Reported QS-based drug-microbe interactions
Quorum sensing (QS), a means of microbial communication, regulates biological behaviors among microbes by synthesizing and releasing signaling molecules such as acyl-homoserine lactones (AHL), 2-heptyl-3-hydroxy-4-quinolone (PQS), and autoinducer-2 (AI-2) also named as autoinducers, which are received by diverse QS receptors [37]. Note that there are most of the reported QS receptors missing their crystal structures in the Protein Data Bank (PDB), except eleven QS receptors (SmcR, LasR, TraR, CviR, QscR, SdiA, YenR, PqsR, LuxP, LsrB, and CckA) (Table 1). Generally, QS systems including these receptors can be divided into four groups: AHL-type, PQS-type, AI-2-type, and two component system (TCS). TCS is a typical QS system which is consist of a histidine kinase receptor such as CckA and a response regulator partner [38]. As in our previous study [33], we also used the maximum homology SmcR from Vibrio vulnificus [39] to represent the common QS receptor LuxR from Vibrio fisheri, of which crystal structure is missed. Although hundreds of QS receptors were reported without crystal structures, the homology modeling of them can be also developed based on the above eleven typical QS receptors (Table 1).
Table 1. Details for various QS systems including eleven QS receptors.
QS
|
Ligand
|
Receptor
|
PDB
|
Binding sites
|
Organism
|
Refs
|
AHL
|
3OC6HSL
|
SmcR
|
3KZ9
|
Asn133, Gln137
|
Vibrio
vulnificus
|
[39]
|
3OC12HSL
|
LasR
|
2UV0
|
Tyr56, Trp60, Asp73, Thr75, Ser129
|
Pseudomonas aeruginosa
|
[40]
|
3OC8HSL
|
TraR
|
1L3L
|
Tyr53, Trp57, Asp70
|
Agrobacterium tumefaciens
|
[41]
|
C6HSL
|
CviR
|
3QP1
|
Tyr80, Trp84, Asp97, Ser155
|
Chromobacterium violaceum
|
[42]
|
3OC12HSL
|
QscR
|
3SZT
|
Tyr58, Trp62, Asp75, Ser38
|
Pseudomonas aeruginosa
|
[43]
|
3OC6HSL
|
SdiA
|
4Y15
|
Tyr63, Trp67, Asp80, Ser43
|
Escherichia
coli
|
[44]
|
3OC6HSL
|
YenR
|
5L09
|
Ser32, Try50, Trp54, Asp67
|
Yersinia enterocolitica
|
[45]
|
PQS
|
PQS
|
PqsR
|
4JVD
|
Ile149, Ile168, Leu207, Ile236, Thr265
|
Pseudomonas aeruginosa
|
[46]
|
AI-2
|
S-THMF-borate
|
LuxP
|
1JX6
|
Gln77, Ser79, Trp82, Asn159, Arg215, Thr266, Asp267, Trp289, Arg310
|
Vibrio
harveyi
|
[47]
|
R-THMF
|
LsrB
|
1TJY
|
Lys35, Asp116, Asp166, Gln167, Ala222, Pro220
|
Salmonella typhimurium
|
[48]
|
TCS
|
c-di-GMP
|
CckA
|
5IDJ
|
Tyr514, Lys518, Trp523
|
Caulobacter
crescentus
|
[38]
|
We have collected examples of the reported binding of drugs to QS receptors. There are diverse drugs such as metformin, berberine, eugenol, salicylic acid, nifuroxazide, and chlorzoxazone can be bound to the QS receptor LasR [31, 49] [32, 50]. For example, compared with the positive control N-3-oxododecanoyl-homoserine lactone (3OC12HSL), there are highly similar binding sites (Tyr56, Trp60) for the berberine and 3OC12HSL when docking with LasR with AutoDock Vina (Figure 1A). With regard to the binding of drugs and another QS receptor CviR, there are same binding sites from Vina-based docking results (Tyr80) for positive control (N-Hexanoyl-L-Homoserine Lactone, C6HSL) and albendazole (Figure 1B). We conducted an analysis for the distribution of the free binding energies (FBE) of diverse QS receptors and various ligands, including original ligands (Figure 1C) and reported docking-validated drugs (Figure 1D). Combined with our previous study [33], we suggested that “-6 kcal/mol” can be also set as the cutoff to separate the binding and non-binding, which can be used to test the validity of Vina-based docking approach.
The set of the docking cutoff formed a good basis for predicting potential binding of the other drugs and QS receptors. Some other cases [16, 51] showed that drug molecules can influence the abundance of the specific microbe or the relevant pathways without pointing out the specific drug-microbe target. Results from docking-based prediction verified that commonly used drugs, such as proton pump inhibitors (Omeprazole, Esomeprazole, Pantoprazole and Rabeprazole), SSRI antidepressants antibiotics (Fluvoxamine, Fluoxetine, Paroxetine, Sertraline, Escitalopram and Citalopram), have some potential to bind to CckA, LasR, LuxR, PqsR and QscR proteins (Figure 1E). It indicates that various QS receptors may be one type of the important targets for drug-microbe interactions. Furthermore, we found that there is drug crosstalk for different QS receptors (Figure 1F). Taking the eleven common drugs from the reported cases (metformin, chlorzoxazone, eugenol, salicylic acid, nifuroxazide, berberine, indoramine, tiaprofenic acid, donepezil, albendazole, fluvoxamine), we developed a crosstalk investigation for the above 11 drugs and QS receptors (Figure 1F). As a result, drug-microbe interaction predictions illustrated in Figure 1F have been partially verified from other reported researches. For example, the salicylic acid (-6.7 kcal/mol) and tiaprofenic acid (-8.9 kcal/mol) were reported to bind to SdiA from Salmonella enteritidis [52]. There is a strongest binding between indoramine and QscR. Tiaprofenic acid has huge potential to bind to the above stated QS receptors except the LsrB. The free binding energies (FBEs) of three drugs (chlorzoxazone, eugenol and salicylic acid) and the eleven QS receptors were relatively close, which indicates the potential drugs crosstalk on different microbes. LsrB and LuxP have stronger specificity than other receptors and have less potential to bind to different drugs.
Predicted QS-based drug-microbe interactions
To have a better understanding for the QS-based drug-microbe interactions at a larger scale, we conducted a docking-based calculation for FBEs distribution of the above 11 QS receptors and more than 8,000 drugs from the DrugBank database (Figure 2). In order to make the FBE distribution more detailed, we have set -8 kCal/mol, -10 kCal/mol, and -12 kCal/mol as three other cutoffs to rank the docking results in a certain gradient. Results shown that there are the most of drugs not binding to LsrB (FBE ≥ -6 kCal/mol), followed by LuxP, YenR, CviR, TraR, SdiA, LasR, QscR, LuxR (SmcR), PqsR, and CckA. Most of FBEs are located in the range of -6~-8 kCal/mol, followed by -10~-12 kCal/mol and smaller than -12 kCal/mol (Figure 2A). Excluded the non-binding cases, we have analyzed docking-based results with FBEs smaller than -6 kCal/mol (Figure 2B). There are the least drugs and weakest binding to LsrB receptor, while the most cases and strongest binding to CckA. The FBEs of the strongest cases for LasR, QscR, SdiA and TraR, are all lower than -12 kCal/mol. The above results indicate that there are changeable drug-receptor interaction strengths among different drugs and microbes.
Furthermore, we have analyzed the strongest binding cases for the above 11 QS receptors (Figure 2C). Results shown that, LsrB, LuxP, YenR, TraR, SdiA, LasR, CckA, QscR, LuxR (SmcR), PqsR, and CviR have the corresponding strongest binding with Perflenapent (DB11625), 3-(5-amino-7-hydroxy-(1,2,3)triazolo(4,5-d)pyrimidin-2-yl)benzoic acid (DB01906), Flavone (DB07776), Indirubin (DB12379), alpha-Naphthoflavone (DB07453), Tecovirim (DB12020), 2-({[3-(3,4-dihydroisoquinolin -2 (1H) ylsulfonyl) phenyl] carbonyl} amino) benzoic acid (DB07691), 2-aminoquinazoline 5 (DB06925), Phthalocyanine (DB12983), (3S)-1-cyclohexyl-N-(3,5-dichlorophenyl)-5-oxopyrrolidine-3-carboxamide(DB07188), and 3-(4-fluorophenyl)-5-phenyl-4H-1,2,4-triazole (DB08470), respectively. As illustrated in Figure 2D, there is highly similar binding site (Ala222) for the positive control (R-THMF) and Perflenapent when docking with LsrB. With respect to the SdiA receptor, there are same binding sites (Tyr63 and Asp80) for positive control (C6HSL) and alpha-Naphthoflavone. Excluded the LsrB receptor, ten other QS receptors would also bind to the other drugs to a certain extent, which also suggests that LsrB have the strongest specificity for various drugs (Figure 1F and 2C). Taking CviR, SdiA, PqsR, LuxP, and CckA as example, we have further analysed the drug crosstalk for the potential drugs with FBEs smaller than -6 kCal/mol (Figure 2E). Results shown that there is the strongest drug crosstalk for CckA (6,633), followed by PqsR (6,094), CviR (2,920), SdiA (3,729), LuxP (1,247). This indicates that the complex drug-microbe interactions entail multilayer drug crosstalk and diverse QS-based regulations including different QS receptors.
Experimental validations
Based on the docking-based results, we have verified some of them by surface plasmon resonance (SPR), which are commonly used to study the direct interaction between small molecules and proteins. Three drug molecules with similar structures, i.e., mandelic acid, aspirin, and salicylic acid were selected for subsequent SPR validation to verify their binding affinity to LsrB receptor from S. typhimurium. As shown in Figure 3A, there are hydrogen-binding intermolecular forces between drugs and receptors. All the binding sites of these three drugs are similar to the active pocket of LsrB for its positive control case, i.e., R-THMF (Table 1). Both of the docking-based and SPR-based results indicate the potential binding of these three drugs and LsrB (Figure 3A).
Another three drugs (alpha-naphthalene, sulfabenzamide, and progesterone) were selected to validate their interactions with SdiA protein from E. coli strain (Figure 3B). Docking results shown that FBEs of these three drugs to SdiA are -13, -10.4, -6.0 kCal/mol. Similarly. all the binding sites of (alpha-naphthalene, sulfabenzamide, progesterone) are similar to the active pocket of SdiA for its positive control case, i.e., 3OC6HSL(Table 1). SPR results shown that binding affinity (Kd) for them are 1.71×10-5 M, 5.23×10-5 M, 9.31×10-5 M, which verified the drug-SdiA interaction. To sum up, it is certain that various drugs can bind to diverse QS receptors to interference with the corresponding biological activities for microbes with the help of docking-based calculations and SPR-based validations.
Construction and analysis for the drug-receptor interaction network
As stated above, QS receptors are the potential targets for diverse drug-microbial interactions. Therefore, we have constructed a potential drug-receptor interaction network based on the above cases with FBEs ≤ -6 kCal/mol, where the absolute value of FBEs were set to be the weight values (Figure S1 in the Supplementary material). This potential network visualizes the complex QS-based drug-receptor interactions. Different QS receptors are linked together through various drugs to be a QS-based drug-receptor interaction network, and the connections will be used to regulate the drug-based interactions among various QS receptors. The giant bipartite network would consist of 8,270 nodes connected via 42,785 edges. The largest degree in the giant network is 6,197 from CckA node, followed by PqsR (6,197), LuxR (5,663), QscR (5,216), LasR (4,671), SdiA (3,924), TraR (3,924), CviR (3,079), YenR (2,251), LuxP (1,411), and LsrB (309). This indicates that fewer of microbes with AI-2 bound receptors (LuxP and LsrB) and most of the microbes with CckA and LuxR-type QS receptors will be affected by diverse drugs.
To have a better understanding on the drug-receptor interactions, we have shrunken the comprehensive network (Figure S1) into a simplified network (Figure 4) with showing only common elements and specific elements like the flower for the 11 QS receptors. Note that each QS receptor has its own specific binding drug, except for the TraR receptor, and there are most of specific drugs for LuxR (73), followed by CckA (62), LuxP (54), LasR (44), PqsR (27), LsrB (26), QscR (15), SdiA (14), CviR (10), and YenR (7). Furthermore, there are 14 drugs that have potential to bind to all of the 11 QS receptors, and their details are listed in Table 2. When these 14 drug molecules are distributed in vivo, all of the 11 QS receptors will be targeted, thus affecting their corresponding microbes. Considering that these 11 receptors represent hundreds of microbes including pathogens and probiotics, it is better to have a systematic understanding of the interactions between these potential broad-spectrum drugs and microbes to prevent some undesirable effects in the treatment of diverse diseases.
Table 2. The potential broad-spectrum drugs for 11 QS receptors
Drugs ID
|
PubChemID
|
Name
|
LuxR
|
LasR
|
TraR
|
CviR
|
QscR
|
PqsR
|
SdiA
|
LuxP
|
LsrB
|
YenR
|
CckA
|
DB07992
|
10258
|
Indoxyl sulfate
|
-6.1
|
-8.1
|
-7.9
|
-7.7
|
-7.9
|
-7.3
|
-8.2
|
-6.4
|
-6.2
|
-8.3
|
-7.1
|
DB02070
|
161166
|
Kynurenine
|
-6.5
|
-8.1
|
-7.3
|
-7.0
|
-7.7
|
-6.8
|
-7.9
|
-6.6
|
-6.2
|
-7.7
|
-6.8
|
DB09531
|
9639
|
Perflexane
|
-8.5
|
-8.9
|
-7.7
|
-8.4
|
-8.8
|
-7.5
|
-8.6
|
-6.6
|
-7.3
|
-9.0
|
-7.0
|
DB01924
|
10313
|
Benzhydroxamic acid
|
-6.7
|
-7.7
|
-7.3
|
-7.5
|
-7.4
|
-6.4
|
-7.3
|
-6.2
|
-6.9
|
-7.6
|
-6.6
|
DB04029
|
445694
|
Phenylalanylamide
|
-7.3
|
-7.6
|
-7.7
|
-7.6
|
-7.1
|
-6.3
|
-7.9
|
-6.7
|
-6.4
|
-7.6
|
-6.5
|
DB04236
|
6951149
|
Tryptophanol
|
-7.4
|
-7.5
|
-7.7
|
-7.3
|
-7.6
|
-7.1
|
-7.9
|
-6.1
|
-6.5
|
-8.0
|
-6.8
|
DB02494
|
444718
|
(S)-3-phenyllactic acid
|
-7.2
|
-7.5
|
-7.5
|
-7.6
|
-7.2
|
-6.4
|
-7.6
|
-6.5
|
-6.8
|
-7.5
|
-6.2
|
DB04476
|
449146
|
Trencam-3,2-hopo
|
-6.4
|
-7.4
|
-6.4
|
-6.3
|
-7.0
|
-6.1
|
-6.9
|
-6.3
|
-6.6
|
-7
|
-6.4
|
DB07673
|
5288102
|
(2S)-2-Methyl-3-phenylpropanoic acid
|
-7.1
|
-7.8
|
-7.9
|
-7.5
|
-7.1
|
-6.4
|
-7.6
|
-6.6
|
-6.9
|
-7.6
|
-6.3
|
DB04157
|
448926
|
N-[(Aminooxy) Carbonyl] Aniline
|
-6.2
|
-7.6
|
-7.1
|
-6.3
|
-7.4
|
-6.2
|
-7.3
|
-6.5
|
-6.9
|
-7.1
|
-6.1
|
DB00909
|
5734
|
Zonisamide
|
-6.2
|
-9.2
|
-8.2
|
-7.8
|
-7.6
|
-7.1
|
-8.5
|
-7.2
|
-6
|
-8.1
|
-7.1
|
DB01662
|
17754112
|
Trans-o-hydroxy-alpha-methyl cinnamate
|
-7.4
|
-7.5
|
-6.9
|
-7.2
|
-7.8
|
-6.7
|
-7.6
|
-6.7
|
-7.6
|
-7.8
|
-6.2
|
DB08327
|
780
|
Homogentisic acid
|
-6.5
|
-6.6
|
-6.3
|
-6.2
|
-6.8
|
-6.0
|
-7.1
|
-6.6
|
-7.5
|
-7.2
|
-6.3
|
DB02556
|
6919011
|
D-Phenylalanine
|
-7.3
|
-7.4
|
-7.9
|
-6.7
|
-7.4
|
-6.3
|
-7.9
|
-6.5
|
-6.5
|
-7.9
|
-6.1
|
Unit of FBEs, kCal/mol
Systematic framework construction for drugs, microbes and diseases
Drugs, microbes, and diseases are located in a complex system, where contains various drug-receptor, drug-microbe, drug-disease, receptor-microbe, and microbe-disease interactions. It is demonstrated that QS receptors can be used as potential targets for some commonly used drugs, not only affecting the efficacy of the drugs themselves, but also affecting certain diseases by affecting the abundance of specific microbes. Herein, we integrated various drugs, receptors, gut microbes, and diseases together to be a systematic framework (Figure 5A) to form a potential key knowledge map of the human gut microbiota to promote the understanding of personalized medicine and developing potential therapies for diverse diseases. Some research has also carefully collected the drug-diseases [53] and drug-microbe [54], and microbe-disease [55] causal connections, respectively. Combined with the data from the above databases and diverse lectures, we have curated various connections carefully for drugs, receptors, microbes, and diseases to form a repository of drug-based microbial interactions, which were listed in Table S1. Note that Table S1 does not include drug-disease connections, which have been collected and can be searched in the DrugBank database.
Take some cases as example, we have illustrated a schematic diagram for diverse connections among drugs, microbial QS receptors, corresponding microbes, and relevant diseases (Figure 5B). For example, Berberine and Metformin, which were commonly used to treat type 2 diabetes, have potential to bind to the LasR, thus interfering the curing of cystic fibrosis caused by P. aeruginosa. Salicylic acid and aspirin were verified by molecular docking and SPR experiments, and the results showed that they bind to LsrB, which may affect the treatment of diseases such as typhoid fever caused by S. typhimurium infection. Furthermore, proton pump inhibitors (PPIs), which are inhibitors of gastric acid production, have been reported to be associated with the increase of typical oral bacteria in the intestinal tract[20]. Docking-based results showed that some PPIs binding to CckA (omeprazole, esomeprazole) and QscR (pantoprazole, rabeprazole), which indicates that PPIs may be able to affect the composition of the corresponding microbes (Faecalibacterium prausnitzii, Caulobacter crescentus, and P. aeruginosa) through QS receptors, thus having an impact on relevant diseases (Breast cancer, Cystic fibrosis, Liver cirrhosis, Diarrhea, Atopic dermatitis, Type 2 diabetes, Septicemia, Crohn's disease (CD), and Inflammatory bowel disease (IBD)). To sum up, drugs used in vivo will bind to various receptors, some of them are relevant to diseases treating, and others are QS-based targets, which affecting the abundance and diversity of corresponding microbes, and then interfere the efficacy for specific diseases.