| Literature DB >> 35846873 |
Yang Zhai1,2, Yangling Chen1, Yihui Luo2, Xiaoping Mei2, Lin Wu3, Xueni Mo3, Min Zou2, Mingzhao Zhou2, Yangling Wu2, Guangshan Zheng2, Peng Yang2, Qingyu He4, Rui Chen4.
Abstract
This study used a metabolomic approach to reveal changes in the levels of metabolic biomarkers and related metabolic pathways before and after Zhuang Yao Shuang Lu Tong Nao granule (YHT) treatment in rats with cerebral ischemia. The neurological deficit scores were significantly higher in the MCAO_R group than in the NC group, indicating that the mice had significantly impaired motor functions. The YHT group had significantly lower scores than the MCAO_R group, suggesting that YHT significantly improved motor function in rats. TTC staining of the brain tissue revealed that YHT significantly reduced the area of cerebral infarction in the treated rats. The MCAO_R group was better separated from the NC rent, sham, and YHT groups via metabolomic PCA. Moreover, there were significant differences in the differential metabolites between the MACO_R and YHT groups. Eighteen common differential metabolites were detected between the MACO_R and NC groups, MACO_R and sham groups, and MACO_R and YHT groups, indicating that YHT significantly increased the levels of various metabolites in the serum of cerebral ischemic stroke (CIS) rats. Moreover, a total of 23 metabolic pathways were obtained. We identified 11 metabolic pathways with the most significant effects in the bubble plots. In conclusion, from a systems biology perspective, this metabolomics-based study showed that YHT could be used to treat ischemic stroke by modulating changes in endogenous metabolites.Entities:
Mesh:
Year: 2022 PMID: 35846873 PMCID: PMC9277214 DOI: 10.1155/2022/8776079
Source DB: PubMed Journal: Anal Cell Pathol (Amst) ISSN: 2210-7177 Impact factor: 4.133
Neurological function scoring criteria.
| Performance | Rating/score |
|---|---|
|
| |
| No defects | 0 |
| Difficulty in full extension of the contralateral forelimb | 1 |
| Rotate to opposite side | 2 |
| Drop to the opposite side | 3 |
| Inability to walk spontaneously | 4 |
|
| |
| No defects | 0 |
| It is difficult to fully extend the contralateral forelimb when lifting the tail | 1 |
| Rotate to opposite side | 2 |
| Reduced contralateral push resistance (and forelimb flexion) without circling | 3 |
| Reduced contralateral push resistance (and forelimb flexion) | 4 |
The gradient of mobile phase.
| Time (min) | Flow rate (mL/min) |
|
|
|---|---|---|---|
| 0.00 | 0.65 | 5 | 95 |
| 0.5 | 0.65 | 5 | 95 |
| 9.5 | 0.65 | 35 | 65 |
| 11.50 | 0.65 | 60 | 40 |
| 13.50 | 0.65 | 60 | 40 |
| 13.60 | 0.65 | 5 | 95 |
| 16.00 | 0.65 | 5 | 95 |
Figure 1Effect of YHT on neurological function and cerebral infarction in the MCAO model rats: (a) neurological function scores; (b) TTC staining to observe the condition of brain tissue in mice. ∗∗∗∗P < 0.0001 compared with the NC group; &&&&P < 0.0001 compared with the MCAO_R group.
Figure 2Principal component analysis and partial least squares discriminant analysis of the components of mouse serum: (a) principal component analysis for each group of rats; (b) principal component analysis between the NC and MCAO_R groups; (c) principal component analysis between the sham and MCAO_R groups; (d) principal component analysis between the MCAO_R and YHT groups; (e) partial least squares discriminant analysis between the NC and MCAO_R groups; (f) partial least squares discriminant analysis between the sham and MCAO_R groups; (g) partial least squares discriminant analysis between the MCAO_R and YHT groups.
Figure 3Identification of serum differential metabolites: (a) identification of heterogeneous metabolites between the NC and MCAO_R groups; (b) identification of heterogeneous metabolites between the sham and MCAO_R groups; (c) identification of heterogeneous metabolites between the MCAO_R and YHT groups; (d) identification of heterogeneous metabolites between the MCAO_R and YHT groups; (e) identification of heterogeneous metabolites between the MCAO_R and YHT groups.
Differential metabolites in serum samples.
| Number | Metabolites | Molecular formula | RT (min) |
| Fold change | ||||
|---|---|---|---|---|---|---|---|---|---|
| MACO_R/NC | MACO_R/sham | MACO_R/YHT | MACO_R/NC | MACO_R/sham | MACO_R/YHT | ||||
| 1 | Creatine | C4H9N3O2 | 10.795 | <0.001 | <0.001 | 0.043 | 1.379 | 1.465 | 0.939 |
| 2 | L-Citrulline | C6H13N3O3 | 11.832 | 0.001 | <0.001 | 0.005 | 0.705 | 0.742 | 0.717 |
| 3 | L-Isoleucine | C6H13NO2 | 8.744 | 0.008 | 0.012 | 0.006 | 1.257 | 1.265 | 0.668 |
| 4 | SM(d18:1/16:0) | C39H79N2O6P | 5.104 | 0.004 | <0.001 | 0.002 | 1.238 | 1.268 | 0.723 |
| 5 | Trigonelline | C7H7NO2 | 9.499 | 0.024 | 0.019 | <0.001 | 0.265 | 0.221 | 0.179 |
| 6 | L-Glutamic acid | C5H9NO4 | 12.589 | <0.001 | <0.001 | 0.013 | 0.410 | 0.460 | 0.638 |
| 7 | 4-Guanidinobutyric acid | C5H11N3O2 | 10.878 | 0.037 | 0.018 | 0.006 | 0.369 | 0.307 | 0.726 |
| 8 | Guanidineacetic acid | C3H7N3O2 | 10.949 | 0.012 | 0.001 | 0.001 | 0.445 | 0.384 | 0.224 |
| 9 | Pyroglutamic acid | C5H7NO3 | 12.540 | <0.001 | 0.001 | 0.026 | 0.425 | 0.477 | 0.670 |
| 10 | Prolylleucine | C11H20N2O3 | 11.366 | 0.026 | 0.001 | 0.045 | 1.499 | 2.079 | 1.770 |
| 11 | L-Aspartic acid | C4H7NO4 | 12.746 | <0.001 | 0.023 | 0.010 | 0.305 | 0.250 | 0.352 |
| 12 | Eicosapentaenoic acid | C20H30O2 | 2.468 | 0.005 | 0.015 | 0.039 | 0.148 | 0.165 | 4.080 |
| 13 | Sphingosine | C18H37NO2 | 5.079 | 0.005 | <0.001 | 0.002 | 0.238 | 0.222 | 2.216 |
| 14 | Argininosuccinic acid | C10H18N4O6 | 13.513 | 0.017 | 0.004 | 0.023 | 0.535 | 0.555 | 0.432 |
| 15 | Arecoline | C8H13NO2 | 10.695 | 0.040 | 0.028 | 0.002 | 0.180 | 0.156 | 2.200 |
| 16 | trans-3-Indoleacrylic acid | C11H9NO2 | 2.538 | 0.004 | 0.003 | 0.043 | 0.353 | 0.311 | 3.577 |
| 17 | Paracetamol | C8H9NO2 | 11.158 | 0.001 | 0.001 | <0.001 | 2.013 | 1.780 | 0.151 |
| 18 | 2′-O-Methylguanosine | C11H15N5O5 | 6.812 | 0.030 | 0.011 | 0.006 | 0.700 | 0.691 | 0.274 |
Metabolic pathway analysis.
| Pathway | Raw | -ln( | FDR | Impact |
|---|---|---|---|---|
| Arginine and proline metabolism | <0.001 | 9.258 | 0.006 | 0.122 |
| Arginine biosynthesis | <0.001 | 8.846 | 0.006 | 0.462 |
| Glutathione metabolism | 0.002 | 6.037 | 0.067 | 0.054 |
| Histidine metabolism | 0.004 | 5.515 | 0.085 | 0.000 |
| Sphingolipid metabolism | 0.009 | 4.723 | 0.149 | 0.069 |
| Aminoacyl-tRNA biosynthesis | 0.017 | 4.084 | 0.236 | <0.001 |
| Alanine, aspartate, and glutamate metabolism | 0.020 | 3.922 | 0.238 | 0.442 |
| Glycine, serine, and threonine metabolism | 0.033 | 3.405 | 0.349 | 0.092 |
| Beta-alanine metabolism | 0.071 | 2.642 | 0.665 | 0.056 |
| Phenylalanine, tyrosine, and tryptophan biosynthesis | 0.082 | 2.498 | 0.691 | 0.500 |
| Nitrogen metabolism | 0.121 | 2.113 | 0.846 | <0.001 |
| D-Glutamine and D-glutamate metabolism | 0.158 | 2.113 | 0.846 | 0.500 |
| Valine, leucine, and isoleucine biosynthesis | 0.158 | 1.846 | 1.000 | <0.001 |
| Phenylalanine metabolism | 0.228 | 1.481 | 1.000 | 0.357 |
| Butanoate metabolism | 0.276 | 1.287 | 1.000 | <0.001 |
| Nicotinate and nicotinamide metabolism | 0.276 | 1.287 | 1.000 | <0.001 |
| Pantothenate and CoA biosynthesis | 0.336 | 1.090 | 1.000 | <0.001 |
| Porphyrin and chlorophyll metabolism | 0.478 | 0.739 | 1.000 | <0.001 |
| Glyoxylate and dicarboxylate metabolism | 0.500 | 0.693 | 1.000 | <0.001 |
| Biosynthesis of unsaturated fatty acids | 0.542 | 0.613 | 1.000 | <0.001 |
| Pyrimidine metabolism | 0.571 | 0.560 | 1.000 | 0.005 |
| Valine, leucine, and isoleucine degradation | 0.581 | 0.544 | 1.000 | <0.001 |
Figure 4Analysis of metabolic pathways.