Lobelia herbs, Active Ingredients, and Known Targets
In this study, a total of 17 herbs belonging to the genus Lobelia were selected. The criteria of their inclusion in this study were based on available information related to the metabolites and their biological targets. For the herbs are known to have at least one experimentally characterized metabolite and one corresponding target was included. The collected herbs are listed in Supplementary Table S1. Initially, the chemical constituents collected from the NPASS database were mapped to the ChEMBL database and the corresponding bioactivity data were retrieved. A total of 194 compounds were successfully mapped in this manner. Additionally, the chemical constituents and corresponding biological targets of Lobelia chinensis were also retrieved from the TCMSP database. A total of 71 chemical constituents were retrieved from the TCMSP database. The redundant chemical structures between the Lobelia chinensis collected from both databases were merged into one and the final selection was based on the TCMSP database. Finally, a total of 233 molecules were subjected to the ADME-filtering. In this study, we used three ADME-filtering criteria; OB ≥ 30%, DL ≥ 0.18, and HL ≥ 4. The aim of this filtering was to select the molecules with good absorption, slow metabolism after oral administration, and suitability for the drug-development. A similar approach has been used in other studies (50). In this work, we did not consider the blood-brain barrier (BBB) permeability as filtering criteria. The reason was that certain natural products (e.g. quercetin) with the theoretical prediction of poor BBB permeability when tested experimentally exhibited satisfactory permeability (51). Following the ADME- filtering, 49 unique chemical compounds associated with 411 corresponding targets were selected. Out of that, 12 targets related to the cytochrome 450 families were discarded from further study. Finally, a total of 153 non-reductant targets were studied.
Neuroprotection and Neurodegenerative Disease-Related Gene
The candidate genes of the five most common neurodegenerative diseases viz. AD (180 genes), ALS (121 genes), epilepsy (2667 genes), HD (65 genes), and PD (127 genes) along with the genes commonly associated with the neuroprotection (NP, 101 genes) mechanism were collected. A series of gene databases (see Material and Methods) were used. Initially, the genes related to each disease and NP mechanism were compared and analyzed individually with the Lobelia genes (153 genes). For the sake of simplicity, this set of genes which includes individual diseases will be called Set I. The overlapped genes of the Set I are shown in Supplementary Figures S1. In the final selection, all the genes associated with five neurodegenerative diseases mentioned above and those of NP mechanisms were overlapped with the Lobelia genes. For the sake of simplicity, this set of genes will be called Set II. To be noted that all the subsequent studies reported in the main paper were performed on Set II. The reason for the selection of Set II for the detailed analysis was because of the fact that different neurodegenerative diseases including those mentioned in this study broadly share the common pathogenesis and signaling pathways related to neuronal damages and protection. Therefore, the genes of the Set II best represented the neuroprotective mechanism associated with different neurodegenerative diseases. Moreover, the analysis related to Set I was also performed (see Supplementary Information).
A total of 31 overlapping genes in Set II were retrieved and the IUPHAR classification (52) was performed on them. The categorical distribution of the overlapped gene is shown in Fig. 1A. As expected, a majority of the genes belonged to the enzyme class (56.3%) followed by ion transporter (12.5%), nuclear hormone receptors (12.5%), other protein (12.5%), and catalytic receptor (6.3%).
We have also performed a clustering analysis to determine the chemical diversity of the collected chemical constituents. The 49 unique compounds could be represented into 10 well-defined clusters as shown in Fig. 1B. The representative chemical structure of each cluster is shown in Fig. 1C. The aglycone part of the flavonoid such as diosmetin and 18 other aglycones constituted the most populated cluster (Cluster 1).
Enrichment Analysis of the Candidate Genes
All the overlapped genes belonging to Set I and Set II were tested for functional enrichment with three GO terms (BP, CC, and MF) and KEGG/REACTOME pathways. The result of the GO terms and KEGG/REACTOME pathways enrichment of genes in Set I are shown in Supplementary Figures S2-S7. The result of the GO enrichment of the genes in Set II is shown in Fig. 2A. The description is provided in the following section.
The “positive regulation of transcription from RNA polymerase II promoter”, “positive regulation of transcription, DNA-templated”, “response to drug”, and “negative regulation of apoptotic process” were the most significantly enriched terms in the BP. Interestingly, the “response to gamma radiation (GO:0010332)” was also found to be within the top ten most enriched BP terms. The ionizing radiation has been shown to have a debilitating impact on neurodegenerative diseases. Several studies reported that radiation inhibits neurogenesis through different mechanisms such as neuroinflammation, elevate reactive oxygen and nitrogen species, oxidative stress, protein degradation, and mitochondrial dysfunctions, among others (53–56). The major cellular components such as nucleus, cytosol, cytoplasm, mitochondria along with synapse were indicated as the location of the overlapped genes in Set II.
The KEGG/REACTOME pathways analysis of overlapped genes were analyzed with BH-corrected P-values < 0.05 (Fig. 2B). The enrichment of the overlapped genes was mostly found in the pathways in cancer, hepatitis B, Akt signaling pathway, proteoglycans in cancer, MAPK signaling pathway, HTLV-1 infection, prostate cancer, colorectal cancer, influenza A, thyroid hormone signaling pathway, tuberculosis, Chagas disease, endometrial cancer, Amyotrophic lateral sclerosis, and bladder cancer, among other. Indeed, a number of studies supported the intriguing cross-talks between cancer and neurodegeneration (57–59). The key candidate pathways-targets interaction network is shown in Fig. 2C.
Compound-Target Networks
The compound-target network was constructed to establish the role of the active ingredients of the genus Lobelia and the overlapped targets found in Set I and Set II. The compound-network diagram diagrams of the overlapped genes in AD, ALS, epilepsy, HD, NP, and PD are shown in Supplementary Figures S8-S13 and for Set II is shown in Fig. 3.
According to the analysis, 12 unique compounds viz. quercetin (MOL000098), luteolin (MOL000006), kaempferol (MOL000422), acacetin (MOL001689), chryseriol (MOL003044), norlobelanine (MOL012216), lobelanine (MOL012208), 2-[(2R,6S)-6-[(2R)-2-hydroxy-2-phenylethyl]-1-methylpiperidin-2-yl]-1-phenylethanone (MOL012209), hydroxygenkwanin (MOL005530), lobelanidine (MOL012207), and diosmetin (MOL002881) were found associated with the 31 target proteins in Set II. Among these compounds, quercetin (degree: 26), luteolin (degree: 10), kaempferol (degree: 7), acacetin (degree: 5), and chryseriol (degree: 4) were found to be a high-degree association (≥ 4 proteins). Among the targets, nitric oxide synthase, brain (NOS1, degree: 5), androgen receptor (ANDR, degree: 5), sodium- and chloride-dependent GABA transporter 1 (SC6A1, degree: 4), BCL2 (degree: 3), AKT1 (degree: 3), P53 (degree: 3), BAX (degree: 3), and TNFA (degree: 3) were found to be associated with at least three compounds. The candidate compounds and target relationships are listed in Supplementary Table S2-S8.
Certain species of Lobelia, such as Lobelia inflata and Lobelia cardinalis are extensively characterized and the pharmacological properties of their chemical constituents are well-studied (60). Among them, the pyridine alkaloids lobeline and lobinaline were thoroughly investigated (27, 61–63). However, due to the structural complexity of the glycosidic components, the evaluation of their bioactivities has been continuing to prove highly challenging. Thus, our study establishing the relationship between the active ingredients of the Lobelia species to their potential targets should aid a tremendous value in elucidating their mechanism of actions in neuroprotection.
Construction and Analysis of Target Proteins PPI Network
The PPI network was constructed to understand the interrelation between the neuroprotection associated candidate genes of the genus Lobelia. The constructed PPI network is shown in Fig. 4. The network edges were first created based on the molecular interaction by keeping the interaction score to ≥ 0.400. A total of 31 nodes and 249 edges were found in the network with an average node degree of 16.1. As expected, AKT1, TP53, MYC, TNF, EGF, EGFR were amongst the central targets in the PPI network. In the next step, the highly interconnected regions in the PPI network were analyzed using the MCODE algorithm. A well-organized and highly interconnected hub region with 20 nodes were retrieved. The targets ESR1, MYC, IL1B, IFNG, CXCL8, CASP9, IL2, CCL2, EGF, EGFR, FOS, MMP2, HSPB1, AKT1, TP53, BCL2L1, AR, HIF1A, TNF, and CCND1 constituted the cluster. Interestingly, all the targets associated in this cluster had an association score ≥ 0.9, which in turn suggests the high confidence in their interactions. The topological parameters of the PPI network are shown in Table 1.
Table 1
The topological parameters of the PPI network.
Genes
|
Degree
|
Betweenness Centrality
|
Average Shortest Path Length
|
Closeness Centrality
|
BCL2L1
|
24
|
0.02054442
|
1.20689655
|
0.82857143
|
TP53
|
24
|
0.02054442
|
1.20689655
|
0.82857143
|
AKT1
|
28
|
0.10602706
|
1.03448276
|
0.96666667
|
GSK3B
|
13
|
0.00616809
|
1.5862069
|
0.63043478
|
EGFR
|
21
|
0.01242899
|
1.31034483
|
0.76315789
|
EGF
|
22
|
0.01542304
|
1.27586207
|
0.78378378
|
BAX
|
9
|
0.00049759
|
1.72413793
|
0.58
|
CASP9
|
19
|
0.01023331
|
1.37931034
|
0.725
|
CXCL8
|
19
|
0.00330769
|
1.37931034
|
0.725
|
IL1B
|
19
|
0.02195186
|
1.34482759
|
0.74358974
|
MYC
|
24
|
0.02054442
|
1.20689655
|
0.82857143
|
BCL2
|
11
|
0.00269961
|
1.65517241
|
0.60416667
|
ESR1
|
21
|
0.01045047
|
1.31034483
|
0.76315789
|
CCND1
|
20
|
0.01077983
|
1.34482759
|
0.74358974
|
TNF
|
25
|
0.04919786
|
1.13793103
|
0.87878788
|
HIF1A
|
17
|
0.00428457
|
1.44827586
|
0.69047619
|
AR
|
17
|
0.00730507
|
1.44827586
|
0.69047619
|
FOS
|
23
|
0.05947999
|
1.20689655
|
0.82857143
|
CCL2
|
17
|
0.00290018
|
1.44827586
|
0.69047619
|
IL2
|
16
|
0.00504994
|
1.48275862
|
0.6744186
|
IFNG
|
14
|
0.00054133
|
1.55172414
|
0.64444444
|
SOD1
|
16
|
0.02218413
|
1.44827586
|
0.69047619
|
CTSD
|
11
|
0.0013186
|
1.65517241
|
0.60416667
|
HSPB1
|
17
|
0.00420332
|
1.44827586
|
0.69047619
|
PRKCB
|
9
|
0.00992863
|
1.68965517
|
0.59183673
|
NOS1
|
7
|
0.00356263
|
1.75862069
|
0.56862745
|
MMP2
|
20
|
0.00586964
|
1.34482759
|
0.74358974
|
SLC6A3
|
6
|
0.02548675
|
1.79310345
|
0.55769231
|
MAOB
|
2
|
0
|
2.62068966
|
0.38157895
|
ACHE
|
7
|
0.04447572
|
1.75862069
|
0.56862745
|
Molecular docking
Molecular docking was carried out to elucidate the binding modes of the 12 active ingredients to the 15 targets (NOS1, BCL2, AR, AKT1, AChE, IL2, EGFR, ER, MAOB, PRKCB, CTSD, MMP2, GSK3B, SOD1, and HIF1A) for which crystal structures were known. Interestingly, most of the Lobelia compounds showed a relatively much higher binding affinity against targets such as MAOB (monoamine oxidase B), PRKCB (protein kinase C beta type), AR (androgen receptor), AChE (acetylcholinesterase), and ER (estrogen receptor) than the other 10 targets. For example, the pyridine alkaloid lobelanidine interacted with MAOB with a docking score of -11.12 kcal/mol. (2-[(2R,6S)-6-[(2R)-2-hydroxy-2-phenylethyl]-1-methylpiperidin-2-yl]-1-phenylethanone) interacted with AChE with a docking score of -11.911, which should be deemed as a potent binding. The docking score of the best-ranked molecules against the selected targets is shown in Table 2. The molecular interactions and binding mode of the selected molecules are shown in Figs. 5–7.
Table 2
Docking results of the best-ranked candidate compounds.
Protein
|
PDB ID
|
Compound ID
|
Docking score (kcal/mol)
|
MAOB
|
2BK3 (119)
|
MOL012207 (lobelanidine)
|
-11.12
|
|
|
MOL012209 (2-[(2R,6S)-6-[(2R)-2-hydroxy-2-phenylethyl]-1-methylpiperidin-2-yl]-1-phenylethanone)
|
-10.506
|
|
|
MOL002881 (diosmetin)
|
-10.488
|
|
|
MOL000098 (quercetin)
|
-10.164
|
|
|
MOL012208 (lobelanine)
|
-10.006
|
PRKCB
|
2I0E (120)
|
MOL005530 (hydroxygenkwanin)
|
-8.264
|
|
|
MOL000098 (quercetin)
|
-8.189
|
|
|
MOL000006 (luteolin)
|
-8.141
|
AR
|
2PIU (121)
|
MOL000006 (luteolin)
|
-9.38
|
|
|
MOL003044 (chryseriol)
|
-9.224
|
|
|
MOL000098 (quercetin)
|
-9.101
|
|
|
MOL000422 (kaempferol)
|
-9.012
|
AChE
|
4EY7 (122)
|
MOL012209 (2-[(2R,6S)-6-[(2R)-2-hydroxy-2-phenylethyl]-1-methylpiperidin-2-yl]-1-phenylethanone)
|
-11.911
|
|
|
MOL012207 (lobelanidine)
|
-11.756
|
|
|
MOL012216 (norlobelanine)
|
-10.967
|
|
|
MOL001689 (acacetin)
|
-9.233
|
ER
|
5TOA (123)
|
MOL000006 (luteolin)
|
-9.985
|
|
|
MOL000098 (quercetin)
|
-9.948
|
|
|
MOL012207 (lobelanidine)
|
-9.782
|
Interestingly, the inhibitory effects of some of the compounds revealed in this study against their targets were reported. For example, quercetin and diosmetin were reported to have MAO inhibitory IC50 values of 90 µM ((64) and 2.10 µM (65), respectively. Both quercetin and luteolin were reported to have inhibitory activity on protein kinase C (66, 67). Luteolin, quercetin, and kaempferol were reported to suppress the function of the AR receptor in different cancer cells (68–70). Likewise, acacetin as well several acacetin derivatives such as linarin (acacetin-7-O-β-d-rutinoside), acacetin-7-O-methyl ether Mannich base derivatives and acacetin-7-O-β-D-galactopyranoside were reported to exhibit AChE inhibition (71–73). Quercetin was reported to stimulate cancer cell proliferation via the estrogen receptor (74).