Abstract

Background

   Sorghum bicolor (SB) is rich in protective phytoconstituents with
   health benefits and regarded as a promising source of natural
   anti-diabetic substance. However, its comprehensive bioactive
   compound(s) and mechanism(s) against type-2 diabetes mellitus (T2DM)
   have not been exposed. Hence, we implemented network pharmacology to
   identify its key compounds and mechanism(s) against T2DM.

Methods

   Compounds in SB were explored through GC-MS and screened by Lipinski’s
   rule. Genes associated with the selected compounds or T2DM were
   extracted from public databases, and the overlapping genes between
   SB-compound related genes and T2DM target genes were identified using
   Venn diagram. Then, the networking between selected compounds and
   overlapping genes was constructed, visualized, and analyzed by RStudio.
   Finally, affinity between compounds and genes was evaluated via
   molecular docking.

Results

   GC-MS analysis of SB detected a total of 20 compounds which were
   accepted by the Lipinski’s rule. A total number of 16 compounds-related
   genes and T2DM-related genes (4,763) were identified, and 81
   overlapping genes between them were selected. Gene set enrichment
   analysis exhibited that the mechanisms of SB against T2DM were
   associated with 12 signaling pathways, and the key mechanism might be
   to control blood glucose level by activating PPAR signaling pathway.
   Furthermore, the highest affinities were noted between four main
   compounds and six genes (FABP3-Propyleneglyco monoleate,
   FABP4-25-Oxo-27-norcholesterol, NR1H3-Campesterol, PPARA-β-sitosterol,
   PPARD-β-sitosterol, and PPARG-β-sitosterol).

Conclusion

   Our study overall suggests that the four key compounds detected in SB
   might ameliorate T2DM severity by activating the PPAR signaling
   pathway.

1. Introduction

   Type 2 diabetes mellitus (T2DM) is a metabolic disease triggered by the
   complex interaction between genetic and/or environmental associations.
   Patients with T2DM have either insulin resistance (sugar transfer
   dysregulation into cells) or lack of optimal insulin secretion to
   sustain natural glucose levels [[28]1]. T2DM can affect at all ages, 1
   in 3 adults has prediabetes, causes serious physiological
   complications, and brings diabetes diagnosis economic difficulties
   [[29]2–[30]4]. The typical signs of T2DM patients include recurrent
   urination, increased thirst, exhaustion, increased appetite, and
   indistinct vision. The main etiology for T2DM is persistent
   hyperglycemia which provokes mitochondrial dysfunction that stimulates
   to abundant reactive oxygen species (ROS) formation in several tissues
   and pancreatic β cells [[31]5, [32]6]. Such ROS accumulation in
   pancreatic β cells causes irreparable mitochondrial damage, resulting
   inhibition of insulin synthesis, and thus leading to diabetes
   progression by failing to produce enough level of insulin [[33]7,
   [34]8]. However, the anti-diabetic drug pathways include stimulating
   the insulin synthesis, inhibiting the production of endogenous glucose,
   and blocking carbohydrate absorption from intestine [[35]9, [36]10].
   Currently, six classes of oral antidiabetic drugs, including Metformin,
   Glimepiride, Repaglinide, Pioglitazone, Sitagliptin, and Acarbose
   [[37]11] are available which are reported to have serious side effects
   such as anorexia, nausea, dyspeptic episodes, and diarrhea [[38]12,
   [39]13]. Hence, the quest for potential drugs has now become more
   concentrated. In this regard, a rich source of phytoconstituents with
   health benefits may be the prospective candidate for T2DM intervention.

   For the past few years, a report has shown that wholegrains including
   sorghum (Sorghum bicolor (L.) Moench) are the potential alternatives to
   ameliorate T2DM symptoms [[40]14]. Sorghum bicolor (SB) contains a
   number of secondary metabolites which are reported to be effective in
   preventing various metabolic diseases, such as cancers, T2DM, obesity,
   and hyperglycemia [[41]15, [42]16]. A recent research demonstrated that
   oral treatment of SB (0.5% and 1%) noticeably reduced the low density
   lipoprotein cholesterol, triglycerides, and glucose level via PPARG in
   mice fed a high-fat diet, conversely, expression level of PPARG
   elevated to 1% [[43]17]. Some reports expound that sorghum extracts at
   1.0 mg/ml activates PPAR binding in mouse macrophage cell line;
   subsequently, PPAR agonists have been emerged as candidates against
   metabolic dysfunctions with T2DM [[44]18, [45]19]. Lecka-Czernik B
   reported that Aleglitazar, being developed by Roche Holding, is a dual
   agonist for PPARD and PPARA for the promising multiple treatment of
   hyperglycemia and dyslipidemia with T2DM patients [[46]20]. It is
   evident that a dual PPAR agonist treatment is more potent than a single
   agonist treatment. A human study on sub-health condition with
   prediabetes confirmed that SB administration significantly decreased
   the level of glucose by 35% [[47]21]. Although literature survey
   revealed that diabetes and obesity complications can be controlled upon
   consumption of SB [[48]22, [49]23]; however, its mechanism(s) against
   T2DM has not been explored to date. Therefore, the research on
   bioactive compound(s) and pathways of SB against T2DM should be
   justified in scientific testing, in order to maintain its
   pharmacological complements for T2DM.

   Network pharmacology is a structured methodical mode, which can
   investigate the interactive networking elements such as compounds,
   genes, proteins, and diseases [[50]24, [51]25]. Network pharmacology
   can decode the mechanism of compounds with a multifunctional point of
   view, which highlights the interaction of diverse factors, instead of
   “one target, one compound” [[52]26]. The network pharmacology is
   therefore a valuable approach to identify potential lead compounds
   (from natural sources) with specific mechanism of action for the
   prevention of various disease, and mostly to elucidate the synergistic
   impact of bioactive compounds [[53]27]. Zhang B. et al. reported that
   the rapid development of bioinformatics, systems biology, and
   poly-pharmacology contributes greatly to network-based drug discovery,
   which is regarded as a cost-effective drug development method [[54]28].
   A report explicates that the network pharmacology is used as a powerful
   tools to identify the mechanisms of actions between traditional
   Ge-Gen-Qin-Lian decoction (GGQLD) formula and target genes [[55]29]. In
   our study, network pharmacology was used to evaluate the bioactive
   compounds and mechanism(s) of SB against T2DM. Firstly, GC-MS analysis
   of SB was conducted to identify the bioactive compounds and their
   “drug-likeness” property was screened by Lipinski’s rule [[56]30].
   Secondly, genes related to selected compounds or T2DM were identified
   using public databases, and overlapping genes between genes related to
   SB and T2DM target genes were also identified. Thirdly, genes related
   to a hub signaling were selected by analyzing gene set enrichment
   analysis. Finally, the selected genes were implemented for molecular
   docking analysis to find the most potent candidates of SB against T2DM.

2. Materials and methods

2.1 Plant preparation, extraction

   The Sorghum bicolor (SB) was purchased from Chuncheon local market,
   Korea. The collected SB was dried and powdered using electric blender.
   Approximately 500 g of SB powder was soaked in 500 mL of 100% methanol
   (Daejung, Korea) for 3 days and repeated for 3 times to collect
   extraction. The solvent extract was collected, filtered, and evaporated
   using a vacuum evaporator (IKA- RV8, Japan). The evaporated sample was
   dried under a boiling water bath (IKA-HB10, Japan) at 40°C to obtain
   yield.

2.2 GC-MS analysis

   Agilent 7890A was used to carry out GC-MS analysis. GC was equipped
   with a DB-5 (30m×0.25mm×0.25μm) capillary column. Initially, the
   instrument was maintained at a temperature of 100°C for 2.1 minutes.
   The temperature was risen to 300°C at the rate of 25°C/min and
   maintained for 20 minutes. Injection port temperature and helium flow
   rate were ensured as 250°C and 1.5 ml/min, respectively. The ionization
   voltage was 70 eV. The samples injected in split mode at 5:1. MS scan
   range was set at 35–550 (m/z). The fragmentation patterns of mass
   spectra were compared with those stored in the using W8N05ST Library MS
   database. The percentage of each compound was calculated from the
   relative peak area of each compound in the chromatogram. The concept of
   integration used the ChemStation integrater algorithms.

2.3 Chemical compounds database construction, drug-likeness, and oral
bioavailability filtering

   The information of chemical compounds from SB was identified by
   utilizing GC-MS analysis which were filtered according to the
   Lipinski’s rule through SwissADME ([57]http://www.swissadme.ch/) to
   identify “Drug-likeness” property and oral bioavailability score. The
   PubChem ([58]https://pubchem.ncbi.nlm.nih.gov/) was utilized for
   identification of the SMILES (Simplified Molecular Input Line Entry
   System) of compounds.

2.4 Target genes related to selected compounds or T2DM

   Based on the SMILES, target genes linked to the compounds were selected
   through both Similarity Ensemble Approach (SEA)
   ([59]http://sea.bkslab.org/) and Swiss Target Prediction (STP)
   ([60]http://www.swisstargetprediction.ch/) with "Homo Sapiens” mode.
   T2DM related genes were identified by DisGeNET
   ([61]https://www.disgenet.org/search) and OMIM
   ([62]https://www.ncbi.nlm.nih.gov/omim) databases. The overlapping
   genes between compounds of SB and T2DM target genes were identified and
   visualized by VENNY 2.1
   ([63]https://bioinfogp.cnb.csic.es/tools/venny/).

2.5 Network construction of interacted overlapping genes

   Through STRING ([64]https://string-db.org/) analysis, the overlapping
   genes were closely correlated, and the signaling pathways of
   overlapping genes were analyzed by RStudio bubble chart. Networking
   between bioactive compounds and genes of SB against T2DM identified a
   hub signaling pathway.

2.6 Preparation for molecular docking of ligand molecules

   The ligand molecules were converted.sdf from PubChem into.pdb format
   using Pymol, and the ligand molecules were converted into.pdbqt format
   through Autodock.

2.7 Preparation for molecular docking of target proteins

   Six target proteins of T2DM i.e. FABP3 (PDB ID: 5HZ9), FABP4 (PDB ID:
   3P6D), NR1H3 (PDB ID: 2ACL), PPARA (PDB ID: ASP6), PPARD (PDB ID:
   5U3Q), PPARG (PDB ID: 3E00) were selected on STRING via RCSB PDB
   ([65]https://www.rcsb.org/). The proteins selected as.PDB format
   converted into.pdbqt format via Autodock
   ([66]http://autodock.scripps.edu/).

2.8 Ligand- protein docking

   The ligand molecules were docked with target proteins utilizing
   autodock4 by setting-up 4 energy range and 8 exhaustiveness as default
   to obtain 10 different poses of ligand molecules [[67]31]. The 2D
   binding interactions was identified through LigPlot+ v.2.2
   ([68]https://www.ebi.ac.uk/thornton-srv/software/LigPlus/). After
   docking, ligands of lowest binding energy were selected to visualize
   the ligand-protein interaction in Pymol.

3. Results

3.1 Potential bioactive compounds from SB

   A total of 20 compounds in SB were detected by the GC-MS analysis
   ([69]Fig 1), and the name of compounds, retention time, peak area (%)
   are enlisted in [70]Table 1. All 20 compounds were checked and accepted
   by Lipinski’s rule (Molecular Weight ≤ 500g/mol; Moriguchi
   octanol-water partition coefficient ≤4.15; Number of Nitrogen or Oxygen
   ≤10; Number of NH or OH ≤5), and all compounds have satisfactory
   “Abbott Bioavailability Score (> 0.1)” identified through SwissADME
   ([71]Table 2).

Fig 1. A typical GC-MS chromatogram of the chemical compounds of the methanol
extract of SB with indication of 4 main chemical constituents.

   [72]Fig 1
   [73]Open in a new tab

Table 1. A list of the identified 20 chemical compounds from SB through
GC-MS.

   No. Compound Pubchem ID Retention time Area (%)
   1 Glycerin 753 3.606 2.34
   2 Glyceraldehyde 751 4.222 0.19
   3 2,3-Dihydro-3,5-dihydroxy-6-methyl-4H-pyran-2-one 67452387 4.76 0.35
   4 β-D-ribofuranosyl-cytosine 596 7.039 3.25
   5 Butanoic acid, ethyl ester 7762 7.318 0.11
   6 2-Propanol, 1-chloro-3-(1-methylethoxy) 94290 7.818, 7.847 0.84
   7 Oxazole, 5-hexyl-2,4-dimethyl- 573438 8.222 0.15
   8 Hexadecanoic acid, methyl ester 8181 8.76 0.24
   9 Palmitic acid 985 8.991,9.270,9.308 13.67
   10 Methyl linoleate 5284421 9.424 2.38
   11 Linoleic acid 5280450 10.106,10.414,10.943 68.2
   12 2-Stearoylglycerol 79075 10.943 0.72
   13 Propyleneglycol monoleate 5365625 11.741 3.02
   14 25-Oxo-27-norcholesterol 165617 11.837 0.95
   15 Cholesterol 5997 12.279 0.14
   16 5,8-Dimethyltocol 86052 14.01 0.26
   17 Campesterol 173183 16.058 0.77
   18 Stigmasterol 5280794 16.356 0.42
   19 β-sitosterol 222284 17.164 1.52
   20 4,5-Dihydroxycoumarin 54690194 19.991 0.28
   [74]Open in a new tab

Table 2. Physicochemical properties of the 20 compounds for good oral
bioavailability.

   No. Compounds Lipinski Rules Lipinski’s Violations Biavailability Score
   MW HBA HBD MLog P
   < 500 < 10 ≤ 5 ≤ 4.15 ≤1 > 0.1
   1 Glycerin 92.09 3 3 -1.51 0 0.55
   2 Propanal, 2,3-dihydroxy- 90.08 3 2 -1.66 0 0.55
   3 2,3-Dihydro-3,5-dihydroxy-6-methyl-4H-pyran-2-one 144.13 4 2 -0.96 0
   0.55
   4 β-D-ribofuranosyl-cytosine 243.22 6 5 -2.29 0 0.55
   5 Butanoic acid, ethyl ester 116.16 2 0 1.27 0 0.55
   6 2-Propanol, 1-chloro-3-(1-methylethoxy) 152.62 2 1 0.96 0 0.55
   7 Oxazole, 5-hexyl-2,4-dimethyl- 181.27 2 0 1.92 0 0.55
   8 Hexadecanoic acid, methyl ester 270.45 2 0 4.44 1 0.55
   9 Palmitic acid 256.42 2 1 4.19 1 0.85
   10 Methyl lineoleate 294.47 2 0 4.70 1 0.55
   11 Linoleic acid 280.45 2 1 4.47 1 0.85
   12 2-Stearoylglycerol 358.56 4 2 3.63 0 0.55
   13 Propyleneglycol monoleate 340.54 3 1 4.37 1 0.55
   14 25-Oxo-27-norcholesterol 386.61 2 1 5.1 1 0.55
   15 Epicholesterol 386.65 1 1 6.34 1 0.55
   16 5,8-Dimethyltocol 416.68 2 1 5.94 1 0.55
   17 Campesterol 400.68 1 1 6.54 1 0.55
   18 (E)-23-ethylcholesta-5,22-dien-3β-ol 412.69 1 1 6.62 1 0.55
   19 β-sitosterol 414.71 1 1 6.73 1 0.55
   20 4,5-Hydroxycoumarin 178.14 3 1 1.04 0 0.55
   [75]Open in a new tab

   MW, Molecular Weight (g/mol); HBA, Hydrogen Bond Acceptor; HBD,
   Hydrogen Bond Donor; LogP, Lipophilicity; Bioavailability Score, the
   ability of a drug or other substance to be absorbed and used by the
   body.

3.2 Overlapping genes between SEA and STP linked to 20 compounds

   Based on the SMILES, a total of 308 genes from SEA and 324 genes from
   STP linked to 20 compounds were extracted ([76]S1 Table). The result of
   Venn diagram exhibited that 118 genes were overlapped between the two
   public databases ([77]Fig 2).

Fig 2. Overlapping genes (118genes) between SEA (308 genes) and STP (324
genes).

   [78]Fig 2
   [79]Open in a new tab

3.3 Overlapping genes between T2DM-related genes and the 118 overlapping
genes

   A total of 4,736 genes related to T2DM were sorted by retrieving
   DisGeNET and OMIM databases ([80]S2 Table). The result of Venn diagram
   unveiled that 81 overlapping genes was identified between 4,763 genes
   related to T2DM and the 118 overlapping genes ([81]Fig 3), ([82]S3
   Table). As shown in [83]S4 Table, a total of 81 overlapping genes
   linked to 16 compounds from aforementioned 20 compounds were
   identified, retrieving from both SEA and STP public databases, and no
   genes were found associated with other 4 compounds (Glycerin;
   Glyceraldehyde; 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-2-one; and
   2-propanol, 1-chloro-3-(1-methylethoxy)) in the two databases.

Fig 3. Overlapping genes between 118 overlapping genes (A) and T2DM related
genes (4,736 genes).

   [84]Fig 3
   [85]Open in a new tab

3.4 Gene-gene network analysis of 16 compounds of SB against T2DM

   The final overlapping 81 genes were closely linked to each other with
   81 nodes and 347 edges ([86]Fig 4). The interaction between 16
   compounds and 81 genes resulted with 97 nodes and 347 edges ([87]Fig
   5), which indicated that the therapeutic efficacy of SB on T2DM. The 16
   compounds were classified as five steroid derivatives
   (25-Oxo-27-norcholesterol, Cholesterol, Campesterol,
   (E)-23-ethylcholesta-5,22-dien-3β-ol, and β-sitosterol), four fatty
   acyls (Palmitic acid, Methyl lineoleate, Linoleic acid, and
   Propylengeglycol monoleate), two fatty acid esters (Butanoic acid-ethyl
   ester and Hexadecanoic acid-ethyl ester), one organooxygen (Propanal,
   2,3-dihydroxy-), one prenol lipid (5,8-Dimethyltocol), one pyrimidine
   nucleoside (β-D-ribofuranosyl-cytosine), one azole (Oxazole,
   5-hexyl-2,4-dimethyl), and one coumarin derivative
   (4,5-Dihydroxycoumarin).

Fig 4. Gene-gene interaction of final overlapping 81 genes (81 nodes and
301edges) in SB against T2DM.

   [88]Fig 4
   [89]Open in a new tab

Fig 5. The interaction between 16 compounds and 81 genes (94 nodes and 356
edges) on T2DM.

   [90]Fig 5
   [91]Open in a new tab

3.5 Signaling pathways and finding of a hub signaling of SB against T2DM

   The result of KEGG pathway enrichment analysis demonstrated that 81
   genes were related to 12 signaling pathways (False Discovery Rate <
   0.05). The 12 signaling pathways were directly related to the
   progression of T2DM and indicated that these 12 signaling pathways
   might be the key pathways of SB against T2DM. The description of 12
   signaling pathways is shown in [92]Table 3. Additionally, a bubble
   chart suggested that PPAR signaling pathway might be a hub signaling
   pathway of SB against T2DM ([93]Fig 6).

Table 3. Target genes in 12 signaling pathways enrichment related to T2DM.

   KEGG ID Target genes False Discovery Rate
   hsa04933:AGE-RAGE signaling pathway in diabetic complications
   AKT1,NFKB1,PRKCA,PRKCE 0.0075
   hsa04920:Adipocytokine signaling pathway AKT1,PPARA,NFKB1 0.0208
   hsa04917:Prolactin signaling pathway ESR1,ESR2,AKT1,NFKB1,CYP17A1
   0.00031
   hsa04915:Estrogen signaling pathway AKT1,GABBR1,ESR1,PGR,ESR2 0.0028
   hsa04664:Fc epsilon RI signaling pathway AKT1,ALOX5,PLA2G4A,PRKCA
   0.0021
   hsa04660:T cell receptor signaling pathway AKT1,NFKB1,PTPRC 0.0408
   hsa04370:VEGF signaling pathway AKT1,PRKCA,PLA2G4A 0.0155
   hsa04152:AMPK signaling pathway AKT1,HNF4A,PPARG,HMGCR 0.013
   hsa04071:Sphingolipid signaling pathway
   ADORA1,S1PR1,AKT1,NFKB1,PRKCA,PRKCE 0.00029
   hsa04021:cAMP signaling pathway ADORA1,GABBR1,AKT1,PTGER3,PPARA,NFKB1
   0.0022
   hsa03320:PPAR signaling pathway FABP3,FABP4,NR1H3,PPARA,PPARD,PPARG
   0.0000419
   hsa04066:HIF-1 signaling pathway GAPDH,AKT1,NFKB1,PRKCA 0.0075
   [94]Open in a new tab

Fig 6. Bubble chart of 12 signaling pathways linked to the occurrence and
progression of T2DM.

   [95]Fig 6
   [96]Open in a new tab

3.6 Molecular docking investigation of 6 genes and 4 compounds related to
PPAR signaling pathway

   From the SEA and STP databases, it was revealed that FABP3 gene is
   associated with six compounds (Methyl lineoleate, Linoleic acid,
   Hexadecanoic acid-methyl ester, Palmitic acid, Propyleneglycol
   monoleate, and 25-Oxo-27-norcholesterol), FABP4 gene is related to six
   compounds (Methyl linoleate, Linoleic acid, Hexadecanoic acid-methyl
   ester, Palmitic acid, Propyleneglycol monoleate, and
   25-Oxo-27-norcholesterol), NR1H3 gene is involved with 8 compounds
   (β-sitosterol, Campesterol, 25-Oxo-27-norcholesterol, Cholesterol,
   Methyl lineoleate, 5,8-Dimethyltocol, Linoleic acid, and Stigmasterol),
   PPARA gene is related to 7 compounds (Palmitic acid, Hexadecanoic
   acid-methyl ester, Linoleic acid, Methyl linoleate, Propyleneglycol
   monoleate, β-sitosterol, and 25-Oxo-27-norcholesterol), PPARD gene is
   associated with 8 compounds (Hexadecanoic acid-methyl ester, Linoleic
   acid, Methyl linoleate, Stigmasterol, 25-Oxo-27-norcholesterol,
   Palmitic acid, Cholesterol, and β-sitosterol), PPARG gene is linked to
   7 compounds (Methyl linoleate, Linoleic acid, Palmitic acid,
   Propyleneglycol monoleate, β-sitosterol, Hexadecanoic acid-methyl
   ester, and 25-Oxo-27-norcholesterol) ([97]Fig 7). Also, each six
   proteins have strong molecular interactions on PPAR signaling pathway
   ([98]Fig 8). The nominated compounds to dock with six proteins were
   selected via both SEA and STP.

Fig 7. Interaction of 11 compounds and 6 genes on PPAR signaling pathway.

   [99]Fig 7
   [100]Open in a new tab

Fig 8. Interaction among 6 genes on PPAR signaling pathway.

   [101]Fig 8
   [102]Open in a new tab

   The molecular docking analysis was performed to evaluate the binding
   energy of these six genes against their related each gene,
   respectively, and the docking figures are depicted in [103]Fig 9.
   Molecular docking score of A1-A6 on FABP3 protein (PDB ID: 5HZ9) is
   analyzed in the “Homo Sapiens” mode. Docking simulation of the affinity
   between A1-A6 and FABP3 protein in the “Homo sapiens” setting was
   analyzed. Based on the docking score, the order of the priority of
   binding energy is given: A5>A6>A2>A3>A1>A4. The six-binding energy of
   A1-FABP3, A2-FABP3, A3-FABP3, A4-FABP3, A5-FABP3, and A6-FABP3
   demonstrated -6.6, -7.4, -6.7, -6.5, -8.4, and -7.8 kcal/mol,
   respectively. Interaction analysis of the best-docked compound namely
   “propyleneglycol monoleate” showed several hydrophobic bonds on FABP3
   protein (PDB ID: 5HZ9). The hydrophobic amino acid residues are Thr-57,
   Phe-58, Val-33, Met-36, Gly-27, GLy-25, Phe-28, and Gln-32. The
   detailed information is enlisted in [104]Table 4. The Propyleneglycol
   monoleate (A5) had the strongest affinity on FABP3. Docking simulation
   of the affinity between B1-B6 and FABP4 protein (PDB ID: 3P6D) in the
   “Homo sapiens” setting revealed promising binding affinity, and the
   order of the priority of binding energy is as follows:
   B6>B5>B2>B1>B3>B4. The six-binding energy of B1-FABP4, B2-FABP4,
   B3-FABP4, B4-FABP4, B5-FABP4, and B6-FABP4 revealed -4.6, -4.9, -4.5,
   -4.4, -5.6, and -7.2 kcal/mol, respectively. The
   25-Oxo-27-norcholesterol (B6) had the strongest affinity on FABP4.
   Interaction analysis of best-docked compound namely
   “25-Oxo-27-norcholesterol” resulted one hydrogen bond (Ser-1) and seven
   hydrophobic bonds (Gly-88, Leu-86, Met-0, Val-44, Cys-1, Gly-46, and
   ASP-47). The detailed information is enlisted in [105]Table 5. Docking
   simulation of the affinity between C1-C8 and NR1H3 protein in the “Homo
   sapiens” setting displayed promising binding affinity, and the order of
   the priority of binding energy is as follows: C2>C8>C3>C4>C1>C7>C6>C4.
   The eight-binding energy of C1-NR1H3, C2-NR1H3, C3-NR1H3, C4-NR1H3,
   C5-NR1H3, C6-NR1H3, C7-NR1H3, and C8-NR1H3 exhibited -7.3, -10.6, -7.9,
   -7.6, -4.9, -5.7, -5.9, and -8.3 kcal/mol, respectively. The
   campesterol (C2) has the strongest affinity on NR1H3. Interaction
   analysis of best- docked compound namely “campesterol” showed several
   hydrophobic bonds on NR1H3 protein (PDB ID: 2ACL). The hydrophobic
   amino acid residues are Gly-328, Arg-248, Leu-329, Gln-330, Val-331,
   Ile-299, Arg-302, Val-298, Asp-295, Leu-294, and Gln-429. The detailed
   information is enlisted in [106]Table 6. Docking simulation of the
   affinity between D1-D8 and PPARA protein in the “Homo sapiens” setting
   revealed promising binding affinity, and the order of the priority of
   binding energy is as follows: D6>D7>D3>D5>D2>D1>D4. The eight-binding
   energy of D1-PPARA, D2-PPARA, D3-PPARA, D4-PPARA, D5-PPARA, D6-PPARA,
   and D7-PPARA exhibited -4.9, -5.2, -6.0, -4.8, -5.8, -6.6, and -6.1
   kcal/mol, respectively. The β-sitosterol (D6) has the strongest
   affinity on NR1H3. Interaction analysis of best- docked compound namely
   “β-sitosterol” displayed several hydrophobic bonds on PPARA protein
   (PDB ID: 3SP6). The hydrophobic amino acid residues are Glu-462,
   Ser-588, Leu-392, Asn-303, Val-306, Thr-307, Lys-310, Tyr-311, Gly-390,
   Pro-389, Arg-465, and Asp-466. The detailed information is enlisted in
   [107]Table 7. Docking simulation of the affinity between E1-E8 and
   PPARD protein in the “Homo sapiens” setting displayed promising binding
   affinity, and the order of the priority of binding energy is as
   follows: The eight-binding energy of E1-PPARD, E2-PPARD, E3-PPARD,
   E4-PPARD, E5-PPARD, E6-PPARD, E7-PPARD, and E8-PPARD exhibited -3.8,
   -5.2, -4.2, -7.3, -7.3, -4.6, -7.3, and -7.4 kcal/mol, respectively.
   The β-sitosterol (E8) has the strongest affinity on PPARD. Interaction
   analysis of best-docked compound namely “β-sitosterol” revealed one
   hydrogen bond (Met-440) and seven hydrophobic bonds (Ala-414, Thr-411,
   Tyr-441, Asp-360, Pro-362, Tyr-284, and Val-410). The detailed
   information is enlisted in [108]Table 8. Docking simulation of the
   affinity between E1-E8 and PPARD protein in the “Homo sapiens” setting
   displayed promising binding affinity, and the order of the priority of
   binding energy is as follows: The seven-binding energy of F1-PPARG,
   F2-PPARG, F3-PPARG, F4-PPARG, F5-PPARG, F6-PPARG, and F7-PPARG
   exhibited -5.2, -5.4, -5.2, -5.9, -7.9, -4.0, and -7.8 kcal/mol,
   respectively. Interaction analysis of best- docked compound namely
   “β-sitosterol” exposed several hydrophobic bonds on PPARG protein (PDB
   ID: 3E00). The hydrophobic amino acid residues are Tyr-169, Tyr-189,
   Leu-167, Thr-168, Lys-336, Arg-350, Glu-351, Lys-354, Gln-193, and
   Tyr-192. The β-sitosterol (F5) has the strongest affinity on PPARG. The
   detailed information is enlisted in [109]Table 9. This result suggested
   that each compound of the highest affinity score on each gene might be
   significant ligand to control glucose homeostasis ([110]Fig 10).

Fig 9. Molecular docking interaction between best docked compounds from SB
and target proteins.

   [111]Fig 9
   [112]Open in a new tab

   (A) Propyleneglycol monoleate on 5HZ9 (B) 25-Oxo-27-norcholesterol on
   3P6D (C) Campesterol on NR1H3 (D) β-sitosterol on 3SP6 (E) β-sitosterol
   on 5U3Q (F) β-sitosterol on 3E00.

Table 4. Binding energy and interactions of potential active compounds on
FABP3 (PDB ID: 5HZ9).

   Hydrogen Bond Interactions Hydrophobic Interactions
   Protein Ligand PubChem ID Symbol Binding energy(kcal/mol) Amino Acid
   Residue Amino Acid Residue
   5HZ9 Methyl lineoleate 5284421 A1 -6.6 n/a Phe-58,Lys-22,
   Gly-25,Phe-28,
   Gln-32, Ala-29
   Linoleic acid 5280450 A2 -7.4 Lys-22 Ala-29,Gln-32,
   Phe-28, Gly-25,
   Gly-27
   Hexadecanoic acid, methyl ester 8181 A3 -6.7 n/a Phe-28, Gln-32,
   Phe-58, Ala-29,
   Lys-22
   Palmitic acid 985 A4 -6.5 n/a Val-33, Gln-32,
   Ala-29, Phe-58,
   Lys-22, Thr-57
   Propyleneglycol monoleate 5365625 A5 -8.4 n/a Thr-57, Phe-58,
   Val-33, Met-36,
   Gly-27, Gly-25,
   Phe-28, Gln-32
   25-Oxo-27-norchoresterol 165617 A6 -7.8 Ser-35, Met-36 Asp-18, Phe-28,
   Met-36 Ala-29, Val-33,
   Asp-99, Gln-32
   Lys-10
   [113]Open in a new tab

Table 5. Binding energy and interactions of potential active compounds on
FABP4 (PDB ID: 3P6D).

   Hydrogen Bond Interactions Hydrophobic Interactions
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Amino Acid
   Residue Amino Acid Residue
   3P6D Methyl linoleate 5284421 B1 -4.6 n/a Ser-1,Cys-1
   Leu-86, Asp-47
   Leu-66, Ile-49
   Linoleic acid 5280450 B2 -4.9 Leu-86 Thr-85, Leu-66
   Asp-47, Cys-1
   Hexadecanoic acid, methyl ester 8181 B3 -4.4 n/a Gly-88, Leu-86
   Ser-1, Gly-46
   Asp-47, Leu-66
   Met-0
   Palmitic acid 985 B4 -4.4 Glu-72,Val-80 Lys-79, Asp-71
   Val-73,Glu-61
   Thr-60
   Propyleneglycol monoleate 5365625 B5 -5.6 Gly-88, Leu-86 Asp-87, Met-0
   Ser-1, Cys-1
   Asp-47, Leu-66
   Ile-65
   25-Oxo-27-norchoresterol 165617 B6 -7.2 Ser-1 Gly-88, Leu-86
   Met-0, Val-44
   Cys-1, Gly-46
   Asp-47
   [114]Open in a new tab

Table 6. Binding energy and interactions of potential active compounds on
NR1H3 (PDB ID: 2ACL).

   Hydrogen Bond Interactions Hydrophobic Interactions
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Amino Acid
   Residue Amino Acid Residue
   2ACL β-sitosterol 222284 C1 -7.3 n/a His-395,Pro-396
   His-397, Gln-243
   Pro-242, Asp-398
   Ser-244, Glu-346
   Asp-241, Gln-348
   Asn-394
   Campesterol 173183 C2 -10.6 n/a Gly-328, Arg-248
   Leu-329, Gln-330
   Val-331, Ile-299
   Arg-302, Val-298
   Asp-295, Leu-294
   Gln-429
   25-Oxo-27-norchoresterol 165617 C3 -7.9 Glu-339, Arg-404 Asp-379,
   Glu-390
   Ala-343, Asp-241
   Pro-242, Leu-347
   Pro-240, Glu-346
   Pro-386
   Cholesterol 5997 C4 -7.6 Asn-385 Trp-236, Ala-391
   Pro-237, Ile-238
   Glu-394, Pro-242
   Asp-241, Pro-240
   Glu-322, Lys-395
   Lys-326, Glu-388
   Methyl linoleate 5284421 C5 -4.9 Arg-342 Ile-238, Pro-378
   Ala-387, Pro-386
   Glu-390, Ser-411
   Phe-340, Asp-379
   Glu-339
   5,8-Dimethyltocol 222284 C6 -5.7 n/a Leu-347, Pro-242
   Ser-244, Asp-241
   Glu-291, Gln-348
   Gln-243, Glu-346
   His-397, Asp-398
   Met-401
   Linoleic acid 5280450 C7 -5.9 Gly-328, Gln-422 Leu-329, Gln-330
   Gln-429, Asp-295
   Val-331, Lys-381
   Ile-299, Ala-303
   Arg-302, Glu-332
   Gly-382, Ala-425
   Val-298
   [115]Open in a new tab

Table 7. Binding energy and interactions of potential active compounds on
PPARA (PDB ID: 3SP6).

   Hydrogen Bond Interactions Hydrophobic Interactions
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Amino Acid
   Residue Amino Acid Residue
   3SP6 Palmitic acid 985 D1 -4.9 n/a Glu-251, Val-332
   Ile-241, Ala-333
   Thr-279, Val-255
   Tyr-334, Leu-258
   Cys-275
   Hexadecanoic acid, methyl ester 8181 D2 -5.2 n/a Ile-317, Ser-323
   Phe-218, Met-220
   Asn-221, Val-324
   Asn-219, Tyr-334
   Ala-333, Thr-279
   Leu-331, Leu-321
   Met-320, Thr-283
   Linoleic acid 5280450 D3 -6 Ser-323 Asn-221, Met-320
   Tyr-214 Val-324, Met-220
   Asn-219, Tyr-334
   Thr-279, Leu-331
   Leu-321, Thr-283
   Ile-317
   Methyl linoleate 5284421 D4 -4.8 n/a Glu-286, Asn-219
   Gly-335, Tyr-334
   Thr-279, Val-324
   Leu-331, Leu-321
   Ile-317, Met-320
   Met-220, Phe-218
   Thr-283
   Propyleneglycol monoleate 5365625 D5 -5.8 n/a Glu-251, Ala-250
   Val-255, Ala-333
   Met-220, Val-324
   Met-320, Tyr-334
   Thr-279, Cys-275
   Leu-254
   β-sitosterol 222284 D6 -6.6 n/a Glu-462, Ser-688
   Leu-392, Asn-303
   Val-306, Thr-307
   Lys-310, Tyr-311
   Gly-390, Pro-389
   Arg-465, Asp-466
   25-Oxo-27-norchoresterol 165617 D7 -6.1 Lys-345 Asp-360, Glu-356
   Pro-357, Glu-439
   His-440, Leu-443
   Asp-353
   [116]Open in a new tab

Table 8. Binding energy and interactions of potential active compounds on
PPARD (PDB ID: 5U3Q).

   Hydrogen Bond Interactions Hydrophobic Interactions
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Amino Acid
   Residue Amino Acid Residue
   5U3Q Hexadecanoic acid, methyl ester 8181 E1 -3.8 n/a Pro-362, Tyr-284
   Arg-407, Glu-288
   Arg-361,Met-440
   Val-410, Thr-411
   Linoleic acid 5280450 E2 -5.2 n/a Val-410, Arg-407
   Met-440, Asp-439
   Thr-411, Tyr-441
   Tyr-284, Asp-360
   Pro-362, Arg-361
   Glu-288
   Methyl linoleate 5284421 E3 -4.2 n/a Arg-407, Glu288
   Tyr-284, Pro-362
   Met-440, Thr-411
   Val-410
   Stigmasterol 5280794 E4 -7.3 Met-440 Ala-414, Thr-411
   Tyr-441, Asp-360
   Pro-362, Tyr-284
   Val-410
   25-Oxo-27-norchoresterol 165617 E5 -7.3 Met-440 Ala-414, Thr-411
   Tyr-441, Asp-360
   Pro-362, Tyr-284
   Val-410
   Palmitic acid 985 E6 -4.6 n/a Tyr-441, Pro-362
   Arg-361, Val-410
   Tyr-284, Glu-288
   Met-440, Thr-411
   Ala-414, Arg-407
   Cholesterol 5997 E7 -7.3 Met-440 Val-410, Ala-414
   Tyr-441, Tyr-284
   Asp-360, Arg-361
   Pro-362, Thr-411
   Val-410
   β-sitosterol 222284 E8 -7.4 Met-440 Ala-414, Thr-411
   Tyr-441, Asp-360
   Pro-362, Tyr-284
   Val-410
   [117]Open in a new tab

Table 9. Binding energy and interactions of potential active compounds on
PPARG (PDB ID: 3E00).

   Hydrogen Bond Interactions Hydrophobic Interactions
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Amino Acid
   Residue Amino Acid Residue
   3E00 Methyl linoleate 5284421 F1 -5.2 Tyr-169, Gln-193 Leu-167, Asp-337
   Lys-336, Val-372
   Lys-373, Glu-369
   Tyr-189, Thr-168
   Tyr-192, Arg-350
   Glu-351
   Linoleic acid 5280450 F2 -5.4 Thr-162, Leu-167 Arg-202, Asp-166
   Lys-336, Glu-369
   Glu-369, Val-372
   Arg-350, Glu-351
   Gln-193, Lys-354
   Tyr-192
   Palmitic acid 985 F3 -5.2 Ser-342, Glu-343 Leu-333, Arg-288
   Glu-291, Glu-295
   Met-329, Ala-292
   Pro-227, Phe-226
   Ile-341, Leu-228
   Propyleneglycol monoleate 5365625 F4 -5.9 Ser-332, Tyr-222 Lys-230,
   Phe-295
   Glu-295, Ile-296
   Ala-292, Arg-288
   Leu-333, Leu-228
   Met-329, Thr-229
   β-sitosterol 222284 F5 -7.9 n/a Tyr-169, Tyr-189
   Leu-167, Thr-168
   Lys-336, Arg-350
   Glu-351, Lys-354
   Gln-193, Tyr-192
   Hexadecanoic acid, methyl ester 8181 F6 -4.0 Glu-343, Ser-342 Leu-340,
   Leu-228
   Ile-341, Met-329
   Phe-226, Ala-292
   Glu-295, Pro-227
   Arg-288, Leu-333
   25-Oxo-27-norchoresterol 165617 F7 -7.8 Asn-375 Asn-335, Lys-336
   Asp-166, Arg-164
   Glu-208, Glu-207
   Val-63, Arg-202
   Glu-203, Val-372
   [118]Open in a new tab

Fig 10. Regulation of glucose homeostasis by potential bioactive compounds of
SB.

   [119]Fig 10
   [120]Open in a new tab

4. Discussion

   Compounds-genes networking system unveiled that therapeutic effect of
   SB against T2DM was related to 16 compounds out of 20 compounds
   detected by GC-MS, including five steroid derivatives, four fatty
   acyls, two fatty acid esters, one organooxygen, one prenol lipid, one
   pyrimidine nucleoside, one azole, and one coumarin. The proportion of
   steroid derivatives to 16 compounds was close to 30%, implying that
   steroid derivatives was the most essential than any other sorts of
   compounds for the amelioration effect of SB on T2DM.

   It was reported that some steroid derivatives have strong effect on
   hypoglycemic activity based on lipophilic properties [[121]32].
   Noticeably, a report showed that β-sitosterol (steroid derivatives)
   controls the glycemic level through regulation of IR (insulin receptor)
   and GLUT4 (glucose transporter 4) proteins in adipocytes of high fat
   and sucrose treated type 2 diabetic rats, interestingly, the in vivo
   result was in line with in silico analysis [[122]33]. In addition,
   researchers found that campesterol (steroid derivatives) decreases the
   LDL (Low Density Lipoprotein) level, associated with the occurrence and
   development of T2DM [[123]34, [124]35]. A patent revealed that
   propyleneglycol monoleate is an agent for treatment or amelioration of
   diabetes, obesity or arteriosclerosis [[125]36]. Another report
   suggested that 25-Oxo-27-norcholesterol interrupts cholesterol
   oxidation, which is related to insulin resistance [[126]37, [127]38].
   These results coincide with our findings of SB on T2DM, suggesting that
   the quantity of the four compounds in SB is greatly enough to exhibit
   anti-diabetic efficacy.

   Compounds-genes networking also specified that the pharmacological
   effect of SB on T2DM was directly associated with 81 genes. The results
   of KEGG pathway enrichment analysis of 81 genes showed that 12
   signaling pathways were directly linked to the occurrence and
   progression of T2DM, demonstrating that these signaling pathways might
   be the key pathways of SB against T2DM. The 12 signaling pathways with
   T2DM were succinctly discussed as follows.

   PPAR signaling pathway: PPAR ligands are the potential therapeutic
   candidates against T2DM, also, alleviate metabolic syndrome including
   obesity and insulin resistance [[128]39]. Furthermore, dual agonists
   approach with both PPARA agonists (such as fibrates) and PPARG agonists
   (such as thiazolidinediones) can have better metabolic efficacy and
   less side effects than its single administration [[129]40]. cAMP
   signaling pathway: cAMP signaling pathway modulates glucose homeostasis
   with insulin and glucagon secretion, glucose uptake, gluconeogenesis,
   glycogen synthesis and breakdown of glucose [[130]41]. HIF-1 signaling
   pathway: Inhibition of HIF-1 signaling caused by diabetes is associated
   with hypoxia and high degradation of HIF-1α protein [[131]42].
   Sphingolipid signaling pathway: Sphingolipid is a significant class of
   signaling lipids, have been recognized as vital players in the
   progression and pathogenesis of insulin resistance and T2DM [[132]43].
   AMPK signaling pathway: The activation of AMPK enhances homeostasis of
   glycemic level, lipid concentration, and blood pressure in
   insulin-resistant rodents, which is considered as an important
   therapeutic target against T2DM [[133]44]. VEGF signaling pathway: VEGF
   is related deeply to the development of T2DM. High content of VEGF is
   generally detected in plasma of T2DM patients [[134]45]. T cell
   receptor activation: Overactivated T cells with T2DM patients might be
   an indication of losing the natural regulatory mechanism, thus,
   intervention of T cells by T2DM might down T cell receptor
   sensitization [[135]46]. Fc epsilon RI signaling pathway: Fc epsilon
   RI- mediated signaling in mouse bone marrow is potentiated by insulin
   [[136]47]. Estrogen signaling pathway: In a research of postmenopausal
   mice and human cells, the report demonstrated that estrogen is related
   to lower risk of T2DM by targeting particular cells in the pancreas and
   gut to improve tolerance to glucose [[137]48]. Prolactin signaling
   pathway: A normal range of prolactin concentration is linked to a lower
   T2DM risk, which may play an inhibitory effect on the development of
   T2DM [[138]49]. Adipocytokine signaling pathway: Adipocutokines leptin
   and adiponectin might be significant biomarkers for first prediction on
   T2DM, which is further associated with diabetic microvascular
   complications [[139]50]. AGE-RAGE signaling pathway in diabetic
   complications: The activation of receptor for AGE (RAGE) is a
   noticeable pathological consequence on T2DM, and thus the design of
   antagonist for the AGE(RAGE) receptor might be a therapeutic strategy
   against T2DM [[140]51].

   Collectively, this study suggests that 12 signaling pathways with 81
   genes are linked to the development of T2DM. In addition, rich factor
   defines that the proportion of the DEGs number and the number of genes
   have been annotated in pathway [[141]52]. In other words, the higher of
   the rich factor is, the higher the degree of enrichment. The rich
   factor of PPAR signaling pathway was the highest degree among 12
   signaling pathways. Reports indicated that PPAR agonists are insulin
   sensitizers and improve insulin resistance with T2DM patients
   [[142]53]. To sum things up, a hub mechanism of SB against T2DM might
   be to maintain glucose homeostasis by activating PPAR signaling
   pathway.

5. Conclusion

   Sorghum bicolor (SB) is rich in beneficial phytoconstituents and seen
   as a possible source of natural antidiabetic agents. However, in this
   report, its comprehensive bioactive compounds and T2DM pathways were
   firstly investigated using the network pharmacology. The findings of
   this study indicate that the antidiabetic ability of the SB could be
   attributed to four main compounds (β-sitosterol, campesterol,
   propyleneglycol monoleate, and 25-Oxo-27-norcholesterol) that were
   strongly related to PPAR signaling pathway. Therefore, our study
   suggests that the four key compounds of SB might ameliorate T2DM by
   activating the PPAR signaling pathway.

Supporting information

   S1 Table

   (PDF)
   [143]Click here for additional data file.^ (97.1KB, pdf)
   S2 Table

   (PDF)
   [144]Click here for additional data file.^ (695.2KB, pdf)
   S3 Table

   (PDF)
   [145]Click here for additional data file.^ (19.8KB, pdf)
   S4 Table

   (PDF)
   [146]Click here for additional data file.^ (36.2KB, pdf)
   S1 File

   (DOCX)
   [147]Click here for additional data file.^ (14.1KB, docx)

Acknowledgments