Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 2460184, 10 pages
http://dx.doi.org/10.1155/2016/2460184

Research Article

A Canonical Correlation Analysis of AIDS Restriction Genes and Metabolic Pathways Identifies Purine Metabolism as a Key Cooperator

Hanhui Ye,1,2 Jinjin Yuan,1,2 Zhengwu Wang,1,2 Aiqiong Huang,1,2 Xiaolong Liu,1,2 Xiao Han,³ and Yahong Chen1,2

¹The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
²Infectious Diseases Hospital of Fuzhou, Fuzhou 350025, China
³Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Received 5 March 2016; Accepted 8 June 2016

Academic Editor: Maria N. D. S. Cordeiro

Copyright © 2016 Hanhui Ye et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Human immunodeficiency virus causes a severe disease in humans, referred to as immune deficiency syndrome. Studies on the interaction between host genetic factors and the virus have revealed dozens of genes that impact diverse processes in the AIDS disease. To resolve more genetic factors related to AIDS, a canonical correlation analysis was used to determine the correlation between AIDS restriction and metabolic pathway gene expression. The results show that HIV-1 postentry cellular viral cofactors from AIDS restriction genes are coexpressed in human transcriptome microarray datasets. Further, the purine metabolism pathway comprises novel host factors that are coexpressed with AIDS restriction genes. Using a canonical correlation analysis for expression is a reliable approach to exploring the mechanism underlying AIDS.

1. Introduction

Human immunodeficiency virus (HIV) is the basis for acquired immune deficiency syndrome (AIDS) pathogenesis and destroys the lymphoid system with prodigious replicates, which reduces a patient’s ability to survive. Since HIV was identified in the 1980s, this pathogen has taken more than 10 million people’s lives throughout the world. Researchers have developed considerable information on HIV involving immunology, virology, host genetics, and treatment over the past few decades.

Human genetics research involving the infectious disease HIV has progressed considerably after initiation of the human genome project (HGP), which is sequencing the entire human genome, both physically and functionally [1]. Many host genetic factors that influence AIDS epidemiological heterogeneity have been characterized [2–4]. From the HIV entry receptor on lymphoid cells to oncogenes in human glioblastomas, AIDS restriction genes (ARGs) are widely involved in biological pathways, and nearly 40 ARGs have been studied in depth through functional analyses [5–12]. Host genomic analysis is a key approach to studying AIDS epidemiology [13].

Further, genome, transcriptome, proteome, and metabolome biodatasets related to HIV have grown exponentially due to advanced sequencing technology. However, an integrative study on these datasets is limited in terms of understanding the complicated biological network.

Recent studies have revealed that metabolic pathways exert certain effects on the control of AIDS disease progression [14]. For example, the oxygen concentration can modulate T-cell differentiation through controlling metabolic status [15]. Metabolizing ATP to adenosine inhibits HIV-specific effector cells. Further, HIV infection is affected by dNTP hydrolysis. Efficient HIV-1 infection of CD4(+) lymphocytes requires sufficient glucose uptake via the Glut1 glucose transporter [16]. Tryptophan and phenylalanine metabolism also play an important role in HIV because HIV pathophysiology is associated with inflammatory stress due to dysregulated amino acid metabolism [17]. The HIV protein NEF impacts lipid-related metabolism through impairing cholesterol metabolism in both infected and bystander cells [18, 19]. This evidence suggests that cross talk between AIDS and the host metabolism is an important research topic that is necessary to resolve the disease mechanism and aid in therapy. Integrating biodatasets with an in-depth analysis of host AIDS restriction genes and metabolic pathways is imperative.

In the transcriptome, gene coexpression is a model for understanding how individual genes are correlated in certain conditions [20, 21]. Based on advances in this field, researchers hypothesize that the coexpression of genes in certain pathways indicates an integrative correlation between the two molecular pathways. Full genes in metabolic pathway are available for the human genome. Identifying correlations between a group of metabolic pathway genes and ARGs is a more comprehensive means for understanding integrative biodatasets. However, traditional methods using a Pearson or partial correlation are only suitable for a single gene. A canonical correlation analysis (CCA) is an efficient and powerful approach for measuring coexpression between two sets of genes. A Childhood Asthma Management Program (CAMP) study using a CCA successfully detected genetic regulatory variants [22]. Using the CCA, the glioblastoma transcriptomes of 45 patients were thoroughly analyzed to identify the glioma pathway genes [23].

In this paper, we used a CCA to analyze coexpression between ARGs and metabolic pathways from KEGG. We discuss the most important metabolic pathways coexpressed with the ARGs, which may imply strategies for AIDS diagnosis and therapy.

2. Methods

2.1. Datasets

Human genome expression datasets were downloaded from the website COPRESDB (http://coxpresdb.jp/), which contains approximately 4000 experiments and expression data on 20,000 human genes. Metabolic pathway genes were downloaded from KEGG (http://www.kegg.jp/); this dataset includes 129 typical metabolic pathways with predicted genes. The ARGs were collected from published literature. Two expression datasets were generated to include metabolic pathway gene and ARG expression data, respectively (Tables 1 and 2).

Table 1: HIV host genetic factor genes.

Gene symbol
Gene ID
Effect

APOBEC3B
9582
Increase infection

APOBEC3G
60489
Accelerates AIDS

CCL11
6356

CCL17
6361

CCL18
6362

CCL2
6347

CCL4
6351

CCL5
6352

CUL5
8065
Accelerates CD4 loss

CXCR1
3577

CXCR6
10663
Accelerates AIDS

DC-SIGN
30835
Decreases infection

DEFB1
1672

GML
2765

HCP5
10866
HIV set point

HLA-A
3105
Delays AIDS

HLA-B
3106
Delays AIDS

HLA-C
3107
Delays AIDS

IDH1
3417
Prevents infection

IFENG
3458
Accelerates AIDS

IL10
3586
Accelerates AIDS

IL4
3565

IRF1
3659

KIR
2669
Delays AIDS

LY6D
8581

MYH9
4627
End stage renal disease

NCOR2
9612
Increase infection

PECI/ECI2
10455
Accelerates AIDS

PPIA/CypA
5478
Accelerates AIDS

PROX1
5629
Delays AIDS progression

SDF1/CXCL12
6387
Delays AIDS

Slurp1
57152

Slurp2/Ly6
6004

TLR4
7099

TLR8
51311

TLR9
54106

TRIM5a
85363
Increase infection

TSG101
7251
Accelerates AIDS

ZNRD1
30834

Table 2: Human metabolism pathway for KEGG.

Pathway name
KEGG ID
Class of metabolism pathway
Gene number

Glycolysis/gluconeogenesis
10
Carbohydrate metabolism
67

Citrate cycle (TCA cycle)
20
Carbohydrate metabolism
31

Pentose phosphate pathway
30
Carbohydrate metabolism
29

Pentose and glucuronate interconversions
40
Carbohydrate metabolism
34

Fructose and mannose metabolism
51
Carbohydrate metabolism
36

Galactose metabolism
52
Carbohydrate metabolism
30

Ascorbate and aldarate metabolism
53
Carbohydrate metabolism
27

Starch and sucrose metabolism
500
Carbohydrate metabolism
56

Amino sugar and nucleotide sugar
520
Carbohydrate metabolism
49

Pyruvate metabolism
620
Carbohydrate metabolism
42

Glyoxylate and dicarboxylate metabolism
630
Carbohydrate metabolism
24

Propanoate metabolism
640
Carbohydrate metabolism
32

Butanoate metabolism
650
Carbohydrate metabolism
29

Inositol phosphate metabolism
562
Carbohydrate metabolism
61

Oxidative phosphorylation
190
Energy metabolism
133

Nitrogen metabolism
910
Energy metabolism
27

Sulfur metabolism
920
Energy metabolism
18

Fatty acid biosynthesis
61
Lipid metabolism
6

Fatty acid elongation
62
Lipid metabolism
23

Fatty acid metabolism
71
Lipid metabolism
44

Ketone bodies
72
Lipid metabolism
9

Steroid biosynthesis
100
Lipid metabolism
18

Primary bile acid biosynthesis
120
Lipid metabolism
17

Steroid hormone biosynthesis
140
Lipid metabolism
56

Glycerolipid metabolism
561
Lipid metabolism
55

Glycerophospholipid metabolism
564
Lipid metabolism
91

Ether lipid metabolism
565
Lipid metabolism
42

Sphingolipid metabolism
600
Lipid metabolism
47

Arachidonic acid metabolism
590
Lipid metabolism
68

Linoleic acid metabolism
591
Lipid metabolism
33

Alpha-linolenic acid metabolism
592
Lipid metabolism
25

Biosynthesis of unsaturated fatty acids
1040
Lipid metabolism
21

Purine metabolism
230
Nucleotide metabolism
173

Pyrimidine metabolism
240
Nucleotide metabolism
107

Alanine, aspartate, and glutamate metabolism
250
Amino acid metabolism
32

Glycine, serine, and threonine metabolism
260
Amino acid metabolism
37

Cysteine and methionine metabolism
270
Amino acid metabolism
34

Valine, leucine, and isoleucine degradation
280
Amino acid metabolism
44

Valine, leucine, and isoleucine biosynthesis
290
Amino acid metabolism
2

Lysine biosynthesis
300
Amino acid metabolism
2

Lysine degradation
310
Amino acid metabolism
49

Arginine and proline metabolism
330
Amino acid metabolism
57

Histidine metabolism
340
Amino acid metabolism
28

Tyrosine metabolism
350
Amino acid metabolism
39

Phenylalanine metabolism
360
Amino acid metabolism
18

Tryptophan metabolism
380
Amino acid metabolism
40

Phenylalanine, tyrosine, and tryptophan biosynthesis
400
Amino acid metabolism
5

Beta-alanine metabolism
410
Metabolism of other amino acids
29

Taurine and hypotaurine metabolism
430
Metabolism of other amino acids
10

Selenocompound metabolism
450
Metabolism of other amino acids
17

Cyanoamino acid metabolism
460
Metabolism of other amino acids
7

D-Glutamine and D-glutamate metabolism
471
Metabolism of other amino acids
4

D-Arginine and D-ornithine metabolism
472
Metabolism of other amino acids
1

Glutathione metabolism
480
Metabolism of other amino acids
51

N-Glycan biosynthesis
510
Glycan biosynthesis and metabolism
49

Mucin type O-glycan biosynthesis
512
Glycan biosynthesis and metabolism
31

Other types of O-glycan biosynthesis
514
Glycan biosynthesis and metabolism
30

Glycosaminoglycan biosynthesis, chondroitin sulfate/dermatan sulfate
532
Glycan biosynthesis and metabolism
20

Glycosaminoglycan biosynthesis, heparan sulfate/heparin
534
Glycan biosynthesis and metabolism
24

Glycosaminoglycan biosynthesis, keratan sulfate
533
Glycan biosynthesis and metabolism
15

Glycosaminoglycan degradation
531
Glycan biosynthesis and metabolism
19

Glycosylphosphatidylinositol- (GPI-) anchor biosynthesis
563
Glycan biosynthesis and metabolism
25

Glycosphingolipid biosynthesis, lacto- and neolactoseries
601
Glycan biosynthesis and metabolism
26

Glycosphingolipid biosynthesis, globoseries
603
Glycan biosynthesis and metabolism
14

Glycosphingolipid biosynthesis, ganglioseries
604
Glycan biosynthesis and metabolism
15

Other glycan degradation
511
Glycan biosynthesis and metabolism
18

Thiamine metabolism
730
Metabolism of cofactors and vitamins
4

Riboflavin metabolism
740
Metabolism of cofactors and vitamins
13

Vitamin B6 metabolism
750
Metabolism of cofactors and vitamins
6

Nicotinate and nicotinamide metabolism
760
Metabolism of cofactors and vitamins
28

Pantothenate and CoA biosynthesis
770
Metabolism of cofactors and vitamins
17

Biotin metabolism
780
Metabolism of cofactors and vitamins
3

Lipoic acid metabolism
785
Metabolism of cofactors and vitamins
3

Folate biosynthesis
790
Metabolism of cofactors and vitamins
14

One carbon pool by folate
670
Metabolism of cofactors and vitamins
20

Retinol metabolism
830
Metabolism of cofactors and vitamins
68

Porphyrin and chlorophyll metabolism
860
Metabolism of cofactors and vitamins
43

Ubiquinone and other terpenoid-quinone biosynthesis
130
Metabolism of cofactors and vitamins
10

Terpenoid backbone biosynthesis
900
Metabolism of terpenoids and polyketides
21

Caffeine metabolism
232
Biosynthesis of other secondary metabolites
7

Butirosin and neomycin biosynthesis
524
Biosynthesis of other secondary metabolites
5

Metabolism of xenobiotics by cytochrome P450
980
Xenobiotics biodegradation and metabolism
80

Drug metabolism, cytochrome P450
982
Xenobiotics biodegradation and metabolism
74

Drug metabolism, other enzymes
983
Xenobiotics biodegradation and metabolism
51

2.2. Canonical Correlation Analysis

To analyze the correlations between ARG and metabolic pathway gene expression, we used a CCA, which integrates multiple correlations into a few significant correlations. This statistical method calculates the correlation between two sets of variables and generates statistically independent pairs of new variables, which are referred to as canonical variables. The linear combination of the variables creates a component of the canonical variable pair in each group of the original variables.

In this study, these variables were defined at each flag as follows: ARG expression described by genes in the vector and metabolic pathway gene expression described by genes in the vector . The respective sets of canonical variables and are results from the linear combination of ARG and metabolic pathway gene expression. The ARG expression canonical variables are included in the vector , which is the result of the linear combination comprising the vector (original ARGs expression) and the canonical coefficients vector as . The vector contains the canonical variables for metabolic pathway gene expression, which result from the linear combination of the vector (original metabolic pathway genes expression) and canonical coefficient vector. The ARG and metabolic pathway gene variance-covariance matrices can be used to estimate the canonical correlation coefficients.

The magnitude of the correlation between each pair of canonical variables is described by the vector eigenvalues. The canonical coefficients exist in the eigenvectors and can be used to estimate the canonical variables. The variance-covariance matrices contain the variances and covariances within the groups for the ARGs and metabolic pathway genes, respectively. The covariances between variables were calculated from the variance-covariance matrices.

2.3. The Study Design and Software Tools

The canonical correlation analysis was performed using the R platform (http://www.r-project.org/). After the canonical variables were generated from the expression datasets composed of ARGs and metabolic pathway genes, we set the absolute value 0.15 as the threshold for selecting ARGs correlated with canonical variables. To select metabolic pathway genes correlated with canonical variables, we sorted the genes using the absolute value, and the top 50 were selected for further enrichment analyses. Functional annotations were generated and enrichment analyses were performed for the metabolic pathway genes using the web-based DAVID tool (http://david.abcc.ncifcrf.gov/). For the pathway enrichment analyses, the “KEGG_PATHWAY” was selected. The pathways with a value < 0.01 were considered significant.

3. Results

3.1. The ARGs and Metabolic Pathway Genes

3.1.1. The General CCA Results

Eight significant (, Wilk’s Lambda, ) canonical correlations were discerned between the ARG and metabolic pathway gene transcriptomes using the CCA. 60% of the total ARG expression variance was explained by the ARGs canonical variables. Significant metabolic pathway canonical variables explained 38% of the metabolic gene transcriptome variation. Thus, ARG-metabolic pathway associations were involved in a substantial proportion of the total variance. The first pair of canonical variables had a correlation of 0.99, while the second pair of canonical variables had a correlation of 0.98.

3.2. Relationships between the Canonical Variables and Original Genes

3.2.1. Pair 1 (C1, P1)

As shown in Table 3, the canonical variable C1 explains 2.4% of the variability in the original ARGs expression variables. We observed positive correlations (absolute value > 0.15) with all ARGs, including PPIA (0.42), ZNRD1 (0.37), MYH9 (0.36), TSG101 (0.31), IDH1 (0.28), TRIM5a (0.17), and CUL5 (0.15), but not GML (−0.17) and NCOR2 (−0.31). The greatest positive correlation was observed between and PPIA. In contrast, the greatest negative correlation was observed between and NCOR2. Among seven ARGs with positive correlations, the four ARGs, PPIA, TSG101, TRIM5a, and CUL5, are postentry cellular viral cofactors.

Table 3: Cross-correlation of Hf genes with canonical variate.

Gene symbol

DEFB1
0.01
0.01
0.02
−0.15
0.06
−0.02
−0.14
−0.04

KIR
0.08
−0.02
−0.05
−0.01
−0.14
0.08
0.17
−0.12

GML
−0.17
−0.16
0.12
−0.06
0.21
−0.06
0.03
0.07

HLA-A
0.10
−0.14
−0.01
0.00
−0.03
−0.22
−0.13
−0.09

HLA-B
0.10
0.09
0.12
0.12
−0.41
0.25
0.07
−0.31

HLA-C
0.07
−0.26
−0.04
−0.08
0.21
−0.33
−0.22
−0.21

IDH1
0.28
0.17
−0.22
−0.17
0.60
−0.20
1.12
0.63

IFENG
0.00
0.03
0.08
0.07
0.05
−0.09
−0.12
−0.07

IL4
−0.12
−0.18
0.08
0.05
0.01
−0.10
−0.15
0.30

CXCR1
−0.07
−0.25
0.20
0.00
0.17
−0.40
−0.14
0.08

IL10
−0.02
−0.05
0.02
0.13
0.05
−0.04
−0.05
−0.01

IRF1
0.07
−0.09
0.08
0.10
0.23
−0.24
−0.14
−0.24

MYH9
0.36
−0.17
0.21
−0.14
−0.49
−0.50
0.14
−0.19

PPIA/CypA
0.42
0.92
1.88
0.58
−0.54
1.11
0.00
1.12

PROX1
−0.14
0.07
0.03
0.16
0.23
0.02
0.55
−0.66

Slurp2/Ly6
−0.04
−0.03
0.00
0.10
−0.13
0.12
0.09
0.02

CCL2
0.02
−0.03
−0.04
0.00
−0.05
0.12
0.08
0.00

CCL4
0.02
−0.06
0.02
0.09
0.02
0.05
0.04
−0.06

CCL5
0.03
−0.06
0.01
0.03
0.00
0.02
−0.14
0.05

CCL11
0.02
−0.05
0.01
0.02
0.09
−0.01
0.25
−0.05

CCL17
0.00
−0.06
0.05
0.07
0.06
−0.02
−0.19
−0.06

CCL18
0.03
−0.04
0.00
0.06
0.05
0.14
0.09
0.05

SDF1/CXCL12
0.09
−0.10
−0.17
−0.18
−0.26
0.14
0.09
0.22

TLR4
0.06
−0.05
−0.02
0.24
−0.15
0.26
0.00
0.02

TSG101
0.31
0.48
−0.25
−0.05
0.17
0.49
−0.54
−1.03

CUL5
0.15
0.51
−0.87
−0.23
0.19
0.40
−0.80
−0.04

LY6D
0.01
−0.05
0.10
−0.24
0.10
0.13
0.01
−0.08

APOBEC3B
0.04
0.03
0.04
0.03
0.15
−0.12
0.02
0.14

NCOR2
−0.31
−0.28
0.37
−0.27
−1.10
−0.24
−0.38
0.52

PECI/ECI2
0.09
0.15
−0.24
0.01
−0.10
0.06
−0.35
0.25

CXCR6
−0.06
−0.10
0.02
−0.06
0.03
0.18
−0.07
0.09

HCP5
−0.04
0.02
−0.01
−0.01
0.32
0.01
0.02
−0.01

ZNRD1
0.37
0.14
0.28
−0.03
0.32
−0.91
−0.04
0.10

DC-SIGN
−0.04
−0.29
0.13
0.00
−0.17
0.03
0.22
0.33

TLR8
0.13
−0.36
−0.03
0.30
0.33
0.70
−0.15
0.17

TLR9
−0.03
−0.18
0.11
−0.06
−0.02
−0.17
−0.24
0.36

Slurp1
−0.03
−0.10
0.19
−0.61
0.21
0.32
0.04
−0.06

APOBEC3G
0.06
−0.17
−0.14
0.11
0.19
0.07
−0.44
0.39

TRIM5a
0.17
−0.13
−0.15
0.00
0.26
−0.30
0.22
0.53

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that correlated with variable were enriched for purine metabolism; these genes include phosphodiesterase 4C (5143), polymerase (RNA) III (DNA directed) polypeptide K (51728), and primase (5558).

Table 4: Cross-correlation of genes enriched in metabolic pathways with canonical variate.

Component
Term
Count
Pop hits
value
Genes

+
Purine metabolism
3
153

5143, 51728, 5558

3+
Glycolysis/gluconeogenesis
3
60

5223, 2597, 57818

3+
Pyrimidine metabolism
3
95

5425, 51727, 7372

4−
Purine metabolism
4
153

1716, 51728, 55703, 5313

4+
Purine metabolism
5
153

55811, 5147, 5425, 5432, 8654

5−
Inositol phosphate metabolism
3
54

8871, 5330, 3707

6−
Pyrimidine metabolism
3
95

5435, 51727, 84172

6+
Pyruvate metabolism
3
40

5162, 4191, 38

6+
Terpenoid backbone biosynthesis
2
15

2224, 38

7−
Pyrimidine metabolism
3
95

54963, 5435, 5430

7+
Methane metabolism
2
6

128, 4524

3.2.2. Pair 2 (C2, P2)

As shown in Table 3, the canonical variable explains 5.3% of the variability in the original ARG expression variables. This variable highly correlated with the ARGs PPIA (0.92), CUL5 (0.51), TSG101 (0.48), IDH1 (0.17), and PECI (0.15), but not GML (−0.16), APOBEC3G (−0.17), MYH9 (−0.17), IL4 (−0.18), TLR9 (−0.18), CXCR1 (−0.25), HLA-C (−0.26), NCOR2 (−0.28), DC-SIGN (−0.29), and TLR8 (−0.36). The greatest positive correlation was observed between and PPIA. However, the greatest negative correlation was observed between and DC-SIGN. Among the ARGs with large correlations, PPIA, TSG101, CUL5, and APOBEC3G are postentry cellular viral cofactors. Among the ARGs with negative correlations, CXCR1 and IL4 are related to cytokines. DC-SIGN is involved in chemokines, which play important role in HIV entry through chemokine receptors.

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that highly correlate with the variable are not enriched in a certain pathway.

3.2.3. Pair 3 (C3, P3)

As shown in Table 3, the canonical variable explains 12.7% of the variability on the original ARG expression variables. This variable positively correlated (absolute value > 0.15) with PPIA (1.88), NCOR2 (0.37), ZNRD1 (0.28), MYH9 (0.21), CXCR1 (0.20), and Slurp1 (0.19); in contrast, it negatively correlated with TRIM5a (−0.15), SDF1 (−0.17), IDH1 (−0.22), PECI (−0.24), TSG101 (−0.25), and CUL5 (−0.87). The greatest positive correlation was observed between and PPIA. However, the greatest negative correlation was observed between and CUL5. Among the ARGs that highly correlated with , PPIA, TSG101, TRIM5a, and CUL5 are postentry cellular viral cofactors. However, only PPIA positively correlated with .

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that highly correlated with the variable are enriched in glycolysis and pyrimidine metabolism. The glycolysis genes include phosphoglycerate mutase 1 (5223), glyceraldehyde-3-phosphate dehydrogenase (2597), and glucose-6-phosphatase (57818). The pyrimidine metabolism genes include polymerase (DNA directed), delta 2 (5425), cytidine monophosphate (UMP-CMP) kinase 1 (51727), and uridine monophosphate synthetase (7372).

3.2.4. Pair 4 (C4, P4)

As shown in Table 3, the canonical variable explains 3.3% of the variability in the original ARG expression variables. This variable highly correlated (absolute value > 0.15) with PPIA (0.58), TLR8 (0.30), TLR4 (0.24), and PROX1 (0.16), but not DEFB1 (−0.15), IDH1 (−0.17), SDF1 (−0.18), CUL5 (−0.23), LY6D (−0.24), NCOR2 (−0.27), and Slurp1 (−0.61). The greatest positive correlation was observed between and PPIA. However, the greatest negative correlation was observed between and Slurp1. Among the ARGs that highly correlated with , only PPIA and CUL5 are postentry cellular viral cofactors.

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that highly correlated with the variable are enriched in purine metabolism. These genes include deoxyguanosine kinase (1716), polymerase (RNA) III (DNA directed) polypeptide K (51728), polymerase (RNA) III (DNA directed) polypeptide B (55703), pyruvate kinase (5313), adenylate cyclase 10 (55811), phosphodiesterase 6D (5147), polymerase (DNA directed), delta 2 (5425), polymerase (RNA) II (DNA directed) polypeptide C (5432), and phosphodiesterase 5A (8654).

3.2.5. Pair 5 (C5, P5)

As shown in Table 3, the canonical variable explains 8.3% of the variability in the original ARG expression variables. This variable highly correlated (absolute value > 0.15) with IDH1 (0.60), TLR8 (0.33), ZNRD1 (0.32), TRIM5a (0.26), IRF1 (0.23), PROX1 (0.23), Slurp1 (0.21), HLA-C (0.21), GML (0.21), CUL5 (0.19), CXCR1 (0.17), TSG101 (0.17), APOBEC3B (0.15), TLR4 (−0.15), DC-SIGN (−0.17), SDF1 (−0.26), HLA-B (−0.41), MYH9 (−0.49), PPIA (−0.54), and NCOR2 (−1.10). The greatest positive correlations were observed between and IDH1. However, the greatest negative correlations were observed between and NCOR2. Among the ARGs that highly correlated with , PPIA, TSG101, APOBEC3B, TRIM5a, and CUL5 are postentry cellular viral cofactors. HLA-C and HLA-B are members of the HLA system. DC-SIGN and SDF1 are related to chemokines. CXCR1 is related to the cytokines pathway.

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that highly correlated with the variable are enriched in inositol phosphate metabolism; these genes include synaptojanin 2 (8871), phospholipase C beta 2 (5330), and inositol-trisphosphate 3-kinase B (3707).

3.2.6. Pair 6 (C6, P6)

As shown in Table 3, the canonical variable explains 10.8% of the variability in the original ARG expression variables. This variable highly correlated (absolute value > 0.15) with PPIA (1.11), TLR8 (0.70), TSG101 (0.49), CUL5 (0.40), Slurp1 (0.32), TLR4 (0.26), HLA-B (0.25), CXCR6 (0.18), TLR9 (−0.17), IDH1 (−0.20), HLA-A (−0.22), IRF1 (−0.24), NCOR2 (−0.24), TRIM5a (−0.30), HLA-C (−0.33), CXCR1 (−0.40), MYH9 (−0.50), and ZNRD1 (−0.91). The greatest positive correlation was observed between and PPIA. However, the greatest negative correlation was observed between and ZNRD1. Among the ARGs that highly correlated with , PPIA, TSG101, TRIM5a, and CUL5 are postentry cellular viral cofactors. HLA-A, HLA-C, and HLA-B are members of the HLA system. CXCR6 is related to chemokine receptors. IRF1 and CXCR1 are related to cytokines.

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that highly correlated with variable are enriched in pyrimidine metabolism and terpenoid backbone biosynthesis. These genes include polymerase (RNA) II (DNA directed) polypeptide F (5435), cytidine monophosphate (UMP-CMP) kinase 1 (51727), polymerase (RNA) I polypeptide B (84172), farnesyl diphosphate synthase (2224), and acetyl-CoA acetyltransferase 1 (38).

3.2.7. Pair 7 (C7, P7)

As shown in Table 3, the canonical variable explains 9% of the variability in the original ARG expression variables. This variable highly correlated (absolute value > 0.15) with IDH1 (1.12), PROX1 (0.55), CCL11 (0.25), DC-SIGN (0.22), TRIM5a (0.22), KIR (0.17), IL4 (−0.15), TLR8 (−0.15), HLA-C (−0.22), TLR9 (−0.24), PECI (−0.35), NCOR2 (−0.38), APOBEC3G (−0.44), TSG101 (−0.54), and CUL5 (−0.80). The greatest positive correlation was observed between and IDH1. However, the greatest negative correlation was observed between and CUL5. Among the ARGs that highly correlated with , TSG101, APOBEC3G, TRIM5a, and CUL5 are postentry cellular viral cofactors. KIR and HLA-C are in the HLA system. DC-SIGN and CCL11 are related to chemokine receptors. IL4 is related to cytokines.

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that highly correlated with variable are enriched in pyrimidine metabolism and methane metabolism. These genes include uridine-cytidine kinase 1-like 1 (54963), polymerase (RNA) II (DNA directed) polypeptide F (5435), polymerase (RNA) II (DNA directed) polypeptide A (5430), alcohol dehydrogenase 5 (class III) (128), and methylenetetrahydrofolate reductase (4524).

3.2.8. Pair 8 (C8, P8)

As shown in Table 3, the canonical variable explains 12% of the variability in the original ARG expression variables. This variable highly correlated (absolute value > 0.15) with PPIA (1.12), IDH1 (0.63), TRIM5a (0.53), NCOR2 (0.52), APOBEC3G (0.39), TLR9 (0.36), DC-SIGN (0.33), IL4 (0.30), PECI (0.25), SDF1 (0.22), TLR8 (0.17), MYH9 (−0.19), HLA-C (−0.21), IRF1 (−0.24), HLA-B (−0.31), PROX1 (−0.66), and TSG101 (−1.03). The greatest positive correlation was observed between and PPIA. However, the greatest negative correlation was observed between and TSG101. Among the ARGs that highly correlated with , TSG101, APOBEC3G, TRIM5a, and PPIA are postentry cellular viral cofactors. KIR and HLA-C are in the HLA system. DC-SIGN and SDF1 are related to chemokine receptors. IL4 and IRF1 are related to cytokines. HLA-C and HLA-B are in the HLA system.

As shown in Table 4, the canonical variable accounts for the variability in the original metabolic pathway gene expression data. The metabolic pathway genes that highly correlated with variable are not enriched in a metabolic pathway.

4. Discussion

Researchers have used numerous approaches to identify host genes related to AIDS [5–13]. Most studies use genomic information but not integration of the genome and transcriptome. However, most SNPs at ARGs impact AIDS through changing host gene transcription [7–10]. This study features novel experiments that focus on ARG cooperation at the transcription level and extends the correlation between ARGs and metabolic pathway genes to discover novel host genes related to AIDS.

For each variable in the canonical correlation analysis, HIV-1 postentry cellular viral cofactors highly cooperated at the transcription level. PPIA, TSG101, TRIM5a, APOBEC3G, and CUL5 frequently appeared together to correlate with the canonical variables. PPIA functions in cyclosporin A-mediated immunosuppression by encoding a member of the peptidyl-prolyl cis-trans isomerase (PPIase) family [24]. Formation of HIV virions requires an interaction between PPIA and HIV viral proteins. TSG101 negatively regulates cell growth and differentiation by producing a protein that interacts with stathmin [25]. TRIM5a is an E3 ubiquitin-ligase, and its ubiquitination function is involved in retroviral restriction [26]. These genes encode HIV-1 postentry cellular viral cofactors involved in different biological processes. Thus, the high correlation between these genes and canonical variables demonstrates that these genes are coordinated at the transcriptional level. These data suggest that a potential transcriptional regulator for these genes may be a key host factor related to AIDS.

The high-frequency ARGs that correlated with canonical variables include PPIA, TSG101, CUL5, NCOR2, IDH1, and MYH9. PPIA, TSG101, and CUL5 are discussed above. NCOR2 with histone deacetylases is a nuclear receptor corepressor [27]. IDH1 encodes isocitrate dehydrogenases involved in cytoplasmic NADPH production and pyruvate metabolism [28]. MYH9 aids in maintaining cell shape, cell motility, and cytokinesis as a conventional nonmuscle myosin [29]. These ARGs are not enriched in a certain biological process. However, many host genetic factors have not been studied.

The low-frequency ARGs that correlated with canonical variables include DEFB1 with , KIR with , HLA-A with , CCL11 with , LY6D with , APOBEC3B with , and CXCR6 with . DEFB1 is a defensin and is implicated in cystic fibrosis pathogenesis [30]. HLA-A is a major histocompatibility complex class I heavy chain paralogue; these paralogues are expressed in nearly all cells [31]. CCL11 is chemokine (C-C motif) ligand 11 and is implicated in immunoregulatory and inflammatory processes [32]. CXCR6 is chemokine (C-X-C motif) receptor [33]. LY6D is a member of the lymphocyte antigen 6 complex [34]. APOBEC3B is a member of the cytidine deaminase gene family. Recent studies have revealed that these ARGs may be RNA-editing enzymes that control the cell cycle [35]. Further, these genes only correlated with one canonical variable, which suggests that the specificity of the correlation may determine the canonical variable correlated with a certain metabolic pathway.

The most significant metabolic pathway in our analysis is purine metabolism, which featured correlations with two canonical variables and the lowest values. Recent studies analyzed purine codon patterns in variable and constant regions of HIV-1 and showed that HIV-1 RNA exhibits extreme enrichment in the purine A compared with most organisms [36]. These data suggest that a potential therapeutic agent against HIV-1 may involve novel purine derivatives [37]. Studies have elucidated twenty-four purine derivatives that act as HIV-1 Tat TAR interaction inhibitors [38]. More recently, research revealed that host cells with a modified purine biosynthesis pathway exhibit increased activity by tenofovir against sensitive and drug resistant HIV-1 [39]. In this study, we show a high correlation between ARG and purine metabolism gene expression. These data imply that purine metabolism genes are significant candidates for studying the host genomic or transcriptome influence on AIDS.

5. Conclusions

In this study, we used a CCA to analyze the correlations between ARG and metabolic pathway gene expression. The results show that HIV-1 postentry cellular viral cofactors are highly coexpressed, which suggests that regulating this group of host genes may be a key factor in studies to understand the AIDS-host interaction mechanism. Furthermore, we show that purine metabolism pathway genes coordinate with ARGs; this novel discovery supports future studies on AIDS therapy using purine derivatives. Both coexpressed ARGs and metabolic pathway genes also provide a new marker for AIDS diagnosis.

Competing Interests

The authors declare no financial interest related to this work.

Authors’ Contributions

Hanhui Ye and Jinjin Yuan contributed equally to this work.

Acknowledgments

The study was supported by the Medical Innovation Project of Fujian Health Department (Grant no. 2015-CXB-28), the Scientific Foundation of Fuzhou City (Grant no. 2015-S-143-6), and the Key Clinical Specialty Discipline Construction Program of Fuzhou, Fujian, China (Grant no. 201510301).

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