Characterization of the Macrophage Transcriptome in Glomerulonephritis-Susceptible and -Resistant Rat Strains

Home , AIG1, ATP6V0B, CAMK2D, CHMP1B, DNM3, PAK1

ORIGINAL ARTICLE Characterization of the macrophage transcriptome in glomerulonephritis-susceptible and -resistant rat strains

K Maratou1, J Behmoaras2, C Fewings1, P Srivastava1, Z D’Souza1, J Smith3, L Game4, T Cook2 and T Aitman1 1Physiological Genomics and Medicine Group, MRC Clinical Sciences Centre, Imperial College London, London, UK; 2Centre for Complement and Inflammation Research, Imperial College London, London, UK; 3Renal Section, Imperial College London, London, UK and 4Genomics Laboratory, MRC Clinical Sciences Centre, London, UK

Crescentic glomerulonephritis (CRGN) is a major cause of rapidly progressive renal failure for which the underlying genetic basis is unknown. Wistar–Kyoto (WKY) rats show marked susceptibility to CRGN, whereas Lewis rats are resistant. Glomerular injury and crescent formation are macrophage dependent and mainly explained by seven quantitative trait loci (Crgn1–7). Here, we used microarray analysis in basal and lipopolysaccharide (LPS)-stimulated macrophages to identify genes that reside on pathways predisposing WKY rats to CRGN. We detected 97 novel positional candidates for the uncharacterized Crgn3–7. We identified 10 additional secondary effector genes with profound differences in expression between the two strains (45-fold change, o1% false discovery rate) for basal and LPS-stimulated macrophages. Moreover, we identified eight genes with differentially expressed alternatively spliced isoforms, by using an in-depth analysis at the probe level that allowed us to discard false positives owing to polymorphisms between the two rat strains. Pathway analysis identified several common linked pathways, enriched for differentially expressed genes, which affect macrophage activation. In summary, our results identify distinct macrophage transcriptome profiles between two rat strains that differ in susceptibility to glomerulonephritis, provide novel positional candidates for Crgn3–7 and define groups of genes that play a significant role in differential regulation of macrophage activity. Genes and Immunity (2011) 12, 78–89; doi:10.1038/gene.2010.61; published online 23 December 2010

Keywords: crescentic glomerulonephritis; macrophage; microarray; rat

Introduction histocompatibility complex haplotype (RT1-l), develop only mild glomerular hypercellularity with no crescents Crescentic glomerulonephritis (CRGN) is a major cause after the administration of the same dose of NTS.3 of rapidly progressive renal failure for which the under- In WKY rats, macrophage depletion studies4 have lying genetic basis is largely unknown.1 The Wistar– shown that glomerular injury in CRGN is macrophage Kyoto (WKY) rat shows marked susceptibility to CRGN, dependent. This suggests, at least in part, shared patho- as administration of a small dose of nephrotoxic serum physiology with human CRGN, in which macrophage (NTS), which is subnephritogenic in other strains, leads accumulation correlates with the degree of histological and to rapid onset of nephrotoxic nephritis. In WKY rats, functional injury.5–7 Furthermore, bone marrow-derived nephrotoxic nephritis is characterized by the develop- macrophages from WKY rats show a number of pheno- ment of albuminuria by day 4, crescent formation in the typic differences compared with those from LEW rats, majority of glomeruli by day 11 and progression to including enhanced antibody-dependent cytotoxicity, Fc- severe scarring, which is characteristic of CRGN, with receptor-mediated phagocytosis and Fc-receptor-depen- renal failure by 6 weeks.2 Monocytes/macrophages and dent oxidative burst, as well as increased inducible nitric CD8 þ cells are the major cell types in the glomerular oxidesynthasegene(Nos2) expression upon lipopolysac- infiltrate, detected as early as 2.5 h after injection of NTS, charide (LPS) stimulation.3,8 However, bone marrow and and reaching a maximal number between days 4 and 8.2 kidney transplantation studies between WKY and LEW The model is highly reproducible and the histology rats show that, in addition to circulating cells, genetic closely resembles that seen in human glomerulonephritis. susceptibility to CRGN is also partly dependent on In contrast Lewis (LEW) rats, which share the same major intrinsic factors within the kidney itself.9,10 In previous studies, we carried out a genome-wide linkage analysis of an F2 population derived from WKY Correspondence: Professor T Aitman, Physiological Genomics and and LEW rats and mapped seven CRGN quantitative Medicine Group, MRC Clinical Sciences Centre, Hammersmith trait loci (QTL) (Crgn1–7) linked to crescent formation, Hospital, Du Cane Road, London W12 0NN, UK. 3 E-mail: [email protected] proteinuria or macrophage infiltration. The two Received 20 July 2010; revised and accepted 17 September 2010; most significant linkage peaks, with logarithm of the published online 23 December 2010 odds score over 8, were on chromosomes 13 (Crgn1) and Macrophage transcriptome and CRGN K Maratou et al 79 16 (Crgn2). By a combination of expression studies, fine between WKY and LEW macrophages, before and after mapping and functional assays, we identified deletion of LPS stimulation. We also identified a small number of an Fc receptor gene, Fcgr3-rs, as a cause of macro- alternative splicing events that cause transcript isoform phage over-activity and CRGN susceptibility at Crgn1. differences between macrophages from the two strains. Subsequently, we identified over-activity of the AP-1 transcription factor JunD as a cause of enhanced macrophage oxygen burst activity as well as inducible nitric Results oxide synthase synthesis and WKY susceptibility to CRGN at Crgn2.8 Recently, we generated a double Differential gene expression between WKY and LEW congenic rat strain where both Crgn1 and Crgn2 from macrophages nephrotoxic nephritis-resistant LEW rats were intro- Exon array analysis identified 800 transcripts that were gressed into the genetic background of the WKY rat.10 differentially expressed between WKY and LEW bone Our results show that Crgn1 and Crgn2 have an additive marrow-derived macrophages, in the basal state, with a protective effect against glomerular crescent formation.10 5% false discovery rate (FDR) threshold (Supplementary However, this study also highlighted the effects of Table S1). Of these transcripts, 52.9% showed higher Crgn3–7, as bone marrow transplantation experiments expression in WKY compared with LEW. Notably, a showed that the double congenic line shows residual subset of 19 genes showed very strong differential susceptibility to CRGN, which is mainly attributable to expression in basal conditions (45-fold change, o1% macrophage activation rather than macrophage number.10 FDR) (Table 1). Following LPS stimulation, 887 trans- The aim of this study was to compare macrophage cripts were differentially expressed (Supplementary gene expression between the CRGN-susceptible WKY Table S2), and 60.0% of these showed higher expression and CRGN-resistant LEW strains to identify genes that in WKY compared with LEW. There was an overlap of reside on effector pathways for macrophage-mediated exactly 400 genes between the basal and LPS conditions, damage to glomeruli in WKY rats. To achieve this whereas 487 genes were differentially expressed between aim, we used rat exon arrays in basal (unstimulated) the two strains only after LPS stimulation. In addition, and LPS-stimulated macrophages. Our results revealed 15 genes showed very strong macrophage gene differential extensive strain differences in macrophage gene expression (45-fold change, o1% FDR) between the two expression, identified several differentially expressed strains following LPS stimulation (Table 2). Eleven of genes that positionally map to Crgn3–7 (primary effector these genes (Arg1, Fcgr3-rs, Grit, Igf2bp1, IMAGE:5598800, candidates), as well as a subset of genes that, although Lilrb3l, Ly49si1, Ly49si2, Mmp7, Orl1684 and Pirb)showed they map outside the known CRGN QTL regions, similarly strong differential expression in both the basal showed highly significant differential expression and LPS-stimulated state.

Table 1 Microarray results of transcripts showing greater than 5-fold difference in expression between WKY and LEW, in basal macrophages, with o1% FDR

Affymetrix transcript Gene name Gene symbol QTL P-value (LEW Fold change (LEW identity position vs WKY basal) vs WKY basal)

7113955 Fc receptor, IgG, low-affinity III, related sequence Fcgr3-rs Crgn1 2.30EÀ07 52.4 7028278 Leukocyte immunoglobulin-like receptor, subfamily B Lilrb3 l 6.91EÀ09 36.7 (with TM and ITIM domains), member 3 like 7332712 Matrix metallopeptidase 7 Mmp7 4.34EÀ06 12.1 7216236 Olfactory receptor 1684 Olr1684 1.64EÀ07 12.0 7047485 Paired-Ig-like receptor B Pirb 3.49EÀ09 11.6 7332774 Transient receptor potential cation channel, subfamily C, Trpc6 4.35EÀ05 11.2 member 6 7317031 Nephroblastoma-overexpressed gene Nov 4.46EÀ09 10.2 7367852 Rattus norvegicus cDNA clone IMAGE:5598800 5-, IMAGE:5598800 1.97EÀ05 7.9 mRNA sequence 7147913 Ligase IV, DNA, ATP dependent Lig4 2.31EÀ07 6.0 7080736 Chemokine (C–C motif) ligand 9 Ccl9 1.04EÀ05 5.4 7294643 Pleckstrin homology domain containing family H Plekhh2 3.56EÀ06 À5.0 (with MyTH4 domain) member 2 7037483 HtrA serine peptidase 1 Htra1 3.28EÀ06 À5.7 7270146 Immunoreceptor Ly49si2 Ly49si2 3.14EÀ06 À5.8 7334400 Rho GTPase-activating protein Grit/Rics 1.39EÀ07 À6.8 7025757 Arginase 1, liver Arg1 1.54EÀ05 À6.9 7080663 Schlafen 8 Slfn8 4.64EÀ06 À8.3 7374731 Carbonic anhydrase VB, mitochondrial Ca5b 1.30EÀ07 À10.0 7081895 Insulin-like growth factor 2 mRNA-binding protein 1 Igf2bp1 1.81EÀ08 À11.3 7270138 Immunoreceptor Ly49si1 Ly49si1 5.04EÀ08 À12.8

Abbreviations: ANOVA, analysis of variance; FDR, false discovery rate; fold-change, positive values show genes downregulated in WKY, whereas negative values show genes upregulated in WKY compared with LEW macrophages; Ig, immunoglobulin; ITIM, immunoreceptor tyrosine-based inhibitory motif; LEW, Lewis; P-value, uncorrected ANOVA P-value; QTL, quantitative trait loci; TM, transmembrane helix; WKY, Wistar–Kyoto. Data are sorted according to fold change.

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 80 Table 2 Microarray results of transcripts showing greater than 5-fold difference in expression between WKY and LEW, in LPS-stimulated macrophages, with o1% FDR

Affymetrix transcript Gene name Gene symbol QTL P-value (LEW Fold change (LEW identity position vs WKY LPS) vs WKY LPS)

7113955 Fc receptor, IgG, low-affinity III, related sequence Fcgr3-rs Crgn1 1.35EÀ07 40.58 7028278 Leukocyte immunoglobulin-like receptor, subfamily B Lilrb3 l 2.44EÀ09 37.47 (with TM and ITIM domains), member 3 like 7332712 Matrix metallopeptidase 7 Mmp7 2.04EÀ07 29.46 7216236 Olfactory receptor 1684 Olr1684 2.92EÀ07 7.22 7047485 Paired-Ig-like receptor B Pirb 9.15EÀ09 6.33 7367852 Rattus norvegicus cDNA clone IMAGE:5598800 5-, IMAGE:5598800 2.01EÀ05 5.97 mRNA sequence 7080742 Chemokine (C–C motif) ligand 6 Ccl6 1.88EÀ07 5.52 7361223 Similar to putative protein (5S487) RGD1310819 2.34EÀ07 5.31 7334400 Rho GTPase-activating protein Grit/Rics 1.23EÀ07 À5.39 7270146 Immunoreceptor Ly49si2 Ly49si2 1.60EÀ06 À5.40 7238766 Rho family GTPase 3 Rnd3 8.74EÀ07 À5.84 7132879 Stathmin-like 4 Stmn4 Crgn7 1.58EÀ07 À7.85 7270138 Immunoreceptor Ly49si1 Ly49si1 5.03EÀ08 À9.07 7025757 Arginase 1, liver Arg1 7.09EÀ07 À14.09 7081895 Insulin-like growth factor 2 mRNA-binding protein 1 Igf2bp1 5.65EÀ10 À31.52

Abbreviations: ANOVA, analysis of variance; FDR, false discovery rate; fold change, positive values show genes downregulated in WKY, whereas negative values show genes upregulated in WKY compared with LEW macrophages; Ig, immunoglobulin; ITIM, immunoreceptor tyrosine-based inhibitory motif; LEW, Lewis; LPS, lipopolysaccharide; P-value, uncorrected ANOVA P-value; QTL, quantitative trait loci; TM, transmembrane helix; WKY, Wistar–Kyoto. Data are sorted according to fold change.

Table 3 Candidate genes for CRGN QTL

QTL Chr. Markers Position (Mb) Phenotypes Candidate genes basal Candidate genes LPS-stimulated macrophages macrophages

Crgn3 1 D1Rat246-D1Rat4 2.4–11.86 Proteinuria RGD1309759, Map3k7ip2, Stxbp5, Map3k7ip2, Stxbp5, RGD1564259, Pex3, Pex3, Aig1, Gpr126 Aig1, Gpr126 Crgn4 2 D2Rat234-D2Rat70 201.2–254.9 Crescents, Tmem77, Csf1, Gstm1, Slc25a24, Tmem77, Kcna3, Csf1, Sars, Slc25a24, proteinuria Ptbp2, Alpk1, Manba, Gbp2, Clca4, Slc44a3, Bcar3, Camk2d, LOC691931, Mcoln3 Manba, Ppp3ca, Eif4e, Mcoln3, Mcoln2 Crgn5 5 D5Rat22-D5Rat33 102–143.5 Crescents Adfp, Cdkn2b, Cyp2j4, Atg4c, Jak1, RGD1305797, Cdkn2b, Cyp2j4, Atg4c, Zcchc11, Kti12, Eps15, Spata6, Jak1, Ak3l1, Zfyve9, Btf3l4, Kti12, Cmpk1, Mmachc, Atp6v0b, Ipo13, Osbpl9, Spata6, Toe1, Plk3, Atp6v0b, Med8, RGD1309802, Zmpste24, Ipo13, RGD1309802, Cap1 Mycl1 Crgn6 10 D10Rat186-D10Rat45 0–20.2 Proteinuria RGD1563547, Mgrn1, Zfp263, Abcc1, Rrn3, RGD1306568, Emp2, Thoc6, LOC100158225, Tsc2, Crebbp, Tmem204, RGD1307381, Clcn7, RGD1307381, Tmem8, Tmem8, RGD1311343, Dusp1 Dusp1 Crgn7 15 D15Rat54-D15Rat96 14.1–71.5 Proteinuria Flnb, Lrp10, Slc7a8, Zfhx2, Irf9, Flnb, Wdhd1, Slc7a7, Acin1, Slc7a8, Ripk3, Kpna3, Mtmr9, Ephx2, Ptk2b, Spata13, Kpna3, Mtmr9, Ccdc25, Ephx2, Trim35, Stmn4, Hr, Htr2a, Cog3, Trim35, Stmn4, Dpysl2, Hr, Dok2, Lcp1, Slc25a30, Epsti1, Elf1, Pcdh8 Slc25a30, Nufip1, Epsti1, Diap3

Abbreviations: Chr., chromosome; CRGN, crescentic glomerulonephritis; LPS, lipopolysaccharide; PCR, polymerase chain reaction; QTL, quantitative trait loci. Genes highlighted in bold were tested by quantitative PCR.

Validation of differentially expressed genes can be considered as candidate genes for Crgn3–7 that Sixty-one of the genes showing differential expression may confer primary susceptibility to CRGN. Thirty-two (o5% FDR) between WKY and LEW in basal macro- of these genes showed similarly strong differential phages and 68 genes showing differential expression expression in both the basal and LPS-stimulated state. in LPS-stimulated macrophages mapped within the To test the accuracy of the microarray results, we 1.5 logarithm of the odds support interval of our randomly selected six genes (Aig1, Csf1, Epsti1, Mcoln3, previously localized crescentic glomerulonephritis sus- Spata6 and Stxbp5) and carried out quantitative real-time ceptibility QTLs (Crgn3–7)3 (Table 3). As these genes are PCR (qPCR) on these six differentially expressed differentially expressed and map to a known QTL, they transcripts (Figure 1a). With the exception of Csf1, the

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 81 * change, o1% FDR) between WKY and LEW basal WKY 8 macrophages, whereas the remaining 10 (Agmat, Ccl6, LEW Ccrl2, Cd4, Cd55, Ifngr1, Il18, Il1rn, P2rx7 and Trf) were 6 selected based on literature searches, as having known or * suspected links to glomerulonephritis. Consistent with 4 * the microarray results, qPCR showed that all 18 genes * were significantly differentially expressed between WKY 2 * and LEW macrophages and matched the directional

Relative expression change of the microarray data (Figures 1b and c). Two of 0 the genes Agmat and Arg1 showed more than 50-fold greater expression by qPCR analysis in WKY compared Aig1 Csf1 Epsti1 Spata6 Mcoln3 Stxbp5 with LEW. Conversely, three genes Mmp7, Lilrb3l and Trpc6 were expressed at over 20-fold higher levels in 250 * LEW compared with WKY macrophages. To ensure that the strong differential expression detected by qPCR 150 * * for Agmat, Arg1 and Mmp7 (the three most strongly differentially expressed genes) is not an artifact owing to 50 genomic sequence variation, we sequenced the qPCR * * 40 primer binding regions by capillary sequencing and 30 * * found no single-nucleotide polymorphisms (SNPs) in the 20 * * WKY or LEW rat strains that were able to affect the Relative expression 10 binding properties of the primers used for qPCR 0 amplification. Overall, therefore, we successfully validated 23 out of the 24 transcripts selected on the basis Arg1 Ca5b Il1rn P2rx7 Trpc6 Lilrb3l Mmp7 Agmat Igf2bp1 that they either (i) map to Crgn3–7, (ii) showed marked inter-strain differential expression or (iii) have pre- * viously reported links to glomerular inflammation. 4 * * * Testing for influence of Crgn1 and Crgn2 on selected gene 3 expression * * To establish whether differential expression of these 2 * * * genes was under the genetic control of our previously identified QTL genes at Crgn1 and Crgn2, we measured 1 the expression of the 23 validated selected genes in

Relative expression a panel of congenic strains. The panel of congenic 0 strains consisted of: congenic rats WKY.LCrgn1 and Trf Il18 Cd4 Pirb Cd55 Ccrl2 Ccl6 Ccl9 WKY.LCrgn2, in which we previously introgressed Ifngr1 the Crgn1 or Crgn2 QTL region from the resistant LEW Figure 1 Validation of microarray data with quantitative PCR strain into the WKY genetic background;8,10 a reciprocal of selected differentially expressed genes. (a) Positional differen- congenic strain on the LEW background for Crgn1 tially expressed QTL candidate genes; (b, c) secondary effector (LEW.WCrgn1); and a double congenic strain (WKY. genes. Relative gene expression was compared with Hprt for WKY and LEW basal macrophages. The secondary effector genes LCrgn1, 2) where both Crgn loci from the resistant LEW are presented in two separate graphs (b, c) according to the strain were introgressed into the WKY genetic back- scale of relative gene expression. All samples were amplified using ground. The expression levels in the macrophages of an independent set of biological duplicates with three technical the congenic animals matched those of the genetic replicates per sample. *Po0.05 using a Mann–Whitney non- background parental strain for all genes, indicating that parametric test (one-tailed). Error bars represent standard deviation. they are not influenced in trans by genes encoded at Crgn1 and/or Crgn2 (Figure 2). Most significantly, our differences in gene expression shown by the microarray data show that none of the five validated positional were confirmed for all of these genes. candidate genes at Crgn3–7 are influenced by Crgn1 or In addition to the 61 genes that map to CRGN QTL Crgn2. regions, a large number of genes (739 transcripts) that were differentially expressed (o5% FDR) between WKY Pathway and functional annotation analysis and LEW basal macrophages do not map to previously To characterize functionally the sets of genes that localized CRGN QTLs. Although these genes are not were differentially expressed between WKY and LEW primary positional candidates for the Crgn3–7 QTLs, macrophages, we performed pathway and functional they may contribute indirectly to the effector pathways annotation analysis using the DAVID (Database for that lead to WKY susceptibility to CRGN. To separate Annotation, Visualization, and Integrated Discovery) these genes from the Crgn3–7 positional candidates, we Bioinformatics Resources.11,12 We used the lists of 800 refer to these 739 transcripts as ‘secondary effector’ and 887 genes that were differentially expressed between genes. To validate these results, we selected a set of 18 of WKY and LEW macrophages before and after LPS these genes for analysis by qPCR. Eight of the 18 genes stimulation, respectively (o5% FDR), and found five (Arg1, Ca5b, Ccl9, Igf2bp1, Lilrb3l, Mmp7, Pirb and Trpc6) KEGG (Kyoto Encyclopedia of Genes and Genomes) or were selected because they were among the 19 genes PANTHER (Protein ANalysis THrough Evolutionary that show profound differences in expression (45-fold Relationships) pathways that were enriched (EASE score

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 82 250 5 250 30 200 4 150 150 20 100 3 50 3

3 Aig1 Arg1 2 ** Ca5b Agmat ****** *** 2 10 2 *** *** 1 1 1 *** *** 0 0 0 0

WKY LEW WKY LEW WKY LEW WKY LEW

WKY.LCrgn1LEW.WCrgn1WKY.LCrgn2 WKY.LCrgn1LEW.WCrgn1WKY.LCrgn2 WKY.LCrgn1LEW.WCrgn1WKY.LCrgn2 WKY.LCrgn1LEW.WCrgn1WKY.LCrgn2 WKY.LCrgn1,2 WKY.LCrgn1,2 WKY.LCrgn1,2 WKY.LCrgn1,2

4 *** *** 3 *** 4 2.5 *** *** 3 2.0 *** 3 2 * 2 *** 1.5

Ccl6 2 Ccrl2 Ccl9 1 Cd4 1.0 1 1 0.5 0 0 0 0.0

6 3 *** 20 ** * 8 * 15 4 6 2 * 10

Cd55 4 Ifngr1 2 1 Igfbp1 Epsti1 5 2 * * *** *** 0 0 0 0

15 6 40 *** * 5.0 30 *** 10 4 20 ** 10 *** 2 2.5 Il18 Il1rn

*** *** Lilrb3l 5 2 Mcoln3 ** *** *** 1 * 0 0 0 0.0

15 4 5 175 *** *** *** *** 3 4 75 10 3 3 2 Pirb

P2rx7 2 ** Mmp7 2 5 Spata6 *** 1 1 1 *** *** 0 0 0 0

4 *** 3 40 *** ** * 30 3 * *** 2 20 10 2 ** 2 Trf * Trpc6

Stxbp5 1 1 1

0 0 0 Figure 2 Selected gene expression in a panel of congenic strains. Relative gene expression compared with Hprt for WKY, LEW, WKY.LCrgn1, LEW.WCrgn1, WKY.LCrgn2 and WKY.LCrgn1,2 in basal macrophages. The sample order shown in the top row of graphs is maintained for all subsequent graphs. All samples were amplified using an independent set of biological duplicates with three technical replicates per sample. *Po0.05; **Po0.01; ***Po0.001 statistically significantly different to WKY, using a one-way ANOVA with Bonferonni’s correction. Error bars represent standard deviation.

o0.01) in basal macrophages and four pathways that strong evidence that the differences in gene expression were enriched in LPS-stimulated macrophages (Table 4). between WKY and LEW (before and after LPS stimula- These results suggest that differential gene expression tion) are likely to impact on the regulation of macro- between the two strains may be functionally related to phage activation, as the top functional annotation terms, altered endocytosis, regulation of the actin cytoskeleton, in both basal and LPS-stimulated macrophages, are cell leukocyte transendothelial migration and Fc gamma- activation and leukocyte or lymphocyte activation. receptor-mediated phagocytosis. Furthermore, we identified 33 Biological Process annotation terms, which were Alternative splicing analysis between WKY and LEW significantly enriched (Benjamini correction o0.05) in macrophages basal macrophages, and five Biological Process anno- Affymetrix exon arrays allow the detection of splicing tation terms, which were significantly enriched in LPS- differences in two groups of data. We initially used the stimulated macrophages (Table 5). These results provide intersect of three commonly used alternative splicing

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 83 Table 4 Canonical pathways enriched for genes differentially expressed between Lewis and WKY macrophages, with EASE score values o0.01

Pathway database Pathway Genes (n) P-value % Differentially expressed genes

Basal macrophages KEGG rno04144: Endocytosis 22 6.89EÀ04 2.9 Igf1r, Vps37c, Pip5k1b, Tfrc, Ap2m1, Git2, Cxcr4, Kit,Fam125a,Psd3, Grk6, Chmp1b, Pld1, RT1-CE10, Itch, Met, Agap3, Ret, Eps15, Erbb3, Ldlr, Rab31 KEGG rno04640: Hematopoietic cell 12 1.25EÀ03 1.6 Anpep, Tfrc, Cd55, Kit, Cd14, Itga1, Csf1, H2-Ea, Cd24, Cd44, lineage Cd8b, Cd4 KEGG rno04810: Regulation of actin 20 5.99EÀ03 2.6 Itgal, Pip5k1b, Myh10, Baiap2, Ssh1, Arpc1b, Arpc3, Pxn, Enah, cytoskeleton Cd14, Pdgfc, Itga1, Pak6, Slc9a1, Pik3cd, Pik3cg, Scin, Itgb8, Rac2, Arhgef12 KEGG rno04670: Leukocyte 13 9.65EÀ03 1.7 Itgal, Sipa1, Pxn, Cxcr4, Rassf5, Ptk2b, Ocln, Pik3cd, Pik3cg, transendothelial migration Rac2, Icam1, Esam, Cybb KEGG rno04666: Fc gamma R-mediated 11 9.90EÀ03 1.4 Pip5k1b, Arpc1b, Arpc3, Pla2g4a, Fcgr2b /// Fcgr3 /// phagocytosis Fcgr3-rs, Prkcd, Pld1, Pik3cd, Pik3cg, Scin, Rac2

LPS-stimulated macrophages KEGG rno04666: Fc gamma R-mediated 15 1.98EÀ04 1.8 Pak1, Pip5k1b, Arpc1b, Pla2g4a, Fcgr3-rs, Prkcd, Syk, Ppap2a, phagocytosis Myo10, Pld1, Gsn, Pik3cg, Asap1, Pla2g6, Was KEGG rno04144: Endocytosis 22 2.20EÀ03 2.6 Igf1r, Vps37c, Adrbk1, Pip5k1b, Arrb2, Cblb, Tfrc, Git2, Grk4, Fam125a, Chmp1b, Dab2, Pld1, RT1-N2, Psd4, Ehd4, Ret, Erbb3, Mdm2, Asap1, Rab31, Sh3kbp1 PANTHER P00016: Cytoskeletal regulation 15 5.57EÀ03 1.8 Pak1, Myh10, Cdgap, Ssh1, Arpc1b, Stmn4, Diaph3, Tubb6, by Rho GTPase Diaph1, Tubb5, Pak6, Rics, Arhgef12, Was, Diaph2 KEGG rno04810: Regulation of actin 21 7.50EÀ03 2.5 Pak1, Actn4, Pip5k1b, Myh10, LOC685269, Ssh1, Arpc1b, Pxn, cytoskeleton Vcl, Diaph3, Fgd3, Diaph1, Gsn, Pak6, Gng12, Slc9a1, Pik3cg, Golga4, Arhgef12, Was, Diaph2

Abbreviations: CRGN, crescentic glomerulonephritis; LEW, Lewis; LPS, lipopolysaccharide; P-value, modified Fisher’s exact test (EASE score); %, percentage (differentially expressed genes/total genes in pathway) involved genes; QTL, quantitative trait loci; WKY, Wistar–Kyoto. detection algorithms (MiDAS, Rank Product of Slice Sgms1), sequenced their differentially expressed exons and Index and Alternative Splice analysis of variance confirmed that they did not contain any SNPs. Next, we (ANOVA)) and found 154 and 94 transcripts with used qPCR to test whether specific isoforms of the two isoform differences between WKY and LEW for basal selected transcripts are differentially expressed between or LPS-stimulated macrophages, respectively. However, WKY and LEW macrophages. We used pairs of primers capillary sequencing of the probe-set regions of seven to amplify the exons showing differential expression selected transcripts that showed evidence of alternative between strains (Dnm3 exon 20, and Sgms1 exon 10) and exon use (Akr7a2, Coq10b, Cxcl10, Pa2g4, Pttg1ip, Reps2 compared the results to a second control pair of primers and Slfn3) revealed that all seven selected transcripts designed to amplify exons showing similar expression contained SNPs within the probe-binding sequence of levels between the two strains (Dnm3 exons 15–16 and the exon that was predicted to be alternatively spliced Sgms1 exons 3–4). Our data confirm the microarray data (Supplementary Table S3). This suggested that the set of predictions for (i) Dnm3 where differential expression transcripts showing apparent alternative splicing con- between strains was found for exon 20, whereas no tained a high number of false positives, owing to SNPs differential expression was detected for exons 15 and 16; affecting the hybridization properties of the correspond- and (ii) Sgms1 where differential expression between ing probe sets. We therefore carried out a more in-depth strains was found for exon 10, whereas no differential analysis of the data at probe level, permitting us to expression was detected for exons 3 and 4 (Figure 3). In remove SNP containing probe sets, by using a filter that summary, although our analysis has revealed a high marks probe sets as false positives when not all four number of polymorphisms between the two strains that probes within a probe set are differentially expressed account for the majority of transcripts showing apparent between the two rat strains. Our revised analysis splicing differences, we also identified a small number of identified a much smaller number of potentially alter- alternatively spliced transcripts that are differentially natively spliced genes. Four transcripts (Dnm3, Hes1, expressed between WKY and LEW macrophages. Ndrg4 and Rtel1) showed isoform differences between WKY and LEW macrophages in the basal state, and four transcripts (Mical2, Polr1a, Sgms1 and Ube3a)showed Discussion isoform differences following LPS stimulation (Table 6). None of these transcripts show a single internal exon that The aim of this study was to identify differentially is included or excluded in a strain-specific pattern. Rather, expressed macrophage genes that reside on effector path- the isoform differences that we detect concern differential ways for macrophage-mediated damage to glomeruli expression of probe sets mapping to the 50-or30-UTR of following CRGN induction in WKY rats. To achieve a gene, or multiple consecutive exons of a transcript. To this aim, we compared gene expression in basal and validate these data, we selected two transcripts (Dmn3 and LPS-stimulated bone marrow-derived macrophages

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 84 Table 5 Enrichment of functional annotation terms using DAVID for genes differentially expressed between Lewis and WKY macrophages, with values o0.05 after Benjamini multiple testing correction

BP_FAT GO term Genes (n) P-value Benjamini

Basal macrophages GO:0001775Bcell activation 29 6.13EÀ07 1.80EÀ03 GO:0045321Bleukocyte activation 26 1.89EÀ06 2.78EÀ03 GO:0002250Badaptive immune response 14 2.07EÀ06 2.03EÀ03 GO:0002460Badaptive immune response based on somatic recombination of immune receptors 14 2.07EÀ06 2.03EÀ03 built from immunoglobulin superfamily domains GO:0046649Blymphocyte activation 22 4.66EÀ06 3.43EÀ03 GO:0010033Bresponse to organic substance 67 5.20EÀ06 3.06EÀ03 GO:0006955Bimmune response 38 6.04EÀ06 2.96EÀ03 GO:0043067Bregulation of programmed cell death 52 2.13EÀ05 8.93EÀ03 GO:0010941Bregulation of cell death 52 2.40EÀ05 8.78EÀ03 GO:0002684Bpositive regulation of immune system process 25 2.63EÀ05 8.56EÀ03 GO:0002443Bleukocyte mediated immunity 13 4.64EÀ05 1.36EÀ02 GO:0051338Bregulation of transferase activity 28 9.21EÀ05 2.44EÀ02 GO:0001666Bresponse to hypoxia 21 1.03EÀ04 2.50EÀ02 GO:0043549Bregulation of kinase activity 27 1.05EÀ04 2.35EÀ02 GO:0030155Bregulation of cell adhesion 16 1.06EÀ04 2.20EÀ02 GO:0045859Bregulation of protein kinase activity 26 1.19EÀ04 2.31EÀ02 GO:0042981Bregulation of apoptosis 49 1.22EÀ04 2.21EÀ02 GO:0001816Bcytokine production 10 1.39EÀ04 2.37EÀ02 GO:0048871Bmulticellular organismal homeostasis 13 1.44EÀ04 2.32EÀ02 GO:0042127Bregulation of cell proliferation 48 1.55EÀ04 2.38EÀ02 GO:0002449Blymphocyte-mediated immunity 11 1.61EÀ04 2.34EÀ02 GO:0042110BT-cell activation 15 1.79EÀ04 2.48EÀ02 GO:0051174Bregulation of phosphorus metabolic process 34 1.85EÀ04 2.44EÀ02 GO:0019220Bregulation of phosphate metabolic process 34 1.85EÀ04 2.44EÀ02 GO:0050778Bpositive regulation of immune response 17 2.00EÀ04 2.52EÀ02 GO:0070482Bresponse to oxygen levels 21 2.37EÀ04 2.86EÀ02 GO:0009611Bresponse to wounding 34 2.78EÀ04 3.22EÀ02 GO:0051347Bpositive regulation of transferase activity 21 3.27EÀ04 3.64EÀ02 GO:0002252Bimmune effector process 15 4.03EÀ04 4.30EÀ02 GO:0042592Bhomeostatic process 49 4.25EÀ04 4.37EÀ02 GO:0042325Bregulation of phosphorylation 32 4.42EÀ04 4.39EÀ02 GO:0033674Bpositive regulation of kinase activity 20 5.00EÀ04 4.79EÀ02 GO:0048584Bpositive regulation of response to stimulus 22 5.27EÀ04 4.89EÀ02

LPS-stimulated macrophages GO:0001775Bcell activation 30 2.01EÀ06 5.87EÀ03 GO:0046649Blymphocyte activation 23 8.36EÀ06 1.21EÀ02 GO:0045321Bleukocyte activation 26 1.46EÀ05 1.41EÀ02 GO:0048872Bhomeostasis of number of cells 17 2.15EÀ05 1.56EÀ02 GO:0006955Bimmune response 38 7.69EÀ05 4.40EÀ02

Abbreviations: Benjamini, Benjamini multiple testing correction value; BP_FAT, subset of Biological Process Gene Ontology (GO) terms, generated by the DAVID team. This subset is missing the broadest GO terms for ease of data interpretation; DAVID, Database for Annotation, Visualization, and Integrated Discovery; LPS, lipopolysaccharide; P-value, modified Fisher’s exact test (EASE score); WKY, Wistar–Kyoto.

between CRGN-susceptible WKY and CRGN-resistant Crgn3–7. Most of these genes are novel candidates as LEW inbred rat strains using rat exon arrays that provide they have not been previously identified to have a role extensive coverage of the rat genome. We used LPS in renal disease. We validated the microarray results by stimulation because LPS, an outer membrane component an independent assay, for five out of six selected genes of Gram-negative bacteria, is widely used as an in vitro (Aig1, Epsti1, Mcoln3, Spata6 and Stxbp5). Analysis in stimulus that mirrors pro-inflammatory macrophage congenic strains showed that none of these five genes activation. Our results reveal extensive strain differences appear to be influenced by Crgn1 or Crgn2. Therefore, as in macrophage gene expression, with several genes these five genes are differentially expressed and map to that reside within Crgn3–7 showing major expression a CRGN susceptibility locus, they can be considered as differences, as well as a subset of genes that, although attractive differentially expressed positional candidate they map outside the known CRGN QTL regions, also genes for glomerulonephritis susceptibility encoded at show highly significant differential expression between Crgn3–7. The potential role of these genes, Aig1 (an WKY vs LEW macrophages, before and after LPS androgen-inducible gene), Epsti1 (an epithelial–stromal stimulation. interaction protein), Mcoln3 (an inward-rectifying cation We found 61 genes, differentially expressed between channel), Spata6 (a spermatogenesis-associated gene) and WKY and LEW in basal macrophages and 68 genes Stxbp5 (a syntaxin-binding protein implicated in the differentially expressed in LPS-stimulated macrophages, formation of complexes involved in neurotransmitter which map within our previously localized CRGN QTLs release), in macrophage function is currently unknown.

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 85 Table 6 Microarray results of transcripts showing isoform differences between WKY and LEW, in basal or LPS-stimulated macrophages

Gene symbol Ensembl ID Transcript ID Probe-set ID P-value Fold change (LEW vs WKY) Differential exon

Basal macrophages Hes1 ENSRNOT00000002346 7094386 6372610 1.52EÀ03 1.61 1 Dnm3 ENSRNOT00000067653 7113169 6694062 1.69EÀ04 À7.83 18 Dnm3 ENSRNOT00000067653 7113169 6589493 1.98EÀ08 À4.57 19 Dnm3 ENSRNOT00000067653 7113169 5968420 1.52EÀ06 À8.40 19 Dnm3 ENSRNOT00000067653 7113169 5697034 4.25EÀ04 À3.87 20 Dnm3 ENSRNOT00000067653 7113169 5799664 4.59EÀ06 À7.75 20 Ndrg4 ENSRNOT00000017413 7182656 6239394 6.97EÀ04 2.94 11 Ndrg4 ENSRNOT00000017413 7182656 6202670 1.03EÀ03 4.75 13 Rtel1 ENSRNOT00000055030 7236395 5939980 2.49EÀ05 2.16 Novel 50-UTR Rtel1 ENSRNOT00000055030 7236395 5780635 7.71EÀ04 2.96 1 Rtel1 ENSRNOT00000055030 7236395 6399147 7.43EÀ06 4.78 1 Rtel1 ENSRNOT00000055030 7236395 6522856 1.64EÀ04 2.33 2

LPS macrophages Ube3a ENSRNOT00000021366 7032220 5976529 2.17EÀ04 2.30 11 Ube3a ENSRNOT00000021366 7032220 6269903 5.78EÀ05 12.24 Novel extension of 11 Mical2 ENSRNOT00000021858 7035525 6277897 2.99EÀ03 À1.93 8 Mical2 ENSRNOT00000021858 7035525 5940132 5.82EÀ04 À3.07 9 Mical2 ENSRNOT00000021858 7035525 6161609 2.91EÀ04 À3.08 Novel 30-UTR Sgms1 ENSRNOT00000054761 7061894 6030014 9.26EÀ06 À2.03 8 Sgms1 ENSRNOT00000054761 7061894 5676967 1.03EÀ04 À2.10 9 Sgms1 ENSRNOT00000054761 7061894 6269809 4.10EÀ04 À2.25 10 Sgms1 ENSRNOT00000054761 7061894 6528432 7.29EÀ04 À2.47 10 Polr1a ENSRNOT00000013587 7254811 6282280 4.42EÀ04 À1.62 31 Polr1a ENSRNOT00000013587 7254811 6206810 3.05EÀ05 À1.70 32 Polr1a ENSRNOT00000013587 7254811 6591259 3.45EÀ05 À1.85 33

Abbreviations: ANOVA, analysis of variance; differential exon, exon differentially expressed between strains; No other exons in each gene showed statistically significant differences between WKY and LEW; fold change, positive values show exons downregulated in WKY, whereas negative values show exons upregulated in WKY compared with LEW macrophages; ID, identity; LEW, Lewis; LPS, lipopolysaccharide; P-value, ANOVA P-value; UTR, untranslated region; WKY, Wistar–Kyoto.

This study has identified a number of differentially identified in other studies as having potential roles in expressed genes for Crgn3–7. However, it should be macrophage function or glomerulonephritis. Particularly, noted that one or more of these QTLs may account for Arg1 competes for substrate with inducible nitric oxide genetic susceptibility mediated by intrinsic rather than synthase (encoded by Nos2), an enzyme that controls the circulating factors. In our recently published research production of nitric oxide (NO), and this can prevent aiming to dissect the contribution of Crgn3–7 to suscepti- NO-mediated apoptosis in activated macrophages.13,14 bility to CRGN in the WKY rat, we performed bone In addition to a primary role in macrophage function, marrow transplantation experiments between congenic Arg1 has been implicated in having a role in glomer- and parental rats.10 Specifically, when LEW bone marrow ulonephritis, as upregulation of this gene has previously is transplanted to a WKY rat, this leads to an almost been shown to occur in macrophages and glomeruli 90% protection in the glomerular crescent formation isolated from rats following induction of acute immune following glomerulonephritis induction. These results complex glomerulonephritis.15–18 We found Arg1 to be suggest that a residual 10% of the CRGN susceptibility markedly overexpressed in WKY compared with LEW may be due to intrinsic cell activation in the glomerulus. basal macrophages. Mmp7 (also referred to as matrilysin) As this work focused on circulating cells, candidates for is a secreted protease, which is important for mediating intrinsic factor CRGN susceptibility are not detectable proteolytic release of tumor nercrosis factor from macro- via this screen. phages,19 as well as the activation of antipathogenic and We found 11 genes (Arg1, Fcgr3-rs, Grit, Igf2bp1, chemotactic proteins such as defensins (reviewed by IMAGE:5598800, Lilrb3l, Ly49si1, Ly49si2, Mmp7, Orl1684 Burke20). Macrophage-produced Mmp-7 has also been and Pirb) that showed profound differences in expres- shown to be necessary for macrophage infiltration.21 We sion between the two strains (45-fold change, o1% found this gene to be highly underexpressed in WKY FDR), for both basal and LPS-stimulated macrophages. compared with LEW basal macrophages. Pirb (also Aside from Fcgr3-rs, the remaining 10 genes do not map known as Lilrb3) is a receptor expressed on immune to previously identified CRGN QTL regions, although cells where it binds to major histocompatibility complex they show significant differential expression both be- class I molecules on antigen-presenting cells and tween WKY and LEW macrophages in the basal state and transduces a negative signal that inhibits stimulation after LPS stimulation. These 10 genes may therefore play of an immune response (reviewed by Takai22). We a significant role in differential regulation of macrophage found this gene to be downregulated in WKY compared activation between WKY and LEW rats and may have with LEW, and studies from Pirb-deficient mice have a secondary effector role in mediating CRGN suscepti- shown that its absence in macrophages causes increased bility. Of these 10 genes, Arg1, Mmp7 and Pirb have been macrophage infiltration and affects integrin signaling.23

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 86 Dnm3 Sgms1

10 4 3 Exon no. 20 16 15 StrainLEW WKY

*** 4 6 ***

3 4 2 Dnm3 Sgms1 2 1

0 0

WKY Lewis WKY Lewis WKY Lewis WKY Lewis exon 20 exons 15+16 exon 10 exons 3+4 control control Figure 3 Comparison of alternative splicing microarray data predictions with quantitative PCR of selected transcript isoforms. (a) Microarray data showing increased expression of probe sets that correspond to Dnm3 exons 17–21, in WKY basal macrophages and increased expression of probe sets that correspond to Sgms1 exons 8–10, for WKY LPS-stimulated macrophages. Data are illustrated as a gene

view plot showing mean log2 signal probe-set intensities by strain. Error bars represent standard errors. (b) qPCR data confirming the microarray predictions. Relative expression, compared with Hprt, for WKY and LEW basal macrophages of Dnm3 exon 20 and control exons 15 and 16, together with WKY and LEW LPS-stimulated macrophages of Sgms1 exon 10 and control exons 3 and 4. All samples were amplified using an independent set of biological duplicates with three technical replicates per sample. ***Po0.001, using a one-way ANOVA with Bonferonni’s correction. Error bars represent standard deviation.

Both of our previously identified Crgn1 and Crgn2 show a compression of measured fold changes,24 which genes3,8 were detected as significantly differentially could have led to missing subtle differences between expressed (o5% FDR) by this study. The molecular basis transcripts if the data were filtered according to fold for Crgn1 is a deletion within Fcgr3-rs, which abolishes change. However, our data analysis is not based on fold- the expression of this gene in WKY macrophages. Such change filtering, but based on use of statistical tests disruptions in gene expression are easily detected by (ANOVA) together with false-discovery rate correction. microarrays and Fcgr3-rs was found to be among the Our previous work has showed that WKY macro- 11 genes that showed profound differences in expression phages are primed for an inflammatory response and between WKY and LEW (45-fold change, o1% FDR), show a number of phenotypic differences relating to for both basal and LPS-stimulated macrophages. The macrophage activation compared with those from LEW molecular basis for Crgn2 is overexpression of JunD rats, including enhanced antibody-dependent cytotoxi- in WKY rats caused, at least in part, by a promoter city, Fc-receptor-mediated phagocytosis and Fc-receptor- polymorphism. This gene was detected by the exon dependent oxidative burst, as well as increased inducible microarray as having modest (1.42-fold), yet highly nitric oxide synthase gene (Nos2) expression upon LPS significant (P ¼ 3.5EÀ4; o5% FDR) differences in macro- stimulation.3,8 Our functional annotation and pathway phages gene expression between the two strains. We find analysis supports these results, because the canonical that the differences in expression for this gene are pathways that associate with the inter-strain differential detected more easily when qPCR is used, in which over expression in basal and LPS-stimulated macrophages fourfold relative gene expression changes are detected also relate to macrophage activation. For example, between WKY and LEW basal macrophages.8 Compared Fc-receptor-mediated phagocytosis is widely used as with quantitative PCR-based methods, microarrays may one of the measures of macrophage activation. The

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 87 canonical pathways that associate with the inter-strain macrophage activity. Many of these genes have not differential expression in basal and LPS-stimulated previously been implicated in CRGN and represent macrophages are also connected. Specifically, phago- novel candidate disease susceptibility genes and poten- cytosis is a specific form of endocytosis, whereas tial targets for therapeutic intervention in immuno- remodeling of the actin cytoskeleton is both an integral logically mediated glomerulonephritis. part of Fc gamma-receptor-mediated phagocytosis25 and being important for macrophage migration.26 Dysregula- tion of Fc gamma receptors is known to associate with Materials and methods glomerulonephritis susceptibility both in WKY rats and Animals in human beings.3,27,28 Therefore, some of the differentially expressed genes that associate with the above- WKY (WKY/NCrl) and LEW (LEW/Crl) rats were connected pathways may represent novel targets for purchased from Charles River (Margate, UK). All pro- therapeutic intervention in nephritis. cedures were performed in accordance with the United RNA splicing is an important mechanism for generat- Kingdom Animals (Scientific Procedures) Act. ing transcriptional diversity and regulating gene expression. We used alternative splicing analysis to identify a Bone marrow collection and macrophage cell culture total of eight genes with alternatively spliced isoforms Femurs from adult (8–10 weeks) WKY and LEW rats differentially expressed between WKY and LEW, in basal were isolated and flushed with Hanks buffer (Invitrogen, (Dnm3, Hes1, Ndrg4 and Rtel1) or LPS-stimulated macro- Paisley, UK). Total bone marrow-derived cells were phages (Mical2, Polr1a, Sgms1 and Ube3a). None of these plated and cultured for 6 days in Dulbecco’s modified genes map to our previously localized CRGN QTLs and Eagle’s medium (Invitrogen) containing 25 mM HEPES their role in macrophage function is currently unknown. (Sigma, Dorset, UK), 25% L929 conditioned medium, However, Dnm3, a dynamin GTPase, has been impli- 25% decomplemented fetal bovine serum (Biosera, À1 cated in phagocytosis,29 whereas Sgms1, a key compo- Ringmer, UK), penicillin (100 U ml ; Invitrogen), À1 nent of lipid rafts, is involved in T-cell receptor signaling streptomycin (100 mgml ; Invitrogen) and L-glutamine and T-cell activation.30 These genes may merit further (2 mM; Invitrogen). These cells were characterized as investigation as regulators of activity in macrophages macrophages by immunohistochemistry for CD68 from the WKY or other rat strains. It is interesting that (Supplementary Figure S1). Bone marrow-derived the inter-strain alternative splicing differences detected macrophages were cultured in serum/antibiotic-free by this study were dependent on the activation state of HBSS (Invitrogen) for 16 h. Next, cells were split into 6 the macrophages. Following our stringent alternative two groups of 1 Â10 cells each and cultured for a further À1 splicing detection analysis, the four differentially alter- 24 h in the presence/absence of 10 ng ml LPS (Sigma). natively spliced transcripts in basal state did not show Cells were finally homogenized in TRIzol (Invitrogen) any inter-strain difference following the LPS treatment. and stored at À70 1C. This was also the case for the four differentially alternatively spliced transcripts in LPS-treated macro- RNA extraction, array hybridization and data analysis phages as the differences were no longer observed in Total RNA was extracted, labeled and hybridized to basal conditions. Our results therefore suggest treatment- Gene Chip Rat Exon 1.0 ST Arrays (Affymetrix, Santa specific alternative splicing differences in rat bone Clara, CA, USA) as described previously.34 Four bone marrow-derived macrophages. marrow-derived macrophage preparations, each gener- Our alternative splicing analysis also revealed that ated from a single animal (biological replicates), were there are a high number of polymorphisms between the used for each condition. The microarray data is available WKY and LEW strains, which affect the hybridization in MIAME-compliant (minimum information about a properties of probe sets and lead to the appearance in microarray experiment) format at the ArrayExpress the microarray data of alternatively spliced transcripts. database (http://www.ebi.ac.uk/arrayexpress)35 under We resolved this difficulty by analyzing the data at the accession code E-MEXP-2624. CEL intensity files were probe, rather than the conventionally used probe-set produced using GeneChip Operating Software version level, together with a filter that marks probe sets as false 1.4 (Affymetrix) and quality tested using the Affymetrix positives when not all four probes within a probe set are Expression Console. All 16 files were deemed suitable for differentially expressed between the two rat strains. The further study. Probe-level data were normalized using problem of SNPs resulting in the appearance of false- robust multi-array average.36 Detection of differential positive alternative splicing events has been noticed expression at the gene level was performed in Partek before.31,32 In human beings, data from the 1000 Genomes Genomics Suite 6.4 (Partek Incorporated, St Louis, MO, Project have allowed production of a list of all the USA) using transcripts with ‘extended’ annotations. Data SNP-containing probe sets in the Affymetrix Gene Chip were summarized at the gene level using a One-Step Human Exon 1.0 ST array, which can now be masked.33 Tukey’s Biweight Algorithm, which reduces the effect of Similarly, full sequencing of the WKY and LEW genomes outlier probe sets (Statistical Algorithms Description will be needed to identify definitively and exclude all Document; Affymetrix-White-Paper). An ANOVA model, SNP-containing probe sets from the rat exon array. using strain, condition and batch as cofactors, was used In summary, our findings reveal distinct macrophage to generate raw P-values, whereas step-up FDR37 was transcriptome profiles between two rat strains that differ used for multiple test corrections. in susceptibility to glomerulonephritis. We have detected novel positional candidate genes for the CRGN suscepti- Interval estimates of QTL location bility loci Crgn3–7 and identified highly differentially To calculate 1.5 logarithm of the odds support interval expressed genes that may play a part in the regulation of estimates, data from the original genome screen for

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 88 nephrotoxic nephritis susceptibility loci in F2 rats3 were normalized probe intensities were mapped to the re-analyzed using the CRAN package R/qtlbim v1.9.4.38 Affymetrix transcript annotation (NetAffx release 28). Probes were median summarized for the four biological Functional association analysis replicates and the respective medians from both strains Lists of differentially expressed genes were uploaded were subtracted and squared. The values obtained into the DAVID v6.7 (http://david.abcc.ncifcrf.gov/)11,12 were subjected to the empirical fluctuation test MOSUM to determine differentially regulated pathways using the based on moving sum estimate implemented in R full rat genome as reference background. Data were package strucchange version 1.4-0.45 P-values less than analyzed with the ‘Functional Annotation Chart’ tool 0.1 suggested that a transcript contained SNPs and was a using KEGG and PANTHER pathways or the Biological false positive for alternative splicing. Such transcripts Process Gene Ontology FAT set term. Cutoff criteria were filtered out. Finally, all results were subjected to used were either a Benjamini multiple testing correction manual analysis using the Partek gene view plots as well P-value less than 0.05 or an EASE score P-value of less as probe-level expression signal plots to remove false than 0.01. EASE score is a modified Fisher’s exact test,39 positives that had escaped the different analytical and it is the default test used by DAVID to identify filtering steps. statistical significance for functional association analysis.

Quantitative PCR Sequencing Primers used are listed in Supplementary Table S4. PCRs WKY and LEW genomic DNA was isolated using a were performed with an ABI 7900 Sequence Detection standard protocol. PCR was performed using KOD DNA System (Applied Biosystems, Warrington, UK). Macro- polymerase (Merk Chemicals Ltd, Nottingham, UK) with phage qPCRs were performed using a two-step protocol primers listed in Supplementary Table S5. The amplified starting with cDNA synthesis using Cells-to-cDNA II products were run on a 4% agarose gel. Bands were (Applied Biosystems/Ambion, Austin, TX, USA), extracted and purified using a QIAquick Gel Extraction followed by PCR using SYBR Green Jumpstart Taq Kit (Qiagen, Crawley, UK). Samples were Cycle Ready mix (Sigma). A total of 10 ng of cDNA per sample Sequenced using BigDye v3.1 (Applied Biosystems) was used. After an initial denaturation step (94 1C for chemistry with an ABI 3730 capillary sequencer (Applied 2 min), samples were cycled 40 times at 94 1C for 15 s, Biosystems). Alignment was performed in Sequencher 60 1C for 1 min and 72 1C for 1 min. All samples were v4.9 (Gene Codes Corporation, Ann Arbor, MI, USA). amplified using an independent set of biological duplicates with three technical replicates per sample. The 7900 Fast system SDS software (Applied Biosystems) was Conflict of interest used to obtain CT values. Results were analyzed using 40 the comparative CT method. Each sample was normal- The authors declare no conflict of interest. ized to the reference gene Hprt, to account for cDNA loading differences. Acknowledgements Alternative splicing analysis The robust multi-array analysis36 algorithm was used We thank Kylie McDonald and Hetal Patel for technical for probe-set (exon-level) intensity analysis. Detection support, and Dr Richard Hull for useful discussions. This of alternative splicing events was performed using work was supported by the 6th Framework Program of three parallel approaches: (1) microarray detection of the European Union, EURATools (LSHG-CT-2005-019015), alternative splicing (MiDAS) (Alternative Transcript by an MRC intramural grant to Tim Aitman and by an Analysis Methods for Exon Arrays; Affymetrix- Imperial College Junior Research Fellowship to Jacques White-Paper) was calculated using the Bioconductor Behmoaras. v2.441 package OneChannelGUI v1.4.5;42 (2) rank product of splice index43 (100 permutations) was calculated also using OneChannelGUI v1.4.5;42 (3) an alternative splice ANOVA model was applied using Partek Genomics References Suite v6.4 (Partek Incorporated) together with a filter to select for probe sets showing significant alternative 1 Segelmark M, Hellmark T. Autoimmune kidney diseases. Autoimmun Rev 2010; 9: A366–A371. splicing score determined at a 5% false discovery rate 2 Tam FW, Smith J, Morel D, Karkar AM, Thompson EM, 37 (FDR). Next, transcripts predicted to be alternatively Cook HT et al. Development of scarring and renal failure in a spliced using an intersect of the above three approaches rat model of crescentic glomerulonephritis. Nephrol Dial were filtered to remove data in which apparent alternative Transplant 1999; 14: 1658–1666. use of exons was most likely due to single-nucleotide 3 Aitman TJ, Dong R, Vyse TJ, Norsworthy PJ, Johnson MD, polymorphisms (SNPs) within the probe-binding regions. Smith J et al. Copy number polymorphism in Fcgr3 predis- For this filter, data were analyzed at the probe level, poses to glomerulonephritis in rats and humans. Nature 2006; as follows: Affymetrix perfect match probe intensities 439: 851–855. were measured using Affymetrix Power Tools version 4 Isome M, Fujinaka H, Adhikary LP, Kovalenko P, El-Shemi AG, Yoshida Y et al. Important role for macrophages in induction of apt-1.12.0 (http://www.affymetrix.com/partners_programs/ crescentic anti-GBM glomerulonephritis in WKY rats. Nephrol programs/developer/tools/powertools.affx#1_1). The data Dial Transplant 2004; 19: 2997–3004. obtained were imported on to R statistical tools version 5 Hooke DH, Hancock WW, Gee DC, Kraft N, Atkins RC. 2.11.0 and subjected to quantiles normalization using the Monoclonal antibody analysis of glomerular hypercellularity Bioconductor package preprocessCore version 1.8.44 The in human glomerulonephritis. Clin Nephrol 1984; 22: 163–168.

Genes and Immunity Macrophage transcriptome and CRGN K Maratou et al 89 6 Hooke DH, Gee DC, Atkins RC. Leukocyte analysis using 26 Jones GE. Cellular signaling in macrophage migration and monoclonal antibodies in human glomerulonephritis. Kidney chemotaxis. J Leukoc Biol 2000; 68: 593–602. Int 1987; 31: 964–972. 27 Tarzi RM, Cook HT. Role of Fcgamma receptors in glomerulo- 7 Markovic-Lipkovski J, Muller CA, Risler T, Bohle A, Muller GA. nephritis. Nephron Exp Nephrol 2003; 95: e7–12. Association of glomerular and interstitial mononuclear leuko- 28 Kovalenko P, Fujinaka H, Yoshida Y, Kawamura H, Qu Z, cytes with different forms of glomerulonephritis. Nephrol Dial El-Shemi AG et al. Fc receptor-mediated accumulation of Transplant 1990; 5:10–17. macrophages in crescentic glomerulonephritis induced by 8 Behmoaras J, Bhangal G, Smith J, McDonald K, Mutch B, anti-glomerular basement membrane antibody administration Lai PC et al. Jund is a determinant of macrophage activation in WKY rats. Int Immunol 2004; 16: 625–634. and is associated with glomerulonephritis susceptibility. 29 Otsuka A, Abe T, Watanabe M, Yagisawa H, Takei K, Yamada H. Nat Genet 2008; 40: 553–559. Dynamin 2 is required for actin assembly in phago- 9 Smith J, Lai PC, Behmoaras J, Roufosse C, Bhangal G, cytosis in Sertoli cells. Biochem Biophys Res Commun 2009; 378: McDaid JP et al. Genes expressed by both mesangial cells 478–482. and bone marrow-derived cells underlie genetic susceptibility 30 Jin ZX, Huang CR, Dong L, Goda S, Kawanami T, Sawaki T to crescentic glomerulonephritis in the rat. J Am Soc Nephrol et al. Impaired TCR signaling through dysfunction of lipid 2007; 18: 1816–1823. rafts in sphingomyelin synthase 1 (SMS1)-knockdown T cells. 10 Behmoaras J, Smith J, D’Souza Z, Bhangal G, Chawanasuntoropoj Int Immunol 2008; 20: 1427–1437. R, Tam FW et al. Genetic loci modulate macrophage activity and 31 Benovoy D, Kwan T, Majewski J. Effect of polymorphisms glomerular damage in experimental glomerulonephritis. JAmSoc within probe-target sequences on oligonucleotide microarray Nephrol 2010; 21: 1136–1144. experiments. Nucleic Acids Res 2008; 36: 4417–4423. 11 Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC 32 Nurtdinov RN, Vasiliev MO, Ershova AS, Lossev IS, et al. DAVID: Database for Annotation, Visualization, and Karyagina AS. PLANdbAffy: probe-level annotation database Integrated Discovery. Genome Biol 2003; 4:3. for Affymetrix expression microarrays. Nucleic Acids Res 2010; 12 Huang dW, Sherman BT, Lempicki RA. Systematic and 38: D726–D730. integrative analysis of large gene lists using DAVID bioinfor- 33 Gamazon ER, Zhang W, Dolan ME, Cox NJ. Comprehensive matics resources. Nat Protoc 2009; 4: 44–57. survey of SNPs in the Affymetrix exon array using the 1000 13 Mori M. Regulation of nitric oxide synthesis and apoptosis by Genomes dataset. PLoS One 2010; 5: e9366. arginase and arginine recycling. J Nutr 2007; 137: 1616S–1620S. 34 Gillett A, Maratou K, Fewings C, Harris RA, Jagodic M, 14 Sonoki T, Nagasaki A, Gotoh T, Takiguchi M, Takeya M, Aitman T et al. Alternative splicing and transcriptome profil- Matsuzaki H et al. Coinduction of nitric-oxide synthase and ing of experimental autoimmune encephalomyelitis using arginase I in cultured rat peritoneal macrophages and rat genome-wide exon arrays. PLoS One 2009; 4: e7773. tissues in vivo by lipopolysaccharide. J Biol Chem 1997; 272: 35 Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, 3689–3693. Shojatalab M, Abeygunawardena N et al. ArrayExpress 15 Jansen A, Lewis S, Cattell V, Cook HT. Arginase is a major update—from an archive of functional genomics experiments pathway of L-arginine metabolism in nephritic glomeruli. to the atlas of gene expression. Nucleic Acids Res 2009; 37: Kidney Int 1992; 42: 1107–1112. D868–D872. 16 Cook HT, Jansen A, Lewis S, Largen P, O’Donnell M, Reaveley D 36 Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis et al. Arginine metabolism in experimental glomerulonephritis: KJ, Scherf U et al. Exploration, normalization, and summaries interaction between nitric oxide synthase and arginase. Am J of high density oligonucleotide array probe level data. Physiol 1994; 267: F646–F653. Biostatistics 2003; 4: 249–264. 17 Waddington SN, Mosley K, Cook HT, Tam FW, Cattell V. 37 Benjamini Y, Hochberg Y. Controlling the false discovery rate: Arginase AI is upregulated in acute immune complex-induced a practical and powerful approach to multiple testing. J Roy inflammation. Biochem Biophys Res Commun 1998; 247: 84–87. Statist Soc Ser B (Methodological) 1995; 57: 289–300. 18 Waddington SN, Tam FW, Cook HT, Cattell V. Arginase acti- 38 Yandell BS, Mehta T, Banerjee S, Shriner D, Venkataraman R, vity is modulated by IL-4 and HOArg in nephritic glomeruli Moon JY et al. R/qtlbim: QTL with Bayesian Interval Mapping and mesangial cells. Am J Physiol 1998; 274: F473–F480. in experimental crosses. Bioinformatics 2007; 23: 641–643. 19 Gearing AJ, Beckett P, Christodoulou M, Churchill M, Clements 39 Hosack DA, Dennis Jr G, Sherman BT, Lane HC, Lempicki RA. JM, Crimmin M et al. Matrix metalloproteinases and processing Identifying biological themes within lists of genes with EASE. of pro-TNF-alpha. J Leukoc Biol 1995; 57: 774–777. Genome Biol 2003; 4: R70. 20 Burke B. The role of matrix metalloproteinase 7 in innate 40 Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the immunity. Immunobiology 2004; 209: 51–56. comparative C(T) method. Nat Protoc 2008; 3: 1101–1108. 21 Haro H, Crawford HC, Fingleton B, Shinomiya K, Spengler DM, 41 Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Matrisian LM. Matrix metalloproteinase-7-dependent release Dudoit S et al. Bioconductor: open software development for of tumor necrosis factor-alpha in a model of herniated disc computational biology and bioinformatics. Genome Biol 2004; resorption. J Clin Invest 2000; 105: 143–150. 5: R80. 22 Takai T. Paired immunoglobulin-like receptors and their MHC 42 Sanges R, Cordero F, Calogero RA. oneChannelGUI: a class I recognition. Immunology 2005; 115: 433–440. graphical interface to Bioconductor tools, designed for life 23 Pereira S, Zhang H, Takai T, Lowell CA. The inhibitory scientists who are not familiar with R language. Bioinformatics receptor PIR-B negatively regulates neutrophil and macro- 2007; 23: 3406–3408. phage integrin signaling. J Immunol 2004; 173: 5757–5765. 43 Della BC, Cordero F, Calogero RA. Dissecting an alternative 24 Canales RD, Luo Y, Willey JC, Austermiller B, Barbacioru CC, splicing analysis workflow for GeneChip Exon 1.0 ST Boysen C et al. Evaluation of DNA microarray results with Affymetrix arrays. BMC Genomics 2008; 9: 571. quantitative gene expression platforms. Nat Biotechnol 2006; 44 Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison 24: 1115–1122. of normalization methods for high density oligonucleotide 25 Coppolino MG, Krause M, Hagendorff P, Monner DA, array data based on variance and bias. Bioinformatics 2003; 19: Trimble W, Grinstein S et al. Evidence for a molecular complex 185–193. consisting of Fyb/SLAP, SLP-76, Nck, VASP and WASP that 45 Zeileis A, Leisch F, Hornik K, Kleiber C. Strucchange: an R links the actin cytoskeleton to Fcgamma receptor signalling package for testing for structural change in linear regression during phagocytosis. J Cell Sci 2001; 114: 4307–4318. models. J Statist Software 2002; 7: 1–38.

Supplementary Information accompanies the paper on Genes and Immunity website (http://www.nature.com/gene)

Genes and Immunity