3.1 Identification for the active components of Cuscuta-Salvia
The total ion flow diagram of Cuscuta-Salvia was obtain from UHPLC-ESI-Q-TOF-MS analysis, and the compounds were identified qualitatively by using SCIEXOS software 1.4. As shown in Fig 2 and Table 2, 13 compounds were identified under positive ion mode and 16 compounds were identified under negative ion mode.
Table 2. High resolution mass spectrometry data and elemental composition of Cuscuta-Salvia. (No. 1-16 was under negative ion mode and 17-29 was under positive ion mode)
No.
|
Molecular Name
|
Molecular Formula
|
Retention Time (min)
|
m/z
|
Measure Molecular Mass (Da)
|
Molecular Mass (Da)
|
1
|
Methyl tanshinate
|
C20H18O5
|
1.29
|
383.1137
|
338.1155
|
338.11542
|
2
|
Caffeate
|
C9H8O4
|
2.48
|
179.0354
|
180.0427
|
180.04226
|
3
|
Neocuscuscutosside A
|
C37H46O21
|
4.75
|
871.2478
|
826.2496
|
826.25316
|
4
|
Cuscutoside A
|
C31H36O16
|
5.89
|
663.1916
|
664.1989
|
664.20034
|
5
|
Salvianolic acid A
|
C26H22O10
|
6.2
|
539.12
|
494.1218
|
494.1213
|
6
|
Salvianolic acid D
|
C20H18O10
|
6.54
|
463.0889
|
418.0907
|
418.09
|
7
|
Tanshiquinone II
|
C19H20O4
|
6.96
|
357.134
|
312.1358
|
312.13616
|
8
|
Salvianolic acid C
|
C26H20O10
|
6.96
|
537.1043
|
492.1061
|
492.10565
|
9
|
Danshensu
|
C9H10O5
|
7.24
|
197.046
|
198.0533
|
198.05282
|
10
|
Isoimperatorin
|
C16H14O4
|
7.51
|
269.085
|
270.0923
|
270.08921
|
11
|
Salvianolic acid F
|
C17H14O6
|
7.51
|
313.0742
|
314.0815
|
314.07904
|
12
|
Tanshinone IIA
|
C19H18O3
|
7.87
|
339.1273
|
294.1291
|
294.12559
|
13
|
Isotanshinone I
|
C18H12O3
|
8.13
|
311.0488
|
276.0794
|
276.07864
|
14
|
Salvia miltiorrhiza
|
C21H20O4
|
10.2
|
371.108
|
336.1386
|
336.13616
|
15
|
Danshinspiroketallactone
|
C18H22O3
|
11.21
|
331.1562
|
286.158
|
286.15689
|
16
|
Methylenedihydrotanshin-quinone
|
C18H16O3
|
11.22
|
325.1086
|
280.1104
|
280.10994
|
17
|
Protocatechualdehyde
|
C7H6O3
|
2.5
|
139.0393
|
138.0321
|
138.03169
|
18
|
Hydroxytanshinone IIA
|
C19H18O4
|
5.89
|
333.1086
|
310.1194
|
310.12051
|
19
|
Quercetin-3-O-β-D-gluco-pyranoside
|
C21H20O12
|
6.23
|
465.1032
|
464.0959
|
464.09548
|
20
|
Quercetin
|
C15H10O7
|
6.54
|
303.0502
|
302.0429
|
302.04265
|
21
|
Kaempferol-3-o-beta-d-gl-ucoside
|
C21H20O11
|
7.09
|
449.1084
|
448.1011
|
448.10056
|
22
|
Kaempferol
|
C15H10O6
|
7.09
|
287.0555
|
286.0482
|
286.04774
|
23
|
Lithospermic acid B
|
C36H30O16
|
7.24
|
736.1883
|
718.1545
|
718.15338
|
24
|
Salvianolic acid G
|
C18H12O7
|
7.52
|
341.0662
|
340.0589
|
340.0583
|
25
|
Lithospermic acid
|
C27H22O12
|
7.83
|
539.1203
|
538.113
|
538.11113
|
26
|
15, 16-dihydrotanshinone I
|
C18H14O3
|
12.67
|
279.1009
|
278.0936
|
278.09429
|
27
|
Neotanshinone A
|
C18H16O4
|
12.67
|
297.1123
|
296.105
|
296.10486
|
28
|
Tanshinone IIB
|
C19H18O4
|
14.11
|
297.1488
|
296.1415
|
296.14124
|
29
|
Cryptotanshinone
|
C19H20O3
|
15.13
|
297.1491
|
296.1418
|
296.14124
|
3.2 Screening of the active components of Cuscuta-Salvia
A total of 231 chemical compounds between Cuscuta and Salvia were collected from TCMSP database, in which included 29 compounds in Cuscuta and 202 compounds in Salvia. Combined with the parameters of OB≥30% and DL≥0.18 in TCMSP, the 69 potential active components were screened out via removing the duplicate values, in which included 10 compounds related to Cuscuta and 59 compounds related to Salvia. In addition, the filtered results in TCMSP and the results of UHPLC-ESI-Q-TOF-MS were intersected. Thus, 14 active components were acquired. And the 14 active components were listed in Table 3.
Table 3. Cuscuta and Salvia active components list.
Herb
|
MOL ID
|
Molecule Name
|
Molecular Formula
|
Cuscuta
|
MOL000422
|
kaempferol
|
C15H10O6
|
Cuscuta
|
MOL000098
|
quercetin
|
C15H10O7
|
Salvia
|
MOL001942
|
isoimperatorin
|
C16H14O4
|
Salvia
|
MOL007101
|
dihydrotanshinone I
|
C18H14O3
|
Salvia
|
MOL007154
|
tanshinone IIa
|
C19H18O3
|
Salvia
|
MOL007045
|
3α-hydroxytanshinone IIa
|
C19H18O4
|
Salvia
|
MOL007155
|
(6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dione
|
C19H18O4
|
Salvia
|
MOL007088
|
cryptotanshinone
|
C19H20O3
|
Salvia
|
MOL007108
|
isocryptotanshi-none
|
C19H20O3
|
Salvia
|
MOL007120
|
miltionone II
|
C19H20O4
|
Salvia
|
MOL007093
|
dan-shexinkum d
|
C20H18O4
|
Salvia
|
MOL007141
|
salvianolic acid g
|
C26H22O10
|
Salvia
|
MOL007130
|
prolithospermic acid
|
C27H22O12
|
Salvia
|
MOL007111
|
Isotanshinone II
|
C18H12O3
|
3.3 Screening of the candidate genes of the active compounds in Cuscuta-Salvia
A total of 404 candidate targets from the 14 active compounds were collected in the TCMSP database. By means of eliminating the overlapping targets, 195 related targets were obtained (Table 4).
Table 4.195 related targets list.
No.
|
Gene Name
|
Protein Name
|
1
|
NOS2
|
Nitric oxide synthase 2
|
2
|
PTGS1
|
Cyclooxygenase-1
|
3
|
AR
|
Androgen receptor
|
4
|
PPARG
|
Peroxisome proliferator-activated receptor
|
5
|
PTGS2
|
Prostaglandin G/H synthase 2
|
6
|
HSP90AB1
|
Heat shock protein HSP 90-beta
|
7
|
PIK3CG
|
PI3-kinase subunit gamma
|
8
|
PRKACA
|
cAMP-dependent protein kinase catalytic subunit alpha
|
9
|
NCOA2
|
Nuclear receptor coactivator 2
|
10
|
DPP4
|
Dipeptidyl peptidase 4
|
11
|
PRSS1
|
Protease serine 1
|
12
|
PGR
|
Progesterone receptor
|
13
|
F2
|
Coagulation factor II
|
14
|
CHRM1
|
Muscarinic acetylcholine receptor M1
|
15
|
NOS3
|
Nitric oxide synthase 3
|
16
|
GABRA2
|
Gamma-aminobutyric acid receptor subunit alpha-2
|
17
|
ACHE
|
Acetylcholinesterase
|
18
|
SLC6A2
|
Sodium-dependent noradrenaline transporter
|
19
|
CHRM2
|
Muscarinic acetylcholine receptor M2
|
20
|
ADRA1B
|
Alpha-1B adrenergic receptor
|
21
|
GABRA1
|
Gamma-aminobutyric acid receptor subunit alpha-1
|
22
|
TOP2A
|
DNA topoisomerase 2-alpha
|
23
|
F7
|
Coagulation factor VII
|
24
|
RELA
|
Transcription factor p65
|
25
|
IKBKB
|
Inhibitor of nuclear factor kappa-B kinase subunit beta
|
26
|
AKT1
|
Threonine-protein kinase
|
27
|
BCL2
|
Apoptosis regulator BCL-2
|
28
|
BAX
|
Apoptosis regulator BAX
|
29
|
TNF
|
Tumor necrosis factor
|
30
|
JUN
|
Transcription factor AP-1
|
31
|
AHSA1
|
Activator of 90 kDa heat shock protein ATPase homolog 1
|
32
|
CASP3
|
Caspase-3
|
33
|
MAPK8
|
Mitogen-activated protein kinase 8
|
34
|
XDH
|
Xanthine dehydrogenase
|
35
|
MMP1
|
Matrix metalloproteinase-1
|
36
|
STAT1
|
Signal transducer and activator of transcription 1
|
37
|
CDK1
|
Cyclin-dependent kinase 1
|
38
|
HMOX1
|
Heme oxygenase 1
|
39
|
CYP3A4
|
Cytochrome P450 3A4
|
40
|
CYP1A2
|
Cytochrome P450 1A2
|
41
|
CYP1A1
|
Cytochrome P450 1A1
|
42
|
ICAM1
|
Intercellular adhesion molecule 1
|
43
|
SELE
|
E-selectin
|
44
|
VCAM1
|
Vascular cell adhesion protein 1
|
45
|
NR1I2
|
Nuclear receptor subfamily 1 group I member 2
|
46
|
CYP1B1
|
Cytochrome P450 1B1
|
47
|
ALOX5
|
Polyunsaturated fatty acid 5-lipoxygenase
|
48
|
HAS2
|
Hyaluronan synthase 2
|
49
|
GSTP1
|
Glutathione S-transferase 2
|
50
|
AHR
|
Aryl hydrocarbon receptor
|
51
|
PSMD3
|
26S proteasome non-ATPase regulatory subunit 3
|
52
|
SLC2A4
|
Solute carrier family 2 member 4
|
53
|
NR1I3
|
Nuclear receptor subfamily 1 group I member 3
|
54
|
INSR
|
Insulin receptor
|
55
|
DIO1
|
Death-inducer obliterator 1
|
56
|
PPP3CA
|
Calmodulin-dependent calcineurin A subunit alpha isoform
|
57
|
GSTM1
|
Glutathione S-transferase Mu 1
|
58
|
GSTM2
|
Glutathione S-transferase Mu 2
|
59
|
AKP1C3
|
Aldo-keto reductase family I member C3
|
60
|
SLPI
|
Antileukoproteinase
|
61
|
AKR1B1
|
Aldo-keto reductase family 1 member B1
|
62
|
KCMH2
|
Potassium voltage gated channel subfamily H member 2
|
63
|
SCN5A
|
Sodium channel protein type 5 subunit alpha
|
64
|
F10
|
Coagulation factor X
|
65
|
ADRB2
|
Beta-2 adrenergic receptor
|
66
|
MMP3
|
Matrix metalloproteinase-3
|
67
|
RXRA
|
Retinoic acid receptor RXR-alpha
|
68
|
MAOB
|
Amine oxidase
|
69
|
EGFR
|
Epidermal growth factor receptor
|
70
|
VEGFA
|
Vascular endothelial growth factor A
|
71
|
CCND1
|
G1/S-specific cyclin-D1
|
72
|
BCL2L1
|
Bcl-2-like protein 1
|
73
|
FOS
|
Proto-oncogene c-Fos
|
74
|
CDKN1A
|
Cyclin-dependent kinase inhibitor 1
|
75
|
EIF6
|
Eukaryotic translation initiation factor 6
|
76
|
CASP9
|
Caspase-9
|
77
|
PLAU
|
Urokinase-type plasminogen activator
|
78
|
MMP2
|
Matrix metalloproteinase-2
|
79
|
MMP9
|
Matrix metalloproteinase-9
|
80
|
MAPK1
|
Mitogen-activated protein kinase 1
|
81
|
IL10
|
Interleukin-10 receptor subunit alpha
|
82
|
EGF
|
Pro-epidermal growth factor
|
83
|
RB1
|
Retinoblastoma-associated protein
|
84
|
IL6
|
Interleukin-6 receptor subunit alpha
|
85
|
CDKN2A
|
Cyclin-dependent kinase inhibitor 2A
|
86
|
TP53
|
Cellular tumor antigen p53
|
87
|
ELK1
|
ETS domain-containing protein Elk-1
|
88
|
NFKBIA
|
NF-kappa-B inhibitor alpha
|
89
|
POR
|
NADPH-cytochrome P450 reductase
|
90
|
ODC1
|
Ornithine decarboxylase
|
91
|
CASP8
|
Caspase-8
|
92
|
TOP1
|
DNA topoisomerase 1
|
93
|
RAF1
|
RAF proto-oncogene serine/threonine-protein kinase
|
94
|
SOD1
|
Superoxide dismutase [Cu-Zn]
|
95
|
PRKCA
|
Protein kinase C alpha type
|
96
|
HIF1A
|
Hypoxia-inducible factor 1-alpha
|
97
|
RUNX1T1
|
Protein CBFA2T1
|
98
|
CDK1
|
Cyclin-dependent kinase inhibitor 1 B
|
99
|
HSPA5
|
Endoplasmic reticulum chaperone BiP
|
100
|
ERBB2
|
Receptor tyrosine-protein kinase erbB-2
|
101
|
ACACA
|
Acetyl-CoA carboxylase 1
|
102
|
CAV1
|
Caveolin-1
|
103
|
MYC
|
Myc proto-oncogene protein
|
104
|
F3
|
Tissue factor
|
105
|
GJA1
|
Gap junction alpha-1 protein
|
106
|
IL1B
|
Interleukin-1 beta
|
107
|
CCL2
|
C-C motif chemokine 2
|
108
|
PTGER3
|
Prostaglandin E2 receptor EP3 subtype
|
109
|
CXCL8
|
Interleukin-8
|
110
|
PRKCB
|
Protein kinase C beta type
|
111
|
BIRC5
|
Baculoviral IAP repeat-containing protein 5
|
112
|
DUOX2
|
Dual oxidase 2
|
113
|
HSPB1
|
Heat shock protein beta-1
|
114
|
TGFB1
|
Transforming growth factor beta-1 proprotein
|
115
|
SULT1E1
|
Sulfotransferase 1E1
|
116
|
MGAM
|
Maltase-glucoamylase
|
117
|
IL2
|
Interleukin-2
|
118
|
CCNB1
|
G2/mitotic-specific cyclin-B1
|
119
|
PLAT
|
Tissue-type plasminogen activator
|
120
|
THBD
|
Thrombomodulin
|
121
|
SERPINE1
|
Plasminogen activator inhibitor 1
|
122
|
COL1A1
|
Collagen alpha-1 (I) chain
|
123
|
IFNG
|
Interferon gamma
|
124
|
ALOX5AP
|
Arachidonate 5-lipoxygenase-activating protein
|
125
|
PTEN
|
Phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase
|
126
|
IL1A
|
Interleukin-1 beta
|
127
|
MPO
|
Myeloperoxidase
|
128
|
NCF1
|
Neutrophil cytosol factor 1
|
129
|
ABCG2
|
Broad substrate specificity ATP-binding cassette transporter ABCG2
|
130
|
NFE2L2
|
Nuclear factor erythroid 2-related factor 2
|
131
|
NQO1
|
NAD (P) H dehydrogenase [quinone] 1
|
132
|
PARP1
|
Poly [ADP-ribose] polymerase 1
|
133
|
COL3A1
|
Collagen alpha-1 (III) chain
|
134
|
CXCL11
|
C-X-C motif chemokine 11
|
135
|
CXCL2
|
C-X-C motif chemokine 2
|
136
|
DCAF5
|
DDB1-and CUL4-associated factor 5
|
137
|
CHEK2
|
Serine/threonine-protein kinase Chk2
|
138
|
CLDN4
|
Claudin-4
|
139
|
PPARA
|
Peroxisome proliferator-activated receptor alpha
|
140
|
PPARD
|
Peroxisome proliferator-activated receptor delta
|
141
|
HSF1
|
Heat shock factor protein 1
|
142
|
CRP
|
Cysteine-rich protein 2-binding protein
|
143
|
CXCL10
|
C-X-C motif chemokine 10
|
144
|
SHUK
|
Inhibitor of nuclear factor kappa-B kinase subunit alpha
|
145
|
SPP1
|
Sphingosine-1-phosphate phosphatase 1
|
146
|
RUNX2
|
Runt-related transcription factor 2
|
147
|
RASSF1
|
Ras association domain-containing protein 1
|
148
|
E2F1
|
Transcription factor E2F1
|
149
|
E2F2
|
Transcription factor E2F2
|
150
|
ACP3
|
Prostatic acid phosphatase
|
151
|
CTSD
|
Cathepsin D
|
152
|
IGFBP3
|
insulin-like growth factor-binding protein 3
|
153
|
IGF2
|
Insulin-like growth factor II
|
154
|
CD40LG
|
CD40 ligand
|
155
|
IRF1
|
Interferon regulatory factor 1
|
156
|
ERBB3
|
Receptor tyrosine-protein kinase erbB-3
|
157
|
PON1
|
Serum paraoxonase/arylesterase 1
|
158
|
PCOLCE
|
Procollagen C-endopeptidase enhancer 1
|
159
|
NPEPPS
|
Puromycin-sensitive aminopeptidase
|
160
|
HK2
|
Hexokinase-2
|
161
|
NKX3-1
|
Homeobox protein Nkx-3.1
|
162
|
RASA1
|
Ras GTPase-activating protein 1
|
163
|
HTR3A
|
5-hydroxytryptamine receptor 3A
|
164
|
ADRA1A
|
Alpha-1A adrenergic receptor
|
165
|
CHRNA7
|
Neuronal acetylcholine receptor subunit alpha-7
|
166
|
IGHG1
|
Immunoglobulin heavy constant gamma 1
|
167
|
NCOA1
|
Nuclear receptor coactivator 1
|
168
|
DRD1
|
D (1A) dopamine receptor
|
169
|
CHRM3
|
Muscarinic acetylcholine receptor M3
|
170
|
CHRM5
|
Muscarinic acetylcholine receptor M5
|
171
|
CHRM4
|
Muscarinic acetylcholine receptor M4
|
172
|
OPRD1
|
Delta-type opioid receptor
|
173
|
OPRM1
|
Mu-type opioid receptor
|
174
|
FASN
|
Fatty acid synthase
|
175
|
EDNRA
|
Endothelin-1 receptor
|
176
|
EDN1
|
Endothlin-1
|
177
|
NPM1
|
Nucleophosmin
|
178
|
ECE1
|
Endothelin-converting enzyme 1
|
179
|
PARP4
|
Protein mono-ADP-ribosyltransferase PARP4
|
180
|
CALCR
|
Calcitonin receptor
|
181
|
ITGB3
|
Intergrin beta-3
|
182
|
CA2
|
Intercellular adhesion molecule 2
|
183
|
ADRA1D
|
Alpha-1D adrenergic receptor
|
184
|
STAT3
|
Signal transducer and activator of transcription 3
|
185
|
APP
|
Amyloid-beta precursor protein
|
186
|
ESR1
|
Estrogen receptor
|
187
|
DRD2
|
D (2) dopamine receptor
|
188
|
CDK2
|
Cyclin-dependent kinase 2
|
189
|
PIM1
|
Serine/threonine-protein kinase pim-1
|
190
|
NR3C1
|
Glucocorticoid receptor
|
191
|
ESR2
|
Estrogen receptor beta
|
192
|
GSK3B
|
Glycogen synthase kinase-3 beta
|
193
|
CHEK1
|
Serine/threonine-protein kinase Chk1
|
194
|
CCNA2
|
Cyclin-A2
|
195
|
PTPN1
|
Tyrosine-protein phosphatase non-receptor type 1
|
3.4 Choosing of the candidate targets of PCOS
To search for candidate targets related to PCOS, we used “Polycystic ovary syndrome” as index keywords to identify 446 potential targets by removing duplicate values from the databases of DisGeNET and Genecard (relevance score≥5). 80 common targets between the targets of Cuscuta-Salvia (195 potential targets) and the targets of PCOS were collected using a Venn diagram (Fig 3).
3.5 Construction of herb compound-PCOS target network
To determine the interaction between herb compounds and PCOS targets, the active components and common targets were input into Cytoscape software to establish the diagram of herb compound-PCOS target network (Fig 4). According to the value of degree, the top 5 of active components were presented (Table 5).
Table 5. The top 5 of active components of herb compound-PCOS target network.
NO.
|
Molecular Name
|
Degree
|
Closenessc Cntrality
|
Cuscuta 2
Cuscuta 1
Salvia 3
Salvia 9
Salvia 12
|
quercetin
kaempferol
tanshinone iia
dan-shexinkum d
Isotanshinone II
|
69
30
14
12
10
|
0.69402985
0.44711538
0.3907563
0.38429752
0.37804878
|
3.6 PPI network and core genes of disease-drug targets
We utilized STRING database to establish a Cuscuta-Salvia target network and PCOS targets network, and PPI network was visualized using Cytoscape v 3.7.2 software, which included 80 nodes and 1350 edges (Fig 5). In addition, according to the ranking of degree of nodes in STRING, the top 20 core targets were screened out. At the same time, PPI network was calculated and analyzed using Cytohubba plugin in Cytoscape v 3.7.2. As illustrated in Fig 6 and Table 6, in the PPI network, there was a total of 10 nodes and 45 edges. Ten nodes included IL6, AKT1, VEGFA, TP53, TNF, MAPK1, JUN, EGF, CASP3, and EGFR.
Table 6. Protein target information.
Gene name
|
Protein name
|
Degree
|
Closeness Centrality
|
Clustering Coefficient
|
IL6
|
Interleukin 6
|
68
|
0.87777778
|
0.51448639
|
AKT1
|
RAC-alpha serine/threonine-protein kinase
|
67
|
0.86813187
|
0.52464948
|
VEGFA
|
Vascular endothelial growth factor A
|
64
|
0.83157895
|
0.56597222
|
TP53
|
Cellular tumor antigen p53
|
63
|
0.82291667
|
0.5483871
|
MAPK1
|
Mitogen-activated protein kinase 1
|
62
|
0.81443299
|
0.54680063
|
TNF
|
Tumor necrosis factor
|
62
|
0.82291667
|
0.57588577
|
JUN
|
Transcription factor AP-1
|
60
|
0.7979798
|
0.58248588
|
EGF
|
Pro-epidermal growth factor
|
59
|
0.7979798
|
0.58036236
|
CASP3
|
Caspase-3
|
58
|
0.78217822
|
0.61766485
|
MAPK8
|
Mitogen-activated protein kinase 8
|
56
|
0.76699029
|
0.63181818
|
3.7 GO functional enrichment analysis and KEGG pathway enrichment analysis
To analyze the biological characteristics of predicted targets of Cuscuta-Salvia on PCOS in detail, a GO analysis and a KEGG enrichment analysis were carried out using “pathview” package in R. In term of GO terms included biological process (BP), cellular component (CC), and molecular function (MF) terms. It was found that BP category was mainly presented in cellular response to drug, response to oxygen levels, response lipopolysaccharide, and response to molecule of bacterial origin (Fig ). CC category was mainly showed in membrane, transcription regulator complex, nuclear chromatin, postsynaptic membrane, and vesicle lumen (Fig ). As shown in , MF category mainly included DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, DNA-binding transcription activator activity, RNA polymerase II-specific, DNA-binding transcription activator activity, and cytokine receptor binding. These terms can exert its therapeutic effects on PCOS.
To investigate the underlying pathways of Cuscuta-Salvia on PCOS, KEGG enrichment pathway analysis was performed. The filter was also set as an adjusted P-value<0.05 and q-value< 0.05. As showed in KEGG pathways were significantly enriched. The results of KEGG enrichment analysis mainly involved in PI3K−Akt signaling pathway, MAPK signaling pathway, cellular senescence, TNF signaling pathway, and IL-17signaling pathway, etc.
3.8 Effect of Cuscuta-Salvia on mice body weight in PCOS mice
To determine whether increased mice body weight in the letrozole-induced PCOS mice model, mice weight was measured. As shown in Fig 8A, compared with the NC group, body weight significantly increased in Model group after day 15. Compared with Model group, body weight in the CS group and Met group decreased, in which body weight in the CS group was on the greater decrease than in the Met group.
3.9 Detection of OGTT in PCOS mice
To examine glucose tolerance of letrozole-induced PCOS mice model, Oral glucose tolerance test (OGTT) assay was conducted. Compared with NC group, the AUC of model group significantly increased (P<0.01). Compared with model group, the AUC of CS group and Met group decreased (P<0.05). At the same time, There were no differences of AUC of blood glucose levels between CS group and Met group (Fig 8B-C).
3.10 Histological change in ovary tissues, liver tissues, and adipose tissue
As shown in Fig 9, in mice ovary tissues, NC group mice have many follicles in various stages of development at the site of a peripheral cortex. We observed normal follicular developmental synchronization, stromal tissue, and normal morphology. In term of model group mice, there were a large number of antral follicles. We also observed incompact follicles, increased medullar area, and enlarged vessels network. Also, there were cystic follicles and hemorrhagic cysts. In CS group and Met group, it was found that morphology in ovary was arranged more neatly than model group. Meanwhile, CS group mice were more neatly than Met group mice.
As shown in Fig 10, in mice liver tissues, there were the number of adipocyte in NC group more than model group. Compared with model group, CS group and Met group results showed that the number of adipocyte significant reduced.
As shown in Fig 11, in mice adipose tissue, there was enhanced irregular abdomen adipocyte and inflammatory infiltration in model group. Compared with model group, CS group and Met group results showed that abdomen adipocyte and inflammatory infiltration got improved significantly.
3.11 Effect of Cuscuta-Salvia on the ovary tissues mRNA expression of IL6, AKT1, VEGFA, TP53, MAPK1, JUN, EGF, AR, LHb, FSHb, CYP17a1, and CYP19a1 in PCOS mice
qRT-PCR was used to examine the mRNA expression of IL6, AKT, VEGFA, TP53, MAPK1, JUN, and EGF from the mice ovary tissue. As shown in Fig 12, we found the mRNA expressions of IL6, JUN, and EGF in model group significantly increased compared with NC group (P<0.05). Compared with model group, the mRNA expressions of IL6 and EGF in CS group decreased, whereas mRNA expression of JUN in CS group significantly decreased (P<0.05); the mRNA expressions of IL6, JUN, and EGF significantly decreased (P<0.01 or P<0.05).
As shown in Fig 12, compared with NC group, the mRNA expressions of AKT1 and TP53 and MAPK1 significantly increased (P<0.001 or P<0.01 or P<0.05). Compared with model group, the mRNA expressions of Akt1, TP53, and MAPK1 markedly decreased in CS group and Met group (P<0.001 or P<0.01).
As shown in Fig 12, compared with NC group, the mRNA expression of VEGFA significantly decreased (P<0.001). Compared with model group, the mRNA expression of VEGFA significantly increased (P<0.001 or P<0.01).
As shown in Fig 13, compared with NC group, the mRNA expression of AR, LHb, and CYP17a1 significantly increased (P<0.01 or P<0.05). Compared with model group, the mRNA expression of CYP17a1 significantly decreased in CS group and Met group (P<0.01 or P<0.05). There were no differences in the mRNA expression of AR and LHb in CS group, though they decreased compared with model group.
As shown in Fig 13, compared with NC group, the mRNA expression of CYP19a1 significantly increased (P<0.05). Compared with model group, the mRNA expression of CYP19a1 significantly decreased in CS group and Met group (P<0.001 or P<0.01).
As shown in Fig 13, compared with NC group, the mRNA expression of FSHb decreased. Compared with model group, the mRNA expressions were no differences in CS group and Met group.