Large sections of the world population depend on medicinal plants (natural products) for their health care needs. The indigenous population use crude preparations of natural products in the form of decoction, paste, or as food supplement. The prevailing belief is that crude mixtures contain many compounds. Several of these compounds might be inactive by themselves but are essential for the effectiveness of the “active molecule” in the crude preparations. In other words, synergism negates the additive effect of individual molecules [
Modern scientific standards emphasize thorough understanding of the molecular mechanism of action of single agent drugs. However, this approach is limited to single molecules, which have been chemically characterized. Even these drugs modulate the activities of disparate metabolic/signaling pathways [
Analytical methods like microarray, pathway focused PCR arrays, RNA seq, and cGH array give a snapshot of large scale changes at genome level. These methodologies generate large data sets. The development of data mining software has enabled discovery of molecular markers from the mass of data possible. Thus, the ability to analyze gene expression changes at genome level had revolutionized the molecular investigations that until now were difficult. For example, understanding global gene expression changes that accompany certain diseases like cancer has enabled the development of personalized medicine. Data analysis of microarray results also facilitated deciphering molecular pathways that are affected by both existing as well as newly discovered drugs [
We have recently published the details about the collection, authentication of
The human pancreatic cancer cell line MIA PaCa-2 (CRL-1420) was purchased from ATCC (USA) and maintained in our laboratory in RPMI 1640 medium supplemented with 10% FBS (Invitrogen, USA) at 37°C in a humidified 5% CO2 atmosphere. It was seeded in a six-well plate at an initial density of
Total RNA was prepared from control and LE treated MiaPaCa-2 cells using TriReagent (Sigma, USA). At the end of the treatment, cells were directly lysed in TriReagent and total RNA isolated. From this point, the rest of the gene expression related procedures were performed at the microarray and gene expression Core facility of the University of Miami. The quality of the extracted RNA was evaluated using BioAnalyzer 2100 (Agilent Technologies, CA). RNA samples with RNA integrity number (RIN) ≥9 have been used for the microarray experiments [
Target RNA was amplified using Amino Allyl Message Amp kit (Ambion, USA). Amplified RNA from the control group was labeled with Cy-3 and the test group with Cy-5 dye (Amersham, USA). Duplicate microarray hybridization was set up using biological replicates that were labeled by dye-swap; that is, control RNA was labeled with Cy-5 and test RNA with Cy-3. The dye labeled cDNA samples were hybridized to the whole human genome microarray (Cat. no. G4112F; Design ID: 014850, Agilent Technologies, USA). We followed the experimental instructions provided by the kit supplier for RNA amplification, cDNA preparation, dye labeling, and array hybridization.
The microarrays were scanned at 5
The microarray gene expression data were verified by quantitative RT-PCR. Premade validated RT-PCR primers (Table
Eight genes were analyzed to validate the microarray data. The fold changes in gene expression as obtained by microarray analyses were verified by quantitative RT-PCR. Experimental details are given in the materials and methods.
No. | Gene symbol | Gene name | Microarray FC | RT PCR FC |
---|---|---|---|---|
1 | TBP | TATA box binding protein | 2.37 | 1.29 |
2 | APOL1 | Apolipoprotein L | 1.64 | 3.59 |
3 | ICAM1 | Intercellular adhesion molecule 1 | 2.63 | 1.94 |
4 | POLR2A | Polymerase (RNA) II (DNA directed) polypeptide A | −1.99 | 0.69 |
5 | SLA | Src-like-adaptor | −1.85 | 0.43 |
6 | HYAL1 | Hyaluronoglucosaminidase 1 | −2.12 | 0.42 |
7 | IRF5 | Interferon regulatory factor 5 | −2.25 | 0.89 |
8 | IL2 | Interleukin 2 | −2.10 | 0.58 |
The whole genome microarray (Agilent, USA) featured approximately 41,000, 60 nucleotide long oligonucleotide probes. These probes represent all known human transcripts. The microarray data were deposited in NCBI’s “Gene Expression Omnibus” database repository; accession number is GSE44290 [
Seven genes differentially regulated in microarray were selected for validation using quantitative real time PCR. The direction of fold change in gene expression observed in microarray analyses matched with the real time PCR data (Table
17,135 features including replicated spots passed technical QC (see “Section
From this data set, we selected genes that passed FDR ≤ 0.01 at each of the three treatment time points. This analyses yielded 971 (637/334), 806 (325/481), and 3,075 (1,758/1,317) genes that were unique to 24 h, 48 h, and 72 h time points, respectively (Supplementary File S-1). The numbers in parenthesis indicate the number of underexpressed and over expressed genes, respectively. A detailed gene list corresponding to the three time points is listed in sheets 24hFDR001, 48hFDR001, and 72hFDR001, respectively (Supplementary File S-1). The genes selected by this method were compared for commonly regulated genes between the different treatment time points. This analysis found 223, 191, and 2,009 genes to be unique to 24, 48, and 72 h, respectively. 38 genes were established to be common between 24 and 48 h, 489 between 24 and 72 hours, and 356 genes between 72 and 48 h. 221 genes were common for all the three time points. Though the genes may be common for the indicated parameters, the direction of regulation may not be the same for these genes at the compared time points. In this list, only the gene names were common. Other than FDR ≤ 0.01 the FC was not factored in this selection. Therefore, the same gene may have a positive and negative fold change at different time points (Sheet “FDR ≤ 0.01,” Supplementary File S-1). This data is graphically represented in Figure
Venn diagram depicting the number of genes regulated by LE at different time points and the number of genes that are common between the time segments (FDR ≤ 0.01). 223, 191, and 2009 genes were unique to 24, 48, and 72 hours, respectively. 259 (221+38) genes were common between 24 and 48 h; 356 between 48 and 72 h; 489 between 24 and 72 h. 221 genes were common among all the three time points. Except for FDR ≤ 0.01, neither fold change nor the direction of regulation is accounted in this representation.
Next, we limited the data analyses to genes that showed similar expression trend either up or down regulated over the three different experimental time points. This analysis yielded the following results. From across three different time points, 941 (516/425) out of 1,178 genes showed similar expression pattern (Sheet “FC Same trend,” Supplementary File S-1). From this list, 362 (248/114) genes showed FC ≥ ±2 (Sheet “FC ≥ ±2,” Supplementary File S-1). This list was further screened for genes with FDR ≤ 0.01 and thus we picked 89 (58/31) genes (Sheet “FDR ≤ 0.01 and FC ≥ ±2,” Supplementary File S-1). Of the 89 genes, 82 had known valid accession IDs. Thus, from 41,000 probe sets on Human Genome microarray, we successfully selected 82 (54/28) genes that showed similar trend across all the three time points with FDR 0.01. In this list, all the genes in at least one of the three points qualified FDR ≤ 0.01 and FC ≥ ±2 (Tables
(a) List of genes whose expression was downregulated at all the three time points. (b) List of genes whose expression was up regulated at all the three time points. Average fold change in gene expression and the corresponding SD for the three different time points is given. Genes that showed FC of ±2 and FDR of <0.01 were selected.
No. | Gene name | NCBI description | AVE FC | SD |
---|---|---|---|---|
1 | SYTL5 | Synaptotagmin-like 5 | −2.92 | 1.88 |
2 | KGFLP1 | Fibroblast growth factor 7 pseudogene | −2.80 | 1.42 |
3 | ARPC3P5 | Actin related protein 2/3 complex, subunit 3 pseudogene 5 | −2.63 | 1.88 |
4 | OSBPL8 | Oxysterol binding protein-like 8 | −2.62 | 0.01 |
5 | C11orf10 | Chromosome 11 open reading frame 10 | −2.56 | 1.09 |
6 | DHX9 | DEAH (Asp-Glu-Ala-His) box polypeptide 9 | −2.51 | 0.77 |
7 | C1orf53 | Chromosome 1 open reading frame 53 | −2.45 | 1.16 |
8 | VWF | Von Willebrand factor | −2.40 | 0.59 |
9 | AF038194 | Homo sapiens clone 23821 mRNA sequence | −2.35 | 0.51 |
10 | POM121 | POM121 transmembrane nucleoporin | −2.33 | 0.36 |
11 | THC2671048 | Q3DWD9_CHLAU (Q3DWD9) YLP motif, partial (6%) | −2.33 | 0.71 |
12 | TOM1L2 | Target of myb1-like 2 (chicken) | −2.33 | 0.76 |
13 | CAPZA3 | Capping protein (actin filament) muscle Z-line, alpha 3 | −2.33 | 0.82 |
14 | CYSLTR2 | Cysteinyl leukotriene receptor 2 | −2.30 | 0.54 |
15 | ANKRD33B | Ankyrin repeat domain 33B | −2.27 | 0.61 |
16 | AA889371 | am40h08.s1 Soares_NFL_T_GBC_S1 Homo sapiens cDNA clone IMAGE:1471263 3′ similar to SW:COQ1_YEAST P18900 HEXAPRENYL PYROPHOSPHATE SYNTHETASE | −2.26 | 0.67 |
17 | FAM101A | Family with sequence similarity 101, member A | −2.26 | 0.91 |
18 | IRF5 | Interferon regulatory factor 5 | −2.25 | 0.68 |
19 | EGFLAM | EGF-like, fibronectin type III, and laminin G domains | −2.25 | 0.72 |
20 | SIRT3 | NAD-dependent protein deacetylase sirtuin-3, mitochondrial isoform a | −2.20 | 0.65 |
21 | NEK8 | NIMA (never in mitosis gene a)—related kinase 8 | −2.19 | 1.07 |
22 | ROD1 | Polypyrimidine tract binding protein 3 | −2.18 | 0.60 |
23 | RBM26 | RNA binding motif protein 26 | −2.18 | 1.05 |
24 | HYAL1 | Hyaluronoglucosaminidase 1 (HYAL1), transcript variant 1, noncoding RNA | −2.12 | 0.45 |
25 | IL2 | Interleukin 2 | −2.10 | 0.17 |
26 | LRRC20 | Leucine-rich repeat-containing protein 20 isoform 3 | −2.10 | 0.67 |
27 | THC2635921 | −2.09 | 0.90 | |
28 | GJD4 | Gap junction protein, delta 4, 40.1 kDa | −2.09 | 0.52 |
29 | SNAPC1 | Small nuclear RNA activating complex, polypeptide 1, 43 kDa | −2.06 | 0.50 |
30 | GNG13 | Guanine nucleotide binding protein (G protein), gamma 13 | −2.00 | 0.45 |
31 | POLR2A | Polymerase (RNA) II (DNA directed) polypeptide A, 220 kDa | −1.99 | 0.23 |
32 | CD44 | CD44 molecule (Indian blood group) | −1.97 | 0.87 |
33 | CR742006 | CR742006 Soares_testis_NHT Homo sapiens cDNA clone IMAGp971J1256; IMAGE:1048737 5′, mRNA sequence | −1.96 | 0.53 |
34 | THC2709441 | Q4VIX2_DROBU (Q4VIX2) Dbuz |
−1.95 | 0.77 |
35 | PIGU | Phosphatidylinositol glycan anchor biosynthesis, class U | −1.95 | 0.25 |
36 | KRT14 | Keratin 14 | −1.91 | 0.62 |
37 | BE564275 | 601343077F1 NIH_MGC_53 Homo sapiens cDNA clone IMAGE:3685338 5′, mRNA sequence | −1.90 | 0.59 |
38 | ZNF622 | Zinc finger protein 622 | −1.89 | 0.76 |
39 | BQ060012 | AGENCOURT_6793913 NIH_MGC_99 Homo sapiens cDNA clone IMAGE:5816175 5′, mRNA sequence | −1.88 | 0.28 |
40 | LCE1D | Late cornified envelope 1D | −1.88 | 0.13 |
41 | BC034623 | Homo sapiens cDNA clone IMAGE:4837603 | −1.86 | 0.45 |
42 | SLA | Src-like-adaptor | −1.85 | 0.30 |
43 | HLA-DOB | Major histocompatibility complex, class II, DO beta | −1.85 | 0.57 |
44 | FOXL1 | Forkhead box L1 | −1.84 | 0.34 |
45 | B3GNT9 | UDP-GlcNAc:betaGal beta-1, 3-N-acetylglucosaminyltransferase 9 | −1.84 | 0.66 |
46 | SLC15A1 | Solute carrier family 15 (oligopeptide transporter), member 1 | −1.82 | 0.43 |
47 | HADHB | hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase (trifunctional protein), beta subunit | −1.79 | 0.61 |
48 | HSPA14 | Heat shock 70 kDa protein 14 | −1.78 | 0.43 |
49 | BC092421 | Homo sapiens cDNA clone IMAGE:30378758 | −1.76 | 0.39 |
50 | POC1A | POC1 centriolar protein homolog A (Chlamydomonas) | −1.75 | 0.27 |
51 | ACVR1 | Activin A receptor, type I | −1.72 | 0.41 |
52 | TPSD1 | Tryptase delta 1 | −1.68 | 0.33 |
53 | MAPK8IP3 | Mitogen-activated protein kinase 8 interacting protein 3 | −1.67 | 0.40 |
54 | UCHL5 | Ubiquitin carboxyl-terminal hydrolase L5 | −1.61 | 0.41 |
No. | Gene name | NCBI description | AVE FC | SD |
---|---|---|---|---|
1 | MBD6 | Methyl-CpG binding domain protein 6 | 1.58 | 0.44 |
2 | BG536553 | 602564961F1 NIH_MGC_77 Homo sapiens cDNA clone IMAGE:4689518 5′, mRNA sequence | 1.64 | 0.34 |
3 | APOL1 | Apolipoprotein L, 1 | 1.64 | 0.44 |
4 | LOC728537 | Uncharacterized LOC728537 | 1.69 | 0.52 |
5 | THC2624002 | Q9BXR7_HUMAN (Q9BXR7) Interleukin 10 (Fragment), partial (93%) | 1.72 | 0.49 |
6 | CASKIN2 | CASK interacting protein 2 | 1.77 | 0.41 |
7 | PML | Promyelocytic leukemia | 1.78 | 0.66 |
8 | ZC3HAV1L | Zinc finger CCCH-type, antiviral 1-like | 1.81 | 0.25 |
9 | THC2653001 | BX098637 Soares fetal liver spleen 1NFLS Homo sapiens cDNA clone IMAGp998F16386; IMAGE:200847, mRNA sequence | 1.81 | 0.67 |
10 | LOC728344 | Glutaredoxin 3 pseudogene | 1.88 | 0.56 |
11 | METRNL | Meteorin, glial cell differentiation regulator-like | 1.89 | 0.89 |
12 | C8orf75 | Long intergenic nonprotein coding RNA 589 | 1.91 | 0.81 |
13 | AK021715 | Homo sapiens cDNA FLJ11653 fis, clone HEMBA1004538 | 1.91 | 0.86 |
14 | TMCO1 | Transmembrane and coiled-coil domains 1 | 1.99 | 0.36 |
15 | UQCC | Ubiquinol-cytochrome c reductase complex chaperone | 2.00 | 0.41 |
16 | RPL7P48 | Ribosomal protein L7 pseudogene 48 | 2.14 | 0.73 |
17 | AF483645 | Homo sapiens capacitative calcium channel protein Trp1 mRNA, partial cds; alternatively spliced | 2.17 | 0.67 |
18 | TAF9B | TAF9B RNA polymerase II, TATA box binding protein (TBP)-associated factor, 31 kDa | 2.31 | 0.37 |
19 | AK026477 | Homo sapiens cDNA:FLJ22824 fis, clone KAIA3991 | 2.34 | 0.39 |
20 | BBS12 | Bardet-Biedl syndrome 12 | 2.37 | 0.48 |
21 | THC2624048 | Q964F8_PLAFA (Q964F8) Merozoite surface protein 8, partial (3%) | 2.37 | 0.44 |
22 | TBP | TATA box binding protein | 2.37 | 0.50 |
23 | BC034627 | Homo sapiens cDNA clone IMAGE:4839213 | 2.38 | 0.36 |
24 | JAG2 | Jagged 2 | 2.42 | 0.49 |
25 | TMEM8 | Transmembrane protein 8A | 2.42 | 0.43 |
26 | CARD18 | Caspase recruitment domain family, member 18 | 2.51 | 0.59 |
27 | AA020958 | ze65a02.s1 Soares retina N2b4HR Homo sapiens cDNA clone IMAGE:363818 3′, mRNA sequence | 2.52 | 0.48 |
28 | ICAM1 | Intercellular adhesion molecule 1 | 2.63 | 0.77 |
Heat map depicting the relatedness of gene expression pattern in MIAPaCa-2 cells treated with LE. This image was generated using the fold changes in the expression of 89 genes that are significantly down (58; green) or up (31; red) regulated at all the three time points. The intensity of the color is proportional to the fold change in gene expression level between untreated and LE treated MiaPaCa-2 cells. The heat map was generated using TM4, microarray data management, and analysis software [
The 82 genes thus selected were uploaded to MetaCore tool in GeneGo suite (GeneGo, USA) to determine the possible signaling pathways affected by LE. This enrichment analysis identified 98 pathways to be regulated by LE. We grouped these 98 pathways that have similar effect. The top ten pathway maps of the 98 identified as above are given in Figure
Signaling pathways affected by LE in Pancreatic cancer cells. The experimental data of 82 genes (see “Section
No. | Pathway map |
|
Ratio |
---|---|---|---|
|
|||
1 | Immune response_MIF-mediated glucocorticoid regulation |
|
2 : 22 |
2 | Immune response_IL-27 signaling pathway |
|
2 : 24 |
3 | Immune response_HSP60 and HSP70/TLR signaling pathway |
|
2 : 54 |
4 | Immune response_IFN alpha/beta signaling pathway |
|
1 : 24 |
5 | Immune response_role of HMGB1 in dendritic cell maturation and migration |
|
1 : 27 |
6 | Immune response_CD137 signaling in immune cell |
|
1 : 29 |
7 | Immune response_Delta-type opioid receptor signaling in T-cells |
|
1 : 29 |
8 | Immune response_IL-22 signaling pathway |
|
1 : 34 |
9 | CCR4-dependent immune cell chemotaxis in asthma and atopic dermatitis |
|
1 : 34 |
10 | Mechanism of action of CCR4 antagonists in asthma and atopic dermatitis (Variant 1) |
|
1 : 34 |
11 | Immune response_regulation of T cell function by CTLA-4 |
|
1 : 36 |
12 | Immune response_role of integrins in NK cells cytotoxicity |
|
1 : 38 |
13 | Immune response_Th1 and Th2 cell differentiation |
|
1 : 40 |
14 | Immune response_IL-5 signalling |
|
1 : 44 |
15 | Immune response_PGE2 signaling in immune response |
|
1 : 45 |
16 | Immune response_NF-AT signaling and leukocyte interactions |
|
1 : 46 |
17 | Immune response_histamine H1 receptor signaling in immune response |
|
1 : 48 |
18 | Immune response_IL-2 activation and signaling pathway |
|
1 : 49 |
19 | Immune response_function of MEF2 in T lymphocytes |
|
1 : 50 |
20 | Immune response_HMGB1/RAGE signaling pathway |
|
1 : 53 |
21 | Immune response _IFN gamma signaling pathway |
|
1 : 54 |
22 | Immune response_CCR5 signaling in macrophages and T lymphocytes |
|
1 : 58 |
23 | Immune response_immunological synapse formation |
|
1 : 59 |
24 | Immune response_TREM1 signaling pathway |
|
1 : 59 |
25 | Immune response_IL-17 signaling pathways |
|
1 : 60 |
26 | Immune response_CD40 signaling |
|
1 : 65 |
27 | Immune response_CD16 signaling in NK cells |
|
1 : 69 |
| |||
|
|||
28 | Mitochondrial ketone bodies biosynthesis and metabolism |
|
1 : 27 |
29 | Propionate metabolism p.1 |
|
1 : 39 |
30 | Selenoamino acid metabolism |
|
1 : 54 |
31 | Phenylalanine metabolism/rodent version |
|
1 : 66 |
32 | Propionate metabolism p.2 |
|
1 : 66 |
33 | Phenylalanine metabolism |
|
1 : 67 |
34 | Leucine, isoleucine and valine metabolism.p.2 |
|
1 : 78 |
35 | Leucine, isoleucine, and valine metabolism/Rodent version |
|
1 : 80 |
36 | Tyrosine metabolism p.2 (melanin) |
|
1 : 83 |
37 | Lysine metabolism |
|
1 : 84 |
38 | Lysine metabolism/rodent version |
|
1 : 86 |
39 | GTP-XTP metabolism |
|
1 : 90 |
40 | Tryptophan metabolism |
|
1 : 101 |
41 | Tryptophan metabolism/rodent version |
|
1 : 102 |
42 | CTP/UTP metabolism |
|
1 : 108 |
43 | NAD metabolism |
|
1 : 119 |
44 | ATP/ITP metabolism |
|
1 : 124 |
| |||
|
|||
45 | Development_glucocorticoid receptor signaling |
|
1 : 24 |
46 | Development_cross-talk between VEGF and angiopoietin 1 signaling pathways |
|
1 : 26 |
47 | Development_osteopontin signaling in osteoclasts |
|
1 : 30 |
48 | Development_BMP signaling |
|
1 : 33 |
49 | Development_lipoxin inhibitory action on PDGF, EGF, and LTD4 signaling |
|
1 : 36 |
50 | Development_beta-adrenergic receptors transactivation of EGFR |
|
1 : 37 |
51 | Development_notch signaling pathway |
|
1 : 43 |
52 | Development_S1P3 receptor signaling pathway |
|
1 : 43 |
53 | Development_VEGF signaling and activation |
|
1 : 43 |
54 | Development_S1P1 signaling pathway |
|
1 : 44 |
55 | Development_beta-adrenergic receptors regulation of ERK |
|
1 : 47 |
56 | Development_WNT signaling pathway, part 2 |
|
1 : 53 |
| |||
|
|||
57 | Transcription_assembly of RNA polymerase II preinitiation complex on TATA-less promoters |
|
3 : 18 |
58 | Translation_IL-2 regulation of translation |
|
1 : 20 |
59 | Transcription_role of akt in hypoxia induced HIF1 activation |
|
1 : 27 |
60 | Transcription_ligand-dependent transcription of retinoid-target genes |
|
1 : 33 |
61 | Transcription_role of AP-1 in regulation of cellular metabolism |
|
1 : 38 |
62 | Translation_(L)-selenoaminoacids incorporation in proteins during translation |
|
1 : 41 |
| |||
|
|||
63 | Cell adhesion_IL-8-dependent cell migration and adhesion |
|
1 : 33 |
64 | Cell adhesion_cell-matrix glycoconjugates |
|
1 : 38 |
65 | Cell adhesion_ephrin signaling |
|
1 : 45 |
66 | Cell adhesion_ECM remodeling |
|
1 : 52 |
67 | Cell adhesion_chemokines and adhesion |
|
1 : 100 |
| |||
|
|||
68 | CFTR folding and maturation (norm and CF) |
|
1 : 14 |
69 | wtCFTR and delta F508 traffic/late endosome and lysosome (norm and CF) |
|
1 : 15 |
70 | Regulation of degradation of delta F508 CFTR in CF |
|
1 : 27 |
71 | Mechanisms of CFTR activation by S-nitrosoglutathione (normal and CF) |
|
1 : 46 |
| |||
|
|||
72 | Chemotaxis_CCR4-induced chemotaxis of immune cells |
|
1 : 34 |
73 | Chemotaxis_lipoxin inhibitory action on fMLP-induced neutrophil chemotaxis |
|
1 : 46 |
74 | Chemotaxis_inhibitory action of lipoxins on IL-8- and leukotriene B4-induced neutrophil migration |
|
1 : 51 |
75 | Chemotaxis_leukocyte chemotaxis |
|
1 : 75 |
| |||
|
|||
76 | Oxidative stress_role of ASK1 under oxidative stress |
|
1 : 34 |
77 | Mitochondrial unsaturated fatty acid beta-oxidation |
|
1 : 45 |
78 | Mitochondrial long chain fatty acid beta-oxidation |
|
1 : 83 |
79 | Peroxisomal branched chain fatty acid oxidation |
|
1 : 83 |
| |||
|
|||
80 | Protein folding_membrane trafficking and signal transduction of G-alpha (i) heterotrimeric G-protein |
|
1 : 19 |
81 | Proteolysis_putative ubiquitin pathway |
|
1 : 23 |
82 | Proteolysis_role of parkin in the ubiquitin-proteasomal pathway |
|
1 : 24 |
83 | Proteolysis_putative SUMO-1 pathway |
|
1 : 29 |
| |||
|
|||
84 | Blood coagulation_blood coagulation |
|
1 : 39 |
85 | Blood coagulation_GPVI-dependent platelet activation |
|
1 : 55 |
86 | Blood coagulation_GPIb-IX-V-dependent platelet activation |
|
1 : 76 |
| |||
|
|||
87 | DNA damage_role of SUMO in p53 regulation |
|
1 : 17 |
88 | DNA damage_NHEJ mechanisms of DSBs repair |
|
1 : 19 |
89 | DNA damage_nucleotide excision repair |
|
1 : 36 |
| |||
|
|||
90 | Apoptosis and survival_role of IAP-proteins in apoptosis |
|
1 : 31 |
91 | Apoptosis and survival_lymphotoxin-beta receptor signaling |
|
1 : 42 |
| |||
|
|||
92 | Cytoskeleton remodeling_keratin filaments |
|
1 : 36 |
93 | Cytokine production by Th17 cells in CF |
|
1 : 39 |
| |||
|
|||
94 | Transport_RAB3 regulation pathway |
|
1 : 14 |
95 | Inhibitory action of lipoxin A4 on PDGF, EGF and LTD4 signaling |
|
1 : 35 |
96 | Signal transduction_calcium signaling |
|
1 : 45 |
97 | Inhibitory action of lipoxins on neutrophil migration |
|
1 : 57 |
| |||
|
|||
98 | Cell cycle_role of Nek in cell cycle regulation |
|
2 : 32 |
Top ten pathway maps identified by MetaCore tool in the microarray data set of MiaPaCa-2 cells treated with LE for three different time points. The list is arranged per descending
In this analysis, the transcription assembly of RNA Polymerase II preinitiation complex on TATA-less promoters pathway passed QC set at FDR ≤ 0.05. The
Canonical pathway analyses in MetaCore tool identified Transcription assembly of RNA polymerase II preinitiation complex on TATA-less promoters to be the top scored pathway map (
Besides the above, additional 11 pathways were significantly affected by LE. They are immune response_MIF-mediated glucocorticoid regulation (
The expression of 82 genes that were identified to be influenced by LE was subjected to disease biomarker enrichment analysis in MetaCore tool. This investigation picked up a large number of diseases from this data set. The top 10 diseases along with the corresponding
A partial list of diseases that may be affected by LE. The top ten diseases identified by MetaCore tool in the microarray data set of MiaPaCa-2 cells treated with LE are listed in this table. The list is arranged per descending
No. | Diseases |
|
Ratio |
---|---|---|---|
1 | Rheumatoid vasculitis |
|
6 : 10 |
2 | Arthritis, experimental |
|
6 : 14 |
3 | Papilloma, intraductal |
|
4 : 4 |
4 | Systemic vasculitis |
|
7 : 47 |
5 | DNA virus infections |
|
15 : 654 |
6 | Herpesviridae infections |
|
12 : 378 |
7 | Vasculitis |
|
10 : 238 |
8 | Lymphangiomyoma |
|
4 : 10 |
9 | Smooth muscle tumor |
|
4 : 10 |
10 | Lymphangioleiomyomatosis |
|
4 : 10 |
In complementary and alternate medicine (CAM), unrefined (crude) preparations of natural products are used for the treatment of various illnesses. It is believed that compounds present in a crude preparation modulate the effect of the active molecule [
We are interested in the anticancer properties of
Mutations in cystic fibrosis gene are identified as a risk factor for chronic pancreatitis and pancreatic ductal adenocarcinoma [
Our results demonstrate that it is possible to pin point possible mechanism of action of crude extract using carefully planned experiments. We applied several assumptions in our data analyses. For instance, we eliminated expressions that did not follow the same pattern. There are instances where a group of genes may be up regulated in the short term and down regulated over long term exposure to LE or vice versa. Though less likely, another set of genes may be up and down regulated in a cyclical manner. By considering unidirectional regulation, we would have eliminated such unique changes effected by LE. Ambiguities in gene expression analysis may be reduced and the robustness of expression data may be increased by using several experimental replicates, multiple different cell lines, and different drug concentrations. Additionally, gene expression analyses may be performed in tumor samples obtained from animal model that had been treated with LE.
The identification of regulation of 82 genes from a large data set encourages us to think that natural products may target a limited set of genes. There are evidences to support such observations. For instance, almost identical results were obtained in MCF-7 breast cancer cells that were treated with a crude extract of
In conclusion, we have demonstrated that LE affected the expression of only a few genes. Using similar methodology, it is possible to get reasonably good overview of the changes induced by crude preparation of natural products with medicinal properties.
Authors indicate no conflict of interests.
Pochi R. Subbarayan thanks Dr. Ram Agarwal and Dr. Pradeep Kumar for the supply of