The combined application of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via network pharmacology approach

doi:10.21203/rs.3.rs-2107064/v1

Non-alcoholic fatty liver disease (NAFLD) is an initial etiology to be developed steatosis, liver fibrosis, cirrhosis, and even hepatocellular carcinoma. However, the noticeable therapeutics were not elucidated completely to dampen the progressive rate involved in NAFLD. In the incomplete project, we combined secondary metabolites (SMs) from gut microbiota (GM) and Hordeum vulgare (HV) as a representative grain with potent NAFLD to exert combinatorial effects via network pharmacology. Hence, we retrieved the SMs of HV from NPASS (Natural product Activity & Species Source Database) and SMs of GM from gutMGene database. Then, targets associated with SMs were identified from both SEA (Similarity Ensemble Approach) and STP (SwissTargetPrediction). The crucial overlapping targets were identified on NAFLD-related targets through Ven diagram plotter. We constructed the protein-protein interaction (PPI) network from the crucial targets and built a bubble plot to identify a key mechanism on NAFLD. Also, we analyzed microbiota or barley – signaling pathways – targets – metabolites (MBSTM) in aspects of combinatorial approach (HV, and GM). To be confirmed a significant SM against NAFLD, we performed Molecular Docking (MD) with Autodock 1.5.6 to verify the affinity between SMs and targets. Finally, drug-likeness and toxicity properties of key SMs were validated via SwissADME and ADMETlab platform. The number of 31 core targets was analyzed by PPI network, the result of which represented JUN as a key target on NAFLD. The key SM bound stably to JUN were Tryptanthrin from HV. On a bubble plot, we identified that Apelin signaling pathway might be an inhibitive mechanism to relieve NAFLD in the combinatorial approach. On the holistic viewpoints, we analyzed MBSTM to obtain components associated with Apelin signaling pathway. As a result, we found the primary GM to fight NAFLD: Microbiota (Eubacterium limosum; Eggerthella sp. SDG-2; Alistipes indistinctus YIT 12060; Odoribacter laneus YIT 12061; Paraprevotella clara YIT 11840; Paraprevotella xylaniphila YIT 11841). The MD provided what the key SM (Dihydroglycitein, 1,3-Diphenylpropan-2-ol, and Acetic) is on each target (HDAC5, NOS1, and NOS2) related directly to Apelin signaling pathway. Overall, these results suggest that combinatorial application could be an effective tactic for ameliorating NAFLD.

Non-alcoholic fatty liver disease

Secondary metabolites

Network pharmacology

Hordeum vulgare

Gut microbiota

Nonalcoholic fatty liver disease (NAFLD) is the most persistent liver disease with a broad spectrum diseases: steatosis, nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and liver failure, including hepatocellular carcinoma ¹. NAFLD, a chronic liver disease, is caused by the excessive fat accumulation, which is manifested by metabolic imbalance related to obesity, diabetes mellitus, and insulin resistance, further triggering a high exposure of cardiovascular diseases (CVDs) ². The manifest evidences demonstrating a connectivity between gut-liver axis and NAFLD pathogenesis have been revealed in the context of therapeutics ³. The key players for digestion of carbohydrates are gut bacteria, producing short-chain fatty acid (SCFA) metabolites (acetate, butyrate, and propionate) ⁴. The short-chain fatty acids (SCFAs) might play important role to enhance glucose, fat metabolism, and drive intestinal homeostasis, the increased SCFAs might alleviate obesity, liver steatosis, and facilitate nutrition utilization in the body ⁵. In particular, Hordeum vulgare (common name; barely) was domesticated about 10,000 years ago, which is healthy food as reliever of metabolic disorder and preventer of colon cancer ^6,7. Notably, an animal test demonstrated that mice fed barley (Hordeum vulgare; HV) combined β-glucan (BG) for 10 weeks diminished the area of liver fibrosis and decreased the expression level of transforming growth factor beta (TGFβ) ⁸. Another report showed that the consumption of rich-BG can be hampered the fat absorption, leading to amelioration of NAFLD ⁹. However, a clinical study for BG polysaccharide effect in 10 healthy male groups reported that the BG was not detected in blood samples after taking 1 g every one time for 1 week ¹⁰, indicating low bioavailability.

In parallel, BG with very hydrophilic properties could lead to diverse unexpected side effects: diarrhea, nausea, vomiting, dizziness, and joint pain ¹¹. Thereby, we selected only lipophilic compounds with drug-like properties from HV, based on Lipinski’s rule. Additionally, it was reported that anti-obesity efficacy level of ginseng was dependent upon the diversity of gut microbiota (GM) ¹². The body weight (BW) and body mass index (BMI) is controlled by the distribution of Subdoligranulum, Oscillibacter, and Akkermansia in the gut, whereas the body fat percentage was determined by Lactobacillus ¹³. It elicits that the composition of GM is a key factor to manage metabolism in the body, and extrinsic elements like diet are also determined to the diversity of gut microbes, including NAFLD. From the same point of view, we investigated the secondary metabolites (SMs) from both GM and HV to be expected the synergistic effects against NAFLD.

With the aid of network pharmacology, we implemented the holistic analysis via the SMs from GM and HV to decode the incorporated effects on NAFLD. Distinctively, network pharmacology is a significant systemic method to uncover the interconnected biological data with complex microbiome analysis ^14,15. The merged analysis between Chinese herbal medicine and harmful GM was conducted to examine key components and mechanisms against ulcerative colitis (UC) via network pharmacology ¹⁶.

Correspondingly, the collective analytical approach including nutrigenomics, metabolomics, and microbiota data might be a keynote to uncover the interplay between SMs, microbiota, and host responses in determining hepatocellular function and healing ¹⁷. Such studies are earnestly required to produce therapeutic possibilities on the SMs from GM and natural product resources. As suggested, we described the therapeutic values from integrated information of GM and HV via network pharmacology concept. Significantly, this approach will gain insight into the mechanism of action and therapeutic benefits of exogenous species (herbal medicines or plants or food) and endogenous species (SMs from GM) with orchestrated system. Accordingly, we constructed microbiota or barley – signaling pathways – targets – metabolites (MBSTM) network to identify their relationships in the treatment of NAFLD. Thus, our study shows that combinatorial application of the SMs from GM and HV might be expected favorable synergistic effects, suggesting that the dual employment might be an alleviative strategy for the treatment of NAFLD. The stepwise work processing is represented (Fig. 1).

Although there is no plenty of microbiome data to draw out a critical mechanistic underpinning for NAFLD treatment, however, our suggestion gives a significant hint to decode what the key components are important between SMs from GM and HV, in even the limited resources.

The SMs of HV and its screening. A total of 24 SMs from HV were reported in NPASS database, targets of which were obtained by SEA (574) and STP (514) databases (Table 2; Supplementary Table S1). The CHEMBL566278 (PubChem ID: 5281113) and Ins (1,2,5,6)P4 (PubChem ID: 14033625) were excluded from the retrieved 24 SMs, due to the violation of Lipinski’s rule. Between the SEA and STP, the number of 79 overlapping targets was finally selected as valid targets associated with HV (Fig. 2a).

The SMs of GM and confirmation of core targets against NAFLD. Then, a total of 208 SMs (Supplementary Table S1) were browsed from the gutMGene database, finally, we identified the 668 overlapping targets between SEA (1256) and STP (947) databases (Supplementary Table S1) (Fig. 2b). The number of 66 intersecting targets was identified by comparison with the overlapping targets of Fig. 2A and Fig. 2B, suggesting that the 66 overlapping targets were considered as critical targets to affect inherently with SMs from GM and SMs from HV (Supplementary Table S1; Fig. 2c). The overlapping 66 targets obtained by the comparison analysis were examined with NAFLD targets, thereby, the number of 31 core targets was identified between the overlapping 66 target and NAFLD targets (Supplementary Table S1; Fig. 2d).

The PPI network analysis. To attain the detailed interaction with each target of HV and GM functioning on NAFLD, the final 31 key targets of SMs and NAFLD were input into STRING database to describe the PPI network. The information extracted by STRING database was placed into R Package to understand what a hub target is. The network study suggested that each target was associated with its neighbor target(s) by establishing 30 nodes linked to 74 edges (Fig. 3a). Among 31 core targets, OXER1 was not any associations with one another. A newly constructed network via R Package indicated that Jun proto-oncogene (JUN) was the most influential protein-coding gene in the entire PPI network, with the highest degree value (16). The classified ranking of each target in the PPI network is profiled in Table 3.

Table 2. The number 24 secondary metabolites of HV from NPASS database.


No.	Compounds		Lipinski's Rules				Lipinski's Violations	Bioavailability Score	TPSA(Å²)
		PubChem ID	MW	HBA	HBD	MLog P	Lipinski's Violations	Bioavailability Score	TPSA(Å²)
			< 500	< 10	≤ 5	≤ 4.15	≤ 1	> 0.1	< 140
1	Salicylic Acid	338	138.12	3	2	0.99	0	0.85	57.53
2	Gramine	6890	174.24	1	1	1.55	0	0.55	19.03
3	Indole-3-Acetic Acid	802	175.18	2	2	1.11	0	0.85	53.09
4	Tyramine	5610	137.18	2	2	1.21	0	0.55	46.25
5	Hordenine	68313	165.23	2	1	1.83	0	0.55	23.47
6	Dormin	5375200	264.32	4	2	1.44	0	0.85	74.6
7	Niacin	938	123.11	3	1	-1.13	0	0.85	50.19
8	Abscisic Acid	5375199	264.32	4	2	1.44	0	0.85	74.6
9	5-hydroxy-2-nonylchromone	365028	288.38	3	1	2.62	0	0.55	50.44
10	S-Methylmethionine Sufonium	145692	164.25	3	2	-1.82	0	0.55	88.62
11	Tryptanthrin	73549	248.24	3	0	2.22	0	0.55	51.96
12	CHEMBL566278	5281113	550.65	8	9	1.34	3	0.17	218.44
13	(+)-Abscisic Acid	5280896	264.32	4	2	1.44	0	0.85	74.6
14	Nonanol	8914	144.25	1	1	2.54	0	0.55	20.23
15	Ethylparaben	8434	166.17	3	1	1.64	0	0.55	46.53
16	Putrescine Hydrochloride	9532	161.07	2	2	0.61	0	0.55	52.04
17	Hordenine Sulfate	77147	263.31	6	3	0.31	0	0.55	106.45
18	Putrescine	1045	88.15	2	2	-0.18	0	0.55	52.04
19	Ins(1,2,5,6)P4	14033625	500.08	18	10	-6.17	3	0.11	346.74
20	Malonic Acid	867	104.06	4	2	-0.99	0	0.85	74.6
21	Jasmonic Acid	5281166	210.27	3	1	1.68	0	0.85	54.37
22	N,N'-Bis(3-Aminopropyl)Heptane-1,7-Diamine	492218	244.42	4	4	1.03	0	0.55	76.1
23	Tryptamine	1150	160.22	1	2	1.26	0	0.55	41.81
24	Gibberellin A3	6466	346.37	6	3	1.66	0	0.55	104.46

MD test on JUN target and bubble plot results. On molecular docking (MD), we confirmed what the most significant SMs on JUN protein. Tryptanthrin derived from HV formed the most stable complex with JUN protein, with − 10.7 kcal/mol as a binding energy (Fig. 3b; Fig. 3c; Table 4). It can be thus elicited that HV exerts the positive effects to ameliorate NAFLD. Next, the bubble plot suggested that the number of 16 overlapping targets was related directly to 6 signaling pathways (Fig. 3d).

**Table 3.** The degree value of the final 30 core targets.
No.	Target name	Degree value	No.	Target name	Degree value
1	JUN	16	16	NR4A1	3
2	ESR1	15	17	EDNRA	3
3	MMP9	10	18	SHBG	3
4	HDAC2	8	19	TGM2	3
5	MMP2	8	20	CNR1	3
6	MCL1	7	21	FABP4	2
7	ACE	7	22	HCAR2	2
8	HDAC3	7	23	FABP5	2
9	HDAC1	7	24	ERN1	2
10	HDAC9	6	25	CNR2	2
11	MMP1	5	26	NOS1	2
12	HDAC5	5	27	STS	2
13	ESR2	5	28	ACLY	2
14	RARA	5	29	MIF	1
15	NOS2	4	30	NAAA	1

Table 5. The description of six signaling pathways on NAFLD.

KEGG ID & Description	Target	False discovery rate
hsa04926: Relaxin signaling pathway	JUN, MMP1, MMP2, MMP9, NOS1, NOS2	0.00000982
hsa04915: Estrogen signaling pathway	JUN, MMP2, MMP9, ESR1, ESR2, RARA	0.00000982
hsa03320: PPAR signaling pathway	MMP1, FABP4, FABP5	0.00700000
hsa04919: Thyroid hormone signaling pathway	ESR1, HDAC1, HDAC2, HDAC3	0.00170000
hsa04657: IL-17 signaling pathway	JUN, MMP1, MMP9	0.01160000
hsa04371: Apelin signaling pathway	NOS1, NOS2, HDAC5	0.02510000

The most significant signaling pathway was Apelin signaling pathway with lowest rich factor, indicating that the signaling pathway dampens to ameliorate NAFLD on SMs from HV and GM (Table 5).

MBSTM networks investigation. The MBSTM networks demonstrated that 45 GM, HV, a key signaling pathway, 3 targets, and 33 metabolites (83 nodes, and 93 edges) were correlated with Apelin signaling pathway (Fig. 4). In the MBSTM network, green circles (nodes) represent the GM; red circles (nodes) stand for Apelin signaling pathway; orange circles (nodes) indicate targets; and sky-blue circles (nodes) indicate SMs. The gray line (edge) represents the relationship of each node.

MD test results on Apelin signaling pathway. On MD, we confirmed what the most significant SMs on each target are. In detail, Dihydroglycitein (-7.8 kcal/mol) converted by Eubacterium limosum ¹⁸ bound stably on HDAC5 (PDB ID: 5UWI) had hydrogen bond interactions of Ser94, and Ser950 and hydrophobic interactions of Thr97, Gly19, Asp18, Asn100, Glu70, Gly68, Lys949, and Val96 provided HDAC5-Dihydroglycitein complex (Fig. 5a). Also, 1,3-Diphenylpropan-2-ol (known as DA-05096) (-7.7 kcal/mol) converted by Eggerthella sp. SDG-2 ¹⁹ had the highest affinity on NOS1 (PDB ID: 4D1N), and the complex provided hydrogen bond interactions of Arg419 and hydrophobic interactions with Leu705, Arg704, Ser708, Phe709, Glu710, Met575, Tyr711, Ser418, Asn416, and Ala417 (Fig. 5b). Then, Acetic (-7.9 kcal/mol) converted by Alistipes indistinctus YIT 12060 ²⁰, Odoribacter laneus YIT 12061 ²⁰, Paraprevotella clara YIT 11840 ²¹, and Paraprevotella xylaniphila YIT 11841 ²¹ had the greatest affinity on NOS2 (PDB ID: 4NOS), containing two hydrogen bonds (Gln149, and Gln192) and six hydrophobic interactions (Ser486, Glu145, Phe146, Asn148, Leu485, and Arg454) (Fig. 5c). Also, its binding energy and interactive residues’ information had been profiled in Table 6.

Drug-likeness and toxicity screening on the four key SMs. Finally, the number of four SMs (Tryptanthrin, Dihydroglycitein, 1,3-Diphenylpropan-2-ol, and Acetic) was confirmed and established on drug-likeness properties and toxicity level via in silico verification (Table 7). The SMs had no physicochemical impediments to be developed as new agents. The chemical structures of the four key SMs were displayed in Fig. 6.

The identification of key components against NAFLD. Above described, the complexes of the key four SMs and its targets were transduced to four signaling pathways: Relaxin signaling pathway, Estrogen signaling pathway, and IL-17 signaling pathway as agonistic mechanism by forming Tryptanthrin-JUN complex, conversely, Apelin signaling pathway as antagonistic mechanism by exerting strong affinity with Dihydroglycitein-HDAC5; 1,3-Diphenylpropan-2-ol-NOS1; and Acetic-NOS2 complexes. The descriptive figure was represented in Fig. 7.

The PPI networks show that JUN is the most significant protein-coding gene to regulate the adjacent targets for the treatment of NAFLD. The previous studies demonstrated that JUN is a crucial target against acute stress like injury of hepatocytes and is a key element to regulate lipid metabolism in hepatocytes ^22,23. Furthermore, JUN attunes the response to severe cellular damages, such as oxidative attack and DNA destruction ²⁴. The Tryptanthrin is a natural alkaloid with potent anti-inflammatory effects, likewise, Jerantinine B is one of alkaloids induces the activation of c-JUN (known as JUN) for the treatment of acute myeloid leukemia (AML) ^25,26. It implies that Tryptanthrin might diminish acute stress in hepatocytes via activating JUN to mitigate NAFLD.

The relationships of six signaling pathways with NAFLD are briefly discussed as follows. (1) Relaxin signaling pathway: Relaxin is a notable therapeutic target to diminish hepatic steatosis by activating endothelial nitric oxide synthase (eNOS) in high-fat diet (HFD) mice subject ²⁷. (2) Estrogen signaling pathway: The lack of estrogen accelerates hepatic steatosis progression, in particular, which is exposed noticeably to post-menopausal women with metabolic disorders ²⁸. (3) Interleukin-17 (IL-17) signaling pathway: A report demonstrated that IL-17 could enhance intestinal epithelial layer and diminish dysbacteriosis-caused intestinal damage and other organs injury incited by a diet ²⁹. Noticeably, Relaxin signaling pathway, Estrogen signaling pathway, and IL-17 signaling pathway are related directly to JUN in the treatment of NAFLD, indicating that the three signaling pathways might exert its therapeutic value on NAFLD. The results line up with our suggestions. It means that Relaxin signaling pathway with the highest rich factor can be activated strongly among the six signaling pathways on the bubble chart. Our study pinpoints that Tryptanthrin from HV is the promising SM to be a therapeutic agent against NAFLD, by activating Relaxin signaling pathway associated with a hub target (JUN). (4) Thyroid hormone signaling pathway: Thyroid Hormone (TH) interplays deeply between liver and thyroid for lipogenesis, fatty acid oxidation, cholesterol biodegradation, and carbohydrate metabolism ³⁰. (5) PPAR signaling pathway: A study reported that Peroxisome Proliferator-Activated Receptor (PPAR) dual agonists might make positive effect on NAFLD, however, its therapeutic efficacy has not been confirmed completely in clinical trials ³¹. It remains unclear to indicate optimal agents on PPAR signaling pathway. (6) Apelin signaling pathway: A study suggests that the activation of Apelin signaling pathway protects hepatocytes exposed to lipid accumulation ³². Conversely, another study demonstrated that Apelin aggravates hepatic fibrosis, furthermore, accelerating metabolic disorders, and even homeostasis imbalance ³³.

Coincidentally, our study shows that inactivation of Apelin signaling pathway might be a crucial mechanism on NAFLD. Specifically, effectors that we are being presented are Dihydroglycitein from Eubacterium limosum, 1,3-Diphenylpropan-2-ol from Eggerthella sp. SDG-2, and Acetic from Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, and Paraprevotella xylaniphila YIT 11841. On Apelin signaling pathway, all three promising SMs are derived from GM, not shown the potential SMs in phytochemicals from HV. As an example, Eubacterium limosum increased by germinated barley foodstuff (GBF) is capable of producing more Dihydroglycitein in humans ³⁴. In addition, by-products processed from barley malts improved the concentration of the SCFAs in cecum of rats ³⁵. Although there was no manifest feedback between HV (barley) and above mentioned six gut microbes, we were able to show beyond doubt concerning likelihood of alleviator on NAFLD, taking the four SMs: (1) Tryptanthrin from HV, and (2) Dihydroglycitein, (3) 1,3-Diphenylpropan-2-ol, (4) Acetic from GM.

Taken together, these suggestions reveal the significance of GM as a key determinant, to be taken HV (barley). However, our study imposes on obvious clinical trials and is required further in-depth investigation.

In conclusion, our study indicates the significance of the combinatorial-administration of GM and HV (barley) against NAFLD, suggesting both an agonist (Tryptanthrin derived from HV) on JUN target, and as antagonists (Dihydroglycitein, 1,3-Diphenylpropan-2-ol, and Acetic) are potential agents to inactivate Apelin signaling pathway. Accordingly, the Apelin signaling pathway in a controversial issue might show beneficial efficacy on NAFLD, blocking the mechanism. These findings provide a new vista about how the key components can facilitate between HV (barley) with dietary fibers and GM.

The utilization of public bioinformatics databases. Specifically, with the employment of biologically significant databases, we analyzed this study to identify pharmacological relationship via combination of GM, and HV. All these web-based platforms are opened for users who attempt to achieve valuable information, furthermore, we applied to reveal key components (GM, signaling pathways, targets, and secondary metabolites) from the combinatorial application. The list of the databases was displayed in Table 1. The SMs from HV were retrieved by Natural Product Activity & Species Source (NPASS) database (http://bidd.group/NPASS/) (accessed on 5 August 2022) ³⁶, suggesting that significant targets associated with the bioactives were identified via Similarity Ensemble Approach (SEA) (https://sea.bkslab.org/) (accessed on 5 August 2022) ³⁷ and SwissTargetPrediction (STP) (http://www.swisstargetprediction.ch/) (accessed on 5 August 2022) ³⁸. The SMs from GM were obtained by gutMGene (http://bio-an- notation.cn/gutmgene/) (accessed on 6 August 2022). Likewise, we meta-analyzed to search for targets related to SMs through SEA and STP. NAFLD targets were selected by DisGeNET (https://www.disgenet.org/) (accessed on 7 August 2022) ³⁹ and OMIM (https://www.omim.org/) (accessed on 7 August 2022) ⁴⁰. Collectively, we found the crucial targets for treatment of NAFLD via STRING bioinformatics database (https://string-db.org/) (accessed on 8 August 2022) ⁴¹. We constructed Protein-Protein Interaction (PPI) network, Microbiota – Barley – Signaling pathways – Targets – Metabolites (MBSTM) using R Package, and Molecular Docking (MD) via AutoDockTools-1.5.6 software ⁴².

The selection of SMs from HV. Step 1: We identified SMs of HV from NPASS, selecting that the targets related to SMs were retrieved by SEA and STP. In parallel, we found the overlapping targets between SEA and STP. The intersecting targets were considered as important elements due to piling on the two significant databases (SEA and STP). Importantly, SEA database was developed by Dr Shoichet’s group to verify the affinity of ligand-protein complex and ultimately uncover its binding stability ⁴³. To be specific, the number of 23 out of 30 targets predicted by SEA was approved by experiment ⁴⁴. Evidently, STP was employed as a tool to identify targets for cudraflavone C, the selected targets on the compound hit via experiment ⁴⁵.

The attainment of SMs from GM. We obtained SMs of GM from gutMGene database, indicating that the targets associated with SMs were identified by SEA and STP. Coordinately, we adopted the overlapping targets identified by the two databases to improve the hit rate in this study.

The mining of core targets against NAFLD from HV and GM. We selected the overlapping targets related to both HV’s and GM’s targets, then, identified core targets by comparison with NAFLD-related targets obtained by DisGeNET and OMIM. The identified targets are critical protein-coding genes to be affected by SMs from HV and GM in the treatment of NAFLD.

PPI network construction. The core targets were input in STRING database to confirm PPI, which was constructed utilizing R Package. Throughout it, we identified a hub target to alleviate NAFLD in PPI analysis. The hub target was defined as a target with “the highest degree value”, meaning that it can make the most effects on other targets.

The bubble plot construction. The establishment of bubble plot for the analysis of Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analysis. From KEGG pathway enrichment analysis, we identified a key signaling pathway against NAFLD, functioning that the signaling pathway might be antagonistic effect in the treatment of NAFLD. The bubble plot was visualized by R Package.

The MBSTM network construction. We constructed the MBSTM network to uncover the relationships between the microbiota, barley, signaling pathway, target, and metabolite via R Package. The size of each circle indicates the number of interactions among each component. The degree value of the microbiota, barley, signaling pathway, target, or metabolite stands for the edge numbers of the microbiota, barley, signaling pathway, target, or metabolite. The described microbiota are defined as converters which enable to produce the SMs in gut.

MD test to identify key SMs. We performed MD using AutoDock 1.5.6 to identify the most stable SMs in both HV and GM on a key signaling pathway. The cut-off value to screen the SMs was − 6.0 kcal/mol ⁴⁶ or SM with the lowest Gibbs energy (the highest negative value) was considered as the crucial SM to exert therapeutic effect on NAFLD. The SMs were extracted as .sdf format from PubChem, converting into .pdb format using PyMOL. The .pdb format was transformed into .pdbqt file to conduct MD through AutoDockTools-1.5.6 software. The PDB IDs of targets were identified by RCSB PDB (https://www.rcsb.org/) (accessed on 08 August 2022). The MD was performed to verify the affinity of each complex on .pdbqt files of SMs and targets. The docking active site was constructed with cubiform (x = 40 Å, y = 40 Å, and z = 40 Å) on the center of each target: HDAC5 (PDB ID: 5UWI) (x=-8.577, y = 42.932, z = 30.245), NOS1 (PDB ID: 4D1N) (x = 218.219, y = 22.957, z = 42.162), and NOS2 (PDB ID: 4NOS) (x = 17.077, y = 63.563, z = 21.453). The hydrophilic and hydrophobic interactions were identified by LigPlot + 2.2 software (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/download2.html) (accessed on 09 August 2022) ⁴⁷. Collectively, MD performed to search for the most stable SMs in key targets.

The verification of drug-like properties and toxicity on the key SMs. We utilized SwissADME (http://www.swissadme.ch/) (accessed on 8 August 2022) platform to validate the properties of drug-likeness of the key SMs ⁴⁸. The criteria are determined by Lipinski’s rule: Molecular weight (< 500g/mol) or Topological Polar Surface Area (TPSA) (< 140 Å²) or Moriguchi octanol-water partition coefficient (MLogP) (≤ 4.15) or Hydrogen Bonding Acceptor (HBA) (< 10) or Hydrogen Bonding Donor (HBD) (≤ 5). Additionally, significant factors to develop a therapeutic agent are to confirm the non-toxicity. Thus, we established the toxicity parameters via ADMETlab 2.0 ⁴⁹, and admetSAR 1.0 ⁵⁰ as follows: Human ether-a-go-go-related gene (hERG) ⁵¹; Human Hepatotoxicity (H-HT) ⁵²; Carcinogens ⁴⁹; Lethal Dose for 50% kill (LD50) ⁵³; Rat Oral Acute Toxicity ⁵⁰; and Eye corrosion ⁵⁴.

BG: β-glucan

BMI: Body Mass Index

CVDs: CardioVascular Diseases

eNOS: endothelial Nitric Oxide Synthase

GBF: Germinated Barley Foodstuff

HBA: Hydrogen Bonding Acceptor

HBD: Hydrogen Bonding Donor

Herg: Human ether-a-go-go-related gene

HFD: High Fat Diet

H-HT: Human Hepatotoxicity

IL-17: Interleukin -17

KEGG: Kyoto Encyclopedia of Gene and Genomes

LD50: Lethal Dose for 50% kill

MBSTM: Microbiota or Barley – Signaling pathways – Targets – Metabolites

MD: Molecular Docking

MLogP : Moriguchi octanol-water partition coefficient

NAFLD: Non-Alcoholic Fatty Liver Disease

NASH: Non-Alcoholic SteatoHepatitis

NPASS: Natural product Activity & Species Source Database

PPAR: Peroxisome Proliferator-Activated Receptor

PPI: Protein-Protein Interaction

SCFA: Short-Chain Fatty Acid

SCFAs: Short-Chain Fatty Acids

SEA: Similarity Ensemble Approach

SMs: Secondary Metabolites

STP: SwissTargetPrediction

TGFβ: Transforming Growth Factor Beta

TPSA: Topological Polar Surface Area

UC: Ulcerative Colitis

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).

Competing interests

There is no conflict of interest declared.

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).

Author contributions: KTS and DJK investigated the project. KTS and KKO conceptualized and designed the study. KTS, KKO, and SBL analyzed the data. HG, BHM, RG, SPS, SMW, JJJ, MGC, GHK, MKJ, JYH, JAE, HJP, SJY, MRC, SB, and GHK visualized the data. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

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.

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Table 1,4,6 and 7 are available in the Supplementary Files section.

No competing interests reported.

The combined application of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via network pharmacology approach

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Version 1

Abstract

Figures

Introduction

Results

Discussion

Conclusion

Materials And Methods

Abbreviations

Declarations

References

Table 1,4,6 And 7

Additional Declarations

Supplementary Files

Status:

Version 1