The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation

Abstract

Plant metabolomics studies haves revealed new bioactive compounds. However, like other omics disciplines, the generated data are not fully exploited, mainly because the commonly performed analyses focus on elucidating the presence/absence of distinctive metabolites (and/or their precursors) and not on providing a holistic view of metabolomic changes and their participation in organismal adaptation to biotic and abiotic stress conditions. Therefore, spectral libraries generated from Cecropia obtusifolia cell suspension cultures in a previous study were considered as a case study and were reanalyzed herein. These libraries were obtained from a time-course experiment under nitrate starvation conditions using both electrospray ionization modes. The applied methodology included the use of ecological analytical tools in a systematic four-step process, including a population analysis of metabolite α diversity, richness, and evenness (i); a chemometrics analysis to identify discriminant groups (ii); differential metabolic marker identification (iii); and enrichment analyses and annotation of active metabolic pathways enriched by differential metabolites (iv). Our species α diversity results referring to the diversity of metabolites represented by mass-to-charge ratio (m/z) values detected at a specific retention time (rt) (an uncommon way to analyze untargeted metabolomic data) suggest that the metabolome is dynamic and is modulated by abiotic stress. A total of 147 and 371 m/z_rt pairs was identified as differential markers responsive to nitrate starvation in ESI− and ESI+ modes, respectively. Subsequent enrichment analysis showed a high degree of completeness of biosynthetic pathways such as those of brassinosteroids, flavonoids, and phenylpropanoids.

Non-Targeted Metabolomic Analysis of Arabidopsis thaliana (L.) Heynh: Metabolic Adaptive Responses to Stress Caused by N Starvation

“…Although the ESI + mode has generally been favored in many previous studies, recently, ESI − has been proven to be a preferrable option owing to its ionization efficiency, which increases its detection limits [ 35 ]. Furthermore, in a previous study, we demonstrated that the ESI modes are complementary and not mutually exclusive [ 24 ]. In the present study, we obtained 7928 signals (spectrometric characteristics)—3407 from ESI + and 4521 from ESI − ( Table S4 and Table S5 , respectively).…”

“…Estimators of diversity, richness, and abundance of species usually applied in community and population ecology studies can be used to analyze the metabolomes by treating each spectrometric feature (m/z_rt) in the metabolome as a species belonging to a diverse community. Under this guidance and using the Chao1, Jack 1, and Jack 2 indices as parametric estimators, the m/z_rt composition of the A. thaliana metabolome was first analyzed using species accumulation curves (SACs) (see [ 24 ] for more details). In total, 3407 and 4521 spectrometric features (m/z_rt) were obtained in the ESI + and ESI − modes, respectively, representing the calculation and graphing of the average number of species (and the SD of the smallest sample size).…”

“…The statistical, diversity, and chemometric analyses were carried out following the methodology proposed by Cadena-Zamudio et al (2022) [ 24 ]. Briefly, this methodology consists of a systematic four-step process, including (i) a population analysis of metabolites’ α diversity, richness, and evenness; (ii) a chemometrics analysis to identify discriminant groups; (iii) differential metabolic marker identification (chemical marker); (iv) and enrichment analyses and annotations of active metabolic pathways enriched by differential metabolites.…”

“…The statistical analyses, metabolic pathway reconstruction, functional enrichment and presumptive annotation were carried out with the statistical software R Studio version 4.3.0 [ 32 ], using the libraries “KEGGREST, KEGGgraph, pathview, circlize, and ggplot2” and MetaboAnalyst [ 24 , 33 , 34 ].…”