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• W3075687179 abstract "Immune aggregates organized as tertiary lymphoid structures (TLS) are observed within the kidneys of patients with systemic lupus erythematosus and lupus nephritis (LN). Renal TLS was characterized in lupus-prone New Zealand black × New Zealand white F1 mice analyzing cell composition and vessel formation. RNA sequencing was performed on transcriptomes isolated from lymph nodes, macrodissected TLS from kidneys, and total kidneys of mice at different disease stages by using a personal genome machine and RNA sequencing. Formation of TLS was found in anti–double-stranded DNA antibody–positive mice, and the structures were organized as interconnected large networks with distinct T/B cell zones with adjacent dendritic cells, macrophages, plasma cells, high endothelial venules, supporting follicular dendritic cells network, and functional germinal centers. Comparison of gene profiles of whole kidney, renal TLS, and lymph nodes revealed a similar gene signature of TLS and lymph nodes. The up-regulated genes within the kidneys of lupus-prone mice during LN development reflected TLS formation, whereas the down-regulated genes were involved in metabolic processes of the kidney cells. A comparison with human LN gene expression revealed similar up-regulated genes as observed during the development of murine LN and TLS. In conclusion, kidney TLS have a similar cell composition, structure, and gene signature as lymph nodes and therefore may function as a kidney-specific type of lymph node. Immune aggregates organized as tertiary lymphoid structures (TLS) are observed within the kidneys of patients with systemic lupus erythematosus and lupus nephritis (LN). Renal TLS was characterized in lupus-prone New Zealand black × New Zealand white F1 mice analyzing cell composition and vessel formation. RNA sequencing was performed on transcriptomes isolated from lymph nodes, macrodissected TLS from kidneys, and total kidneys of mice at different disease stages by using a personal genome machine and RNA sequencing. Formation of TLS was found in anti–double-stranded DNA antibody–positive mice, and the structures were organized as interconnected large networks with distinct T/B cell zones with adjacent dendritic cells, macrophages, plasma cells, high endothelial venules, supporting follicular dendritic cells network, and functional germinal centers. Comparison of gene profiles of whole kidney, renal TLS, and lymph nodes revealed a similar gene signature of TLS and lymph nodes. The up-regulated genes within the kidneys of lupus-prone mice during LN development reflected TLS formation, whereas the down-regulated genes were involved in metabolic processes of the kidney cells. A comparison with human LN gene expression revealed similar up-regulated genes as observed during the development of murine LN and TLS. In conclusion, kidney TLS have a similar cell composition, structure, and gene signature as lymph nodes and therefore may function as a kidney-specific type of lymph node. Systemic lupus erythematosus (SLE) and lupus nephritis (LN) are chronic autoimmune diseases characterized by inflammation and damage in the kidneys.1Rekvig O.P. Thiyagarajan D. Pedersen H.L. Horvei K.D. Seredkina N. Future perspectives on pathogenesis of lupus nephritis: facts, problems, and potential causal therapy modalities.Am J Pathol. 2016; 186: 2772-2782Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar,2Seredkina N. van der Vlag J. Berden J. Mortensen E. Rekvig O.P. Lupus nephritis - enigmas, conflicting models and an emerging concept.Mol Med. 2013; 19: 161-169Crossref PubMed Scopus (44) Google Scholar The deposition of immune complexes within the glomeruli and within the tubular interstitial membranes stimulates glomerular and tubular cells to produce chemokines that attract immune cells and can also activate intrinsic immune cells, such as macrophages and dendritic cells (DCs).3Fenton K. Fismen S. Hedberg A. Seredkina N. Fenton C. Mortensen E.S. Rekvig O.P. Anti-dsDNA antibodies promote initiation, and acquired loss of renal Dnase1 promotes progression of lupus nephritis in autoimmune (NZB×NZW)F1 mice.PLoS One. 2009; 4: e8474Crossref PubMed Scopus (86) Google Scholar, 4Kalaaji M. Fenton K.A. Mortensen E.S. Olsen R. Sturfelt G. Alm P. Rekvig O.P. Glomerular apoptotic nucleosomes are central target structures for nephritogenic antibodies in human SLE nephritis.Kidney Int. 2007; 71: 664-672Abstract Full Text Full Text PDF PubMed Scopus (149) Google Scholar, 5Kanapathippillai P. Hedberg A. Fenton C.G. Fenton K.A. Nucleosomes contribute to increase mesangial cell chemokine expression during the development of lupus nephritis.Cytokine. 2013; 62: 244-252Crossref PubMed Scopus (22) Google Scholar, 6Yung S. Cheung K.F. Zhang Q. Chan T.M. Mediators of inflammation and their effect on resident renal cells: implications in lupus nephritis.Clin Dev Immunol. 2013; 2013: 317682Crossref PubMed Scopus (37) Google Scholar, 7Maria N.I. Davidson A. Renal macrophages and dendritic cells in SLE nephritis.Curr Rheumatol Rep. 2017; 19: 81Crossref PubMed Scopus (34) Google Scholar This deposition led to an increased accumulation of immune cells within the kidneys during the development of LN.8Tsokos G.C. Lo M.S. Costa R.P. Sullivan K.E. New insights into the immunopathogenesis of systemic lupus erythematosus.Nat Rev Rheumatol. 2016; 12: 716-730Crossref PubMed Scopus (636) Google Scholar Chronic inflammatory processes, such as infection and autoimmunity, cause tertiary lymphoid structures (TLS) to develop within different organs.9Aloisi F. Pujol-Borrell R. Lymphoid neogenesis in chronic inflammatory diseases.Nat Rev Immunol. 2006; 6: 205-217Crossref PubMed Scopus (730) Google Scholar These structures, resembling secondary lymphoid organs,10van de Pavert S.A. Mebius R.E. New insights into the development of lymphoid tissues.Nat Rev Immunol. 2010; 10: 664-674Crossref PubMed Scopus (440) Google Scholar have been detected in patients with different diseases that affect the kidneys, such as IgA nephropathies11Pei G. Zeng R. Han M. Liao P. Zhou X. Li Y. Zhang Y. Liu P. Zhang C. Liu X. Yao Y. Xu G. Renal interstitial infiltration and tertiary lymphoid organ neogenesis in IgA nephropathy.Clin J Am Soc Nephrol. 2014; 9: 255-264Crossref PubMed Scopus (43) Google Scholar and LN,12Chang A. Henderson S.G. Brandt D. Liu N. Guttikonda R. Hsieh C. Kaverina N. Utset T.O. Meehan S.M. Quigg R.J. Meffre E. Clark M.R. In situ B cell-mediated immune responses and tubulointerstitial inflammation in human lupus nephritis.J Immunol. 2011; 186: 1849-1860Crossref PubMed Scopus (247) Google Scholar,13He N. Chen W.L. Long K.X. Zhang X. Dong G.F. Association of serum CXCL13 with intrarenal ectopic lymphoid tissue formation in lupus nephritis.J Immunol Res. 2016; 2016: 4832543Crossref PubMed Scopus (10) Google Scholar and in calcineurin Aα heterozygous mice,14Kelly F.M. Reddy R.N. Roberts B.R. Gangappa S. Williams I.R. Gooch J.L. TGF-beta upregulation drives tertiary lymphoid organ formation and kidney dysfunction in calcineurin A-alpha heterozygous mice.Am J Physiol Renal Physiol. 2009; 296: F512-F520Crossref PubMed Scopus (9) Google Scholar lupus-prone mice,15Kang S. Fedoriw Y. Brenneman E.K. Truong Y.K. Kikly K. Vilen B.J. BAFF induces tertiary lymphoid structures and positions T cells within the glomeruli during lupus nephritis.J Immunol. 2017; 198: 2602-2611Crossref PubMed Scopus (45) Google Scholar and during aging.16Huang Y. Caputo C.R. Noordmans G.A. Yazdani S. Monteiro L.H. van den Born J. van Goor H. Heeringa P. Korstanje R. Hillebrands J.L. Identification of novel genes associated with renal tertiary lymphoid organ formation in aging mice.PLoS One. 2014; 9: e91850Crossref PubMed Scopus (17) Google Scholar,17Sato Y. Mii A. Hamazaki Y. Fujita H. Nakata H. Masuda K. Nishiyama S. Shibuya S. Haga H. Ogawa O. Shimizu A. Narumiya S. Kaisho T. Arita M. Yanagisawa M. Miyasaka M. Sharma K. Minato N. Kawamoto H. Yanagita M. Heterogeneous fibroblasts underlie age-dependent tertiary lymphoid tissues in the kidney.JCI Insight. 2016; 1: e87680Crossref PubMed Scopus (60) Google Scholar The size and location of kidney biopsies make it difficult to assess the extent of the developed TLS in human LN. Whether TLS are sites for activation or inhibition of immune cells is still not known.15Kang S. Fedoriw Y. Brenneman E.K. Truong Y.K. Kikly K. Vilen B.J. BAFF induces tertiary lymphoid structures and positions T cells within the glomeruli during lupus nephritis.J Immunol. 2017; 198: 2602-2611Crossref PubMed Scopus (45) Google Scholar Changes in gene expression during the development of LN have been extensively studied in human and murine LN.18Bethunaickan R. Berthier C.C. Zhang W. Kretzler M. Davidson A. Comparative transcriptional profiling of 3 murine models of SLE nephritis reveals both unique and shared regulatory networks.PLoS One. 2013; 8: e77489Crossref PubMed Scopus (37) Google Scholar, 19Berthier C.C. Bethunaickan R. Gonzalez-Rivera T. Nair V. Ramanujam M. Zhang W. Bottinger E.P. Segerer S. Lindenmeyer M. Cohen C.D. Davidson A. Kretzler M. Cross-species transcriptional network analysis defines shared inflammatory responses in murine and human lupus nephritis.J Immunol. 2012; 189: 988-1001Crossref PubMed Scopus (130) Google Scholar, 20Berthier C.C. Kretzler M. Davidson A. A systems approach to renal inflammation in SLE.Clin Immunol. 2017; 185: 109-118Crossref PubMed Scopus (8) Google Scholar Most of the differences in gene expression observed in diseased LN kidneys compared with healthy kidneys are involved in the activation of the innate and the adaptive immune systems.21Berthier C.C. Kretzler M. Davidson A. From the large scale expression analysis of lupus nephritis to targeted molecular medicine.J Data Mining Genomics Proteomics. 2012; 3: 1000123Crossref PubMed Google Scholar This finding indicates that the formation of TLS might be an important feature of LN. In a longitudinal study on lupus-prone, New Zealand black × New Zealand white (NZB/W) F1 mice, the formation of renal immune aggregates resembling TLS during the progression of LN has been observed.22Dorraji S.E. Hovd A.K. Kanapathippillai P. Bakland G. Eilertsen G.O. Figenschau S.L. Fenton K.A. Mesenchymal stem cells and T cells in the formation of tertiary lymphoid structures in lupus nephritis.Sci Rep. 2018; 8: 7861Crossref PubMed Scopus (22) Google Scholar We hypothesize that the gene profile of kidney-specific TLS is similar to the lymph nodes of lupus-prone mice in an active stage of disease. The present study was undertaken to characterize the cell composition and analyze the gene expression profile of macrodissected kidney-specific TLS compared with whole kidney and lymph nodes of lupus-prone mice during the development of anti–double-stranded DNA (dsDNA) antibody production and progression of LN in three different stages of the disease. NZB/W mice were obtained from Jackson Laboratory (Bar Harbor, ME). Treatment and care of animals were conducted in accordance with guidelines of the Norwegian Ethical and Welfare Board for Animal Research, and the institutional review board approved the study. Seven-week–old NZB/W mice (Table1) (n = 5), 20- to 41-week–old mice 4 to 14 weeks anti-dsDNA–positive (n = 12), and 28- to 39-week–old nephritic mice 5 to 13 weeks anti-dsDNA positive (n = 13) were sacrificed as described previously.3Fenton K. Fismen S. Hedberg A. Seredkina N. Fenton C. Mortensen E.S. Rekvig O.P. Anti-dsDNA antibodies promote initiation, and acquired loss of renal Dnase1 promotes progression of lupus nephritis in autoimmune (NZB×NZW)F1 mice.PLoS One. 2009; 4: e8474Crossref PubMed Scopus (86) Google Scholar The kidneys and renal lymph nodes were isolated and processed for RNA isolation immunohistochemistry analysis as described.5Kanapathippillai P. Hedberg A. Fenton C.G. Fenton K.A. Nucleosomes contribute to increase mesangial cell chemokine expression during the development of lupus nephritis.Cytokine. 2013; 62: 244-252Crossref PubMed Scopus (22) Google ScholarTable 1Mice Used in This StudyMouse no.Age, wkTime of Anti-dsDNA antibody Positivity, wkProteinuria uristixACRHistology Score (range, 0–4)TLS test resultRNA Sequencing1700122NegativeM1270042NegativeM23700322NegativeM34700282NegativeM45700422NegativeM56355427294PositiveNP7357022PositiveNP8294022PositiveM692840NP2PositiveM710284083PositiveM8112940613PositiveM9122070612PositiveNP1336402263PositiveNP1436401083PositiveNP15265072PositiveM101637134NPNPPositiveNP173694NP4PositiveNP1836114NPNPPositiveNP193054NP4PositiveNP2041142NP2PositiveNP2130Negative4NP3PositiveNP223884NPNPPositiveNP234162NPNPPositiveNP2441132NPNPPositiveNP2539134NP4PositiveM11262894NPNPPositiveNP2736104NP4PositiveM122836134NP3PositiveM13292854NP4PositiveM143037114NP4PositiveM15Bold indicates selected for RNA sequencing.ACR, albumin/creatinine ratio (normal, 0 to <30; microalbuminuria, 30 to ≤300; clinical proteinuria, >300); NP, not performed; TLS, tertiary lymphoid structures. Open table in a new tab Bold indicates selected for RNA sequencing. ACR, albumin/creatinine ratio (normal, 0 to <30; microalbuminuria, 30 to ≤300; clinical proteinuria, >300); NP, not performed; TLS, tertiary lymphoid structures. Urine samples were tested every week until the onset of proteinuria. Full-blown LN was defined when proteinuria reached 4+, as determined by Urine Stix (Bayer Diagnostics, Bridgend, UK): 0 to 1+ was defined as <1 g/L (physiologic proteinuria); 2+, ≥1 to 3 g/L; 3+, ≥3 to 20 g/L; and 4+, ≥20 g/L. End point urine were collected if possible and analyzed using the albumin/creatinine ratio (ACR) assay kit from PromoKine (Heidelberg, Germany). ACR results were defined as follows: normal, 0 to <30; microalbuminuria, 30 to ≤300; and clinical proteinuria, >300. However, chronic kidney disease may be present if ACR ≥30. Blood samples were taken every week until anti-dsDNA positivity was detected; thereafter, samples were taken every second week until proteinuria was detected. Serum samples were collected and stored at −20°C until use. Serum antibodies against dsDNA were determined by enzyme-linked immunosorbent assay as previously described.23Kalaaji M. Sturfelt G. Mjelle J.E. Nossent H. Rekvig O.P. Critical comparative analyses of anti-alpha-actinin and glomerulus-bound antibodies in human and murine lupus nephritis.Arthritis Rheum. 2006; 54: 914-926Crossref PubMed Scopus (81) Google Scholar,24Rekvig O.P. Moens U. Sundsfjord A. Bredholt G. Osei A. Haaheim H. Traavik T. Arnesen E. Haga H.J. Experimental expression in mice and spontaneous expression in human SLE of polyomavirus T-antigen: a molecular basis for induction of antibodies to DNA and eukaryotic transcription factors.J Clin Invest. 1997; 99: 2045-2054Crossref PubMed Scopus (104) Google Scholar Sera were diluted twofold from 1/100 to 1/6400 in phosphate-buffered saline (PBS) (0.02% Tween), and the positive control 163c3 anti-dsDNA monoclonal antibody (kindly provided by T.N. Marion, The University of Tennessee Health Science Center, Memphis, TN25Tillman D.M. Jou N.T. Hill R.J. Marion T.N. Both IgM and IgG anti-DNA antibodies are the products of clonally selective B cell stimulation in (NZB × NZW)F1 mice.J Exp Med. 1992; 176: 761-779Crossref PubMed Scopus (236) Google Scholar) was included in each enzyme-linked immunosorbent assay for assay validation and determination of cut-off value. The optical density cut-off values were set to >0.2 at A493, and positive titers were determined when 40% of positive control mAbs was reached. Classification of kidney damage was analyzed based on the 2019 European League Against Rheumatism and the American College of Rheumatology criteria.26Aringer M. Costenbader K. Daikh D. Brinks R. Mosca M. Ramsey-Goldman R. et al.2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus.Arthritis Rheumatol. 2019; 71: 1400-1412Crossref PubMed Scopus (627) Google Scholar Pathologic findings were scored by observers who were blinded to the genotype. Paraffin-embedded sections 4 mm thick were dewaxed and stained with hematoxylin and eosin. The sections were scanned using a VS120 virtual slide microscope (Olympus, Asker, Norway). Each kidney was examined at ×400 magnification and scored from 0 to 4 based on the following features: glomerular size and hypercellularity, changes in glomerular matrix, and the degree of hypercellularity in the tubulointerstitium. Immunohistochemistry using Polink-2 Plus HRP detection kits for tissue (anti-rabbit and anti-rat) (Golden Bridge International Inc., Mukilteo, WA) was performed on Zink- or 4% paraformaldehyde-fixed kidneys embedded in paraffin. Anti-mouse CD3 was obtained from Dako (Glostrup, Denmark). Anti-mouse CD45R (B220) was purchased from R&D Systems (Minneapolis, MN). Antibodies against mouse B-cell lymphoma 6 (BCL6), CD21, and lymphotoxin β-receptor (LTBr) were purchased from Abcam (Cambridge, UK). Anti-mouse peripheral lymph node addressin (PNAD) and anti-mouse F4/80 were obtained from BioLegend (San Diego, CA). Anti-mouse monoclonal anti-DC antibody (MIDC)-8 was obtained from Nordic BioSite (Oslo, Norway). Images were collected with an Olympus microscope (BX51 and DP74). Immunofluorescence staining was performed on 5-μm kidney cryosections. The sections were dried at room temperature for 30 minutes and then fixed for 5 minutes in 4% paraformaldehyde. Sections were washed three times in 1× PBS for 5 minutes each and incubated with blocking serum (1× PBS with 10% donkey serum) (AB7475, Abcam) for 30 minutes. The sections were incubated with primary antibodies [B220 and muscle, intestine and stomach expression (Mist) (BHLHA15, Biorbyt, Cambridge, UK) or B220 and forkhead box P3 (FoxP3) (Novus Biologicals, Bio-Techne Ltd., Abingdon, UK)] for 30 minutes and washed three times in 1× PBS for 5 minutes each. The sections were incubated with secondary antibody (AF488 anti-rat IgG and AF546 anti-rabbit IgG) for 30 minutes in the dark and were washed three times in 1× PBS for 5 minutes each. The sections were carefully dried and mounted using 10 μL of DAPI (Invitrogen, Fisher Scientific, Oslo, Norway). For each sample, a negative control was prepared by the same procedure with an irrelevant control antibody. Images were collected with an Olympus microscope (BX51 and DP74). Serial kidney sections (10-μm thick) from a NZB/W mouse were stained with hematoxylin, and images were obtained at ×4 magnification. The TLS, arteries, and veins were outlines, reconstructed, and visualized in three dimensions using the TrakEM2 plugin in ImageJ software version 1.52 (NIH, Bethesda, MD; http://imagej.nih.gov/ij). Total RNA of lymph nodes from 4- and 8-week–old and proteinuric mice, or immune aggregates/TLS dissected manually under the loupe (magnification, ×10) from proteinuric kidney, or kidney tissue from the same kidney, were purified by TRIzol regent (Ambion, Thermo Fisher, Oslo, Norway) according to manufacturer's instructions. Total RNA of lymph nodes from anti-dsDNA antibody–positive mice were isolated using miRNeasy mini kit (Qiagen, Oslo, Norway). The concentration and quality of extracted total RNA were assessed using the Agilent RNA 6000 nano kit with the Agilent 2100 Bioanalyzer instrument (Agilent, Matriks AS, Norway). All RNA used in this study had an RNA integrity number ≥7. Total RNA samples were prepared for sequencing using the Qiagen Allprep total RNA kit (Qiagen). Briefly, tissue sections (20 to 30 μg) taken from the middle of the kidneys were lyzed and homogenized in 600 μL of Buffer RLT in 2 mL of Magna Lyser green beads tubes (Roche Life Sciences, Oslo, Norway) at 600 × g for 30 seconds using the Precellys 24 tissue homogenizer (Bertin Technologies, Aix-en-Provence, France). The lysates were centrifuged at full speed for 3 minutes, the supernatant was carefully transferred to the AllPrep spin column, and RNA isolation was performed according to the manufacturer's protocol. The isolated RNA was analyzed by Agilent. Only RNA samples of high quality (RNA integrity number ≥8.0) were used. Sequencing of total kidney mRNA was performed by Eurofins Genomics (Ebersberg, Germany) using the Illumina HiSeq 2000 system. Polyadenylated mRNA was isolated from total RNA using Dynabeads mRNA Direct Micro Kit (Ambion, Thermo Fisher) followed by its fragmentation using RNase III with mean sizes of 100 to 200 nt. The fragmented mRNA was cleaned and the yield and size distribution determined using the Agilent RNA 6000 Pico kit with the Agilent 2100 Bioanalyzer instrument. After adapter ligation at both ends of fragmented mRNA, reverse transcription was performed, followed by PCR amplification. After purification of amplified cDNA, the yield and size distribution were analyzed using an Agilent High Sensitivity DNS kit with Agilent 2100 Bioanalyzer instrument. All libraries were amplified with emulsion PCR (Ion One Touch 2 instrument), and enrichment of template Ion Sphere Particles was performed with the Ion One Touch ES system. Quality control of Ion Sphere Particles was performed with an Ionsphere Quality Control kit using Qubit version 2.0 (Life Technologies, Thermo Fisher, Oslo, Norway). The enriched Ion Sphere Particles were sequenced using an Ion Torrent 316 chip with sequencer Ion Torrent Personal Genome Machine (Ion Torrent PGM) according to manufacturer's instructions. All sequences were imported and analyzed in CLC Genomics Workbench 8 (CLCbio, Aarhus Denmark). Adaptor sequences were trimmed, and RNA sequencing was conducted using Mus_musculus GRCm 38.80 as a reference genome by default. RNA sequencing cDNA libraries were prepared from the total RNA isolated from kidneys of 15 NZB/W F1 mice (Table 1). Fifteen 3′-fragments with an insert size of approximately 200 to 450 bp were prepared and sequenced using the Illumina HiSeq 2000 version 3.0 system (Eurofins Genomics). The samples were divided into three channels with five libraries per channel containing 1 × 100-bp single-read module. The sequences were demultiplexed according to the 6-bp index code allowing 1 mismatch (Eurofins Genomics). Eurofins Genomics performed alignments and assignment of reads to genes. The alignment of reads to a reference sequence was performed using the BWA-backtrack version 0.6.2-r126 (http://bio-bwa.sourceforge.net, last accessed June 18, 2019). Raw read counts were created using HTSeq with Python software version 2.7 (Python Software Foundation, Fredericksburg, VA; https://www.python.org). Reads with unique mapping positions were considered for read counting. Paired-end reads that were mapped to the same reference with approximately the expected insert size were counted as one read. Paired-end reads that were mapped to different references or with an unexpected insert size were counted as two reads. If only one read of a pair was mapped, it was counted as one read. Only reads that overlapped exon features were counted. All reads mapping to features with the same identifier were summed. The gene attribute was used as feature identifier. Reads mapping to multiple features with different identifier were ignored for read counting. The mean read length was 100.0 (Eurofins Genomics). The CPM (counts per million) function from the edgeR library was used to generate the CPM values; the CPM values were further filtered. The ratio of RNA production was estimated by using a weighted trimmed mean of log expression ratios called the trimmed mean of M values. The calcNormFactors function from edgeR package calculated the normalization factors among libraries. These normalization factors are rescaled by the mean of the normalized library sizes. Normalized read counts were obtained by dividing raw read counts by these rescaled normalization factors. This was performed to eliminate composition biases among libraries.27Robinson M.D. Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data.Genome Biol. 2010; 11: R25Crossref PubMed Scopus (4091) Google Scholar A multidimensional scaling plot was generated with the plotMDS function from limma package. Distances between samples was calculated with leading fold change defined as the root mean square of the largest 500 log2 fold changes among samples. Differential expression analysis was performed using the edgeR package.28Robinson M.D. McCarthy D.J. Smyth G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.Bioinformatics. 2010; 26: 139-140Crossref PubMed Scopus (20826) Google Scholar Differential expression data were filtered to contain a false discovery rate <0.05. An empirical Bayes procedure was used to shrink the dispersions toward a consensus value, borrowing information among genes. The results were tested for differential expression using the generalized linear model likelihood ratio test. The likelihood ratio test was performed by estimating two groups and by comparing the fit of one group with the fit of the other. To deal with multiple tests, individual tests were made with separate computations to test for different contrasts. Three different contrasts were made to test the hypothesis that the different coefficients in each contrast (ie, the three different groups) were equal. The glmLRT function from edgeR was used to conduct likelihood ratio tests for the coefficients in the linear model, such as the Fisher exact test, and adapted for over dispersed data.27Robinson M.D. Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data.Genome Biol. 2010; 11: R25Crossref PubMed Scopus (4091) Google Scholar The results were corrected for multiple hypothesis testing via the Benjamini-Hochberg procedure. For each gene, an adjusted P value was calculated to enable the expected proportion of positive results returned that were false-positive results (ie, false discovery rate). Venn diagrams were drawn to visualize the overlap between up-regulated and down-regulated genes among all three groups using the vennDiagram package from limma. All relevant data have been deposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov; accession number GSE155405). GraphPad Prism software version 5.0 (GraphPad Software, San Diego, CA) was used to perform statistical comparison analyses. Results are expressed as means ± SEM. Statistical significance was set at P < 0.05. Statistical significance was assessed using a paired t-test, one-way or two-way analysis of variance, followed by the Bonferroni posttest as indicated in the figures. Differentially expressed (DE) genes were divided into up-regulated and down-regulated genes, according to an adjusted P <0.05 and logFC value 1.0/−1.0, respectively. Venn diagrams were drawn to visualize the overlapping of DE genes using the groupings from the multidimensional scaling plot plot. Statistical significance is indicated as follows in the figures. A Spearman's correlation matrix was generated based on clinical parameters and gene expression. The production of anti-dsDNA antibody was measured every week until antibody-positive titer and then every second week until onset of proteinuria or until the mice reached 4 to 5 weeks with antibody positivity (Figure 1A and Table 1). All mice with anti-dsDNA antibody-positive titer for 4 to 5 weeks had developed TLS near the pelvic wall, large arteries, and large veins (Figure 1, B–E). Some mice had TLS within the perirenal fat (Figure 1, C and E). Smaller TLS were also detected in the cortex of kidneys from proteinuric mice (Figure 1, C and F). To examine the structural specifications of TLS and to investigate whether the TLS developed in separate regions of the kidneys, a three-dimensional structure of serial sections of a whole kidney was made (Figure 1G). The three-dimensional structure revealed an interconnected large network of immune aggregates located close to or surrounding the arteries and veins (Figure 1G). The larger structures were found in the renal pelvic area between the pelvic wall and the biggest veins (Figure 1G), whereas the smaller structures were located around the arteries in the superior and inferior sections of the kidney (Figure 1G). Most cells in TLS were CD3+ T cells with surrounding characteristic B220+ B-cell areas (Figure 2A). The B-cell areas contained a network of CD21+ follicular DCs (FDCs) (Figure 2A), and the T-cell areas were rich in activated MIDC-8+ DCs (Figure 2A8) and a few FoxP3+ cells (Figure 2B). F4/80+ macrophages were detected within the pelvic wall, in the TLS close to the kidney tissue or perirenal fat, and interspersed within the structure (Figure 2A). PNAD and high endothelial venules (HEVs) were detected within the pelvic wall (Figure 2A). The presence of germinal centers (BCL6) was detected in larger TLS (Figure 2A). Mist and plasma cells were detected within the TLS in areas outside the dense B-cell zones and germinal centers (Figure 2C). In the large aggregates, many small microvessels were detected throughout the structure, whereas HEVs were mostly located along the pelvic wall (Figure 2D). Large lymphatic vessel endothelial hyaluronan receptor 1–positive thin vessels were identified and contained CD3+ and B220+ T and B cells, in addition to a few MIDC-8+ DCs (Figure 2E). Previously obtained animals (n = 493Fenton K. Fismen S. Hedberg A. Seredkina N. Fenton C. Mortensen E.S. Rekvig O.P. Anti-dsDNA antibodies promote initiation, and acquired loss of renal Dnase1 promotes progression of lupus nephritis in autoimmune (NZB×NZW)F1 mice.PLoS One. 2009; 4: e8474Crossref PubMed Scopus (86) Google Scholar,5Kanapathippillai P. Hedberg A. Fenton C.G. Fenton K.A. Nucleosomes contribute to increase mesangial cell chemokine expression during the development of lupus nephritis.Cytokine. 2013; 62: 244-252Crossref PubMed Scopus (22) Google Scholar) were" @default.
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• W3075687179 title "Kidney Tertiary Lymphoid Structures in Lupus Nephritis Develop into Large Interconnected Networks and Resemble Lymph Nodes in Gene Signature" @default.
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• W3075687179 doi "https://doi.org/10.1016/j.ajpath.2020.07.015" @default.
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