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

Abstract

Lipo-chitooligosaccharides (LCOs) are historically known for their role as microbial-derived signaling molecules that shape plant symbiosis with beneficial rhizobia or mycorrhizal fungi. Recent studies showing that LCOs are widespread across the fungal kingdom have raised questions about the ecological function of these compounds in organisms that do not form symbiotic relationships with plants. To elucidate the ecological function of these compounds, we investigate the metabolomic response of the ubiquitous human pathogen Aspergillus fumigatus to LCOs. Our metabolomics data revealed that exogenous application of various types of LCOs to A. fumigatus resulted in significant shifts in the fungal metabolic profile, with marked changes in the production of specialized metabolites known to mediate ecological interactions. Using network analyses, we identify specific types of LCOs with the most significant effect on the abundance of known metabolites. Extracts of several LCO-induced metabolic profiles significantly impact the growth rates of diverse bacterial species. These findings suggest that LCOs may play an important role in the competitive dynamics of non-plant-symbiotic fungi and bacteria. This study identifies specific metabolomic profiles induced by these ubiquitously produced chemicals and creates a foundation for future studies into the potential roles of LCOs as modulators of interkingdom competition. IMPORTANCE The activation of silent biosynthetic gene clusters (BGC) for the identification and characterization of novel fungal secondary metabolites is a perpetual motion in natural product discoveries. Here, we demonstrated that one of the best-studied symbiosis signaling compounds, lipo-chitooligosaccharides (LCOs), play a role in activating some of these BGCs, resulting in the production of known, putative, and unknown metabolites with biological activities. This collection of metabolites induced by LCOs differentially modulate bacterial growth, while the LCO standards do not convey the same effect. These findings create a paradigm shift showing that LCOs have a more prominent role outside of host recognition of symbiotic microbes. Importantly, our work demonstrates that fungi use LCOs to produce a variety of metabolites with biological activity, which can be a potential source of bio-stimulants, pesticides, or pharmaceuticals.

Keywords: Aspergillus; bacteria; biosynthetic gene clusters; lipo-chitooligosaccharides; network analyses; secondary metabolites; synergism.

FIG 1

Chemical structures of LCO standards. (a to d) The LCO structures are (a) C16:0 sulfated LCOs, (b) C16:0 nonsulfated LCOs, (c) C18:1 sulfated LCOs, and (d) C18:1 nonsulfated LCOs. The palmitic acid chain is in purple, the oleic acid chain is in green, the sulfated group is in red, and the nonsulfated group is in blue. The chemical formula for each LCO is shown below the structure. LCO standards were dissolved in 0.005% EtOH for use as the solvent control. The concentration of LCOs used in the experiments was 10⁻⁸ M.

FIG 2

LCO-induced A. fumigatus metabolomic profiles at 25°C. (a) The total significant features produced were counted and distributed by treatments that coincidentally or uniquely induced their production. Set size is the number of features coincidentally or uniquely produced by each of the treatments. The plots are separated based on features detected under the 25°C growth condition. Features showing significant differences (P < 0.05) in pairwise comparison between treatment and solvent control with intersections with at least 10 members are displayed (n = 10 per treatment). (b to e) Volcano plots representing the number of features and known secondary metabolites identified in (b) C16:0 sLCO-, (c) C16:0 nsLCO-, (d) C18:1 sLCO-, and (e) C18:1 nsLCO-treated samples. Features showing significant differences (P < 0.05) in pairwise comparison between treatment and control are displayed (n = 10 per treatment).

FIG 3

LCO-induced A. fumigatus metabolomic profiles at 37°C. (a) The total significant features produced were counted and distributed by treatments that coincidentally or uniquely induced their production. Set size is the number of features coincidentally or uniquely produced by each of the treatments. The plots are separated based on features detected under the 37°C growth condition. Features showing significant differences (P < 0.05) in pairwise comparison between treatment and solvent control with intersections with at least 10 members are displayed (n = 10 per treatment). (b to e) Volcano plots representing the number of features and known secondary metabolites identified in (b) C16:0 sLCO-, (c) C nsLCO-, (d) C18:1 sLCO-, and (e) C18:1 nsLCO-treated samples. Features showing significant differences (P < 0.05) in pairwise comparison between treatment and control are displayed (n = 10 per treatment).

FIG 4

Network analysis of metabolomic changes in response to LCO treatments at 25°C. Graphical representation of the directed network analysis showing the influence of the treatments on secondary metabolite production. (a) Directed PageRank measures: (b) broadcasting for treatments and (c) receiving for secondary metabolites. (d) Auxiliary network analysis of significantly differentially regulated XICs uniquely (degree of 1 noted as “deg. 1”) or coincidentally (degrees of 2, 3, or 4 noted as “deg. 2,” “deg. 3,” or “deg. 4”) produced by all treatments. Metabolomic regulation was determined by the log₂ fold change and is indicated by edge color, where red shows upregulation and blue shows downregulation.

FIG 5

Network analysis of metabolomic changes in response to LCO treatments at 37°C. Graphical representation of the direct network analysis showing the influence of the treatments on secondary metabolite production. (a) PageRank measures: (b) broadcasting for treatments and (c) receiving for secondary metabolites. (d) Auxiliary network analysis of significantly differentially regulated XICs uniquely (degree of 1 noted as “deg. 1”) or coincidentally (degrees of 2, 3, or 4 noted as “deg. 2,” “deg. 3,” or “deg. 4”) produced by all treatments. Metabolomic regulation was determined by the log₂ fold change and is indicated by edge color, where red shows upregulation and blue shows downregulation.

FIG 6

Log-phase growth rates of bacterial species following exposure to LCO-induced secreted metabolites. (a) Poplar-promoting bacteria that were examined in this study and their taxonomic placement. Data are mean quantiles based on the inflection point of growth for each bacterial species. Statistical analysis was conducted with a Welch one-way ANOVA test and a Dunnett T3. (b to h) Welch’s ANOVA P values were (b) 0.0050, (c) not significant, (d) not significant, (e) not significant, (f) 0.0266, (g) 0.0011, and (f) 0.0013. *, P < 0.05; **, P < 0.01; ***, P < 0.001. The point of inflection for each bacterial strain can be found in Table S3. There were eight biological replications for each bacterial species. (Figure 6a created with www.BioRender.com.)