A guidance into the fungal metabolomic abyss: Network analysis for revealing relationships between exogenous compounds and their outputs

Abstract

Fungal specialized metabolites include many bioactive compounds with potential applications as pharmaceuticals, agrochemical agents, and industrial chemicals. Exploring and discovering novel fungal metabolites is critical to combat antimicrobial resistance in various fields, including medicine and agriculture. Yet, identifying the conditions or treatments that will trigger the production of specialized metabolites in fungi can be cumbersome since most of these metabolites are not produced under standard culture conditions. Here, we introduce a data-driven algorithm comprising various network analysis routes to characterize the production of known and putative specialized metabolites and unknown analytes triggered by different exogenous compounds. We use bipartite networks to quantify the relationship between the metabolites and the treatments stimulating their production through two routes. The first, called the direct route, determines the production of known and putative specialized metabolites induced by a treatment. The second, called the auxiliary route, is specific for unknown analytes. We demonstrated the two routes by applying chitooligosaccharides and lipids at two different temperatures to the opportunistic human fungal pathogen Aspergillus fumigatus. We used various network centrality measures to rank the treatments based on their ability to trigger a broad range of specialized metabolites. The specialized metabolites were ranked based on their receptivity to various treatments. Altogether, our data-driven techniques can track the influence of any exogenous treatment or abiotic factor on the metabolomic output for targeted metabolite research. This approach can be applied to complement existing LC/MS analyses to overcome bottlenecks in drug discovery and development from fungi.

Lipo-Chitooligosaccharides Induce Specialized Fungal Metabolite Profiles That Modulate Bacterial Growth

“…A subset of the samples (4 out of 10 biological replications) was randomly selected and subjected to LC-MS/MS for confirmation of all secondary metabolites listed above by matching their fragmentation patterns to procured standards or an in silico database search if a standard was unavailable for purchase. LC-MS/MS feature confirmation data were previously shown in reference 26. Later, we evaluated whether the production of these known metabolites is regulated at the transcriptional level through quantitative PCR (qPCR) analysis of their specific backbone genes (core biosynthetic genes) or transcription factors (Fig.…”

“…Network analysis determines which LCO treatments had the largest influence on known, putative, and unknown metabolite production. Leveraging from our previous efforts, we used direct and auxiliary network analysis approaches (26) to determine the influence a treatment has on known or putative metabolites and unknown features. The direct network analysis approach was performed to determine which treatments impacted negatively or positively the relative abundance of a specific known metabolite produced by A. fumigatus (Fig.…”

“…4b and c and Fig. 5b and c) as described in our previous work (26,27). On the other hand, we employed the auxiliary network analysis approach illustrated in reference 26 to extract unknown features from the raw LC/MS data sets (Fig.…”

“…Fumitremorgin C was not used in the bacterial assay due to the limited amount of compound available. However, trypacidin and fumitremorgin C were used in the confirmation of metabolites in our LC/MS samples (26). All solutions were placed in a sterile 25-mL or 50-mL class A glass medium bottle sonicated in a water bath for 15 min at 25°C before use.…”

Olive mill solid waste induces beneficial mushroom-specialized metabolite diversity: a computational metabolomics study

“…Data-dependent MS/MS spectrum files were converted from .raw to .mzXML using the GNPS vendor-conversion (Mass Spectrometry File Conversion – GNPS Documentation; ccms-ucsd.github.io) 29 . Data preprocessing was performed using MZmine 3.3.0 software and included the following steps: mass1 and mass2 detection with noise levels of 4.0E 5 and 1.0E 4 respectively, LC-MS chromatogram building and resolution with intensity threshold of 9.0E 5 and minimum highest intensity of 2.0E 6 , isotope grouping, alignment, isotope pattern filtering, gap filling and filtering out of duplicate peaks 30 , M/Z and RT tolerance were set to 5 and 0.5 respectively. More details about MZmine 3 parameters can be found in the batch file in the supplementary data.…”