Toxicogenomic analysis of publicly available transcriptomic data can predict food, drugs, and chemical-induced asthma

Figures & data

Figure 1 Flowchart outlining the steps of the bioinformatics approach to identify differentially expressed genes in severe asthmatic bronchial epithelium compared to healthy controls.

Abbreviations: GEO omnibus, Gene Expression Omnibus; RMA, Robust Multiarray Averaging; GC-RMA, GeneChip RMA; MAS5, Affymetrix Microarray Suite 5.

Figure 2 The flowchart of the bioinformatics approach to identify gene sets related to chemical, toxins, and drugs.

Table 1 Details of datasets extracted from DSigDB and DrugMatrix and used for GSEA

Collection	Description	Unique Number of Genes	Number of Gene Sets
D1: FDA Approved	FDA Approved Drug Gene Sets.	1,288	1,202
D2: Kinase Inhibitors	Kinase Inhibitors Gene Sets based on in vitro kinase profiling assays.	407	1,220
FDA	FDA Approved Kinase Inhibitors.	341	28
HMS LINCS	Kinase inhibition assays extracted from HMS LINCS database.	381	90
MRC	Kinase inhibition assays extracted from MRC Kinome Inhibition database.	137	157
GSK	GSK Published Kinase Inhibitor Set (PKIS), kinase inhibitors used as chemical probes.	116	204
Roche	Kinase Inhibitors profiled by Roche.	153	570
RBC	Kinase Inhibitors profiled by Reaction Biology Corporation.	246	99
KinomeScan	Kinase Inhibitors profiled by DiscoveryRx using KinomeScan technology.	374	72
D3: Perturbagen Signatures	7,064 gene expression profiles from three cancer cell lines perturbed by 1,309 compounds from CMap (build 02).	11,137	1,998
CMAP	7,064 gene expression profiles from three cancer cell lines perturbed by 1,309 compounds from CMap (build 02).	11,137	1,998
D4: Computational Drug Signatures	Drug signatures extracted from literature using a mixture of manual curation and by automatic computational approaches.	18,854	18,107
BOSS	Text mining approaches of drug-gene targets using Biomedical Object Search System (BOSS).	3,354	2,114
CTD	Curation of targets from Comparative Toxicogenomics Database (CTD).	18,700	5,163
TTD	Manual curation of targets from the Therapeutics Targets Database (TTD).	1,389	10,830
DrugMatrix database	The DrugMatrix database is one of the world’s largest toxicogenomic reference resources	5209	7876

Figure 3 The flowchart outline using the Comparative Toxicogenomics Database (CTD) batch query tool (http://ctdbase.org/tools/batchQuery) to identify common pathways targeted by most of the GSEA-identified chemicals. (A) All the earlier identified drugs and chemicals were uploaded to the query tool to search for genes and (B) pathways that were documented to be affected by queried chemicals. The tool will generate (C) a list of pathways where the given chemical affects genes related to that pathway significantly (adjusted p-value <0.05). (D) Only pathways that are shared by at least 50 percent and above of the identified chemicals are selected.

Figure 4 Gene Set Enrichment Analysis (GSEA) of the differentially expressed genes between severe asthmatic bronchial epithelium (n=22) and healthy bronchial epithelium (n=37) in GSE64913. (A) Distribution of the identified genes ranked according to their position (B) Heatmap image generated from the 2952 DEG between severe asthma and healthy controls which were later filtered into 225 genes (C) the top enriched pathways whether upregulated or downregulated in severe asthma compared to healthy controls using metascape (http://metascape.org): a gene annotation and analysis online resource that generates a graphical representation.

Table 2 List of the significantly enriched pathways related to chemicals, toxins, and drugs for the genes that showed significant differential expression in severe asthmatic bronchial epithelium compared to healthy controls

#	Gene Set Name	Size	Enrichment score	Normalized Enrichment score	Nominal p-value	False Discovery Rate q-value	Familywise-error rate p-value	Rank at Max	Leading Edge
1	Purpurogallin	28	0.630629	1.827431	0.002024292	0.0122542	0.033	1538	tags=64%, list=26%, signal=86%
2	Tyrphostin AG 538	16	0.631126	1.846969	0.003937008	0.0136133	0.028	797	tags=38%, list=13%, signal=43%
3	7-amino-4-hydroxy-2-naphthalenesulfonic acid	15	0.640253	1.799482	0.002016129	0.0146326	0.049	176	tags=27%, list=3%, signal=27%
4	Corilagin	17	0.676499	1.8472	0.003898636	0.02042	0.028	964	tags=53%, list=16%, signal=63%
5	4-hydroxytamoxifen	17	0.588731	1.724107	0.012219959	0.0285716	0.096	699	tags=35%, list=12%, signal=40%
6	Salazinic Acid	15	0.646698	1.672565	0.011857707	0.0403463	0.147	1700	tags=73%, list=28%, signal=102%
7	Ellagic Acid	27	0.559782	1.639618	0.004008016	0.0463795	0.183	1067	tags=48%, list=18%, signal=58%
8	Baicalein	15	0.607701	1.556171	0.032719836	0.076446	0.273	1067	tags=47%, list=18%, signal=57%
9	Curcumin	25	0.542949	1.509353	0.024439918	0.0802608	0.333	978	tags=48%, list=16%, signal=57%
10	SB 202190	22	0.489577	1.518684	0.032258064	0.082731	0.323	699	tags=36%, list=12%, signal=41%
11	Acrylamide	15	0.586047	1.661663	0.009451796	0.1950874	0.477	125	tags=20%, list=2%, signal=20%
12	Myricetin	40	0.547651	1.654645	0.01713062	0.1982278	0.487	1174	tags=48%, list=20%, signal=59%
13	Cupric Oxide	131	0.413138	1.663593	0.012526096	0.2041864	0.471	936	tags=29%, list=16%, signal=34%
14	Microcystin RR	20	0.547977	1.66728	0.00990099	0.2108996	0.462	462	tags=25%, list=8%, signal=27%
15	Arachidonic Acid	78	0.524402	1.64341	0.004115226	0.212851	0.518	998	tags=38%, list=17%, signal=46%
16	CLOFOP [ISO] (2-[4-(4-chlorophenoxy)phenoxy]propanoic acid)	16	0.667372	1.550415	0.02586207	0.2191059	0.681	1328	tags=69%, list=22%, signal=88%
17	Luteolin	44	0.54766	1.634669	0.024844721	0.2199198	0.532	1012	tags=43%, list=17%, signal=52%
18	Ammonium Hexachloroplatinate (IV)	16	0.600401	1.550776	0.040733196	0.2241297	0.681	648	tags=38%, list=11%, signal=42%
19	Bisindolylmaleimide I	23	0.587615	1.552711	0.030991735	0.2265287	0.679	964	tags=39%, list=16%, signal=46%
20	Phytoestrogens	16	0.768827	1.667617	0.022	0.2282088	0.462	856	tags=63%, list=14%, signal=73%
21	Thapsigargin (67526-95-8)	327	0.333608	1.533748	0.002061856	0.2255021	0.711	1303	tags=34%, list=22%, signal=41%
22	1-(5-deoxypentofuranosyl)-5-fluoropyrimidine-2,4 (1 h, 3 h)-dione	23	0.565554	1.552936	0.0385439	0.2324028	0.679	1330	tags=52%, list=22%, signal=67%
23	Nelfinavir	18	0.513173	1.529705	0.029661017	0.2336185	0.716	1169	tags=56%, list=19%, signal=69%
24	Vinblastine	75	0.419995	1.523846	0.036885247	0.234146	0.724	995	tags=31%, list=17%, signal=36%
25	Thimerosal	53	0.525926	1.601744	0.018867925	0.2357184	0.592	1071	tags=47%, list=18%, signal=57%
26	MG-132 (N-Benzyloxycarbonyl-L-leucyl-L-leucyl-L-leucinal)	67	0.455003	1.554736	0.017391304	0.2358129	0.675	1012	tags=39%, list=17%, signal=46%
27	Thalidomide	61	0.512241	1.525544	0.025641026	0.2359595	0.719	1101	tags=48%, list=18%, signal=58%
28	Protoporphyrin IX	22	0.540459	1.536768	0.048625793	0.2360099	0.704	1355	tags=50%, list=23%, signal=64%
29	Phosphine	37	0.573179	1.534214	0.038793102	0.2361448	0.71	1479	tags=51%, list=25%, signal=68%
30	Adenine	25	0.575044	1.752054	0.007968128	0.2367139	0.286	650	tags=40%, list=11%, signal=45%
31	Caffeic Acid	24	0.516087	1.539089	0.032388665	0.2367951	0.7	1803	tags=67%, list=30%, signal=95%
32	Lucanthone	71	0.547325	1.609604	0.046653144	0.2390304	0.571	742	tags=44%, list=12%, signal=49%
33	Dronabinol	134	0.414731	1.603898	0.008230452	0.241325	0.586	1067	tags=34%, list=18%, signal=40%
34	Antimony Potassium Tartrate	37	0.50915	1.613491	0.012711864	0.24176	0.561	1181	tags=49%, list=20%, signal=60%
35	Rottlerin	35	0.511257	1.554947	0.020408163	0.2423282	0.674	1221	tags=40%, list=20%, signal=50%
36	DMNQ (2,3-Dimethoxy-1,4-naphthoquinone)	62	0.493426	1.592946	0.022916667	0.2429922	0.604	874	tags=44%, list=15%, signal=50%
37	Rapamycin	89	0.435861	1.58848	0.012765957	0.2433278	0.61	1346	tags=45%, list=22%, signal=57%
38	Gefitinib	33	0.622531	1.769392	0.006276151	0.2467885	0.242	753	tags=42%, list=13%, signal=48%
39	Atenolol	17	0.598956	1.667728	0.006012024	0.2487788	0.462	120	tags=24%, list=2%, signal=24%
40	Antimony	35	0.540104	1.615625	0.02096436	0.2491837	0.556	1181	tags=51%, list=20%, signal=64%
41	Acrolein	43	0.538176	1.555005	0.0392562	0.2494786	0.674	1067	tags=47%, list=18%, signal=56%

Notes:  Column Headings as per GSEA website (https://software.broadinstitute.org/gsea/): Size; Number of genes in the gene set. Enrichment score for the gene set; the degree to which this gene set is overrepresented at the top or bottom of the ranked list of genes in the expression dataset. Normalized enrichment score; the enrichment score for the gene set after it has been normalized across analyzed gene sets. Nominal p-value; the statistical significance of the enrichment score. The nominal p-value is not adjusted for gene set size or multiple hypothesis testing; therefore, it is of limited use in comparing gene sets. False discovery rate; the estimated probability that the normalized enrichment score represents a false-positive finding. Familywise-error rate; a more conservatively estimated probability that the normalized enrichment score represents a false-positive finding. Rank at Max; the position in the ranked list at which the maximum enrichment score occurred. Three statistics are used to define the leading edge subset. Tags; the percentage of gene hits before (for positive ES) or after (for negative ES) the peak in the running enrichment score. This indicates the percentage of genes contributing to the enrichment score. List; the percentage of genes in the ranked gene list before (for positive ES) or after (for negative ES) the peak in the running enrichment score. This indicates where in the list, the enrichment score is attained. Signal, the enrichment signal strength that combines the two previous statistics.

Table 3 The top enriched chemicals by GSEA categorized into different subgroups

Occupational	Drugs	Plant/Plant toxins/Food
1. Ammonium Hexachloroplatinate (IV)	1. Nelfinavir	1. Adenine
2. Phosphine	2. Thalidomide	2. Arachidonic Acid
3. Acrylamide	3. Antimony Potassium Tartrate	3. Baicalein
Pesticide/herbicide	4. 4-Hydroxytamoxifen	4. Caffeic Acid
5. Acrolein	5. SB 202190 (4-[4-(4-fluorophenyl)-5-(4-pyridinyl)-1H-imidazol-2-yl]-phenol)	6. Corilagin
7. CLOFOP [ISO] (2-[4-(4-chlorophenoxy)phenoxy]propanoic acid)	6. Myricetin	8. Curcumin
Chemical compounds	7. Lucanthone	9. Ellagic Acid
10. Bisindolylmaleimide I	8. Dronabinol	11. Luteolin
12. Thapsigargin (67526-95-8)	9. Rapamycin	13. Microcystin RR
14. MG-132 (N-Benzyloxycarbonyl-L-leucyl-L-leucyl-L-leucinal)	10. Atenolol	15. Phytoestrogens
16. DMNQ (2,3-Dimethoxy-1,4-naphthoquinone)	Chemotherapy	17. Protoporphyrin IX
18. Tyrphostin AG 538	19. Vinblastine	20. Purpurogallin
21. 1-(5-Deoxypentofuranosyl)-5-Fluoropyrimidine-2,4 (1 h, 3 h)-Dione	22. 7-Amino-4-Hydroxy-2-Naphthalenesulfonic Acid	23. Rottlerin
24. Antimony	25. Gefitinib	26. Salazinic Acid

Table 4 List of pathways significantly associated with the largest number of the identified 41 chemicals using Comparative Toxicogenomics Database (CTD) batch query webtool (http://ctdbase.org/tools/batchQuery). Only pathways that are shared by more than 50% of the identified chemicals were listed

Significant Chemicals Associated Pathways	Shared by how many chemicals (Total=41)	Percentage
Immune System	33	80%
Cytokine Signaling in the Immune system	31	76%
IL-17 signaling pathway	31	76%
Pathways in cancer	31	76%
Signaling by Interleukins	31	76%
Signal Transduction	31	76%
HTLV-I infection	30	73%
Interleukin-4 and 13 signaling	30	73%
Chagas disease (American trypanosomiasis)	29	71%
Fluid shear stress and atherosclerosis	29	71%
Hepatitis B	29	71%
Innate Immune System	29	71%
Metabolism	29	71%
Apoptosis	28	68%
Pertussis	28	68%
TNF signaling pathway	28	68%
Toxoplasmosis	28	68%
Tuberculosis	28	68%
AGE-RAGE signaling pathway in diabetic complications	27	66%
Endocrine resistance	27	66%
Gene Expression	27	66%
MAPK signaling pathway	27	66%
PI3K-Akt signaling pathway	27	66%
Signaling by NGF	27	66%
Viral carcinogenesis	27	66%
Cellular responses to stress	27	66%
Hemostasis	27	66%
Herpes simplex infection	27	66%
Non-alcoholic fatty liver disease (NAFLD)	27	66%
Platinum drug resistance	27	66%
MicroRNAs in cancer	26	63%
NOD-like receptor signaling pathway	26	63%
Proteoglycans in cancer	26	63%
Toll-like receptor signaling pathway	26	63%
Amoebiasis	26	63%
HIF-1 signaling pathway	26	63%
Influenza A	26	63%
Legionellosis	26	63%
Progesterone-mediated oocyte maturation	26	63%
Prostate cancer	26	63%
Amyotrophic lateral sclerosis (ALS)	25	61%
Adaptive Immune System	25	61%
Bladder cancer	25	61%
Cell cycle	25	61%
Colorectal cancer	25	61%
Downstream signaling events of B Cell Receptor (BCR)	25	61%
Generic Transcription Pathway	25	61%
Leishmaniasis	25	61%
Senescence-Associated Secretory Phenotype (SASP)	25	61%
FoxO signaling pathway	25	61%
Measles	25	61%
p53 signaling pathway	25	61%
Rheumatoid arthritis	25	61%
Developmental Biology	24	59%
Downstream signal transduction	24	59%
Epstein-Barr virus infection	24	59%
Estrogen signaling pathway	24	59%
Fc epsilon receptor (FCERI) signaling	24	59%
Metabolism of lipids and lipoproteins	24	59%
NGF signaling via TRKA from the plasma membrane	24	59%
Osteoclast differentiation	24	59%
Signaling by EGFR	24	59%
Signaling by PDGF	24	59%
Th17 cell differentiation	24	59%
Toll-Like Receptors Cascades	24	59%
Cellular Senescence	24	59%
Insulin resistance	24	59%
Interleukin-10 signaling	24	59%
Transcriptional misregulation in cancer	24	59%
Transcriptional Regulation by TP53	24	59%
Cell Cycle, Mitotic	24	59%
Activated TLR4 signalling	23	56%
Breast cancer	23	56%
Central carbon metabolism in cancer	23	56%
DAP12 interactions	23	56%
DAP12 signaling	23	56%
MyD88 cascade initiated on the plasma membrane	23	56%
MyD88 dependent cascade initiated on endosome	23	56%
MyD88-independent TLR3/TLR4 cascade	23	56%
MyD88:Mal cascade initiated on plasma membrane	23	56%
Neurotrophin signaling pathway	23	56%
Prolactin signaling pathway	23	56%
Salmonella infection	23	56%
Signaling by SCF-KIT	23	56%
Th1 and Th2 cell differentiation	23	56%
Toll Like Receptor 10 (TLR10) Cascade	23	56%
Toll-Like Receptor 2 (TLR2) Cascade	23	56%
Toll-Like Receptor 3 (TLR3) Cascade	23	56%
Toll-Like Receptor 5 (TLR5) Cascade	23	56%
Toll-Like Receptor 7/8 (TLR7/8) Cascade	23	56%
Toll-Like Receptor 9 (TLR9) Cascade	23	56%
Toll-Like Receptor TLR1: TLR2 Cascade	23	56%
Toll-Like Receptor TLR6: TLR2 Cascade	23	56%
TRAF6 mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation	23	56%
TRIF-mediated TLR3/TLR4 signaling	23	56%
Chronic myeloid leukemia	23	56%
Cytokine-cytokine receptor interaction	23	56%
Disease	23	56%
Hepatitis C	23	56%
Inflammatory bowel disease (IBD)	23	56%
Pancreatic cancer	23	56%
Signaling by the B Cell Receptor (BCR)	23	56%
Alzheimer’s disease	23	56%
Cell Cycle	23	56%
ErbB signaling pathway	22	54%
FCERI mediated MAPK activation	22	54%
GAB1 signalosome	22	54%
Metabolism of proteins	22	54%
PI3K/AKT activation	22	54%
PIP3 activates AKT signaling	22	54%
Shigellosis	22	54%
Signaling by VEGF	22	54%
T cell receptor signaling pathway	22	54%
Toll-Like Receptor 4 (TLR4) Cascade	22	54%
VEGF signaling pathway	22	54%
Apoptosis	22	54%
Diseases of signal transduction	22	54%
Epithelial cell signaling in Helicobacter pylori infection	22	54%
Intrinsic Pathway for Apoptosis	22	54%
Malaria	22	54%
Mitotic G1-G1/S phases	22	54%
NF-kappa B signaling pathway	22	54%
Programmed Cell Death	22	54%
Ras signaling pathway	22	54%
Small cell lung cancer	22	54%
Prion diseases	22	54%
Activation of the AP-1 family of transcription factors	21	51%
Autophagy - animal	21	51%
B cell receptor signaling pathway	21	51%
cAMP signaling pathway	21	51%
Endometrial cancer	21	51%
MAPK family signaling cascades	21	51%
Thyroid hormone signaling pathway	21	51%
VEGFA-VEGFR2 Pathway	21	51%
Acute myeloid leukemia	21	51%
Adipocytokine signaling pathway	21	51%
Apoptosis - multiple species	21	51%
Chemokine signaling pathway	21	51%
EGFR tyrosine kinase inhibitor resistance	21	51%
Glioma	21	51%
Jak-STAT signaling pathway	21	51%
Longevity regulating pathway	21	51%
Oxidative Stress-Induced Senescence	21	51%
Renal cell carcinoma	21	51%
Role of LAT2/NTAL/LAB on calcium mobilization	21	51%
Sphingolipid signaling pathway	21	51%
Platelet activation, signaling, and aggregation	21	51%