The seamless integration of dietary plant-derived natural flavonoids and gut microbiota may ameliorate non-alcoholic fatty liver disease: a network pharmacology analysis

Abstract

We comprised metabolites of gut microbiota (GM; endogenous species) and dietary plant-derived natural flavonoids (DPDNFs; exogenous species) were known as potent effectors against non-alcoholic fatty liver disease (NAFLD) via network pharmacology (NP). The crucial targets against NAFLD were identified via GM and DPDNFs. The protein interaction (PPI), bubble chart and networks of GM or natural products- metabolites-targets-key signalling (GNMTK) pathway were described via R Package. Furthermore, the molecular docking test (MDT) to verify the affinity was performed between metabolite(s) and target(s) on a key signalling pathway. On the networks of GNMTK, Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1 as key microbiota; flavonoid-rich products as key natural resources; luteolin and myricetin as key metabolites (or dietary flavonoids); AKT Serine/Threonine Kinase 1 (AKT1), CF Transmembrane conductance Regulator (CFTR) and PhosphoInositide-3-Kinase, Regulatory subunit 1 (PIK3R1) as key targets are promising components to treat NAFLD, by suppressing cyclic Adenosine MonoPhosphate (cAMP) signalling pathway. This study shows that components (microbiota, metabolites, targets and a key signalling pathway) and DPDNFs can exert combinatorial pharmacological effects against NAFLD. Overall, the integrated pharmacological approach sheds light on the relationships between GM and DPDNFs.

Keywords:

• gut microbiota
• dietary plant-derived natural flavonoids
• non-alcoholic fatty liver disease
• network pharmacology
• cAMP signalling pathway
• combinatorial pharmacological effects

Introduction

Non-alcoholic fatty liver disease (NAFLD) is an initial stage to develop malignant cascade, which might lead to hepatocellular death as irreversible condition: cirrhosis [1] and hepatocellular carcinoma (HCC) [2]. These diseases represent chronic inflammation, including liver damage to be aggravated by NAFLD [3,4]. Moreover, alterations of gut microbiota (GM) have notable characteristics such as intestinal dysbacteriosis; enhancement of intestinal penetrability and disparity of endogenous compounds: bile acids, choline and free fatty acids [5].

In the context of metabolites from GM, they play critical roles to modulate inflammatory level via gut-liver crosstalk, suggesting that metabolites from GM can traverse liver through portal vein [6,7]. Most recently, it has been suggested that equol converted from daidzein in the gut has potent antioxidative, antiandrogenic and oestrogenic effects in host system [8,9]. Interestingly, individual variation to produce equol depends on the diversity of GM, which is responsible for converting from precursor [10]. It implies that GM might play important roles to produce valuable flavonoids against diseases. Furthermore, dietary plant-derived natural flavonoids (DPDNFs) and GM have interconnected intricately to biological benefits for human health [11]. The DPDNFs in myriad plants, fruits and vegetables own the therapeutic efficacy such as antioxidant, anti-inflammatory, anticancer and antiviral qualities [12].

An animal experiment indicated that myricetin in flavonoid species has alleviative effects by modulating hepatic lipid mechanism on NAFLD in obese mice [13]. The spinach, peas, turnip, carrot, cauliflower and cranberry are in plentiful supply of myricetin, its key mechanism diminished the amount of triglycerides and cholesterol [14]. Additionally, another animal test suggested that luteolin reduces inflammatory factors and alleviates dysbiosis by GM, which leads to the restoration in aggravated intestinal mucosal barrier [15]. The peppers, carrots, broccoli, celery, cabbages and parsley are abundant providers of luteolin, which have been taken as alternatives for alleviation in hypertension, inflammatory diseases and cancer [16].

Collectively, we investigated promising components to ameliorate NAFLD by comprising GM and DPDNFs. In parallel, we explored its interactive metabolites, targets and a key signalling pathway against NAFLD, via incorporating GM and DPDNFs.

Specifically, network pharmacology (NP) is an important methodology to elucidate the integrated biological information, including complex microbiome analysis [17,18]. To pioneer the pharmacological pathways and relevant mechanisms is significant processing to interpret the combinatorial effects via NP concept [19]. Thus, we utilized NP-based analysis to expound the relationships between GM-metabolites-targets and a key signalling pathway with DPDNFs against NAFLD. From this holistic viewpoint, we needed to make a new methodology and concept to reveal the combinatorial effects on GM and DPDNFs. At present, four crucial components (GM, targets, metabolites and a key signalling pathway) and key DPDNFs have yet to be documented.

The aim of this study is to uncover the combinatorial value in microbiome components and DPDNFs against NAFLD. Firstly, we retrieved GM targets and key DPDNFs from public database. Then, we browsed flavonoids-related and NAFLD-related targets with bioinformatics database. Also, the identified crucial targets were utilized to analyse PPI and a bubble chart. Moreover, we utilized molecular docking test (MDT) to obtain affinity between the targets and molecules on a key signalling pathway against NAFLD. More importantly, we integrated relationships with GM or DPDNFs – metabolites – targets – a key signalling pathway. In the merged networks, it has been shown that GM or DPDNFs might exert combinatorial effects. Collectively, we interpret the medicinal value of GM as endogenous species, DPDNFs as exogenous species in integrated pharmacological viewpoints. The workflow of this study is displayed in Figure 1.

Figure 1. The stepwise workflow of this study.

Materials and methods

Database platforms and data analysis associated with NP

Biologically significant databases that give a large amount of data linked to the relationship between ligands and targets, which is enables the researcher to utilize NP as a powerful tool for drug development. The utilized databases in this study were profiled in Supplementary Table S1. All these web-based databases are accessible freely to users who retrieve valuable information, moreover, which are useful resources to employ microbiome and NP concept at the same time. In this study, we incorporated the connectivity of the GM and DPDNFs, based on public databases.

The selection of metabolites from GM and its targets

The metabolites from GM were identified by gutMGene as a collective database of GM’s targets and the metabolites (http://bio-annotation.cn/gutmgene) (accessed on 3 July 2022) [20]. The targets were collected by the Similarity Ensemble Approach (SEA) (https://sea.bkslab.org/) (accessed on 3 July 2022) [21] and SwissTargetPrediction (STP) (http://www.swisstargetprediction.ch/) (accessed on 4 July 2022) [22] as cheminformatics databases to identify potential targets. Specially, we selected only overlapping targets from the two databases to achieve the rigorousness. The Venny 2.1 tool was utilized to select the overlapping targets between SEA and STP, which was visualized by Venn diagram plotter.

The collection of DPDNFs and identification of its targets

The DPDNFs were collected by literature and the drug-likeness qualities of them were evaluated by SwissADME platform based on Lipinski’s rule of five [23]. With exactness and rigour, the targets responded to DPDNFs were retrieved by SEA and STP.

The retrieval of intersecting targets and NAFLD-related targets

The targets identified by metabolites from GM and DPDNFs-related targets were analysed by Venny 2.1. The targets linked directly to NAFLD were found in both DisGENet as a platform of target-disease correlation (https://www.disgenet.org/) (accessed on 5 July 2022) and Online Mendelian Inheritance in Man (OMIM) as a database of target-etiology association (accessed on 5 July 2022) (https://omim.org/).

The identification of crucial targets and its PPI networks

The overlapping targets between the intersecting targets and NAFLD-related targets were considered as crucial targets deeply associated with NAFLD, based on the GM metabolites’ targets and DPDNFs targets. The crucial targets were input into STRING database version 11.5 to construct PPI network (accessed on 7 July 2022) [24], the result of which was obtained via Excel file. The Excel file was input into R Package to describe the connectivity between each target. The PPI network depicts the networks of protein interacted by biochemical responses and/or distinctive biological events in host system [25]. Noticeably, we found two significant targets on PPI network analysis.

The construction of bubble chart to identify a key signalling pathway

We obtained crucial targets with combinatorial properties on the GM metabolites’ targets and DPDNFs targets. On the bubble chart, the rich factor (the number of targets in the pathway/number of all targets in the total target set) was considered as a key selective factor [26]. Then, a signalling pathway with the lowest rich factor was chosen as the uppermost mechanism, indicating that the mechanism might function as a suppressor to ameliorate NAFLD.

The preparation of metabolites and targets for MDT

The metabolites (or flavonoids) associated with the key signalling pathway were changed .sdf format from PubChem into .pdb format using Pymol, and they were changed into .pdbqt format through Autodock tool. The three targets were linked to a key signalling pathway, ie AKT Serine/Threonine Kinase 1 (AKT1) (PDB ID: 3O96), PhosphoInositide-3-Kinase, Regulatory subunit 1 (PIK3R1) (PDB ID: 4JPS) and CF Transmembrane conductance Regulator (CFTR) (PDB ID: 6O1V) were extracted by STRING on RCSB PDB (https://www.rcsb.org/), (accessed 8 July 2022). Additionally, the PDB formats of proteins were converted into .pdbqt via Autodock tool to perform the MDT.

MDT of a key signalling pathway

Based on formatting 4 energy range and 8 exhaustiveness as default to identify 10 different poses of metabolites, we performed MDT between metabolites and targets [27]. The centre of each target on the key signalling pathway was AKT1 (x = 6.313, y= −7.926, z = 17.198), PIK3R1 (x= −9.551, y= −24.547, z = 31.350), CFTR (x = 152.958, y = 156.025, z = 132.814). The definition of “center” is middle point in the target frame [28]. The box size of active cube was x = 40 Å, y = 40 Å, z = 40 Å, setting up as automatic in protein-ligand complexes. The complex of metabolites (or flavonoids) – targets was conducted by AutoDock 1.5.6 for 3D diagram and Ligplot + 2.2 for 2D diagram [29], including hydrophobic and hydrophilic interaction.

Also, each metabolite formed the most stable conformer on the key three targets (AKT1, PIK3R1 and CFTR) was considered as a crucial effector. In other words, a molecule with the lowest binding energy on each target is defined as the most stable effector [30]. After MDT, the metabolites with the lowest binding energy were chosen to describe conformers on targets in Pymol.

Toxicological verification of two key metabolites

During drug development, the level of toxicity is an important factor to be successful therapeutic agents. The toxicity was verified by the following assays: hERG blockers [31], Human Hepato-Toxicity (H-HT) [32], Rat Oral Acute Toxicity [33], Carcinogenicity [34], Eye Corrosion [35], Respiratory Toxicity [36]. The toxicity parameters are assessed by using ADMETlab platform [37].

The networks of GM or natural resources - metabolites-targets-key signalling pathway (GNMTK)

To describe a holistic viewpoint, we constructed the network of GM or natural resources- metabolites -targets-key signalling pathway (GNMTK) via R Package. The GNMTK was depicted as nodes in the network, and their correlations were indicated as edges [38]. The GNMTK network was depicted on a size plot with the number of edges connected to each node. In the network diagram, yellow circle (node) represented GM; purple circle (node) stood for metabolites; orange circle (node) indicated targets and green circle (node) described a key signalling pathway. The merged network was visualized utilizing R Package.

Results

The identification of potential targets from GM

In the gutMGene, we identified a total of 208 metabolites from GM (Supplementary Table S2) and the metabolites were searched for its targets through SEA (1256 targets) and STP (947 targets) cheminformatics platforms (Supplementary Table S2). The number of 668 overlapping targets was identified between 1256 targets in SEA and 947 targets in STP (Figure 2(A); Supplementary Table S2). The targets were considered as respondents modulated by metabolites from GM.

Figure 2. (A) The number of 668 overlapping targets between SEA (1256 targets) and STP (947 targets). (B) The number of 112 overlapping targets related to 14 flavonoids between SEA (173 targets) and STP (200 targets). (C) The number of 112 overlapping targets between (A) and (B). (D) The number of 47 crucial targets against NAFLD.

The DPDNFs’ mining and drug-likeness selection

The sixteen DPDNFs were selected through a literature study, and their chemical information was profiled in Table 1. The selected compounds’ physicochemical properties or drug-likeness qualities were assessed utilizing Lipinski’s rule of five, the parameters of which were confirmed by SwissADME web-based tool. Based on Lipinski’s rule of five, 14 out of 16 DPDNFs were accepted as drug-likeness molecules without violating of the criteria: (i) molecular weight ≤500; (ii) H-bond donors ≤5; (iii) H-bond acceptors ≤10; (iv) MLogP ≤4.15 1 (v) and Topological Polar Surface Area (TPSA) <140 (Ų) [39,40]. The violated compounds were Epigallocatechin-gallate and Procyanidins, which were excluded in the analysis.

Table 1. The physicochemical properties of the number of 16 dietary plant derived flavonoids.

No.	Compounds		Lipinski’s rules				Lipinski’s violations	Bioavailability score	TPSA(Ų)
		PubChem ID	MW	HBA	HBD	MLog P
			< 500	< 10	≤ 5	≤ 4.15	≤1	> 0.1	<140
1	Epicatechin	72276	290.27	6	5	0.24	0	0.55	110.38
2	Catechin	9064	290.27	6	5	0.24	0	0.55	110.38
3	Epigallocatechin-gallate	65064	458.37	11	8	−0.44	2	0.17	197.37
4	Quercetin	5280343	302.24	7	5	−0.56	0	0.55	131.36
5	Myricetin	5281672	318.24	8	6	−1.08	1	0.55	151.59
6	Kaemferol	5280863	286.24	6	4	−0.03	0	0.55	111.13
7	Naringenin	932	272.25	5	3	0.71	0	0.55	86.99
8	Hesperetin	72281	302.28	6	3	0.41	0	0.55	96.22
9	Eriodictyol	440735	288.25	6	4	0.16	0	0.55	107.22
10	Apigenin	5280443	270.24	5	3	0.52	0	0.55	90.90
11	Luteolin	5280445	286.24	6	4	−0.03	0	0.55	111.13
12	Cyanidin	128861	287.24	6	5	0.32	0	0.55	114.29
13	Malvidin	159287	331.30	7	4	0.28	0	0.55	112.52
14	Delphinidin	68245	338.70	7	6	0.03	1	0.55	134.52
15	Pelargonidin	440832	271.24	5	4	0.86	0	0.55	94.06
16	Procyanidins	107876	594.52	13	10	−0.60	3	0.17

Note: Bold font: Epigallocatechin-gallate and Procyanidins are excluded due to violation of Lipinski’s rule.

The identification of crucial targets against NAFLD

The targets related to the fourteen DPDNFs were retrieved via SEA and STP databases, indicating 173 targets in SEA and 200 targets in STP (Figure 2(B); Supplementary Table S3). The 112 intersecting targets were identified by the two databases (Supplementary Table S3). The 668 overlapping targets were associated with metabolites from GM, suggesting that the 112 intersecting targets were a subset of the 668 metabolites’ targets (Figure 2(C)). Coordinately, NAFLD-related targets were retrieved by DisGeNET, and OMIM online database, resulting in 1836 targets (Supplementary Table S3). Collectively, the number of 47 overlapping targets (Supplementary Table S3) was identified between NAFLD-related targets (1836) and the 112 intersecting targets (Figure 2(D)). The obtained 47 targets were considered as significant targets in determining integrated application of GM and DPDNFs against NAFLD.

PPI network construction

The STRING database was incorporated to construct PPI network on the crucial 47 targets, constructing via R Package. The 44 out of 47 targets were interacted closely with each other, however, the three targets (CA3, ST6GAL1 and ERN1) were not correlated with one another. The 44 targets were represented with 44 nodes and 241 edges (Figure 3). In the PPI networks, the two targets with the highest degree value were AKT1 (31) and VEGFA (31) (Table 2). The two targets were considered as the most significant protein-coding gene in the entire PPI network.

Figure 3. PPI network (44 nodes and 241 edges).

Table 2. The degree of value in PPI network.

No.	Target	Degree of value	No.	Target	Degree of value
1	VEGFA	31	23	ALOX5	9
2	AKT1	31	24	INSR	9
3	EGFR	27	25	PARP1	9
4	ESR1	25	26	CYP1B1	8
5	PTGS2	24	27	F2	8
6	MMP9	20	28	PIK3CG	8
7	IGF1R	17	29	AKR1B1	7
8	KDR	17	30	CD38	7
9	MMP2	16	31	NOX4	6
10	AR	16	32	TERT	6
11	CYP3A4	15	33	FUT4	5
12	PIK3R1	14	34	ALOX15	5
13	ABCG2	13	35	SYK	5
14	GSK3B	13	36	CBR1	4
15	MET	13	37	MMP13	4
16	AHR	12	38	ALOX12	3
17	ESR2	11	39	TTR	3
18	APP	11	40	ESRRB	3
19	CYP19A1	11	41	AKR1B10	2
20	PGF	10	42	CFTR	2
21	ABCB1	10	43	MAOA	1
22	MPO	10	44	GPR35	1

Bubble chart on signalling pathways against NAFLD

The results of Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that the 47 targets were associated with 30 signalling pathways (False Discovery Rate < 0.05). The 30 signalling pathways were involved in occurrence and development of NAFLD, suggesting that the implicated pathways might be significant mechanisms against NAFLD. The description of the 30 signalling pathways was profiled in Table 3. Additionally, the bubble chart represented that cAMP (cyclic Adenosine MonoPhosphate) signalling pathway with the lowest rich factor might be a key mechanism against NAFLD (Figure 4).

Figure 4. Bubble chart of 30 signalling pathways associated with progression of NAFLD.

Table 3. The description of 30 signalling pathways against NAFLD.

KEGG ID & description	Target	False discovery rate
hsa04370: VEGF signalling pathway	AKT1,VEGFA,PTGS2,PIK3R1,KDR	0.00000623
hsa04917: Prolactin signalling pathway	AKT1,ESR1,ESR2,PIK3R1,GSK3B	0.00001180
hsa04664: Fc epsilon RI signalling pathway	AKT1,ALOX5,PIK3R1,SYK	0.00020000
hsa04066: HIF-1 signalling pathway	AKT1,VEGFA,EGFR,OIK3R1,IGF1R,INSR	0.00000502
hsa04926: Relaxin signalling pathway	AKT1,VEGFA,EGFR,PIK3R1,MMP2,MMP9,MMP13	0.00000088
hsa04915: Oestrogen signalling pathway	AKT1,EGFR,ESR1,ESR2,PIK3R1,MMP2,MMP9	0.00000104
hsa04933: AGE-RAGE signalling pathway in diabetic complications	AKT1,VEGFA,PIK3R1,MMP2,NOX4	0.00005220
hsa04662: B cell receptor signalling pathway	AKT1,GSK3B,PIK3R1,SYK	0.00029000
hsa04072: Phospholipase D signalling pathway	AKT1,EGFR,PIK3R1,SYK,PIK3CG,INSR	0.00000163
hsa04012: ErbB signalling pathway	AKT1,EGFR,PIK3R1,GSK3B	0.00035000
hsa04015: Rap1 signalling pathway	AKT1,VEGFA,EGFR,IGF1R,PIK3R1,PGF,INSR,MET,KDR	0.00000007
hsa04657: IL-17 signalling pathway	GSK3B,PTGS2,MMP9,MMP13	0.00049000
hsa04152: AMPK signalling pathway	AKT1,PIK3R1,IGF1R,INSR	0.00013000
hsa04014: Ras signalling pathway	AKT1,VEGFA,EGFR,PIK3R1,IGF1R,INSR,PGF,MET,KDR	0.00000017
hsa04068: FoxO signalling pathway	AKT1,EGFR,UGF1R,PIK3R1,INSR	0.00014000
hsa04625: C-type lectin receptor signalling pathway	AKT1,PIK3R1,PTGS2,SYK	0.00071000
hsa04668: TNF signalling pathway	AKT1,PTGS2,PIK3R1,MMP9	0.00097000
hsa04151: PI3K-Akt signalling pathway	AKT1,VEGFA,EGFR,GSK3B,IGF1R,PIK3R1,INSR,PIK3CG,PGF,MET,KDR	0.00000000
hsa04919: Thyroid hormone signalling pathway	AKT1,GSK3B,ESR1,PIK3R1	0.00120000
hsa04150: mTOR signalling pathway	AKT1,GSK3B,IGF1R,PIK3R1,INSR	0.00025000
hsa04910: Insulin signalling pathway	AKT1,GSK3B,PIK3R1,INSR	0.00170000
hsa04660: T cell receptor signalling pathway	AKT1,GSK3B,PIK3R1	0.00840000
hsa04064: NF-kappa B signalling pathway	PTGS2,PARP1,SYK	0.00840000
hsa04010: MAPK signalling pathway	AKT1,VEGFA,EGFR,IGF1R,INSR,PGF,MET,KDR	0.00000706
hsa04921: Oxytocin signalling pathway	PTGS2,EGFR,PIK3CG,CD38	0.00250000
hsa04722: Neurotrophin signalling pathway	AKT1,GSK3B,PIK3R1	0.01080000
hsa04062: Chemokine signalling pathway	AKT1,GSK3B,PIK3R1,PIK3CG	0.00510000
hsa04630: JAK-STAT signalling pathway	AKT1,EGFR,PIK3R1	0.02300000
hsa04022: cGMP-PKG signalling pathway	AKT1,INSR,PIK3CG	0.02360000
hsa04024: cAMP signalling pathway	AKT1,PIK3R1	0.04260000

MDT on cAMP signalling pathway

Through MDT analysis, it was uncovered that AKT1 (PDB ID: 3O96) was related to 21 metabolites: (1) Luteolin, (2) Myricetin, (3) Kaempferol, (4) Cyanidin, (5) Apigenin, (6) Diosmetin, (7) Delphinidin, (8) Pelargonidin, (9) Glycocholic acid, (10) Equol, (11) Naringenin chalcone, (12) Malvidin, (13) Quercetin, (14) Icaritin, (15) Eriodictyol, (16) Urolithin A, (17) Iuro-a, (18) Indole-3-lactic acid, (19) Lunularin, (20) 3-Indolepropionic acid and (21) Phenylalanine.

PIK3R1 (PDB ID: 4JPS) was associated with 20 metabolites: (1) Myricetin, (2) Quercetin, (3) Kaempferol, (4) Luteolin, (5) Ponciretin, (6) Apigenin, (7) Pelargonidin, (8) Acacetin, (9) 11-Methoxycurvularin, (10) Cyanidin, (11) Delphinidin, (12) Icaritin, (13) Diosmetin, (14) Malvidin, (15) Arctigenin, (16) Naringenin chalcone, (17) Secoisolariciresinol, (18) 1-Caffeoylglycerol, (19) DIF-3 and (20) 3-Hydroxyphenethyl alcohol.

CFTR (PDB ID: 6O1V) was connected with 19 metabolites: (1) Luteolin, (2) Icaritin, (3) Apigenin, (4) Kaempferol, (5) Baicalein, (6) 6,7,4′-Trihydroxyisoflavone, (7) Chrysin, (8) Genistein, (9) Diosmetin, (10) Acacetin, (11) Naringenin chalcone, (12) Indole-3-acrylic acid, (13) Indole-3-lactic acid, (14) 3-Indolepropionic acid, (15) Indoxyl sulphate, (16) Indole-3-carboxylic acid, (17) Dihydroisoferulic acid, (18) 3-Methylindole and (19) Indole-3-carboxaldehyde (Table 4).

Table 4. The binding energy and interactions of key metabolites and targets.

				Grid box		Hydrogen bond interactions	Hydrophobic interactions
Protein	Ligand	PubChem ID	Binding energy(kcal/mol)	Centre	Dimension	Amino acid residue	Amino acid residue
AKT1 (PDB ID: 3O96)	Luteolin	5280445	−8.7	x = 6.313	Size x = 40	Asn199,Trp80,Ser56	Phe225,Gln59,Leu78
				y= −7.926	Size y = 40	Asn53	Ala58,Gln79,Val201
				z = 17.198	Size z = 40
	Myricetin	5281672	−8.6	x = 6.313	Size x = 40	Asn199,Trp80,Asn53	Gln59,Leu78,Ala58
				y= −7.926	Size y = 40		Ser56,Gln79,Val201
				z = 17.198	Size z = 40
	Kaempferol	5280863	−8.5	x = 6.313	Size x = 40	Asn199,Asn53,Ser56	Phe225,Val201,Gln59
				y= −7.926	Size y = 40	Trp80	Gln79,Ala58,Leu78
				z = 17.198	Size z = 40
	Cyanidin	128861	−8.5	x = 6.313	Size x = 40	Asn199,Trp80	Phe225,Leu78,Gln59
				y= −7.926	Size y = 40		Asn53,Ser56,Ala58
				z = 17.198	Size z = 40		Gln79,Val201
	Apigenin	5280443	−8.4	x = 6.313	Size x = 40	Asn199,Trp80,Ser56	Gln59,Phe225,Leu78
				y= −7.926	Size y = 40	Asn53	Ala58,Gln79,Val201
				z = 17.198	Size z = 40
	Diosmetin	5281612	−8.2	x = 6.313	Size x = 40	Asn53,Ser56,Asn199	Ala58,Cys77,Gln79
				y= −7.926	Size y = 40		Leu78,Gln59,Leu213
				z = 17.198	Size z = 40		Trp80,Val201
	Delphinidin	68245	−8.2	x = 6.313	Size x = 40	Asn199,TRP80	Gln59,Ala58,Leu78
				y= −7.926	Size y = 40		Ser56,Asn53,Gln79
				z = 17.198	Size z = 40		Val201
	Pelargonidin	440832	−8.2	x = 6.313	Size x = 40	Asn199,Trp80	Phe225,Leu78,Gln59
				y= −7.926	Size y = 40		Ala58,Ser56,Gln79
				z = 17.198	Size z = 40		Asn53,Val201
	Glycocholic acid	10140	−7.8	x = 6.313	size x = 40	Ala50,Glu322,Asn324	Leu52,Gly327,Pro388
				y= −7.926	Size y = 40	Lys389	Tyr326,Glu319,Lys39
				z = 17.198	Size z = 40		Asp325,Arg328,Glu49
	Equol	91469	−7.5	x = 6.313	Size x = 40	Asn199,Asn53,Gln79	Trp80,Leu78,Ala58
				y= −7.926	Size y = 40	Ser56	Gln59,Val201
				z = 17.198	Size z = 40
	Naringenin chalcone	5280960	−7.4	x = 6.313	Size x = 40	Gln59	Trp80,Val270,Asn54
				y= −7.926	Size y = 40		Asn53,Gln79,Asn199
				z = 17.198	Size z = 40
	Malvidin	159287	−7.4	x = 6.313	Size x = 40	Asn199,Asn53	Val201,Trp80,Phe225
				y= −7.926	Size y = 40		Gln59,Leu78,Gln79
				z = 17.198	Size z = 40		Ala58,Val270,Ser56
	Quercetin	5280343	−7.2	x = 6.313	Size x = 40	Gln414,Asn204,Ser259	Tyr417,Glu267,Arg206
				y= −7.926	Size y = 40		Tyr263,His207,Asp262
				z = 17.198	Size z = 40
	Icaritin	5318980	−7.2	x = 6.313	Size x = 40	Gly395	Gly394,Arg328,Ala50
				y= −7.926	Size y = 40		Leu52,Gly327,Pro51
				z = 17.198	Size z = 40		Phe55,Tyr326,Ile36
							Lys389
	Eriodictyol	440735	−7.2	x = 6.313	Size x = 40	Ser259,Gln414,Asn204	Tyr417,Glu267,Tyr263
				y= −7.926	Size y = 40	Arg206	Asp262,His207
				z = 17.198	Size z = 40
	Urolithin A	5488186	−7.0	x = 6.313	Size x = 40	Tyr229	Leu156, Gly157,Val164
				y= −7.926	Size y = 40		Leu295,Phe293,Met281
				z = 17.198	Size z = 40
	Iuro-a	10376459	−6.9	x = 6.313	Size x = 40	Gln218,Leu78,Ser216	Leu223,Gln59,Trp80
				y= −7.926	Size y = 40		Leu202,Asn199
				z = 17.198	Size z = 40
	Indole-3-lactic acid	92904	−6.5	x = 6.313	Size x = 40	Gly37	Phe27,Asp323,Arg25
				y= −7.926	Size y = 40		Lys14,Asn324,Arg273
				z = 17.198	Size z = 40		Glu17,Phe55,Tyr326
							Arg86
	Lunularin	181511	−6.0	x = 6.313	Size x = 40	Glu298	Gly157,Leu156,Phe293
				y= −7.926	Size y = 40		Met281,Glu234,Glu278
				z = 17.198	Size z = 40		Leu295
	3-Indolepropionic acid	3744	−5.6	x = 6.313	Size x = 40	Ala329	Gly394,Gly395,Arg328
				y= −7.926	Size y = 40		Pro51,Leu52,Phe55
				z = 17.198	Size z = 40		Ala50,Tyr326,Gly327
	Phenylalanine	6140	−5.5	x = 6.313	Size x = 40	Asp274,Lys276,Asn279	Glu298, Gly157,Leu156
				y= −7.926	Size y = 40		Val164,Phe293
				z = 17.198	Size z = 40
PIK3R1 (PDB ID: 4JPS)	Myricetin	5281672	−9.3	x= −9.551	Size x = 40	His676,Ser7,Asp717	Ser673,Gly842,Glu674
				y= −24.547	Size y = 40	Asn803,Lys672,Met675	Glu710,Val845,Asp843
				z = 31.350	Size z = 40		Ile713
	Quercetin	5280343	−9.2	x= −9.551	Size x = 40	His676,Lys672,Met675	Ser673,Glu674,Gly842
				y= −24.547	Size y = 40	Asn803,Asp717,Ser7	Asp843,Val845,Ile713
				z = 31.350	Size z = 40		Glu710,Gly122
	Kaempferol	5280863	−9.0	x= −9.551	Size x = 40	Met675,Asp717,Asn803	Ser673,Glu710,Ile713
				y= −24.547	Size y = 40	Lys672,His676	Val845,Gly842,Glu674
				z = 31.350	Size z = 40
	Luteolin	5280445	−9.0	x= −9.551	Size x = 40	Lys672,His676,Asp717	Ser673,Met675,Gly842
				y= −24.547	Size y = 40	Asn803	Ile713,Asp843,Glu710
				z = 31.350	Size z = 40		Glu674
	Ponciretin	25201019	−8.9	x= −9.551	Size x = 40	Met675,Asp843,Lys672	Gly842,Glu710,Gly118
				y= −24.547	Size y = 40	His676	Phe119,Arg93,Glu76
				z = 31.350	Size z = 40		Arg4,Gly122,Glu674
							Ser673
	Apigenin	5280443	−8.8	x= −9.551	Size x = 40	Lys672,Met675,Asp717	Ser673,Glu710,Asp843
				y= −24.547	Size y = 40	Asn803,His676	Gly842,Ile713,Val845
				z = 31.350	Size z = 40		Glu674
	Pelargonidin	440832	−8.7	x= −9.551	Size x = 40	Met675,His676,Asn803	Glu674,Asp843,Gly842
				y= −24.547	Size y = 40	Ser7,Lys672	Asp717,Val845,Ile713
				z = 31.350	Size z = 40		Glu710,Ser673
	Acacetin	5280442	−8.6	x= −9.551	Size x = 40	Met675,His676,Lys672	Ser673,Glu674,Arg4
				y= −24.547	Size y = 40	Asp843	Gly122,Phe119,Arg93
				z = 31.350	Size z = 40		Gly118,Glu76,Glu710
							Gly842
	11-Methoxycurvularin	10381440	−8.6	x= −9.551	Size x = 40	Lys271,Ser629	Arg662,Arg818,Phe666
				y= −24.547	Size y = 40		Gln630,Asp626
				z = 31.350	Size z = 40
	Cyanidin	128861	−8.6	x= −9.551	Size x = 40	His670,Ser629,Arg662	Ile633,Glu259,Asn756
				y= −24.547	Size y = 40	Asn170	Met811,Phe666,Leu632
				z = 31.350	Size z = 40
	Delphinidin	68245	−8.6	x= −9.551	Size x = 40	His670,Ser629,Arg662	Glu259,Asn756,Phe666
				y= −24.547	Size y = 40	Asn170	Met811,Ile633,Leu632
				z = 31.350	Size z = 40
	Icaritin	5318980	−8.5	x= −9.551	Size x = 40	His670,Cys838,Pro835	Met811,Gly837,Tyr836
				y= −24.547	Size y = 40	Arg818	Asn756,Asn170,Ser629
				z = 31.350	Size z = 40		Phe666,Ile633
	Diosmetin	5281612	−8.5	x= −9.551	Size x = 40	Met675,Lys672,Asn803	Gly122,Glu710,Ile713
				y= −24.547	Size y = 40	His676	Val845,Asp717,Asp843
				z = 31.350	Size z = 40		Gly842,Ser673,Glu674
	Malvidin	159287	−8.1	x= −9.551	Size x = 40	Glu259,Asn170,His670	Arg662,Ile633,Met811
				y= −24.547	Size y = 40	Asn756	Ser629,Leu755,Phe666
				z = 31.350	Size z = 40		Cys838
	Arctigenin	64981	−8.0	x= −9.551	Size x = 40	Pro835	Gly837,Leu755,Arg818
				y= −24.547	Size y = 40		Gln630,Ile633,Arg662
				z = 31.350	Size z = 40		Ser629,Phe666,Asn756
							Tyr836
	Naringenin chalcone	5280960	−7.7	x= −9.551	Size x = 40	Lys672,His676,Asp717	Ser673,Gly842,Glu710
				y= −24.547	Size y = 40	Asn803,Met675	Ile713,Val845,Ile841
				z = 31.350	Size z = 40		Glu674
	Secoisolariciresinol	65373	−7.6	x= −9.551	Size x = 40	His670,Lys271,Arg818	Ile633,Leu632,Ser629
				y= −24.547	Size y = 40		Gln630,Asn170,Arg662
				z = 31.350	Size z = 40		Gly837,Tyr836,Cys838
							Phe666
	1-Caffeoylglycerol	5315606	−6.9	x= −9.551	Size x = 40	Ile841,Lys672,Arg93	Arg4,Glu76,Pro3
				y= −24.547	Size y = 40	Phe119,His676	Val706,Gly122,Glu710
				z = 31.350	Size z = 40		Ser673,Met675,Gly842
	DIF-3	3081033	−6.5	x= −9.551	Size x = 40	Ser7,Arg4	Gly842,Val845,Ile713
				y= −24.547	Size y = 40		Glu710,Phe119,Ser6
				z = 31.350	Size z = 40		Val706,Arg93,Gly122
							Lys672,Glu76,Pro3
	3-Hydroxyphenethyl alcohol	83404	−5.9	x= −9.551	Size x = 40	Asn756,Ala758	Arg662,Glu259,Pro757
				y= −24.547	Size y = 40		Gln661,Val166
				z = 31.350	Size z = 40
CFTR (PDB ID: 6O1V)	Luteolin	5280445	−8.0	x = 152.958	Size x = 40	Val1056,Gly1342,Ile1295	Leu1059,Lys1060,Glu267
				y = 156.025	Size y = 40	Ser263	Phe1294,Glu264,Ile177
				z = 132.814	Size z = 40
	Icaritin	5318980	−7.9	x = 152.958	Size x = 40	Trp356,Thr360	Glu193,Gly194,Leu986
				y = 156.025	Size y = 40		Lys190,Asn186,Arg303,Gln359
				z = 132.814	Size z = 40
	Apigenin	5280443	−7.8	x = 152.958	Size x = 40	Asp1341,Val1293,Ser263	Ile177,Leu1059,Ile1295
				y = 156.025	Size y = 40		Gly1342,Phe1294,Asn268
				z = 132.814	Size z = 40		Glu267,Glu264
				x = 152.958	Size x = 40	Ile177,Ile1295,Val1293	Ser176,Glu264,Gly1342
				y = 156.025	Size y = 40	Glu267	Asn268,Phe1294,Lys1060
				z = 132.814	Size z = 40		Leu1059,Asp1341
	Kaempferol	5280863	−7.7	x = 152.958	Size x = 40	Ile177,Ile1295,Val1293	Ser176,Glu264,Gly1342
				y = 156.025	Size y = 40	Glu267	Asn268,Phe1294,Lys1060
				z = 132.814	Size z = 40		Leu1059,Asp1341
	Baicalein	5281605	−7.6	x = 152.958	Size x = 40	Ile1295	Ile177,Lys1060,Leu1059
				y = 156.025	Size y = 40		Glu267,Phe494,Val1293
				z = 132.814	Size z = 40		Phe1294,Asn268,Glu264
							Gly1342,Val1056
	6,7,4′-Trihydroxyisoflavone	5284649	−7.5	x = 152.958	Size x = 40	Ile1295,Val1056	Gly1342,Glu264,Glu267
				y = 156.025	Size y = 40		Leu1059,Ile177,Lys1060
				z = 132.814	Size z = 40		Val1293,Phe1294
	Chrysin	5281607	−7.4	x = 152.958	Size x = 40	Asp1341,Ser263	Asn268,Val1293,Gly1342
				y = 156.025	Size y = 40		Leu1059,Ile177,Glu264
				z = 132.814	Size z = 40		Glu267,Phe1294,Ile1295
	Genistein	5280961	−7.2	x = 152.958	Size x = 40	Val1056,Val1293,Ile1295	Ile177,Glu267,Lys1060
				y = 156.025	Size y = 40		Asn268,Phe1294,Gly1342
				z = 132.814	Size z = 40		Asp1341,Leu1059
	Diosmetin	5281612	−7.2	x = 152.958	Size x = 40	Asn186,Lys978,Arg251	Lys190,Asp363,Gln359
				y = 156.025	Size y = 40		Trp356,Asp985,Arg248
				z = 132.814	Size z = 40
	Acacetin	5280442	−7.1	x = 152.958	Size x = 40	Lys370,Asp363	Ala367,Gly366,Arg248
				y = 156.025	Size y = 40		Lys190,Arg303,Leu986
				z = 132.814	Size z = 40		Asp985,Asn186
	Naringenin chalcone	5280960	−7.0	x = 152.958	Size x = 40	Ser263,Val1293,Ile1295	Ser176,Leu1059,Val260
				y = 156.025	Size y = 40	Ile177	Glu267,Glu264,Asn268
				z = 132.814	Size z = 40		Phe1294,Gly1342
	Indole-3-acrylic acid	5375048	−6.7	x = 152.958	Size x = 40	Glu264,Gly1342,Ser263	Val260,Ser176,Asp1341
				y = 156.025	Size y = 40		Lys1060,Leu1059,Val1056
				z = 132.814	Size z = 40		Ile177,Ser1297
	Indole-3-lactic acid	92904	−6.4	x = 152.958	Size x = 40	Asp1341,Ser1297	Ser176,Ser263,Val1056
				y = 156.025	Size y = 40		Glu267,Lys1060,Leu1059
				z = 132.814	Size z = 40		Glu264,Ile177,Val260
	3-Indolepropionic acid	3744	−6.1	x = 152.958	Size x = 40	Gly1342,Asp1341,Ser263	Val1340,Ser176,Val260
				y = 156.025	Size y = 40	Glu264,Ser1297	Ile177,Glu267,Val1056
				z = 132.814	Size z = 40		Leu1059,Lys1060
	Indoxyl sulphate	10258	−6.0	x = 152.958	Size x = 40	Ser1297,Glu264,Ser263	Asp1341,Leu1059,Val1056
				y = 156.025	Size y = 40		Ile177,Glu267,Val260
				z = 132.814	Size z = 40		Ser176
	Indole-3-carboxylic acid	69867	−5.9	x = 152.958	Size x = 40	Gly1342,Ser1297,Glu264	Val260,Ser263,Ile177
				y = 156.025	Size y = 40		Ser176,Asp1341
				z = 132.814	Size z = 40
	Dihydroisoferulic acid 2752054		−5.9	x = 152.958	Size x = 40	N/A	Ile546,Glu543,Glu267
				y = 156.025	Size y = 40		Ala969,Leu973,Ser263
				z = 132.814	Size z = 40		Ile972,Ile266,Lys968
							Leu967,Leu964
	3-Methylindole 6736		−5.7	x = 152.958	Size x = 40	N/A	Arg74,Met152,Ile70
				y = 156.025	Size y = 40		Leu73,Leu195,Trp361
				z = 132.814	Size z = 40		Phe81,Phe78
	Indole-3-carboxaldehyde 10256		−5.6	x = 152.958	Size x = 40	Asp1341,Ser1297	Val260,Ser176,Glu264
				y = 156.025	Size y = 40		Ile177,Ser263
				z = 132.814	Size z = 40

We observed that Luteolin could bind stably to AKT1 (−8.7 kcal/mol) (PDB ID: 3O96) and CFTR (−8.0 kcal/mol) (PDB ID: 6O1V), and Myricetin could form stable conformer on PIK3R1 (−9.3 kcal/mol) (PDB ID: 4JPS). It has been suggested that these metabolites could make strong affinity with the key targets. Its figures were displayed in Figure 5.

Figure 5. Molecular docking test and its binding scores between key targets and compounds. (A) AKT1 (PDB ID: 3O96) – luteolin conformer. (B) PIK3R1 (PDB ID: 4JPS) – mMyricetin conformer. (C) CFTR (PDB ID: 6O1V) – luteolin conformer.

Toxicological evaluation of two key metabolites in silico

The two metabolites (luteolin and myricetin) was assessed through ADMETlab web-based tool. The result has been shown that the two compounds had no noticeable toxic properties (Table 5).

Table 5. The verification of toxic parameters on two key metabolites.

Parameter	Luteolin	Myricetin
hERG (hERG blockers)	Non-blockers	Non-blockers
H-HT (human hepatotoxicity)	Negative	Negative
Rat oral acute toxicity	Negative	Negative
Carcinogenecity	Negative	Negative
Eye corrosion	Negative	Negative
Respiratory toxicity	Negative	Negative

The networks of microbiota or natural resources-metabolites-targets-key signalling pathway

The GM or natural resources-metabolites-targets-key signalling pathway (GNMTK) suggested that they have strong relationships to exert the therapeutic effects. Hence, GNMTK network represented that the orchestration of DPDNFs and favourable GM might have more beneficial effects against NAFLD. All in all, both Luteolin and Myricetin might bind stably to AKT1, PIK3R1 and CFTR to dampen cAMP signalling pathway, suggesting that the effectors are bona fide agents in DPDNFs and Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1 are critical GM in the treatment of NAFLD (Figure 6).

Figure 6. Network of the gut microbiota or natural resources-metabolites-targets-key signalling pathway (GNMTK).

Discussion

Our study suggested that combinatorial application with natural resources and beneficial GM might favourable pharmacological efficacy on NAFLD. Most recently, a report demonstrated that NP is an efficient methodology to elucidate active molecules in Silybum marianum against NAFLD [41]. Similarly, another report has been shown that Xiaochaihu decoction plays an important role to ameliorate NAFLD via NP-based analysis [42]. Certainly, we assembled the GM database on natural products database to unravel the relationships between GM and DPDNFs, in the pursuit of scaled-up approach. This methodology is needed to optimize key GM and metabolites against some diseases and would provide new insight into NAFLD therapeutics.

Accordingly, we employed the systemic analysis by integrating key components to be identified by data mining, thereby, luteolin (referred to as postbiotics or metabolites), myricetin (referred to as postbiotics or metabolites), AKT1 (target), CFTR (target), PIK3R1 (target), Enterococcus sp. 45 (probiotics), Escherichia sp.12 (probiotics), Escherichia sp.33 (probiotics) and Bacterium MRG-PMF-1 (probiotics) were associated with cAMP signalling pathway. In parallel, we surveyed luteolin-rich and myricetin-rich natural food resources. As a result, the pepper, carrot, broccoli, celery, cabbage and parsley are luteolin-rich natural resources, which are related deeply to AKT1 and CFTR. Likewise, Enterococcus sp. 45 (probiotics) can convert orientin (prebiotics) (flavone species) and cymaroside (prebiotics) (flavone species) into luteolin (postbiotics) [43,44].

In addition, the spinach, pea, turnip, carrot, cauliflower and cranberry are significant suppliers of myricetin, which are associated closely with PIK3R1. Similarly, Escherichia sp.12 (probiotics), Escherichia sp.33 (probiotics) and Bacterium MRG-PMF-1 (probiotics) can convert myricitrin (prebiotics) into myricetin (postbiotics) [45].

An animal test demonstrated that cAMP level in NAFLD rats was elevated to as fivefold as control groups, conversely, the cAMP level had negative correlation with high density lipoprotein (HDL) cholesterol [46]. Furthermore, cAMP triggers gluconeogenesis in NAFLD as well as causes insulin resistance [47–49]. It has been suggested that inhibitors of cAMP signalling pathway might be important therapeutic agents in the treatment of NAFLD.

Both luteolin and myricetin are secondary metabolites in natural resources or postbiotics in specific GM. The luteolin improves sensitivity level of insulin as an antidiabetic agent, including antioxidant and anti-inflammatory properties [50]. Likewise, myricetin has not only potent stimulatory effect of insulin, but accelerates lipogenesis in rat adipocytes [51]. The MK-2206 as an AKT inhibitor reached at a phase II clinical trial suppressed the progression of NAFLD [52]. Uncommonly, there was no relationship between CFTR and hepatic steatosis in the development of NAFLD [53]. Wortmannin as PIK3R1 (PI3K) inhibitor diminished the expression level of interleukin 1β (IL-1β) and interleukin 18 (IL-18), which leads to protecting mice from NAFLD [54]. The results suggested that targets (AKT1, PIK3R1) and effectors (luteolin, myricetin) might be critical elements to alleviate NAFLD on a pharmacological viewpoint. Thus, our innovative methodology gives significant clues to clarify the nuanced relationships between GM and DPDNFs.

The top 10 signalling pathways related to NAFLD were briefly discussed as follows, the selective criteria of which was based on false discovery rate (FDR< 0.05). cAMP signalling pathway: Metformin with AMPK (AMP-activated protein Kinase)-dependent mechanisms dampens mitochondrial respiratory system, activating AMPK and lowering cAMP and thereby enhancing liver enzymes and fat content in NAFLD [55]. Interestingly, an animal test demonstrated that metformin as cAMP suppressor is associated with diversity of GM and reduced the production of bacterial endotoxin in NAFLD [56]. It elicits that cAMP blocker might have synergistic effects via positive pharmacological mechanism and favourable GM composition on NAFLD.

Cyclic Guanosine MonoPhosphate Protein Kinase G (cGMP-PKG) signalling pathway: The Gene Expression Omnibus (GEO) database analysis shows that cyclic Guanosine MonoPhosphate Protein Kinase G (cGMP-PKG) signalling pathway is related directly to insulin resistance and gives rise to steatosis via aggravating cytotoxic cascades of NAFLD [57]. JAnus Kinase-Signal Transducer and Activator of Transcription (JAK-STAT) signalling pathway: An animal experiment demonstrated that the deletion of JAK2 induces fatty liver, thereby elevating liver triglyceride as twenty times as normal group [58]. Neurotrophin signalling pathway: A logistic regression study demonstrated that brain-derived neurotrophic factor (BDNF) has a negative independent factor with 0.041 (Odds ratio) in NAFLD patients, by comparison with control groups [59]. T cell receptor signalling pathway: The recognition of antigen on T cell is an initial step to maintain the homeostasis of the GM, affecting liver immune system in gut-liver axis [60]. NF-kappa B (NFKB) signalling pathway: O. indicum seed extract (OISE) with anti-inflammatory effects inhibits the activity of NFKB, leading to the development non-alcoholic steatohepatitis (NASH) [61]. Chemokine signalling pathway: The concentrations of the C-C Motif Chemokine Ligand 2 (CCL2) and CXC Motif Ligand 9 (CXCL9) in NAFLD group were significantly greater than the control group [62]. Oxytocin signalling pathway: Oxytocin is an accelerator to promote liver regeneration via autophagy, liver metabolic homeostasis [63,64]. Insulin signalling pathway: Insulin resistance a key risk factor to aggravate NAFLD, insulin sensitizers (thiazolidinediones) taken to ameliorate NAFLD might be critical therapeutic agents [65,66]. Thyroid hormone signalling pathway: The thyroid hormone can reduce the concentration of blood lipid and attenuate the progression of NAFLD [67]. It implies that thyroid hormone can alleviate the hyperlipidaemia and NAFLD.

To sum up, we believe that the integrated analysis of prebiotics, probiotics, postbiotics and targets is an essential strategy to decode the complex microbiome system. In parallel, DPDNFs and GM play important role against NAFLD. Collectively, we indicate relationships of the key GM or natural resources, metabolites, targets and a key signalling pathway in the treatment of NAFLD. From a holistic perspective, we suggest the GM and DPDNFs components might exert the combinatorial effects on NAFLD. The key findings of this study were displayed in Figure 7.

Figure 7. The key findings of this study.

The pros and cons of this study

With the aid of NP, the connectivity of GM and DPDNFs is a strategy to decrypt novel uses for existing, validated, or known therapeutic ingredients that are outside extent of its real clinical features in microbiome research. This study is based on the scientific evidence that metabolites and DPDNFs from GM affect some key targets against NAFLD. At present, thanks to the development of bioinformatics, cheminformatics and microbiome databases, a great deal of biodata has been achieved. This enables to analyse the promising natural resources, GM, mechanisms, targets and metabolites against human diseases, including NAFLD. The aim of this applicability is to represent the utilization of these integrated pharmacological method to predict the relationships and to discover for new effectors in certain diseases. At this stage, the devised platform allows new data to input in the stylized format. Nevertheless, there are some limitations that our suggests are theoretical points and dependent on data-driven study. It is a challenge that we should overcome in further study.

Conclusion

Overall, this study indicated key probiotics (Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1) or natural resources, postbiotics (luteolin and myricetin) and targets (AKT1, PIK3R1 and CFTR) might block cAMP signalling pathway against NAFLD. In the scenario, we shed light on the significance of both luteolin and myricetin as metabolites from GM (Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1) and DPDNFs in the treatment of NAFLD. To conclude, we suggest that combinatorial application of favourable GM and DPDNFs might make positive efficacy in the alleviation of NAFLD.

Author contributions

Ki-Tae Suk, Dong Joon Kim and Ki-Kwang Oh had substantial contributions to the conception or design for the work. Ki-Tae Suk, Ki-Kwang Oh, Su-Been Lee, Min-Gi Cha, Goo-Hyun Kwon, Haripriya Gupta and Raja Ganesan were involved in the acquisition, or analysis for the work. Satya Priya Sharma, Sung-Min Won, Jin-Ju Jeong and Min-Kyo Jeong performed the interpretation of data for the work. Byeong-Hyun Min, Ji-Ye Hyun and Jung-A Eom performed draft of the figures. Hee-Jin Park, Sang-Jun Yoon and Mi-Ran Choi conducted the revising of the figures. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Also, all authors confirmed the final approval of the version to be published.

Acknowledgments

A preliminary version of this paper appeared as a Preprint [68] which was reelaborated during authors’ stay at Center for Microbiome, Institute for Liver and Digestive Diseases in August 2022 and December 2022 respectively. The authors wish to thank the Hallym university for exceptional working conditions. This study has been implemented with the support of the Hallym University Research Fund, the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education, Science and Technology.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Additional information

Funding

This research was supported by the Hallym University Research Fund, the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education, Science and Technology (NRF-2020R1I1A3073530 and NRF-2020R1A6A1A03043026).