Structuring Microbial Metabolic Responses to Multiplexed Stimuli via Self-Organizing Metabolomics Maps

Abstract

Secondary metabolite biosynthesis in microorganisms responds to discrete chemical and biological stimuli; however, untargeted identification of these responses presents a significant challenge. Herein we apply multiplexed stimuli to Streptomyces coelicolor and collect the resulting response metabolomes via ion mobility-mass spectrometric analysis. Self-organizing map (SOM) analytics adapted for metabolomic data demonstrate efficient characterization of the subsets of primary and secondary metabolites that respond similarly across stimuli. Over 60% of all metabolic features inventoried from responses are either not observed under control conditions or produced at greater than two-fold increase in abundance in response to at least one of the multiplexing conditions, reflecting how metabolites encode phenotypic changes in an organism responding to multiplexed challenges. Using abundance as an additional filter, each of sixteen known S. coelicolor secondary metabolites is prioritized via SOM and observed at increased levels (1.2 – 22-fold compared to unperturbed) in response to one or more challenge conditions.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

“…Secondary metabolite production can be activated in microorganisms through a variety of chemical and environmental stimulation, 29,30,183,184 and PCA and PLS are commonly applied to identify abundantly produced features in these conditions. 40,185-187 Binary comparisons using S-plots can be a used to identify group specific features of a PLS model.…”

“…For instance, it is common to screen a target organism under dozens of stimulus conditions to optimize compound production, or to induce silent biosynthetic gene clusters, and in these cases we have previously demonstrated that an alternative method utilizing Kohonen self-organizing map (SOM) analytics can be more effective at representing multiplexed stimuli data than PCA. 184 As discussed above, metabolomic acquisition via LC-MS results in the acquisition of thousands of detectable features. Through SOM analyses these features are organized using an artificial neural network into a 2-dimensional grid based on feature response patterns across all experimental conditions.…”

“…We applied these SOM analytics to map stimuli-induced metabolomic responses from 23 distinct conditions in Streptomyces coelicolor. 184 In this study 16 detected secondary metabolites produced by S. coelicolor were induced in one of the 23 conditions and prioritized through the SOM analysis. 184 Application of SOM analyses to investigate metabolomic changes engendered through microbial competition in our Nocardiopsis case study prioritized several metabolites unique to mixed culture conditions including the ciromicins.…”

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

“…SOM differential analysis allows numerous comparisons between SOMs originating from discrete samples and/or time points of the experiment. Another benefit of the SOM approach is the ability to “decrypt” specific nodes on the map to extract primary feature information, such as retention time and m/z (148,149). A recent example of using SOMs to systems-level mapping of molecules is contained in Figure 8 for an “organ-on-chip” human liver bioreactor exposed to acetaminophen (APAP) (150).…”

Systems-Wide High-Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies

“…Self-organizing data techniques and MEDI approaches have been used to rapidly characterize molecular indicators of drug addiction phenotype (34) and to prioritize candidate molecules in drug discovery efforts from natural products (39,40). The latter demonstrates a different feature of MEDI maps, namely the ability to perform mathematical operations on the untargeted data to drive further interpretive power.…”