Abstract
Treatment of uremia is now dominated by dialysis, in some cases, patients are treated with dialysis for decades, but overall outcomes are disappointing. A number of studies have confirmed the relevance of several experimental insights to the pathogenesis of uremia, but the specific biomarkers of uremia have not been fully elucidated. A total of 15 uremia patients and 15 healthy controls were collected in the present study. The aim of this study was to explain the etiology of uremia, MHC gene capture technology, hMeDIP-chip, T-UCR microarray and bioinformatics analysis were utilised in the uremia and normal control group. The result showed 8 CpG methylated enrichment in MHC segment. We found 1 SNP in CpG promoter of lncRNA and 1 SNP in chr6: 28890951-28892013, 1 SNP in CpG chr6:29521110-29521833 and 1 SNP in CpG chr6:30684836-30685503. The CpG methylated corresponding gene wasn’t found significant immune correlated process GO term and KEGG pathway enrichment in uremia. In this experiment, T-UCR was not discovered in MHC segment. The T-UCR corresponding gene wasn’t found significant immune correlated process GO term and KEGG pathway enrichment too. Analysis of SNP (rs2301754, rs11545587, rs17184255, rs4713354) and expression of the gene in peripheral blood lymphocytes indicated these SNP were associated with the occurrence of uremia. Future studies should examine the roles of these SNP in the pathogenesis of uremia. Integrative analysis technology provided an expansive view of molecular signaling pathways in uremia.
Key words
major histocompatibility complex, methylation, uremia, transcribed ultra-conserved regions
Introduction
Uremia refers to the condition that occurs when kidney function regresses during chronic kidney disease. Chronic kidney disease represents the progressive loss of renal function, and its latest stage-uremia, where little or no kidney function is present, requires either transplantation or dialysis [1]. In all stages of the disease, but particularly in uremia, patients present a many-fold increased mortality rate for cardiovascular disease than the general population. Despite intensive research, the pathologic mechanisms of uremia phenotype are still not completely understood and are probably multifactorial. Both genetic and environmental factors have been associated with uremia phenotype, but these factors cannot entirely explain the onset of uremia phenotype. Further studies are still encouraged to shed light on the true associations between uremia and its susceptibility genes. Novel methods should be looked into in this area.
Using a predictive bioinformatics algorithm, Mantila Roosa et al. created a linear model of gene expression and identified 44 transcription factor binding motifs and 29 miRNA binding sites that were predicted to regulate gene expression across the time course. Known and novel transcription factor binding motifs were identified throughout the time course, as were several novel miRNA binding sites. These time-dependent regulatory mechanisms may be important in controlling the loading-induced bone formation process [2]. This integrated bioinformatics analysis method may be looked into in our study. The link between MHC and uremia still unclear. Further investigations are likely to reveal the involvement of MHC in uremia. We are interested in studying MHC, CpG methylated and T-UCR as a first step toward better understanding regulation of gene expression in uremia. We report an expansive view of uremia from an integrated bioinformatics analysis of MHC, CpG methylated and T-UCR data sets.
Materials and methods
Human subjects
Thirty subjects were enrolled in the study including15 uremia patients on dialysis and 15 healthy volunteers. All uremia patients were recruited from the inpatient unit in the Department of Nephrology in the181st Hospital and were free of active infections, diabetes mellitus, and autoimmune diseases.
Written informed consent was obtained from all the subjects or their guardians. The local Ethics Committee approved the study, and peripheral blood samples were obtained with informed consent from all participating individuals. This study abides by the Helsinki Declaration on ethical principles for medical research involving human subjects.
MHC gene capture
Genomic DNA was isolated from the peripheral blood samples. According to the MHC genomic sequence, a completely complementary probe was designed and fixed on a support, and then applied to the genomic DNA after coupling with a probe connector. Unhybridized probe was washed away; then, probe that had hybridized with the DNA was eluted to directly build a library for DNA sequencing (Hiseq 2000 high-throughput sequencing). MHC region capture technology based on the NimbleGen SeqCap EZ Choice Library that enables deep sequencing coverage of the human MHC region. Data were analyzed by using the chi-squared test with Yates’ correction for continuity.
hMeDIP-chip
Genomic DNA was extracted using a DNeasy Blood & Tissue Kit (Qiagen, Fremont, CA). One microgram of the sonicated genomic DNA was used for immunoprecipitation using a mouse monoclonal antibody. For DNA labeling, the NimbleGen Dual-Color DNA Labeling Kit was used according to the manufacturer’s guidelines that are detailed in the NimbleGen hMeDIP-chip protocol (NimbleGen Systems, Inc., Madison, WI, USA). The microarrays were hybridized in Nimblegen hybridization buffer/hybridization component A in a hybridization chamber (Hybridization System - Nimblegen Systems, Inc., Madison, WI, USA). For array hybridization, Roche NimbleGen's Promoter plus CpG Island Array was used.
T-UCR microarray analysis
Sample RNA labeling and array hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology) with minor modifications. The hybridized arrays were washed, fixed and scanned with using the Agilent DNA Microarray Scanner (part number G2505C). Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed with using the GeneSpring GX v12.1 software package (Agilent Technologies).
Bioinformatics analysis
MHC segment CpG methylated enrichment and difference enrichment analysis: MHC gene capture sequencing segment was chr6:30146860-33375560. To search enrichment location, we analyzed CpG peak in MHC segment.
T-UCR expression in MHC segment
To search transcript location, we analyzed T-UCR expression in MHC segment.
The effect of CpG methylated level and T-UCR expression level in immunologic process: We analyzed all methylated CpG, T-UCR and their corresponding gene. Then, we analyzed the related gene in immunologic process. In this experiment, to further understand the functions of the gene, we used the online gene ontology tool EASE（http://david.abcc.ncifcrf.gov/ease/ease1.htm. The differential expression gene was to classify in biological process. GO and KEGG pathway mapping of genes were performed by web-accessible DAVID annotation system.
The correlation of MHC mutation and CpG methylated
To search the correlation, we calculated data of differential CpG methylated, MHC mutation and analyzed correlation coefficient.
Results
Capturing the quantity of genes and SNP loci in the MHC region
We obtained 170 genes and 27,454 SNPs by MHC gene capturing and high-throughput sequencing in patients compared with the normal controls.
hMeDIP-chip: The 4063 genes of CpG islands showed significantly different methylation levels in the patients compared with the normal controls.
T-UCR microarray analysis: To identify potential T-UCRs differentially expressed, we performed a fold change filtering in the patients compared with the normal controls. There are 119 potential T-UCRs, which have been collected from authoritative databases such as Refseq, UCSC knowngenes, and Ensembl.
CpG peak in MHC segment
To search enrichment location, we analyzed CpG peak in MHC segment. The result showed 16 CpG methylated enrichment (Table 1), including 8 in uremia and 8 in the normal controls.
|
CpG Name (hg19)
|
Length (bp)
|
Control
|
Uremia
|
Gene Name
|
Type
|
|
chr6:30038881-30039477
|
596
|
|
1
|
NCRNA00171
|
Promoter
|
|
chr6:30095173-30095610
|
437
|
|
1
|
TRIM40
|
Promoter
|
|
chr6:32046815-32047094
|
279
|
|
1
|
TNXB
|
Intragenic
|
|
chr6:32163292-32164383
|
1091
|
|
1
|
PBX2
|
Promoter
|
|
chr6:32163292-32164383
|
1091
|
|
1
|
GPSM3
|
Promoter
|
|
chr6:32847498-32847846
|
348
|
|
1
|
PPP1R2P1
|
Intragenic
|
|
chr6:33266302-33267582
|
1280
|
|
1
|
RGL2
|
Promoter
|
|
chr6:33266302-33267582
|
1280
|
|
1
|
WDR46
|
Promoter
|
|
chr6:28890951-28892013
|
1062
|
1
|
|
TRIM27
|
Promoter
|
|
chr6:29521110-29521833
|
723
|
1
|
|
UBD
|
Intragenic
|
|
chr6:30684836-30685503
|
667
|
1
|
|
MDC1
|
Promoter
|
|
chr6:30684836-30685503
|
667
|
1
|
|
TUBB
|
Promoter
|
|
chr6:31795467-31797384
|
1917
|
1
|
|
SNORD48
|
Promoter
|
|
chr6:31795467-31797384
|
1917
|
1
|
|
HSPA1B
|
Promoter
|
|
chr6:31795467-31797384
|
1917
|
1
|
|
C6orf48
|
Promoter
|
|
chr6:31795467-31797384
|
1917
|
1
|
|
SNORD52
|
Promoter
|
Table 1. 16 CpG methylated enrichment in MHC segment
T-UCR expression in MHC segment
In this study, we analysis all the T-UCR expression, but UCR overlap gene and UCR proximal gene were not discovered in MHC segment.
The effect of CpG methylated level in immunologic process: In this experiment, we annotated CpG methylated corresponding gene with GO schemes by DAVID gene annotation tool. The genes produced total 55 GO terms in uremia (Table 2), and immune correlated process GO term such as GO:0006955-immune response wasn’t found significant enrichment.
|
GO Term
|
Gene Count
|
P Value
|
FDR
|
|
GO:0006350~transcription
|
378
|
8.17E-12
|
1.52E-08
|
|
GO:0006355~regulation of transcription, DNA-
dependent
|
316
|
2.29E-09
|
4.25E-06
|
|
GO:0051252~regulation of RNA metabolic process
|
321
|
3.44E-09
|
6.39E-06
|
|
GO:0045449~regulation of transcription
|
436
|
4.54E-09
|
8.43E-06
|
|
GO:0007409~axonogenesis
|
55
|
3.41E-08
|
6.32E-05
|
|
GO:0030182~neuron differentiation
|
99
|
5.71E-08
|
1.06E-04
|
|
GO:0000904~cell morphogenesis involved in differentiation
|
64
|
7.04E-08
|
1.31E-04
|
|
GO:0048667~cell morphogenesis involved in neuron differentiation
|
57
|
9.74E-08
|
1.81E-04
|
|
GO:0006357~regulation of transcription from RNA polymerase II promoter
|
145
|
1.85E-07
|
3.44E-04
|
|
GO:0048812~neuron projection morphogenesis
|
56
|
4.70E-07
|
8.72E-04
|
|
GO:0048666~neuron development
|
77
|
1.62E-06
|
0.003011
|
|
GO:0006355~regulation of transcription, DNA-dependent
|
61
|
4.61E-06
|
0.008556
|
|
GO:0051252~regulation of RNA metabolic process
|
60
|
9.58E-06
|
0.017786
|
|
GO:0045449~regulation of transcription
|
58
|
1.00E-05
|
0.018585
|
|
GO:0007409~axonogenesis
|
61
|
1.83E-05
|
0.034300
|
|
GO:0030182~neuron differentiation
|
76
|
2.01E-05
|
0.037274
|
|
GO:0000904~cell morphogenesis involved in differentiation
|
36
|
2.52E-05
|
0.046809
|
|
GO:0048667~cell morphogenesis involved in neuron differentiation
|
39
|
5.75E-05
|
0.106653
|
|
GO:0006357~regulation of transcription from RNA polymerase II promoter
|
30
|
9.17E-05
|
0.170063
|
|
GO:0048812~neuron projection morphogenesis
|
80
|
9.86E-05
|
0.182883
|
|
GO:0048666~neuron development
|
75
|
1.12E-04
|
0.208311
|
|
GO:0031175~neuron projection development
|
46
|
1.31E-04
|
0.242746
|
|
GO:0032990~cell part morphogenesis
|
68
|
1.90E-04
|
0.351983
|
|
GO:0048858~cell projection morphogenesis
|
114
|
2.21E-04
|
0.409970
|
|
GO:0007389~pattern specification process
|
68
|
2.49E-04
|
0.461054
|
|
GO:0000902~cell morphogenesis
|
106
|
2.85E-04
|
0.528252
|
|
GO:0007156~homophilic cell adhesion
|
71
|
3.38E-04
|
0.625256
|
|
GO:0030900~forebrain development
|
116
|
3.41E-04
|
0.630759
|
|
GO:0006355~regulation of transcription, DNA-dependent
|
105
|
3.58E-04
|
0.662752
|
|
GO:0051252~regulation of RNA metabolic process
|
103
|
3.80E-04
|
0.703840
|
|
GO:0045449~regulation of transcription
|
106
|
3.93E-04
|
0.727092
|
|
GO:0007409~axonogenesis
|
87
|
4.04E-04
|
0.746483
|
|
GO:0030182~neuron differentiation
|
73
|
4.18E-04
|
0.772374
|
|
GO:0000904~cell morphogenesis involved in differentiation
|
103
|
4.68E-04
|
0.864433
|
|
GO:0048667~cell morphogenesis involved in neuron differentiation
|
60
|
4.69E-04
|
0.866098
|
|
GO:0006357~regulation of transcription from RNA polymerase II promoter
|
71
|
5.54E-04
|
1.022866
|
|
GO:0048812~neuron projection morphogenesis
|
93
|
6.35E-04
|
1.171092
|
|
GO:0048666~neuron development
|
60
|
6.70E-04
|
1.236425
|
|
GO:0031175~neuron projection development
|
94
|
7.01E-04
|
1.293726
|
|
GO:0032990~cell part morphogenesis
|
95
|
7.33E-04
|
1.351237
|
|
GO:0048858~cell projection morphogenesis
|
99
|
8.20E-04
|
1.510345
|
|
GO:0007389~pattern specification process
|
115
|
9.05E-04
|
1.666747
|
|
GO:0000902~cell morphogenesis
|
56
|
0.001007
|
1.853066
|
|
GO:0007156~homophilic cell adhesion
|
33
|
0.001143
|
2.099637
|
|
GO:0030900~forebrain development
|
87
|
0.001209
|
2.220594
|
|
GO:0007411~axon guidance
|
126
|
0.001287
|
2.362594
|
|
GO:0032989~cellular component morphogenesis
|
87
|
0.001377
|
2.525408
|
|
GO:0030030~cell projection organization
|
118
|
0.001636
|
2.992538
|
|
GO:0003002~regionalization
|
60
|
0.001677
|
3.066779
|
|
GO:0043009~chordate embryonic development
|
87
|
0.001789
|
3.269362
|
|
GO:0045935~positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process
|
30
|
0.001931
|
3.523561
|
|
GO:0009792~embryonic development ending in birth or egg hatching
|
53
|
0.002101
|
3.828081
|
|
GO:0016192~vesicle-mediated transport
|
12
|
0.002437
|
4.427231
|
|
GO:0045892~negative regulation of transcription, DNA-dependent
|
12
|
0.002437
|
4.427231
|
|
GO:0051173~positive regulation of nitrogen compound metabolic process
|
118
|
0.002722
|
4.933267
|
Table 2. The CpG methylated corresponding genes annotation GO terms in uremia.
Note: If p-value<0.005, it is considered significant. The False Discovery Rate (FDR) of a set of predictions is the expected percent of false predictions in the set of predictions. Meanwhile an FDR<5% might be quite meaningful.
In addition, we obtained 4 KEGG pathways of the genes in uremia (Table 3), immune correlated process KEGG pathway wasn’t significantly enriched.
|
Pathways
|
Gene Count
|
P Value
|
FDR
|
|
hsa04012:ErbB signaling pathway
|
24
|
3.41E-04
|
0.418186
|
|
hsa05220:Chronic myeloid leukemia
|
20
|
0.001899
|
2.308109
|
|
hsa04144:Endocytosis
|
38
|
0.002299
|
2.787276
|
|
hsa05214:Glioma
|
17
|
0.004113
|
4.936479
|
Table 3. The CpG methylated corresponding genes annotation KEGG pathways in uremia.
Note: If p-value<0.005, it is considered significant. The False Discovery Rate (FDR) of a set of predictions is the expected percent of false predictions in the set of predictions. Meanwhile an FDR<5% might be quite meaningful.
The effect of T-UCR expression level in immunologic process: In this experiment, we annotated T-UCR corresponding gene with GO schemes by DAVID gene annotation tool. The genes produced total 52 GO terms in uremia (Table 4), and immune correlated process GO term wasn’t found significant enrichment. In addition, we don’t obtained immune correlated process KEGG pathway of the genes in SLE. They weren’t significantly enriched.
|
GO Term
|
Gene Count
|
P Value
|
FDR
|
|
GO:0045449~regulation of transcription
|
240
|
8.45E-14
|
1.52E-10
|
|
GO:0051252~regulation of RNA metabolic process
|
176
|
1.11E-11
|
1.99E-08
|
|
GO:0006350~transcription
|
191
|
3.49E-10
|
6.27E-07
|
|
GO:0006357~regulation of transcription from RNA polymerase II promoter
|
87
|
4.20E-10
|
7.54E-07
|
|
GO:0006355~regulation of transcription, DNA-
dependent
|
167
|
5.03E-10
|
9.05E-07
|
|
GO:0003002~regionalization
|
35
|
1.59E-08
|
2.85E-05
|
|
GO:0045941~positive regulation of transcription
|
66
|
1.74E-07
|
3.13E-04
|
|
GO:0045935~positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process
|
70
|
3.44E-07
|
6.17E-04
|
|
GO:0010628~positive regulation of gene expression
|
66
|
5.15E-07
|
9.25E-04
|
|
GO:0051173~positive regulation of nitrogen compound metabolic process
|
71
|
5.44E-07
|
9.78E-04
|
|
GO:0016071~mRNA metabolic process
|
48
|
6.14E-07
|
0.001104
|
|
GO:0048598~embryonic morphogenesis
|
42
|
8.78E-07
|
0.001579
|
|
GO:0051254~positive regulation of RNA metabolic process
|
57
|
9.23E-07
|
0.001659
|
|
GO:0006397~mRNA processing
|
43
|
1.12E-06
|
0.002013
|
|
GO:0007389~pattern specification process
|
38
|
1.22E-06
|
0.002200
|
|
GO:0006396~RNA processing
|
62
|
1.30E-06
|
0.002335
|
|
GO:0045944~positive regulation of transcription from RNA polymerase II promoter
|
47
|
1.60E-06
|
0.002875
|
|
GO:0045893~positive regulation of transcription, DNA-dependent
|
56
|
1.60E-06
|
0.002876
|
|
GO:0048706~embryonic skeletal system development
|
18
|
2.01E-06
|
0.003616
|
|
GO:0008380~RNA splicing
|
39
|
2.10E-06
|
0.003773
|
|
GO:0009952~anterior/posterior pattern formation
|
25
|
2.31E-06
|
0.004161
|
|
GO:0031328~positive regulation of cellular biosynthetic process
|
72
|
2.61E-06
|
0.004690
|
|
GO:0048562~embryonic organ morphogenesis
|
24
|
3.21E-06
|
0.005772
|
|
GO:0010557~positive regulation of macromolecule biosynthetic process
|
69
|
3.84E-06
|
0.006907
|
|
GO:0009891~positive regulation of biosynthetic process
|
72
|
4.37E-06
|
0.007853
|
|
GO:0031327~negative regulation of cellular biosynthetic process
|
61
|
6.09E-06
|
0.010950
|
|
GO:0010604~positive regulation of macromolecule metabolic process
|
83
|
9.65E-06
|
0.017339
|
|
GO:0048568~embryonic organ development
|
27
|
9.77E-06
|
0.017567
|
|
GO:0030900~forebrain development
|
25
|
1.01E-05
|
0.018085
|
|
GO:0010558~negative regulation of macromolecule biosynthetic process
|
59
|
1.13E-05
|
0.020368
|
|
GO:0009890~negative regulation of biosynthetic process
|
61
|
1.17E-05
|
0.021059
|
|
GO:0010605~negative regulation of macromolecule metabolic process
|
72
|
2.79E-05
|
0.050223
|
|
GO:0001501~skeletal system development
|
39
|
3.18E-05
|
0.057209
|
|
GO:0010629~negative regulation of gene expression
|
53
|
6.67E-05
|
0.119750
|
|
GO:0021537~telencephalon development
|
14
|
9.53E-05
|
0.171182
|
|
GO:0051172~negative regulation of nitrogen compound metabolic process
|
52
|
2.63E-04
|
0.470982
|
|
GO:0021543~pallium development
|
11
|
2.97E-04
|
0.531764
|
|
GO:0016481~negative regulation of transcription
|
47
|
3.37E-04
|
0.603146
|
|
GO:0045934~negative regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process
|
51
|
3.48E-04
|
0.623182
|
|
GO:0051253~negative regulation of RNA metabolic process
|
39
|
4.47E-04
|
0.800670
|
|
GO:0048704~embryonic skeletal system morphogenesis
|
12
|
4.50E-04
|
1.142592
|
|
GO:0045892~negative regulation of transcription, DNA-dependent
|
38
|
6.39E-04
|
1.268796
|
|
GO:0030902~hindbrain development
|
12
|
7.10E-04
|
1.636420
|
|
GO:0048705~skeletal system morphogenesis
|
17
|
9.18E-04
|
2.027251
|
|
GO:0000122~negative regulation of transcription from RNA polymerase II promoter
|
30
|
0.001139
|
2.820892
|
|
GO:0007411~axon guidance
|
16
|
0.001591
|
3.154926
|
|
GO:0030326~embryonic limb morphogenesis
|
14
|
0.001782
|
3.154926
|
|
GO:0035113~embryonic appendage morphogenesis
|
14
|
0.001782
|
3.206449
|
|
GO:0009954~proximal/distal pattern formation
|
7
|
0.001812
|
4.553220
|
|
GO:0009953~dorsal/ventral pattern formation
|
11
|
0.002589
|
4.620857
|
|
GO:0051051~negative regulation of transport
|
18
|
0.002629
|
4.877394
|
|
GO:0045665~negative regulation of neuron differentiation
|
8
|
0.002778
|
4.825061
|
Table 4. The T-UCR corresponding genes annotation GO terms in uremia.
Note: If p-value<0.005, it is considered significant. The False Discovery Rate (FDR) of a set of predictions is the expected percent of false predictions in the set of predictions. Meanwhile an FDR<5% might be quite meaningful.
The correlation of MHC mutation and CpG methylated: In this experiment, we found 1 SNP in CpG promoter of lncRNA and 1 SNP in chr6: 28890951-28892013,1 SNP in CpG chr6:29521110-29521833 and 1 SNP in CpG chr6:30684836-30685503 (Table 5).
|
Chromosome segment
|
SNP
|
Gene
|
Function
|
|
chr6: 30039098
|
rs2301754
|
RNF3
|
exonic
|
|
chr6: 28891522
|
rs11545587
|
TRIM27
|
2021 Copyright OAT. All rights reserv
UTR5
|
|
chr6: 29521289
|
rs17184255
|
LOC100507362
|
intergenic
|
|
chr6: 30685420
|
Rs4713354
|
MDC1
|
UTR5
|
Table 5. 4 CpG methylated SNPs of MHC segment in uremia
Discussion
The first genetic factors to be identified as important in the pathogenesis of uremia were those of the MHC on chromosome 6. It is now widely accepted that MHC genes constitute a part of the genetic susceptibility to uremia. Previous studies in uremia have lacked statistical power and genetic resolution to fully define MHC influences. In this research, we tried to identify MHC, CpG methylated and T-UCR, and reveal potential mechanism in uremia by a novel and combinatorial approach involving MHC gene capture technology, hMeDIP-chip, T-UCR microarray, and bioinformatic analysis. 27,454 SNPs were detected significantly different which may be involved in uremia. Moreover, in this study, we integrated the datasets and identified 4 most important SNPs in uremia. Function research on these SNPs is in our plan.
It is reported that H3K4me3 altered in uremia patients but not in healthy people [3]. Their results indicate that H3K9 trimethylation is involved in unphysiological uremic environment and these novel candidate genes may become potential biomarkers or future therapeutic targets [4]. Epigenetic events play a central role in the priming, differentiation and subset determination of T lymphocytes. CpG-DNA methylation and post-translational modifications to histone tails are the two most well accepted epigenetic mechanisms. The involvement of epigenetic mechanisms in the pathogenesis of uremia has been suggested A better understanding of the molecular events that contribute to epigenetic alterations and subsequent immune imbalance is essential for the establishment of disease biomarkers and identification of potential therapeutic targets. These findings may facilitate the selection of better target molecules for further studies. Our study might also aid in suggesting new pathways to be studied in a more focused approach with confirmation at the protein levels and investigation of the clinical significance.
Long non-coding RNAs (lncRNAs) are transcripts longer than ~200 nucleotides with little or no protein-coding capacity [5]. Growing evidence shows that lncRNAs present important function in development and are associated with many human diseases such as cancers, Alzheimer disease, and heart diseases. T-UCR transcripts are a novel class of lncRNAs transcribed from ultraconserved regions (UCRs). UCRs are a class of 481 noncoding sequences located in both intra- and inter-genic regions of the genome. UCRs are absolutely conserved (100%) between the orthologous regions of the human, rat, and mouse genomes, and are actively transcribed. It has recently been proven in cancer systems that differentially expressed T-UCRs could alter the functional characteristics of malignant cells. Recent data suggest that T-UCRs are altered at the transcriptional level in human tumorigenesis and the aberrant T-UCRs expression profiles can be used to differentiate human cancer types [6,7] . Researchers observed that DNA hypomethylation induces release of T-UCR silencing in cancer cells. The analysis of a large set of primary human tumors demonstrated that hypermethylation of the described T-UCR CpG islands was a common event among the various tumor types [8]. However, in our study, we integrated the MHC and T-UCR datasets. We examined the expression levels of T-UCR in MHC segment by T-UCR microarray. We annotated T-UCR corresponding gene by DAVID gene annotation tool. The immune correlated process GO term and KEGG pathway wasn’t found significant enrichment. T-UCR expression levels were not correlated with commonly used clinicopathological features of uremia.
Conclusions
Taken together, we identified 4 most important SNPs (rs2301754, rs11545587, rs17184255, rs4713354) in uremia. Our work indicates that SNPs in MHC segment are potential biomarkers and probable factors involved in the pathogenesis of uremia. However, further studies are required to investigate the mechanism by which polymorphisms in this gene lead to uremia. A major advantage of combining multiple planes of measurement is the ability to dissect mechanisms not apparent in a single dimension. Integrating MHC, CpG methylated and T-UCR data sets is a powerful strategy for understanding uremia biology. Our findings proved insights into the anomalous regulated SNPs’ potential contribution to the abnormalities in uremia and could help us to structure antenatal diagnostic biomarkers of uremia, as well as get the novel therapeutic targets in the treatment of individual with uremia. Besides, our study of SNPs may lead to finding novel methods to treat and prevent other diseases.
Acknowledgements
The authors thank the patients with uremia and healthy volunteers who participated in this study. This work was supported by grants from the Health system research project of Pingshan, Shenzhen (no. 201417) and Incubation fund of medical and health development of Pingshan (no.201317) Bioinformatics analysis was performed by Shanghai BIOTREE biotech Co. Ltd., Shanghai, China.
Disclosure
The authors of this manuscript have no conflicts of interest to disclose.
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