BioMed Research International
Volume 2016 (2016), Article ID 1634730, 13 pages
http://dx.doi.org/10.1155/2016/1634730

Research Article

Rapid Identification of Potential Drugs for Diabetic Nephropathy Using Whole-Genome Expression Profiles of Glomeruli

Jingsong Shi, Song Jiang, Dandan Qiu, Weibo Le, Xiao Wang, Yinhui Lu, and Zhihong Liu

National Clinical Research Center of Kidney Diseases, Research Institute of Nephrology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210016, China

Received 23 October 2015; Revised 25 January 2016; Accepted 8 February 2016

Academic Editor: Gianluigi Zaza

Copyright © 2016 Jingsong Shi 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.

Objective. To investigate potential drugs for diabetic nephropathy (DN) using whole-genome expression profiles and the Connectivity Map (CMAP). Methodology. Eighteen Chinese Han DN patients and six normal controls were included in this study. Whole-genome expression profiles of microdissected glomeruli were measured using the Affymetrix human U133 plus 2.0 chip. Differentially expressed genes (DEGs) between late stage and early stage DN samples and the CMAP database were used to identify potential drugs for DN using bioinformatics methods. Results. (1) A total of 1065 DEGs (FDR < 0.05 and fold change > 1.5) were found in late stage DN patients compared with early stage DN patients. (2) Piperlongumine, 15d-PGJ2 (15-delta prostaglandin J2), vorinostat, and trichostatin A were predicted to be the most promising potential drugs for DN, acting as NF-κB inhibitors, histone deacetylase inhibitors (HDACIs), PI3K pathway inhibitors, or PPARγ agonists, respectively. Conclusion. Using whole-genome expression profiles and the CMAP database, we rapidly predicted potential DN drugs, and therapeutic potential was confirmed by previously published studies. Animal experiments and clinical trials are needed to confirm both the safety and efficacy of these drugs in the treatment of DN.

1. Introduction

Diabetic nephropathy (DN), which is clinically characterized by proteinuria and morphological and ultrastructural changes in the kidney, is a serious complication of diabetes mellitus and is a major cause of end-stage renal disease worldwide. DN is a multifactorial progressive disease with complex pathogenesis, involving hyperglycemia, advanced glycation end products (AGEs), hemodynamic disorder, metabolic abnormalities, inflammatory factors, and oxidative stress [1]. Although our knowledge of DN is continuously increasing, no treatment strategies specifically target the pathogenesis of DN beyond controlling glucose levels, blood lipid levels, and high blood pressure [2]. As a result, the prognosis for most DN patients is poor, especially for those in late stages of the disease. The identification of potential drugs targeting the molecular pathogenesis of DN is critical for improving the prognosis and survival of patients with DN.

Whole-genome expression profiling is the simultaneous measurement of the expression of thousands of genes by microarray technology (or RNA-Seq) to create a global picture of tissue or cellular function. Comparing the whole-genome expression profiles of tissues (or cells) under physiologic and pathologic conditions may reveal the pathogenesis of DN. In addition to identifying differentially expressed and coexpressed genes, from which one can generate new hypotheses about the molecular mechanism of complex diseases, whole-genome expression data are also used to identify therapeutic drugs. The Connectivity Map (CMAP) database is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules [3]. Lamb et al. findings showed that genomic signatures in the CMAP database can be used to identify potential new therapeutics, and signatures are often conserved across diverse cell types [3]. Therefore, the CMAP database can be used with whole-genome expression profiles to identify potential drugs for DN in the glomeruli of patients. Using the CMAP database, Zhong et al. predicted that the combination of an angiotensin-converting enzyme (ACE) inhibitor and a histone deacetylase inhibitor would maximally reverse the disease-associated expression of genes in a mouse model of HIV-associated nephropathy (Tg26 mice), and the renoprotective effect of the combined use of these inhibitors was proven in Tg26 mice [4]. It is feasible to utilize gene expression profiles of tissues under normal and physiopathological conditions to investigate potential therapeutic drugs based on bioinformatics methods. However, this kind of therapeutic drug identification in DN research is lacking. In our study, we utilized the gene expression profiles of microdissected glomeruli from DN patients and explored potential therapeutic drugs using in silico screening approaches.

2. Materials and Methods

2.1. Patients

The clinical study used a cross-sectional design. The control and DN kidney samples were obtained from leftover portions of diagnostic kidney biopsies. For the kidney biopsies, informed consent was obtained from the donors and patients. All of the participants provided written informed consent. The institutional review board of Jinling Hospital specifically approved this study [5].

A total of 18 DN patients diagnosed by renal biopsy were enrolled in the study. The baseline clinical characteristics of the DN patients are listed in Table 1.

Table 1: The baseline clinical and pathologic characteristics of DN patients.

Early stage
Late stage

6
12
—

Age (years)
45.9  ± 6.4
51.0  ± 6.8
0.103

Sex (female, %)
3 (50%)
3 (25%)
0.344

Ethnicity
Han
Han
—

BMI (kg/m²)
25.3  ± 1.5
24.9  ± 1.3
0.892

Serum creatinine (mg/dL)
0.63  ± 0.13
2.34  ± 0.74
0.000

eGFR (mL/min)
112.8  ± 8.1
32.9  ± 13.3
0.000

Proteinuria (g/24 h)
0.82  ± 0.47
5.18  ± 2.01
0.000

HbA1C (%)
8.4  ± 1.6
7.2  ± 1.9
0.146

BUN (mg/dL)
15.4  ± 4.8
35.8  ± 11.8
0.003

Glomerular lesions

0.000

    Class I
3
0

    Class IIa
3
0

    Class IIb
0
0

    Class III
0
7

    Class IV
0
5

IFTA

0.006

    0
2
0

    1
4
2

    2
0
4

    3
0
6

Interstitial inflammation

0.004

    0
2
0

    1
3
1

    2
1
11

Arteriolar hyalinosis

0.333

    0
0
0

    1
1
0

    2
5
12

Arteriosclerosis

0.504

    NA
0
0

    0
2
1

    1
1
2

    2
3
9

BMI: body mass index; eGFR: estimated glomerular filtration rate, calculated using the EPI-CKD formula; HbA1C: glycated hemoglobin; BUN: blood urea nitrogen. Values are presented as or means ± SD. values were obtained using the Wilcoxon rank sum test for continuous variables and Fisher’s exact test for categorical variables.

For each biopsy specimen, light microscopy, immunofluorescence, and electron microscopy were routinely performed. Sections for light microscopy were stained with hematoxylin eosin, periodic acid-Schiff, Masson’s trichrome, and periodic acid methenamine silver. All of the patients were categorized based on the pathologic classification of the Renal Pathology Society [5]. The glomerular classifications were as follows: class I, glomerular basement membrane thickening; class IIa, mild mesangial expansion; class IIb, severe mesangial expansion; class III, nodular sclerosis; and class IV, global glomerulosclerosis in >50% of glomeruli. Interstitial fibrosis and tubular atrophy (IFTA) were scored as follows: 0, absent; 1, <25%; 2, 25–50%; and 3, >50% of the total area. Interstitial inflammation was scored as follows: 0, absent; 1, inflammation only in relation to IFTA; and 2, inflammation in areas without IFTA. Arteriolar hyalinosis was scored as follows: 0, absent; 1, at least one area of arteriolar hyalinosis; and 2, more than one area of arteriolar hyalinosis. Arteriosclerosis was scored as follows: NA, absence of large vessels; 0, no intimal thickening; 1, intimal thickening less than thickness of media; and 2, intimal thickening greater than thickness of media. All of the specimens were scored by the same pathologist (Dr. Feng Xu) who was blinded to the clinical findings. In order to assess the reliability and reproducibility of the classification, biopsy slides were scored independently by another pathologist (Dr. Dandan Liang). The pathologic characteristics of the DN patients are listed in Table 1.

The DN patients were divided into 2 groups according to the following criteria: early stage DN group (), glomeruli isolated from the renal tissue of early stage DN patients who were identified with eGFR > 90 mL/min; late stage DN group (), glomeruli isolated from the renal tissue of late stage DN patients with eGFR between 15 mL/min and 60 mL/min. Control samples () were obtained from living donor kidney biopsies. Control subjects were defined as having an eGFR of more than 90 mL/min, the absence of proteinuria, normal serum creatinine, and BUN.

2.2. Tissue Handling and Microdissection

Tissues placed in RNALater (SIGMA, St. Louis, MO, USA) were manually microdissected at 4°C for glomeruli. In general, 10 glomeruli were collected from each biopsy tissue and were placed into cold RNeasy lysis buffer solution (Qiagen, Valencia, CA, USA).

2.3. RNA Extraction and Amplification

Dissected glomeruli were homogenized, and RNA was prepared using RNeasy mini columns (Qiagen, Valencia, CA, USA), according to the manufacturer’s instructions. RNA quality and quantity were determined using the Laboratory-on-Chip Total RNA Pico Kit Agilent Bioanalyzer. Samples without evidence of degradation were further amplified using the Ovation RNA amplification system kit (NuGEN, San Carlos, CA, USA).

2.4. Affymetrix Microarray Data and Preprocessing

The Affymetrix microarray platform (Human U133 Plus 2.0) was used to produce the whole-genome gene expression profile data. Quality control and data processing were performed using R [6] and Bioconductor [7]. The CEL source files were processed into expression estimates, and background correction and quartile data normalization were performed using the RMA (robust multiarray average) algorithm [8].

2.5. Screening of Differentially Expressed Genes (DEGs)

The limma package [9] in R language was used to screen DEGs by pairwise comparison between groups. The statistical method implemented in the limma package is based on an approach called linear models. We used the method proposed by Benjamini-Hochberg (BH) for multiple testing correction. The adjusted values were the false discovery rates (FDR). The threshold criterion is a combination of FDR < 0.05 and fold change > 1.5. The DEGs between late stage and early stage DN samples were chosen for DN drug identification.

2.6. Validation of Microarray Expression Data

The relative mRNA levels of 10 genes were validated in new selected glomerular samples. The clinical and pathologic characteristics of these DN patients are listed in Table 2. The processes used for patient screening, tissue handling, microdissection, and total RNA were performed as previously described. The mRNA levels of the target genes were analyzed by quantitative real-time RT-PCR (qRT-PCR) using the Applied Biosystems™ 7900HT Fast Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA). The qRT-PCR results were normalized to 18S ribosomal RNA using the method [10], and significance was set to .

Table 2: The baseline clinical and pathologic characteristics of DN patients for validation.

Early stage
Late stage

5
4
—

Age (years)
43.1  ± 11.8
52.1  ± 7.3
0.226

Sex (female, %)
3 (60%)
2 (50%)
1

Ethnicity
Han
Han
—

BMI (kg/m²)
25.5  ± 4.7
22.9  ± 2.3
0.342

Serum creatinine (mg/dL)
0.65  ± 0.15
1.76  ± 0.54
0.003

eGFR (mL/min)
112.3  ± 4.3
42.1  ± 17.4
0.000

Proteinuria (g/24 h)
0.50  ± 0.23
4.43  ± 0.41
0.000

HbA1C (%)
6.5  ± 0.9
7.3  ± 1.0
0.285

BUN (mg/dL)
14.2  ± 2.7
30.0  ± 6.8
0.002

Glomerular lesions

0.016

    Class I
1
0

    Class IIa
4
0

    Class IIb
0
0

    Class III
0
2

    Class IV
0
2

IFTA

0.087

    0
1
0

    1
4
1

    2
0
3

    3
0
0

Interstitial inflammation

0.286

    0
1
0

    1
4
2

    2
0
2

Arteriolar hyalinosis

1

    0
0
0

    1
0
0

    2
5
4

Arteriosclerosis

1

    NA
0
0

    0
1
0

    1
2
1

    2
2
3

2.7. Drug Identification Using a Kolmogorov-Smirnov (KS) Statistic Algorithm

From the CMAP database (http://www.broadinstitute.org/cmap/), we downloaded the ranked lists of probe tables. This table is freely available for download and provides the ranks of all genes in the form of probes based on the change of gene expression after approximately 6000 drug perturbation experiments. The table contains 22283 probes, which have 22277 common probes with Human U133 Plus 2.0. Finally, we obtained a table containing 24 samples and 22277 probes. With the limma package in R language, we obtained the 1000 largest changing probes between the late and early stage DN samples, and the upregulated probes and the downregulated probes were saved in GRP files. The CMAP website provides a KS statistic algorithm; we uploaded the GRP files as the query gene signature, which was then compared to each rank-ordered list to determine whether upregulated query genes appeared near the top of the list and downregulated query genes near the bottom (positive connectivity) or vice versa (negative connectivity), yielding a connectivity score ranging from 1 to −1. A high negative connectivity score indicated that the corresponding perturbagen reversed the expression of the query signature and might have the potential to treat DN.

2.8. Drug Identification Using a Matching Algorithm [4]

The IDs of the probe table downloaded from the CMAP database were converted to Entrez gene symbols using the Affymetrix lookup table associated with the platform. If more than one probe ID corresponded to the same gene, the gene rank was condensed to the median of the probe ranks. Then, we extracted the 500 top and bottom DEGs. Potential DN drugs should reverse the DEGs of DN; genes upregulated in DN should be downregulated by a potential DN drug and vice versa. The DEGs (FDR < 0.05 and fold change > 1.5) between the late and early groups were used to calculate the reversing scores using formula (1). The perturbagen with the highest reversing scores might have the potential to treat DN. Consider

where indicates the reversing score for a drug in th experiment in the CMAP database; indicates the intersection between two sets; up indicates a list of upregulated genes during disease; down indicates a list of downregulated genes during disease; indicates genes upregulated by a drug in the th experiment in the CMAP database; and indicates genes downregulated by a drug in the th experiment in the CMAP database.

2.9. Functional Enrichment Analysis

The functional enrichment analysis of the screened DEGs and the genes reversed by potential drugs was performed via the GeneAnswers package in R language [11]. The GeneAnswers package functionally categorizes genes based on Fisher’s exact test with annotation libraries of the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG).

3. Results

3.1. DEGs in the Glomeruli of DN Patients

We separately compared the samples in the 2 stages of DN with the controls. A total of 105 DEGs were identified between the early stage DN samples and the controls, including 54 upregulated genes and 51 downregulated genes; 2572 DEGs were identified between the late stage DN samples and the controls, including 1626 upregulated genes and 946 downregulated genes.

Only 105 DEGs were identified between the early stage DN samples and the controls, and the enriched GO categories were primarily involved in “response to stimulus.” These results were in accordance with the mild pathological changes in the glomeruli from early stage DN patients.

In contrast, 1065 DEGs were identified between the late and early stages of DN samples, including 815 upregulated genes and 250 downregulated genes. The heat map in Figure 1 shows the expression levels of the top 100 regulated genes across the 24 samples. As shown in Figure 2, the most enriched GO categories of the DEGs were “extracellular matrix,” “protein binding,” “cell adhesion,” and “immune system process.” The most enriched KEGG pathways were “ECM-receptor interaction,” “complement and coagulation cascades,” “focal adhesion,” “cytokine-cytokine receptor interaction,” and “PI3K-Akt signaling pathway.” These GO categories and KEGG pathways are closely related to DN progression [12, 13].

Figure 1: Heat map of the top 100 DEGs between glomeruli in the late and early stages of DN.

(a) Cellular component ontology

(b) Molecular function ontology

(c) Biological process ontology

(d) KEGG pathway enrichment

Figure 2: GO category (cellular component, molecular function, and biological process) and KEGG pathways enriched in the DEGs between glomeruli in the late and early stages of DN.

As shown in Figure 3, qRT-PCR analysis was performed to confirm the degree and direction of the expression changes in 10 genes. All 10 genes assayed by qRT-PCR were found to be significantly differentially expressed in the microarray analysis between the late and early stage DN samples. As determined by qRT-PCR analysis, 10 out of the 10 selected genes demonstrated a change in expression in the same direction (i.e., up- or downregulated) (Figure 3(a)). Similarly, the direction of the change in gene expression determined by qRT-PCR analysis agreed with the directions obtained for the genes that were found to be significantly differentially expressed by microarray analysis between the early stage DN samples and the controls (5 out of 5 genes, Figure 3(b)) and between the late stage DN samples and the controls (10 out of 10 genes, Figure 3(c)).

(a)

(b)

(c)

Figure 3: qRT-PCR confirmation. qRT-PCR was performed to confirm the direction of the fold change in expression (shown as log₂ fold change). The gene expression changes determined by qRT-PCR were compared with those obtained from the microarray analysis of late versus early stage DN (a), early stage DN versus Ctrl (b), and late stage DN versus Ctrl (c). .

3.2. Potential DN Drugs Predicted by the KS Statistic Algorithm

To explore the potential drugs targeting the molecular mechanisms of DN, we used the DEGs between the late and early stages of DN. These genes were enriched for their specific contribution to nephropathy because genes that are differentially regulated in human diabetes per se, in the absence of nephropathy, were excluded by this strategy. After uploading 812 upregulated and 188 downregulated probe IDs to the CMAP database, the top 20 drug perturbations that most strongly reversed the DRGs are listed in Table 3. Among these drugs, MG-132 [14] and MG-262 [15] are proteasome inhibitors, piperlongumine inhibits PI3K/Akt/mTOR signaling [16] and NF-κB activity [17], 15d-PGJ2 (15-delta prostaglandin J2) activates PPARγ [18], and vorinostat and trichostatin A are histone deacetylase inhibitors (HDACIs).

Table 3: Top 20 drug perturbations with high negative connectivity scores.

Rank
Instance ID
CMAP name
Dose
Cell
Score
Up
Down

1
1274
Bepridil
10 µM
HL60
−1
−0.077
0.223

2
7063
MG-262
100 nM
MCF7
−0.96
−0.163
0.125

3
7345
Alcuronium chloride
5 µM
MCF7
−0.953
−0.108
0.178

4
942
Prazosin
10 µM
MCF7
−0.953
−0.111
0.175

5
1764
Piperlongumine
13 µM
HL60
−0.94
−0.071
0.212

6
7022
Dyclonine
12 µM
MCF7
−0.931
−0.104
0.175

7
3351
(±)-Catechin
14 µM
MCF7
−0.923
−0.126
0.151

8
7017
Mesoridazine
7 µM
MCF7
−0.922
−0.101
0.176

9
909
HC toxin
100 nM
MCF7
−0.921
−0.107
0.17

10
1694
Metformin
24 µM
MCF7
−0.909
−0.102
0.17

11
1656
15d-PGJ2
10 µM
MCF7
−0.901
−0.112
0.158

12
7020
Xylometazoline
14 µM
MCF7
−0.898
−0.1
0.17

13
1058
Vorinostat
10 µM
MCF7
−0.898
−0.12
0.15

14
7178
Tetrandrine
6 µM
MCF7
−0.897
−0.151
0.118

15
1069
15d-PGJ2
10 µM
MCF7
−0.895
−0.12
0.149

16
1140
MG-132
21 µM
MCF7
−0.884
−0.122
0.143

17
1112
Trichostatin A
100 nM
MCF7
−0.882
−0.104
0.161

18
6936
Chlorpromazine
1 µM
MCF7
−0.88
−0.115
0.149

19
5310
Puromycin
7 µM
MCF7
−0.874
−0.111
0.151

20
5304
Moroxydine
19 µM
MCF7
−0.874
−0.125
0.137

Instance: a treatment and control pair and the list of probe sets ordered by their extent of differential expression between this treatment and control pair; instance ID: the ID uniquely identifying each instance; CMAP name: the name given to a perturbagen; dose: perturbagen dose; cell: cell line; up: the up score, a value between +1 and −1 representing the absolute enrichment of an up tag list in a given instance; down: the down score, a value between +1 and −1 representing the absolute enrichment of a down tag list in a given instance; score: the connectivity score, a combination of the up score and the down score. A high negative connectivity score indicates that the corresponding perturbagen reversed the expression of the query signature.

3.3. Potential DN Drugs Predicted by the Matching Algorithm

We further utilized a matching algorithm and the DEGs between early and late stage DN samples to calculate the reversing score for each drug in the CMAP database. The top 20 drug perturbations that had the highest scores are listed in Table 4. We clustered the drugs by the similarity of reversed DEGs, and the drugs with similar pharmacological characteristics were clustered together (Figure 4). For example, HDACIs, including vorinostat, trichostatin A, and valproic acid, were clustered together. Among these drugs, piperlongumine, 15d-PGJ2, vorinostat, and trichostatin A were also identified by the KS statistic algorithm. Valproic acid and parthenolide are also histone deacetylase inhibitors [19], resveratrol inhibits cAMP phosphodiesterase [20], and LY-294002 inhibits PI3K [21].

Table 4: Top 20 drug perturbations with high reversing scores.

Perturbagen name-instance ID
Score
Reversed
Aggravated

Vorinostat-6179
46
75
29

15d-PGJ2-1231
43
65
22

Piperlongumine-1764
36
55
19

Resveratrol-662
34
54
20

Ciclopirox-2456
33
67
34

Resveratrol-1715
32
59
27

LY-294002-6186
32
63
31

Valproic acid-410
31
53
22

Trichostatin A-1175
31
59
28

Ethoxyquin-3764
31
57
26

0173570-0000-7389
31
68
37

Parthenolide-1736
30
47
17

Ellipticine-1765
30
46
16

Etoposide-3241
30
73
43

Withaferin A-3819
30
68
38

Vorinostat-1161
29
57
28

Naproxen-2533
29
63
34

Rosiglitazone-2693
29
55
26

Monobenzone-3391
29
74
45

Alclometasone-5752
29
63
34

Perturbagen name: the name given to a perturbagen; instance ID: the ID uniquely identifying each instance; score: the reversing score calculated using formula (1); reversed: number of genes in the query signature, the expression of these genes was reversed by the corresponding perturbagen; aggravated: number of genes in the query signature, the expression of these genes was aggravated by the corresponding perturbagen.

Figure 4: The heat map and hierarchical cluster of effects of the top 20 drug perturbations. Each row indicates a drug perturbation, and each column represents a gene. The blue vertical bars in the heat map indicate that the gene was reversed by the corresponding perturbagen, whereas the red bars indicate that the gene was aggravated by the corresponding perturbagen. Drug perturbations having similar effects were clustered together.

3.4. Functional Enrichment Analysis of the Drug-Reversed Genes

Piperlongumine, 15d-PGJ2, vorinostat, and trichostatin A were identified by both algorithms. The genes that could be reversed by these drugs are shown in Table 5. To indicate the target molecular mechanisms of these drugs, we conducted a functional analysis of these potentially reversed genes in the glomeruli of DN patients (Figure 5). The genes reversed by piperlongumine are mostly involved in the “immune response,” “response to stimulus,” “complement and coagulation cascades,” “NF-κB signaling pathway,” and “Toll-like receptor signaling pathway.” The genes reversed by 15d-PGJ2 are primarily involved in “cell division,” “FoxO signaling pathway,” and “cytokine-cytokine receptor interaction,” and the genes reversed by vorinostat are mostly involved in the “immune response,” “response to stimulus,” “signal transduction,” and “osteoclast differentiation.” The genes reversed by trichostatin A are mostly involved in the “immune response,” “response to stress,” “signal transduction,” “cell migration,” and “osteoclast differentiation.”

Table 5: DEGs reversed by drug perturbations.

Drugs
Direction
Genes

Piperlongumine
Decrease
CLEC5A, TNFAIP6, FCGR1B, C3, NEIL3, RNASE6, C3AR1, SAMSN1, CAPG, CD86, TREM1, IRF8, EVI2A, PBK, FCGR2A, RGS13, KCNN4, GPR183, TLR2, GINS2, CLEC7A, RRM2, RGS1, CD300A, CD14, CTSC, TMEM158, CDCA3, NCF2, SYK, IL1B, GINS1, SLC43A3, PSRC1, MYC, FILIP1L, MYO1F, PLAUR, PFKP, CCR1, HELLS, PLAU, PTPRE, MNDA, TNFAIP8, MAD2L1, CYP1B1, RASSF2, GALNT7, ECT2, CD300C, CDC45

Increase
HPS5, EAF2, RND1

15d-PGJ2
Decrease
MLPH, CXCL6, TOP2A, LPAR1, GPR39, DLGAP5, HMGA2, SCG5, CXCL1, PTX3, INHBA, TNFRSF11A, COL5A2, TENM4, ASPM, KIF18B, CENPF, KIF20A, IL33, SYTL2, CCNB2, CCNA1, HJURP, CDC20, CDH11, LMNB1, NCAPH, MKI67, CDCA3, FST, TTK, NDP, GINS1, MTCL1, PSRC1, NEK2, HAS2, CDCA8, NPM3, ETV1, KIF2C, FAM110B, PRSS23, CDK1, PLAU, MALL, P2RY6, AURKB, BNC2, FJX1, CCNB1, TROAP, IGSF3, CYP1B1, ECT2, TK1, IRS1

Increase
PRKCE, SLC16A10, ERVMER34-1, EAF2, RND1, EGF, ZNF804A, SERPINI2

Vorinostat
Decrease
CLEC5A, PLA2G7, CTSG, FCGR1A, FCGR1B, VCAN, LAIR1, BCAT1, HP, PTAFR, C3AR1, ADORA3, LILRB4, HGF, IGSF6, CCR2, NCF4, MS4A6A, CD33, MMP9, GNA15, EVI2A, TLR1, ATP8B4, CACNA2D3, LILRB1, KCNN4, GPR183, ANXA3, TLR2, CLEC7A, RRM2, RGS1, LMNB1, NINJ2, CD300A, CSF2RA, NCAPH, CD14, INHBE, LILRA2, SIGLEC9, LAT2, SYK, SLC38A1, SLC43A3, MYC, GLIPR1, PTENP1, MYO1F, PSTPIP1, PLAUR, CCR1, PLAU, DOK3, SELPLG, SASH3, P2RY6, VAV1, PTPRE, MNDA, SPI1, DEF6, CYBRD1, CXorf21, CYP1B1, RASSF2, DOK2

Increase
ACOX2, EXPH5, PRKAR2B, GDPD3, MLXIPL, RND1, CTSV

Trichostatin A
Decrease
CLEC5A, FCGR1B, VCAN, LAIR1, RNASE6, C3AR1, HGF, IGSF6, NCF4, MS4A6A, SLC7A11, EVI2A, KIF20A, ATP8B4, CACNA2D3, RGS13, KCNN4, GPR183, ANXA3, CLEC7A, RRM2, RGS1, LMNB1, CD300A, CD14, INHBE, PYCARD, LILRA2, SYK, SLC38A1, SLC43A3, PIK3CG, MYC, FILIP1L, PSTPIP1, PLAUR, FOXM1, PLAU, SASH3, P2RY6, VAV1, MNDA, SPI1, CSF3R, CYBRD1, CXorf21, MICAL1, CYP1B1, RCC1, P2RX1, ARRB2, RASSF2, GALNT7, DOK2, FAM129A

Increase
PRKAR2B, CTNNBIP1, RND1, CTSV

(a) GO enrichment

(b) KEGG pathway enrichment

(c) GO enrichment

(d) KEGG pathway enrichment

(e) GO enrichment

(f) KEGG pathway enrichment

(g) GO enrichment

(h) KEGG pathway enrichment

Figure 5: GO categories and KEGG pathways enriched in the DEGs that were reversed by the drug perturbation. (a), (b) GO categories and KEGG pathways enriched in the DEGs that were reversed by piperlongumine; (c), (d) GO categories and KEGG pathways enriched in the DEGs that were reversed by 15d-PGJ2; (e), (f) GO categories and KEGG pathways enriched in the DEGs that were reversed by vorinostat; (g), (h) GO categories and KEGG pathways enriched in the DEGs that were reversed by trichostatin A.

4. Discussion

Current clinical strategies to treat DN focus on the intensification of glycemic control and the control of blood pressure and blood fat. Renoprotective drugs based on the molecular pathogenesis of DN are unavailable because the molecular pathogenesis of DN is complicated. Recently, high-throughput transcriptome technology has been used to explore the molecular pathogenesis of complex diseases [22, 23], and drug screening methods based on transcriptome data have attracted increasing attention [4, 24].

The most common kidney lesions in people with diabetes are those that affect the glomeruli, and DN is characterized by morphological and ultrastructural changes in the kidney, including expansion of the molecular matrix and loss of the charge barrier on the glomerular basement membrane [25]. The gene expression profiles of glomeruli microdissected from DN biopsy samples will provide an excellent opportunity to explore the molecular mechanisms of this complex disease and to identify potential drugs.

In this study, we obtained the whole-genome transcriptome profiles of glomeruli from DN patients and normal controls. The glomeruli contain podocytes, endothelial cells, and mesangial cells, whereas the CMAP database was built upon four types of cultured human cell lines [3]. Because the signatures of drugs are often conserved across diverse cell types [3], we can utilize the CMAP database to identify potential drugs for DN.

There were only 105 DEGs between early stage DN samples and controls, mainly involving “response to stimulus.” These results indicated that there was minimal gene expression change involving molecular pathogenesis in the early stage of DN. The DEGs between the late and early stages of DN samples were related to “extracellular matrix,” “cell adhesion,” “immune system process,” “ECM-receptor interaction,” “complement and coagulation cascades,” “cytokine-cytokine receptor interaction,” and “PI3K-Akt signaling pathway” (Figure 2). These functional categories and pathways have been widely related to the pathogenesis of DN [13]. Coexpression network analysis and association analysis indicated that some DEGs between DN samples and controls exhibit no correlation with the progression or prognosis of DN (data not shown). These genes, which may be differentially regulated in human diabetes per se, were excluded from the comparison of the late and early stage DN samples. Therefore, it is more suitable to use the DEGs between the late and early stages to identify potential therapeutic drugs for DN treatment.

A KS statistic algorithm and a matching algorithm were applied in this study. However, using either algorithm, some drugs among the top 20 are unlikely to have renal protection on the basis of prior clinical and pharmacological knowledge. Therefore, the results based on the two different algorithms were combined to enhance the reliability of the potential therapeutic drugs, which provided a good foundation for the in vitro and in vivo studies.

Piperlongumine, 15d-PGJ2, vorinostat, and trichostatin A were identified by both algorithms. The molecular mechanisms of the 4 drugs include inhibition of NF-κB activity, histone deacetylase (HDAC) activity, PI3K-Akt signaling pathway, and the activation of PPARγ. The transcription factor NF-κB is induced by various cell stress-associated stimuli, including growth factors, vasoactive agents, cytokines, and oxidative stress. NF-κB in turn controls the regulation of genes encoding proteins involved in immune and inflammatory responses. The activation and nuclear translocation of NF-κB in human DN have been demonstrated in the intrinsic cells of the kidney [26]. Upregulation of HDACs has been reported in the kidneys of patients with DN as well as in type 1 and type 2 in vivo animal models of diabetes [27]. HDACIs have anti-inflammatory and antifibrotic effects in the kidney and may prove to be a novel class of agents in the treatment of diabetic nephropathy [27]. The PI3K-Akt signaling pathway is directly related to cellular proliferation, migration, differentiation, and survival. There are many known factors that enhance the PI3K-Akt pathway, including insulin, EGF, and IGF-1. In rat mesangial cells and db/db mice, high-glucose decreased the expression of MNSOD via the PI3K-Akt signaling pathway and further aggravated oxidative stress [28]. The nuclear receptor PPARγ is located in all three types of glomerular cells, with prominent expression in podocytes. PPARγ agonists, which have emerged as promising candidates for treating DN, are effective in delaying and even preventing disease progression.

Piperlongumine can inhibit both NF-κB activation and the PI3K-Akt signaling pathway [16, 17] and increase mRNA levels of PPARγ2 [29]. Piperlongumine is a main component of the root of Piper longum, a plant used by some Indian tribes to treat diabetes, digestive disorders, and obesity [30]. In streptozotocin- (STZ-) induced diabetic rats, Piper longum root aqueous extract can significantly decrease fasting blood glucose levels and protect liver and kidney function [30].

15d-PGJ2 is the endogenous ligand of PPARγ and can regulate metabolism of adipose tissue and restrain insulin resistance [18]. In rat mesangial cells, 15d-PGJ2 significantly decreased alpha-smooth muscle actin (α-SMA) expression, a marker of mesangial cell dedifferentiation. In mouse mesangial cells, 15d-PGJ2 repressed TGF-β1-mediated α-SMA, fibronectin, and plasminogen activator inhibitor-1 expression; induced HGF expression; and attenuated Smad nuclear translocation in response to TGF-β1 stimulation [31]. In rat renal interstitial fibroblasts, 15d-PGJ2 inhibited TGF-β1-induced renal fibroblast activation, CTGF expression, and ECM synthesis through abrogating the TGF-β1/Smad signaling pathway [32].

Vorinostat and trichostatin A belong to the same group of HDACIs based on their chemical structures (hydroxamic acid). They are broad inhibitors of HDAC activity and inhibit class I and class II enzymes [33]. Vorinostat is FDA approved for use against refractory cutaneous T cell lymphoma [34, 35]. HDACIs have beneficial effects in diabetic nephropathy. In cultured proximal tubule cells, vorinostat treatment reduced EGFR protein and mRNA and attenuated cellular proliferation [36]. Daily treatment of diabetic rats with vorinostat for 4 weeks blunted renal growth and glomerular hypertrophy [36]. In STZ-induced diabetic mice, long-term administration of vorinostat decreased albuminuria, mesangial collagen IV deposition, and oxidative-nitrosative stress through an eNOS-dependent mechanism [37]. In STZ-induced diabetic rats, trichostatin A prevented extracellular matrix accumulation and epithelial-to-mesenchymal transition in diabetic kidney [38].

In addition to these four drugs, we have identified potential therapeutic drugs to treat DN. Proteasome inhibitors, including MG-132 and MG-262, have anti-inflammatory and antifibrotic effects [15]. MG132 alleviates kidney damage by inhibiting Smad7 ubiquitin degradation, SnoN degradation, and TGF-β activation in STZ-induced DN rats [39, 40]. Valproic acid, another histone deacetylase inhibitor, has beneficial effects on proteinuria, glomerulosclerosis, and renal inflammation in Adriamycin-induced nephropathic mice [41]. LY-294002, a PI3K inhibitor, prevented the quantitative and distributional changes of CD2AP induced by high-glucose and advanced glycosylation end products in mouse podocytes [42]. In db/db mice, LY-294002 decreased the levels of phosphorylated Akt and phosphorylated FoxO3a, increased the level of MnSOD expression, and further decreased oxidative stress [28]. Resveratrol, a phosphodiesterase inhibitor, improved diabetic nephropathy in several animal models of types 1 and 2 diabetes through its antioxidative effects resulting from direct radical scavenging or modulation of antioxidant enzymes [43].

In summary, this study utilized the gene expression profiles of glomeruli from DN patients to identify potential drugs for DN using the CMAP database and bioinformatics methods. Four drugs were identified by two different algorithms, and therapeutic potential was shown to be promising by literature analysis. Our study provides useful information for further animal experiments and clinical trials to confirm both the safety and efficacy of these drugs.

Competing Interests

The authors have no competing interests to declare. All of the authors have approved the final version of the paper and agreed to submit it for publication.

Authors’ Contributions

Jingsong Shi and Song Jiang contributed equally to the paper.

Acknowledgments

The work was supported by grants from the National Key Technology R&D Program (2013BAI09B04) and the National Natural Science Foundation of China (81500556, 81500548).

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