http://informahealthcare.com/cts ISSN: 0300-8207 (print), 1607-8438 (electronic) Connect Tissue Res, 2014; 55(3): 187–196 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/03008207.2014.905548
RESEARCH ARTICLE
Analysis of microRNAs in patients with systemic lupus erythematosus, using Solexa deep sequencing Wenbiao Chen1*, Kuibi Tan2*, Jianrong Huang1, Xiangqi Yu1, Wujian Peng3, Yuyu Chen1, Xiaocong Lin4, Deheng Chen1, and Yong Dai1 Second Clinical Medical College, Jinan University, Shenzhen People’s Hospital, Shenzhen, Guangdong Province, China, 2Ningbo No. 2 Hospital, Ningbo, Zhejiang, China, 3The Third People’s Hospital of Shenzhen, Shenzhen, Guangdong, China, and 4Institute of Biochemistry and Molecular Biology, Guangdong Medical College, Zhanjiang, China Abstract
Keywords
Objectives: Our aim in this study was to identify and examine the differential expression of microRNAs in patients with systemic lupus erythematosus (SLE). Methods: We employed high-quality, high-throughput Solexa sequencing to clone and identify microRNAs in SLE patients and a control group. Results: From the sequencing data, we identified numerous microRNAs displaying significantly different levels of expression in patients with SLE and in healthy controls. The 212 and 199 microRNAs were upregulated and downregulated, respectively. Only 61 novel microRNAs exhibited significantly different levels of expression in the two groups. The target genes of the novel microRNAs identified in the SLE group were found to have cell metabolism functions. We also analyzed the chromosomal locations of the microRNAs with high level of expression between the two groups. A profile comparison revealed that the majority of transcripts were expressed at a similar level. The functional classes of the most abundant microRNAs were equally represented on each chromosome. Conclusion: We identified novel and known microRNAs significantly enhancing our understanding of the microRNA expression profiles of SLE patients. These data also provide insight into the function of microRNAs in SLE and provide new strategies for future therapies.
Expression and distribution, high-throughput Solexa sequencing, microRNA, novel microRNA, systemic lupus erythematosus, target gene
Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown etiology that is characterized by immunological dysfunction and the presence of multiple autoantibodies in the serum (1). Organ damage in SLE patients results from the deposition of immune complexes and the infiltration of activated T cells into susceptible organs (2). The clinical progression and prognosis of SLE can vary. SLE can involve any organ or multiple organs and can be quiescent between periods of aggravation (3). The majority of SLE patients are young females and 90–95% are of reproductive age (4). While modern treatment has greatly improved patient care, SLE patients often experience relapses and severe adverse effects (5). Hereditary and environmental factors and hormones play a predominant role in causing SLE (6); however, he mechanism underlying the development of SLE remains unclear (7) and it presents with a variety of features *These authors contributed equally to this work. Correspondence: Yong Dai, Second Clinical Medical College, Jinan University, People’s Hospital, Guangdong Province, Shenzhen 518020, China. Tel: +86 13802201510. Fax: +86 75525626750. E-mail: [email protected]
History Received 6 January 2014 Revised 4 March 2014 Accepted 13 March 2014 Published online 3 April 2014
and manifestations (8). In several studies, scientists have examined the comparative expression of microRNAs in SLE patients in order to advance the understanding of the molecular events that underlie SLE. MicroRNAs are endogenous, noncoding RNAs that are 19–25 nucleotides in length. MicroRNAs are capable of regulating gene expression at both the transcriptional and post-transcriptional level (9). They play important roles in development and cellular processes such as differentiation, growth and cell death (10). MicroRNAs reportedly play an important role in the development of SLE (11) and are linked to many other diseases including cancer (12). Several recent studies have suggested that microRNAs can act as oncogenes and tumor suppressors (13). To expand the repertoire of small regulatory RNAs known to be expressed in SLE patients and to gain a further understanding of the molecular events underlying SLE pathogenesis, we generated and sequenced two small RNA libraries prepared from SLE patients and normal controls. We identified novel microRNAs that possessed unique features, distinguishing them from other endogenous small RNAs. Notable features included a characteristic stem-loop hairpin secondary structure in the microRNA precursors (14).
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1
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In this study, we used high-throughput Solexa sequencing to study the global microRNA expression profiles of SLE patients and healthy subjects. We also identified and analyzed the expression of novel microRNAs in the SLE group. The size distribution of small RNAs in the SLE and control group indicated that numerous microRNAs are relevant to SLE and present opportunities for therapeutic intervention. Notably, we identified a set of microRNAs in the SLE group that may be associated with disease relapse, providing valuable insights into the pathogenesis of SLE. We present a comprehensive picture of microRNA expression that advances our knowledge of microRNA expression profiles in SLE and suggests new avenues for future research into the pathogenesis of the disease.
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Materials and methods Patient materials A total of 40 peripheral blood mononuclear cell (PBMC) samples, 20 samples from SLE patients and 20 samples from normal controls (NC), were obtained from the Second Affiliated Hospital of Jinan University. The subjects were selected in accordance with the American Rheumatism Association diagnostic criteria for SLE (1997 revision). The patient blood samples were evaluated for cytotoxic drugs, immunosuppressants and high-dose glucocorticoids (41 mgkg1 d1). Patients in both groups underwent standard health screening to rule out infection, cancer, hypertension, diabetes and other systemic diseases. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Jinan University.
Figure 1. RNA sequence analysis workflow.
ligated to the purified small RNAs; (c) 30 RNA adapters were ligated to the precipitated RNA; (d) ligation products were reverse-transcribed and amplified using real-time polymerase chain reaction (RT-PCR) and (e) the amplification products were clustered and sequenced using an Illumina Genome Analyzer (BGI, Shenzhen, China). The experimental workflow outlining the small RNA sequence analysis is shown in Figure 1. The small RNA library sequencing data were deposited in the NCBI Sequence Read Archive (accession number SRP028945). Data analysis
Sample processing Under aseptic conditions, fasting venous blood samples (5 ml) were drawn and stored in EDTA tubes. To generate two pooled groups (SLC and NC) equal volumes of the 20 SLE and 20 NC blood samples were combined. The PBMC blood samples were separated using Ficoll-Paque (Sigma, St. Louis, MO). The RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA), in accordance with the manufacturer’s instructions. The integrity of the RNA and presence of microRNAs were assessed by microcapillary electrophoresis using the RNA 6000 and Small RNA kit (Agilent Technologies, Santa Clara, CA), respectively. The concentration and quality of the RNA were assessed by absorbance spectrometry using a NanoDrop 2000 (Thermo, Waltham, MA). Sequence analysis The small RNA digital analysis was based on high-through put Solexa sequencing method. We used the sequencing by synthesis method because it decreased the loss of nucleotides. This method requires a small amount of sample material, has a high level of accuracy, and runs on a straightforward automatic platform. A single round of analysis can generate millions of small RNA sequence tags. The RNA sequencing was performed as follows: (a) small RNAs [18–30 nucleotides (nt)] were isolated from total RNA; (b) 50 adapters were
Data cleaning was performed on the sequence tags (50 nt) from the high-throughput Solexa sequencing. This included removal of low quality tags, trimming of the terminal 30 primer adapter sequence, and elimination of the 50 adapter contaminants formed by ligation. The remaining sequences (18 nt minimum) were aligned to the human reference genome (hg19, NCBI) using the SOAP V2.0 program. Sequences that were a perfect match to the genome along their entire length were analyzed further. Next, we removed tags that originated from protein-coding genes, repeat sequences, and rRNA, tRNA, snRNA, and snoRNA sequences that were found in the NCBI GenBank database (http://www.bi.nih.gov) and the Rfam 9.1 database. Repeat overlapping sequences and sequences that overlapped with predicted exons and introns were also filtered. The remaining unique small RNA sequences were mapped to the miRBase database (http:// www.mirbase.org/). Perfectly matched sequences were considered conserved microRNAs. The data analysis workflow is shown in Figure 2. Novel microRNA prediction To obtain matched sequences, we compared the clean sequences to the NCBI GenBank database. The matched sequences were annotated in the following priority order: GenBank4Rfam(10.0)4miRBase18.04repeat4 exon4intron. In total, we obtained 11 classes of small RNAs: exonic, intronic, miRNAs, rRNAs, repeats, scRNAs, snRNAs,
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Figure 2. Data analysis workflow.
Table 1. Summary of small RNAs. Class exon intron miRNA rRNA repeat scRNA snRNA snoRNA srpRNA tRNA unann
NC
SLE
177,585 36,584 7,159,443 238,489 70,102 1,770,169 174,337 150,501 305 148,355 2,137,340
270,162 68,167 12,238,468 344,554 46,726 78,237 113,457 308,183 126 108,144 2,183,421
snoRNAs, srpRNAs, tRNAs and unannotated (Table 1). An unannotated sequence was defined as one that could not be matched and annotated. Notably, the unannotated sequence was the source of several novel microRNAs. We used Mireap (http://sourceforge.net/projects/mireap) to identify novel microRNA candidates based on their secondary structures. We assessed the stability of their hairpin structures and confirmed the presence of Dicer cleavage sites in the microRNA tags. Candidates that fulfilled the following five criteria were considered high-confidence microRNAs: (1) The RNA displayed a folding free energy value of 18 kcal/mol, (2) The candidate was expressed at a detectable level in at least two samples, (3) The hairpin-shaped stemloop region contained no more than 35 nucleotides, (4) More than two sequences were located within the hairpin and (5) The sequences did not span the loop. Novel microRNA prediction was conducted as described by Liu et al. (15). Quantitative real-time PCR verification of candidate microRNAs We performed quantitative real-time PCR (qRT-PCR) to verify the results of the deep sequencing analysis for six
randomly selected differentially expressed novel microRNAs. The qRT-PCR primers are listed in Table 2. Total RNA (2 mg) from each sample was reverse-transcribed into cDNA using a reverse transcription kit, in accordance with the manufacturer’s protocol (Promega, Madison, WI). The cDNA was amplified using the following PCR program: 30 min at 16 C, 40 min at 42 C and 5 min at 84 C. The reactions were performed in triplicate. The comparative threshold cycle (2DDCt) method of quantification was used to calculate differences in expression levels between the SLE and groups. The microRNA expression levels were normalized to U6 snRNA reference RNA. The Rotor-Gene 6000 Series Software 1.7 (QIAGEN) was used to calculate the DDCt value, using the following formula: DDCt ¼ Ct(SLE NC).
Results Length distribution of small RNAs The small RNAs were typically 18–30 nt in length. The length distribution is shown in Figure 3. The 22 nt class was the most abundant group of small RNAs in both the SLE and NC groups. The sequences 20–24 nt in length accounted for 91.4% of the total sequence counts in the SLE library. This class was significantly more than any other class (Figure 3A). In the control library, sequences 18–29 nt in length were more abundant than the other classes in the group, accounting for 96.81% of the total sequence counts (Figure 3B). Differentially expressed microRNAs in the SLE and NC groups We compare the microRNA expression patterns of the two groups to identify differentially expressed microRNAs. This process involved two steps: (1) normalizing the counts of each microRNA in each group to the total number of small RNAs.
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Table 2. qRT-PCR primers. miRNA name
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novel_mir_8 novel_mir_9 novel_mir_42 novel_mir_95 novel_mir_194 novel_mir_189 U6 snRNA
qRT-PCR primers 0
F:5 ACACTCCAGCTGGGCCTCACCATC CCT TCTGCCTG 30 F:50 ACACTCCAGCTGGGATCTCCACTACT G CGGCCAC 30 F:50 ACACTCCAGCTGGGTGGGCAGGGGCT TATTGTAG 30 F:50 ACACTCCAGCTGGGTTGGGCCCCTCC T AAGACA 30 F:50 ACACTCCAGCTGGGTGGGCAGGGGCTTATTGTA 30 F:50 ACACTCCAGCTGGGGAGAATATGGGA GTCTGTG 30 F:50 GCTTCGGCAGCACATATACTAAAAT 30
Figure 3. The length distribution of small RNAs in (A) SLE and (B) NC groups.
The ratio was then multiplied by a constant of 1 106 using the following formula: normalized expression ¼ (actual number of microRNAs)/(total number of small microRNAs) 106. (2) The fold-changes and p values were calculated using the normalized expression values. The following formulae were used: fold change ¼ log2(SLE/ NC) (16) and p(y/x) ¼ (N2/N1)y (x + y)!/x!y!(1 + N2/ N1)(x + y + 1), which was based on the statistical test developed by Audic and Claverie (17). The number of sequences across a microRNA in the NC group and the number of sequences across the corresponding microRNA in the SLE group were represented by x and y, respectively. N1 and N2 represented the total number of clean sequences in the NC and SLE groups, respectively. MicroRNAs with expression levels of 0 after normalization were changed to 0.01 for the differential expression analysis. A fold change [log2(SLE/NC)] and p value 0.01 were considered significant. We found 411 microRNAs that exhibited aberrant expression in the SLE group. Of these microRNAs, 212 and 199 were upregulated and downregulated, respectively, in SLE patients compared with the NC group. The number of
upregulated microRNAs was greater than the number of downregulated microRNAs, suggesting that microRNAs play a significant role in the pathogenesis of SLE. The top 20 upregulated and downregulated microRNAs are listed in Table 3. The average fold-change(log2)47 was 15 and 13 for the upregulated and downregulated microRNAs, respectively. The greatest change was observed for microRNA-31-3p, whose expression level decreased 10-fold. Interestingly, 10 members of the let-7 family showed altered expression. let-7a-3p, and let-7f-2-3p were upregulated, whereas let-7a-5p, let-7b-5p, let-7c, let-7d-5p, let-7e-5p, let7g-5p, let-7i-3p and let-7i-5p were downregulated. Of these microRNAs, let-7f-2-3p exhibited the most significant change, with a fold-change(log2) of 7.4. The differential expression of the microRNAs in the SLE and NC groups also differed significantly. For example, microRNA-143-3p, microRNA-21-5p, microRNA-378a-3p and microRNA-146b5p were significantly increased, and microRNA-1271-3p, microRNA-179-5p, microRNA-4736 and microRNA-3200-3p displayed the most significant reductions in the SLE group. In the NC group, hsa-let-7a-5p, hsa-let-7g-5p,
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Table 3. Differentially expressed microRNAs in SLE and NC groups: the top 20 upregulated and downregulated microRNAs.
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microRNA
NC expression
Up-regulated microRNAs hsa-miR-5683 0.01 hsa-miR-381 0.1658 hsa-miR-508-3p 0.0829 hsa-miR-143-3p 328.934 hsa-miR-126-3p 28.2678 hsa-miR-514a-3p 0.0829 hsa-miR-182-5p 8.3726 hsa-miR-506-3p 0.01 hsa-let-7f-2-3p 0.01 hsa-miR-203 0.01 hsa-miR-10b-5p 0.0829 hsa-miR-5000-3p 0.01 hsa-miR-379-5p 6.2173 hsa-miR-3911 0.01 hsa-miR-4784 0.01 hsa-miR-380-5p 0.0829 hsa-miR-668 0.01 hsa-miR-330-5p 0.3316 hsa-miR-378a-3p 535.5954 hsa-miR-30e-3p 58.1935 Down-regulated microRNAs hsa-miR-31-3p 11.1082 hsa-miR-1275 6.3001 hsa-miR-766-5p 33.0758 hsa-miR-193b-5p 4.8909 hsa-miR-3130-3p 4.2277 hsa-miR-23a-5p 23.4598 hsa-miR-363-5p 2.9014 hsa-miR-4707-3p 2.4869 hsa-miR-3916 1.9066 hsa-miR-548i 1.8237 hsa-miR-4752 1.6579 hsa-miR-1284 1.4092 hsa-miR-3141 1.4092 hsa-miR-1271-3p 1.1606 hsa-miR-197-5p 1.1606 hsa-miR-4736 1.1606 hsa-miR-107 9916.7634 hsa-miR-3200-3p 1.0777 hsa-miR-301a-5p 6.4659 hsa-miR-23b-5p 23.6256
SLE expression
Fold-change(log2)
p Value
7.9317 98.1621 48.161 155 255.718 11 984.2166 27.7925 1832.592 1.967 1.6498 1.6498 13.1983 1.396 846.4023 1.3325 1.3325 8.9469 1.0787 32.5515 49 027.9445 4900.1738
9.6314863 9.2095783 9.18227753 8.88263255 8.72776028 8.3891078 7.77399421 7.61985315 7.36614733 7.36614733 7.3147643 7.12515513 7.08891154 7.05799172 7.05799172 6.75387198 6.75314988 6.61713621 6.5163169 6.39583109
1.57E31 0 1.62E185 0 0 1.63E106 0 2.52E08 4.32E07 4.32E07 4.68E50 4.20E06 0 7.42E06 7.42E06 1.11E33 7.21E05 2.77E119 0 0
0.01 0.01 0.0635 0.01 0.01 0.0635 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 85.9156 0.01 0.0635 0.3173
10.11740928 9.29923091 9.02480376 8.93395613 8.72372921 8.52921843 8.18060539 7.95820469 7.5748584 7.51072462 7.37321318 7.13873257 7.13873257 6.85872702 6.85872702 6.85872702 6.85080541 6.75181183 6.6699508 6.21835964
2.02E49 2.26E28 3.04E143 3.35E22 2.68E19 2.73E101 1.72E13 1.12E11 3.89E09 8.98E09 4.78E08 5.86E07 5.86E07 7.19E06 7.19E06 7.19E06 0 1.66E05 1.95E27 3.14E95
microRNA-140-3P, microRNA-let-7b-5p exhibited the highest abundance and microRNA-5683, microRNA-506-3p, let7f-2-3p and microRNA-203 displayed the most significant reductions. The number of microRNAs was also significantly different between the groups. This ranged from 0 (microRNA3200-3p) to 2 446 775 (microRNA-143-3p) in the SLE group and from 0 (microRNA-668) to 100 279 (microRNA-let-7a5p) in the NC group. Novel microRNAs differentially expressed in the SLE and NC groups To investigate the potential disease relevance of the novel microRNAs, we compared microRNA expression between the SLE and NC groups. Only 61 novel microRNAs exhibited significantly different expression between the two groups, with fold-changes of 41 or 5–1 and associated p values of 50.01. We identified 43 and 18 novel microRNAs that were upregulated and downregulated, respectively. The top 10 upregulated and downregulated novel microRNAs are listed in Table 4.
The fold-change value was relatively large for the majority of the novel microRNAs that were differentially expressed between the two groups. Fold-changes 46 were observed for 57 microRNAs. The five novel microRNAs with the highest level of upregulation were microRNA-41, microRNA-42, microRNA-157, microRNA-64 and microRNA-147. The four novel microRNAs with the highest level of downregulation were microRNA-34, microRNA-7, microRNA-45 and microRNA-50. We also found a large discrepancy between the numbers of individual novel microRNAs in the two groups. For example, 99 microRNA-100 were present in the SLE group, whereas no microRNA-100 was found in the control group. Only 10 novel microRNAs were expressed at relatively similar levels in the two groups. The chromosomal distribution of microRNAs We compared microRNAs that were expressed by both groups to the MiRBase database (http://www.mirbase.org/) (18) that uses the DAVID software suite to facilitate the functional
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Table 4. Differentially expressed novel microRNAs in SLE and NC groups: the top 10 upregulated and downregulated novel microRNAs.
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Novel microRNA
NC expression
SLE expression
Up-regulated novel microRNAs novel-mir-157 0.01 novel-mir-64 0.01 novel-mir-147 0.01 novel-mi-160 0.01 novel-mir-65 0.01 novel-mir-120 0.01 novel-mir-100 0.01 novel-mir-194 0.01 novel-mir-217 0.01 novel-mir-173 0.01 Down-regulated novel microRNAs novel-mir-41 66.1515 novel-mir-42 42.9405 novel-mir-27 5.5541 novel-mir-8 3.6475 novel-mir-21 1.8237 novel-mir-3 1.6579 novel-mir-40 1.6579 novel-mir-28 1.4921 novel-mi-9 1.4921 novel-mir-49 1.4092
11.8023 10.4698 10.0891 9.8987 9.5814 8.8834 6.2819 6.2184 4.8859 4.2514 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Table 5. The number of microRNAs on each chromosome.
Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X/Y
Total microRNAs 127 98 76 56 68 54 67 70 71 61 69 57 37 88 57 48 83 30 108 40 15 36 108
SLE microRNAs 57 25 30 15 25 20 39 20 31 17 21 17 17 69 17 13 48 6 33 17 5 20 74
Fold-change(log2) 10.20485232 10.03201817 9.97858177 9.95109526 9.90409266 9.79496814 9.29505717 9.28039961 8.93248053 8.73179419 12.6915577 12.06812316 9.11740936 8.51076419 7.51072462 7.37321318 7.37321318 7.22120041 7.22120041 7.13873257
Table 6. Differentially chromosome. NC microRNAs 59 25 32 16 27 20 39 21 30 17 21 17 17 68 17 14 49 6 34 17 5 21 74
Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X/Y
p Value 1.38E46 2.10E41 6.37E40 3.51E39 6.01E38 3.12E35 4.13E25 7.28E25 1.11E19 3.27E17 2.06E290 8.63E189 4.18E25 9.31E17 8.98E09 4.78E08 4.78E08 2.54E07 2.54E07 5.86E07
expressed
microRNAs
categorized
by
Upregulateda
Downregulateda
Upregulatedb
Downregulatedb
29 17 15 6 20 10 23 10 17 8 10 11 10 38 12 6 22 5 18 9 1 8 35
30 9 17 10 7 10 16 11 14 9 11 6 7 32 4 8 27 1 16 88 3 13 11
5 4 6 1 9 5 10 4 2 3 1 8 3 16 5 4 8 0 7 1 0 1 11
7 8 3 2 2 5 2 5 1 2 2 3 1 5 2 2 9 1 4 2 0 3 11
a
annotation and analysis of large lists of genes (19). This type of analysis can provide a large amount of information pertaining to the differential expression of microRNAs and number of microRNAs on particular chromosomes. We statistically analyzed the microRNAs on each chromosome in both groups. In addition, we counted the mature microRNAs in both the SLE and NC groups. The number of microRNAs on each chromosome ranged from 15 (chromosome 21) to 127 (chromosome 1) and were virtually equal between the SLE and NC groups (Table 5). For example, 57 and 59 microRNAs were expressed from chromosome 1 in the SLE and NC group, respectively. Similarly, 20 (SLE) and 21 (NC) microRNAs were expressed
Differentially expressed microRNAs,regardless of fold-change and p value. b Differentially expressed microRNAs,considered fold-change and p value.
from chromosome 8, and 69 (SLE) and 68 (NC) were expressed from chromosome 14. We analyzed the differential expression of microRNAs on each chromosome between the two groups. The results are presented in Table 6. Regardless of the fold-change, a p value 50.01 was considered significant. An analysis of the upregulated and downregulated microRNAs revealed that the two groups had similar numbers of differentially expressed microRNAs on most chromosomes, with the exception of chromosomes 2, 5, 12, 15, 16 and 21. When
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Table 7. Novel microRNA-regulated target genes associated with SLE. Novel microRNA
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novel_mir_57 novel_mir_9 novel_mir_10 novel_mir_194 novel_mir_189 novel_mir_42 novel_mir_95
Target gene related to SLE IKZF1 UBE2L3 NMNAT2,TOB1 TRAF1,PRDM1 TSC1,PRDM1 TRAF1,PRDM1 NCF2
we considered fold-changes of41 or5–1 and p values 0.01, few microRNAs remained and the number of differentially expressed microRNAs on each chromosome was reduced. However, the differences in the expression of the upregulated and downregulated microRNAs remained similar after this analysis (Table 6). Overall, 12 chromosomes had a greater number of upregulated microRNAs than downregulated microRNAs and 8 chromosomes had a greater number of downregulated than upregulated microRNAs. Three chromosomes had equal numbers of microRNAs. Target gene prediction and functional analysis The novel microRNAs potentially reveal target genes that are involved in the pathogenesis of SLE. We used TargetScan (http://www.targetscan.org/) to predict the target genes of the novel microRNAs and analyzed their function using Gene Ontology (GO) (http://www.geneontology.org/). Gene Ontology is a standardized, international classification system of gene function, which supplies a set of controlled vocabulary to comprehensively describe the properties of genes and gene products. The GO enrichment analysis was used to classify the predicted target gene candidates of the novel microRNAs. It compared the microRNAs to a background reference gene and genes that correspond to particular biological functions. We identified functions that were associated with the predicted target gene candidates. This analysis generated a large data set (Table S1) for eight novel microRNAs (novel-mir-8, novel-mir-9, novel-mir-42, novelmir-57, novel-mir-94, novel-mir-95, novel-mir-189 and novelmir-194) and their predicted gene targets. The target genes that were regulated by the SLE-associated novel microRNAs are shown in Table 7. It was evident that the target genes play important roles in various biological processes (Table S2). The novel microRNA target genes associated with SLE were primarily involved in macromolecular cell metabolism, adjustment of cell metabolism and biosynthesis regulation. These data indicate that the pathogenesis of SLE is closely linked to the process of cell metabolism. Validation of microRNA expression by using qRT-PCR We examined microRNA expression by using qRT-PCR, a commonly used in transcriptome analyses, because it is considered the most appropriate method for the confirmation of microarray-generated data (20,21). The expression levels of several microRNAs were too low to be detected. Therefore, we used U6 snRNA as an internal reference to avoid false
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negative results. We randomly selected six novel microRNAs (novel-mir-8, novel-mir-9, novel-mir-42, novel-mir-95, novelmir-19 and novel-mir-189) and assessed their expression by using qRT-PCR. The data were analyzed using the 2DDCt method and normalized to U6 snRNA. We compared the log2(SLE/NC) values with the previous fold-change values to confirm the experimental results. Similar to the microarray results, the log2(SLE/NC) values for novel-mir-8, novel-mir9, novel-mir-42, novel-mir-95, novel-mir-194 and novel-mir189 were 1.8, 1.67, 1.5, 0.59, 0.38 and 0.69, respectively (Table 8). Also in accordance with the microarray results, novel-mir-8, novel-mir-9 and novel-mir-42 were found to be downregulated, and novel-mir-95, novel-mir-194 and novelmir-189 were upregulated.
Discussion Here, we present our analysis of microRNA expression in SLE patients and healthy controls. We found that the total number of upregulated microRNAs in the SLE group was greater than that of downregulated microRNAs. These data are consistent with previous studies (10,22). The first study that examined the differential microRNA expression between SLE patients and healthy controls was published in 2007. In that study, peripheral blood mononuclear cells from 23 Chinese SLE patients, 10 idiopathic thrombocytopenic purpura patients and 10 healthy controls were analyzed. MicroRNA microarray chip analysis identified 16 microRNAs that were differentially expressed in the SLE patients. Of these microRNAs, nine were upregulated and seven were downregulated (23). The expression levels of microRNA have been investigated in other studies. MicroRNA-21 is over expressed in CD4+T cells in both lupus patients and lupus-prone MR/LPR mice, suggesting its expression is correlated with SLE (24,25). In our study, the number of microRNA-21 in the SLE samples was 161 993, indicating upregulated expression. Consistent with previous reports, our data also showed that all members of the microRNA-146 family (microRNA-146a-5p, microRNA-146a-3p, microRNA-146b-5p and microRNA146b-3p) were upregulated. MicroRNA-146a is a negative regulator of the interferon pathway and its reduced expression contributes to alterations in the type 1 interferon pathway in lupus patients (26). Moreover, a novel genetic variant in the promoter region of microRNA-146b is directly correlated with reduced expression of microRNA-146a and SLE susceptibility (27). Li et al. (28) reported that microRNA-223 is upregulated in patients with rheumatoid arthritis and is involved in osteoclastogenesis, contributing to erosive disease. However, microRNA-223 was found to be downregulated in this study. Dai et al. (29) examined microRNA-223 knockout mice, revealing that it acts as a negative regulator of granulocyte differentiation. The microRNA-223 knockout mice exhibited an increased number of granulocyte progenitors and spontaneously developed inflammatory lung pathologies. It is not clear whether this experiment would produce the same data in humans as the results may differ by species. Further research is required to understand the species-specific functions of microRNA-223.
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0.69 1.61
0.38 1.29
0.59 1.49
1.5 0.35
1.67 0.31
novel-mir-189
novel-mir-194
novel-mir-95
novel-mir-42
novel-mir-9
SLE NC SLE NC SLE NC SLE NC SLE NC SLE NC
11.37 10.88 11.37 10.88 11.37 10.88 11.37 10.88 11.37 10.88 11.37 10.88
25.83 23.74 25.66 23.5 32.51 30,52 26.3 26.4 23.44 23.34 23.72 23.92
14.46 12.66 14.29 12.62 21.14 19.64 14.93 15.52 12.07 12.45 12.35 13.04
1.8 0 1.67 0 1.5 0 0.59 0 0.38 0 0.69 0
0.28 1 0.31 1 0.35 1 1.49 1 1.29 1 1.61 1
0.28
1.8
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novel-mir-8
Ct(MN-CG) ¼ DDCtSLE DDCtNC DCt ¼ (CtmicroRNA CtU6) CtmicroRNA CtU6 Groups miRNAs name
Table 8. Validation of microRNA expression by qRT-PCR.
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2DDCT
SLE/NC ratio
log2(SLE/NC ratio)
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In previous studies, scientists have suggested that hsamicroRNA-181, hsa-microRNA-186 and hsa-microRNA-5903P work together to target a large number of lupus genes (30). Te et al. (22) established that Epstein–Barr virus (EBV)transformed B cell lines are a useful model for the discovery of microRNAs that could serve as biomarkers for SLE. In particular, hsa-microRNA-181 is differentially expressed in EBV-transformed B cell lines derived from SLE patients. Our analysis of chromosomal distribution showed that hsamicroRNA-181 and hsa-microRNA-186 are both located on chromosome 1. However, we unable to identify the chromosomal location of hsa-microRNA-590-3p. We attribute this to the methods used in this study. Our results highlight the need to focus on chromosome 1, which contains important information regarding the relation between microRNA and SLE. We found that microRNA-142-5p was downregulated and microRNA-142-3p was upregulated in the SLE compared with the NC group. However, Ding et al. reported that both microRNAs are significantly downregulated in SLE CD4+T cells compared with healthy controls. This reduced expression induced T cell activity and B cell hyperstimulation. In contrast, both microRNAs were found to directly inhibit SLE-related target signaling (31). Interestingly, microRNA142-3p levels were shown to be significantly higher in patients with systemic sclerosis than in those with SLE. The SLE patients participating in that study did not fulfill the American College of Rheumatology criteria for systemicsclerosis; however, these patients were likely to develop systemic sclerosis in the future (32). We speculate that the upregulation of microRNA-142-3p is an essential factor in the development of SLE into systemic sclerosis. Several novel microRNAs that were differentially expressed between the two groups were identified in our study. In general, the numbers of the novel microRNAs were significantly lower than those of conserved microRNAs (33). We identified just 61 novel microRNAs that exhibited significant differences in expression. This is a relatively low number compared to the total number of microRNAs (411) that showed significantly different levels of expression. Genetic studies conducted using the lupus-prone NZM2410 mouse strain have implicated genomic regions on chromosomes 1, 4 and 7 in lupus susceptibility (34). To verify their chromosomal distribution, we searched for microRNAs shared between the SLE and NC group in the miRBase database (http://www.mirbase.org/). Equal numbers of shared microRNAs and similar distributions of upregulated and downregulated microRNAs were observed on each chromosome. A comparison of euploid and trisomic profiles revealed that the majority of abundant genes are equally represented (35). However, 12 chromosomes contained a greater number of upregulated than downregulated microRNAs and 8 chromosomes contained a greater number of downregulated than upregulated microRNAs. We established that SLE-associated novel microRNA gene targets are important factors in cellular metabolism. The relationship between metabolism of SLE has been well studied (36,37). SLE patients typically suffer from systemic metabolism disorders. Three prime repair exonuclease 1 (TREX1), the most abundant exonuclease in mammalian
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cells, has been linked to inflammatory diseases including SLE (38). Microparticles carry IgG increased in SLE were associated with activity in SLE (39). Moreover, McPhee et al. (40) implicated polymorphisms in the pleiotropic cytokine interleukin-21 and its receptor in the SLE. We subjected the novel microRNA gene targets to GO analysis. The target genes were enriched in the micromolecule binding metabolic functional activity category (data not show). For example, 25.8% and 25.4% of the target genes were enriched in the ion binding and cation binding categories, respectively. The micromolecule-binding group includes many factors that participate in cellular metabolism. Approximately 15.1% and 14.1% of the target genes enriched in hydrolase activity and molecular transducer activity, respectively. Active molecular compounds included signal transducers, receptors, enzyme regulators and protein kinase. These active molecular factors play important roles in the function of cell metabolism. Further analyses are required to establish the relationship between SLE pathogenesis and metabolism. Tan et al. (41) used novel microRNAs to predict target genes and analyzed the function of these genes in the pathogenesis of immunoglobulin A nephropathy. In addition, they developed a gene function heat map to comprehensively understand the relationship between microRNA, target genes and pathogenesis. This type of analysis is the next step in understanding the function of microRNAs and target genes in the pathogenesis of SLE. We used high-throughput Solexa sequencing to analyze microRNAs found in SLE patients. These findings provide valuable experimental data that can be used for the functional characterization of microRNAs associated with SLE. With the advancement of molecular biology applications in medicine and genetic research, most studies have focused on DNA or RNA (42). MicroRNAs could act as powerful research tools, laying the foundation for further research. There are many unanswered questions surrounding SLE. Additional work is required to clarify the role of microRNAs in SLE and to determine whether microRNAs are the only negative regulatory factor involved in sporadic SLE. In the near future, we anticipate that novel and effective microRNA-based genetic therapies will be developed to diagnose and treat SLE. We expect microRNAbased therapies to replace traditional approaches and improve patient prognosis.
Acknowledgements We thank the SLE patients and healthy volunteers for providing valuable experimental specimens. We thank Dr Dai for helpful opinions and guidance. In addition, we thank the research team of the Shenzhen Clinical Medical Research Center for supporting our research.
Declaration of interest This research was supported by the Shenzhen Scientific Project of 2010 (201205) and by a 2012 Key Project Grant from the Guangxi Province Science Foundation (2012GXNSFDA053017).
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References 1. Elkon KB, Stone VV. Type I interferon and systemic lupus erythematosus. J Interferon Cytokine Res 2011;31:803–12. 2. Apostolidis SA, Lieberman LA, Kis-Toth K, Crispin JC, Tsokos GC. The dysregulation of cytokine networks in systemic lupus erythematosus. J Interferon Cytokine Res 2011;31:769–79. 3. Askanase A, Shum K, Mitnick H. Systemic lupus erythematosus: an overview. Soc Work Health Care 2012;51:576–86. 4. Stojan G, Baer AN. Flares of systemic lupus erythematosus during pregnancy and the puerperium: prevention, diagnosis and management. Expert Rev Clin Immunol 2012;8:439–53. 5. Bezalel S, Asher I, Elbirt D, Sthoeger ZM. Novel biological treatments for systemic lupus erythematosus: current and future modalities. Isr Med Assoc J 2012;14:508–14. 6. Khan WA, Uddin M, Khan MW, Chabbra HS. Catecholoestrogens: possible role in systemic lupus erythematosus. Rheumatology (Oxford) 2009;48:1345–51. 7. Maidhof W, Hilas O. Lupus: an overview of the disease and management options. P T 2012;37:240–9. 8. Cojocaru M, Cojocaru IM, Silosi I, Vrabie CD. Manifestations of systemic lupus erythematosus. Maedica (Buchar) 2011;6:330–6. 9. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116:281–97. 10. Dai Y, Sui W, Lan H, Yan Q, Huang H, Huang Y. Comprehensive analysis of microRNA expression patterns in renal biopsies of lupus nephritis patients. Rheumatol Int 2009;29:749–54. 11. Liang D, Shen N. MicroRNA involvement in lupus: the beginning of a new tale. Curr Opin Rheumatol 2012;24:489–98. 12. Chang TC, Mendell JT. microRNAs in vertebrate physiology and human disease. Annu Rev Genomics Hum Genet 2007;8:215–39. 13. Zen K, Zhang CY. Circulating microRNAs: a novel class of biomarkers to diagnose and monitor human cancers. Med Res Rev 2012;32:326–48. 14. Berezikov E, Cuppen E, Plasterk RH. Approaches to microRNA discovery. Nat Genet 2006;38 Suppl:S2–7. 15. Liu S, Li D, Li Q, Zhao P, Xiang Z, Xia Q. MicroRNAs of Bombyx mori identified by Solexa sequencing. BMC Genomics 2010;11: 148. 16. Audic S, Claverie JM. The significance of digital gene expression profiles. Genome Res 1997;7:986–95. 17. Tino P. Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays. BMC Bioinformatics 2009;10: 310. 18. Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003;4:P3. 19. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Clifford Lane H, Lempicki RA. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007;35:W169–75. 20. Kazubek M, Dlugosz A, Pawlik K. [Application of PCR techniques in toxicology]. Postepy Hig Med Dosw (Online) 2010;64:482–9. 21. Provenzano M, Mocellin S. Complementary techniques: validation of gene expression data by quantitative real time PCR. Adv Exp Med Biol 2007;593:66–73. 22. Te JL, Dozmorov IM, Guthridge JM, Nguyen KL, Cavett JW, Kelly JA, Bruner GR, Harley JB, Ojwang JO. Identification of unique microRNA signature associated with lupus nephritis. PLoS One 2010;5:e10344. 23. Dai Y, Huang YS, Tang M, Lv TY, Hu CX, Tan YH, Xu ZM, Yin YB. Microarray analysis of microRNA expression in peripheral blood cells of systemic lupus erythematosus patients. Lupus 2007; 16:939–46. 24. Garchow BG, Bartulos Encinas O, Leung YT, Tsao PY, Eisenberg RA, Caricchio R, Obad S, Petri A, Kauppinen S, Kiriakidou M. Silencing of microRNA-21 in vivo ameliorates autoimmune splenomegaly in lupus mice. EMBO Mol Med 2011;3: 605–15. 25. Pan W, Zhu S, Yuan M, Cui H, Wang L, Luo X, Li J, Zhou H, Tang Y, Shen N. MicroRNA-21 and microRNA-148a contribute to DNA hypomethylation in lupus CD4+ T cells by directly and indirectly targeting DNA methyltransferase 1. J Immunol 2010;184: 6773–81.
Connect Tissue Res Downloaded from informahealthcare.com by Michigan University on 03/14/15 For personal use only.
196
W. Chen et al.
26. Tang Y, Luo X, Cui H, Ni X, Yuan M, Guo Y, Huang X, Zhou H, de Vries N, Tak PP, Chen S, Shen N. MicroRNA-146A contributes to abnormal activation of the type I interferon pathway in human lupus by targeting the key signaling proteins. Arthritis Rheum 2009;60:1065–75. 27. Luo X, Yang W, Ye DQ, Cui H, Zhang Y, Hirankarn N, Qian X, Tang Y, Lau YL, de Vries N, Tak PP, Tsao BP, Shen N. A functional variant in microRNA-146a promoter modulates its expression and confers disease risk for systemic lupus erythematosus. PLoS Genet 2011;7:e1002128. 28. Li YT, Chen SY, Wang CR, Liu MF, Lin CC, Jou IM, Shiau AL, Wu CL. Brief report: amelioration of collagen-induced arthritis in mice by lentivirus-mediated silencing of microRNA-223. Arthritis Rheum 2012;64:3240–5. 29. Dai R, Ahmed SA. MicroRNA, a new paradigm for understanding immunoregulation, inflammation, and autoimmune diseases. Transl Res 2011;157:163–79. 30. Vinuesa CG, Rigby RJ, Yu D. Logic and extent of miRNAmediated control of autoimmune gene expression. Int Rev Immunol 2009;28:112–38. 31. Ding S, Liang Y, Zhao M, Liang G, Long H, Zhao S, Wang Y, Yin H, Zhang P, Zhang Q, Lu Q. Decreased microRNA-142-3p/5p expression causes CD4+ T cell activation and B cell hyperstimulation in systemic lupus erythematosus. Arthritis Rheum 2012;64:2953–63. 32. Makino K, Jinnin M, Kajihara I, Honda N, Sakai K, Masuguchi S, Fukushima S, Inoue Y, Ihn H. Circulating miR-142-3p levels in patients with systemic sclerosis. Clin Exp Dermatol 2012;37:34–9. 33. Chen YH, Wang SQ, Wu XL, Shen M, Chen ZG, Chen XG, Liu YX, Zhu XL, Guo F, Duan XZ, Han XC, Tao ZH. Characterization
Connect Tissue Res, 2014; 55(3): 187–196
34.
35. 36. 37. 38. 39. 40.
41. 42.
of microRNAs expression profiling in one group of Chinese urothelial cell carcinoma identified by Solexa sequencing. Urol Oncol 2013;31:219–27. Mohan C, Yu Y, Morel L, Yang P, Wakeland EK. Genetic dissection of SLE pathogenesis: SLE3 on murine chromosome 7 impacts T cell activation, differentiation, and cell death. J Immunol 1999;162:6492–502. Sommer CA, Pavarino-Bertelli EC, Goloni-Bertollo EM, HenriqueSilva F. Identification of dysregulated genes in lymphocytes from children with Down syndrome. Genome 2008;51:19–29. Gordon C, Bradley H, Waring RH, Emery P. Abnormal sulphur oxidation in systemic lupus erythematosus. Lancet 1992;339: 25–6. Rovin BH. The chemokine network in systemic lupus erythematous nephritis. Front Biosci 2008;13:904–22. Pereira-Lopes S, Celhar T, Sans-Fons G, Serra M, Fairhurst AM, Lloberas J, Celada A. The exonuclease Trex1 restrains macrophage proinflammatory activation. J Immunol 2013;191:6128–35. Nielsen CT. Circulating microparticles in systemic Lupus erythematosus. Dan Med J 2012;59:B4548. McPhee CG, Bubier JA, Sproule TJ, Park G, Steinbuck MP, Schott WH, Christianson GJ, Morse HC 3rd, Roopenian DC. IL-21 is a double-edged sword in the systemic lupus erythematosus-like disease of BXSB.Yaa mice. J Immunol 2013;191:4581–8. Tan K, Chen J, Li W, Chen Y, Sui W, Zhang Y, Dai Y. Genomewide analysis of microRNAs expression profiling in patients with primary IgA nephropathy. Genome 2013;56:161–9. El-Osta A, Wolffe AP. DNA methylation and histone deacetylation in the control of gene expression: basic biochemistry to human development and disease. Gene Expr 2000;9:63–75.
RESEARCH ARTICLE
Analysis of microRNAs in patients with systemic lupus erythematosus, using Solexa deep sequencing Wenbiao Chen1*, Kuibi Tan2*, Jianrong Huang1, Xiangqi Yu1, Wujian Peng3, Yuyu Chen1, Xiaocong Lin4, Deheng Chen1, and Yong Dai1 Second Clinical Medical College, Jinan University, Shenzhen People’s Hospital, Shenzhen, Guangdong Province, China, 2Ningbo No. 2 Hospital, Ningbo, Zhejiang, China, 3The Third People’s Hospital of Shenzhen, Shenzhen, Guangdong, China, and 4Institute of Biochemistry and Molecular Biology, Guangdong Medical College, Zhanjiang, China Abstract
Keywords
Objectives: Our aim in this study was to identify and examine the differential expression of microRNAs in patients with systemic lupus erythematosus (SLE). Methods: We employed high-quality, high-throughput Solexa sequencing to clone and identify microRNAs in SLE patients and a control group. Results: From the sequencing data, we identified numerous microRNAs displaying significantly different levels of expression in patients with SLE and in healthy controls. The 212 and 199 microRNAs were upregulated and downregulated, respectively. Only 61 novel microRNAs exhibited significantly different levels of expression in the two groups. The target genes of the novel microRNAs identified in the SLE group were found to have cell metabolism functions. We also analyzed the chromosomal locations of the microRNAs with high level of expression between the two groups. A profile comparison revealed that the majority of transcripts were expressed at a similar level. The functional classes of the most abundant microRNAs were equally represented on each chromosome. Conclusion: We identified novel and known microRNAs significantly enhancing our understanding of the microRNA expression profiles of SLE patients. These data also provide insight into the function of microRNAs in SLE and provide new strategies for future therapies.
Expression and distribution, high-throughput Solexa sequencing, microRNA, novel microRNA, systemic lupus erythematosus, target gene
Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown etiology that is characterized by immunological dysfunction and the presence of multiple autoantibodies in the serum (1). Organ damage in SLE patients results from the deposition of immune complexes and the infiltration of activated T cells into susceptible organs (2). The clinical progression and prognosis of SLE can vary. SLE can involve any organ or multiple organs and can be quiescent between periods of aggravation (3). The majority of SLE patients are young females and 90–95% are of reproductive age (4). While modern treatment has greatly improved patient care, SLE patients often experience relapses and severe adverse effects (5). Hereditary and environmental factors and hormones play a predominant role in causing SLE (6); however, he mechanism underlying the development of SLE remains unclear (7) and it presents with a variety of features *These authors contributed equally to this work. Correspondence: Yong Dai, Second Clinical Medical College, Jinan University, People’s Hospital, Guangdong Province, Shenzhen 518020, China. Tel: +86 13802201510. Fax: +86 75525626750. E-mail: [email protected]
History Received 6 January 2014 Revised 4 March 2014 Accepted 13 March 2014 Published online 3 April 2014
and manifestations (8). In several studies, scientists have examined the comparative expression of microRNAs in SLE patients in order to advance the understanding of the molecular events that underlie SLE. MicroRNAs are endogenous, noncoding RNAs that are 19–25 nucleotides in length. MicroRNAs are capable of regulating gene expression at both the transcriptional and post-transcriptional level (9). They play important roles in development and cellular processes such as differentiation, growth and cell death (10). MicroRNAs reportedly play an important role in the development of SLE (11) and are linked to many other diseases including cancer (12). Several recent studies have suggested that microRNAs can act as oncogenes and tumor suppressors (13). To expand the repertoire of small regulatory RNAs known to be expressed in SLE patients and to gain a further understanding of the molecular events underlying SLE pathogenesis, we generated and sequenced two small RNA libraries prepared from SLE patients and normal controls. We identified novel microRNAs that possessed unique features, distinguishing them from other endogenous small RNAs. Notable features included a characteristic stem-loop hairpin secondary structure in the microRNA precursors (14).
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In this study, we used high-throughput Solexa sequencing to study the global microRNA expression profiles of SLE patients and healthy subjects. We also identified and analyzed the expression of novel microRNAs in the SLE group. The size distribution of small RNAs in the SLE and control group indicated that numerous microRNAs are relevant to SLE and present opportunities for therapeutic intervention. Notably, we identified a set of microRNAs in the SLE group that may be associated with disease relapse, providing valuable insights into the pathogenesis of SLE. We present a comprehensive picture of microRNA expression that advances our knowledge of microRNA expression profiles in SLE and suggests new avenues for future research into the pathogenesis of the disease.
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Materials and methods Patient materials A total of 40 peripheral blood mononuclear cell (PBMC) samples, 20 samples from SLE patients and 20 samples from normal controls (NC), were obtained from the Second Affiliated Hospital of Jinan University. The subjects were selected in accordance with the American Rheumatism Association diagnostic criteria for SLE (1997 revision). The patient blood samples were evaluated for cytotoxic drugs, immunosuppressants and high-dose glucocorticoids (41 mgkg1 d1). Patients in both groups underwent standard health screening to rule out infection, cancer, hypertension, diabetes and other systemic diseases. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Jinan University.
Figure 1. RNA sequence analysis workflow.
ligated to the purified small RNAs; (c) 30 RNA adapters were ligated to the precipitated RNA; (d) ligation products were reverse-transcribed and amplified using real-time polymerase chain reaction (RT-PCR) and (e) the amplification products were clustered and sequenced using an Illumina Genome Analyzer (BGI, Shenzhen, China). The experimental workflow outlining the small RNA sequence analysis is shown in Figure 1. The small RNA library sequencing data were deposited in the NCBI Sequence Read Archive (accession number SRP028945). Data analysis
Sample processing Under aseptic conditions, fasting venous blood samples (5 ml) were drawn and stored in EDTA tubes. To generate two pooled groups (SLC and NC) equal volumes of the 20 SLE and 20 NC blood samples were combined. The PBMC blood samples were separated using Ficoll-Paque (Sigma, St. Louis, MO). The RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA), in accordance with the manufacturer’s instructions. The integrity of the RNA and presence of microRNAs were assessed by microcapillary electrophoresis using the RNA 6000 and Small RNA kit (Agilent Technologies, Santa Clara, CA), respectively. The concentration and quality of the RNA were assessed by absorbance spectrometry using a NanoDrop 2000 (Thermo, Waltham, MA). Sequence analysis The small RNA digital analysis was based on high-through put Solexa sequencing method. We used the sequencing by synthesis method because it decreased the loss of nucleotides. This method requires a small amount of sample material, has a high level of accuracy, and runs on a straightforward automatic platform. A single round of analysis can generate millions of small RNA sequence tags. The RNA sequencing was performed as follows: (a) small RNAs [18–30 nucleotides (nt)] were isolated from total RNA; (b) 50 adapters were
Data cleaning was performed on the sequence tags (50 nt) from the high-throughput Solexa sequencing. This included removal of low quality tags, trimming of the terminal 30 primer adapter sequence, and elimination of the 50 adapter contaminants formed by ligation. The remaining sequences (18 nt minimum) were aligned to the human reference genome (hg19, NCBI) using the SOAP V2.0 program. Sequences that were a perfect match to the genome along their entire length were analyzed further. Next, we removed tags that originated from protein-coding genes, repeat sequences, and rRNA, tRNA, snRNA, and snoRNA sequences that were found in the NCBI GenBank database (http://www.bi.nih.gov) and the Rfam 9.1 database. Repeat overlapping sequences and sequences that overlapped with predicted exons and introns were also filtered. The remaining unique small RNA sequences were mapped to the miRBase database (http:// www.mirbase.org/). Perfectly matched sequences were considered conserved microRNAs. The data analysis workflow is shown in Figure 2. Novel microRNA prediction To obtain matched sequences, we compared the clean sequences to the NCBI GenBank database. The matched sequences were annotated in the following priority order: GenBank4Rfam(10.0)4miRBase18.04repeat4 exon4intron. In total, we obtained 11 classes of small RNAs: exonic, intronic, miRNAs, rRNAs, repeats, scRNAs, snRNAs,
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Figure 2. Data analysis workflow.
Table 1. Summary of small RNAs. Class exon intron miRNA rRNA repeat scRNA snRNA snoRNA srpRNA tRNA unann
NC
SLE
177,585 36,584 7,159,443 238,489 70,102 1,770,169 174,337 150,501 305 148,355 2,137,340
270,162 68,167 12,238,468 344,554 46,726 78,237 113,457 308,183 126 108,144 2,183,421
snoRNAs, srpRNAs, tRNAs and unannotated (Table 1). An unannotated sequence was defined as one that could not be matched and annotated. Notably, the unannotated sequence was the source of several novel microRNAs. We used Mireap (http://sourceforge.net/projects/mireap) to identify novel microRNA candidates based on their secondary structures. We assessed the stability of their hairpin structures and confirmed the presence of Dicer cleavage sites in the microRNA tags. Candidates that fulfilled the following five criteria were considered high-confidence microRNAs: (1) The RNA displayed a folding free energy value of 18 kcal/mol, (2) The candidate was expressed at a detectable level in at least two samples, (3) The hairpin-shaped stemloop region contained no more than 35 nucleotides, (4) More than two sequences were located within the hairpin and (5) The sequences did not span the loop. Novel microRNA prediction was conducted as described by Liu et al. (15). Quantitative real-time PCR verification of candidate microRNAs We performed quantitative real-time PCR (qRT-PCR) to verify the results of the deep sequencing analysis for six
randomly selected differentially expressed novel microRNAs. The qRT-PCR primers are listed in Table 2. Total RNA (2 mg) from each sample was reverse-transcribed into cDNA using a reverse transcription kit, in accordance with the manufacturer’s protocol (Promega, Madison, WI). The cDNA was amplified using the following PCR program: 30 min at 16 C, 40 min at 42 C and 5 min at 84 C. The reactions were performed in triplicate. The comparative threshold cycle (2DDCt) method of quantification was used to calculate differences in expression levels between the SLE and groups. The microRNA expression levels were normalized to U6 snRNA reference RNA. The Rotor-Gene 6000 Series Software 1.7 (QIAGEN) was used to calculate the DDCt value, using the following formula: DDCt ¼ Ct(SLE NC).
Results Length distribution of small RNAs The small RNAs were typically 18–30 nt in length. The length distribution is shown in Figure 3. The 22 nt class was the most abundant group of small RNAs in both the SLE and NC groups. The sequences 20–24 nt in length accounted for 91.4% of the total sequence counts in the SLE library. This class was significantly more than any other class (Figure 3A). In the control library, sequences 18–29 nt in length were more abundant than the other classes in the group, accounting for 96.81% of the total sequence counts (Figure 3B). Differentially expressed microRNAs in the SLE and NC groups We compare the microRNA expression patterns of the two groups to identify differentially expressed microRNAs. This process involved two steps: (1) normalizing the counts of each microRNA in each group to the total number of small RNAs.
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Table 2. qRT-PCR primers. miRNA name
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novel_mir_8 novel_mir_9 novel_mir_42 novel_mir_95 novel_mir_194 novel_mir_189 U6 snRNA
qRT-PCR primers 0
F:5 ACACTCCAGCTGGGCCTCACCATC CCT TCTGCCTG 30 F:50 ACACTCCAGCTGGGATCTCCACTACT G CGGCCAC 30 F:50 ACACTCCAGCTGGGTGGGCAGGGGCT TATTGTAG 30 F:50 ACACTCCAGCTGGGTTGGGCCCCTCC T AAGACA 30 F:50 ACACTCCAGCTGGGTGGGCAGGGGCTTATTGTA 30 F:50 ACACTCCAGCTGGGGAGAATATGGGA GTCTGTG 30 F:50 GCTTCGGCAGCACATATACTAAAAT 30
Figure 3. The length distribution of small RNAs in (A) SLE and (B) NC groups.
The ratio was then multiplied by a constant of 1 106 using the following formula: normalized expression ¼ (actual number of microRNAs)/(total number of small microRNAs) 106. (2) The fold-changes and p values were calculated using the normalized expression values. The following formulae were used: fold change ¼ log2(SLE/ NC) (16) and p(y/x) ¼ (N2/N1)y (x + y)!/x!y!(1 + N2/ N1)(x + y + 1), which was based on the statistical test developed by Audic and Claverie (17). The number of sequences across a microRNA in the NC group and the number of sequences across the corresponding microRNA in the SLE group were represented by x and y, respectively. N1 and N2 represented the total number of clean sequences in the NC and SLE groups, respectively. MicroRNAs with expression levels of 0 after normalization were changed to 0.01 for the differential expression analysis. A fold change [log2(SLE/NC)] and p value 0.01 were considered significant. We found 411 microRNAs that exhibited aberrant expression in the SLE group. Of these microRNAs, 212 and 199 were upregulated and downregulated, respectively, in SLE patients compared with the NC group. The number of
upregulated microRNAs was greater than the number of downregulated microRNAs, suggesting that microRNAs play a significant role in the pathogenesis of SLE. The top 20 upregulated and downregulated microRNAs are listed in Table 3. The average fold-change(log2)47 was 15 and 13 for the upregulated and downregulated microRNAs, respectively. The greatest change was observed for microRNA-31-3p, whose expression level decreased 10-fold. Interestingly, 10 members of the let-7 family showed altered expression. let-7a-3p, and let-7f-2-3p were upregulated, whereas let-7a-5p, let-7b-5p, let-7c, let-7d-5p, let-7e-5p, let7g-5p, let-7i-3p and let-7i-5p were downregulated. Of these microRNAs, let-7f-2-3p exhibited the most significant change, with a fold-change(log2) of 7.4. The differential expression of the microRNAs in the SLE and NC groups also differed significantly. For example, microRNA-143-3p, microRNA-21-5p, microRNA-378a-3p and microRNA-146b5p were significantly increased, and microRNA-1271-3p, microRNA-179-5p, microRNA-4736 and microRNA-3200-3p displayed the most significant reductions in the SLE group. In the NC group, hsa-let-7a-5p, hsa-let-7g-5p,
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Table 3. Differentially expressed microRNAs in SLE and NC groups: the top 20 upregulated and downregulated microRNAs.
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microRNA
NC expression
Up-regulated microRNAs hsa-miR-5683 0.01 hsa-miR-381 0.1658 hsa-miR-508-3p 0.0829 hsa-miR-143-3p 328.934 hsa-miR-126-3p 28.2678 hsa-miR-514a-3p 0.0829 hsa-miR-182-5p 8.3726 hsa-miR-506-3p 0.01 hsa-let-7f-2-3p 0.01 hsa-miR-203 0.01 hsa-miR-10b-5p 0.0829 hsa-miR-5000-3p 0.01 hsa-miR-379-5p 6.2173 hsa-miR-3911 0.01 hsa-miR-4784 0.01 hsa-miR-380-5p 0.0829 hsa-miR-668 0.01 hsa-miR-330-5p 0.3316 hsa-miR-378a-3p 535.5954 hsa-miR-30e-3p 58.1935 Down-regulated microRNAs hsa-miR-31-3p 11.1082 hsa-miR-1275 6.3001 hsa-miR-766-5p 33.0758 hsa-miR-193b-5p 4.8909 hsa-miR-3130-3p 4.2277 hsa-miR-23a-5p 23.4598 hsa-miR-363-5p 2.9014 hsa-miR-4707-3p 2.4869 hsa-miR-3916 1.9066 hsa-miR-548i 1.8237 hsa-miR-4752 1.6579 hsa-miR-1284 1.4092 hsa-miR-3141 1.4092 hsa-miR-1271-3p 1.1606 hsa-miR-197-5p 1.1606 hsa-miR-4736 1.1606 hsa-miR-107 9916.7634 hsa-miR-3200-3p 1.0777 hsa-miR-301a-5p 6.4659 hsa-miR-23b-5p 23.6256
SLE expression
Fold-change(log2)
p Value
7.9317 98.1621 48.161 155 255.718 11 984.2166 27.7925 1832.592 1.967 1.6498 1.6498 13.1983 1.396 846.4023 1.3325 1.3325 8.9469 1.0787 32.5515 49 027.9445 4900.1738
9.6314863 9.2095783 9.18227753 8.88263255 8.72776028 8.3891078 7.77399421 7.61985315 7.36614733 7.36614733 7.3147643 7.12515513 7.08891154 7.05799172 7.05799172 6.75387198 6.75314988 6.61713621 6.5163169 6.39583109
1.57E31 0 1.62E185 0 0 1.63E106 0 2.52E08 4.32E07 4.32E07 4.68E50 4.20E06 0 7.42E06 7.42E06 1.11E33 7.21E05 2.77E119 0 0
0.01 0.01 0.0635 0.01 0.01 0.0635 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 85.9156 0.01 0.0635 0.3173
10.11740928 9.29923091 9.02480376 8.93395613 8.72372921 8.52921843 8.18060539 7.95820469 7.5748584 7.51072462 7.37321318 7.13873257 7.13873257 6.85872702 6.85872702 6.85872702 6.85080541 6.75181183 6.6699508 6.21835964
2.02E49 2.26E28 3.04E143 3.35E22 2.68E19 2.73E101 1.72E13 1.12E11 3.89E09 8.98E09 4.78E08 5.86E07 5.86E07 7.19E06 7.19E06 7.19E06 0 1.66E05 1.95E27 3.14E95
microRNA-140-3P, microRNA-let-7b-5p exhibited the highest abundance and microRNA-5683, microRNA-506-3p, let7f-2-3p and microRNA-203 displayed the most significant reductions. The number of microRNAs was also significantly different between the groups. This ranged from 0 (microRNA3200-3p) to 2 446 775 (microRNA-143-3p) in the SLE group and from 0 (microRNA-668) to 100 279 (microRNA-let-7a5p) in the NC group. Novel microRNAs differentially expressed in the SLE and NC groups To investigate the potential disease relevance of the novel microRNAs, we compared microRNA expression between the SLE and NC groups. Only 61 novel microRNAs exhibited significantly different expression between the two groups, with fold-changes of 41 or 5–1 and associated p values of 50.01. We identified 43 and 18 novel microRNAs that were upregulated and downregulated, respectively. The top 10 upregulated and downregulated novel microRNAs are listed in Table 4.
The fold-change value was relatively large for the majority of the novel microRNAs that were differentially expressed between the two groups. Fold-changes 46 were observed for 57 microRNAs. The five novel microRNAs with the highest level of upregulation were microRNA-41, microRNA-42, microRNA-157, microRNA-64 and microRNA-147. The four novel microRNAs with the highest level of downregulation were microRNA-34, microRNA-7, microRNA-45 and microRNA-50. We also found a large discrepancy between the numbers of individual novel microRNAs in the two groups. For example, 99 microRNA-100 were present in the SLE group, whereas no microRNA-100 was found in the control group. Only 10 novel microRNAs were expressed at relatively similar levels in the two groups. The chromosomal distribution of microRNAs We compared microRNAs that were expressed by both groups to the MiRBase database (http://www.mirbase.org/) (18) that uses the DAVID software suite to facilitate the functional
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Table 4. Differentially expressed novel microRNAs in SLE and NC groups: the top 10 upregulated and downregulated novel microRNAs.
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Novel microRNA
NC expression
SLE expression
Up-regulated novel microRNAs novel-mir-157 0.01 novel-mir-64 0.01 novel-mir-147 0.01 novel-mi-160 0.01 novel-mir-65 0.01 novel-mir-120 0.01 novel-mir-100 0.01 novel-mir-194 0.01 novel-mir-217 0.01 novel-mir-173 0.01 Down-regulated novel microRNAs novel-mir-41 66.1515 novel-mir-42 42.9405 novel-mir-27 5.5541 novel-mir-8 3.6475 novel-mir-21 1.8237 novel-mir-3 1.6579 novel-mir-40 1.6579 novel-mir-28 1.4921 novel-mi-9 1.4921 novel-mir-49 1.4092
11.8023 10.4698 10.0891 9.8987 9.5814 8.8834 6.2819 6.2184 4.8859 4.2514 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Table 5. The number of microRNAs on each chromosome.
Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X/Y
Total microRNAs 127 98 76 56 68 54 67 70 71 61 69 57 37 88 57 48 83 30 108 40 15 36 108
SLE microRNAs 57 25 30 15 25 20 39 20 31 17 21 17 17 69 17 13 48 6 33 17 5 20 74
Fold-change(log2) 10.20485232 10.03201817 9.97858177 9.95109526 9.90409266 9.79496814 9.29505717 9.28039961 8.93248053 8.73179419 12.6915577 12.06812316 9.11740936 8.51076419 7.51072462 7.37321318 7.37321318 7.22120041 7.22120041 7.13873257
Table 6. Differentially chromosome. NC microRNAs 59 25 32 16 27 20 39 21 30 17 21 17 17 68 17 14 49 6 34 17 5 21 74
Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X/Y
p Value 1.38E46 2.10E41 6.37E40 3.51E39 6.01E38 3.12E35 4.13E25 7.28E25 1.11E19 3.27E17 2.06E290 8.63E189 4.18E25 9.31E17 8.98E09 4.78E08 4.78E08 2.54E07 2.54E07 5.86E07
expressed
microRNAs
categorized
by
Upregulateda
Downregulateda
Upregulatedb
Downregulatedb
29 17 15 6 20 10 23 10 17 8 10 11 10 38 12 6 22 5 18 9 1 8 35
30 9 17 10 7 10 16 11 14 9 11 6 7 32 4 8 27 1 16 88 3 13 11
5 4 6 1 9 5 10 4 2 3 1 8 3 16 5 4 8 0 7 1 0 1 11
7 8 3 2 2 5 2 5 1 2 2 3 1 5 2 2 9 1 4 2 0 3 11
a
annotation and analysis of large lists of genes (19). This type of analysis can provide a large amount of information pertaining to the differential expression of microRNAs and number of microRNAs on particular chromosomes. We statistically analyzed the microRNAs on each chromosome in both groups. In addition, we counted the mature microRNAs in both the SLE and NC groups. The number of microRNAs on each chromosome ranged from 15 (chromosome 21) to 127 (chromosome 1) and were virtually equal between the SLE and NC groups (Table 5). For example, 57 and 59 microRNAs were expressed from chromosome 1 in the SLE and NC group, respectively. Similarly, 20 (SLE) and 21 (NC) microRNAs were expressed
Differentially expressed microRNAs,regardless of fold-change and p value. b Differentially expressed microRNAs,considered fold-change and p value.
from chromosome 8, and 69 (SLE) and 68 (NC) were expressed from chromosome 14. We analyzed the differential expression of microRNAs on each chromosome between the two groups. The results are presented in Table 6. Regardless of the fold-change, a p value 50.01 was considered significant. An analysis of the upregulated and downregulated microRNAs revealed that the two groups had similar numbers of differentially expressed microRNAs on most chromosomes, with the exception of chromosomes 2, 5, 12, 15, 16 and 21. When
MicroRNAs in patients with systemic lupus erythematosus
DOI: 10.3109/03008207.2014.905548
Table 7. Novel microRNA-regulated target genes associated with SLE. Novel microRNA
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novel_mir_57 novel_mir_9 novel_mir_10 novel_mir_194 novel_mir_189 novel_mir_42 novel_mir_95
Target gene related to SLE IKZF1 UBE2L3 NMNAT2,TOB1 TRAF1,PRDM1 TSC1,PRDM1 TRAF1,PRDM1 NCF2
we considered fold-changes of41 or5–1 and p values 0.01, few microRNAs remained and the number of differentially expressed microRNAs on each chromosome was reduced. However, the differences in the expression of the upregulated and downregulated microRNAs remained similar after this analysis (Table 6). Overall, 12 chromosomes had a greater number of upregulated microRNAs than downregulated microRNAs and 8 chromosomes had a greater number of downregulated than upregulated microRNAs. Three chromosomes had equal numbers of microRNAs. Target gene prediction and functional analysis The novel microRNAs potentially reveal target genes that are involved in the pathogenesis of SLE. We used TargetScan (http://www.targetscan.org/) to predict the target genes of the novel microRNAs and analyzed their function using Gene Ontology (GO) (http://www.geneontology.org/). Gene Ontology is a standardized, international classification system of gene function, which supplies a set of controlled vocabulary to comprehensively describe the properties of genes and gene products. The GO enrichment analysis was used to classify the predicted target gene candidates of the novel microRNAs. It compared the microRNAs to a background reference gene and genes that correspond to particular biological functions. We identified functions that were associated with the predicted target gene candidates. This analysis generated a large data set (Table S1) for eight novel microRNAs (novel-mir-8, novel-mir-9, novel-mir-42, novelmir-57, novel-mir-94, novel-mir-95, novel-mir-189 and novelmir-194) and their predicted gene targets. The target genes that were regulated by the SLE-associated novel microRNAs are shown in Table 7. It was evident that the target genes play important roles in various biological processes (Table S2). The novel microRNA target genes associated with SLE were primarily involved in macromolecular cell metabolism, adjustment of cell metabolism and biosynthesis regulation. These data indicate that the pathogenesis of SLE is closely linked to the process of cell metabolism. Validation of microRNA expression by using qRT-PCR We examined microRNA expression by using qRT-PCR, a commonly used in transcriptome analyses, because it is considered the most appropriate method for the confirmation of microarray-generated data (20,21). The expression levels of several microRNAs were too low to be detected. Therefore, we used U6 snRNA as an internal reference to avoid false
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negative results. We randomly selected six novel microRNAs (novel-mir-8, novel-mir-9, novel-mir-42, novel-mir-95, novelmir-19 and novel-mir-189) and assessed their expression by using qRT-PCR. The data were analyzed using the 2DDCt method and normalized to U6 snRNA. We compared the log2(SLE/NC) values with the previous fold-change values to confirm the experimental results. Similar to the microarray results, the log2(SLE/NC) values for novel-mir-8, novel-mir9, novel-mir-42, novel-mir-95, novel-mir-194 and novel-mir189 were 1.8, 1.67, 1.5, 0.59, 0.38 and 0.69, respectively (Table 8). Also in accordance with the microarray results, novel-mir-8, novel-mir-9 and novel-mir-42 were found to be downregulated, and novel-mir-95, novel-mir-194 and novelmir-189 were upregulated.
Discussion Here, we present our analysis of microRNA expression in SLE patients and healthy controls. We found that the total number of upregulated microRNAs in the SLE group was greater than that of downregulated microRNAs. These data are consistent with previous studies (10,22). The first study that examined the differential microRNA expression between SLE patients and healthy controls was published in 2007. In that study, peripheral blood mononuclear cells from 23 Chinese SLE patients, 10 idiopathic thrombocytopenic purpura patients and 10 healthy controls were analyzed. MicroRNA microarray chip analysis identified 16 microRNAs that were differentially expressed in the SLE patients. Of these microRNAs, nine were upregulated and seven were downregulated (23). The expression levels of microRNA have been investigated in other studies. MicroRNA-21 is over expressed in CD4+T cells in both lupus patients and lupus-prone MR/LPR mice, suggesting its expression is correlated with SLE (24,25). In our study, the number of microRNA-21 in the SLE samples was 161 993, indicating upregulated expression. Consistent with previous reports, our data also showed that all members of the microRNA-146 family (microRNA-146a-5p, microRNA-146a-3p, microRNA-146b-5p and microRNA146b-3p) were upregulated. MicroRNA-146a is a negative regulator of the interferon pathway and its reduced expression contributes to alterations in the type 1 interferon pathway in lupus patients (26). Moreover, a novel genetic variant in the promoter region of microRNA-146b is directly correlated with reduced expression of microRNA-146a and SLE susceptibility (27). Li et al. (28) reported that microRNA-223 is upregulated in patients with rheumatoid arthritis and is involved in osteoclastogenesis, contributing to erosive disease. However, microRNA-223 was found to be downregulated in this study. Dai et al. (29) examined microRNA-223 knockout mice, revealing that it acts as a negative regulator of granulocyte differentiation. The microRNA-223 knockout mice exhibited an increased number of granulocyte progenitors and spontaneously developed inflammatory lung pathologies. It is not clear whether this experiment would produce the same data in humans as the results may differ by species. Further research is required to understand the species-specific functions of microRNA-223.
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0.69 1.61
0.38 1.29
0.59 1.49
1.5 0.35
1.67 0.31
novel-mir-189
novel-mir-194
novel-mir-95
novel-mir-42
novel-mir-9
SLE NC SLE NC SLE NC SLE NC SLE NC SLE NC
11.37 10.88 11.37 10.88 11.37 10.88 11.37 10.88 11.37 10.88 11.37 10.88
25.83 23.74 25.66 23.5 32.51 30,52 26.3 26.4 23.44 23.34 23.72 23.92
14.46 12.66 14.29 12.62 21.14 19.64 14.93 15.52 12.07 12.45 12.35 13.04
1.8 0 1.67 0 1.5 0 0.59 0 0.38 0 0.69 0
0.28 1 0.31 1 0.35 1 1.49 1 1.29 1 1.61 1
0.28
1.8
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novel-mir-8
Ct(MN-CG) ¼ DDCtSLE DDCtNC DCt ¼ (CtmicroRNA CtU6) CtmicroRNA CtU6 Groups miRNAs name
Table 8. Validation of microRNA expression by qRT-PCR.
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2DDCT
SLE/NC ratio
log2(SLE/NC ratio)
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In previous studies, scientists have suggested that hsamicroRNA-181, hsa-microRNA-186 and hsa-microRNA-5903P work together to target a large number of lupus genes (30). Te et al. (22) established that Epstein–Barr virus (EBV)transformed B cell lines are a useful model for the discovery of microRNAs that could serve as biomarkers for SLE. In particular, hsa-microRNA-181 is differentially expressed in EBV-transformed B cell lines derived from SLE patients. Our analysis of chromosomal distribution showed that hsamicroRNA-181 and hsa-microRNA-186 are both located on chromosome 1. However, we unable to identify the chromosomal location of hsa-microRNA-590-3p. We attribute this to the methods used in this study. Our results highlight the need to focus on chromosome 1, which contains important information regarding the relation between microRNA and SLE. We found that microRNA-142-5p was downregulated and microRNA-142-3p was upregulated in the SLE compared with the NC group. However, Ding et al. reported that both microRNAs are significantly downregulated in SLE CD4+T cells compared with healthy controls. This reduced expression induced T cell activity and B cell hyperstimulation. In contrast, both microRNAs were found to directly inhibit SLE-related target signaling (31). Interestingly, microRNA142-3p levels were shown to be significantly higher in patients with systemic sclerosis than in those with SLE. The SLE patients participating in that study did not fulfill the American College of Rheumatology criteria for systemicsclerosis; however, these patients were likely to develop systemic sclerosis in the future (32). We speculate that the upregulation of microRNA-142-3p is an essential factor in the development of SLE into systemic sclerosis. Several novel microRNAs that were differentially expressed between the two groups were identified in our study. In general, the numbers of the novel microRNAs were significantly lower than those of conserved microRNAs (33). We identified just 61 novel microRNAs that exhibited significant differences in expression. This is a relatively low number compared to the total number of microRNAs (411) that showed significantly different levels of expression. Genetic studies conducted using the lupus-prone NZM2410 mouse strain have implicated genomic regions on chromosomes 1, 4 and 7 in lupus susceptibility (34). To verify their chromosomal distribution, we searched for microRNAs shared between the SLE and NC group in the miRBase database (http://www.mirbase.org/). Equal numbers of shared microRNAs and similar distributions of upregulated and downregulated microRNAs were observed on each chromosome. A comparison of euploid and trisomic profiles revealed that the majority of abundant genes are equally represented (35). However, 12 chromosomes contained a greater number of upregulated than downregulated microRNAs and 8 chromosomes contained a greater number of downregulated than upregulated microRNAs. We established that SLE-associated novel microRNA gene targets are important factors in cellular metabolism. The relationship between metabolism of SLE has been well studied (36,37). SLE patients typically suffer from systemic metabolism disorders. Three prime repair exonuclease 1 (TREX1), the most abundant exonuclease in mammalian
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DOI: 10.3109/03008207.2014.905548
cells, has been linked to inflammatory diseases including SLE (38). Microparticles carry IgG increased in SLE were associated with activity in SLE (39). Moreover, McPhee et al. (40) implicated polymorphisms in the pleiotropic cytokine interleukin-21 and its receptor in the SLE. We subjected the novel microRNA gene targets to GO analysis. The target genes were enriched in the micromolecule binding metabolic functional activity category (data not show). For example, 25.8% and 25.4% of the target genes were enriched in the ion binding and cation binding categories, respectively. The micromolecule-binding group includes many factors that participate in cellular metabolism. Approximately 15.1% and 14.1% of the target genes enriched in hydrolase activity and molecular transducer activity, respectively. Active molecular compounds included signal transducers, receptors, enzyme regulators and protein kinase. These active molecular factors play important roles in the function of cell metabolism. Further analyses are required to establish the relationship between SLE pathogenesis and metabolism. Tan et al. (41) used novel microRNAs to predict target genes and analyzed the function of these genes in the pathogenesis of immunoglobulin A nephropathy. In addition, they developed a gene function heat map to comprehensively understand the relationship between microRNA, target genes and pathogenesis. This type of analysis is the next step in understanding the function of microRNAs and target genes in the pathogenesis of SLE. We used high-throughput Solexa sequencing to analyze microRNAs found in SLE patients. These findings provide valuable experimental data that can be used for the functional characterization of microRNAs associated with SLE. With the advancement of molecular biology applications in medicine and genetic research, most studies have focused on DNA or RNA (42). MicroRNAs could act as powerful research tools, laying the foundation for further research. There are many unanswered questions surrounding SLE. Additional work is required to clarify the role of microRNAs in SLE and to determine whether microRNAs are the only negative regulatory factor involved in sporadic SLE. In the near future, we anticipate that novel and effective microRNA-based genetic therapies will be developed to diagnose and treat SLE. We expect microRNAbased therapies to replace traditional approaches and improve patient prognosis.
Acknowledgements We thank the SLE patients and healthy volunteers for providing valuable experimental specimens. We thank Dr Dai for helpful opinions and guidance. In addition, we thank the research team of the Shenzhen Clinical Medical Research Center for supporting our research.
Declaration of interest This research was supported by the Shenzhen Scientific Project of 2010 (201205) and by a 2012 Key Project Grant from the Guangxi Province Science Foundation (2012GXNSFDA053017).
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References 1. Elkon KB, Stone VV. Type I interferon and systemic lupus erythematosus. J Interferon Cytokine Res 2011;31:803–12. 2. Apostolidis SA, Lieberman LA, Kis-Toth K, Crispin JC, Tsokos GC. The dysregulation of cytokine networks in systemic lupus erythematosus. J Interferon Cytokine Res 2011;31:769–79. 3. Askanase A, Shum K, Mitnick H. Systemic lupus erythematosus: an overview. Soc Work Health Care 2012;51:576–86. 4. Stojan G, Baer AN. Flares of systemic lupus erythematosus during pregnancy and the puerperium: prevention, diagnosis and management. Expert Rev Clin Immunol 2012;8:439–53. 5. Bezalel S, Asher I, Elbirt D, Sthoeger ZM. Novel biological treatments for systemic lupus erythematosus: current and future modalities. Isr Med Assoc J 2012;14:508–14. 6. Khan WA, Uddin M, Khan MW, Chabbra HS. Catecholoestrogens: possible role in systemic lupus erythematosus. Rheumatology (Oxford) 2009;48:1345–51. 7. Maidhof W, Hilas O. Lupus: an overview of the disease and management options. P T 2012;37:240–9. 8. Cojocaru M, Cojocaru IM, Silosi I, Vrabie CD. Manifestations of systemic lupus erythematosus. Maedica (Buchar) 2011;6:330–6. 9. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116:281–97. 10. Dai Y, Sui W, Lan H, Yan Q, Huang H, Huang Y. Comprehensive analysis of microRNA expression patterns in renal biopsies of lupus nephritis patients. Rheumatol Int 2009;29:749–54. 11. Liang D, Shen N. MicroRNA involvement in lupus: the beginning of a new tale. Curr Opin Rheumatol 2012;24:489–98. 12. Chang TC, Mendell JT. microRNAs in vertebrate physiology and human disease. Annu Rev Genomics Hum Genet 2007;8:215–39. 13. Zen K, Zhang CY. Circulating microRNAs: a novel class of biomarkers to diagnose and monitor human cancers. Med Res Rev 2012;32:326–48. 14. Berezikov E, Cuppen E, Plasterk RH. Approaches to microRNA discovery. Nat Genet 2006;38 Suppl:S2–7. 15. Liu S, Li D, Li Q, Zhao P, Xiang Z, Xia Q. MicroRNAs of Bombyx mori identified by Solexa sequencing. BMC Genomics 2010;11: 148. 16. Audic S, Claverie JM. The significance of digital gene expression profiles. Genome Res 1997;7:986–95. 17. Tino P. Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays. BMC Bioinformatics 2009;10: 310. 18. Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003;4:P3. 19. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Clifford Lane H, Lempicki RA. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007;35:W169–75. 20. Kazubek M, Dlugosz A, Pawlik K. [Application of PCR techniques in toxicology]. Postepy Hig Med Dosw (Online) 2010;64:482–9. 21. Provenzano M, Mocellin S. Complementary techniques: validation of gene expression data by quantitative real time PCR. Adv Exp Med Biol 2007;593:66–73. 22. Te JL, Dozmorov IM, Guthridge JM, Nguyen KL, Cavett JW, Kelly JA, Bruner GR, Harley JB, Ojwang JO. Identification of unique microRNA signature associated with lupus nephritis. PLoS One 2010;5:e10344. 23. Dai Y, Huang YS, Tang M, Lv TY, Hu CX, Tan YH, Xu ZM, Yin YB. Microarray analysis of microRNA expression in peripheral blood cells of systemic lupus erythematosus patients. Lupus 2007; 16:939–46. 24. Garchow BG, Bartulos Encinas O, Leung YT, Tsao PY, Eisenberg RA, Caricchio R, Obad S, Petri A, Kauppinen S, Kiriakidou M. Silencing of microRNA-21 in vivo ameliorates autoimmune splenomegaly in lupus mice. EMBO Mol Med 2011;3: 605–15. 25. Pan W, Zhu S, Yuan M, Cui H, Wang L, Luo X, Li J, Zhou H, Tang Y, Shen N. MicroRNA-21 and microRNA-148a contribute to DNA hypomethylation in lupus CD4+ T cells by directly and indirectly targeting DNA methyltransferase 1. J Immunol 2010;184: 6773–81.
Connect Tissue Res Downloaded from informahealthcare.com by Michigan University on 03/14/15 For personal use only.
196
W. Chen et al.
26. Tang Y, Luo X, Cui H, Ni X, Yuan M, Guo Y, Huang X, Zhou H, de Vries N, Tak PP, Chen S, Shen N. MicroRNA-146A contributes to abnormal activation of the type I interferon pathway in human lupus by targeting the key signaling proteins. Arthritis Rheum 2009;60:1065–75. 27. Luo X, Yang W, Ye DQ, Cui H, Zhang Y, Hirankarn N, Qian X, Tang Y, Lau YL, de Vries N, Tak PP, Tsao BP, Shen N. A functional variant in microRNA-146a promoter modulates its expression and confers disease risk for systemic lupus erythematosus. PLoS Genet 2011;7:e1002128. 28. Li YT, Chen SY, Wang CR, Liu MF, Lin CC, Jou IM, Shiau AL, Wu CL. Brief report: amelioration of collagen-induced arthritis in mice by lentivirus-mediated silencing of microRNA-223. Arthritis Rheum 2012;64:3240–5. 29. Dai R, Ahmed SA. MicroRNA, a new paradigm for understanding immunoregulation, inflammation, and autoimmune diseases. Transl Res 2011;157:163–79. 30. Vinuesa CG, Rigby RJ, Yu D. Logic and extent of miRNAmediated control of autoimmune gene expression. Int Rev Immunol 2009;28:112–38. 31. Ding S, Liang Y, Zhao M, Liang G, Long H, Zhao S, Wang Y, Yin H, Zhang P, Zhang Q, Lu Q. Decreased microRNA-142-3p/5p expression causes CD4+ T cell activation and B cell hyperstimulation in systemic lupus erythematosus. Arthritis Rheum 2012;64:2953–63. 32. Makino K, Jinnin M, Kajihara I, Honda N, Sakai K, Masuguchi S, Fukushima S, Inoue Y, Ihn H. Circulating miR-142-3p levels in patients with systemic sclerosis. Clin Exp Dermatol 2012;37:34–9. 33. Chen YH, Wang SQ, Wu XL, Shen M, Chen ZG, Chen XG, Liu YX, Zhu XL, Guo F, Duan XZ, Han XC, Tao ZH. Characterization
Connect Tissue Res, 2014; 55(3): 187–196
34.
35. 36. 37. 38. 39. 40.
41. 42.
of microRNAs expression profiling in one group of Chinese urothelial cell carcinoma identified by Solexa sequencing. Urol Oncol 2013;31:219–27. Mohan C, Yu Y, Morel L, Yang P, Wakeland EK. Genetic dissection of SLE pathogenesis: SLE3 on murine chromosome 7 impacts T cell activation, differentiation, and cell death. J Immunol 1999;162:6492–502. Sommer CA, Pavarino-Bertelli EC, Goloni-Bertollo EM, HenriqueSilva F. Identification of dysregulated genes in lymphocytes from children with Down syndrome. Genome 2008;51:19–29. Gordon C, Bradley H, Waring RH, Emery P. Abnormal sulphur oxidation in systemic lupus erythematosus. Lancet 1992;339: 25–6. Rovin BH. The chemokine network in systemic lupus erythematous nephritis. Front Biosci 2008;13:904–22. Pereira-Lopes S, Celhar T, Sans-Fons G, Serra M, Fairhurst AM, Lloberas J, Celada A. The exonuclease Trex1 restrains macrophage proinflammatory activation. J Immunol 2013;191:6128–35. Nielsen CT. Circulating microparticles in systemic Lupus erythematosus. Dan Med J 2012;59:B4548. McPhee CG, Bubier JA, Sproule TJ, Park G, Steinbuck MP, Schott WH, Christianson GJ, Morse HC 3rd, Roopenian DC. IL-21 is a double-edged sword in the systemic lupus erythematosus-like disease of BXSB.Yaa mice. J Immunol 2013;191:4581–8. Tan K, Chen J, Li W, Chen Y, Sui W, Zhang Y, Dai Y. Genomewide analysis of microRNAs expression profiling in patients with primary IgA nephropathy. Genome 2013;56:161–9. El-Osta A, Wolffe AP. DNA methylation and histone deacetylation in the control of gene expression: basic biochemistry to human development and disease. Gene Expr 2000;9:63–75.
