Illuminating a plant's tissue-specific metabolic diversity using computational metabolomics and information theory

Abstract

Secondary metabolite diversity is considered an important fitness determinant for plants' biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue-metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function.

Keywords: Nicotiana attenuata; information theory; mass spectrometry; metabolomics; secondary metabolism.

Fig. 1.

Integration of MS-based metabolomics and information theory analysis highlights tissue-specific metabolome specialization. (A) Tissues were collected and analyzed separately for metabolomic profiling. Detailed explanations of the tissue collection procedure are provided in Materials and Methods. (B) Hierarchical clustering, using the Euclidean distance as the clustering metric, of tissue-specific idMS/MS relative expression profiles. The heat map coloring depicts the scaled intensities. Z-score–normalized median absolute distances captured the cross-tissue variations for idMS/MS intensities (895 idMS/MSs) obtained for each tissue. (C) Information theory analysis of tissue-level idMS/MS composition δj and Hj based on idMS/MS cross-tissue distributions is displayed in a 2D space to reveal gradients of metabolic specialization. ANT, anthers; BUD, floral bud; COR, corolla tube; FIL, filaments; LEA, rosette leaves; LIM, corolla limb; PED, floral pedicel; ROO, root; SEE, seeds; SEP, floral sepals; STE, stem; STY, floral style and stigma.

Fig. 2.

Large-scale analysis of idMS/MS tissue specificity. (A) Cross-tissue distribution patterns for three idMS/MS examples. Z-score–normalized median absolute distances captured cross-tissue variations for idMS/MS intensities. idMS/MSs deconvoluted for m/z 295.102 @ 374 s and 901.404 @ 1,032 s revealed clear tissue specificity for one and two tissue types, respectively. idMS/MS for m/z 627.340 @ 1473 s was not associated with a particular tissue. (B) Density of intensity levels of each idMSMS across all analyzed tissues is computed and filtered using a reduction of kurtosis method to determine idMS/MS with significant tissue specificity. (C) Bar chart showing the number of idMS/MSs per tissue using an intensity threshold of 2 (Left), and bar chart showing the percentage of idMS/MSs illustrating tissue specificity per tissue (Right). (D) Heat map matrix visualizing idMSMS sharing among tissues as measured using the Jacquard index. The idMS/MS classifications to main compound classes in N. attenuata as obtained by idMS/MS alignments to public libraries and manual curation are shown in Dataset S1).

Fig. 3.

Combination of structural classifications of idMS/MS and tissue specificity of expression. (A) Biclustering analysis to classify idMS/MSs according to structural similarities. The analysis used two scoring methods: one based on shared fragments among spectra, whereas the other scored shared common NLs among spectra. Using biclustering, which favors clustering based on iterative alignments of spectra based on the two scoring methods, produces large modules (M) with structurally related idMS/MSs. Some of these modules were congruent with known compound families, whereas others were composed of yet unknown or poorly characterized metabolites. Module annotation and idMS/MS intensity distribution are reported in Dataset S1. (B) Relative contribution of each module to the idMS/MSs associated with a given tissue. The visualization highlights the complete absence of specific metabolic groups, corresponding here to particular modules, such as O-acyl sugars (O-AS), in anthers. (C) Molecular networks constructed for each module. Nodes represent idMS/MSs and edges represent similarity values based on the two scoring types. Tissue specificity can easily be mapped to the molecular networks.

Fig. 4.

Distribution of a flavonoid-enriched module among different flower parts. (A) Network representation and annotation of module M4 from the biclustering analysis. Nodes correspond to idMS/MS spectra, and edges correspond to their pairwise similarity as measured according to the fragment (NDP; >0.6) and NL (>0.6) similarity. Many of the spectra correspond to flavonoid glycosides, albeit O-acyl sugars of type II are also present due to shared NLs. (B) Cross-tissue coexpression (based on an Ochiai score > 0.6) between idMS/MS spectra discriminates flavonoid glycosides from O-acyl sugar. The analysis reveals metabolites within the M4 module with high tissue specificity, such as idMS/MS at m/z 295.102, predicted to be a tuliposide derivative, which is abundant in anthers (Fig. 3A). (C) Examples of visualization of cross-tissue variations for idMS/MSs of M4. Node size is proportional to the cross-tissue relative intensity of each idMS/MS. Color mapping denotes rules presented in A. Gray nodes do not exhibit tissue specificity, whereas yellow nodes were detected as tissue-specific. Red-circled nodes are annotated as flavonoid glycosides. Identifications of KG, kaempferol-3-O-sophoroside (glucosyl(1-2)glucoside) (KGG), KGR, QG, QGG, and kaempferol-3-O-rutinoside (glucosyl(1-2)rhamnoside) [QGR (Rutin)] are according to Snook et al. (53).

Fig. 5.

Silencing UGT-A and UGT-B reveals their involvement as UDP-glucosyltransferase and UDP-rhamnosyltransferase, respectively, in floral flavonoid glycoside metabolism, two predictions of the tissue coexpression analysis. (A) Results of the kurtosis filtering analysis for preferential tissue–gene associations. Examples are provided for the tissue specificity of members of large metabolic gene families. Notably, 71% of all predicted UDP-glycosyltransferases (GT) exhibit tissue specificity in the transcriptome dataset. (B, Left) Number of tissue-specific idM/MS spectra containing glucose or rhamnose NLs, and therefore predicted to be glycosylated secondary metabolites, compared with the total number of tissue-specific idMS/MSs per biclustering module. M4 is enriched in flavonoid glycosides, M7 in 17-HGL-DTGs, and M8 in O-acyl sugars. (B, Right) Number of UDP-GT coassociated across tissues (Ochiai score > 0.4) with at least one idMS/MS containing glucose (G) or rhamnose (R) NL of each module. (C) Relative levels of precursors corresponding to idMS/MSs referred to in B after analysis of flower buds of plants inoculated with empty vector and gene silencing constructs for UGT-A and UGT-B (SI Appendix, Fig. S11). As supported by the annotation of idMS/MS spectra, silencing UGT-A decreases the glucosylation of flavonols, whereas silencing UGT-B decreases their additional rhamnosylation. Identifications of KG, KGG, KGR, QG, QGG, and QGR (Rutin) are according to Snook et al. (53). *P < 0.05; **P < 0.01; ***P < 0.001.