Murine Renal Transcriptome Profiles Upon Leptospiral Infection: Implications for Chronic Kidney Diseases

Murine Renal Transcriptome Profiles Upon Leptospiral Infection: Implications for Chronic Kidney... Abstract Background Leptospirosis caused by pathogenic Leptospira spp leads to kidney damage that may progress to chronic kidney disease. However, how leptospiral infections induced renal damage is unclear. Methods We apply microarray and next-generation sequencing technologies to investigate the first murine transcriptome-wide, leptospires-mediated changes in renal gene expression to identify biological pathways associated with kidney damage. Results Leptospiral genes were detected in renal transcriptomes of mice infected with Leptospira interrogans at day 28 postinfection, suggesting colonization of leptospires within the kidney with propensity of chronicity. Comparative differential gene expression and pathway analysis were investigated in renal transcriptomes of mice infected with pathogens and nonpathogens. Pathways analysis showed that Toll-like receptor signaling, complements activation, T-helper 1 type immune response, and T cell-mediated immunity/chemotaxis/proliferation were strongly associated with progressive tubulointerstitial damage caused by pathogenic leptospiral infection. In addition, 26 genes related with complement system, immune function, and cell-cell interactions were found to be significantly up-regulated in the L interrogans-infected renal transcriptome. Conclusions Our results provided comprehensive knowledge regarding the host transcriptional response to leptospiral infection in murine kidneys, particularly the involvement of cell-to-cell interaction in the immune response. It would provide valuable resources to explore functional studies of chronic renal damage caused by leptospiral infection. chronic kidney diseases, leptospiral infection, leptospirosis, renal transcriptome Leptospirosis, an infectious disease caused by pathogenic Leptospira spp, occurs more often in tropical areas with heavy rainfall and is a re-emerging worldwide public health problem with an increasing incidence [1]. Renal failure is one of the clinical syndromes observed in leptospirosis, characterized by tubulointerstitial nephritis and tubular dysfunction [2]. In chronic infection, pathogenic leptospires may colonize and persist in renal proximal tubules, leading to the progression of tubulointerstitial nephritis and fibrosis in mice [3]. Clinical studies of renal lesions associated with leptospirosis have indicated that Leptospira spp may induce kidney injury in humans [4, 5]. In our previous study, we conducted a multistage sampling survey in Taiwan that indicated Leptospira exposure may induce chronic kidney disease (CKD) in humans [5]. The mechanism of leptospirosis-induced tubulointerstitial nephritis and fibrosis during chronic leptospiral infection have not been fully elucidated. Numerous studies of leptospirosis-associated interstitial nephritis and chronic infection have been described in mice [6]. It has been previously shown that that Toll-like receptors (TLRs), inducible nitric-oxide synthase, and Na/K-ATPase may play roles in leptospiral infection in mice [7–9]. In addition, in vitro studies suggest that pathogenic Leptospira spp may evade the host innate immune response to infection through its resistance to the complement system [10]. Significant differences in transcript levels of cytokine and chemokine genes were investigated and reported in kidneys of Leptospira-infected animals [11]. Findings regarding cytokine and chemokine cascades could explain the role of renal microenvironments involved in kidney damage caused by Leptospira. A number of transcriptomic studies associated with leptospiral infection have been performed; however, most of these studies focused on genome-wide transcriptional profiling in bacteria [12, 13]. To date, studies of the host transcriptomic changes associated with leptospiral infection have been reported in cell infection models [14, 15]. To understand global changes in renal gene expression during leptospiral infection, we performed comprehensive transcriptome profiling in murine kidneys-leptospires interactions. The aim of this study is to discuss a global analysis of renal gene expression associated with renal damage that was induced by leptospiral infection using experimental murine models. We profiled the renal transcriptome of C57BL/6 mice infected with pathogenic and non-pathogenic leptospires, respectively, and analyzed the contribution of possible signaling pathways to a chronic leptospiral infection-induced nephritis and renal fibrosis. Our results provide important information for understanding the biology and pathogenesis of the infectious disease. Details about the transcriptional regulation during infection may shed light on the molecular pathogenic mechanisms underlying leptospiral infection-mediated kidney injury. MATERIALS AND METHODS Bacterial Culture Leptospira interrogans serovar Copenhageni Fiocruz L1-130 (American Type Culture Collection [ATCC] BAA-1198; pathogenic species) and Leptospira biflexa serovar Patoc (ATCC 23582; non-pathogenic species) were propagated at 28°C under aerobic conditions in medium containing Leptospira enrichment Elinghausen-McCullough-Johnson-Harris (EMJH) medium (BD Diagnostics) and Leptospira medium base EMJM medium (Difco, Sparks, MD). Bacterial densities were measured using a CASY-Model TT cell counter and analyzer (Casy-Technology, Roche Innovatis AG, Reutlingen, Germany). Mouse Strains and Infection C57BL/6 female mice aged 6–8 weeks were inoculated intraperitoneally with a high infective dose of Leptospira spp, and the control groups were inoculated with sterile EMJH medium [16]. The mice were sacrificed at 7 and 28 days postinfection, respectively, and the kidneys were harvested. All animal experiments required Animal Biosafety Level 2 conditions and followed all appropriate guidelines for the use and handling of infected animals. All animal procedures and experimental protocols were approved by the Institutional Animal Care and Use Committee of the Chang Gung Memorial Hospital in Taiwan (no. 2015120101). Ribonucleic Acid Preparation Total ribonucleic acid (RNA) was extracted from kidneys using the TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s protocol. The quantity and integrity of extracted RNA were verified using a Nano-Drop spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Next-Generation Sequencing and Microarray Processing Total RNA samples for RNA-sequencing (RNA-seq) were converted to complementary deoxyribonucleic acid (cDNA) using the Ovation RNA-seq System V2 (NuGEN Technologies, San Carlos, CA). Library construction was performed using the Ovation Ultralow Library System V2 1–96 (NuGEN). Sequencing was performed on the Illumina HiSeq 2000 sequencing platform (Illumina, San Diego, CA) with a 100-nucleotide (nt) paired-end setting. The raw data were filtered using the CLC Genomics Workbench veresion 8.0, on the basis of the Per Base Sequence Quality Score ≥20. To quantify transcript expression, the sequencing data were mapped to the Mus_musculus GRCm38 reference genome and mapped the remaining unmapped reads to the leptospiral genome. Total RNA samples for microarray detection were transcribed into cDNA and subjected to the GeneChip Mouse Transcriptome Assay 1.0 (Affymetrix, Santa Clara, CA). The Affymetrix data have been deposited in National Center for Biotechnology Information’s Gene Expression Omnibus [17] and are accessible through Gene Expression Omnibus accession number GSE111249. Bioinformatics Analyses Gene expression levels in the RNA-seq analysis were measured as reads per kilobase million (RPKM) value. A set of 15949 genes (of the total 46202 annotated genes) having a median RPKM value for all samples larger than 0.05 were considered to be expressed. The RPKM values were log transformed and tested using analysis of variance with Partek Genomics Suite software (Partek, St. Louis, MO). Genes presenting a fold change greater than 2 or less than −2 and P < .05 were selected as differentially expressed genes (DEGs). The hierarchical clustering was plotted using the Partek Genomics Suite software. The DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis using the Partek Pathway (Partek). Real-Time Quantitative Polymearse Chain Reaction Real-time quantitative polymearse chain reaction (qPCR) was performed on an ABI ViiA7 real-time PCR system (Applied Biosystems) using TaqMan gene expression assays. The 24 genes of interest were selected for confirmation, using TBP as an endogenous control (Supplementary Table 1). RESULTS Experimental Design: Renal Transcriptome in Mice Infected With Leptospira Species To study global transcriptional responses in kidneys after leptospiral infection, the kidneys of mice infected with L interrogans and L biflexa, respectively, and those of EMJH medium-treated control mice, were harvested for histological observations. Histopathological examination of kidneys from L interrogans-infected mice at day 28 postinfection showed inflammatory infiltrates and fibrosis with a milder intensity than those in the kidneys from L biflexa-infected and uninfected mice. The renal histopathological investigations revealed the degree of tubulointerstitial lesions slightly increased in kidneys from L interrogans-infected mice at day 28 postinfection (Supplementary Figure 1A, B, and C). Concomitant to evaluation of tubulointerstitial lesions in kidneys, the presence of pathogenic leptospires was also detected in kidney tissue and urine from infected mice (Supplementary Figure 1D and E). Hence, we selected 3 independent samples in each group for renal transcriptome studies, and the workflow is outlined in Figure 1. Figure 1. View largeDownload slide Schematic representation of simultaneous transcriptional profiling of kidney tissues from mice upon Leptospira spp infection. Abbreviations: DNA, deoxyribonucleic acid; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NGS, next-generation sequencing; PCR, polymerase chain reaction. Figure 1. View largeDownload slide Schematic representation of simultaneous transcriptional profiling of kidney tissues from mice upon Leptospira spp infection. Abbreviations: DNA, deoxyribonucleic acid; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NGS, next-generation sequencing; PCR, polymerase chain reaction. Leptospiral Genes Identified in the Kidneys of Mice We monitored renal transcriptome profiles by applying a RNA-seq analysis. An average of 57 million clean 94- to 95-nt reads were obtained for each sample, and approximately 98% of these reads were successfully mapped to the mouse genome (Supplementary Table 2). Moreover, we mapped the remaining unmapped reads to the leptospiral genome. There were 29 leptospiral genes detected in the RNA-seq data at day 28 postinfection with leptospires, whereas none were detected in kidneys at day 7 postinfection (Supplementary Table 3). Three of these genes were consistent with our previous work [18] and were validated by PCR (Supplementary Figure 2). Together with previous results (Supplementary Figure 1D and E), these data support that pathogenic leptospires were localized in the kidney at day 28 postinfection. Therefore, we further investigated the signaling pathways involved in renal lesions caused by chronic leptospirosis. Identification of Differentially Expressed Renal Genes We identified DEGs from the RNA-seq and microarray data (Table 1). At day 7 postinfection, the group infected with L interrogans versus the noninfected group (714 genes) included 629 up-regulated and 85 down-regulated genes identified from the RNA-seq data. At day 28 postinfection, the group infected with L interrogans versus the noninfected group (1089 genes) included 1062 up-regulated and 27 down-regulated DEGs. The microarray analysis identified the 467 significantly DEGs in the kidneys of mice infected with L interrogans at day 7 postinfection and the 927 transcripts significantly differentially expressed in these kidneys at day 28 postinfection. Table 1. The Number of DEGs in Mouse Kidneys After Leptospiral Infection Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Abbreviations: DEG, differentially expressed gene; ID, identification; L, Leptospira; RNA, ribonucleic acid; seq, sequencing. aNumbers of overlapping DEGs between the RNA-seq and microarray results based on corresponding Ensembl gene IDs. View Large Table 1. The Number of DEGs in Mouse Kidneys After Leptospiral Infection Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Abbreviations: DEG, differentially expressed gene; ID, identification; L, Leptospira; RNA, ribonucleic acid; seq, sequencing. aNumbers of overlapping DEGs between the RNA-seq and microarray results based on corresponding Ensembl gene IDs. View Large According to the corresponding Ensembl Gene identification, comprehensive analysis of RNA-seq and microarray gene expression consistency showed that 202 (the 512 and 265 genes that were uniquely identified from the RNA-seq and microarray datasets, respectively) and 424 (the 665 and 503 genes that were uniquely identified from the RNA-seq and microarray datasets, respectively) genes were found to be differentially expressed in mice with pathogenic leptospiral infection after day 7 and 28, respectively (Table 1). The validation of DEGs in the renal transcriptome was conducted using real-time qPCR, as shown in Table 2. The 24 genes (Supplementary Table 1) with predicted functions in renal injury/fibrosis (FGF2, TGFβ1, CTGF, LTBP2, FBN2, TIMP1, BGN, IL18, LCN2, and HAVCR1), the proinflammatory/inflammarory signaling molecules/immune responses (FOXP3, P2RX7, IFNγ, IL1β, IL6, IL10, MCP1, TNFα, IL18, TGFβ1, CSF1, CSF1R, and IL34), and cell-cell interactions (SIGLEC-1, SPN, and MUCI) were selected for validation [19–26]. We infer that DEGs that overlapped between the RNA-seq and microarray datasets were more significantly affected in response to leptospiral infection (for example, in groups for L interrogans at day 7 postinfection: BGN, P2RX7, CSF1, and HAVCR1; groups for L interrogans at day 28 postinfection: IL1β, BGN, P2RX7, MCP1, and CSF1) (Table 2). The real-time qPCR results are consistent with the RNA-seq data for SIGLEC-1, SPN, MUCI, TGFβ1, LTBP2, TIMP1, IFNγ, IL1β, IL6, IL10, BGN, P2RX7, MCP1, TNFα, CSF1, CSF1R, LCN2, IL34, HAVCR1 (in groups for L interrogans at day 7 postinfection) and SIGLEC-1, SPN, MUCI, FGF2, TGFβ1, LTBP2, TIMP1, FBN2, IFNγ, IL1β, IL6, IL10, BGN, FOXP3, P2RX7, MCP1, TNFα, IL18, CSF1, LCN2, and HAVCR1 (in groups for L interrogans at day 28 postinfection) (Table 2). Our findings showed that the real-time qPCR data were more consistent with the RNA-seq data. Hence, our after analysis focused on the DEGs identified from RNA-seq results. Table 2. Validation of Renal Transcriptome Data Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Abbreviations: CT, cycle threshold; L, Leptospira; NGS, next-generation sequencing; qPCR, quantitative polymerase chain reaction; RPKM, reads per kilobase million SIGLEC-1, sialic acid-binding immunoglobulin-like lectin-1; SPN, sialophorin; MUCI, mucin 1; FGF2, fibroblast growth factor 2; TGFb1, transforming growth factor beta 1; CTGF, connective tissue growth factor; LTBP2, latent transforming growth factor beta binding protein-2; TIMP1, tissue inhibitor of metalloproteinase 1; FBN2, fibrillin-2; IFNg, interferon gamma; IL1b, interleukin 1 beta; IL6, interleukin-6; IL10, interleukin-10; BGN, biglycan; FOXP3, forkhead box P3; P2RX7, purinergic receptor P2X, ligand-gated ion channel, 7; MCP1,monocyte chemotactic protein 1; TNFa, tumor necrosis factor-alpha; IL18, interleukin 18; CSF1, colony-stimulating factor 1; CSF1R, colony stimulating factor 1 receptor; LCN2, lipocalin 2; IL34, interleukin 34; HAVCR1, hepatitis A virus cellular receptor 1. aExpression was presented as the fold change compared with noninfected mice. bFold change using the RPKM values of the transcript. Fold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). cFold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). dFold change was determined using the comparative CT method [50]. Fold-differences were calculated using the standard deviation of the ΔΔCT value and are expressed as a range (minimum to maximum) of fold change; TBP was used as an internal control; n = 5–6 separate mice in groups for L biflexa infection, and n = 6–7 separate mice in groups for L interrogans infection; technical duplication for all of the genes in samples. View Large Table 2. Validation of Renal Transcriptome Data Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Abbreviations: CT, cycle threshold; L, Leptospira; NGS, next-generation sequencing; qPCR, quantitative polymerase chain reaction; RPKM, reads per kilobase million SIGLEC-1, sialic acid-binding immunoglobulin-like lectin-1; SPN, sialophorin; MUCI, mucin 1; FGF2, fibroblast growth factor 2; TGFb1, transforming growth factor beta 1; CTGF, connective tissue growth factor; LTBP2, latent transforming growth factor beta binding protein-2; TIMP1, tissue inhibitor of metalloproteinase 1; FBN2, fibrillin-2; IFNg, interferon gamma; IL1b, interleukin 1 beta; IL6, interleukin-6; IL10, interleukin-10; BGN, biglycan; FOXP3, forkhead box P3; P2RX7, purinergic receptor P2X, ligand-gated ion channel, 7; MCP1,monocyte chemotactic protein 1; TNFa, tumor necrosis factor-alpha; IL18, interleukin 18; CSF1, colony-stimulating factor 1; CSF1R, colony stimulating factor 1 receptor; LCN2, lipocalin 2; IL34, interleukin 34; HAVCR1, hepatitis A virus cellular receptor 1. aExpression was presented as the fold change compared with noninfected mice. bFold change using the RPKM values of the transcript. Fold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). cFold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). dFold change was determined using the comparative CT method [50]. Fold-differences were calculated using the standard deviation of the ΔΔCT value and are expressed as a range (minimum to maximum) of fold change; TBP was used as an internal control; n = 5–6 separate mice in groups for L biflexa infection, and n = 6–7 separate mice in groups for L interrogans infection; technical duplication for all of the genes in samples. View Large Comparative Pathway Analysis Between Renal Transcriptome of Pathogen and Nonpathogen-Infected Mice We compared the different renal transcriptome of mice infected with L interrogans or L biflexa at different time points postinfection (Table 1; Supplementary Figures 3 and 4). Supplementary Tables 4 and 5 list the unique and shared DEGs of pathogenic and non-pathogenic Leptospira species-infected mice between both groups at 2 different time points after infection. Overall, the number of DEGs was higher in pathogen infection groups than in nonpathogen infection groups at both postinfection time points, and most DEGs were up-regulated in pathogenic Leptospira species-infected mice. This might reflect that the complexity of renal transcriptome responses were induced by L interrogans. To understand the biological processes associated with host responses to leptospiral infection, we performed GO enrichment analysis (P < 1E-5). The results showed that 714 and 1089 significantly DEGs identified from renal transcriptomes of mice infected with L interrogans were enriched, with 549 and 779 GO terms at day 7 and 8 postinfection, respectively. In Figure 2, we list the top 30 significantly enriched GO terms, whereas these enriched GO terms were also found in the microarray results. In addition, the number of DEGs enriched in each GO terms was higher in groups at day 28 than in groups at day 7 postinfection (Figure 2). Moreover, the differentially expressed genes in the non-pathogenic leptospiral infection group at day 7 postinfection showed no significant hits in the GO database. Figure 2. View largeDownload slide Gene Ontology (GO) enrichment analysis of the differentially regulated genes in murine kidneys after Leptospira interrogans infection. The top 30 significantly enriched GO terms at day 7 and 28 postinfection, respectively. The left of the x-axis indicates the GO category, and the bar chart indicates the number of differentially expressed genes in each category. M and B represent categories of the GO terms: M, “molecular function” (green); B, “biological process” (blue). Figure 2. View largeDownload slide Gene Ontology (GO) enrichment analysis of the differentially regulated genes in murine kidneys after Leptospira interrogans infection. The top 30 significantly enriched GO terms at day 7 and 28 postinfection, respectively. The left of the x-axis indicates the GO category, and the bar chart indicates the number of differentially expressed genes in each category. M and B represent categories of the GO terms: M, “molecular function” (green); B, “biological process” (blue). To further elucidate the function of DEGs in renal transcriptome after leptospiral infection, a KEGG pathway enrichment analysis (P < 1E-4) was performed, and results are depicted in Supplementary Tables 6 and 7. No pathway was significantly enriched in the non-pathogenic L biflexa infection group at day 7 postinfection. Comparing pathways between pathogenic and non-pathogenic Leptospira species infection groups at day 28 postinfection shows that TLR signaling pathways, complement/coagulation cascades, tumor necrosis factor (TNF) signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interaction were significantly enriched in the pathogenic L interrogans infection group but not in L biflexa infection group. Hence, we infer that these functionally enriched pathways obtained from pathogenic leptospires infection groups might have important roles in kidney injury caused by leptospiral infection. Leptospires Induced Toll-Like Receptor Signaling Pathways in Kidneys The activation of TLR signaling through recognition of bacterial constituents leads to the host defense against invading pathogens and subsequent production of chemokines [27]. At day 7 postinfection with pathogen, DEGs were significantly enriched in TLR signaling pathways, leading to regulate gene expression profiles including the production of early inflammatory (TNFα, CCL5, and CCL2) and costimulatory molecules such as CD86, intercellular adhesion molecule (ICAM)1, and major histocompatibility complex (MHC) class I/II (Figure 3A). Figure 3B shows that TLRs initiate downstream signaling cascades to induce inflammatory cytokines (TNFα/IL1β), chemokines (CCL2/CCL5), chemokine receptors (CCR2/CCR1), and led to the production of CD86, CD83, CD80, CD40, CD69, ICAM1, and MHC class I/II molecules. Previous studies have shown that CCL2 is mainly expressed in the tubulointerstitial regions of the kidney in lupus-prone mice and CCL2/CCR2 are involved in the regulation of T-cell activation/differentiation [28]. In addition, CCL5/CCR1 is essential for T cell, macrophage, and neutrophil infiltration in the tubulointerstitial region of the kidney [29]. Hence, TLRs expressed on kidney injury caused by leptospiral infection plays a crucial role in the induction of inflammation and injury, indicating that TLRs may modulate the microenvironment and induce the complex regulation of cytokine gene expression in kidney. Figure 3. View largeDownload slide Toll-like receptor (TLR) signaling pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; and * indicate that genes were consistent with the results obtained from microarray results. Figure 3. View largeDownload slide Toll-like receptor (TLR) signaling pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; and * indicate that genes were consistent with the results obtained from microarray results. Leptospires Induced Complement-Related Pathways in Kidneys The activation of the classic, alternative, and/or lectin-induced complement pathways triggers phagocytosis through the opsonization of antigens, inflammation by attracting macrophages/neutrophils, and membrane attack by rupturing the membranes of pathogens/damaged cells [30]. Figure 4 shows that the C5/C6/C9 transcript that is not differentially expressed and the down-regulation of the C8 gene were found in the pathogen infection groups at day 7 postinfection. In addition, the C5, C6, C8, and C9 transcripts that were not differentially expressed were found in the pathogen infection groups at day 28 postinfection, suggesting that the membrane attack complex pore is not formed or formed as an inactive complex in the kidneys of mice infected with leptospires. Furthermore, the up-regulation of transcripts for C5AR1, C3AR1, α-M integrin, and α-X integrin suggests the activation of proinflammatory responses and the crosstalk of the immune system such as phagocytosis, cell-mediated cytotoxicity, chemotaxis, anaphylation, and cellular activation [30, 31]. Hence, we infer that the complement-induced immune responses might be present in the kidneys of mice infected with L interrogans. Figure 4. View largeDownload slide Complement-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. Figure 4. View largeDownload slide Complement-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. Animal studies have shown that the activation of C3 contribute to the development of progressive renal fibrosis during experimental obstructive nephropathy [32]. Our results showed the elevation of C3 and C4 in the renal transcriptome after pathogen infection, suggesting that C3 may play an important role in the renal defense mechanism. Genes related to complement-modulating proteins, such as the C1 inhibitor, factor H and factor I, were significantly up-regulated and revealed the regulation of complement activation upon L interrogans infection. We infer that pathogenic leptospires escape the complement-mediated killing and conclude that complement-mediated tissue damage might be implicated in the progression of pathogenic leptospiral infection-induced kidneys injury. Leptospires Induced Immune-Related Pathways in Kidneys As listed in Supplementary Table 6, results show that immunity plays a significant role in renal injuries after L interrogans infection. A schematic illustration of the cell-to-cell interactions during immune regulation is shown in Figure 5. It is interesting to note that genes involved in the formation of the immunological synapse, a nanoscale gap between T cells and antigen presenting cells, were enriched in the kidneys of mice infected with pathogen, but not in those infected with nonpathogen. The formation of the immunological synapse supports the notion that these supramolecular clusters might induce T-cell activation and signaling. Figure 5. View largeDownload slide Immune system-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. (C) Heat map of differentially expressed immune-related genes in response to pathogenic leptospiral infection. Heat map is arranged according to hierarchical clustering, based on the expression pattern of differentially expressed genes. The color scale indicates the expression value (red, up-regulation; green, down-regulation; black, no change). Figure 5. View largeDownload slide Immune system-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. (C) Heat map of differentially expressed immune-related genes in response to pathogenic leptospiral infection. Heat map is arranged according to hierarchical clustering, based on the expression pattern of differentially expressed genes. The color scale indicates the expression value (red, up-regulation; green, down-regulation; black, no change). Interleukin (IL)18 plays an important role in the T-helper 1(Th1)-mediated immune responses and in the host protection against intracellular pathogens infection [33]. The up-regulated expression of IL18 and its receptors IL18R1/IL18RAP was found in the renal transcriptomes of mice infected with pathogen at day 28 postinfection, suggesting an important contribution of IL18-mediated Th1 immunity in kidney injury after leptospiral infection (Figure 5B). Interleukin 18 may contribute to the renal damage caused by L interrogans infection. We suggest that IL18 blockade, such as IL18-binding protein production, and IL37, the IL18 contraregulatory protein, may have potential as a therapeutic strategies in CKD caused by L interrogans infection [34, 35]. We performed hierarchical clustering analysis on these significant DEGs. This analysis revealed that 26 immune-related genes such as LBP, FCGR1, SYK, IL33, COLLA1, IRF7, NCF1, and TLR2 in the L interrogans-infected renal transcriptome were differentially expressed, as shown in Figure 5C and Supplementary Table 8. These genes are involved in the cytoskeletal regulation, superoxide anion production, complement systems activation, immune responses regulation, and cell-cell recognition. DISCUSSION The study presented the first report characterizing the renal transcriptional profile of mice infected with Leptospira spp, and this work was performed for detection of both leptospires and renal transcripts during infection. We found that molecules from leptospires such as LipL32 and OmpL47 were identified in renal transcriptome of mice infected with L interrogans. These outer membrane proteins have been reported as host interaction proteins in leptospires [36–38]. LipL32 is known to interact directly with TLR2 and OmpL47 and has been reported to bind to laminin, fibronectin, fibrinogen, and collagen type III [38]. Our findings provide insights into the possibility of the interactions of leptospires within kidneys that may establish chronic leptospiral colonization of the renal tubules to induce persistent immune cell-mediated renal damage. Several immune response-related enriched pathways were found in non-pathogenic leptospiral infection groups at day 28; however, key molecules such as TCR molecules for T-cell activation were not significantly differentially expressed. Meanwhile, those molecules were found significantly expressed in pathogenic leptospiral infection groups. Therefore, we speculate that some pathogenic leptospires such as L interrogans induces persistent immune responses and T-cell activation/development, leading to CKD. Chemokines participate Th1 immune responses during renal injury and tissue fibrosis [39, 40]. Evidences reveal that increase levels of CCL2 are associated with progressive tubulointerstitial disease/renal fibrosis and up-regulation of CCR5 ligands (CCL3, CCL4, and CCL5) correlates with the recruitment of monocytes/T cells [41, 42]. Our results show that CCL2 versus its receptor CCR2 and CCL4/CCL3/CCL5 versus their receptors CCR1/CCR5 were up-regulated during progressive kidney injury in pathogenic leptospiral-infected mice. Furthermore, an increased expression level of CXCL9/CXCL10 and their receptors CXCR3 was found in the renal transcriptomes of mice infected with pathogenic leptospires at day 28 postinfection. Experimental evidence for renal fibrosis in hamsters caused by chronic leptospiral infection has demonstrated that proinflammatory IL1β/TNFα, IL10, chemokines macrophage-inflammatory protein-1α/CCL3, and IP-10/CXCL10 were up-regulated in damaged kidneys [11]. Our results show that the expressions of IL1β/TNFα, IL10, and CCL3/CXCL10 messenger RNA were also up-regulated in damaged kidneys caused by infection of pathogenic Leptospira spp in mice. In our study, the expression of the TGFβ transcript, COLLA1 for collagen type I, and COL3A1 for collagen type III were all up-regulated in the renal transcriptomes of mice infected with pathogenic leptospires. Hence, collagen I and III might participate in leptospiral infection-induced renal fibrosis [43]. Furthermore, MARCKS-like protein 1 ([MARCKSL1] cytoskeleton-associated proteins associated with cell spreading) transcript was elevated in the renal transcriptomes of mice infected with pathogenic leptospires, suggesting a role in dynamics of cellular spreading in kidneys during leptospiral infection [44]. Reactive oxygen species exert modulating effects on inflammation and have roles in the regulation of immune responses [45]. Our finding also suggests that the production of superoxide anions may be a portion of injury mechanisms participated in renal damage caused by chronic leptospiral infection. It is notable that the up-regulated expression of runt-related transcription factors 3 (RUNX3) transcript (7.75-fold change; P < .05) was only found in the renal transcriptomes of mice infected with pathogenic leptospires at day 28 postinfection. Previous studies demonstrated that Runx3 expression is up-regulated during Th1 differentiation and circulating human CD4+CD25highCD127− T regulatory (Treg) cells [46, 47]. Future studies are needed to further investigate the roles of Runx3 in Treg cells from kidney injury caused by leptospiral infection. Previous studies have reported that subversion of innate immunity by pathogen-induced complement-TLR crosstalk pathways has the potential to modulate the host response in ways that favor pathogen persistence and interfere with host protective immunity [48, 49]. Moreover, C57BL/6 mice deficient for the decay-accelerating factor, an early regulator of complement cascades, are more susceptible to infection with L interrogans [43]. Our results confirm that TLR signaling complements activation, and Th1 type immune responses were strongly associated with progressive tubulointerstitial damage caused by pathogenic leptospiral infection. Hence, we speculate that pathogenic leptospiral infections may alter the microenvironment to induce cell-to-cell interactions, leading to chronic inflammatory disorder in kidneys. Moreover, chronic leptospiral infections via complement-TLR crosstalk pathways may play a detrimental role in T cell-mediated renal injury. CONCLUSIONS The new insight of the molecular mechanisms underlying complement-TLR crosstalk pathways in our study could bring focus on development either alone or both of the therapeutic strategies in the treatment of kidney diseases caused by chronic leptospiral infection. These results provide a better understanding of the interaction between the host and Leptospira spp in chronic renal infections. Supplementary Data Supplementary materials are available at The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author. Notes Author contributions. L.-F. C. designed the study, performed the experiments and data analysis, and wrote the manuscript. T.-W. C. performed the bioinformatic analysis and edited the manuscript. Y.-C. K. carried out the mouse infection experiments, and C.-T. H. performed the ribonucleic acid extraction. H.-Y. Y., M.-Y. C., Y.-C. T., C.-C. H., S.-H. H., and C.-Y. T. participated in the interpretation and discussion of the results and provided critical comments during the drafting of the manuscript. C.-W. Y. conceived and designed the study, participated in the interpretation and discussion of the results, and assisted in the drafting of the manuscript. All authors have read and approved the final version of the manuscript. Acknowledgments.  We gratefully acknowledge Microscope Core Laboratory and the Genomic Medicine Core Laboratory at the Chang Gung Memorial Hospital, Linkou for technical assistance. We thank the Tissue Bank (Chang Gung Memorial Hospital, Lin-Kou, Taiwan) for excellent tissue processing. Financial support. This work was funded by grants from the Ministry of Science and Technology, Taiwan (MOST 103-2320-B-182A-020, MOST 105-2628-B-182A-006-MY3 and MOST-106-2311-B-182-005) and Chang Gung Memorial Hospital grants (CMRPG3E1931-1, CMRPG3E1931-2, and CMRPG3E1931-3). Potential conflicts of interest.  All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Infectious Diseases Oxford University Press

Murine Renal Transcriptome Profiles Upon Leptospiral Infection: Implications for Chronic Kidney Diseases

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.
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0022-1899
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10.1093/infdis/jiy339
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Abstract

Abstract Background Leptospirosis caused by pathogenic Leptospira spp leads to kidney damage that may progress to chronic kidney disease. However, how leptospiral infections induced renal damage is unclear. Methods We apply microarray and next-generation sequencing technologies to investigate the first murine transcriptome-wide, leptospires-mediated changes in renal gene expression to identify biological pathways associated with kidney damage. Results Leptospiral genes were detected in renal transcriptomes of mice infected with Leptospira interrogans at day 28 postinfection, suggesting colonization of leptospires within the kidney with propensity of chronicity. Comparative differential gene expression and pathway analysis were investigated in renal transcriptomes of mice infected with pathogens and nonpathogens. Pathways analysis showed that Toll-like receptor signaling, complements activation, T-helper 1 type immune response, and T cell-mediated immunity/chemotaxis/proliferation were strongly associated with progressive tubulointerstitial damage caused by pathogenic leptospiral infection. In addition, 26 genes related with complement system, immune function, and cell-cell interactions were found to be significantly up-regulated in the L interrogans-infected renal transcriptome. Conclusions Our results provided comprehensive knowledge regarding the host transcriptional response to leptospiral infection in murine kidneys, particularly the involvement of cell-to-cell interaction in the immune response. It would provide valuable resources to explore functional studies of chronic renal damage caused by leptospiral infection. chronic kidney diseases, leptospiral infection, leptospirosis, renal transcriptome Leptospirosis, an infectious disease caused by pathogenic Leptospira spp, occurs more often in tropical areas with heavy rainfall and is a re-emerging worldwide public health problem with an increasing incidence [1]. Renal failure is one of the clinical syndromes observed in leptospirosis, characterized by tubulointerstitial nephritis and tubular dysfunction [2]. In chronic infection, pathogenic leptospires may colonize and persist in renal proximal tubules, leading to the progression of tubulointerstitial nephritis and fibrosis in mice [3]. Clinical studies of renal lesions associated with leptospirosis have indicated that Leptospira spp may induce kidney injury in humans [4, 5]. In our previous study, we conducted a multistage sampling survey in Taiwan that indicated Leptospira exposure may induce chronic kidney disease (CKD) in humans [5]. The mechanism of leptospirosis-induced tubulointerstitial nephritis and fibrosis during chronic leptospiral infection have not been fully elucidated. Numerous studies of leptospirosis-associated interstitial nephritis and chronic infection have been described in mice [6]. It has been previously shown that that Toll-like receptors (TLRs), inducible nitric-oxide synthase, and Na/K-ATPase may play roles in leptospiral infection in mice [7–9]. In addition, in vitro studies suggest that pathogenic Leptospira spp may evade the host innate immune response to infection through its resistance to the complement system [10]. Significant differences in transcript levels of cytokine and chemokine genes were investigated and reported in kidneys of Leptospira-infected animals [11]. Findings regarding cytokine and chemokine cascades could explain the role of renal microenvironments involved in kidney damage caused by Leptospira. A number of transcriptomic studies associated with leptospiral infection have been performed; however, most of these studies focused on genome-wide transcriptional profiling in bacteria [12, 13]. To date, studies of the host transcriptomic changes associated with leptospiral infection have been reported in cell infection models [14, 15]. To understand global changes in renal gene expression during leptospiral infection, we performed comprehensive transcriptome profiling in murine kidneys-leptospires interactions. The aim of this study is to discuss a global analysis of renal gene expression associated with renal damage that was induced by leptospiral infection using experimental murine models. We profiled the renal transcriptome of C57BL/6 mice infected with pathogenic and non-pathogenic leptospires, respectively, and analyzed the contribution of possible signaling pathways to a chronic leptospiral infection-induced nephritis and renal fibrosis. Our results provide important information for understanding the biology and pathogenesis of the infectious disease. Details about the transcriptional regulation during infection may shed light on the molecular pathogenic mechanisms underlying leptospiral infection-mediated kidney injury. MATERIALS AND METHODS Bacterial Culture Leptospira interrogans serovar Copenhageni Fiocruz L1-130 (American Type Culture Collection [ATCC] BAA-1198; pathogenic species) and Leptospira biflexa serovar Patoc (ATCC 23582; non-pathogenic species) were propagated at 28°C under aerobic conditions in medium containing Leptospira enrichment Elinghausen-McCullough-Johnson-Harris (EMJH) medium (BD Diagnostics) and Leptospira medium base EMJM medium (Difco, Sparks, MD). Bacterial densities were measured using a CASY-Model TT cell counter and analyzer (Casy-Technology, Roche Innovatis AG, Reutlingen, Germany). Mouse Strains and Infection C57BL/6 female mice aged 6–8 weeks were inoculated intraperitoneally with a high infective dose of Leptospira spp, and the control groups were inoculated with sterile EMJH medium [16]. The mice were sacrificed at 7 and 28 days postinfection, respectively, and the kidneys were harvested. All animal experiments required Animal Biosafety Level 2 conditions and followed all appropriate guidelines for the use and handling of infected animals. All animal procedures and experimental protocols were approved by the Institutional Animal Care and Use Committee of the Chang Gung Memorial Hospital in Taiwan (no. 2015120101). Ribonucleic Acid Preparation Total ribonucleic acid (RNA) was extracted from kidneys using the TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s protocol. The quantity and integrity of extracted RNA were verified using a Nano-Drop spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Next-Generation Sequencing and Microarray Processing Total RNA samples for RNA-sequencing (RNA-seq) were converted to complementary deoxyribonucleic acid (cDNA) using the Ovation RNA-seq System V2 (NuGEN Technologies, San Carlos, CA). Library construction was performed using the Ovation Ultralow Library System V2 1–96 (NuGEN). Sequencing was performed on the Illumina HiSeq 2000 sequencing platform (Illumina, San Diego, CA) with a 100-nucleotide (nt) paired-end setting. The raw data were filtered using the CLC Genomics Workbench veresion 8.0, on the basis of the Per Base Sequence Quality Score ≥20. To quantify transcript expression, the sequencing data were mapped to the Mus_musculus GRCm38 reference genome and mapped the remaining unmapped reads to the leptospiral genome. Total RNA samples for microarray detection were transcribed into cDNA and subjected to the GeneChip Mouse Transcriptome Assay 1.0 (Affymetrix, Santa Clara, CA). The Affymetrix data have been deposited in National Center for Biotechnology Information’s Gene Expression Omnibus [17] and are accessible through Gene Expression Omnibus accession number GSE111249. Bioinformatics Analyses Gene expression levels in the RNA-seq analysis were measured as reads per kilobase million (RPKM) value. A set of 15949 genes (of the total 46202 annotated genes) having a median RPKM value for all samples larger than 0.05 were considered to be expressed. The RPKM values were log transformed and tested using analysis of variance with Partek Genomics Suite software (Partek, St. Louis, MO). Genes presenting a fold change greater than 2 or less than −2 and P < .05 were selected as differentially expressed genes (DEGs). The hierarchical clustering was plotted using the Partek Genomics Suite software. The DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis using the Partek Pathway (Partek). Real-Time Quantitative Polymearse Chain Reaction Real-time quantitative polymearse chain reaction (qPCR) was performed on an ABI ViiA7 real-time PCR system (Applied Biosystems) using TaqMan gene expression assays. The 24 genes of interest were selected for confirmation, using TBP as an endogenous control (Supplementary Table 1). RESULTS Experimental Design: Renal Transcriptome in Mice Infected With Leptospira Species To study global transcriptional responses in kidneys after leptospiral infection, the kidneys of mice infected with L interrogans and L biflexa, respectively, and those of EMJH medium-treated control mice, were harvested for histological observations. Histopathological examination of kidneys from L interrogans-infected mice at day 28 postinfection showed inflammatory infiltrates and fibrosis with a milder intensity than those in the kidneys from L biflexa-infected and uninfected mice. The renal histopathological investigations revealed the degree of tubulointerstitial lesions slightly increased in kidneys from L interrogans-infected mice at day 28 postinfection (Supplementary Figure 1A, B, and C). Concomitant to evaluation of tubulointerstitial lesions in kidneys, the presence of pathogenic leptospires was also detected in kidney tissue and urine from infected mice (Supplementary Figure 1D and E). Hence, we selected 3 independent samples in each group for renal transcriptome studies, and the workflow is outlined in Figure 1. Figure 1. View largeDownload slide Schematic representation of simultaneous transcriptional profiling of kidney tissues from mice upon Leptospira spp infection. Abbreviations: DNA, deoxyribonucleic acid; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NGS, next-generation sequencing; PCR, polymerase chain reaction. Figure 1. View largeDownload slide Schematic representation of simultaneous transcriptional profiling of kidney tissues from mice upon Leptospira spp infection. Abbreviations: DNA, deoxyribonucleic acid; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NGS, next-generation sequencing; PCR, polymerase chain reaction. Leptospiral Genes Identified in the Kidneys of Mice We monitored renal transcriptome profiles by applying a RNA-seq analysis. An average of 57 million clean 94- to 95-nt reads were obtained for each sample, and approximately 98% of these reads were successfully mapped to the mouse genome (Supplementary Table 2). Moreover, we mapped the remaining unmapped reads to the leptospiral genome. There were 29 leptospiral genes detected in the RNA-seq data at day 28 postinfection with leptospires, whereas none were detected in kidneys at day 7 postinfection (Supplementary Table 3). Three of these genes were consistent with our previous work [18] and were validated by PCR (Supplementary Figure 2). Together with previous results (Supplementary Figure 1D and E), these data support that pathogenic leptospires were localized in the kidney at day 28 postinfection. Therefore, we further investigated the signaling pathways involved in renal lesions caused by chronic leptospirosis. Identification of Differentially Expressed Renal Genes We identified DEGs from the RNA-seq and microarray data (Table 1). At day 7 postinfection, the group infected with L interrogans versus the noninfected group (714 genes) included 629 up-regulated and 85 down-regulated genes identified from the RNA-seq data. At day 28 postinfection, the group infected with L interrogans versus the noninfected group (1089 genes) included 1062 up-regulated and 27 down-regulated DEGs. The microarray analysis identified the 467 significantly DEGs in the kidneys of mice infected with L interrogans at day 7 postinfection and the 927 transcripts significantly differentially expressed in these kidneys at day 28 postinfection. Table 1. The Number of DEGs in Mouse Kidneys After Leptospiral Infection Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Abbreviations: DEG, differentially expressed gene; ID, identification; L, Leptospira; RNA, ribonucleic acid; seq, sequencing. aNumbers of overlapping DEGs between the RNA-seq and microarray results based on corresponding Ensembl gene IDs. View Large Table 1. The Number of DEGs in Mouse Kidneys After Leptospiral Infection Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Groups Time of Infection (Days) DEGs Based on RNA-seq Results DEGs Based on Microarray Results Overlapping DEGsa Total Up (%) Down (%) Total Up (%) Down (%) Total Up (%) Down (%) L interrogans 7 714 629 (88.10) 85 (11.90) 467 439 (94.00) 28 (6.00) 202 197 (97.52) 5 (2.48) L biflexa 7 57 31 (54.39) 26 (45.61) 508 200 (39.37) 308 (60.63) 0 0 (0.00) 0 (0.00) L interrogans 28 1089 1062 (97.52) 27 (2.48) 927 852 (91.91) 75 (8.09) 424 418 (98.58) 6 (1.42) L biflexa 28 405 144 (35.56) 261 (64.44) 1067 851 (79.76) 216 (20.24) 162 142 (87.65) 20 (12.35) Abbreviations: DEG, differentially expressed gene; ID, identification; L, Leptospira; RNA, ribonucleic acid; seq, sequencing. aNumbers of overlapping DEGs between the RNA-seq and microarray results based on corresponding Ensembl gene IDs. View Large According to the corresponding Ensembl Gene identification, comprehensive analysis of RNA-seq and microarray gene expression consistency showed that 202 (the 512 and 265 genes that were uniquely identified from the RNA-seq and microarray datasets, respectively) and 424 (the 665 and 503 genes that were uniquely identified from the RNA-seq and microarray datasets, respectively) genes were found to be differentially expressed in mice with pathogenic leptospiral infection after day 7 and 28, respectively (Table 1). The validation of DEGs in the renal transcriptome was conducted using real-time qPCR, as shown in Table 2. The 24 genes (Supplementary Table 1) with predicted functions in renal injury/fibrosis (FGF2, TGFβ1, CTGF, LTBP2, FBN2, TIMP1, BGN, IL18, LCN2, and HAVCR1), the proinflammatory/inflammarory signaling molecules/immune responses (FOXP3, P2RX7, IFNγ, IL1β, IL6, IL10, MCP1, TNFα, IL18, TGFβ1, CSF1, CSF1R, and IL34), and cell-cell interactions (SIGLEC-1, SPN, and MUCI) were selected for validation [19–26]. We infer that DEGs that overlapped between the RNA-seq and microarray datasets were more significantly affected in response to leptospiral infection (for example, in groups for L interrogans at day 7 postinfection: BGN, P2RX7, CSF1, and HAVCR1; groups for L interrogans at day 28 postinfection: IL1β, BGN, P2RX7, MCP1, and CSF1) (Table 2). The real-time qPCR results are consistent with the RNA-seq data for SIGLEC-1, SPN, MUCI, TGFβ1, LTBP2, TIMP1, IFNγ, IL1β, IL6, IL10, BGN, P2RX7, MCP1, TNFα, CSF1, CSF1R, LCN2, IL34, HAVCR1 (in groups for L interrogans at day 7 postinfection) and SIGLEC-1, SPN, MUCI, FGF2, TGFβ1, LTBP2, TIMP1, FBN2, IFNγ, IL1β, IL6, IL10, BGN, FOXP3, P2RX7, MCP1, TNFα, IL18, CSF1, LCN2, and HAVCR1 (in groups for L interrogans at day 28 postinfection) (Table 2). Our findings showed that the real-time qPCR data were more consistent with the RNA-seq data. Hence, our after analysis focused on the DEGs identified from RNA-seq results. Table 2. Validation of Renal Transcriptome Data Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Abbreviations: CT, cycle threshold; L, Leptospira; NGS, next-generation sequencing; qPCR, quantitative polymerase chain reaction; RPKM, reads per kilobase million SIGLEC-1, sialic acid-binding immunoglobulin-like lectin-1; SPN, sialophorin; MUCI, mucin 1; FGF2, fibroblast growth factor 2; TGFb1, transforming growth factor beta 1; CTGF, connective tissue growth factor; LTBP2, latent transforming growth factor beta binding protein-2; TIMP1, tissue inhibitor of metalloproteinase 1; FBN2, fibrillin-2; IFNg, interferon gamma; IL1b, interleukin 1 beta; IL6, interleukin-6; IL10, interleukin-10; BGN, biglycan; FOXP3, forkhead box P3; P2RX7, purinergic receptor P2X, ligand-gated ion channel, 7; MCP1,monocyte chemotactic protein 1; TNFa, tumor necrosis factor-alpha; IL18, interleukin 18; CSF1, colony-stimulating factor 1; CSF1R, colony stimulating factor 1 receptor; LCN2, lipocalin 2; IL34, interleukin 34; HAVCR1, hepatitis A virus cellular receptor 1. aExpression was presented as the fold change compared with noninfected mice. bFold change using the RPKM values of the transcript. Fold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). cFold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). dFold change was determined using the comparative CT method [50]. Fold-differences were calculated using the standard deviation of the ΔΔCT value and are expressed as a range (minimum to maximum) of fold change; TBP was used as an internal control; n = 5–6 separate mice in groups for L biflexa infection, and n = 6–7 separate mice in groups for L interrogans infection; technical duplication for all of the genes in samples. View Large Table 2. Validation of Renal Transcriptome Data Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Groupsa Time of Infection (Days) Gene NGSb Microarrayc Real-Time qPCR (Fold-Differences) d L interrogans infection 7 SIGLEC-1 9.77 1.64* 9.61 (3.90–23.70) SPN 5.60* 1.49 3.96 (1.26–12.47) MUCI 4.21* 2.76 2.34 (1.20–4.56) FGF2 1.08 1.20 2.26 (1.12–4.57) TGFβ1 2.13 1.75* 2.21 (1.21–4.06) CTGF 2.45 1.38 1.49 (−1.39–3.08) LTBP2 4.98 1.33 22.55 (5.30–95.87) TIMP1 84.29* 2.05 74.59 (26.77–207.84) FBN2 −1.15 1.05 4.52 (1.58–12.95) IFNγ 28840.91 −1.09 6.45 (2.05–20.32) IL1β 2.16 1.50 4.57 (1.69–12.34) IL6 107878.60 1.28 35.58 (5.82–217.65) IL10 49218.11 −1.00 48.05 (20.65–111.81) BGN 2.06* 2.59* 2.15 (−1.27–5.88) FOXP3 −1.91 1.01 5.99 (1.46–24.53) P2RX7 2.48* 2.34* 3.09 (1.19–8.00) MCP1 3.51* 1.89* 7.04 (2.24–22.13) TNFα 9.70* 1.23 7.29 (−1.27–67.16) IL18 2.14 1.19 1.82 (−1.89–6.28) CSF1 3.04* 2.30* 3.50 (2.00–6.14) CSF1R 1.23 1.85* 1.91 (1.02–3.58) LCN2 37.12* 10.22 64.34 (10.02–413.31) IL34 3.40* 1.60 4.00 (1.60–9.99) HAVCR1 108.53* 22.47* 113.75 (34.71–372.81) L biflexa infection 7 SIGLEC-1 −1.60 −1.07 1.52 (1.14–2.02) SPN 1.16 −1.05 1.36 (−1.20–2.23) MUCI 1.41 −2.30* −1.04 (−1.64–−1.53) FGF2 −1.56 −1.13 −1.14 (−1.82–1.39) TGFβ1 −1.15 −1.19 −1.16 (−1.33–1.00) CTGF −1.70 −1.37 −1.52 (−2.13–−1.08) LTBP2 −4.95 −1.02 −1.10 (−1.96–−1.65) TIMP1 3.55 −1.10 −1.11 (1.61–1.31) FBN2 −1.35 −1.05 1.07 (−2.33–2.67) IFNγ 1.00 −1.23 −1.05 (−1.85–1.66) IL1β −1.07 −1.07 −1.22 (−1.92–1.28) IL6 1.00 −1.06 −2.33 (−6.25–1.19) IL10 1.00 −1.10 1.46 (1.92–4.11) BGN −1.23 −1.59* −1.11 (−1.56–1.25) FOXP3 −1.60 −1.02 1.49 (1.06–2.11) P2RX7 1.67 1.04 1.36 (−1.22–2.28) MCP1 −1.39 −1.20 1.07 (−1.82–2.08) TNFα 3.25 −1.23 −1.11 (−2.17–1.75) IL18 1.33 −1.08 1.42 (−1.12–2.28) CSF1 −1.29 −1.27* 1.09 (−1.54–1.83) CSF1R −1.27 −1.15 1.02 (−1.79–1.87) LCN2 1.01 −1.27* 1.18 (−2.17–2.98) IL34 −1.11 −1.18 1.12 (1.72–2.14) HAVCR1 2.18 1.18 1.62 (−1.64–4.32) L interrogans infection 28 SIGLEC-1 7.81 1.23* 8.76 (5.74–13.35) SPN 119.85* 1.77* 9.31 (5.43–15.96) MUCI 1.55 1.74 1.51 (−1.30–2.96) FGF2 1.31 1.11* 1.74 (1.42–2.13) TGFβ1 3.08* 1.69* 2.49 (1.47–4.22) CTGF 2.12 1.74* 1.13 (−1.20–1.53) LTBP2 22.01* 1.17 11.90 (5.79–24.50) TIMP1 15.60* 1.42 15.17 (6.34–36.29) FBN2 7.51 −1.02 4.08 (1.92–8.63) IFNγ 27.02 2.41 78.30 (28.95–211.78) IL1β 6.09* 2.90* 7.12 (3.39–14.96) IL6 20418.16 −1.10 21.54 (9.15–50.72) IL10 48365.51 1.00 36.79 (14.57–92.89) BGN 2.90* 2.40* 2.59 (1.34–5.00) FOXP3 3.87 −1.01 26.91 (9.05–79.98) P2RX7 6.39* 3.82* 4.54 (2.67–7.73) MCP1 14.29* 2.45* 16.74 (3.4–79.03) TNFα 21.77* 2.07 15.48 (1.06–226.36) IL18 3.52* 1.38* 2.21 (1.29–3.79) CSF1 3.90* 2.36* 2.55 (1.11–5.84) CSF1R 2.59 1.73* 1.30 (−2.17–3.63) LCN2 2.64 1.60* 2.72 (1.32–5.62) IL34 2.16 1.29 1.24 (−2.44–3.73) HAVCR1 4.93* 1.55 4.99 (2.28–10.89) L biflexa infection 28 SIGLEC-1 3.75 1.32* 2.68 (−1.04–7.49) SPN 4.40* 1.76* 2.85 (−1.08–8.79) MUCI 2.20 3.10* 1.77 (−1.22–3.82) FGF2 1.08 1.09* −1.22 (−2.5–1.63) TGFβ1 1.15 1.69* 1.58 (1.00–2.51) CTGF 1.21 1.38 −2.00 (−4.17–1.05) LTBP2 6.53 1.12 1.17 (−1.79–2.45) TIMP1 4.70 1.43 4.06 (−1.25–20.62) FBN2 3.77 −1.02 1.03 (−4.35–4.68) IFNγ 7.21 1.73* 5.79 (1.02–32.80) IL1β 4.14 4.32* 1.03 (−4.55–4.89) IL6 38092.39 1.07 3.27 (−4.55–45.57) IL10 1.00 1.01 2.38 (−2.44–13.79) BGN 1.27 1.72* −1.11 (−2.08–1.68) FOXP3 −1.10 −1.01 1.93 (−2.27–8.43) P2RX7 2.20 2.63* −1.12 (−3.45–2.73) MCP1 4.10* 2.91* 3.24 (−1.39–14.59) TNFα 4.65 1.53* 1.08 (−3.33–3.91) IL18 1.96 1.34 −1.22 (−3.03–2.00) CSF1 2.46* 2.62* −1.22 (−2.70–1.81) CSF1R 1.30 1.54* −1.96 (−4.00–1.04) LCN2 3.17 2.85* −1.09 (−5.88–4.96) IL34 1.39 1.37* −2.08 (−4.35–−1.04) HAVCR1 1.47 1.51* −1.05 (−4.35–3.99) Abbreviations: CT, cycle threshold; L, Leptospira; NGS, next-generation sequencing; qPCR, quantitative polymerase chain reaction; RPKM, reads per kilobase million SIGLEC-1, sialic acid-binding immunoglobulin-like lectin-1; SPN, sialophorin; MUCI, mucin 1; FGF2, fibroblast growth factor 2; TGFb1, transforming growth factor beta 1; CTGF, connective tissue growth factor; LTBP2, latent transforming growth factor beta binding protein-2; TIMP1, tissue inhibitor of metalloproteinase 1; FBN2, fibrillin-2; IFNg, interferon gamma; IL1b, interleukin 1 beta; IL6, interleukin-6; IL10, interleukin-10; BGN, biglycan; FOXP3, forkhead box P3; P2RX7, purinergic receptor P2X, ligand-gated ion channel, 7; MCP1,monocyte chemotactic protein 1; TNFa, tumor necrosis factor-alpha; IL18, interleukin 18; CSF1, colony-stimulating factor 1; CSF1R, colony stimulating factor 1 receptor; LCN2, lipocalin 2; IL34, interleukin 34; HAVCR1, hepatitis A virus cellular receptor 1. aExpression was presented as the fold change compared with noninfected mice. bFold change using the RPKM values of the transcript. Fold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). cFold changes followed by a * have P < .05. The P values were calculated from comparing infected (n = 3) with noninfected mice (n = 3). dFold change was determined using the comparative CT method [50]. Fold-differences were calculated using the standard deviation of the ΔΔCT value and are expressed as a range (minimum to maximum) of fold change; TBP was used as an internal control; n = 5–6 separate mice in groups for L biflexa infection, and n = 6–7 separate mice in groups for L interrogans infection; technical duplication for all of the genes in samples. View Large Comparative Pathway Analysis Between Renal Transcriptome of Pathogen and Nonpathogen-Infected Mice We compared the different renal transcriptome of mice infected with L interrogans or L biflexa at different time points postinfection (Table 1; Supplementary Figures 3 and 4). Supplementary Tables 4 and 5 list the unique and shared DEGs of pathogenic and non-pathogenic Leptospira species-infected mice between both groups at 2 different time points after infection. Overall, the number of DEGs was higher in pathogen infection groups than in nonpathogen infection groups at both postinfection time points, and most DEGs were up-regulated in pathogenic Leptospira species-infected mice. This might reflect that the complexity of renal transcriptome responses were induced by L interrogans. To understand the biological processes associated with host responses to leptospiral infection, we performed GO enrichment analysis (P < 1E-5). The results showed that 714 and 1089 significantly DEGs identified from renal transcriptomes of mice infected with L interrogans were enriched, with 549 and 779 GO terms at day 7 and 8 postinfection, respectively. In Figure 2, we list the top 30 significantly enriched GO terms, whereas these enriched GO terms were also found in the microarray results. In addition, the number of DEGs enriched in each GO terms was higher in groups at day 28 than in groups at day 7 postinfection (Figure 2). Moreover, the differentially expressed genes in the non-pathogenic leptospiral infection group at day 7 postinfection showed no significant hits in the GO database. Figure 2. View largeDownload slide Gene Ontology (GO) enrichment analysis of the differentially regulated genes in murine kidneys after Leptospira interrogans infection. The top 30 significantly enriched GO terms at day 7 and 28 postinfection, respectively. The left of the x-axis indicates the GO category, and the bar chart indicates the number of differentially expressed genes in each category. M and B represent categories of the GO terms: M, “molecular function” (green); B, “biological process” (blue). Figure 2. View largeDownload slide Gene Ontology (GO) enrichment analysis of the differentially regulated genes in murine kidneys after Leptospira interrogans infection. The top 30 significantly enriched GO terms at day 7 and 28 postinfection, respectively. The left of the x-axis indicates the GO category, and the bar chart indicates the number of differentially expressed genes in each category. M and B represent categories of the GO terms: M, “molecular function” (green); B, “biological process” (blue). To further elucidate the function of DEGs in renal transcriptome after leptospiral infection, a KEGG pathway enrichment analysis (P < 1E-4) was performed, and results are depicted in Supplementary Tables 6 and 7. No pathway was significantly enriched in the non-pathogenic L biflexa infection group at day 7 postinfection. Comparing pathways between pathogenic and non-pathogenic Leptospira species infection groups at day 28 postinfection shows that TLR signaling pathways, complement/coagulation cascades, tumor necrosis factor (TNF) signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interaction were significantly enriched in the pathogenic L interrogans infection group but not in L biflexa infection group. Hence, we infer that these functionally enriched pathways obtained from pathogenic leptospires infection groups might have important roles in kidney injury caused by leptospiral infection. Leptospires Induced Toll-Like Receptor Signaling Pathways in Kidneys The activation of TLR signaling through recognition of bacterial constituents leads to the host defense against invading pathogens and subsequent production of chemokines [27]. At day 7 postinfection with pathogen, DEGs were significantly enriched in TLR signaling pathways, leading to regulate gene expression profiles including the production of early inflammatory (TNFα, CCL5, and CCL2) and costimulatory molecules such as CD86, intercellular adhesion molecule (ICAM)1, and major histocompatibility complex (MHC) class I/II (Figure 3A). Figure 3B shows that TLRs initiate downstream signaling cascades to induce inflammatory cytokines (TNFα/IL1β), chemokines (CCL2/CCL5), chemokine receptors (CCR2/CCR1), and led to the production of CD86, CD83, CD80, CD40, CD69, ICAM1, and MHC class I/II molecules. Previous studies have shown that CCL2 is mainly expressed in the tubulointerstitial regions of the kidney in lupus-prone mice and CCL2/CCR2 are involved in the regulation of T-cell activation/differentiation [28]. In addition, CCL5/CCR1 is essential for T cell, macrophage, and neutrophil infiltration in the tubulointerstitial region of the kidney [29]. Hence, TLRs expressed on kidney injury caused by leptospiral infection plays a crucial role in the induction of inflammation and injury, indicating that TLRs may modulate the microenvironment and induce the complex regulation of cytokine gene expression in kidney. Figure 3. View largeDownload slide Toll-like receptor (TLR) signaling pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; and * indicate that genes were consistent with the results obtained from microarray results. Figure 3. View largeDownload slide Toll-like receptor (TLR) signaling pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; and * indicate that genes were consistent with the results obtained from microarray results. Leptospires Induced Complement-Related Pathways in Kidneys The activation of the classic, alternative, and/or lectin-induced complement pathways triggers phagocytosis through the opsonization of antigens, inflammation by attracting macrophages/neutrophils, and membrane attack by rupturing the membranes of pathogens/damaged cells [30]. Figure 4 shows that the C5/C6/C9 transcript that is not differentially expressed and the down-regulation of the C8 gene were found in the pathogen infection groups at day 7 postinfection. In addition, the C5, C6, C8, and C9 transcripts that were not differentially expressed were found in the pathogen infection groups at day 28 postinfection, suggesting that the membrane attack complex pore is not formed or formed as an inactive complex in the kidneys of mice infected with leptospires. Furthermore, the up-regulation of transcripts for C5AR1, C3AR1, α-M integrin, and α-X integrin suggests the activation of proinflammatory responses and the crosstalk of the immune system such as phagocytosis, cell-mediated cytotoxicity, chemotaxis, anaphylation, and cellular activation [30, 31]. Hence, we infer that the complement-induced immune responses might be present in the kidneys of mice infected with L interrogans. Figure 4. View largeDownload slide Complement-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. Figure 4. View largeDownload slide Complement-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. Animal studies have shown that the activation of C3 contribute to the development of progressive renal fibrosis during experimental obstructive nephropathy [32]. Our results showed the elevation of C3 and C4 in the renal transcriptome after pathogen infection, suggesting that C3 may play an important role in the renal defense mechanism. Genes related to complement-modulating proteins, such as the C1 inhibitor, factor H and factor I, were significantly up-regulated and revealed the regulation of complement activation upon L interrogans infection. We infer that pathogenic leptospires escape the complement-mediated killing and conclude that complement-mediated tissue damage might be implicated in the progression of pathogenic leptospiral infection-induced kidneys injury. Leptospires Induced Immune-Related Pathways in Kidneys As listed in Supplementary Table 6, results show that immunity plays a significant role in renal injuries after L interrogans infection. A schematic illustration of the cell-to-cell interactions during immune regulation is shown in Figure 5. It is interesting to note that genes involved in the formation of the immunological synapse, a nanoscale gap between T cells and antigen presenting cells, were enriched in the kidneys of mice infected with pathogen, but not in those infected with nonpathogen. The formation of the immunological synapse supports the notion that these supramolecular clusters might induce T-cell activation and signaling. Figure 5. View largeDownload slide Immune system-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. (C) Heat map of differentially expressed immune-related genes in response to pathogenic leptospiral infection. Heat map is arranged according to hierarchical clustering, based on the expression pattern of differentially expressed genes. The color scale indicates the expression value (red, up-regulation; green, down-regulation; black, no change). Figure 5. View largeDownload slide Immune system-related pathways derived from the renal transcriptome after Leptospira interrogans infection. (A) Days 7 and (B) 28 postinfection. Symbols are as follows: red italic letters indicate significantly up-regulated transcripts; green italic letters indicate significantly down-regulated transcripts; dark italic letters underlined indicate that these genes had no significant differences in expression levels; blue square indicates receptors in the plasma membrane; * indicates that genes were consistent with the results obtained from microarray results. (C) Heat map of differentially expressed immune-related genes in response to pathogenic leptospiral infection. Heat map is arranged according to hierarchical clustering, based on the expression pattern of differentially expressed genes. The color scale indicates the expression value (red, up-regulation; green, down-regulation; black, no change). Interleukin (IL)18 plays an important role in the T-helper 1(Th1)-mediated immune responses and in the host protection against intracellular pathogens infection [33]. The up-regulated expression of IL18 and its receptors IL18R1/IL18RAP was found in the renal transcriptomes of mice infected with pathogen at day 28 postinfection, suggesting an important contribution of IL18-mediated Th1 immunity in kidney injury after leptospiral infection (Figure 5B). Interleukin 18 may contribute to the renal damage caused by L interrogans infection. We suggest that IL18 blockade, such as IL18-binding protein production, and IL37, the IL18 contraregulatory protein, may have potential as a therapeutic strategies in CKD caused by L interrogans infection [34, 35]. We performed hierarchical clustering analysis on these significant DEGs. This analysis revealed that 26 immune-related genes such as LBP, FCGR1, SYK, IL33, COLLA1, IRF7, NCF1, and TLR2 in the L interrogans-infected renal transcriptome were differentially expressed, as shown in Figure 5C and Supplementary Table 8. These genes are involved in the cytoskeletal regulation, superoxide anion production, complement systems activation, immune responses regulation, and cell-cell recognition. DISCUSSION The study presented the first report characterizing the renal transcriptional profile of mice infected with Leptospira spp, and this work was performed for detection of both leptospires and renal transcripts during infection. We found that molecules from leptospires such as LipL32 and OmpL47 were identified in renal transcriptome of mice infected with L interrogans. These outer membrane proteins have been reported as host interaction proteins in leptospires [36–38]. LipL32 is known to interact directly with TLR2 and OmpL47 and has been reported to bind to laminin, fibronectin, fibrinogen, and collagen type III [38]. Our findings provide insights into the possibility of the interactions of leptospires within kidneys that may establish chronic leptospiral colonization of the renal tubules to induce persistent immune cell-mediated renal damage. Several immune response-related enriched pathways were found in non-pathogenic leptospiral infection groups at day 28; however, key molecules such as TCR molecules for T-cell activation were not significantly differentially expressed. Meanwhile, those molecules were found significantly expressed in pathogenic leptospiral infection groups. Therefore, we speculate that some pathogenic leptospires such as L interrogans induces persistent immune responses and T-cell activation/development, leading to CKD. Chemokines participate Th1 immune responses during renal injury and tissue fibrosis [39, 40]. Evidences reveal that increase levels of CCL2 are associated with progressive tubulointerstitial disease/renal fibrosis and up-regulation of CCR5 ligands (CCL3, CCL4, and CCL5) correlates with the recruitment of monocytes/T cells [41, 42]. Our results show that CCL2 versus its receptor CCR2 and CCL4/CCL3/CCL5 versus their receptors CCR1/CCR5 were up-regulated during progressive kidney injury in pathogenic leptospiral-infected mice. Furthermore, an increased expression level of CXCL9/CXCL10 and their receptors CXCR3 was found in the renal transcriptomes of mice infected with pathogenic leptospires at day 28 postinfection. Experimental evidence for renal fibrosis in hamsters caused by chronic leptospiral infection has demonstrated that proinflammatory IL1β/TNFα, IL10, chemokines macrophage-inflammatory protein-1α/CCL3, and IP-10/CXCL10 were up-regulated in damaged kidneys [11]. Our results show that the expressions of IL1β/TNFα, IL10, and CCL3/CXCL10 messenger RNA were also up-regulated in damaged kidneys caused by infection of pathogenic Leptospira spp in mice. In our study, the expression of the TGFβ transcript, COLLA1 for collagen type I, and COL3A1 for collagen type III were all up-regulated in the renal transcriptomes of mice infected with pathogenic leptospires. Hence, collagen I and III might participate in leptospiral infection-induced renal fibrosis [43]. Furthermore, MARCKS-like protein 1 ([MARCKSL1] cytoskeleton-associated proteins associated with cell spreading) transcript was elevated in the renal transcriptomes of mice infected with pathogenic leptospires, suggesting a role in dynamics of cellular spreading in kidneys during leptospiral infection [44]. Reactive oxygen species exert modulating effects on inflammation and have roles in the regulation of immune responses [45]. Our finding also suggests that the production of superoxide anions may be a portion of injury mechanisms participated in renal damage caused by chronic leptospiral infection. It is notable that the up-regulated expression of runt-related transcription factors 3 (RUNX3) transcript (7.75-fold change; P < .05) was only found in the renal transcriptomes of mice infected with pathogenic leptospires at day 28 postinfection. Previous studies demonstrated that Runx3 expression is up-regulated during Th1 differentiation and circulating human CD4+CD25highCD127− T regulatory (Treg) cells [46, 47]. Future studies are needed to further investigate the roles of Runx3 in Treg cells from kidney injury caused by leptospiral infection. Previous studies have reported that subversion of innate immunity by pathogen-induced complement-TLR crosstalk pathways has the potential to modulate the host response in ways that favor pathogen persistence and interfere with host protective immunity [48, 49]. Moreover, C57BL/6 mice deficient for the decay-accelerating factor, an early regulator of complement cascades, are more susceptible to infection with L interrogans [43]. Our results confirm that TLR signaling complements activation, and Th1 type immune responses were strongly associated with progressive tubulointerstitial damage caused by pathogenic leptospiral infection. Hence, we speculate that pathogenic leptospiral infections may alter the microenvironment to induce cell-to-cell interactions, leading to chronic inflammatory disorder in kidneys. Moreover, chronic leptospiral infections via complement-TLR crosstalk pathways may play a detrimental role in T cell-mediated renal injury. CONCLUSIONS The new insight of the molecular mechanisms underlying complement-TLR crosstalk pathways in our study could bring focus on development either alone or both of the therapeutic strategies in the treatment of kidney diseases caused by chronic leptospiral infection. These results provide a better understanding of the interaction between the host and Leptospira spp in chronic renal infections. Supplementary Data Supplementary materials are available at The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author. Notes Author contributions. L.-F. C. designed the study, performed the experiments and data analysis, and wrote the manuscript. T.-W. C. performed the bioinformatic analysis and edited the manuscript. Y.-C. K. carried out the mouse infection experiments, and C.-T. H. performed the ribonucleic acid extraction. H.-Y. Y., M.-Y. C., Y.-C. T., C.-C. H., S.-H. H., and C.-Y. T. participated in the interpretation and discussion of the results and provided critical comments during the drafting of the manuscript. C.-W. Y. conceived and designed the study, participated in the interpretation and discussion of the results, and assisted in the drafting of the manuscript. All authors have read and approved the final version of the manuscript. Acknowledgments.  We gratefully acknowledge Microscope Core Laboratory and the Genomic Medicine Core Laboratory at the Chang Gung Memorial Hospital, Linkou for technical assistance. We thank the Tissue Bank (Chang Gung Memorial Hospital, Lin-Kou, Taiwan) for excellent tissue processing. Financial support. This work was funded by grants from the Ministry of Science and Technology, Taiwan (MOST 103-2320-B-182A-020, MOST 105-2628-B-182A-006-MY3 and MOST-106-2311-B-182-005) and Chang Gung Memorial Hospital grants (CMRPG3E1931-1, CMRPG3E1931-2, and CMRPG3E1931-3). Potential conflicts of interest.  All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. 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The Journal of Infectious DiseasesOxford University Press

Published: Jun 2, 2018

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