Molecules 2012, 17(10), 12406-12426; doi:10.3390/molecules171012406

Article
The Effect of Curcumin on Human Bronchial Epithelial Cells Exposed to Fine Particulate Matter: A Predictive Analysis
Zhiguo Zhang 1,, Xuyan Niu 2,, Cheng Lu 2, Miao Jiang 2, Gary G. Xiao 3,* and Aiping Lu 2,4,*
1
Institute of Basic Theory, China Academy of Chinese Medical Sciences, No.16 Nanxiaojie, Dongzhimennei, Beijing 100700, China; Email: zzgtcm@163.com
2
Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No.16 Nanxiaojie, Dongzhimennei, Beijing 100700, China; Email: niuxuyan@yahoo.cn (X.N.); lv_cheng0816@163.com (C.L.); miao_jm@vip.126.com (M.J.)
3
Functional Genomics & Proteomics Laboratory, Osteoporosis Research Center, Creighton University Medical Center, 601N 30th ST, Suite 6730, Omaha, NE 68131, USA
4
School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Kowloon Tong, Hong Kong, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed; Email: lap64067611@126.com (A.L.); gxiao@creighton.edu (G.G.X.); Tel.: +86-10-6406-7611 (A.L.); Fax: +86-10-8403-2881 (A.L.); Tel.: +1-402-280-5911 (G.G.X.); Fax: +1-402-280-4284 (G.G.X.).
Received: 12 July 2012; in revised form: 15 October 2012 / Accepted: 16 October 2012 /
Published: 22 October 2012

Abstract

: Fine particulate matter (PM2.5) has been associated in humans with inflammation, oxidative stress and cancer. Studies had shown that curcumin could potentially inhibit these effects; however, there had been no in vivo or in vitro reports about the effects of curcumin on organisms exposed to PM2.5. This predictive study explored the possible biological functions and pathways involved in the mechanism of curcumin inhibition of the hazardous effects of PM2.5. For predictive analysis, microarray data were used to investigate the effect of PM2.5 on human bronchial epithelial cells (HBEC), and human target proteins of curcumin were retrieved from PubChem. Two protein-protein interaction (PPI) networks were established based upon differential genes and target proteins, respectively, and the common network of these two networks was found. Functional and pathway analysis of the common network was performed using the Ingenuity Pathways Analysis (IPA) software. The results suggested that the predictive effects of curcumin on HBEC exposed to PM2.5 were involved in bio-functions, including inflammatory response of airway, cancerogenesis, and apoptosis, and in pathways such as cancer, glucocorticoid receptor signaling, and NF-kappaB signaling. This study predicted for the first time that curcumin could be a potential therapeutic agent for protecting the human airway from the hazardous effects of PM2.5.
Keywords:
curcumin; fine particulate matter; protein-protein interaction network; bioinformatics prediction; pathway analysis

1. Introduction

Air pollution had long been considered a hazard to human health. Ambient airborne particulate matter (PM), an important environmental pollutant, had been associated with multiple cardiopulmonary diseases and cancers [1]. In the past few decades, many studies had highlighted the role of the size and surface area of PM in determining the potential to elicit inflammatory injury, oxidative damage, and other biological effects [2]. These effects were stronger for fine particles (diameter < 2.5 μm, known as PM2.5), because they could penetrate deeper into the airways of the respiratory tract and reach the alveoli, where 50% of the PM2.5 were retained in the lung parenchyma [3]. In recent years, the hazardous effects of PM2.5 had captured more and more public attention. However, do we have other methods to protect us from the hazardous of PM2.5 in addition to reducing the discharge of PM2.5 into the atmosphere? Furthermore, can certain food or herbal additives intake actively defend the body against the damaging effects of PM2.5?

Curcumin, a yellow pigment extracted from the rhizome of the plant Curcuma longa (turmeric), had been widely used as a spice, food additive, and herbal medicine in Asia [4]. In recent years, extensive in vitro and in vivo studies had suggested that curcumin had anticancer, antiviral, antiarthritic, anti-amyloid, antioxidant, anti-inflammatory, and anti-aging properties [5]. Interestingly, these therapeutic effects of curcumin were in direct opposition to the detrimental effects of PM2.5. Therefore, we speculated that curcumin as a therapeutic agent might control or decrease the damage induced by PM2.5. In the present study, we predicted the underlying protective mechanism of curcumin on human airway epithelial cells (HBEC) exposed to PM2.5 based on gene expression profiling in Gene Expression Omnibus (GEO) and target protein data in PubChem.

2. Results and Discussion

2.1. Results

Using a t-test, we identified 89 genes differentially expressed between HBEC exposed to PM2.5 and vehicle control (Table 1). These genes could clearly distinguish primary HBEC exposed to PM2.5 from the HBEC in control. Of the 89 genes, 38 genes were significantly up-regulated and 51 genes were remarkably down-regulated.

Table Table 1. Differentially expressed genes in HBEC exposed to PM2.5 versus control.

Click here to display table

Table 1. Differentially expressed genes in HBEC exposed to PM2.5 versus control.
Probe Set IDRefSeq IDGene Symbolp-valueFold ChangeRegulation
203665_atNM_002133HMOX10.004524.58 up
209921_atNM_014331SLC7A110.000410.45 up
202436_s_atNM_000104CYP1B10.00157.02 up
201266_atNM_003330TXNRD10.00146.96 up
205749_atNM_000499CYP1A10.01855.54 up
203925_atNM_002061GCLM0.00063.71 up
202923_s_atNM_001498GCLC0.01103.29 up
201468_s_atNM_000903NQO10.01392.92 up
204151_x_atNM_001353AKR1C10.00422.91 up
206172_atNM_000640IL13RA20.01102.72 up
211653_x_atNM_001354AKR1C20.00832.70 up
209387_s_atNM_014220TM4SF10.04722.47 up
210845_s_atNM_002659PLAUR0.01042.18 up
206683_atNM_003447ZNF1650.00902.14 up
212907_atNM_021194SLC30A10.03572.14 up
214211_atNM_002032 FTH10.02702.08 up
208963_x_atNM_013402FADS10.02022.03 up
205767_atNM_001432EREG0.01601.98 up
219475_atNM_182981OSGIN10.01031.98 up
207675_x_atNM_057091ARTN0.03131.97 up
202842_s_atNM_012328DNAJB90.03091.96 up
202266_atNM_016614TDP20.00011.95 up
201625_s_atNM_005542INSIG10.04461.93 up
209882_atNM_006912RIT10.01141.93 up
201489_atNM_005729PPIF0.00931.92 up
213112_s_atNM_003900SQSTM10.01911.91 up
204420_atNM_005438FOSL10.03231.80 up
202284_s_atNM_000389CDKN1A0.03241.77 up
206907_atNM_003811TNFSF90.00321.74 up
219697_atNM_006043HS3ST20.02911.72 up
204970_s_atNM_002359MAFG0.00321.69 up
213187_x_atNM_000146FTL0.04711.68 up
212717_atNM_014798PLEKHM10.03191.66 up
206498_atNM_000275OCA20.02211.66 up
202672_s_atNM_001674ATF30.00411.57 up
202021_x_atNM_005801EIF10.04601.55 up
202067_s_atNM_000527LDLR0.01281.54 up
204958_atNM_004073PLK30.01531.50 up
202207_atNM_005737ARL4C0.01393.24 down
202887_s_atNM_019058DDIT40.01232.66 down
201890_atNM_001034RRM20.00972.26 down
211450_s_atNM_000179MSH60.04862.26 down
201849_atNM_004052BNIP30.02932.20 down
219250_s_atNM_013281FLRT30.04252.10 down
209120_atNM_021005NR2F20.00562.01 down
202464_s_atNM_004566PFKFB30.02891.99 down
208808_s_atNM_002129HMGB20.04621.99 down
203344_s_atNM_002894RBBP80.02801.97 down
218718_atNM_016205PDGFC0.00441.97 down
207173_x_atNM_001797CDH110.04321.95 down
201669_s_atNM_002356MARCKS0.04481.92 down
207826_s_atNM_002167ID30.02851.84 down
204967_atNM_001649SHROOM20.01411.80 down
202628_s_atNM_000602SERPINE10.04861.77 down
212599_atNM_015570AUTS20.00531.77 down
203274_atNM_012151F8A10.01181.76 down
208673_s_atNM_003017SRSF30.01881.76 down
203476_atNM_006670TPBG0.04001.75 down
209189_atNM_005252FOS0.03661.72 down
209784_s_atNM_002226JAG20.00321.70 down
203625_x_atNM_005983SKP20.00401.70 down
222036_s_atNM_005914MCM40.04391.66 down
202219_atNM_005629SLC6A80.02531.65 down
205449_atNM_013299SAC3D10.03281.65 down
212168_atNM_006047RBM120.00311.64 down
209286_atNM_006449CDC42EP30.00601.63 down
204334_atNM_003709KLF70.01051.63 down
208579_x_atNM_017445H2BFS0.01731.62 down
204069_atNM_002398MEIS10.02811.60 down
203797_atNM_003385VSNL10.01721.58 down
203764_atNM_014750DLGAP50.01811.58 down
213051_atNM_020119ZC3HAV10.01041.58 down
208051_s_atNM_006451PAIP10.03211.57 down
203405_atNM_003720PSMG10.03041.57 down
211744_s_atNM_001779CD580.02731.57 down
206277_atNM_002564P2RY20.01791.56 down
204715_atNM_015368PANX10.03751.56 down
201312_s_atNM_003022SH3BGRL0.03831.55 down
213088_s_atNM_015190DNAJC90.02531.55 down
203803_atNM_016297PCYOX10.03501.54 down
201624_atNM_001349DARS0.02251.54 down
214214_s_atNM_001212C1QBP0.04681.54 down
212320_atNM_178014TUBB0.01851.53 down
208405_s_atNM_006016CD1640.04651.51 down
213019_atNM_012416RANBP60.00021.51 down
212922_s_atNM_020197SMYD20.00021.50 down
209025_s_atNM_006372SYNCRIP0.04811.50 down
201163_s_atNM_001553IGFBP70.04581.50 down
214800_x_atNM_001207BTF30.00361.50 down

57 human target proteins of curcumin (CID: 969516) were obtained from the PubChem database by PubChem Promiscuity online and identified by UniProt protein IDs (Table 2).

Table Table 2. Human target proteins of curcumin in PubChem.

Click here to display table

Table 2. Human target proteins of curcumin in PubChem.
GIUniProtKB ID
45079491433B_HUMAN
31542303ABHD5_HUMAN
37622910ACM1_HUMAN
21361176AL1A1_HUMAN
4885057APJ_HUMAN
47132611ATG4B_HUMAN
6683500BAZ2B_HUMAN
53832009CAC1H_HUMAN
4502601CBR3_HUMAN
37187860CCR6_HUMAN
67551261CLK1_HUMAN
153791372CLK3_HUMAN
13435386CP3A4_HUMAN
32307159CRFR2_HUMAN
30219CRHBP_HUMAN
4503383DRD1_HUMAN
4503385DRD2_HUMAN
10835013ESR2_HUMAN
4885263GEM_HUMAN
122921310HCD2_HUMAN
155969707IDE_HUMAN
98986450KC1G1_HUMAN
153791733KC1G2_HUMAN
325651834KCNH2_HUMAN
221046486KD4DL_HUMAN
22035600M4K2_HUMAN
11386165MCL1_HUMAN
89993689MDM2_HUMAN
88702791MDM4_HUMAN
20986531MK01_HUMAN
4505209MMP13_HUMAN
66911845MRGX1_HUMAN
34577122NFKB1_HUMAN
222080095OX1R_HUMAN
32307152OXYR_HUMAN
4505587PA1B3_HUMAN
5031975PAK4_HUMAN
31881630PE2R2_HUMAN
31542939PGDH_HUMAN
4505811PIM1_HUMAN
42821112PIM2_HUMAN
223718196PLIN1_HUMAN
116734717PPBT_HUMAN
4826962RAC3_HUMAN
41281453SLK_HUMAN
23943882STK33_HUMAN
8400711TAU_HUMAN
223468676TF65_HUMAN
4507533TLR4_HUMAN
8394456TLR9_HUMAN
4507615TNNC1_HUMAN
151101270TNNI3_HUMAN
48255881TNNT2_HUMAN
4507681TRFR_HUMAN
118600387UBP1_HUMAN
4502331V1AR_HUMAN
4507883VDR_HUMAN

Based on the differentially expressed genes in Table 1 and human target proteins in Table 2, two biological networks showing protein-protein interactions were constructed. The two protein-protein interaction (PPI) networks were visualized using Cytoscape. The nodes represented proteins in the PPI network and the edges represented the biological relationship between two nodes. There were 1,962 nodes and 15,455 edges in the PPI network of HBEC exposed to PM2.5 (Supplementary Material Figure S1), and 1,284 nodes and 11,541 edges in the PPI network of human target proteins of curcumin (Supplementary Material Figure S2). Appling the function “Intersection” of the Advanced Network Merge plugin in Cytoscape, we found the common proteins and relationships (common network) in the two PPI networks. The common network had 1,197 nodes and 9,521 edges (Figure 1).

The top five functions of the common network and the number of proteins associated with each function were found using Ingenuity Pathways Analysis (IPA). The most significant biological functions were grouped into three categories: (1) Diseases and Disorders, (2) Molecular and Cellular Functions, and (3) Physiological System Development and Function (Table 3).

Table 4 lists the top five canonical pathways associated with the common network as calculated by IPA (Figure 2, Figure 3, Figure 4 and Figure 5, Supplementary Material 3). Calculation was either according to ratio (the number of genes from the data set that map to the canonical pathway in question divided by the total number of proteins that map to the same canonical pathway) or significance (Fischer’s exact test was used to calculate a P-value determining the probability that the association between the proteins in the dataset and the canonical pathway was explained by chance alone).

To partially validate the pathways listed in Table 4, we measured the expression of NF-kappaB p65 and IL-6 in human bronchial epithelial cells (16HBE) exposed to PM2.5. 16HBE were pre−treated with 10, 20, 40 μM curcumin for 30 min followed by exposure to PM2.5 (250 μg/mL) for 24 h in the presence or absence of curcumin. After 24 h, cells were collected and measured for NF-kappaB p65 and IL-6 expression by Western blot. Notably, NF-kappaB p65 or IL-6 expression level was markedly increased in 16HBE exposed to PM2.5 as compared with the control cells. However, curcumin treatment could attenuate the high expression of NF-kappaB p65 or IL-6 in cells induced by PM2.5 (Figure 6).

Molecules 17 12406 g001 200
Figure 1. Common network of two PPI networks based on differentially expressed genes of HBEC exposed to PM2.5 and human target proteins of curcumin. Red cycles represent seed nodes, and blue cycles represent neighbor nodes. All edges represent interactions between the nodes.

Click here to enlarge figure

Figure 1. Common network of two PPI networks based on differentially expressed genes of HBEC exposed to PM2.5 and human target proteins of curcumin. Red cycles represent seed nodes, and blue cycles represent neighbor nodes. All edges represent interactions between the nodes.
Molecules 17 12406 g001 1024
Molecules 17 12406 g002 200
Figure 2. Molecular mechanisms of cancer associated with the common network. Blue legends represent proteins contained in the common network.

Click here to enlarge figure

Figure 2. Molecular mechanisms of cancer associated with the common network. Blue legends represent proteins contained in the common network.
Molecules 17 12406 g002 1024
Molecules 17 12406 g003 200
Figure 3. PI3K/AKT signaling associated with the common network. Blue legends represent proteins contained in the common network.

Click here to enlarge figure

Figure 3. PI3K/AKT signaling associated with the common network. Blue legends represent proteins contained in the common network.
Molecules 17 12406 g003 1024
Molecules 17 12406 g004 200
Figure 4. NF-kappaB signaling associated with the common network. Blue legends represent proteins contained in the common network.

Click here to enlarge figure

Figure 4. NF-kappaB signaling associated with the common network. Blue legends represent proteins contained in the common network.
Molecules 17 12406 g004 1024
Molecules 17 12406 g005 200
Figure 5. 14-3-3-mediated signaling associated with the common network. Blue legends represent proteins contained in the common network.

Click here to enlarge figure

Figure 5. 14-3-3-mediated signaling associated with the common network. Blue legends represent proteins contained in the common network.
Molecules 17 12406 g005 1024
Table Table 3. Key functions associated with the common network using IPA.

Click here to display table

Table 3. Key functions associated with the common network using IPA.
Top Bio Functionsp-valueNumber of Molecules
Diseases and Disorders
Infectious Disease1.26E−12–4.25E−0235
Cancer3.45E–3.01E−028
Genetic Disorder1.33E–3.01E−025
Respiratory Disease1.33E–3.01E−026
Inflammatory Response2.79E–2.79E−021
Molecular and Cellular Functions
Cell Death9.91E−20–3.01E−0231
Cellular Growth and Proliferation5.64E−15–2.79E−0232
Cellular Development1.56E−08–2.79E−0217
Cell Cycle1.84E−07–2.79E−0212
Cellular Movement1.01E−04–2.79E−0210
Physiological System Development and Function
Organismal Survival2.02E−03–2.02E−034
Respiratory System Development and Function2.28E−03–2.28E−032
Tissue Development2.28E−03–2.79E−022
Connective Tissue Development and Function1.94E−02–1.94E−022
Tissue Morphology2.79E−02–2.79E−021
Table Table 4. Key canonical pathways associated with the common network using IPA.

Click here to display table

Table 4. Key canonical pathways associated with the common network using IPA.
Canonical Pathwaysp-valueRatio
Glucocorticoid Receptor Signaling2.57E−4261/238 (0.256)
Molecular Mechanisms of Cancer6.68E−3965/314 (0.207)
PI3K/AKT Signaling6.87E−3641/110 (0.373)
NF-kappaB Signaling1.33E−3041/143 (0.287)
14-3-3-mediated Signaling1.37E−3036/102 (0.353)
Molecules 17 12406 g006 200
Figure 6. The effect of curcumin on the NF-kappaB p65 and IL-6 of 16HBE exposed to PM2.5. Cells were pre−treated with 10, 20, 40 μM curcumin for 30 min followed by exposure to PM2.5 (250 μg/mL) for 24 h in the presence or absence of curcumin. After 24 h, cells were collected and measured for NF-kappaB p65 and IL-6 expression by Western blot. (A) Expression of NF-kappaB p65. (B) Expression of IL-6. (C) Bar graphs showing the quantification of Western blot bands. Beta-actin was used as an internal control. ** p < 0.01, compared with the control group, # p < 0.05, ## p < 0.01, compared with the PM2.5 group.

Click here to enlarge figure

Figure 6. The effect of curcumin on the NF-kappaB p65 and IL-6 of 16HBE exposed to PM2.5. Cells were pre−treated with 10, 20, 40 μM curcumin for 30 min followed by exposure to PM2.5 (250 μg/mL) for 24 h in the presence or absence of curcumin. After 24 h, cells were collected and measured for NF-kappaB p65 and IL-6 expression by Western blot. (A) Expression of NF-kappaB p65. (B) Expression of IL-6. (C) Bar graphs showing the quantification of Western blot bands. Beta-actin was used as an internal control. ** p < 0.01, compared with the control group, # p < 0.05, ## p < 0.01, compared with the PM2.5 group.
Molecules 17 12406 g006 1024

2.2. Discussion

Predictive analysis was a general method for predicting the accuracy of quantitative experiments. The use of predictive analysis allowed the designer of an experiment to estimate the accuracy that should be obtained from the experiment before the experimental setup was finalized [6]. Until now, there had been no in vivo or in vitro reports about the effects of curcumin on organisms exposed to PM2.5; therefore, we collected limited data associated with PM2.5 or curcumin available from online databases such as GEO and PubChem. Because the aim of our study was to outline the potential biofunctions and pathways associated with the effect of curcumin on HBEC exposed to PM2.5 predictively, we did not restrict all data reanalyzed at identical molecular level.

PubChem [7] is a public repository for biological properties of small molecules hosted by the US National Institutes of Health (NIH). The PubChem BioAssay database contained biological test results for more than 700,000 compounds. From the PubChem BioAssay database, we could retrieve the target proteins of compounds [8]. In our study, 57 human target proteins of curcumin (CID: 969516) were obtained.

PPI were extremely important cellular events that affected many of the most important molecular processes in the cell, such as DNA replication. They formed the basis for many signal transduction pathways and transcriptional regulatory networks. The availability of complete and annotated genome sequences of several organisms had led to a paradigm shift from the study of individual proteins in a cell to proteome−wide analysis in an organism. The whole proteome analysis had illustrated that PPI affected cellular biological functions through many orchestrating networks such as metabolic, signaling and regulatory pathways in an organism [9].

Within the airway, the epithelium forms the mucosal immune barrier, the first structural cell defense against common environmental insults such as microorganisms and particulate matter. Hence, respiratory infectious diseases share similar pathologic processes such as the inflammatory response or oxidative stress with bronchial diseases induced by PM2.5 [10,11,12]. The inflammatory response was the main acute effect induced by PM2.5 in the respiratory tract, a target organ of PM2.5. In vitro studies had shown that airway epithelial cells responded to PM2.5 exposure by the release of inflammatory cytokines such as IL-1beta, TNF-alpha, and IL-6 [13], chemokines such as IL-8 [14], and erythropoietic cytokines such as G-CSF and GM-CSF [15,16]. Because curcumin was observed to inhibit secretion of the pro-inflammatory cytokines NF-kappaB mediating in HBEC exposed to pollutants [17,18], we predicted that curcumin might also have an anti-inflammatory effect on HBEC exposed to PM2.5.

Some researchers conducting large epidemiological cohort studies in the United States and Europe had comfirmed the relationship between long-term exposure to particulate air pollution (PM10 and PM2.5) and increased mortality from lung cancer, especially in combination with other known risk factors, such as smoking, passive smoking, and occupational exposure [19,20]. By contrast, curcumin, a natural antitumor compound, had been shown to have the effect of inhibiting lung cancer cell invasion and metastasis in several studies [21,22,23] and have promising potential as a diet-derived cancer chemopreventive agent [24]. Thus, we inferred that curcumin could inhibit the carcinogenesis of airway epithelial cells resulting from PM2.5 exposure.

Generally, PM2.5 led to the proliferation inhibition and apoptosis of HBEC [25,26,27]. PM2.5 could induce cell cycle arrest in G1 phase, inhibit DNA synthesis, and block airway epithelial cell proliferation [28]. The P53 pathway, tumor necrosis factor-alpha (TNF-alpha) pathway, and mitochondrial pathway played critical roles in the apoptosis processes induced by PM2.5 [29,30]. However, as a dietary antioxidant, curcumin had been proven to have preventive potential against apoptosis induced by peroxide or cigarette smoke extract in HBEC though inhibition of NF-kappaB [17,31]. Moreover, curcumin was a selective apoptosis modulator. For most noncancerous cells, curcumin was a protector and prevents cells from apoptosis induced by various adverse factors, but for cancer cells, curcumin was a killer and arrested cell cycle, inhibited cell proliferation, and/or caused apoptosis. For example, when mammary epithelial cells and breast cancer cells accumulated a similar amount of curcumin, a significantly higher percentage of apoptotic cells was induced in cancer cells compared to epithelial cells [32]. Similarly, we speculated that curcumin might have a two-way regulating effect on HBEC when exposed to PM2.5.

Glucocorticoids (GCs) could control airway inflammation in respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma, and the airway epithelium was a primary target of GC anti-inflammatory actions [33]. GC effects were mediated through the GC receptor (GR). Previous studies had indicated that cultured HBEC from smokers possess GRs with a lower binding affinity, and this might result from the inflammation found in the airways in smokers [34]. Although there had been no studies involving the effect of PM2.5 on GRs in HBEC, cigarette smoke and PM2.5 shared a similar inflammatory effect on HBEC, and we could speculate that PM2.5 might decrease GR signaling. In addition, GR action was shown to be tightly regulated by histone deacetylase 2 (HDAC2), which suppressed inflammatory gene expression in inflammatory airway disease [35]. Acting as an HDAC activator, curcumin was found to restore HDAC2 activity, thereby restoring the function of the GR [36]. In summary, regulation of the GR pathway was a possible mechanism by which curcumin inhibits the hazardous effects of PM2.5.

Recent studies have suggested that numerous components of phosphoinositide 3-kinase (PI3K)-dependent signaling, mediated by Akt kinase, played a crucial role in the expression and activation of inflammatory mediators, inflammatory cell recruitment, immune cell function, airway remodeling, and corticosteroid insensitivity in chronic inflammatory respiratory disease [37], especially in COPD and asthma [38]. PM2.5 or cigarette smoke could induce activation of the PI3K/Akt pathway in HBEC and promote transcription of downstream inflammatory mediators [39,40,41]. However, studies had proved that curcumin could inhibit PI3K/Akt/NF-kappaB signals in human lung epithelial cells [42], block Akt translocation to the nucleus and further decrease inflammation in human tracheal smooth muscle cells [43]. Therefore, we predicted that curcumin also might have potential to prevent HBEC from the toxicity effects of PM2.5 by modulating PI3K/Akt signaling.

Recent research indicated that the NF-kappaB/IkappaB pathway played an important role in the inflammatory response induced by PM2.5 in the lung [44]. The activation of the NF-kappaB/IkappaB complex preceded cytotoxicity or inflammation in PM2.5-exposed human bronchial or lung epithelial cells through the reactive oxygen species (ROS)-dependent NF-kappaB pathway [45,46]. As an inhibitor of NF-kappaB, curcumin exhibited a potent anti-inflammatory effect, and could decrease the airway epithelial cell inflammatory cytokine response to the pollutant cadmium or cigarette smoke extract [17,18]. Like cadmium and cigarette smoke, PM2.5 was also a pollutant in the environment, so we hypothesized that curcumin might perform its anti-inflammatory effect on PM2.5 by inhibiting the NF-kappaB pathway.

14-3-3 family members tightly regulated cell fate through interaction with a wide spectrum of proteins that were targeted by various classes of protein kinases [47]. 14-3-3 proteins played particularly important roles in coordinating the progression of cells through the cell cycle, regulating their response to DNA damage and influencing life−death decisions [48]. Studies reported that 14-3-3 might contribute to lung tumorigenesis. In H322 cells, over-expression of 14-3-3 protein resulted in abnormal DNA replication and polyploidization [49], and in A549 cells, 14-3-3 promoted cellular proliferation [50]. Other studies found that curcumin could induce the typical features of apoptosis and inhibited the expression of 14-3-3 in HT-29 cells [51]. Based on the evidence mentioned above, we predicted that curcumin might prevent HBEC exposed to PM2.5 from carcinogenesis by inhibiting the 14-3-3 pathway.

In the NF-kappaB signaling pathway, NF-kappaB played a pivotal role as inflammatory response regulator, and IL-6 was an important inflammatory factor regulated by NF-kappaB and caused the damage response of PM2.5 [52]. Therefore, in a validating experiment, we selected NF-kappaB p65 and IL-6 as validated molecules and found that curcumin treatment could attenuate the high expression of NF-kappaB p65 or IL-6 in cells induced by PM2.5. These results supported our part prediction.

3. Experimental

3.1. Microarray Data Analysis

A microarray dataset (accession number GSE7010) [53] was downloaded from the GEO [54], and analyzed it based on the Affymetrix Human Genome U133A Array. This dataset was derived from a study observing global gene expression in HBEC and identifying cellular pathways associated with coarse, fine and ultrafine particulate matter exposure. Ambient PM was collected in three different size fractions from Chapel Hill air; particles were extracted from foam or filter matrices and lyophilized. Primary HBEC were exposed to PM2.5 at 250 μg/mL or vehicle control for 6 h in culture [55]. In this study, we used three samples from the control group (GSM161787, GSM161793, GSM161798) and three samples from the fine particulate matter (PM2.5) group (GSM161790, GSM161796, GSM161801). Probes showing differential expression were extracted by volcano plot analysis with the filtering criteria of a 1.5-fold change using GeneSpring GX version 11.0 after per chip and per gene normalization.

3.2. Target Proteins of Curcumin

The human target proteins of curcumin (CID: 969516) in PubChem [56] were retrieved using PubChem Promiscuity [57] online [58] with the filtering criteria of “not less than one Active Bioassay”.

3.3. Construction of PPI Networks and Detection of Common Network

PPI represented a basic blueprint for the analysis of self-organization and homeostasis in living organisms [59]. In this study, a Cytoscape [60] plugin, BisoGenet [61], was applied for assembling the PPI network. Information on human PPI networks involving relevant genes was obtained from various databases, including HPRD (Human Protein Reference Database), BIND (Biomolecular Interaction Network Database), BioGRID (The General Repository for Interaction Datasets), DIP (Database of Interacting Proteins), IntAct (Database system and analysis tools for protein interaction data), and MINT (Molecular Interactions Database). Two PPI networks were constructed based on the differential expression of genes from microarray data analysis and the target proteins of curcumin from PubChem. Another Cytoscape plugin, Advanced Network Merge, was used to find the common proteins and relations (common network) in the two PPI networks.

3.4. Functional and Pathway Analysis of Common Network

For further analysis, a data file was uploaded into IPA (Ingenuity® Systems, www.ingenuity.com, Redwood City, CA, USA). This file contained the proteins in the common network. Each protein identifier was mapped to its corresponding protein object in the Ingenuity Pathways Knowledge Base (IPKB). The functional analysis identified the biological functions and/or diseases that were most significant to the data set. Proteins from the data set that met the P-value threshold of 0.05 (Fisher’s exact test) and were associated with biological functions and/or diseases in the IPKB were kept for analysis. Canonical pathway analysis identified the pathways most significant to the data set, based on two parameters: (1) a ratio of the number of proteins from the data set that map to the pathway divided by the total number of proteins that map to the canonical pathway and (2) a p-value calculated with Fisher’s exact test determining the probability that the association between the proteins in the dataset and the canonical pathway is explained by chance alone.

3.5. Validating Experiment

3.5.1. Chemicals

All reagents used in this validating experiment including curcumin (purity: 70%) were purchased from Sigma (Sigma-Aldrich, St. Louis, MO, USA) unless specified.

3.5.2. Cell Culture

Human bronchial epithelial cells 16HBE were purchased from American Type Culture Collection (ATCC, Manassas, VA). Cells were maintained at 37 °C and 5% CO2 in DMEM medium supplemented with 10% heat-inactivated fetal bovine serum, 10 U/mL of penicillin and 10 U/mL of streptomycin.

3.5.3. Preparation of Particles

Urban atmospheric PM2.5 was kindly provided by Prof. Xiaohong Zhao of College of Arts and Sciences of Beijing Union University. PM2.5 was collected on 150 mm diameter nitrocellulose filters (HAWP, Sartorius, La Fert’esous-Jouarre, France) with a high volume sampler machine (DA-80 Digitel, Cugy, Switzerland, flowrate: 30 m3/h) during the winter of 2008 on the roof of a five−story building in Xueyuan Road, Haidian District, Beijing. Particles were processed as previously described [55].

3.5.4. Treatment of Cells with Curcumin and PM2.5

The cells were pretreated with curcumin (10, 20, 40 μM) for 30 min followed by exposure to PM2.5 (250 μg/mL) for 24 h in the presence or absence of curcumin. After 24 h, total cell lysates were prepared and 30 μg protein was subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), followed by immunoblot analysis.

3.5.5. Western Blot

Rabbit polyclonal anti-NF-kappaB p65, anti-IL-6 antibody and mouse monoclonal anti-beta-actin antibody were purchased from Cell Signaling Technology, Abcam Inc. and Applygen Technologies Inc., respectively. Goat anti-rabbit horseradish peroxidase−conjugated immunoglobulin G (IgG-HRP; Santa Cruz Biotechnology) and goat anti-mouse IgG-HRP (Santa Cruz Biotechnology) were used as secondary antibodies for the rabbit and mouse primary antibodies, respectively. Western blot was performed following the standard protocol. Precision Plus ProteinTM Dual Color Standards (Bio-Rad Laboratories) and PageRulerTM Plus Prestained Protein Ladder (Fermentas) were used as molecular weight markers. The immunoblot was finally visualized by exposure on film with ECL Plus Western Blotting Detection Reagents (Amersham & Pharmacia Biotech). Each experiment was independently repeated in triplicate.

4. Conclusions

In this study, we predicted for the first time that the anticancer and anti-inflammatory effects of curcumin might play a key role in protecting human airway from the hazardous effect of PM2.5. Curcumin had the potential to be an airway-protective agent against PM2.5. The current findings were based on bioinformatic studies and require further investigation to confirm.

Supplementary Materials

Supplementary materials can be accessed at: http://www.mdpi.com/1420-3049/17/10/12406/s1.

Acknowledgments

This work is supported by National Natural Science Foundation of China (No. 81102680) and China Postdoctoral Science Foundation (No. 20100470524, No. 20110490548).

  • Sample Availability: Not Available.

References

  1. Valavanidis, A.; Fiotakis, K.; Vlachogianni, T. Airborne particulate matter and human health: toxicological assessment and importance of size and composition of particles for oxidative damage and carcinogenic mechanisms. J. Environ. Sci. Heal. C 2008, 26, 339–362.
  2. Sacks, J.D.; Stanek, L.W.; Luben, T.J.; Johns, D.O.; Buckley, B.J.; Brown, J.S.; Ross, M. Particulate matter-induced health effects: who is susceptible? Environ. Health Perspect. 2011, 119, 446–454.
  3. Polichetti, G.; Cocco, S.; Spinali, A.; Trimarco, V.; Nunziata, A. Effects of particulate matter (PM(10), PM(2.5) and PM(1)) on the cardiovascular system. Toxicology 2009, 261, 1–8, doi:10.1016/j.tox.2009.04.035.
  4. Hatcher, H.; Planalp, R.; Cho, J.; Torti, F.M.; Torti, S.V. Curcumin: From ancient medicine to current clinical trials. Cell. Mol. Life Sci. 2008, 65, 1631–1652, doi:10.1007/s00018-008-7452-4.
  5. Zhou, H.; Beevers, C.S.; Huang, S. The targets of curcumin. Curr. Drug Targets 2011, 12, 332–347, doi:10.2174/138945011794815356.
  6. Wolberg, J.R. Designing Quantitative Experiments : Prediction Analysis; Springer: Berlin, Germany, 2010.
  7. PubChem, Available online: http://pubchem.ncbi.nlm.nih.gov (accessed on 16 October 2012).
  8. Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Zhou, Z.; Han, L.; Karapetyan, K.; Dracheva, S.; Shoemaker, B.A.; et al. PubChem’s BioAssay Database. Nucleic Acids Res. 2012, 40, D400–D412, doi:10.1093/nar/gkr1132.
  9. Raman, K. Construction and analysis of protein-protein interaction networks. Autom. Exp. 2010, 2, 2, doi:10.1186/1759-4499-2-2.
  10. Phung, T.T.; Sugamata, R.; Uno, K.; Aratani, Y.; Ozato, K.; Kawachi, S.; Thanh Nguyen, L.; Nakayama, T.; Suzuki, K. Key role of regulated upon activation normal T-cell expressed and secreted, nonstructural protein1 and myeloperoxidase in cytokine storm induced by influenza virus PR-8 (A/H1N1) infection in A549 bronchial epithelial cells. Microbiol. Immunol. 2011, 55, 874–884, doi:10.1111/j.1348-0421.2011.00396.x.
  11. Dergham, M.; Lepers, C.; Verdin, A.; Billet, S.; Cazier, F.; Courcot, D.; Shirali, P.; Garcon, G. Prooxidant and proinflammatory potency of air pollution particulate matter (PM(2).(5)(-)(0).(3)) produced in rural, urban, or industrial surroundings in human bronchial epithelial cells (BEAS-2B). Chem. Res. Toxicol. 2012, 25, 904–919, doi:10.1021/tx200529v.
  12. Koarai, A.; Sugiura, H.; Yanagisawa, S.; Ichikawa, T.; Minakata, Y.; Matsunaga, K.; Hirano, T.; Akamatsu, K.; Ichinose, M. Oxidative stress enhances toll-like receptor 3 response to double-stranded RNA in airway epithelial cells. Am. J. Respir. Cell Mol. Biol. 2010, 42, 651–660, doi:10.1165/rcmb.2008-0345OC.
  13. Alfaro-Moreno, E.; Torres, V.; Miranda, J.; Martinez, L.; Garcia-Cuellar, C.; Nawrot, T.S.; Vanaudenaerde, B.; Hoet, P.; Ramirez-Lopez, P.; Rosas, I.; et al. Induction of IL-6 and inhibition of IL-8 secretion in the human airway cell line Calu-3 by urban particulate matter collected with a modified method of PM sampling. Environ. Res. 2009, 109, 528–535, doi:10.1016/j.envres.2009.02.010.
  14. Veranth, J.M.; Moss, T.A.; Chow, J.C.; Labban, R.; Nichols, W.K.; Walton, J.C.; Watson, J.G.; Yost, G.S. Correlation of in vitro cytokine responses with the chemical composition of soil-derived particulate matter. Environ. Health Perspect. 2006, 114, 341–349, doi:10.1289/ehp.114-a341.
  15. Baulig, A.; Sourdeval, M.; Meyer, M.; Marano, F.; Baeza-Squiban, A. Biological effects of atmospheric particles on human bronchial epithelial cells. Comparison with diesel exhaust particles. Toxicol. In Vitro 2003, 17, 567–573, doi:10.1016/S0887-2333(03)00115-2.
  16. Reibman, J.; Hsu, Y.; Chen, L.C.; Kumar, A.; Su, W.C.; Choy, W.; Talbot, A.; Gordon, T. Size fractions of ambient particulate matter induce granulocyte macrophage colony-stimulating factor in human bronchial epithelial cells by mitogen-activated protein kinase pathways. Am. J. Respir. Cell Mol. Biol. 2002, 27, 455–462.
  17. Liu, X.; Togo, S.; Al-Mugotir, M.; Kim, H.; Fang, Q.; Kobayashi, T.; Wang, X.; Mao, L.; Bitterman, P.; Rennard, S. NF-kappaB mediates the survival of human bronchial epithelial cells exposed to cigarette smoke extract. Respir. Res. 2008, 9, 66, doi:10.1186/1465-9921-9-66.
  18. Rennolds, J.; Malireddy, S.; Hassan, F.; Tridandapani, S.; Parinandi, N.; Boyaka, P.N.; Cormet-Boyaka, E. Curcumin regulates airway epithelial cell cytokine responses to the pollutant cadmium. Biochem. Biophys. Res. Commun. 2012, 417, 256–261, doi:10.1016/j.bbrc.2011.11.096.
  19. Turner, M.C.; Krewski, D.; Pope, C.A., 3rd; Chen, Y.; Gapstur, S.M.; Thun, M.J. Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never-smokers. Am. J. Respir. Crit. Care Med. 2011, 184, 1374–1381, doi:10.1164/rccm.201106-1011OC.
  20. Pope, C.A., 3rd.; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D.; Ito, K.; Thurston, G.D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 2002, 287, 1132–1141, doi:10.1001/jama.287.9.1132.
  21. Chen, H.W.; Lee, J.Y.; Huang, J.Y.; Wang, C.C.; Chen, W.J.; Su, S.F.; Huang, C.W.; Ho, C.C.; Chen, J.J.; Tsai, M.F.; et al. Curcumin inhibits lung cancer cell invasion and metastasis through the tumor suppressor HLJ1. Cancer Res. 2008, 68, 7428–7438, doi:10.1158/0008-5472.CAN-07-6734.
  22. Lin, S.S.; Lai, K.C.; Hsu, S.C.; Yang, J.S.; Kuo, C.L.; Lin, J.P.; Ma, Y.S.; Wu, C.C.; Chung, J.G. Curcumin inhibits the migration and invasion of human A549 lung cancer cells through the inhibition of matrix metalloproteinase-2 and -9 and Vascular Endothelial Growth Factor (VEGF). Cancer Res. 2009, 285, 127–133.
  23. Alexandrow, M.G.; Song, L.J.; Altiok, S.; Gray, J.; Haura, E.B.; Kumar, N.B. Curcumin: A novel Stat3 pathway inhibitor for chemoprevention of lung cancer. Eur. J. Cancer Prev. 2012, 21, 407–412, doi:10.1097/CEJ.0b013e32834ef194.
  24. Gescher, A.J.; Sharma, R.A.; Steward, W.P. Cancer chemoprevention by dietary constituents: a tale of failure and promise. Lancet Oncol. 2001, 2, 371–379, doi:10.1016/S1470-2045(00)00392-2.
  25. Agopyan, N.; Head, J.; Yu, S.; Simon, S.A. TRPV1 receptors mediate particulate matter-induced apoptosis. Am. J. Physiol. Lung Cell. Mol. Physiol. 2004, 286, L563–l572, doi:10.1152/ajplung.00299.2003.
  26. Nel, A.E.; Diaz-Sanchez, D.; Li, N. The role of particulate pollutants in pulmonary inflammation and asthma: Evidence for the involvement of organic chemicals and oxidative stress. Curr. Opin. Pulm. Med. 2001, 7, 20–26, doi:10.1097/00063198-200101000-00004.
  27. Gualtieri, M.; Ovrevik, J.; Mollerup, S.; Asare, N.; Longhin, E.; Dahlman, H.J.; Camatini, M.; Holme, J.A. Airborne urban particles (Milan winter-PM2.5) cause mitotic arrest and cell death: Effects on DNA, mitochondria, AhR binding and spindle organization. Mutat. Res. 2011, 713, 18–31, doi:10.1016/j.mrfmmm.2011.05.011.
  28. Zhang, J.; Ghio, A.J.; Gao, M.; Wei, K.; Rosen, G.D.; Upadhyay, D. Ambient particulate matter induces alveolar epithelial cell cycle arrest: role of G1 cyclins. FEBS Lett. 2007, 581, 5315–5320, doi:10.1016/j.febslet.2007.10.020.
  29. Soberanes, S.; Panduri, V.; Mutlu, G.M.; Ghio, A.; Bundinger, G.R.; Kamp, D.W. p53 mediates particulate matter-induced alveolar epithelial cell mitochondria-regulated apoptosis. Am. J. Respir. Crit. Care Med. 2006, 174, 1229–1238, doi:10.1164/rccm.200602-203OC.
  30. Dagher, Z.; Garcon, G.; Billet, S.; Gosset, P.; Ledoux, F.; Courcot, D.; Aboukais, A.; Shirali, P. Activation of different pathways of apoptosis by air pollution particulate matter (PM2.5) in human epithelial lung cells (L132) in culture. Toxicology 2006, 225, 12–24, doi:10.1016/j.tox.2006.04.038.
  31. Siddiqui, M.A.; Ahamed, M.; Ahmad, J.; Majeed Khan, M.A.; Musarrat, J.; Al-Khedhairy, A.A.; Alrokayan, S.A. Nickel oxide nanoparticles induce cytotoxicity, oxidative stress and apoptosis in cultured human cells that is abrogated by the dietary antioxidant curcumin. Food Chem. Toxicol. 2012, 50, 641–647, doi:10.1016/j.fct.2012.01.017.
  32. Ramachandran, C.; You, W. Differential sensitivity of human mammary epithelial and breast carcinoma cell lines to curcumin. Breast Cancer Res. Treat. 1999, 54, 269–278, doi:10.1023/A:1006170224414.
  33. Pujolsa, L.; Mullol, J.; Picado, C. Glucocorticoid receptor in human respiratory epithelial cells. Neuroimmunomodulation 2009, 16, 290–299, doi:10.1159/000216187.
  34. Verheggen, M.M.; Adriaansen-Soeting, P.W.; Berrevoets, C.A.; van Hal, P.T.; Brinkmann, A.O.; Hoogsteden, H.C.; Versnel, M.A. Glucocorticoid receptor expression in human bronchial epithelial cells: effects of smoking and COPD. Mediat. Inflamm. 1998, 7, 275–281, doi:10.1080/09629359890965.
  35. Barnes, P.J. Histone deacetylase-2 and airway disease. Ther. Adv. Respir. Dis. 2009, 3, 235–243, doi:10.1177/1753465809348648.
  36. Marwick, J.A.; Ito, K.; Adcock, I.M.; Kirkham, P.A. Oxidative stress and steroid resistance in asthma and COPD: pharmacological manipulation of HDAC-2 as a therapeutic strategy. Expert Opin. Ther. Tar. 2007, 11, 745–755, doi:10.1517/14728222.11.6.745.
  37. Ito, K.; Caramori, G.; Adcock, I.M. Therapeutic potential of phosphatidylinositol 3-kinase inhibitors in inflammatory respiratory disease. J. Pharmacol. Exp. Ther. 2007, 321, 1–8, doi:10.1124/jpet.106.111674.
  38. Barnes, P.J. Targeting the epigenome in the treatment of asthma and chronic obstructive pulmonary disease. Proc. Am. Thorac. Soc. 2009, 6, 693–696, doi:10.1513/pats.200907-071DP.
  39. Watterson, T.L.; Hamilton, B.; Martin, R.S.; Coulombe, R.A., Jr. Urban particulate matter activates Akt in human lung cells. Arch. Toxicol. 2012, 86, 121–135, doi:10.1007/s00204-011-0739-5.
  40. Syed, D.N.; Afaq, F.; Kweon, M.H.; Hadi, N.; Bhatia, N.; Spiegelman, V.S.; Mukhtar, H. Green tea polyphenol EGCG suppresses cigarette smoke condensate-induced NF-kappaB activation in normal human bronchial epithelial cells. Oncogene 2007, 26, 673–682, doi:10.1038/sj.onc.1209829.
  41. Yu, H.; Li, Q.; Kolosov, V.P.; Perelman, J.M.; Zhou, X. Regulation of cigarette smoke-induced mucin expression by neuregulin1beta/ErbB3 signalling in human airway epithelial cells. Basic Clin. Pharmacol. Toxicol. 2011, 109, 63–72, doi:10.1111/j.1742-7843.2011.00686.x.
  42. Moriyuki, K.; Sekiguchi, F.; Matsubara, K.; Nishikawa, H.; Kawabata, A. Curcumin Inhibits the proteinase-activated receptor-2-triggered prostaglandin E2 production by suppressing cyclooxygenase-2 upregulation and Akt-dependent activation of nuclear factor-kappaB in human lung epithelial cells. J. Pharmacol. Sci. 2010, 114, 225–229, doi:10.1254/jphs.10126SC.
  43. Lee, C.W.; Lin, C.C.; Lin, W.N.; Liang, K.C.; Luo, S.F.; Wu, C.B.; Wang, S.W.; Yang, C.M. TNF-alpha induces MMP-9 expression via activation of Src/EGFR, PDGFR/PI3K/Akt cascade and promotion of NF-kappaB/p300 binding in human tracheal smooth muscle cells. Am. J. Physiol. Lung Cell. Mol. Physiol. 2007, 292, L799–l812.
  44. Mantecca, P.; Farina, F.; Moschini, E.; Gallinotti, D.; Gualtieri, M.; Rohr, A.; Sancini, G.; Palestini, P.; Camatini, M. Comparative acute lung inflammation induced by atmospheric PM and size-fractionated tire particles. Toxicol. Lett. 2010, 198, 244–254, doi:10.1016/j.toxlet.2010.07.002.
  45. Dagher, Z.; Garcon, G.; Billet, S.; Verdin, A.; Ledoux, F.; Courcot, D.; Aboukais, A.; Shirali, P. Role of nuclear factor-kappa B activation in the adverse effects induced by air pollution particulate matter (PM2.5) in human epithelial lung cells (L132) in culture. J. Appl. Toxicol. 2007, 27, 284–290, doi:10.1002/jat.1211.
  46. Zhao, Y.; Usatyuk, P.V.; Gorshkova, I.A.; He, D.; Wang, T.; Moreno-Vinasco, L.; Geyh, A.S.; Breysse, P.N.; Samet, J.M.; Spannhake, E.W.; et al. Regulation of COX-2 expression and IL-6 release by particulate matter in airway epithelial cells. Am. J. Respir. Cell Mol. Biol. 2009, 40, 19–30.
  47. Freeman, A.K.; Morrison, D.K. 14–3-3 Proteins: diverse functions in cell proliferation and cancer progression. Semin. Cell Dev. Biol. 2011, 22, 681–687, doi:10.1016/j.semcdb.2011.08.009.
  48. Gardino, A.K.; Yaffe, M.B. 14–3-3 proteins as signaling integration points for cell cycle control and apoptosis. Semin. Cell Dev. Biol. 2011, 22, 688–695, doi:10.1016/j.semcdb.2011.09.008.
  49. Qi, W.; Liu, X.; Chen, W.; Li, Q.; Martinez, J.D. Overexpression of 14–3-3gamma causes polyploidization in H322 lung cancer cells. Mol. Carcinog. 2007, 46, 847–856, doi:10.1002/mc.20314.
  50. Kawamoto, S.; Iemura, N.; Inoue, Y.; Katakura, Y.; Shirahata, S. Effect of 14–3-3 protein induction on cell proliferation of A549 human lung adenocarcinoma. Cytotechnology 2000, 33, 253–257, doi:10.1023/A:1008129020728.
  51. Wang, J.B.; Qi, L.L.; Zheng, S.D.; Wang, H.Z.; Wu, T.X. Curcumin suppresses PPARdelta expression and related genes in HT-29 cells. World J. Gastroenterol. 2009, 15, 1346–1352, doi:10.3748/wjg.15.1346.
  52. Miyata, R.; van Eeden, S.F. The innate and adaptive immune response induced by alveolar macrophages exposed to ambient particulate matter. Toxicol. Appl. Pharmacol. 2011, 257, 209–226, doi:10.1016/j.taap.2011.09.007.
  53. GSE7010. Available online: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7010 (accessed on 16 October 2012).
  54. Edgar, R.; Domrachev, M.; Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30, 207–210, doi:10.1093/nar/30.1.207.
  55. Huang, Y.C.; Karoly, E.D.; Dailey, L.A.; Schmitt, M.T.; Silbajoris, R.; Graff, D.W.; Devlin, R.B. Comparison of gene expression profiles induced by coarse, fine, and ultrafine particulate matter. J. Toxicol. Environ. Health A 2011, 74, 296–312, doi:10.1080/15287394.2010.516238.
  56. Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Bryant, S.H. PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic acids Res. 2009, 37, W623–w633, doi:10.1093/nar/gkp456.
  57. Canny, S.A.; Cruz, Y.; Southern, M.R.; Griffin, P.R. PubChem promiscuity: A web resource for gathering compound promiscuity data from PubChem. Bioinformatics 2012, 28, 140–141, doi:10.1093/bioinformatics/btr622.
  58. PubChem Promiscuity. Available online: http://chemutils.florida.scripps.edu:8080/pcpromiscuity (Accessed on 16 October 2012).
  59. Real-Chicharro, A.; Ruiz-Mostazo, I.; Navas-Delgado, I.; Kerzazi, A.; Chniber, O.; Sanchez-Jimenez, F.; Medina, M.A.; Aldana-Montes, J.F. Protopia: A protein-protein interaction tool. BMC Bioinformatics 2009, 10 Suppl. 12, S17.
  60. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504, doi:10.1101/gr.1239303.
  61. Martin, A.; Ochagavia, M.E.; Rabasa, L.C.; Miranda, J.; Fernandez-de-Cossio, J.; Bringas, R. BisoGenet: A new tool for gene network building, visualization and analysis. BMC Bioinformatics 2010, 11, 91, doi:10.1186/1471-2105-11-91.
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert