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Integration of major histocompatibility complex, methylation, and transcribed ultra-conserved regions analyses in uremia

Jinjun Qiu

Pingshan People’s Hospital, Shenzhen, Guangdong, People’s Republic of China

Huiyan He

Clinical Medical Research Center, The Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital), 518020, Shenzhen, Guangdong, People’s Republic of China

Weiguo Sui

Nephrology Department of 181st Hospital, Guangxi Key laboratory of Metabolic Diseases Research, 541002, Guilin, Guangxi, People’s Republic of China

Dong’e Tang

Clinical Medical Research Center, The Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital), 518020, Shenzhen, Guangdong, People’s Republic of China

Yong Dai

Clinical Medical Research Center, The Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital), 518020, Shenzhen, Guangdong, People’s Republic of China

E-mail : daiyong22@aliyun.com

DOI: 10.15761/IMM.1000179

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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

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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Editorial Information

Editor-in-Chief

Masayoshi Yamaguchi
Emory University School of Medicine

Article Type

Research Article

Publication history

Received date: October 22, 2015
Accepted date: November 10, 2015
Published date: November 16, 2015

Copyright

©2015 Qiu J. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation

Qiu J, He H, Sui W, Tang D, Dai Y (2015) Integration of major histocompatibility complex, methylation, and transcribed ultra-conserved regions analyses in uremia. Integr Mol Med 2: DOI: 10.15761/IMM.1000179.

Corresponding author

Yong Dai

Clinical Medical Research Center, The Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital), 5518020, Shenzhen, Guangdong, People’s Republic of China, Fax: +86-75525626750.

E-mail : daiyong22@aliyun.com

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

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.

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.

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.

Chromosome segment

SNP

Gene

Function

chr6: 30039098

rs2301754

RNF3

exonic

chr6: 28891522

rs11545587

TRIM27

UTR5

chr6: 29521289

rs17184255

LOC100507362

intergenic

chr6: 30685420

Rs4713354

MDC1

UTR5

Table 5. 4 CpG methylated SNPs of MHC segment in uremia