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Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis
GENE-40590; No. of pages: 9; 4C: Gene xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
Research paper
Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis Suad AlFadhli a,⁎, Aqeel A.M. Ghanem b, Rasheeba Nizam a a b
Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, Kuwait University, Kuwait Department of Rheumatology, Mubarak Al-Kabeer Hospital, Kuwait
a r t i c l e
i n f o
Article history: Received 9 February 2015 Received in revised form 16 May 2015 Accepted 7 June 2015 Available online xxxx Keywords: Lupus Lupus nephritis Alternative splicing Arabs Splice variants
a b s t r a c t Systemic lupus erythematosus (lupus) is a genetically heterogeneous autoimmune disorder with an obscure etiology. With 92–94% of human genes exhibiting alternative splicing, gaining insights to such events may lead to better diagnostics. Herein, we explored the genome-wide peripheral blood transcriptome of lupus and its severe form lupus-nephritis (LN) compared to healthy controls (HC). Age/gender/ethnically-matched Arab females were tested using high-density arrays and statistical analysis was carried out using appropriate software. Analysis revealed 15 splice variants that are differentially expressed between lupus/HC and 99 variants between LN/HC (p ≤ 0.05, SIN or ≤0.5, Benjamin Hochberg-False discovery rate correction). Comparison between LN/lupus revealed 7 variants that significantly differed in expression. Pathway analysis of differentially spliced-genes postulated 11 significant pathways in lupus and 12 in LN (p b 0.05). Analysis of peripheral blood transcriptome possibly revealed signature causative genes that are alternatively spliced, signifying their clinical relevance. Present study is the first to reveal the significance of alternative variants in lupus and LN. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Systemic lupus erythematosus (lupus) is a multifactorial autoimmune disease affecting the population worldwide. It is characterized by the loss of immune tolerance, increased antigenic load, defective B cell suppression, excess T helper (Th) cells and the shifting of Th1 to Th2 immune responses leading to B cell hyperactivity and autoantibody production (Mok and Lau, 2003). For many years, the focus in the literature was mainly on the association of specific loci with the development and the clinical manifestation of autoimmune disease in general and lupus in particular. The exact etio-pathology of lupus remains unknown, although multiple genes have been associated with its severity and susceptibility (Hom et al., 2008; Tsokos, 2011; Yang et al., 2009). Lupus nephritis (LN) is considered as one of the most serious clinical manifestations of lupus which usually occurs within five years of its
Abbreviations: ABCA1, ATP-binding cassette transporter subfamily A1; ABLIM, actin binding LIM protein-3; ADAM8, a disintegrin and metalloproteinase domain-containing protein 8; ANK1, ankryin 1; CD, Cluster of differentiation; CMTM5, CKLF-Like MARVEL trans-membrane domain-containing protein; GO, Gene Ontology; IL, interleukin; IRF, interferon regulatory factor; ITGA2B, integrin alpha 2B; LILRB3, leukocyte immunoglobulin-like receptor, subfamily B with TM and ITIM domains; LN, lupus nephritis; RERE, arginine-glutamic acid dipeptide repeat; SLC35A5, solute carrier family 35 member A5; SLE, lupus; TAPBP, TAP binding protein; TNFa, tumor necrosis factor alpha; VNN1, vanin1; ZBP1, Z-DNA binding protein 1. ⁎ Corresponding author at: Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, Kuwait University, PO Box 31470 Sulaibekhat, Kuwait. E-mail addresses: [email protected], [email protected] (S. AlFadhli).
onset and is characterized by inflammation in kidneys leading severe illness or death. Up to 60% of lupus cases are diagnosed with LN (Lee et al., 2010). To date, little is known regarding the genetic factors that predispose to LN. Although multiple articles have been found illustrating the various aspects of lupus such as genome wide association, copy number variation and linkage studies (Bentham and Vyse, 2013; Cheng et al., 2013; Forabosco et al., 2006; Koskenmies et al., 2004; Lv et al., 2012), no study has so far fully characterized the role of alternate splicing. Very limited studies were found indicating the significance of individual splice variants in the pathogenesis of lupus (AlFadhli and Nizam, 2014; Vandiedonck et al., 2011). Alternate splicing has recently emerged as a pioneering event transforming our perspective of genome function. It increases protein complexity by differential inclusion or exclusion of regions of premRNA. Current estimates suggest that 92–94% of human coding genes exhibit alternate splicing (http://www.uptodate.com/contents/ epidemiology-and-pathogenesis-of-systemic-lupus-erythematosus) indicating its significance in human pathophysiology and disease. However, the diversity of proteins encoded by a single gene or the proposed modulating and regulatory activities of each variant remain largely unidentified. Gaining insights to such events may lead to novel biomarker discovery and better diagnostics. Violating the theory of single genesingle disorder, new research indicates that complex diseases arise from interplay of multiple genes, therefore studying an individual gene might not be very informative. A whole genome screen is mandatory to identify the modifier genes for its regulatory role in the pathogenesis of complex diseases.
http://dx.doi.org/10.1016/j.gene.2015.06.020 0378-1119/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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S. AlFadhli et al. / Gene xxx (2015) xxx–xxx
In the present study, we aim to adopt a high density Gene chip Human exon 1.0 ST array (Affymetrix) to study the genome-wide alternate splicing of Arab female systemic lupus erythematosus (lupus) and lupus nephritis (LN) cases compared to healthy subjects (HC). 2. Results We aimed to explore the alternatively spliced genes of the lupus and its severe form LN compared to healthy subjects (HC). Age, gender and ethnically matched Arab lupus (n = 5), LN (n = 3) and HC (n = 3) were carefully selected and recruited for this study. Whole human genome expression profiling was carried out using high density Affymetrix Human Exon 1.0 ST arrays with 1.4 million probe sets targeting every known and predicted exon in the entire genome. Data quality was evaluated by Principle component analysis (PCA) which depicted a clear segregation of lupus and healthy controls. An outlier healthy control sample which showed conflicting clustering was further removed from the analysis, to evade false discovery rate. Samples were filtered based on detection above background threshold (DABG, p ≤ 0.05), resulting in 14,455/20,369 transcript clusters that passed signal threshold for subsequent analysis. Data comparison was carried out in three pairs (i) lupus versus healthy control (ii) LN versus healthy control and (iii) lupus versus LN. Analysis of expression profiling revealed 15 genes that are differentially spliced in lupus compared to healthy control with a p value ≤ 0.05 and splice index (SI) b −0.5 or N +0.5 after Benjamini and Hochberg false discovery rate (FDR) correction (Table 1). Of these, LILRB3 (Leukocyte immunoglobulin-like receptor, subfamily B with TM and ITIM domains) and SLC35A5 (Solute carrier family 35 member A5) are expected to be highly spliced with p value of 8.90E-05 after Benjamini and Hochberg FDR correction. A list of 99 genes were found to be differentially spliced in LN compared to healthy control (p ≤ 0.05, SI b −0.5 or N +0.5, after Benjamini and Hochberg FDR correction, Table 2). Three specific genes showed a splice index of b -2 or ≥ +2 and these include IRF5 (Interferon regulatory factor 5), ANK1 (Ankryin 1) and LILRB3. Additionally, both lupus and LN were found to differ in the splicing spectrum of 7 genes (p ≤ 0.005, SI b − 0.5 or N + 0.5, after Benjamini and Hochberg FDR correction, Table 3). 2.1. Hierarchical cluster analysis Hierarchical cluster analysis further enabled the visualization of overall splicing pattern (Fig. 1). Analysis was carried on gene normalized intensities of differentially spliced genes of lupus and LN with Pearson's uncentered (Cosine) distance metric and average linkage Table 1 Differentially spliced genes in lupus cases compared to healthy control. Gene symbol SPOCD1 SLC35A5 MASP1 CENPE ERAP1|CAST CD74 HIST1H4H HLA-B|HLA-C MACC1 ARHGEF10 ZNF250 KIAA0020|GPS2 LILR-| A1|A2| A3 |A6 | B1| B3 IL2RG|CXorf65 SOX3
Probeset_ID 2404766 2636185 2709631 2780172 2868131 2881370 2946383 2948926 3040518 3082874 3159061 3196691 3870611 4011844 4024310
P value 5.99E-05 2.04E-08 4.59E-05 2.85E-05 5.20E-06 4.17E-07 9.89E-06 6.40E-05 2.32E-05 6.19E-05 5.36E-05 3.90E-05 1.92E-08 1.80E-05 3.93E-05
⁎Corrected p value
Splicing index (SI)
0.04935 0.00009 0.04634 0.04161 0.01363 0.00137 0.02162 0.04935 0.03802 0.04935 0.04935 0.04292 0.00009 0.03377 0.04292
−1.454 1.542 −0.952 1.0588 −1.715 1.0076 −0.735 1.116 1.689 −0.985 −1.801 −1.26 3.262 1.436 −0.649
⁎ p value ≤ 0.05 obtained from Splicing ANOVA test after Benjamin and Hochberg FDR correction. Bold indicates P ≤ 0.005 and SI N 2.
Table 2 Differentially spliced genes in lupus nephritis cases compared to healthy control. Gene symbol LEPR|LEPROT CCDC55|FAM73A BTG2 RCOR3 DISC1 CHD5|LOC100131864 RERE MTOR PAQR6 ARHGEF11 SYT2 ITGA4|CERKL GLS SPAG16|VWC2L SLC11A1 TIA1 IL17RE ALAS1 PPM1M SLC35A5 C3orf10 SLC4A7 ROBO1 SEMA5B MASP1 RGS12 NSUN7|ARL4A NSD1 CCDC125 ERAP1|CAST RAPGEF6|FNIP1 CD74 CD109 HIST1H3D|HIST1H2AD HIST1H4H SLC26A8 CRISP2 LPAL2 PFTK1 PILRA|PILRB IRF5 RNF148 PTPRN2 ADAM28 ANK1 DOCK8 C9orf89 SPTAN1 TRPM6 POLE3 TSC1 FBXO18 BUB3 ADAM8 IFITM1 CAPN1 CSTF3|TCP11L1 CYBASC3 AHNAK|IGHG1 LRP1 TMTC3 PAN2 CIT MPHOSPH9 OLFM4 KLF5 ARHGEF7 IRF9 KTN1 PCNX TEP1 ADCY4 RDH11 SYNJ2BP
Probeset_ID 2340433 2343170 2375664 2378584 2385343 2394478 2395245 2396537 2438016 2438657 2451261 2518272 2520291 2526419 2527747 2558511 2610056 2623515 2623568 2636185 2662581 2666904 2683763 2692199 2709631 2716025 2724853 2842951 2860614 2868131 2874794 2881370 2913694 2946324 2946383 2951730 2956536 2982630 3012064 3015519 3023246 3070507 3081862 3090209 3132940 3159330 3179669 3190558 3210013 3221646 3228373 3233431 3268669 3315024 3315675 3334954 3368520 3375307 3375735 3417842 3425134 3457696 3474104 3476012 3490892 3493543 3501661 3529701 3536905 3542689 3555340 3558168 3569401 3570475
p value 1.40E-04 9.96E-05 1.53E-04 6.76E-05 2.86E-04 7.72E-05 1.47E-05 2.90E-04 1.77E-04 7.59E-05 3.57E-04 2.35E-04 2.14E-04 3.73E-05 1.57E-04 1.12E-05 1.85E-04 5.50E-05 1.57E-05 2.33E-04 1.75E-04 2.35E-05 1.34E-05 9.15E-06 2.43E-04 1.96E-04 3.39E-04 7.54E-05 2.96E-04 4.46E-06 1.73E-04 3.09E-06 1.36E-04 7.36E-06 2.15E-04 1.85E-04 4.80E-05 3.02E-04 4.99E-05 3.62E-04 3.01E-05 6.12E-05 1.21E-04 3.01E-04 5.08E-09 2.05E-04 3.78E-05 1.46E-04 1.40E-05 1.02E-04 3.75E-04 6.19E-05 2.50E-04 9.15E-07 2.09E-04 6.85E-05 2.87E-04 3.42E-04 2.09E-06 3.84E-05 2.50E-04 2.92E-04 2.24E-04 3.82E-05 1.36E-05 8.02E-05 1.99E-04 2.55E-04 1.05E-04 5.29E-06 2.78E-04 5.40E-06 1.86E-04 1.65E-04
⁎Corrected p value
Splicing index (SI)
0.0325 0.0263 0.0333 0.0215 0.0423 0.0221 0.0100 0.0423 0.0343 0.0221 0.0481 0.0378 0.0359 0.0169 0.0333 0.0100 0.0346 0.0202 0.0100 0.0378 0.0343 0.0135 0.0100 0.0093 0.0386 0.0358 0.0470 0.0221 0.0424 0.0071 0.0343 0.0071 0.0320 0.0081 0.0359 0.0346 0.0187 0.0424 0.0188 0.0482 0.0165 0.0204 0.0290 0.0424 0.0001 0.0358 0.0169 0.0328 0.0100 0.0264 0.0494 0.0204 0.0388 0.0040 0.0358 0.0215 0.0423 0.0470 0.0069 0.0169 0.0388 0.0423 0.0369 0.0169 0.0100 0.0225 0.0358 0.0391 0.0266 0.0071 0.0422 0.0071 0.0346 0.0337
−0.815 1.944 1.210 1.012 0.913 0.713 1.321 0.876 1.225 −1.231 −1.028 −0.583 1.188 1.158 1.251 −0.615 −0.766 −1.016 0.830 1.594 −1.184 −1.417 1.197 −1.389 −1.010 −0.706 1.330 −0.968 1.301 −1.600 1.492 1.116 1.090 −0.650 −0.961 −0.972 1.372 −1.505 0.884 1.032 2.064 1.085 1.509 −1.343 −2.287 1.103 1.021 −0.696 −1.080 1.610 −0.945 0.685 1.151 0.714 0.892 −0.743 −1.471 −0.592 1.616 1.109 −0.822 1.082 1.036 1.543 −1.734 1.376 0.821 −0.784 1.325 0.881 −0.907 0.948 0.837 −0.577
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
S. AlFadhli et al. / Gene xxx (2015) xxx–xxx Table 2 (continued) Gene symbol
Probeset_ID
SECISBP2L MT1H|MT1F|MT1P2 ROGDI YPEL3 PSMB10|CTRL COG1 CCDC40 ALOX12B TOB1 SFRS2 SLC39A6 MAN2B1|MORG1 PHLDB3 LILR-A1|-A2|-A3-|-A6|B1|B3 RASSF2 CPNE1|RBM12 ZBP1 SYNJ1 RCAN1 APOL6 PRPS2 SLC25A14 HUWE1 PHF8 IL2RG|CXorf65
3623320 3662201 3678369 3687452 3696016 3733938 3737079 3744072 3762473 3771800 3804195 3851545 3864390 3870611 3896034 3904119 3911177 3929325 3930235 3944243 3969047 3990762 4009315 4009506 4011844
p value 3.36E-06 6.09E-05 3.27E-05 4.18E-05 3.40E-05 4.82E-05 1.56E-04 2.02E-04 1.47E-04 1.59E-05 1.64E-04 1.66E-04 3.57E-04 8.51E-05 1.48E-05 1.16E-04 1.11E-07 9.57E-05 4.64E-05 6.58E-06 2.08E-04 1.14E-04 7.26E-05 2.02E-05 4.12E-06
⁎Corrected p value
Splicing index (SI)
0.0071 0.0204 0.0169 0.0178 0.0169 0.0187 0.0333 0.0358 0.0328 0.0100 0.0337 0.0337 0.0481 0.0234 0.0100 0.0284 0.0007 0.0258 0.0187 0.0079 0.0358 0.0284 0.0221 0.0121 0.0071
0.946 −1.144 −1.439 1.067 0.937 0.829 −0.714 −0.586 1.007 1.639 −0.996 1.066 −0.665 3.767 1.302 −1.038 1.028 1.062 −0.716 1.050 0.756 −0.976 0.870 0.828 1.457
⁎ p value ≤ 0.05 obtained from Splicing ANOVA test after Benjamin and Hochberg FDR correction. Bold indicates P ≤ 0.005 and SI N 2.
rule on sample conditions. The clustering algorithm groups rows (entities) and columns (individual samples) by similarity in splicing pattern which is depicted by color codes. The red indicates maximum, blue minimum and yellow medium level of expression. Co-regulated entities across individual groups are also designated by dendrograms. 2.2. Gene Ontology (GO) analysis Genes were evaluated based on their functional similarity and classified into three GO categories such as biological, molecular and cell component by the software. GO hierarchy analysis on alternatively spliced genes of lupus revealed 66 GO terms satisfying a p ≤ 0.05. A more stringent analysis revealed 34 GO terms satisfying a p ≤ 0.005 (Fig. 2). Much of the alternatively spliced genes of lupus were found to be associated with biological process such as immune response, T cell differentiation, antigen processing and presentation, defense response and IL4/IL2/IL7 mediated signaling. Similarly, molecular category included antigen binding/phosphatase binding/receptor and regulatory activities and interleukin receptor mediated signaling process. GO analysis of LN entity list revealed 11 GO terms satisfying a p ≤ 0.05. These were mostly associated with biological process such as immune response, positive regulation of CD4 positive, CD25 positive and alpha-beta regulatory T-cell differentiation. The molecular category included protein phosphatase binding, antigen binding, apo-lipoprotein
Table 3 Comparative expression profile of differentially spliced genes between lupus nephritis and lupus cases. Gene symbol
Probeset_ID
p value
⁎Corrected p value
Splicing index (SI)
RERE ABLIM3 TAPBP VNN1 ABCA1 CMTM5 ITGA2B
2395245 2834863 2950629 2974592 3218528 3529113 3759137
3.66E-07 9.70E-06 2.82E-06 2.61E-05 1.66E-05 7.86E-06 1.33E-06
0.0047 0.0251 0.0122 0.0483 0.0358 0.0251 0.0086
1.028 −0.808 0.734 −1.537 −0.862 0.903 0.960
⁎ p value ≤ 0.05 obtained from Splicing ANOVA test after Benjamin and Hochberg FDR correction. Bold indicates P ≤ 0.005.
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binding, cadmium ion binding and calcium dependent cysteine type endopeptidase inhibitor activity. A single GO term indicating the protein phosphatase binding was obtained with a p-value cut off of p ≤ 0.005. 2.3. Pathway analysis We further carried out a pathway based analysis of differentially spliced genes comparing to all known pathways (315) of BioPAX database. It revealed 11 significant pathways with a p ≤ 0.05 (Table 4). The proteasome degradation and allograft rejection pathway showed significant association with a p-value cut off of b 0.005, indicating its significance in the pathogenesis of lupus. Similarly, entity list of spliced genes in LN revealed 12 significant pathways with a p ≤ 0.05 (Table 4). The Wnt pathway showed significant association with a p-value cut off of b 0.005 (Fig. 3). 3. Discussion Lupus is known to be a complex polygene autoimmune disease with a wide range of clinical and immunological spectrum. The incidence and prevalence rate of lupus vary across different geographical areas with African-Americans more significantly affected than rest of the population and higher prevalence rate among Asians and Hispanics (http://www.uptodate.com/contents/epidemiology-and-pathogenesisof-systemic-lupus-erythematosus; Petri, 2002). We have previously reported increasing cases of lupus among Arabs and its association with specific gene variants (AlFadhli and Jahabani, 2014; AlFadhli and Ghanem, 2014). Diagnosis of lupus is more common in females and its incidence rate has tripled over the past four decades (Uramoto et al., 1999). Nevertheless, the exact etiopathology remains unknown. Recently, alternate splicing has emerged as a key event contributing to the diversity and complexity of mRNA/ protein variants. Despite being the hotspot area, not much is known regarding the expression spectrum of alternatively spliced genes and it role in diseases progression. Very limited studies have characterized the functional role of splice variants with majority being centered on algorithm based computational methods and RNA-seq technology (Li et al., 2014a, 2014b; Eksi et al., 2013; Wang et al., 2008). In the present study, we aimed to investigate the genome-wide peripheral blood transcriptome of Arab female lupus and LN cases compared to healthy control. Our analysis indicates that alternate splice events are differentially regulated in both lupus and LN with higher degree of splicing events in LN. We have identified 15 genes that are differentially spliced in lupus; while an exhaustive list of 99 genes were obtained for LN cases with distinguishable roles in immune response and T cell differentiation. Certain interesting mRNA variants of leukocyte immunoglobulinlike receptor (LILR-A1|A2|A3|A6|B1|B3) and solute carrier family 35 member A5 (SLC35A5) that are differentially spliced between lupus and healthy control (p b 0.0005) were detected. LILRs are family of receptors encoded within the leukocyte receptor complex that exhibits immune-modulatory effects on a wide spectrum of immune cells. LILR-A1|A2|A6 are stimulatory receptors, while LILR-A3 represents a soluble receptor and LILR-B1|B3 is known for its inhibitory effect on cell activation. Polymorphisms within LILR family have been previously associated with susceptibility to lupus and rheumatoid arthritis and its role in nephropathy has been marked by increased urine level of LILRA2 in LN cases (Mamegano et al., 2008; Kuroki et al., 2005; Du et al., 2014; Yeh et al., 2011). Additionally, we report increased splice index of LILRB3 variant (SI = 3) in LN cases suggesting aberrant spicing event. Not much is known regarding the role of SLC35A5 in autoimmunity. It is characterized to be involved in the nucleotide-sugar transmembrane transporter activity and has been reported to be differentially expressed in lupus cases (Lyons et al., 2010). IRF5 and ANK1 also draw attention with a splice index of 2 in LN cases. Variations in IRF5 gene have been previously associated the initiation and progression of lupus and LN (Qin et al., 2010; Alfadhli and
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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Fig. 1. Hierarchical Cluster (HC) analysis: HC analysis was carried on gene normalized intensities of differentially spliced genes of Lupus and Lupus nephritis (LN) with Pearson's uncentered (Cosine) distance metric and average linkage rule on sample conditions (GeneSpring 13.0 Software, Agilent). Heat maps signifying overall splicing pattern in (A) Lupus and healthy control: Individual splice variants of all tested subjects; lupus (n = 5), lupus nephritis (n = 3) and healthy control (n = 2). Only 15 splice variants were considered to be differentially expressed. N/A indicates probes sets for which annotations are not available. (B) Lupus subjects with and without nephritis; clustergram indicates the expression profile of individual splice variants in lupus subjects with (n = 3) and without nephritis (n = 5). SLE 6, 7, 8 represents lupus subjects with nephritis, while SLE 1, 2, 3, 4, 5 indicates lupus subjects without nephritis. Red indicates maximum, blue minimum and yellow medium level of expression. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Jahabani, 2014). It plays a key regulatory role in innate and adaptive immune responses and is crucial for the production of the proinflammatory cytokines such as TNFa, IL12 and IL6 subsequent to
the activation of Toll-like receptor signaling (Alfadhli and Jahabani, 2014). Ankyrin proteins link the integral membrane proteins to the cytoskeleton elements and plays critical role in mediating cellular
Fig. 2. Represents Gene Ontology (GO) analysis of alternatively spliced genes of (A) lupus and (B) lupus nephritis indicating their GO Id, function and p-value obtained from GeneSpring 13.0 Software (Agilent). GO analysis was performed to explore the Gene Ontology terms associated with the spliced genes for their functional classification.
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
S. AlFadhli et al. / Gene xxx (2015) xxx–xxx
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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Table 4 Represents significant pathways of alternatively spiced genes in lupus and lupus nephritis. Pathway (lupus)
p-Value
Matched entities
Pathway entities
IL-2 Signaling pathway Complement and Coagulation Cascades Inflammatory Response Pathway Proteasome Degradation Kinesins Complement Activation, Classical Pathway Type II interferon signaling (IFNG) IL-9 signaling pathway IL-4 signaling pathway Allograft Rejection IL-7 signaling pathway
0.0139 0.0193 0.0124 0.0002⁎ 0.0019 0.0047 0.0143 0.0047 0.0166 0.0000⁎ 0.0070
1 1 1 2 1 1 1 1 1 2 1
37 63 33 63 6 12 37 12 46 40 20
Pathway (Lupus nephritis)
p-Value
Matched entities
Pathway entities
Insulin Signaling AMPK signaling Interferon type I Nucleotide Metabolism Complement Activation, Classical Pathway Senescence and Autophagy Wnt Signaling Pathway IL-9 signaling pathway Heme Biosynthesis MicroRNAs in cardiomyocyte hypertrophy Regulation of Actin Cytoskeleton IL-7 signaling pathway
0.0184 0.0332 0.0051 0.0448 0.0285 0.0135 0.0015⁎ 0.0285 0.0215 0.0118 0.0188 0.0425
2 1 2 1 1 2 2 1 1 2 2 1
89 49 45 19 12 79 25 12 9 90 91 20
⁎ Bold indicates p b 0.005.
events. Although its direct role in LN is unknown, its significantly altered expression has been previously reported in chronic kidney disease patients (Costa et al., 2008). Variants of two other genes; ADAM8 and ZBP1 were found to be differentially spliced in LN cases (p b 0.005). These variants were not previously associated with the etiopathogenesis of LN. ADAM8 is primarily involved in cell adhesion during neurodegeneration. Owing to its functional involvement in leukocyte recruitment it is suggested to be a potential target for inflammatory diseases (Zarbock and Rossaint, 2011). Similarly, ZBP1 has been found to play an essential role in innate immune response regulating the type I interferon responses (Takaoka et al., 2007). Likewise, we observed 7 variants that are differentially spliced between LN and lupus, indicating their probable role as markers for nephritis development. These include variants of VNN1, TAPBP, ABCA1, RERE, ABLIM, CMTM5 and ITGA2B. Our study highlights VNN1 involved in hematopoietic cell trafficking to be a potential biomarker for nephritis with higher expression in LN and minimum in lupus. Concordant to our results, very recently VNN1 was reported to be a potential marker for LN (Sanchez-Munoz et al., 2013). TAPBP is a transmembrane glycoprotein that facilitates antigen loading on to major histocompatibility complex and has been previously reported to be up-regulated in glomeruli of LN mouse (Teramoto et al., 2008). Likewise, ABCA1 codes for membrane-associated protein that facilitate transport of various molecules across intra/extra cellular membrane and plays a major role in cholesterol and phospholipid homeostasis. Down-regulation of ABCA1 in renal cells was reported to promote cholesterol accumulation contributing to the development of diabetic nephropathy (Tsun et al., 2014). ITGA2B probably accounts for the intravascular and cellular events involving nephritis, with its prominent role in coagulation and cell surface mediated signaling involving cell growth, migration and apoptosis (http:// www.genecards.org/cgi-bin/carddisp.pl?gene=ITGA2B). Though variants in ITGAM have been widely associated with lupus, the role of ITGA2B in the latter remains yet to be explored. Similarly, CMTM5 encodes
for chemokine like superfamily; while several chemokine families have previously shown to be involved in renal diseases (Segerer and Schlondorff, 2007), the role of CMTM5 is obscure. Additionally, the role of two other variants RERE and ABLIM3 in renal involvement also remains unidentified. RERE conveys a crucial role in the development and function of multiple organs and acts as nuclear receptor co-regulator and induces apoptosis on up-regulation (Kim et al., 2013). ABLIM3 is essentially involved in embryonic development, cell lineage determination and cancer (Krupp et al., 2006). Further research need to be carried out to investigate the spatial and sequential roles of these variants in susceptibility to lupus and LN. We also identified pathways that are deregulated in lupus and LN subjects apparently elucidating the broader aspects of disease manifestation with multiple entities involved. Allograft rejection and proteasome degradation pathways have been shown to be active in wide array of inflammatory and intracellular events indicating its significance in the pathophysiology of the disease (Bunnapradist et al., 2006; Ciechanover, 1994). Agents that inhibit proteasomes are considered to be a therapeutic target for inflammatory diseases and cancer (Hershko, 1997). WNT signaling pathway has been recently reported to be deregulated in human LN and in renal tissue of a lupus mouse model, indicating its probable role in renal fibrosis (Wang et al., 2014). A detailed study of these pathways may open gateways to molecularly targeted therapy. Majority of the genes that tend to be differentially spliced in lupus and LN were found to be involved in immune dysfunction, specifically immune cell infiltration and activation. Consistently, transcriptome analysis of murine model of LN has also shown the significance inflammatory events, aside from renal hypoxia and metabolic stress during the progression of disease (Bethunaickan et al., 2014). The global expression profiling of peripheral blood mononuclear cells has consistently revealed the up-regulation of IFN-inducible genes in lupus, though not splice variants (Baechler et al., 2003). The lack of co-relation between relative abundance or the expression of individual variants observed in blood (PMBC) and organs such as kidney in literature could be due to stage specific or tissue dependent factors indicating varied role of genes in different lineages. In conclusion, our study highlights the likelihood of alternate splicing in the genetic etiology of lupus and LN. Identification of principal isoforms that are responsible for the canonical functions of genes may serve as a breakthrough leading to better diagnostics and therapeutics. We identified individual variants that are differentially expressed in each study group that may serve an essential role in disease pathogenesis. Higher extent of splice variant deregulation in LN indicates its possibility in clinical application. A further study on mechanism and control of these variants in a relatively large cohort is mandatory, to assess their therapeutic potential. Our study presents novel findings signifying the potential future of in-depth research in the same domain. 4. Materials and method 4.1. Study subjects Blood samples were collected from a total of 11 Arab female subjects: (5 lupus and 3 LN) patients and 3 — age/gender/ethnically matched healthy individuals (GEO accession number: GSE62764). Lupus cases were selected when they had at least four of the Systemic Lupus International Collaboration Clinics/American College of Rheumatology (SLICC/ACR) criteria. The diagnosis of lupus was made if four or more of the 11 manifestations are present, either one after the other or at the same time. These include discoid rash, malar rash, photosensitivity, hematological disorder, antinuclear antibody, immunologic
Fig. 3. Pathway analysis of differentially spliced genes comparing to all known pathways of BioPAX database postulated central gene regulators and significant pathways that are deregulated in study subjects with a p-value cut-off of less than 0.005. These include: (A) proteasome degradation and (B) Allograft rejection pathway in lupus and (C) Wnt pathway in lupus nephritis (GeneSpring 13.0 Software, Agilent).
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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disorder, mucosal ulcers, arthritis, renal disorders, serositis and neurological disorder. LN cases were confirmed by renal biopsy. Average age of lupus and LN were 33 ± 3.5 and 27.67 ± 3.1 respectively. All patients were in active disease state. The biochemical and immunological profiles were conducted in Mubarak Al-Kabeer Hospital as part of the patient diagnosis and follow-up. Healthy volunteers were randomly selected with inclusion criteria of general good health and no first degree relatives with autoimmune diseases. Exclusion criteria were any recent history of acute or chronic debilitating illness. Average age of healthy controls was 32.5 ± 3.5. The conduct of the study was approved by the research ethics committees of Kuwait University and all the participants provided written informed consent. Blood samples were collected from patients and healthy subjects in EDTA treated tubes. RNA was extracted from 2 ml of whole blood using QIAamp® RNA Blood Mini kit (Qiagen, Germany) following the manufacture's protocol. Extracted RNA was further purified using DNase (Qiagen) to remove potential genomic DNA contamination. Purified RNA was then quantified using a NanoDrop spectrophotometer (NanoDrop Technologies, DE, USA) and RNA integrity was determined by Agilent Bioanalyzer 2100 (Agilent, CA, USA). Total RNA samples with a RNA integrity number (RIN) greater than 7 were used for further microarray studies. 4.2. Array hybridization RNA (100 ng) from each sample was processed using Ambion expression kit (Life technologies, USA) for Affymetrix® whole transcript expression array. A double amplification procedure was carried out for cDNA preparation following the manufacturer's instructions. The prepared cDNA was purified (Ambion kit) and labeled using WT Terminal Labeling Kit (Affymetrix, Inc., Santa Clara, CA). Hybridization cocktails were prepared and hybridized to the GeneChip® Human Exon 1.0 ST Arrays (Affymetrix, Inc., Santa Clara, CA) followed by incubation in a rotating hybridization oven 640 (Affymetrix) at 45 °C and 60 rpm for 16 h. Following hybridization, the arrays were washed and stained on an GeneChip® Fluidics 450 workstation (Affymetrix, Santa Clara, USA) using the FS450_0001 fluidics script. The arrays were then scanned using the Affymetrix GeneChip® Scanner 3000 7G (Affymetrix, Santa Clara, USA). 4.3. Data analysis Data analysis was carried out using GeneSpring 12.5GX (Agilent) software. Data quality was evaluated by Principal component analysis (PCA) and any outlier samples identified were removed. Normalization was carried out using Robust Multi-array Average (RMA) method on core probsets and baseline to median summarization was performed. At exon level, only probesets characterized by detection above background signal (DABG) p value b0.05 in atleast 50% of the samples in either group were accepted for subsequent analysis. Alternate splicing events between patients and control subjects were characterized by “Splice index (SI) model” (Affymerix Inc., 2006, Gardina et al., 2006). SI model enables identification of exons that have differential inclusion rates between the two tested sample groups. It is represented as the log ratio of the gene normalized intensity (NI) between the two tested sample groups. NI in turn indicates the ratio of exon-level probe set expression to gene-level transcript cluster expression. Gene‐level Normalized Intensity ðNIÞ ¼
Splicing Index ¼ log2
Probe set intensity Expression level of the }gene}
Sample1 NI : Sample2 NI
To identify the significant alternatively spliced genes, splicing ANOVA with Benjamini and Hochberg false discovery rate (FDR)
correction was employed on gene normalized intensities of the tested sample groups. True positives with a p ≤ 0.05, SI 0.05 and Fold change (FC) N 1 were considered as alternatively spliced. Cluster, Gene Ontology and pathway analysis were carried out using GeneSpring software. Hierarchical clustering heatmaps were generated from gene normalized intensities of differentially spliced genes of lupus and LN in terms of Pearson's uncentered (Cosine) distance metric and average linkage rule. The red indicates maximum, blue minimum and yellow medium level of variant expression. Gene Ontology analysis was performed to explore the Gene Ontology terms associated with the entities for their functional classification. Pathway analysis further enabled the visual representation of our experimental data. Conflict of interest Authors declare that no conflict of interest exists. Funding source This work was supported by Kuwait University research administration Grant NM02/07. The funders had no role in study design data collection and analysis, decision to publish or preparation of the manuscript. Acknowledgment This work was supported by Kuwait University Research administration Grant NM 02/07 and General facility Grant #SRUL02/13. References AlFadhli, S., Ghanem, A.A., 2014. Influence of HumDN1 VNTR polymorphism on DNASE1 expression in systemic lupus erythematosus and rheumatoid arthritis. Immunol. Invest. 43, 411–423. AlFadhli, S., Jahabani, I., 2014. Association of interferon regulatory factor 5 (IRF5) markers with an increased risk of lupus and overlapping autoimmunity in a Kuwaiti population. Ann. Hum. Biol. 41, 531–539. AlFadhli, S., Nizam, R., 2014. Differential expression of alternative splice variants of CTLA4 in Kuwaiti autoimmune disease patients. Gene 534, 307–312. Baechler, E.C., Batliwalla, F.M., Karypis, G., Gaffney, P.M., Ortmann, W.A., Espe, K.J., et al., 2003. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl. Acad. Sci. U. S. A. 100, 2610–2615. Bentham, J., Vyse, T.J., 2013. The development of genome-wide association studies and their application to complex diseases, including lupus. Lupus 22, 1205–1213. Bethunaickan, R., Berthier, C.C., Zhang, W., Eksi, R., Li, H.D., et al., 2014. Identification of stage-specific genes associated with lupus nephritis and response to remission induction in (NZB × NZW)F1 and NZM2410 mice. Arthritis Rheumatol. 66, 2246–2258. Bunnapradist, S., Chung, P., Peng, A., Hong, A., Chung, P., et al., 2006. Outcomes of renal transplantation for recipients with lupus nephritis: Analysis of the Organ Procurement and Transplantation Network database. Transplantation 82, 612–618. Cheng, F.J., Zhou, X.J., Zhao, Y.F., Zhao, M.H., Zhang, H., 2013. Alpha-defensin DEFA1A3 gene copy number variation in Asians and its genetic association study in Chinese systemic lupus erythematosus patients. Gene 517, 158–163. Ciechanover, A., 1994. The ubiquitin-proteasome proteolytic pathway. Cell 79, 13–21. Costa, E., Rocha, S., Rocha-Pereira, P., Castro, E., Miranda, V., et al., 2008. Altered erythrocyte membrane protein composition in chronic kidney disease stage 5 patients under haemodialysis and recombinant human erythropoietin therapy. Blood Purif. 26, 267–273. Du, Y., Su, Y., He, J., Yang, Y., Shi, Y., et al., 2014. Impact of the leucocyte immunoglobulinlike receptor A3 (LILRA3) on susceptibility and subphenotypes of systemic lupus erythematosus and Sjogren's syndrome. Ann. Rheum. Dis. (pii: annrheumdis- 2013204441). Eksi, R., Li, H.D., Menon, R., Wen, Y., Omenn, G.S., et al., 2013. Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data. PLoS Comput. Biol. 9, e1003314. Forabosco, P., Gorman, J.D., Cleveland, C., Kelly, J.A., Fisher, S.A., et al., 2006. Gorman JD, Cleveland C, et al. Meta-analysis of genome-wide linkage studies of systemic lupus erythematosus. Genes Immun. 7, 609–614. Hershko, A., 1997. Roles of ubiquitin-mediated proteolysis in cell cycle control. Curr. Opin. Cell Biol. 9, 788–799. Hom, G., Graham, R.R., Modrek, B., Taylor, K.E., Ortmann, W., et al., 2008. Association of Systemic Lupus Erythematosus with C8orf13–BLK and ITGAM–ITGAX. N. Engl. J. Med. 358, 900–909. http://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGA2B (Accessed: 2015).
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Qin, L., Lv, J., Zhou, X., Hou, P., Yang, H., et al., 2010. Association of IRF5 gene polymorphisms and lupus nephritis in a Chinese population. Nephrology 15, 710–713. Sanchez-Munoz, F., Amezcua-Guerra, L.M., Macias-Palacios, M., Márquez-Velasco, R., Bojalil, R., 2013. Vanin1 a potential novel biomarker for active nephritis in systemic lupus erythematosus. Lupus 22, 333–335. Segerer, S., Schlondorff, D., 2007. Role of chemokines for the localization of leukocyte subsets in the kidney. Semin. Nephrol. 27, 260–274. Takaoka, A., Wang, Z., Choi, M.K., Yanai, H., Negishi, H., Ban, T., et al., 2007. DAI (DLM-1/ZBP1) is a cytosolic DNA sensor and an activator of innate immune response. Nature 448, 501–505. Teramoto, K., Negoro, N., Kitamoto, K., Iwai, T., Iwao, H., et al., 2008. Microarray analysis of glomerular gene expression in murine lupus nephritis. J. Pharmacol. Sci. 106, 56–67. Tsokos, George C., 2011. Systemic Lupus Erythematosus. N. Engl. J. Med. 365, 2110–2121. Tsun, J.G., Yung, S., Chau, M.K., Shiu, S.W., Chan, T.M., et al., 2014. Cellular cholesterol transport proteins in diabetic nephropathy. PLoS One 9, e105787. Uramoto, K.M., Michet Jr., C.J., Thumboo, J., Sunku, A., O'Fallon, M., et al., 1999. Trends in the incidence and mortality of systemic lupus erythematosus, 1950–1992. Arthritis Rheum. 42, 46. Vandiedonck, C., Taylor, M.S., Lockstone, H.E., Plant, K., Taylor, J.M., et al., 2011. Pervasive haplotypic variation in the spliceo-transcriptome of the human major histocompatibility complex. Genome Res. 21, 1042–1054. Wang, E.T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., et al., 2008. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476. Wang, X.D., Huang, X.F., Yan, Q.R., Bao, C.D., 2014. Aberrant activation of the WNT/βcatenin signaling pathway in lupus nephritis. PLoS One 9, e84852. Yang, W., Ng, P., Zhao, M., Hirankarn, N., Lau, C.S., et al., 2009. Population differences in SLE susceptibility genes: STAT4 and BLK, but not PXK, are associated with systemic lupus erythematosus in Hong Kong Chinese. Genes Immun. 10, 219–226. Yeh, Mary Ya-Ping, Tseng, Tzu-Ling, Lu, Ching-Fang, Lin, Wei-Ya, Hsu, Tsai-Wei, et al., 2011. Biomarkers associated with nephropathy (US20110086372 A1). Zarbock, A., Rossaint, J., 2011. Regulating inflammation: ADAM8—a new player in the game. Eur. J. Immunol. 41, 3419–3422.
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Research paper
Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis Suad AlFadhli a,⁎, Aqeel A.M. Ghanem b, Rasheeba Nizam a a b
Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, Kuwait University, Kuwait Department of Rheumatology, Mubarak Al-Kabeer Hospital, Kuwait
a r t i c l e
i n f o
Article history: Received 9 February 2015 Received in revised form 16 May 2015 Accepted 7 June 2015 Available online xxxx Keywords: Lupus Lupus nephritis Alternative splicing Arabs Splice variants
a b s t r a c t Systemic lupus erythematosus (lupus) is a genetically heterogeneous autoimmune disorder with an obscure etiology. With 92–94% of human genes exhibiting alternative splicing, gaining insights to such events may lead to better diagnostics. Herein, we explored the genome-wide peripheral blood transcriptome of lupus and its severe form lupus-nephritis (LN) compared to healthy controls (HC). Age/gender/ethnically-matched Arab females were tested using high-density arrays and statistical analysis was carried out using appropriate software. Analysis revealed 15 splice variants that are differentially expressed between lupus/HC and 99 variants between LN/HC (p ≤ 0.05, SIN or ≤0.5, Benjamin Hochberg-False discovery rate correction). Comparison between LN/lupus revealed 7 variants that significantly differed in expression. Pathway analysis of differentially spliced-genes postulated 11 significant pathways in lupus and 12 in LN (p b 0.05). Analysis of peripheral blood transcriptome possibly revealed signature causative genes that are alternatively spliced, signifying their clinical relevance. Present study is the first to reveal the significance of alternative variants in lupus and LN. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Systemic lupus erythematosus (lupus) is a multifactorial autoimmune disease affecting the population worldwide. It is characterized by the loss of immune tolerance, increased antigenic load, defective B cell suppression, excess T helper (Th) cells and the shifting of Th1 to Th2 immune responses leading to B cell hyperactivity and autoantibody production (Mok and Lau, 2003). For many years, the focus in the literature was mainly on the association of specific loci with the development and the clinical manifestation of autoimmune disease in general and lupus in particular. The exact etio-pathology of lupus remains unknown, although multiple genes have been associated with its severity and susceptibility (Hom et al., 2008; Tsokos, 2011; Yang et al., 2009). Lupus nephritis (LN) is considered as one of the most serious clinical manifestations of lupus which usually occurs within five years of its
Abbreviations: ABCA1, ATP-binding cassette transporter subfamily A1; ABLIM, actin binding LIM protein-3; ADAM8, a disintegrin and metalloproteinase domain-containing protein 8; ANK1, ankryin 1; CD, Cluster of differentiation; CMTM5, CKLF-Like MARVEL trans-membrane domain-containing protein; GO, Gene Ontology; IL, interleukin; IRF, interferon regulatory factor; ITGA2B, integrin alpha 2B; LILRB3, leukocyte immunoglobulin-like receptor, subfamily B with TM and ITIM domains; LN, lupus nephritis; RERE, arginine-glutamic acid dipeptide repeat; SLC35A5, solute carrier family 35 member A5; SLE, lupus; TAPBP, TAP binding protein; TNFa, tumor necrosis factor alpha; VNN1, vanin1; ZBP1, Z-DNA binding protein 1. ⁎ Corresponding author at: Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, Kuwait University, PO Box 31470 Sulaibekhat, Kuwait. E-mail addresses: [email protected], [email protected] (S. AlFadhli).
onset and is characterized by inflammation in kidneys leading severe illness or death. Up to 60% of lupus cases are diagnosed with LN (Lee et al., 2010). To date, little is known regarding the genetic factors that predispose to LN. Although multiple articles have been found illustrating the various aspects of lupus such as genome wide association, copy number variation and linkage studies (Bentham and Vyse, 2013; Cheng et al., 2013; Forabosco et al., 2006; Koskenmies et al., 2004; Lv et al., 2012), no study has so far fully characterized the role of alternate splicing. Very limited studies were found indicating the significance of individual splice variants in the pathogenesis of lupus (AlFadhli and Nizam, 2014; Vandiedonck et al., 2011). Alternate splicing has recently emerged as a pioneering event transforming our perspective of genome function. It increases protein complexity by differential inclusion or exclusion of regions of premRNA. Current estimates suggest that 92–94% of human coding genes exhibit alternate splicing (http://www.uptodate.com/contents/ epidemiology-and-pathogenesis-of-systemic-lupus-erythematosus) indicating its significance in human pathophysiology and disease. However, the diversity of proteins encoded by a single gene or the proposed modulating and regulatory activities of each variant remain largely unidentified. Gaining insights to such events may lead to novel biomarker discovery and better diagnostics. Violating the theory of single genesingle disorder, new research indicates that complex diseases arise from interplay of multiple genes, therefore studying an individual gene might not be very informative. A whole genome screen is mandatory to identify the modifier genes for its regulatory role in the pathogenesis of complex diseases.
http://dx.doi.org/10.1016/j.gene.2015.06.020 0378-1119/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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S. AlFadhli et al. / Gene xxx (2015) xxx–xxx
In the present study, we aim to adopt a high density Gene chip Human exon 1.0 ST array (Affymetrix) to study the genome-wide alternate splicing of Arab female systemic lupus erythematosus (lupus) and lupus nephritis (LN) cases compared to healthy subjects (HC). 2. Results We aimed to explore the alternatively spliced genes of the lupus and its severe form LN compared to healthy subjects (HC). Age, gender and ethnically matched Arab lupus (n = 5), LN (n = 3) and HC (n = 3) were carefully selected and recruited for this study. Whole human genome expression profiling was carried out using high density Affymetrix Human Exon 1.0 ST arrays with 1.4 million probe sets targeting every known and predicted exon in the entire genome. Data quality was evaluated by Principle component analysis (PCA) which depicted a clear segregation of lupus and healthy controls. An outlier healthy control sample which showed conflicting clustering was further removed from the analysis, to evade false discovery rate. Samples were filtered based on detection above background threshold (DABG, p ≤ 0.05), resulting in 14,455/20,369 transcript clusters that passed signal threshold for subsequent analysis. Data comparison was carried out in three pairs (i) lupus versus healthy control (ii) LN versus healthy control and (iii) lupus versus LN. Analysis of expression profiling revealed 15 genes that are differentially spliced in lupus compared to healthy control with a p value ≤ 0.05 and splice index (SI) b −0.5 or N +0.5 after Benjamini and Hochberg false discovery rate (FDR) correction (Table 1). Of these, LILRB3 (Leukocyte immunoglobulin-like receptor, subfamily B with TM and ITIM domains) and SLC35A5 (Solute carrier family 35 member A5) are expected to be highly spliced with p value of 8.90E-05 after Benjamini and Hochberg FDR correction. A list of 99 genes were found to be differentially spliced in LN compared to healthy control (p ≤ 0.05, SI b −0.5 or N +0.5, after Benjamini and Hochberg FDR correction, Table 2). Three specific genes showed a splice index of b -2 or ≥ +2 and these include IRF5 (Interferon regulatory factor 5), ANK1 (Ankryin 1) and LILRB3. Additionally, both lupus and LN were found to differ in the splicing spectrum of 7 genes (p ≤ 0.005, SI b − 0.5 or N + 0.5, after Benjamini and Hochberg FDR correction, Table 3). 2.1. Hierarchical cluster analysis Hierarchical cluster analysis further enabled the visualization of overall splicing pattern (Fig. 1). Analysis was carried on gene normalized intensities of differentially spliced genes of lupus and LN with Pearson's uncentered (Cosine) distance metric and average linkage Table 1 Differentially spliced genes in lupus cases compared to healthy control. Gene symbol SPOCD1 SLC35A5 MASP1 CENPE ERAP1|CAST CD74 HIST1H4H HLA-B|HLA-C MACC1 ARHGEF10 ZNF250 KIAA0020|GPS2 LILR-| A1|A2| A3 |A6 | B1| B3 IL2RG|CXorf65 SOX3
Probeset_ID 2404766 2636185 2709631 2780172 2868131 2881370 2946383 2948926 3040518 3082874 3159061 3196691 3870611 4011844 4024310
P value 5.99E-05 2.04E-08 4.59E-05 2.85E-05 5.20E-06 4.17E-07 9.89E-06 6.40E-05 2.32E-05 6.19E-05 5.36E-05 3.90E-05 1.92E-08 1.80E-05 3.93E-05
⁎Corrected p value
Splicing index (SI)
0.04935 0.00009 0.04634 0.04161 0.01363 0.00137 0.02162 0.04935 0.03802 0.04935 0.04935 0.04292 0.00009 0.03377 0.04292
−1.454 1.542 −0.952 1.0588 −1.715 1.0076 −0.735 1.116 1.689 −0.985 −1.801 −1.26 3.262 1.436 −0.649
⁎ p value ≤ 0.05 obtained from Splicing ANOVA test after Benjamin and Hochberg FDR correction. Bold indicates P ≤ 0.005 and SI N 2.
Table 2 Differentially spliced genes in lupus nephritis cases compared to healthy control. Gene symbol LEPR|LEPROT CCDC55|FAM73A BTG2 RCOR3 DISC1 CHD5|LOC100131864 RERE MTOR PAQR6 ARHGEF11 SYT2 ITGA4|CERKL GLS SPAG16|VWC2L SLC11A1 TIA1 IL17RE ALAS1 PPM1M SLC35A5 C3orf10 SLC4A7 ROBO1 SEMA5B MASP1 RGS12 NSUN7|ARL4A NSD1 CCDC125 ERAP1|CAST RAPGEF6|FNIP1 CD74 CD109 HIST1H3D|HIST1H2AD HIST1H4H SLC26A8 CRISP2 LPAL2 PFTK1 PILRA|PILRB IRF5 RNF148 PTPRN2 ADAM28 ANK1 DOCK8 C9orf89 SPTAN1 TRPM6 POLE3 TSC1 FBXO18 BUB3 ADAM8 IFITM1 CAPN1 CSTF3|TCP11L1 CYBASC3 AHNAK|IGHG1 LRP1 TMTC3 PAN2 CIT MPHOSPH9 OLFM4 KLF5 ARHGEF7 IRF9 KTN1 PCNX TEP1 ADCY4 RDH11 SYNJ2BP
Probeset_ID 2340433 2343170 2375664 2378584 2385343 2394478 2395245 2396537 2438016 2438657 2451261 2518272 2520291 2526419 2527747 2558511 2610056 2623515 2623568 2636185 2662581 2666904 2683763 2692199 2709631 2716025 2724853 2842951 2860614 2868131 2874794 2881370 2913694 2946324 2946383 2951730 2956536 2982630 3012064 3015519 3023246 3070507 3081862 3090209 3132940 3159330 3179669 3190558 3210013 3221646 3228373 3233431 3268669 3315024 3315675 3334954 3368520 3375307 3375735 3417842 3425134 3457696 3474104 3476012 3490892 3493543 3501661 3529701 3536905 3542689 3555340 3558168 3569401 3570475
p value 1.40E-04 9.96E-05 1.53E-04 6.76E-05 2.86E-04 7.72E-05 1.47E-05 2.90E-04 1.77E-04 7.59E-05 3.57E-04 2.35E-04 2.14E-04 3.73E-05 1.57E-04 1.12E-05 1.85E-04 5.50E-05 1.57E-05 2.33E-04 1.75E-04 2.35E-05 1.34E-05 9.15E-06 2.43E-04 1.96E-04 3.39E-04 7.54E-05 2.96E-04 4.46E-06 1.73E-04 3.09E-06 1.36E-04 7.36E-06 2.15E-04 1.85E-04 4.80E-05 3.02E-04 4.99E-05 3.62E-04 3.01E-05 6.12E-05 1.21E-04 3.01E-04 5.08E-09 2.05E-04 3.78E-05 1.46E-04 1.40E-05 1.02E-04 3.75E-04 6.19E-05 2.50E-04 9.15E-07 2.09E-04 6.85E-05 2.87E-04 3.42E-04 2.09E-06 3.84E-05 2.50E-04 2.92E-04 2.24E-04 3.82E-05 1.36E-05 8.02E-05 1.99E-04 2.55E-04 1.05E-04 5.29E-06 2.78E-04 5.40E-06 1.86E-04 1.65E-04
⁎Corrected p value
Splicing index (SI)
0.0325 0.0263 0.0333 0.0215 0.0423 0.0221 0.0100 0.0423 0.0343 0.0221 0.0481 0.0378 0.0359 0.0169 0.0333 0.0100 0.0346 0.0202 0.0100 0.0378 0.0343 0.0135 0.0100 0.0093 0.0386 0.0358 0.0470 0.0221 0.0424 0.0071 0.0343 0.0071 0.0320 0.0081 0.0359 0.0346 0.0187 0.0424 0.0188 0.0482 0.0165 0.0204 0.0290 0.0424 0.0001 0.0358 0.0169 0.0328 0.0100 0.0264 0.0494 0.0204 0.0388 0.0040 0.0358 0.0215 0.0423 0.0470 0.0069 0.0169 0.0388 0.0423 0.0369 0.0169 0.0100 0.0225 0.0358 0.0391 0.0266 0.0071 0.0422 0.0071 0.0346 0.0337
−0.815 1.944 1.210 1.012 0.913 0.713 1.321 0.876 1.225 −1.231 −1.028 −0.583 1.188 1.158 1.251 −0.615 −0.766 −1.016 0.830 1.594 −1.184 −1.417 1.197 −1.389 −1.010 −0.706 1.330 −0.968 1.301 −1.600 1.492 1.116 1.090 −0.650 −0.961 −0.972 1.372 −1.505 0.884 1.032 2.064 1.085 1.509 −1.343 −2.287 1.103 1.021 −0.696 −1.080 1.610 −0.945 0.685 1.151 0.714 0.892 −0.743 −1.471 −0.592 1.616 1.109 −0.822 1.082 1.036 1.543 −1.734 1.376 0.821 −0.784 1.325 0.881 −0.907 0.948 0.837 −0.577
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
S. AlFadhli et al. / Gene xxx (2015) xxx–xxx Table 2 (continued) Gene symbol
Probeset_ID
SECISBP2L MT1H|MT1F|MT1P2 ROGDI YPEL3 PSMB10|CTRL COG1 CCDC40 ALOX12B TOB1 SFRS2 SLC39A6 MAN2B1|MORG1 PHLDB3 LILR-A1|-A2|-A3-|-A6|B1|B3 RASSF2 CPNE1|RBM12 ZBP1 SYNJ1 RCAN1 APOL6 PRPS2 SLC25A14 HUWE1 PHF8 IL2RG|CXorf65
3623320 3662201 3678369 3687452 3696016 3733938 3737079 3744072 3762473 3771800 3804195 3851545 3864390 3870611 3896034 3904119 3911177 3929325 3930235 3944243 3969047 3990762 4009315 4009506 4011844
p value 3.36E-06 6.09E-05 3.27E-05 4.18E-05 3.40E-05 4.82E-05 1.56E-04 2.02E-04 1.47E-04 1.59E-05 1.64E-04 1.66E-04 3.57E-04 8.51E-05 1.48E-05 1.16E-04 1.11E-07 9.57E-05 4.64E-05 6.58E-06 2.08E-04 1.14E-04 7.26E-05 2.02E-05 4.12E-06
⁎Corrected p value
Splicing index (SI)
0.0071 0.0204 0.0169 0.0178 0.0169 0.0187 0.0333 0.0358 0.0328 0.0100 0.0337 0.0337 0.0481 0.0234 0.0100 0.0284 0.0007 0.0258 0.0187 0.0079 0.0358 0.0284 0.0221 0.0121 0.0071
0.946 −1.144 −1.439 1.067 0.937 0.829 −0.714 −0.586 1.007 1.639 −0.996 1.066 −0.665 3.767 1.302 −1.038 1.028 1.062 −0.716 1.050 0.756 −0.976 0.870 0.828 1.457
⁎ p value ≤ 0.05 obtained from Splicing ANOVA test after Benjamin and Hochberg FDR correction. Bold indicates P ≤ 0.005 and SI N 2.
rule on sample conditions. The clustering algorithm groups rows (entities) and columns (individual samples) by similarity in splicing pattern which is depicted by color codes. The red indicates maximum, blue minimum and yellow medium level of expression. Co-regulated entities across individual groups are also designated by dendrograms. 2.2. Gene Ontology (GO) analysis Genes were evaluated based on their functional similarity and classified into three GO categories such as biological, molecular and cell component by the software. GO hierarchy analysis on alternatively spliced genes of lupus revealed 66 GO terms satisfying a p ≤ 0.05. A more stringent analysis revealed 34 GO terms satisfying a p ≤ 0.005 (Fig. 2). Much of the alternatively spliced genes of lupus were found to be associated with biological process such as immune response, T cell differentiation, antigen processing and presentation, defense response and IL4/IL2/IL7 mediated signaling. Similarly, molecular category included antigen binding/phosphatase binding/receptor and regulatory activities and interleukin receptor mediated signaling process. GO analysis of LN entity list revealed 11 GO terms satisfying a p ≤ 0.05. These were mostly associated with biological process such as immune response, positive regulation of CD4 positive, CD25 positive and alpha-beta regulatory T-cell differentiation. The molecular category included protein phosphatase binding, antigen binding, apo-lipoprotein
Table 3 Comparative expression profile of differentially spliced genes between lupus nephritis and lupus cases. Gene symbol
Probeset_ID
p value
⁎Corrected p value
Splicing index (SI)
RERE ABLIM3 TAPBP VNN1 ABCA1 CMTM5 ITGA2B
2395245 2834863 2950629 2974592 3218528 3529113 3759137
3.66E-07 9.70E-06 2.82E-06 2.61E-05 1.66E-05 7.86E-06 1.33E-06
0.0047 0.0251 0.0122 0.0483 0.0358 0.0251 0.0086
1.028 −0.808 0.734 −1.537 −0.862 0.903 0.960
⁎ p value ≤ 0.05 obtained from Splicing ANOVA test after Benjamin and Hochberg FDR correction. Bold indicates P ≤ 0.005.
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binding, cadmium ion binding and calcium dependent cysteine type endopeptidase inhibitor activity. A single GO term indicating the protein phosphatase binding was obtained with a p-value cut off of p ≤ 0.005. 2.3. Pathway analysis We further carried out a pathway based analysis of differentially spliced genes comparing to all known pathways (315) of BioPAX database. It revealed 11 significant pathways with a p ≤ 0.05 (Table 4). The proteasome degradation and allograft rejection pathway showed significant association with a p-value cut off of b 0.005, indicating its significance in the pathogenesis of lupus. Similarly, entity list of spliced genes in LN revealed 12 significant pathways with a p ≤ 0.05 (Table 4). The Wnt pathway showed significant association with a p-value cut off of b 0.005 (Fig. 3). 3. Discussion Lupus is known to be a complex polygene autoimmune disease with a wide range of clinical and immunological spectrum. The incidence and prevalence rate of lupus vary across different geographical areas with African-Americans more significantly affected than rest of the population and higher prevalence rate among Asians and Hispanics (http://www.uptodate.com/contents/epidemiology-and-pathogenesisof-systemic-lupus-erythematosus; Petri, 2002). We have previously reported increasing cases of lupus among Arabs and its association with specific gene variants (AlFadhli and Jahabani, 2014; AlFadhli and Ghanem, 2014). Diagnosis of lupus is more common in females and its incidence rate has tripled over the past four decades (Uramoto et al., 1999). Nevertheless, the exact etiopathology remains unknown. Recently, alternate splicing has emerged as a key event contributing to the diversity and complexity of mRNA/ protein variants. Despite being the hotspot area, not much is known regarding the expression spectrum of alternatively spliced genes and it role in diseases progression. Very limited studies have characterized the functional role of splice variants with majority being centered on algorithm based computational methods and RNA-seq technology (Li et al., 2014a, 2014b; Eksi et al., 2013; Wang et al., 2008). In the present study, we aimed to investigate the genome-wide peripheral blood transcriptome of Arab female lupus and LN cases compared to healthy control. Our analysis indicates that alternate splice events are differentially regulated in both lupus and LN with higher degree of splicing events in LN. We have identified 15 genes that are differentially spliced in lupus; while an exhaustive list of 99 genes were obtained for LN cases with distinguishable roles in immune response and T cell differentiation. Certain interesting mRNA variants of leukocyte immunoglobulinlike receptor (LILR-A1|A2|A3|A6|B1|B3) and solute carrier family 35 member A5 (SLC35A5) that are differentially spliced between lupus and healthy control (p b 0.0005) were detected. LILRs are family of receptors encoded within the leukocyte receptor complex that exhibits immune-modulatory effects on a wide spectrum of immune cells. LILR-A1|A2|A6 are stimulatory receptors, while LILR-A3 represents a soluble receptor and LILR-B1|B3 is known for its inhibitory effect on cell activation. Polymorphisms within LILR family have been previously associated with susceptibility to lupus and rheumatoid arthritis and its role in nephropathy has been marked by increased urine level of LILRA2 in LN cases (Mamegano et al., 2008; Kuroki et al., 2005; Du et al., 2014; Yeh et al., 2011). Additionally, we report increased splice index of LILRB3 variant (SI = 3) in LN cases suggesting aberrant spicing event. Not much is known regarding the role of SLC35A5 in autoimmunity. It is characterized to be involved in the nucleotide-sugar transmembrane transporter activity and has been reported to be differentially expressed in lupus cases (Lyons et al., 2010). IRF5 and ANK1 also draw attention with a splice index of 2 in LN cases. Variations in IRF5 gene have been previously associated the initiation and progression of lupus and LN (Qin et al., 2010; Alfadhli and
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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Fig. 1. Hierarchical Cluster (HC) analysis: HC analysis was carried on gene normalized intensities of differentially spliced genes of Lupus and Lupus nephritis (LN) with Pearson's uncentered (Cosine) distance metric and average linkage rule on sample conditions (GeneSpring 13.0 Software, Agilent). Heat maps signifying overall splicing pattern in (A) Lupus and healthy control: Individual splice variants of all tested subjects; lupus (n = 5), lupus nephritis (n = 3) and healthy control (n = 2). Only 15 splice variants were considered to be differentially expressed. N/A indicates probes sets for which annotations are not available. (B) Lupus subjects with and without nephritis; clustergram indicates the expression profile of individual splice variants in lupus subjects with (n = 3) and without nephritis (n = 5). SLE 6, 7, 8 represents lupus subjects with nephritis, while SLE 1, 2, 3, 4, 5 indicates lupus subjects without nephritis. Red indicates maximum, blue minimum and yellow medium level of expression. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Jahabani, 2014). It plays a key regulatory role in innate and adaptive immune responses and is crucial for the production of the proinflammatory cytokines such as TNFa, IL12 and IL6 subsequent to
the activation of Toll-like receptor signaling (Alfadhli and Jahabani, 2014). Ankyrin proteins link the integral membrane proteins to the cytoskeleton elements and plays critical role in mediating cellular
Fig. 2. Represents Gene Ontology (GO) analysis of alternatively spliced genes of (A) lupus and (B) lupus nephritis indicating their GO Id, function and p-value obtained from GeneSpring 13.0 Software (Agilent). GO analysis was performed to explore the Gene Ontology terms associated with the spliced genes for their functional classification.
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
S. AlFadhli et al. / Gene xxx (2015) xxx–xxx
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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Table 4 Represents significant pathways of alternatively spiced genes in lupus and lupus nephritis. Pathway (lupus)
p-Value
Matched entities
Pathway entities
IL-2 Signaling pathway Complement and Coagulation Cascades Inflammatory Response Pathway Proteasome Degradation Kinesins Complement Activation, Classical Pathway Type II interferon signaling (IFNG) IL-9 signaling pathway IL-4 signaling pathway Allograft Rejection IL-7 signaling pathway
0.0139 0.0193 0.0124 0.0002⁎ 0.0019 0.0047 0.0143 0.0047 0.0166 0.0000⁎ 0.0070
1 1 1 2 1 1 1 1 1 2 1
37 63 33 63 6 12 37 12 46 40 20
Pathway (Lupus nephritis)
p-Value
Matched entities
Pathway entities
Insulin Signaling AMPK signaling Interferon type I Nucleotide Metabolism Complement Activation, Classical Pathway Senescence and Autophagy Wnt Signaling Pathway IL-9 signaling pathway Heme Biosynthesis MicroRNAs in cardiomyocyte hypertrophy Regulation of Actin Cytoskeleton IL-7 signaling pathway
0.0184 0.0332 0.0051 0.0448 0.0285 0.0135 0.0015⁎ 0.0285 0.0215 0.0118 0.0188 0.0425
2 1 2 1 1 2 2 1 1 2 2 1
89 49 45 19 12 79 25 12 9 90 91 20
⁎ Bold indicates p b 0.005.
events. Although its direct role in LN is unknown, its significantly altered expression has been previously reported in chronic kidney disease patients (Costa et al., 2008). Variants of two other genes; ADAM8 and ZBP1 were found to be differentially spliced in LN cases (p b 0.005). These variants were not previously associated with the etiopathogenesis of LN. ADAM8 is primarily involved in cell adhesion during neurodegeneration. Owing to its functional involvement in leukocyte recruitment it is suggested to be a potential target for inflammatory diseases (Zarbock and Rossaint, 2011). Similarly, ZBP1 has been found to play an essential role in innate immune response regulating the type I interferon responses (Takaoka et al., 2007). Likewise, we observed 7 variants that are differentially spliced between LN and lupus, indicating their probable role as markers for nephritis development. These include variants of VNN1, TAPBP, ABCA1, RERE, ABLIM, CMTM5 and ITGA2B. Our study highlights VNN1 involved in hematopoietic cell trafficking to be a potential biomarker for nephritis with higher expression in LN and minimum in lupus. Concordant to our results, very recently VNN1 was reported to be a potential marker for LN (Sanchez-Munoz et al., 2013). TAPBP is a transmembrane glycoprotein that facilitates antigen loading on to major histocompatibility complex and has been previously reported to be up-regulated in glomeruli of LN mouse (Teramoto et al., 2008). Likewise, ABCA1 codes for membrane-associated protein that facilitate transport of various molecules across intra/extra cellular membrane and plays a major role in cholesterol and phospholipid homeostasis. Down-regulation of ABCA1 in renal cells was reported to promote cholesterol accumulation contributing to the development of diabetic nephropathy (Tsun et al., 2014). ITGA2B probably accounts for the intravascular and cellular events involving nephritis, with its prominent role in coagulation and cell surface mediated signaling involving cell growth, migration and apoptosis (http:// www.genecards.org/cgi-bin/carddisp.pl?gene=ITGA2B). Though variants in ITGAM have been widely associated with lupus, the role of ITGA2B in the latter remains yet to be explored. Similarly, CMTM5 encodes
for chemokine like superfamily; while several chemokine families have previously shown to be involved in renal diseases (Segerer and Schlondorff, 2007), the role of CMTM5 is obscure. Additionally, the role of two other variants RERE and ABLIM3 in renal involvement also remains unidentified. RERE conveys a crucial role in the development and function of multiple organs and acts as nuclear receptor co-regulator and induces apoptosis on up-regulation (Kim et al., 2013). ABLIM3 is essentially involved in embryonic development, cell lineage determination and cancer (Krupp et al., 2006). Further research need to be carried out to investigate the spatial and sequential roles of these variants in susceptibility to lupus and LN. We also identified pathways that are deregulated in lupus and LN subjects apparently elucidating the broader aspects of disease manifestation with multiple entities involved. Allograft rejection and proteasome degradation pathways have been shown to be active in wide array of inflammatory and intracellular events indicating its significance in the pathophysiology of the disease (Bunnapradist et al., 2006; Ciechanover, 1994). Agents that inhibit proteasomes are considered to be a therapeutic target for inflammatory diseases and cancer (Hershko, 1997). WNT signaling pathway has been recently reported to be deregulated in human LN and in renal tissue of a lupus mouse model, indicating its probable role in renal fibrosis (Wang et al., 2014). A detailed study of these pathways may open gateways to molecularly targeted therapy. Majority of the genes that tend to be differentially spliced in lupus and LN were found to be involved in immune dysfunction, specifically immune cell infiltration and activation. Consistently, transcriptome analysis of murine model of LN has also shown the significance inflammatory events, aside from renal hypoxia and metabolic stress during the progression of disease (Bethunaickan et al., 2014). The global expression profiling of peripheral blood mononuclear cells has consistently revealed the up-regulation of IFN-inducible genes in lupus, though not splice variants (Baechler et al., 2003). The lack of co-relation between relative abundance or the expression of individual variants observed in blood (PMBC) and organs such as kidney in literature could be due to stage specific or tissue dependent factors indicating varied role of genes in different lineages. In conclusion, our study highlights the likelihood of alternate splicing in the genetic etiology of lupus and LN. Identification of principal isoforms that are responsible for the canonical functions of genes may serve as a breakthrough leading to better diagnostics and therapeutics. We identified individual variants that are differentially expressed in each study group that may serve an essential role in disease pathogenesis. Higher extent of splice variant deregulation in LN indicates its possibility in clinical application. A further study on mechanism and control of these variants in a relatively large cohort is mandatory, to assess their therapeutic potential. Our study presents novel findings signifying the potential future of in-depth research in the same domain. 4. Materials and method 4.1. Study subjects Blood samples were collected from a total of 11 Arab female subjects: (5 lupus and 3 LN) patients and 3 — age/gender/ethnically matched healthy individuals (GEO accession number: GSE62764). Lupus cases were selected when they had at least four of the Systemic Lupus International Collaboration Clinics/American College of Rheumatology (SLICC/ACR) criteria. The diagnosis of lupus was made if four or more of the 11 manifestations are present, either one after the other or at the same time. These include discoid rash, malar rash, photosensitivity, hematological disorder, antinuclear antibody, immunologic
Fig. 3. Pathway analysis of differentially spliced genes comparing to all known pathways of BioPAX database postulated central gene regulators and significant pathways that are deregulated in study subjects with a p-value cut-off of less than 0.005. These include: (A) proteasome degradation and (B) Allograft rejection pathway in lupus and (C) Wnt pathway in lupus nephritis (GeneSpring 13.0 Software, Agilent).
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
S. AlFadhli et al. / Gene xxx (2015) xxx–xxx
Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020
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disorder, mucosal ulcers, arthritis, renal disorders, serositis and neurological disorder. LN cases were confirmed by renal biopsy. Average age of lupus and LN were 33 ± 3.5 and 27.67 ± 3.1 respectively. All patients were in active disease state. The biochemical and immunological profiles were conducted in Mubarak Al-Kabeer Hospital as part of the patient diagnosis and follow-up. Healthy volunteers were randomly selected with inclusion criteria of general good health and no first degree relatives with autoimmune diseases. Exclusion criteria were any recent history of acute or chronic debilitating illness. Average age of healthy controls was 32.5 ± 3.5. The conduct of the study was approved by the research ethics committees of Kuwait University and all the participants provided written informed consent. Blood samples were collected from patients and healthy subjects in EDTA treated tubes. RNA was extracted from 2 ml of whole blood using QIAamp® RNA Blood Mini kit (Qiagen, Germany) following the manufacture's protocol. Extracted RNA was further purified using DNase (Qiagen) to remove potential genomic DNA contamination. Purified RNA was then quantified using a NanoDrop spectrophotometer (NanoDrop Technologies, DE, USA) and RNA integrity was determined by Agilent Bioanalyzer 2100 (Agilent, CA, USA). Total RNA samples with a RNA integrity number (RIN) greater than 7 were used for further microarray studies. 4.2. Array hybridization RNA (100 ng) from each sample was processed using Ambion expression kit (Life technologies, USA) for Affymetrix® whole transcript expression array. A double amplification procedure was carried out for cDNA preparation following the manufacturer's instructions. The prepared cDNA was purified (Ambion kit) and labeled using WT Terminal Labeling Kit (Affymetrix, Inc., Santa Clara, CA). Hybridization cocktails were prepared and hybridized to the GeneChip® Human Exon 1.0 ST Arrays (Affymetrix, Inc., Santa Clara, CA) followed by incubation in a rotating hybridization oven 640 (Affymetrix) at 45 °C and 60 rpm for 16 h. Following hybridization, the arrays were washed and stained on an GeneChip® Fluidics 450 workstation (Affymetrix, Santa Clara, USA) using the FS450_0001 fluidics script. The arrays were then scanned using the Affymetrix GeneChip® Scanner 3000 7G (Affymetrix, Santa Clara, USA). 4.3. Data analysis Data analysis was carried out using GeneSpring 12.5GX (Agilent) software. Data quality was evaluated by Principal component analysis (PCA) and any outlier samples identified were removed. Normalization was carried out using Robust Multi-array Average (RMA) method on core probsets and baseline to median summarization was performed. At exon level, only probesets characterized by detection above background signal (DABG) p value b0.05 in atleast 50% of the samples in either group were accepted for subsequent analysis. Alternate splicing events between patients and control subjects were characterized by “Splice index (SI) model” (Affymerix Inc., 2006, Gardina et al., 2006). SI model enables identification of exons that have differential inclusion rates between the two tested sample groups. It is represented as the log ratio of the gene normalized intensity (NI) between the two tested sample groups. NI in turn indicates the ratio of exon-level probe set expression to gene-level transcript cluster expression. Gene‐level Normalized Intensity ðNIÞ ¼
Splicing Index ¼ log2
Probe set intensity Expression level of the }gene}
Sample1 NI : Sample2 NI
To identify the significant alternatively spliced genes, splicing ANOVA with Benjamini and Hochberg false discovery rate (FDR)
correction was employed on gene normalized intensities of the tested sample groups. True positives with a p ≤ 0.05, SI 0.05 and Fold change (FC) N 1 were considered as alternatively spliced. Cluster, Gene Ontology and pathway analysis were carried out using GeneSpring software. Hierarchical clustering heatmaps were generated from gene normalized intensities of differentially spliced genes of lupus and LN in terms of Pearson's uncentered (Cosine) distance metric and average linkage rule. The red indicates maximum, blue minimum and yellow medium level of variant expression. Gene Ontology analysis was performed to explore the Gene Ontology terms associated with the entities for their functional classification. Pathway analysis further enabled the visual representation of our experimental data. Conflict of interest Authors declare that no conflict of interest exists. Funding source This work was supported by Kuwait University research administration Grant NM02/07. The funders had no role in study design data collection and analysis, decision to publish or preparation of the manuscript. Acknowledgment This work was supported by Kuwait University Research administration Grant NM 02/07 and General facility Grant #SRUL02/13. References AlFadhli, S., Ghanem, A.A., 2014. Influence of HumDN1 VNTR polymorphism on DNASE1 expression in systemic lupus erythematosus and rheumatoid arthritis. Immunol. Invest. 43, 411–423. AlFadhli, S., Jahabani, I., 2014. Association of interferon regulatory factor 5 (IRF5) markers with an increased risk of lupus and overlapping autoimmunity in a Kuwaiti population. Ann. Hum. Biol. 41, 531–539. AlFadhli, S., Nizam, R., 2014. Differential expression of alternative splice variants of CTLA4 in Kuwaiti autoimmune disease patients. Gene 534, 307–312. Baechler, E.C., Batliwalla, F.M., Karypis, G., Gaffney, P.M., Ortmann, W.A., Espe, K.J., et al., 2003. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl. Acad. Sci. U. S. A. 100, 2610–2615. Bentham, J., Vyse, T.J., 2013. The development of genome-wide association studies and their application to complex diseases, including lupus. Lupus 22, 1205–1213. Bethunaickan, R., Berthier, C.C., Zhang, W., Eksi, R., Li, H.D., et al., 2014. Identification of stage-specific genes associated with lupus nephritis and response to remission induction in (NZB × NZW)F1 and NZM2410 mice. Arthritis Rheumatol. 66, 2246–2258. Bunnapradist, S., Chung, P., Peng, A., Hong, A., Chung, P., et al., 2006. Outcomes of renal transplantation for recipients with lupus nephritis: Analysis of the Organ Procurement and Transplantation Network database. Transplantation 82, 612–618. Cheng, F.J., Zhou, X.J., Zhao, Y.F., Zhao, M.H., Zhang, H., 2013. Alpha-defensin DEFA1A3 gene copy number variation in Asians and its genetic association study in Chinese systemic lupus erythematosus patients. Gene 517, 158–163. Ciechanover, A., 1994. The ubiquitin-proteasome proteolytic pathway. Cell 79, 13–21. Costa, E., Rocha, S., Rocha-Pereira, P., Castro, E., Miranda, V., et al., 2008. Altered erythrocyte membrane protein composition in chronic kidney disease stage 5 patients under haemodialysis and recombinant human erythropoietin therapy. Blood Purif. 26, 267–273. Du, Y., Su, Y., He, J., Yang, Y., Shi, Y., et al., 2014. Impact of the leucocyte immunoglobulinlike receptor A3 (LILRA3) on susceptibility and subphenotypes of systemic lupus erythematosus and Sjogren's syndrome. Ann. Rheum. Dis. (pii: annrheumdis- 2013204441). Eksi, R., Li, H.D., Menon, R., Wen, Y., Omenn, G.S., et al., 2013. Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data. PLoS Comput. Biol. 9, e1003314. Forabosco, P., Gorman, J.D., Cleveland, C., Kelly, J.A., Fisher, S.A., et al., 2006. Gorman JD, Cleveland C, et al. Meta-analysis of genome-wide linkage studies of systemic lupus erythematosus. Genes Immun. 7, 609–614. Hershko, A., 1997. Roles of ubiquitin-mediated proteolysis in cell cycle control. Curr. Opin. Cell Biol. 9, 788–799. Hom, G., Graham, R.R., Modrek, B., Taylor, K.E., Ortmann, W., et al., 2008. Association of Systemic Lupus Erythematosus with C8orf13–BLK and ITGAM–ITGAX. N. Engl. J. Med. 358, 900–909. http://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGA2B (Accessed: 2015).
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Please cite this article as: AlFadhli, S., et al., Genome-wide peripheral blood transcriptome analysis of Arab female lupus and lupus nephritis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.06.020