Disclosures gest that, although mononuclear phagocytes in different renal The authors have no financial conflicts of interest. locations share some functional characteristics, the different microenvironments are associated with unique gene-expression References http://www.jimmunol.org/ profiles that may profoundly influence cell function. 1. Doria, A., L. Iaccarino, A. Ghirardello, S. 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Diffuse Severe Mouse model specificity Sclerotic GN Proliferative GN proliferative GN

Age (weeks) 16 7 8

Gender Female Female Male

Sample size 8 5 4

Proteinuria range* 1+ 1+ 1+ Control Control Mean interstitial score 0.25 0.00 0.75

Mean glomerulonephritis score 0.67 0.70 1.63

Age (weeks) 23 17

Gender Female Male

Sample size 11 6

Proteinuria range* 1+ 1-2+ Prenephritic Mean interstitial score 0.45 0.25

Mean glomerulonephritis score 1.14 1.67

23 (onset) Age (weeks) 21-31 18-21 36 (established)

Gender Female Female Male

6 (onset) Sample size 5 6 10 (established)

Proteinuria range* 3+ 3+ 3+ Nephritic 1.40 (onset) Mean interstitial score 4.00 1.67 2.72 (established)

2.40 (onset) Mean glomerulonephritis score 3.00 2.70 2.83 (established) Patients with Healthy living

Human Characteristic lupus nephritis donor controls

(LN) (LD)

Arrays 32 15 Number subjects Real-time PCR 9 11

Arrays 25:7 6:9 Gender (Female:Male) Real-time PCR 7:2 5:6

Arrays 35.1±2.4** 56.9±4.7 Age (years) Real-time PCR 36.1±5.3** 46.0±4.6 Demographical characteristics

Arrays 23.1±0.7 23.9±0.3 Body mass index Real-time PCR 21.6±1.0 NA

Arrays 101.7±3.4 98.1±7.8 Mean blood pressure (mm Hg)† Real-time PCR 110.4±5.7 NA

Arrays 1.4±0.2 1.0±0.1 Serum creatinine (mg/dL) Real-time PCR 1.1±0.2** NA Clinical

characteristics Arrays 2.9±0.6** absent Proteinuria (g/day) Real-time PCR 2.4±0.5** absent

Arrays 63.7±5.4 78.4±6.1 2 eGFRMDRD (ml/min/1.73m ) Real-time PCR 75.4±11.3 NA

Arrays 90.3 Positive-ANA (% of patients) NA Real-time PCR 88.9

Positive anti-dsDNA (% of Arrays 90.9 NA patients) Real-time PCR 77.8

Arrays 8 Class II Real-time PCR 2 Histological characteristics WHO class Arrays 10 NA Class III Real-time PCR 3

Class IV Arrays 12 Real-time PCR 3

Arrays 2 Class V Real-time PCR 1

Arrays 48.3 ACE-I (% of patients) NA Real-time PCR 44.4

Arrays 17.2 Beta-blockers (% of patients) NA Real-time PCR 22.2

Ca-channel blockers (% of Arrays 20.7 NA patients) Real-time PCR 22.2

Arrays 75.9 Steroids (% of patients) NA Real-time PCR 66.7

Other anti-hypertensive (% of Arrays 23.3 NA Therapeutic characteristics patients) Real-time PCR 11.1

Immunosuppressant (% of Arrays 37.5 NA patients) Real-time PCR 55.6

Arrays 34.5 Diuretics (% of patients) NA Real-time PCR 44.4

Lipid lowering agent (% of Arrays 6.9 NA patients) Real-time PCR 11.1

* as measured by dipstick. ** Significant compared to LD (p-value < 0.05). NA: not applicable. ANA: anti-nuclear antibody. † Mean BP = RR dias. + (RR sys. – RR dias.)/3.

References:

Schiffer L, Bethunaickan R, Ramanujam M, Huang W, Schiffer M, Tao H, Madaio MP, Bottinger EP,

Davidson A. Activated renal macrophages are markers of disease onset and disease remission in lupus nephritis. J Immunol. 2008 Feb 1;180(3):1938-47. Erratum in: J Immunol. 2008 Mar 1;180(5):3613.

Madaio, Michael M [corrected to Madaio, Michael P]. Bethunaickan R, Berthier CC, Ramanujam M, Sahu R, Zhang W, Sun Y, Bottinger EP, Ivashkiv L,

Kretzler M, Davidson A. A unique hybrid renal mononuclear phagocyte activation phenotype in murine systemic lupus erythematosus nephritis. J Immunol. 2011 Apr 15;186(8):4994-5003. Supplementary Table II. TALE nodes.

(A) 125 TALE nodes shared between the NZM2410 LN mouse model and the human LN.

Legend: a in nephritic compared to pre-nephritic. b in LN compared to LD.

Human gene Mouse Mouse Mouse Human Human Human

symbol Entrez fold- q-value Entrez fold- q-value

Gene ID changea Gene ID changeb

23 nodes down-regulated in both species

1 DUSP1 19252 0.69 0.017 1843 0.27 0.000

2 LDLR 16835 0.76 0.031 3949 0.56 0.000

3 KLF9 16601 0.77 0.004 687 0.58 0.000

4 PROZ 66901 0.49 0.001 8858 0.73 0.002

5 VEGFA 22339 0.61 0.000 7422 0.75 0.000

6 HMGCR 15357 0.40 0.000 3156 0.76 0.001

7 SLC12A6 107723 0.78 0.001 9990 0.77 0.000

8 FXR2 27528 0.474 0.002 9513 0.77 0.000

9 PARD6A 56513 0.83 0.010 50855 0.79 0.000

10 TFRC 22042 0.35 0.000 7037 0.79 0.019

11 SLC4A4 54403 0.69 0.001 8671 0.79 0.001

12 GK 14933 0.690 0.001 2710 0.80 0.028

13 DHCR7 13360 0.60 0.000 1717 0.80 0.000

14 SLC2A4 20528 0.63 0.000 6517 0.81 0.000

15 GHR 14600 0.70 0.004 2690 0.81 0.005

16 PTGER3 19218 0.78 0.044 5733 0.81 0.000

17 MTOR 56717 0.82 0.001 2475 0.82 0.000

18 YBX2 53422 0.69 0.005 51087 0.83 0.000

19 HNF4A 15378 0.59 0.001 3172 0.83 0.000

20 TENC1 209039 0.71 0.000 23371 0.83 0.000 21 TOMM40 53333 0.79 0.001 10452 0.83 0.000

22 CRYL1 68631 0.57 0.000 51084 0.83 0.004

23 OVOL1 18426 0.82 0.012 5017 0.83 0.000

58 nodes up-regulated in both species

1 FN1 14268 1.61 0.003 2335 1.20 0.000

2 DOCK2 94176 1.53 0.001 1794 1.21 0.015

3 RAB8B 235442 1.54 0.000 51762 1.21 0.004

4 SPINT2 20733 1.47 0.001 10653 1.22 0.000

5 SYNCRIP 56403 1.21 0.031 10492 1.22 0.001

6 NFE2L2 18024 1.69 0.000 4780 1.22 0.000

7 THBS1 21825 1.67 0.006 7057 1.22 0.018

8 AXL 26362 1.70 0.000 558 1.22 0.000

9 TLR2 24088 3.24 0.000 7097 1.22 0.002

10 MYD88 17874 1.40 0.000 4615 1.23 0.000

11 F2R 14062 2.40 0.001 2149 1.24 0.000

12 CD44 12505 14.60 0.000 960 1.25 0.004

13 COL18A1 12822 2.34 0.000 80781 1.26 0.000

14 SERPINB1 66222 1.47 0.020 1992 1.26 0.002

15 BAX 12028 1.21 0.008 581 1.27 0.010

16 FXYD5 18301 1.48 0.003 53827 1.28 0.007

17 HOMER3 26558 1.36 0.001 9454 1.29 0.004

18 F2RL1 14063 2.42 0.000 2150 1.29 0.000

19 NFKB1 18033 1.43 0.000 4790 1.29 0.000

20 EFEMP1 216616 2.06 0.002 2202 1.32 0.003

21 NCF2 17970 1.23 0.038 4688 1.33 0.003

22 XPO1 103573 1.24 0.004 7514 1.33 0.000

23 FNDC3B 72007 1.34 0.003 64778 1.33 0.003 24 UGT8 22239 1.22 0.047 7368 1.37 0.011

25 DNMT1 13433 1.25 0.002 1786 1.40 0.000

26 IGSF6 80719 1.66 0.001 10261 1.43 0.001

27 CASP4 12363 3.39 0.000 837 1.44 0.000

28 CD2AP 12488 2.08 0.001 23607 1.46 0.000

29 UCHL1 22223 1.54 0.011 7345 1.52 0.006

30 PSME2 19188 1.78 0.000 5721 1.53 0.000

31 SMAD1 17125 1.49 0.004 4086 1.58 0.000

32 MYOF 226101 2.33 0.000 26509 1.63 0.000

33 LYN 17096 1.44 0.001 4067 1.63 0.000

34 B2M 12010 2.57 0.000 567 1.67 0.000

35 CYBB 13058 2.79 0.000 1536 1.70 0.000

36 TLR3 142980 1.44 0.006 7098 1.76 0.000

37 CCL5 20304 10.59 0.001 6352 1.81 0.001

38 VCAM1 22329 10.71 0.000 7412 1.85 0.000

39 ANXA2 12306 3.51 0.000 302 1.89 0.000

40 SLPI 20568 1.63 0.017 6590 2.00 0.005

41 PTPRC 19264 3.06 0.000 5788 2.03 0.000

42 EVI2A 14017 2.77 0.001 2123 2.06 0.000

43 CLU 12759 3.04 0.000 1191 2.14 0.000

44 FGL2 14190 2.23 0.002 10875 2.25 0.000

45 CASP1 12362 1.51 0.003 834 2.27 0.000

46 VIM 22352 2.92 0.000 7431 2.37 0.000

47 HLA-DMA 14998 3.22 0.000 3108 2.51 0.000

48 TIMP1 21857 4.99 0.000 7076 2.73 0.000

49 PSMB8 16913 4.35 0.000 5696 2.74 0.000

50 WFDC2 67701 2.01 0.001 10406 2.85 0.000 51 TNC 21923 1.75 0.014 3371 2.87 0.000

52 CXCL10 15945 4.36 0.000 3627 3.01 0.000

53 PSMB9 16912 2.50 0.004 5698 3.29 0.000

54 C1S 50908 1.76 0.003 716 3.45 0.000

55 STAT1 20846 1.71 0.005 6772 3.62 0.000

56 TYROBP 22177 2.87 0.000 7305 3.64 0.000

57 LYZ 17105 4.61 0.000 4069 4.09 0.000

58 MX1 17858 1.21 0.042 4599 10.81 0.000

44 nodes differentially regulated in both species

1 NID2 18074 0.77 0.012 22795 1.22 0.014

2 KDR 16542 0.70 0.002 3791 1.21 0.000

3 CCND1 12443 0.62 0.000 595 1.22 0.004

4 FUT8 53618 0.78 0.000 2530 1.22 0.002

5 KIAA0644 66873 0.77 0.046 9865 1.26 0.000

6 EDNRB 13618 0.56 0.000 1910 1.27 0.040

7 AGTR1 11607 0.79 0.032 185 1.32 0.017

8 GJA1 14609 0.51 0.001 2697 1.36 0.026

9 PTER 19212 0.77 0.005 9317 1.45 0.003

10 ID1 15901 0.69 0.007 3397 1.76 0.000

11 CXCL12 20315 0.68 0.018 6387 2.10 0.000

12 TNFAIP8 106869 0.69 0.001 25816 2.40 0.000

13 ATF3 11910 3.85 0.000 467 0.09 0.000

14 MAFF 17133 2.09 0.001 23764 0.11 0.000

15 FOS 14281 2.22 0.008 2353 0.20 0.000

16 GDF15 23886 4.84 0.000 9518 0.28 0.000

17 EGR1 13653 3.16 0.001 1958 0.31 0.000

18 JUN 16476 2.17 0.000 3725 0.35 0.000 19 SERPINE1 18787 5.83 0.001 5054 0.50 0.000

20 GADD45A 13197 1.49 0.003 1647 0.52 0.000

21 CDKN1A 12575 2.05 0.000 1026 0.57 0.000

22 ZFAND5 22682 1.20 0.011 7763 0.57 0.000

23 LBP 16803 1.27 0.010 3929 0.62 0.006

24 DUSP5 240672 1.85 0.001 1847 0.66 0.000

25 NFKBIA 18035 1.88 0.001 4792 0.68 0.000

26 PPP2R1B 73699 1.27 0.000 5519 0.72 0.000

27 CEBPB 12608 1.46 0.041 1051 0.72 0.001

28 ELF3 13710 1.70 0.004 1999 0.73 0.046

29 CREM 12916 1.83 0.002 1390 0.75 0.000

30 MYC 17869 2.50 0.001 4609 0.77 0.027

31 LIF 16878 1.46 0.000 3976 0.77 0.000

32 FOSL1 14283 1.33 0.001 8061 0.78 0.000

33 IL1F6 54448 3.71 0.000 27179 0.78 0.000

34 NGF 18049 2.16 0.000 4803 0.79 0.000

35 XDH 22436 1.73 0.005 7498 0.79 0.000

36 BCL3 12051 2.53 0.000 602 0.79 0.000

37 PTPN1 19246 1.24 0.001 5770 0.79 0.009

38 TNFRSF1A 21937 1.42 0.000 7132 0.80 0.001

39 GNA13 14674 1.34 0.007 10672 0.81 0.001

40 NOTCH1 18128 1.30 0.001 4851 0.81 0.000

41 SOCS3 12702 7.62 0.000 9021 0.81 0.000

42 TGFB3 21809 1.62 0.031 7043 0.82 0.000

43 TNIP2 231130 1.42 0.000 79155 0.82 0.001

44 LRRFIP2 71268 1.28 0.016 9209 0.82 0.000

(B) 86 TALE nodes shared between the NZB/W LN mouse model and the human LN. Legend: a in 36 weeks old with established proteinuria compared to 23 weeks old pre-nephritic. b in LN compared to LD.

Human gene Mouse Mouse Mouse Human Human Human

symbol Entrez fold- q-value Entrez fold- q-value

Gene ID changea Gene ID changeb

16 nodes down-regulated in both species

1 ALB 11657 0.81 0.035 213 0.36 0.000

2 LDLR 16835 0.82 0.006 3949 0.56 0.000

3 PROZ 66901 0.73 0.000 8858 0.73 0.002

4 VEGFA 22339 0.63 0.000 7422 0.75 0.000

5 SEMA4A 20351 0.77 0.000 64218 0.77 0.000

6 AMN 93835 0.69 0.000 81693 0.78 0.000

7 TFRC 22042 0.74 0.000 7037 0.79 0.019

8 RHBG 58176 0.72 0.000 57127 0.79 0.000

9 SNCG 20618 0.72 0.001 6623 0.79 0.000

10 SLC4A4 54403 0.64 0.000 8671 0.79 0.001

11 NPPA 230899 0.81 0.028 4878 0.79 0.000

12 SLC2A4 20528 0.67 0.000 6517 0.81 0.000

13 PTGER3 19218 0.76 0.006 5733 0.81 0.000

14 ALAD 17025 0.81 0.003 210 0.82 0.011

15 HNF4A 15378 0.69 0.000 3172 0.83 0.000

16 CRYL1 68631 0.75 0.001 51084 0.83 0.004

47 nodes up-regulated in both species

1 FN1 14268 1.81 0.000 2335 1.20 0.000

2 RAB8B 235442 1.64 0.000 51762 1.21 0.004

3 THBS1 21825 1.51 0.016 7057 1.22 0.018

4 TLR2 24088 2.08 0.000 7097 1.22 0.002 5 CNP 12799 1.20 0.002 1267 1.22 0.000

6 MYD88 17874 1.36 0.000 4615 1.23 0.000

7 SYK 20963 1.57 0.000 6850 1.24 0.001

8 F2R 14062 2.34 0.000 2149 1.24 0.000

9 CD44 12505 3.47 0.000 960 1.25 0.004

10 COL18A1 12822 1.30 0.050 80781 1.26 0.000

11 FXYD5 18301 1.92 0.000 53827 1.28 0.007

12 CYTIP 227929 1.29 0.004 9595 1.28 0.028

13 F2RL1 14063 1.55 0.000 2150 1.29 0.000

14 SWAP70 20947 1.25 0.001 23075 1.33 0.000

15 PSME1 19186 1.29 0.000 5720 1.33 0.000

16 MUC1 17829 1.36 0.000 4582 1.34 0.003

17 FAS 14102 1.21 0.001 355 1.36 0.038

18 ASNS 27053 1.58 0.000 440 1.37 0.000

19 CD2 12481 1.47 0.001 914 1.44 0.001

20 PSME2 19188 1.35 0.000 5721 1.53 0.000

21 IFI30 65972 1.26 0.001 10437 1.58 0.000

22 IRF8 15900 1.54 0.000 3394 1.62 0.003

23 MYOF 226101 1.64 0.001 26509 1.63 0.000

24 LYN 17096 1.59 0.000 4067 1.63 0.000

25 B2M 12010 1.63 0.000 567 1.67 0.000

26 SPARC 20692 1.41 0.001 6678 1.73 0.000

27 RFTN1 76438 1.32 0.000 23180 1.74 0.000

28 TLR3 142980 1.32 0.000 7098 1.76 0.000

29 CCL5 20304 3.41 0.000 6352 1.81 0.001

30 VCAM1 22329 2.56 0.000 7412 1.85 0.000

31 CXCR4 12767 2.24 0.000 7852 1.86 0.001 32 ANXA2 12306 1.71 0.000 302 1.89 0.000

33 PTPRC 19264 4.55 0.000 5788 2.03 0.000

34 EVI2A 14017 3.20 0.000 2123 2.06 0.000

35 CLU 12759 1.46 0.038 1191 2.14 0.000

36 C1R 50909 1.79 0.000 715 2.16 0.000

37 FGL2 14190 2.67 0.000 10875 2.25 0.000

38 CXCL9 17329 2.23 0.001 4283 2.26 0.020

39 CASP1 12362 1.78 0.000 834 2.27 0.000

40 VIM 22352 2.05 0.000 7431 2.37 0.000

41 TIMP1 21857 2.65 0.000 7076 2.73 0.000

42 PSMB8 16913 2.96 0.000 5696 2.74 0.000

43 CXCL10 15945 2.86 0.000 3627 3.01 0.000

44 PSMB9 16912 3.00 0.000 5698 3.29 0.000

45 C1S 50908 1.61 0.000 716 3.45 0.000

46 STAT1 20846 2.02 0.000 6772 3.62 0.000

47 TYROBP 22177 3.64 0.000 7305 3.64 0.000

23 nodes differentially regulated in both species

1 ATF3 11910 1.82 0.000 467 0.09 0.000

2 FOS 14281 4.46 0.000 2353 0.20 0.000

3 EGR1 13653 4.03 0.000 1958 0.31 0.000

4 JUN 16476 2.07 0.000 3725 0.35 0.000

5 SERPINE1 18787 3.01 0.000 5054 0.50 0.000

6 CDKN1A 12575 1.38 0.029 1026 0.57 0.000

7 LBP 16803 1.24 0.000 3929 0.62 0.006

8 NFKBIA 18035 1.55 0.000 4792 0.68 0.000

9 ARG2 11847 1.89 0.000 384 0.68 0.000

10 CEBPB 12608 1.79 0.000 1051 0.72 0.001 11 CCL4 20303 1.21 0.039 6351 0.76 0.001

12 MYC 17869 1.65 0.002 4609 0.77 0.027

13 XDH 22436 1.51 0.000 7498 0.79 0.000

14 TNFRSF1A 21937 1.24 0.011 7132 0.80 0.001

15 GNA13 14674 1.20 0.001 10672 0.81 0.001

16 SOCS3 12702 4.59 0.000 9021 0.81 0.000

17 GUCA2A 14915 1.24 0.002 2980 0.82 0.000

18 KDR 16542 0.80 0.000 3791 1.21 0.000

19 NR3C1 14815 0.80 0.000 2908 1.30 0.000

20 PTPRK 19272 0.75 0.000 5796 1.31 0.000

21 AGTR1 11607 0.65 0.000 185 1.32 0.017

22 BEND5 67621 0.68 0.000 79656 1.42 0.000

23 PTER 19212 0.77 0.004 9317 1.45 0.003

(C) 67 TALE nodes shared between the NZW/BXSB LN mouse model and the human LN.

Legend: a in nephritic compared to pre-nephritic. b in LN compared to LD.

Human gene Mouse Mouse Mouse Human Human Human

symbol Entrez fold- q-value Entrez fold- q-value

Gene ID changea Gene ID changeb

7 nodes down-regulated in both species

1 ALB 11657 0.74 0.027 213 0.36 0.000

2 FTCD 14317 0.46 0.000 10841 0.69 0.000

3 VEGFA 22339 0.70 0.000 7422 0.75 0.000

4 OSBP2 74309 0.83 0.009 23762 0.78 0.000

5 CYP2B6 13088 0.50 0.003 1555 0.82 0.002

6 YBX2 53422 0.66 0.003 51087 0.83 0.000

7 OVOL1 18426 0.79 0.004 5017 0.83 0.000

45 nodes up-regulated in both species 1 FN1 14268 2.43 0.000 2335 1.20 0.000

2 SLFN12 20558 1.47 0.005 55106 1.21 0.000

3 RAB8B 235442 1.23 0.014 51762 1.21 0.004

4 NID2 18074 1.37 0.000 22795 1.22 0.014

5 TLR2 24088 1.72 0.001 7097 1.22 0.002

6 SYK 20963 1.28 0.005 6850 1.24 0.001

7 F2R 14062 1.96 0.005 2149 1.24 0.000

8 CD44 12505 3.89 0.000 960 1.25 0.004

9 COL18A1 12822 1.91 0.000 80781 1.26 0.000

10 BAX 12028 1.42 0.000 581 1.27 0.010

11 CKAP2 80986 1.58 0.001 26586 1.28 0.000

12 NFKB1 18033 1.34 0.000 4790 1.29 0.000

13 EFEMP1 216616 1.86 0.000 2202 1.32 0.003

14 PRDX4 53381 1.22 0.001 10549 1.33 0.001

15 FNDC3B 72007 1.20 0.002 64778 1.33 0.003

16 MUC1 17829 1.48 0.000 4582 1.34 0.003

17 IGSF6 80719 1.57 0.000 10261 1.43 0.001

18 NUCB2 53322 1.34 0.001 4925 1.44 0.000

19 CD47 16423 1.35 0.000 961 1.46 0.000

20 MATN2 17181 1.33 0.012 4147 1.52 0.000

21 KRT7 110310 1.58 0.003 3855 1.52 0.001

22 GPX7 67305 1.70 0.000 2882 1.54 0.000

23 CAV1 12389 1.26 0.001 857 1.57 0.002

24 LYN 17096 1.37 0.001 4067 1.63 0.000

25 IRF7 54123 2.35 0.001 3665 1.66 0.000

26 B2M 12010 1.41 0.000 567 1.67 0.000

27 SPARC 20692 2.33 0.000 6678 1.73 0.000 28 RFTN1 76438 1.28 0.000 23180 1.74 0.000

29 CCL5 20304 2.71 0.001 6352 1.81 0.001

30 VCAM1 22329 2.97 0.000 7412 1.85 0.000

31 ANXA2 12306 2.22 0.000 302 1.89 0.000

32 SLPI 20568 1.24 0.024 6590 2.00 0.005

33 PTPRC 19264 2.35 0.000 5788 2.03 0.000

34 CLU 12759 2.04 0.001 1191 2.14 0.000

35 LGALS3BP 19039 2.01 0.000 3959 2.19 0.000

36 VIM 22352 2.57 0.000 7431 2.37 0.000

37 NNMT 18113 1.44 0.000 4837 2.50 0.004

38 TIMP1 21857 5.36 0.000 7076 2.73 0.000

39 PSMB8 16913 2.04 0.000 5696 2.74 0.000

40 WFDC2 67701 2.29 0.000 10406 2.85 0.000

41 CXCL10 15945 3.43 0.007 3627 3.01 0.000

42 PSMB9 16912 1.78 0.005 5698 3.29 0.000

43 LY96 17087 1.35 0.003 23643 3.60 0.000

44 STAT1 20846 1.74 0.017 6772 3.62 0.000

45 LYZ 17105 2.45 0.000 4069 4.09 0.000

15 nodes differentially regulated in both species

1 SERPINE1 18787 1.45 0.036 5054 0.50 0.000

2 CDKN1A 12575 1.40 0.043 1026 0.57 0.000

3 HP 15439 3.01 0.003 3240 0.59 0.003

4 NFKBIA 18035 1.29 0.034 4792 0.68 0.000

5 IL6 16193 2.26 0.001 3569 0.75 0.000

6 MYC 17869 1.45 0.013 4609 0.77 0.027

7 SEMA4A 20351 1.25 0.000 64218 0.77 0.000

8 LIF 16878 1.42 0.001 3976 0.77 0.000 9 IL1F6 54448 2.75 0.000 27179 0.78 0.000

10 TNFRSF1A 21937 1.42 0.000 7132 0.80 0.001

11 FAM64A 109212 1.20 0.019 54478 0.82 0.000

12 MITF 17342 0.83 0.011 4286 1.31 0.000

13 CDKN1B 12576 0.67 0.008 1027 1.54 0.000

14 CXCL12 20315 0.70 0.000 6387 2.10 0.000

15 CXCL3 20310 3.45 0.000 2921 0.88 0.000

(D) List of the nodes common to all three TALE analyses and regulated in the same direction, and lists of the nodes regulated in the same direction and shared only between each mouse and human

LN tubulointerstitium.

20 nodes shared between 32 nodes shared only 18 nodes shared only 17 nodes shared only the NZM2410, NZB/W, between the NZM2410 between the NZB/W between the

NZW/BXSB LN mouse LN mouse model and LN mouse model and NZW/BXSB LN mouse

models and human LN human LN human LN model and human LN

VEGFA DUSP1 SEMA4A FTCD

FN1 KLF9 AMN OSBP2

RAB8B HMGCR RHBG CYP2B6

TLR2 SLC12A6 SNCG SLFN12

F2R C5ORF13 NPPA NID2

CD44 PARD6A ALAD CKAP2

COL18A1 GK CNP PRDX4

LYN DHCR7 CYTIP NUCB2

B2M GHR SWAP70 CD47

CCL5 MTOR PSME1 MATN2

VCAM1 TENC1 FAS KRT7

ANXA2 TOMM40 ASNS GPX7

PTPRC DOCK2 CD2 CAV1 CLU SPINT2 IFI30 IRF7

VIM SYNCRIP IRF8 LGALS3BP

TIMP1 NFE2L2 CXCR4 NNMT

PSMB8 THBS1 C1R LY96

CXCL10 AXL CXCL9

PSMB9 SERPINB1

STAT1 HOMER3

NCF2

XPO1

UGT8

DNMT1

CASP4

CD2AP

UCHL1

SMAD1

CYBB

HLA-DMA

TNC

MX1 Supplementary Table III: Ingenuity canonical pathways.

(A) Top 15 canonical pathways significantly regulated (p-value<0.05) from the nodes regulated in the same direction in both species of the three TALE list, as assessed by IPA (Ingenuity Pathway Analysis).

Legend: * Proteinuric vs. non-proteinuric; ** LN vs. LD.

Number of regulated Canonical pathways (number of genes in the pathway) p-value genes in the pathway

From the 20 shared nodes regulated in the same direction in NZM2410, NZB/W, NZW/BXSB mouse model*s and human LN**

(tubulointerstitium)

1 Hepatic Fibrosis / Hepatic Stellate Cell Activation (147) 1.32E-07 6

2 Communication between Innate and Adaptive Immune Cells (109) 6.46E-06 4

3 Antigen Presentation Pathway (43) 2.75E-05 3

4 Dendritic Cell Maturation (188) 1.10E-04 4

5 Pathogenesis of Multiple Sclerosis (9) 1.20E-04 2

6 Glioma Invasiveness Signaling (60) 1.26E-04 3

7 Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis (363) 1.26E-04 5

8 IL-17A Signaling in Gastric Cells (25) 9.77E-04 2

9 Interferon Signaling (36) 1.62E-03 2

10 Role of Hypercytokinemia/hyperchemokinemia in the Pathogenesis of Influenza (45) 2.29E-03 2

11 ILK Signaling (192) 3.39E-03 3

12 Leukocyte Extravasation Signaling (199) 3.98E-03 3

13 GM-CSF Signaling (67) 5.37E-03 2 14 Role of MAPK Signaling in the Pathogenesis of Influenza (74) 5.75E-03 2

15 Protein Ubiquitination Pathway (273) 6.61E-03 3

From the 81 regulated nodes shared between the NZM2410 LN mouse model* and the human LN** (tubulointerstitium)

1 Dendritic Cell Maturation (188) 8.13E-07 9

2 Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis (363) 6.17E-06 11

3 Hepatic Fibrosis / Hepatic Stellate Cell Activation (147) 7.76E-06 8

4 Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses (87) 1.23E-05 6

5 TREM1 Signaling (66) 3.16E-05 5

6 Interferon Signaling (36) 9.12E-05 4

7 Antigen Presentation Pathway (43) 9.12E-05 4

8 Communication between Innate and Adaptive Immune Cells (109) 1.45E-04 5

9 IL-8 Signaling (193) 2.04E-04 7

10 Toll-like Receptor Signaling (55) 3.55E-04 4

11 Rac Signaling (124) 7.94E-04 5

12 IL-17A Signaling in Gastric Cells (25) 8.32E-04 3

13 Colorectal Cancer Metastasis Signaling (257) 1.17E-03 7

14 Pathogenesis of Multiple Sclerosis (9) 1.95E-03 2

15 LXR/RXR Activation (93) 2.00E-03 4

From the 62 regulated nodes shared between the NZB/W LN mouse model* and the human LN** (tubulointerstitium)

1 Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis (363) 3.16E-07 11 2 Hepatic Fibrosis / Hepatic Stellate Cell Activation (147) 8.51E-07 8

3 Dendritic Cell Maturation (188) 9.12E-07 8

4 Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses (87) 2.29E-06 6

5 Pathogenesis of Multiple Sclerosis (9) 1.41E-05 3

6 Communication between Innate and Adaptive Immune Cells (109) 3.55E-05 5

7 TREM1 Signaling (66) 1.62E-04 4

8 Antigen Presentation Pathway (43) 7.59E-04 3

9 Acute Phase Response Signaling (178) 2.04E-03 5

10 Toll-like Receptor Signaling (55) 2.04E-03 3

11 IL-12 Signaling and Production in Macrophages (133) 2.75E-03 4

12 Glioma Invasiveness Signaling (60) 3.31E-03 3

13 p70S6K Signaling (130) 3.31E-03 4

14 Polyamine Regulation in Colon Cancer (29) 5.01E-03 2

15 Protein Ubiquitination Pathway (273) 6.31E-03 5

From the 52 regulated nodes shared between the NZW/BXSB LN mouse model* and the human LN** (tubulointerstitium)

1 Hepatic Fibrosis / Hepatic Stellate Cell Activation (147) 1.62E-08 9

2 Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses (87) 2.04E-05 5

3 IL-17A Signaling in Gastric Cells (25) 2.34E-04 3

4 Communication between Innate and Adaptive Immune Cells (109) 3.09E-04 4

5 Interferon Signaling (36) 5.01E-04 3 6 Antigen Presentation Pathway (43) 5.01E-04 3

7 Dendritic Cell Maturation (188) 5.25E-04 5

8 Pathogenesis of Multiple Sclerosis (9) 8.32E-04 2

9 ILK Signaling (192) 1.20E-03 5

10 Toll-like Receptor Signaling (55) 1.35E-03 3

11 Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis (363) 2.14E-03 6

12 Glioma Invasiveness Signaling (60) 2.19E-03 3

13 PI3K Signaling in B Lymphocytes (143) 2.34E-03 4

14 Activation of IRF by Cytosolic Pattern Recognition Receptors (73) 3.09E-03 3

15 Role of MAPK Signaling in the Pathogenesis of Influenza (74) 3.24E-03 3

(B) Top 10 canonical pathways significantly regulated (p-value<0.05) from the defined “tubulointerstitial and glomerular macrophage genes”, as assessed by IPA (Ingenuity Pathway Analysis).

Number of regulated Canonical pathways (number of genes in the pathway) p-value genes in the pathway

From the 103” tubulointerstitial-restricted macrophage genes”

1 N-Glycan Degradation (26) 9.77E-04 3

2 Axonal Guidance Signaling (432) 1.00E-02 8

3 14-3-3-mediated Signaling (120) 1.29E-02 4 4 P2Y Purigenic Receptor Signaling Pathway (135) 1.29E-02 4

5 Cellular Effects of Sildenafil (Viagra) (151) 1.82E-02 4

6 GNRH Signaling (145) 1.91E-02 4

7 IL-17 Signaling (74) 2.24E-02 3

8 BMP signaling pathway (80) 2.24E-02 3

9 Protein Kinase A Signaling (327) 2.88E-02 6

10 Tight Junction Signaling (164) 3.02E-02 4

From the 224 :glomerular-restricted macrophage genes”

1 Protein Ubiquitination Pathway (274) 6.46E-04 12

2 Glioma Invasiveness Signaling (60) 3.31E-03 5

3 Death Receptor Signaling (65) 4.57E-03 5

4 Fc Receptor-mediated Phagocytosis in Macrophages and Monocytes (102) 7.08E-03 6

5 Mitotic Roles of Polo-Like Kinase (64) 1.41E-02 4

6 Signaling (260) 1.48E-02 9

7 IL-8 Signaling (193) 3.47E-02 7 8 Cell Cycle: G2/M DNA Damage Checkpoint Regulation (49) 4.07E-02 3

9 Molecular Mechanisms of Cancer (377) 4.17E-02 11

10 Leukocyte Extravasation Signaling (199) 4.79E-02 7

From the 110 shared “tubuolointerstitial and glomerular macrophage genes”

1 Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses (87) 6.31E-04 5

2 Atherosclerosis Signaling (107) 1.51E-03 5

3 Leukocyte Extravasation Signaling (199) 1.74E-03 7

4 Fc Receptor-mediated Phagocytosis in Macrophages and Monocytes (102) 1.95E-03 5

5 Complement System (35) 3.63E-03 3

6 Acute Phase Response Signaling (178) 5.37E-03 6

7 Hepatic Fibrosis / Hepatic Stellate Cell Activation (147) 1.17E-02 5

8 Polyamine Regulation in Colon Cancer (29) 1.51E-02 2

9 Ephrin Receptor Signaling (199) 2.14E-02 5

10 Intrinsic Prothrombin Activation Pathway (34) 2.95E-02 2

Supplementary Figure 1. Human-mouse shared glomerular transcriptional networks from TALE analysis. Network sharing the most connection between human and (A) NZM2410 (nephritic vs. young control) (157 nodes), (B) NZB/W (36 wks with established nephritis vs. 16 wks pre-nephritic) (111 nodes), and (C) NZW/BXSB (nephritic vs. prenephritic) (62 nodes). (D) represents the overlap of the node lists using only the nodes regulated in the same direction in both species in each network (103/157 nodes in the NZM2410, 84/111 nodes in the NZB/W and 49/62 modes in the NZW/BXSB). Legend: Each node represents a gene; each edge (blue line) represents a connection between two nodes. The nodes having more than 20 connections, between 2 and 19 connections and only one connection are displayed in the inner layer, the middle layer and the outside layer, respectively. In red, green, white are respectively the nodes up- regulated in both species, down-regulated in both species and discordantly regulated among species.

A Human-NZM2410 B Human-NZB/W efhd2 myo1b hoxb6 aim1 157 nodes 111 nodes ddx21 praf2 abhd5 slc7a7 rab8b ccdc91 rhob abca1 fpr2 nqo1 hladma slc11a1 tfpi2 bmp1 inpp5d hladra psme1 plin2 postn mcam cish id4 psmb9 ctnna1 sh3bp4 itm2c cs plaur ctsd bmp7 lck myd88 egr1 fgf1 cybb pth1r yy1 pipox npr3 jam3 ccr2 odc1 ccr5 dusp1 prkcz il18 socs3 ywhaq wsb1 npr1 lyz

sh2b3 cd86 tlr3 nos3 itgam egf igf1 casp8 a2m ube2c

camlg cdc2 agps pparg cd40 bax agtr1 igf2 jun cxcl10 scly spint2 ccnb1

dsp btk trem2 usp2 ccnd1 lpl ccl5 agt ctgf tlr4 myc cldn4 tlr7 bcat2 rab32 hla-dqa1 cd44 vcam1 cdh1 cdkn1a lif fos timp1 il8rb lmf1 plac8 nans cdkn3 atf3 pigt mmp2 kdr fn1 apoe hmox1 lyn thbs1 pdhx pdgfa rnf123 mmrn2 lcp1 f2rl1 cep170 nqo2 tlr2 ptprc ngf stat1 sod2 vegfa angpt1 cnp ecm1 vav1 slc4a4 cebpa nusap1 coasy anpep tnfaip8 cd38 sema3b dixdc1 prkcb taf12 calb1 slpi casp1 fah aacs kcnn2 fxyd5 macrod1 hmgcl ppic pacsin3 zc3hav1 clcnkb

C Human-NZW/BXSB D 62 nodes sosdc1 cep170 Human-NZM2410 Human-NZB/W 103 nodes 84 nodes bmp7 btk cep55 cebpa lyz sparc ptpn6 26

cklf itgam cdkn1a cdc2 smad7 ccl5 mmp2 47 26

cd47 cd34 tlr2 sod2 zc3hav1

pbk stat1 cd40 vav1

sulf1 ccnb1 syk alb 24

acads lipg apoe cd44 slpi 6 8 kcnn2 ly96 timp1 egf c5ar1 fgl2

tlr1 ptprc myc ccr5

vegfa fgr vcam1 cav1 rab8b lif fn1 il6 psmb9 ctsb

lyn lpl bcat2 11 cxcl10 cubn cd86 Human-NZW/BXSB lmf1 49 nodes