Ethnopharmacological relevance: Achyranthes bidentata Blume (AB) is a well-known traditional Chinese medicine for treating osteoporosis and bone fracture. In the current, researches on pharmacological mechanism of AB mostly focused on molecular pathways, knowledge about its metabolic signatures is largely unclear. This study aims to develop an integrative metabolomics and metallomic approach for deciphering the biochemical basis of anti-osteoporosis effects of raw and salt-processed AB. Method: Gas chromatography–mass spectrometry (GC–MS) and inductively coupled plasma mass spectrometry (ICP-MS) were combined for metabolomic and metallomic profiling of rats serum, liver and kidney derived from the sham group, model group, E2, raw and salt-processed AB treated groups. Meanwhile, micro-CT and biomechanical analysis were carried out to ensure the success of the osteoporosis model and to validate the antiosteoporosis effect of raw and salt-processed AB. Partial least squares discriminant analysis (PLS-DA) was employed to screen potential biomarkers and the MetaboAnalyst and KEGG PATHWAY Database were used to investigate the metabolic pathway. Results: Raw and salt-processed AB protected the rats against osteoporosis, as evidenced by the restoration of the alkaline phosphatase activity, osteocalcin concentration, urine calcium/creatinine ratio and urine phosphorus/ creatinine ratio. The combination of PCA and PLS-DA revealed deviations in ninety-four differential biomarkers between raw AB treated group and model group. The identified biomarkers were primarily engaged in the metabolic pathways including galactose metabolism, urea cycle, arginine and proline metabolism, alanine metabolism, lactose degradation, ammonia recycling and glycine and serine metabolism. The levels of these biomarkers showed significant alterations and a tendency to be restored to normal values in raw and saltprocessed AB treated osteoporosis rats. Of note, the levels of trace elements, such as Zn, Se, Mn, Cu and Fe, were elevated after raw and salt-processed AB treatment. Finally, a correlation network diagram was constructed to show the biomarkers perturbed by raw and salt-processed AB. Conclusion: Our findings indicate that raw and salt-processed AB has positive effects on osteoporosis rats. Meanwhile, metabolomic and metallomic method coupled with metabolites enrichment analysis and pattern recognition serves as a useful tool for revealing the action mechanism of traditional Chinese medicine.
Keywords: Anti-osteoporosis | Achyranthes bidentata Blume | GC-MS | ICP-MS | Pattern recognition

This study aimed to investigate the differences in the metabolic profiles in serum of dairy cows that were normal or overconditioned when dried off for elucidating the pathophysiological reasons for the increased health disturbances commonly associated with overconditioning. Fifteen weeks antepartum, 38 multiparous Holstein cows were allocated to either a high body condition (HBCS; n = 19) group or a normal body condition (NBCS; n = 19) group and were fed different diets until dry-off to amplify the difference. The groups were also stratified for comparable milk yields (NBCS: 10,361 ± 302 kg; HBCS: 10,315 ± 437 kg; mean ± standard deviation). At dry-off, the cows in the NBCS group (parity: 2.42 ± 1.84; body weight: 665 ± 64 kg) had a body condition score (BCS) <3.5 and backfat thickness (BFT) <1.2 cm, whereas the HBCS cows (parity: 3.37 ± 1.67; body weight: 720 ± 57 kg) had BCS >3.75 and BFT >1.4 cm. During the dry period and the subsequent lactation, both groups were fed identical diets but maintained the BCS and BFT differences. A targeted metabolomics (AbsoluteIDQ p180 kit, Biocrates Life Sciences AG, Innsbruck, Austria) approach was performed in serum samples collected on d −49, +3, +21, and +84 relative to calving for identifying and quantifying up to 188 metabolites from 6 different compound classes (acylcarnitines, AA, biogenic amines, glycerophospholipids, sphingolipids, and hexoses). The concentrations of 170 metabolites were above the limit of detection and could thus be used in this study. We used various machine learning (ML) algorithms (e.g., sequential minimal optimization, random forest, alternating decision tree, and naïve Bayes–updatable) to analyze the metabolome data sets. The performance of each algorithm was evaluated by a leave-one-out crossvalidation method. The accuracy of classification by the ML algorithms was lowest on d 3 compared with the other time points. Various ML methods (partial least squares discriminant analysis, random forest, information gain ranking) were then performed to identify those metabolites that were contributing most significantly to discriminating the groups. On d 21 after parturition, 12 metabolites (acetylcarnitine, hexadecanoyl-carnitine, hydroxyhexadecenoyl-carnitine, octadecanoyl-carnitine, octadecenoyl-carnitine, hydroxybutyryl-carnitine, glycine, leucine, phosphatidylcholine-diacyl-C40:3, trans-4-hydroxyproline, carnosine, and creatinine) were identified in this way. Pathway enrichment analysis showed that branched-chain AA degradation (before calving) and mitochondrial β-oxidation of long-chain fatty acids along with fatty acid metabolism, purine metabolism, and alanine metabolism (after calving) were significantly enriched in HBCS compared with NBCS cows. Our results deepen the insights into the phenotype related to overconditioning from the preceding lactation and the pathophysiological sequelae such as increased lipolysis and ketogenesis and decreased feed intake
Key words: metabolomics | machine learning | metabolic pathway | transition cow