Metabolite accumulation and metabolic network in developing roots of Rehmannia glutinosa reveals its root developmental mechanism and quality

Abstract

Rehmannia glutinosa root contains many compounds with important medicinal properties and nutritional benefits, but only more than 140 compounds have been reported so far. Many other compounds and their accumulation and metabolic networks during its development remain unclear. In order to clarify them, its metabolic profiles at three different developmental stages were analyzed using untargeted LC-MS analysis. Multivariate analysis revealed that 434 metabolites differently accumulated in its different stages, suggesting different change trends. The metabolites having the same trend share common metabolic pathways, the metabolites showing increasing contents during its development have medical and nutritional values, and some mature root-specific metabolites may be better candidates for its quality control; 434 metabolites were mapped onto 111 KEGG pathways including 62 enzymes, whose increasing and decreasing patterns were shown during its development. Some metabolites complicatedly interacted with some enzymes and the top-10 pathways enriched from 111 KEGG pathways in network analysis. These findings extended the dataset of its identified compounds, and revealed that its development and quality were associated with the accumulation of different metabolites. Our work will lay the foundation for the better understanding of its chemical constituents, quality and developmental mechanism.

Multi-omics Analysis Reveals Whether and how Naphthylacetic Acid Affects the Quality of Rehmannia Glutinosa

“…Anyway, NAA treatment could affect the contents of some metabolites in R. glutinosa, suggesting some change in its quality. On the other hand, these result of our current study also indicated that LC-MS technology had a potential to identify many metabolites in R. glutinosa as stated by previous reports [1,2], but could not identify any metabolite in R. glutinosa.…”

“…For the samples with biological replicates: Prior to differential gene expression analysis, for each sequenced library, the read counts were adjusted by edgeR program package through one scaling normalized factor. Differential expression analysis of two samples was performed using the EBSeq R package [2]. The resulting FDR (false discovery rate) were adjusted using the PPDE(posterior probability of being DE).…”

“…In our current study, based on our NT and CK yield measurement of R. glutinosa in advance, we integrated transcriptomic, small RNAs, degradome and metabolomics analyses to compare the gene expression, miRNA and metabolomic pro les between NTs and CKs. The aims of this study were (1) to provide abundant transcriptome resources for a transcript isoform catalog, (2) to detect NAA-regulated metabolites and (3) to systematically identify potential NAA-responsive DETs, DEMs, miRNAs and their targets for providing valuable information regarding NAA-improving quality of R. glutinosa Libosh and its the mechanisms.…”

Treatment with catalpol protects against cisplatin‑induced renal injury through Nrf2 and NF‑κB signaling pathways

“…In certain cases, Rehmannia glutinosa has also been used to treat diabetes, anemia and urinary tract infections ( 17 ). A previous study extracted the effective component in Rehmannia glutinosa and identified it as catalpol, whose molecular formula is C 15 H 22 O 10 ( 18 ). Studies have since revealed that catalpol exhibits anti-inflammatory and anti-apoptotic effects ( 19 , 20 ).…”

Analysis of Young Shoots of ‘Anji Baicha’ (Camellia sinensis) at Three Developmental Stages Using Nontargeted LC‐MS‐Based Metabolomics

“…Those variables with a relative standard deviation (RSD) below 30% in the QC samples analyses were kept for further data processing. Then, the resulting data were normalized to the total peak area of each sample using Microsoft Excel 2007 software (Microsoft, Redmond, WA, USA) (Tan et al., ; Xiong et al., ; Zhou et al., ). The acquired Excel file with three dimension data sets including m/z, peak RT and peak intensities, and RT–m/z pairs were used as the identifier for each ion.…”