Pyruvate decarboxylase and its lysine succinylation contribute to the development and metabolite biosynthesis in pathogenic fungus Aspergillus flavus

ABSTRACT

This study evaluates the effects of pyruvate decarboxylase and its lysine succinylation (Ksucc) on the development of Aspergillus flavus and the production of secondary metabolites. Mutant strains, including the knockout (∆pdc1), pdc1 point mutants (K258R and K258E) and complementary (∆pdc1.com) were constructed. The results showed that both Δpdc1 and K258R strains exhibited decreased conidiophore and conidia production and failed to generate sclerotia. The production of aflatoxin B1 (AFB1) was significantly increased in the Δpdc1 and K258R strains, while it decreased in the K258E strain. The results also indicate that pdc1 and its Ksucc are involved in the stress response and pathogenicity of A. flavus. Through GC-MS analysis, Δpdc1 was found to produce several significantly decreased compounds, including n-hexadecanoic acid, 2-bromotetradecane, and palmitic acid, among others. Additionally, different volatile metabolites, such as 1-iodo-decane, n, n-dimethyloctanamide and n, n-dimethyl-7-octynamide, were not detected in the Δpdc1 strain compared to WT and Δpdc1.com. HPLC results showed that the production of pyruvic acid, malic acid and succinic acid increased in both the Δpdc1 and K258R strains when compared to WT and Δpdc1.com strains. Comparative RNA-seq analysis revealed a total of 3,817 differentially expressed genes (DEGs) including 1,913 up-regulated and 1,904 down-regulated genes in Δpdc1 vs. WT. These results suggest that PDC1 and K258 play a crucial role in the biosynthesis of secondary metabolites, development and stress responses of A. flavus.

GRAPHICAL ABSTRACT

1. Introduction

The statistics indicate that over two million patients worldwide suffer infections caused by pathogenic fungi every year (Yan et al. 2022). Several fungal species such as Aspergillus spp., Cryptococcus spp., and Candida spp. have been identified as the main agents of fungal pathogenesis (Perfect 2017; Yan et al. 2022). Aspergillus flavus is a ubiquitous filamentous fungus prevalent in air, plants, soil and water, decomposing organic debris and other diverse substrates (Ahmad et al. 2019; Haug 2019). This fungus can infect various seed crops, including peanuts, rice, corn, cotton, wheat and beans, which can lead to significant losses in agricultural commodities (Tumukunde et al. 2020; Qin et al. 2021). Long-term exposure to A. flavus can cause several diseases, such as asthma, allergic bronchopulmonary aspergillosis, pericarditis and endocarditis in both animals and humans (Li et al. 2019). Aspergillus species are considered among the most aflatoxigenic fungi (Davari et al. 2015). For example, A. flavus produces several types of secondary metabolites, including aspertoxin, aflatrem, cyclopiazonic acid, aspergillic acid and aflatoxins (Uka et al. 2019). Among these metabolites, aflatoxins are the most intensively studied mycotoxins worldwide due to their significant effects on health hazards, food safety risks and the marketability of crop supplies globally (Tumukunde et al. 2020; Wang et al. 2022). It is also important to note that A. flavus has been identified as the main producer of several types of aflatoxins, with AFB₁ being recognised as the most toxic and carcinogenic (Davari et al. 2015; Tumukunde et al. 2019). Daily exposure to these toxins can result in acute and chronic toxicity leading to serious health issues (Wild and Montesano 2009). Biosynthesis of aflatoxins has been reported as a complex process that requires several transcription factors and signalling proteins (Yao et al. 2017; Caceres et al. 2020). Although the aflatoxin gene cluster has been widely studied, the mechanism behind aflatoxin biosynthesis in A. flavus remains poorly understood.

Pyruvate decarboxylases (PDCs) are a set of enzymes that catalyse the nonoxidative decarboxylation of pyruvate to acetaldehyde using the cofactor thiamin diphosphate and divalent cations such as Mg²⁺. PDCs are key enzymes in homo-fermentative pathways that cleave pyruvate into CO₂ and acetaldehyde (Gocke et al. 2009), and they are commonly found in yeasts, plants, fungi, animals, and bacteria (Choo et al. 2018). The pyruvate decarboxylase gene (pdc) encoding PDC has been studied in several organisms, including Saccharomyces cerevisiae, Zymomonas mobilis, Acetobacter pasteurianus, Sarcina ventriculi, Gluconacetobacter diazotrophicus, and Torulopsis glabrata (Alharbi 2022). Several filamentous fungi utilise pdc for growth, and in some cases, for the production of organic acids, such as fumaric, malic and citric acids. In Gibberella zeae, pdc1 is required for the vegetative growth of embedded mycelia and lipid production (Son et al. 2012). Another study demonstrated that under oxygen-limited conditions, the Δpdc1 strain exhibited minimal growth on YPD media compared to the WT strain in Kluyveromyces marxianus, indicating that pdc1 is essential for K. marxianus growth in such environments. This research also found that the levels of ethanol, glycerol and acetate metabolites in the culture supernatant of Δpdc1 strain were barely detectable compared to those of the WT strain (Qin et al. 2021). In the absence of oxygen, most vertebrates perish within a short time as they cannot meet the cellular energy demands of anaerobic metabolism. However, some fish species, such as crucian carp and goldfish, possess pdc1, which allows for ethanol fermentation to provide energy when oxygen is scarce, enabling their survival for extended periods (Fagernes et al. 2017). Although pdc1 has been characterised in several organisms, its role in A. flavus remains unknown.

Post-translational modifications (PTMs) including methylation, acetylation, sumoylation, glycosylation, phosphorylation, ubiquitination, and succinylation are crucial mechanisms that help elucidate the roles of various protein modifications involved in numerous biological processes (Nie et al. 2018; Tumukunde et al. 2021). These modifications can regulate virulence, development and secondary metabolite production in A. flavus (Tumukunde et al. 2021). Among these PTMs, lysine succinylation (Ksucc) occurs through the transfer of the succinyl group (–CO–CH₂–CH₂–CO₂H) to the lysine residue of a protein (Yang and Gibson 2019) and is considered a primary modification for regulating enzyme activity associated with metabolite synthesis (Weinert et al. 2013). Ksucc has been shown to play an essential role in responses to shifts in carbon sources and perform pathophysiological functions in pathogens (Colak et al. 2013; Xie et al. 2015). A study by Zhang et al. (2011) identified lysine succinylation (Ksucc) as a novel PTM, and they suggested that succinyl-CoA could serve as a cofactor for this modification. Other studies have revealed that Ksucc regulates several metabolic processes across various organisms (Park et al. 2013; Rardin et al. 2013; Ren et al. 2018; Yang and Gibson 2019). Recently, our laboratory conducted a successful investigation into the lysine succinylome in A. flavus using high-resolution mass spectrometry and found that succinylated proteins are involved in diverse biological processes, including cellular and metabolic processes. In particular, the study revealed that Ksucc sites on the norsolorinic acid reductase NorA (AflE) affect the production of both sclerotia and aflatoxin B1 (AFB₁) in A. flavus (Ren et al. 2018).

In this study, we demonstrated that PDC1 and its succinylated site K258 are involved in the regulation of sporulation, growth rate, sclerotia formation, seed pathogenicity, and stress resistance in A. flavus. Furthermore, we found that PDC1 and K258 regulate the biosynthesis of several secondary metabolites, including AFB₁, organic acids, and volatile compounds. RNA-seq analysis revealed a total of 3,817 and 2,950 differentially expressed genes for the Δpdc1 and K258R strains, respectively. These results suggest that PDC1 and K258 play a crucial role in the biosynthesis of secondary metabolites, development, and stress responses of A. flavus.

2. Materials and methods

2.1. Strains and culture conditions

All fungal strains used in this study are listed in Table S1. The TXZ 21.3 and NRRL3357 strains were obtained from Pfannenstiel et al. (2017). A. flavus strains were cultured on different media including Potato Dextrose Broth (PDB), Potato Dextrose Agar (PDA), Yeast Extract-Sucrose (YES), Complete Medium (CM), Yeast Glucose Extract agar (YGT), and Minimal Media (MM) (Pfannenstiel et al. 2017; Ren et al. 2018). Supplements including uridine (1 g/L), uracil (1 g/L), and arginine (1 g/L) were added to the media as a requirement. All strains (WT, ∆pdc1, ∆pdc1.com, K258R, and K258E) were cultured on four replicate plates and grown at 29 °C or 37 °C without shaking (solid media) or shaking at 180 r/min in dark conditions. The experiments were at least repeated thrice, and the strains were stored at −80 °C in 30% glycerol.

2.2. In silico analysis

The amino acid sequence of PDC1 with the accession number AFLA_031570 and protein sequence with query ID EED55885.1 were downloaded from the National Center For Biotechnology Information Database (NCBI). Other protein sequences of PDC1 homologues from different Aspergillus species (A. oryzae, A. parasiticus, A. nomiae, A. tamari, A. bombycis, A. pseudonomiae, A. pseudotamarii, A. terreus, A. fischeri, A. fumigatus, A. niger, A. campestris, A. nidulans, A. chevalieri, A. cristatus, and A. clavatus) were also obtained from NCBI database by using blast algorithm with A. flavus PDC1 protein sequence as inquiry. The domain enclosed in the protein sequence of each organism was predicted using InterPro (http://www.ebi.ac.uk/interpro/scan.html) and constructed using the IBS 1.0 software (Liu et al. 2015). The phylogenetic tree of the aforementioned species was generated by Molecular Evolutionary Genetic Analysis Version 7.0.26 (MEGA 7) software (Kumar et al. 2016). Multiple sequence alignments were carried out using the Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) and viewed using Jalview Version 2 (JV2) software (Waterhouse et al. 2009).

2.3. Preparation of pdc1 mutant strains

The pdc1 gene knockout strain (∆pdc1) was prepared using the previous method of homologous recombination (Nie et al. 2018; Li et al. 2019). The preparation of protoplast and the polyethylene glycol (PEG)-mediated fungal transformation were carried out following procedures described previously (Ren et al. 2017). The pdc1 complementary strain (∆pdc.com) was obtained following the previously described method (Ren et al. 2018). RT-PCR and diagnostic PCR were used to verify the positive transformants, and the primers are listed in Table S2. To generate the pdc1 site-directed mutants, we followed the method described by Ren et al. (2017) with minor modifications. The succinylated residue of pdc1 at K258 was replaced by both arginine (R) and glutamic acid (E). The point-mutation fragments of pdc1, the pyrG selective marker, and the pdc1 downstream fragment were amplified using specific primers (Table S2) and joined by fusion PCR. Both the PCR and DNA sequencing analyses were used to verify the site-mutated transformants, and the used primers are listed in Table S2.

2.4. Protein extraction and western blot analysis

A. flavus strains were point inoculated onto MM solid medium and grown for 48 h at 37 °C, and whole cell extraction and Western blot were carried out as formerly described (Waterhouse et al. 2009; Ren et al. 2018). In brief, 100 mg of mycelium powder from each strain was dissolved into 1 mL of radio immunoprecipitation assay (RIPA) lysis buffer, and protein extracts (30 μg) were separated using 12% sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). After the primary antibody and secondary antibody were, respectively, added, the membrane was washed with TBST and dipped into an equal volume of HRP Substrate Peroxide Solution and HRP Substrate Luminol Reagent for colour development. The GBox XT4 Chemiluminescence and Fluorescence Imaging System (Gene Company Limited, Shanghai, China) was utilised for signal detection and photography.

2.5. Morphological analysis

For colony development analysis, MM solid media without or with 1% carbon source (ethanol, glucose, glycerol, and sodium acetate) were poured into Petri dishes (15 mL per plate). To determine the effect of pdc1 on conidia production, conidia were collected and counted as a formerly described protocol (Davari et al. 2015; Xie et al. 2015). For the evaluation of conidiophore formation, the aforementioned media were also used, and the A. flavus strains were cultured and grown at 37 °C in the dark. After 2 days, hyphae were harvested, cut and observed as previously described (Li et al. 2019; Tumukunde et al. 2020). To examine the influence of pdc1 on sclerotia production, A. flavus strains were inoculated on both CM and YGT solid media and grown for 7 days at 37 °C in the dark. For sclerotia visualisation and counting, conidia were washed with 75% ethanol.

2.6. AFB₁ extraction and TLC assay

AFB₁ extraction and TLC analysis were performed according to the previous method (Wang et al. 2017) with minor modification. Briefly, A. flavus WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains were inoculated into a Petri dish containing 30 mL of PDB liquid media and incubated for 4 days at 29 °C in the dark. The filtrate (2 mL) from every Petri dish was collected and transferred into a 4-mL micro-centrifuge tube. Then, 2 mL dichloromethane was added into each tube with the sample and then vortexed for better dispersal, and 1 mL of dichloromethane from each sample was transferred to a new tube and air-dried. The residue in each sample was dissolved in 50 μL of chloroform, and 5 μL of the suspension was then loaded onto a thin layer chromatography (TLC) plate. A TLC plate was placed into a developing chamber with a developing solvent (acetone:chloroform = 1:9, v/v). The plate was air-dried, and the results were visualised under 365 nm UV radiation. GeneTools image analysis software was then used for quantitative analysis of AFB₁.

2.7. Pathogenicity assay

The protocol of these A. flavus strains to contaminate crop seeds and the analysis of their ability to produce aflatoxin were carried out following a previously illustrated procedure (Lan et al. 2016) with slight modification. All samples of maize seeds and peanut cotyledons were sterilised with 0.05% sodium hypochlorite and were inoculated with 5 × 10⁶ conidia/mL (each strain). After incubation, the seeds were harvested into 50 mL centrifuge tubes containing 15 mL of sterile water with 0.05% Tween-80 and vortexed for 1 min. The number of conidia from each sample was then counted haemo-cytometrically, and AFB₁ from the infected seeds was extracted following the previous procedure (Hu et al. 2018).

2.8. Stress assay

The MMG agar medium supplemented with/without cell wall stress agents (100 µg/mL CFW or 200 µg/mL CR), cell membrane stress (100 µg/mL SDS), osmotic stress agents (0.6 mol/L D-Sorbitol, 1.2 mol/L NaCl, or 1.2 mol/L KCl), oxidative stress agents (5 mmol/L H₂O₂ or 0.5 mmol/L tBooH) and genotoxic stress (0.02% MMS or 0.02% HU) were inoculated with WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains. Five replicate plates were used for each strain and incubated at 37 °C for 4 days in dark conditions. The diameter of mycelium was subsequently measured, and the growth inhibition rate was then calculated. The following formula was used for this calculation: Growth inhibition rate = (diameter of the unstressed strain – diameter of the stressed strain)/diameter of the unstressed strain × 100%.

2.9. Quantitative reverse transcription polymerase chain reaction (qRT-PCR)

The total RNA was extracted from 50 mg of smashed mycelia (every sample) using a Total RNA isolation kit (Promega, Madison, WI, USA). The qRT-PCR analysis was completed by mixing the product of the synthesised cDNA (a template), a pair of primers (Table S2) for the target gene and SYBR Green qPCR mix (TaKaRa Biotechnology, Japan) and then amplified using the PikoReal 96 Real-time PCR system (Thermo Fisher Scientific, Vantaa, Finland). The relative transcript level of each target gene was calculated using the $2^{-\Delta \mathrm{\Delta }\mathit{CT}}$ method (Zhang et al. 2011), and the β-actin gene was used as an endogenous standard. All qRT-PCR tests were completed with a technological quadruplet for each strain, and each experiment was repeated at least three times.

2.10. RNA-Seq and enrichment analysis of differentially expressed genes

The total RNA of WT, Δpdc1, and K258R strains with three biological replicates was sequenced. RNA-Seq library was constructed following the Illumina standard protocol and sequenced for a paired-end 150 bp on the Illumina Novaseq platform. The sequenced clean reads were mapped to the A. flavus NRRL 3357 reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_014117465.1) using Hisat2 (2.0.5) software (Mortazavi et al. 2008). The mapped reads of each sample were assembled using StringTie (1.3.3b) software (Pertea et al. 2015). FeatureCounts (1.5.0-p3) software (Liao et al. 2014) was used to count the number of reads mapped to each transcript, and RPKM (fragments per kilobase per million mapped reads) was used for the quantification of transcript expression. Gene ontology (GO) analysis of DEGs was carried out by agriGo2 database, and significant enriched GO terms (including molecular function, cellular component and biological process) were defined with Padj < 0.05. KEGG (Kyoto Encyclopaedia of Genes and Genomes) enrichment analysis was also carried out using clusterProfiler 3.81 software (Kanehisa and Goto 2000). For protein-protein interaction (PPI) network analysis, the STRING database was used to construct a PPI network of DEGs and subsequently visualised using Cytoscape 3.9.1 software.

2.11. Analysis of metabolites by gas chromatography-mass spectrometry (GC-MS) analysis

An equal amount (1 × 10⁶ conidia/mL) of each A. flavus strain (WT, Δpdc1, ∆pdc1.com, K258R, and K258E) was inoculated into a 200 mL conical flask containing 50 mL of PDB liquid medium supplemented with 1% (wt/vol) carbon source (glucose or sodium acetate) and incubated at 29 °C in a shaker at 180 r/min in the dark. Then, the extraction was performed by adding 15 mL of dichloromethane to each sample in a conical flask. The samples were then sonicated for 2 min, incubated at 4 °C for 5 min, and then shaken at 180 r/min for 5 min at 29 °C in the dark. Afterwards, the samples were left to stand for 1 h, and then 3 mL of dichloromethane from each sample was filtered through a 0.22 μm syringe filter and transferred to a 4 mL tube and air-dried. The residue in each sample was dissolved in 1 mL of dichloromethane, and 100 μL of the suspension was then added into a 1.5 mL 9-mm-wide opening screw glassware tube for GC-MS analysis of the volatile compound. A triple quadrupole GC-MS system, GCMS-TQ8040 (Shimadzu Corporation, Japan) equipped with a highly efficient detector and SH-Rxi-5Sil MS capillary column (Shimadzu, Japan) was used to detect volatile metabolites.

2.12. Analysis of metabolites by high-pressure liquid chromatography (HPLC) analysis

An equal amount (1 × 10⁶ conidia/mL) of every A. flavus strain was grown in 25 mL liquid cultures of MM and PDB with 1% glucose or sodium acetate as carbon sources and incubated at 37 °C for 5 days with 180 r/min in the dark condition. 1 mL of culture supernatant from each sample was collected by centrifugation and filtered through a 0.22 μm syringe filter before being used for HPLC analysis. Concentrations of malic acid, pyruvic acid, acetic acid, lactic acid, and succinic acid in the culture supernatant were measured via HPLC (LD-20AD, Shimadzu, Japan). Xtimate Sugar-H, 5 μm, 7.8 × 300 mm column (Welch Materials Inc.) was adopted for separation, and the column temperature was 30 °C. The mobile phase consisted of 0.1% potassium dihydrogen phosphate (KH₂PO₄) solution (A) at pH 2.5 and 100% methanol (B), in a binary gradient of 97% A/3% B and flowed at a rate of 0.8 mL/min.

2.13. Statistical analysis

All data in the study were presented by means ± SD (standard deviation) of at least three biological replicate samples. For statistical and significance analysis, GraphPad Prism 5 software and Dunnett’s multiple comparison test method were used. A significant difference between the samples was confirmed when p-values were < 0.05. Error bars in every column of the graph represent the standard error of three replicates.

3. Results

3.1. Domain and sequence analysis of PDC1

Pdc1 sequence in A. flavus has been obtained from the NCBI database with the sequence ID AFLA_031570 and protein ID EED55885.1, and the putative PDC1 protein EED55885.1 in A. flavus has been predicted to encode 570 amino acids. PDC1 protein sequences from other 16 different Aspergillus species including A. oryzae (XP_001819813.1), A. parasiticus (KAB8200962.1), A. nomiae (XP_015402704.1), A. tamari (KAE8161105.1), A. bombycis (XP_022385111.1), A. pseudonomiae (KAB8257064.1), A. pseudotamarii (XP_031911839.1), A. terreus (XP_001213811.1), A. fischeri (XP_001263352.1), A. fumigatus (XP_754512.1), A. niger (XP_001399817.2), A. campestris (XP_024696690.1), A. nidulans (P87208.3), A. chevalieri (BCR85621.1), A. cristatus (ODM19256.1), and A. clavatus (XP_001271110.1) were also downloaded from the NCBI database. All downloaded sequences were then used for the analysis of the revolutionary relationship among all the aforementioned species by Mega 7 software. The results revealed that A. flavus PDC1 has the highest similarity to its homologue from A. oryzae (100% Identity, 100% Query Cover) (Figure 1a). The results in Figure 1b indicates that thiamine pyrophosphate enzyme central domain (TPP_enzyme_M), N terminal TPP binding domain (TPP_enzyme_N), and C terminal TPP binding domain (TPP_enzyme_C) are conserved in all 17 species. The alignment of protein sequences revealed that lysine at 258 of PDC1 was highly conserved in the A. flavus orthologues, suggesting that this residue of PDC1 may be important for an evolutionarily conserved function (Figure 1c).

Figure 1.

Structural characterisation of PDC1 protein from 17 species. (a) The phylogenetic tree of PDC1 proteins from different species. The phylogenetic tree was constructed using MEGA 7 software and the Neighbour-joining method with 1,000 replications. (b) Comparison of the conserved domains among PDC1 proteins from different species. Non-conserved parts are shown in gray colour, while the thiamine pyrophosphate enzyme central domain (TPP_enzyme_M), N terminal TPP binding domain (TPP_enzyme_N), and C terminal TPP binding domain (TPP_enzyme_C) are shown in green, red and blue respectively. (c) Alignment of partial amino acid sequences of PDC1 from 17 different Aspergillus species. Protein ID numbers from NCBI reference sequence are shown on the right side. XP_015402704.1 (A. nomiae), KAB8257064.1 (A. pseudonomiae), A. flavus (ID EED55885.1), KAB8200962.1 (A. parasiticus), XP_001819813.1 (A. oryzae), KAE8161105.1 (A. tamari), XP_031911839.1 (A. pseudotamarii), XP_022385111.1 (A. bombycis), XP_001213811.1 (A. terreus), BCR85621.1 (A. chevalieri), ODM19256.1 (A. cristatus), P87208.3 (A. nidulans), XP_001263352.1 (A. fischeri), XP_754512.1 (A. fumigatus), XP_001271110.1 (A. clavatus), XP_024696690.1 (A. campestris), and XP_001399817.2 (A. niger) were used for alignment. The conserved succinylation sites (K258) are marked by the black frame.

The homologous recombination methods shown in Figure S1a and S1b were used to generate pdc1 deletion mutant (∆pdc1) and site mutant strains, respectively. Transformants were further confirmed by PCR (Figure S1c), RT-PCR (Figure S1d), and qRT-PCR (Figure S1e). To obtain strains for the mutated Ksucc, lysine (K) at 258 was replaced by both arginine and glutamic acid, which were named K258R and K258E, respectively. The transformants were verified by PCR and DNA sequencing analysis (Figure S1f and Figure S1g). The Western blot analysis of Ksucc levels was performed on the WT, ∆pdc1, ∆pdc1.com, K258R, and K258E strains using a pan anti-succinyl lysine antibody (anti-Ksucc) on the whole cell extracts. The obtained succinylation immunoblot signals revealed the presence of Ksucc in the WT, ∆pdc1, ∆pdc1.com, K258R, and K258E in A. flavus. In particular, ∆pdc1, K258R, and K258E cells showed a decrease in succinylation levels compared to the WT and ∆pdc1.com cells in the MM media containing 40 mmol/L acetate (Figure S1h). All these results showed that Ksucc is present in K258 of A. flavus PDC1, and all the mutant strains in this study were successfully constructed.

3.2. Effect of PDC1 and its Ksucc on A. flavus growth and conidia production

To identify the potential functions of PDC1 and its Ksucc in A. flavus growth and conidiation, A. flavus strains (WT, Δpdc, ∆pdc1.com, K258R, and K258E) were cultured on MM media without or with 1% (wt/vol) ethanol (MME), acetate (MMA), glycerol (MMGly), or glucose (MMGlu) as the sole carbon sources and grown in the dark for 4 days at 37 °C (Figure 2a). It was found that the colony diameter of both Δpdc1 and K258R strains was significantly shorter than that of the WT and ∆pdc1.com strains when grown on MMGlu and MMGly media (Figure 2b). For the analysis of conidia production, we found that the number of conidia produced by Δpdc1 and K258R strains on MME, MMGlu and MMGly media was considerably reduced in contrast to that in the WT and ∆pdc1.com strains. Whereas, no significant difference was observed in conidia production between the WT and mutant strains grown on MMA media (Figure 2c). Furthermore, it is notable that in the absence of a carbon source, all strains hardly produce conidia (Figure 2c).

Figure 2.

Vegetative growth and conidiation analysis of WT, Δpdc, ∆pdc1.com, K258E, and K258R strains. (a) The phenotype of WT, Δpdc, ∆pdc1.com, K258E, and K258R strains cultured on MM solid media with or without 1% (wt/vol) of the indicated carbon sources at 37 °C for 4 days in dark conditions. (b) Colony diameter of WT, Δpdc, ∆pdc1.com, K258E, and K258R strains. (c) The number of conidia produced by WT, Δpdc, ∆pdc1.com, K258E, and K258R strains on MM media with or without carbon sources. (d) Microscopic examination of conidiophores produced by each strain cultured on MM media with or without carbon sources at 37 °C for 48 h. (e) qRT-PCR analysis of the expression levels of conidia-related gene brlA from WT and mutant strains. (f) qRT-PCR analysis of the expression levels of abaA from WT and mutant strains. The expression levels were calculated by $2{ }^{-\Delta \mathrm{\Delta CT}}$ method, and β-actin gene was used as a control. The asterisks indicate significant difference levels when mutant strains were compared with WT (i.e., *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001). Standard deviation is indicated by error bars.

To measure the fungal biomass, 5 × 10⁶ conidia/mL of each A. flavus strain (WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains) were added into PDB and MM liquid media supplemented with 1% (wt/vol) glucose as the sole carbon sources and grown for 4 days in the dark with shaking (180 r/min) at 29 °C (Figure S2a). The media were then filtered through Whatman filter paper No. 1, and the filter paper along with fungal filtrate was dried at 65 °C for 12 h in an air incubator, and the results demonstrated that the mycelial dry weight of both the ∆pdc1 and K258R strains was decreased when compared to that of the WT and ∆pdc1.com strains (Figure S2b). The diameters of the fungal balls of the samples were assessed, and the result indicated that the diameters of the fungal balls for the ∆pdc1 and K258R strains were significantly decreased in contrast to that for the WT and ∆pdc1.com strains (Figure S2c). We also observed that conidiophores produced under MMA media were almost absent in all strains (Figure 2d). Furthermore, qRT-PCR analysis of the expression levels for two conidia-related genes abaA and brlA was carried out to confirm the obtained variation in conidial production. The results demonstrated that with or without a carbon source, the expression levels of these genes were down-regulated in the ∆pdc1 and K258R strains compared to that in the WT and ∆pdc1.com stains (Figure 2e,f). Together, these results suggest that PDC1 might play a critical function in vegetative growth and conidiation, and K258 succinylation could be important for the function of PDC1 in regulating vegetative growth and conidiation in A. flavus under the investigated conditions.

3.3. PDC1 and Ksucc are important for sclerotial formation in A. flavus

Aspergillus flavus produces sclerotia, which are the hardened masses of hyphae essential for fungal survival during unfavourable conditions (Park et al. 2013). To evaluate the function of PDC1 in the production of sclerotia formation, WT, Δpdc, ∆pdc1.com, K258R, and K258E strains were grown for 7 days in CM and YGT media at 37 °C in the dark condition, and 75% ethanol was utilised to wash off conidia. Our findings demonstrated that in contrast to the WT and Δpdc1.com strains, the Δpdc1 strain could not produce any sclerotia (Figure 3a–c), suggesting that pdc1 is involved in the production of sclerotia in A. flavus. Similar to the results obtained in the Δpdc1 strain, sclerotia were not detected in the pdc1 site mutant K258R (Figure 3a–c), suggesting that succinylation modification site K258 is important for PDC1 function in the production of sclerotia. Additionally, the qRT-PCR analysis showed that the expression levels of the genes related to the sclerotial formation (nsdD and nsdC) were significantly lower in both the Δpdc1 and K258R strains than that in other strains (Figure 3d,e). Taken together, these findings indicated that PDC1 and its succinylation modification were essential for regulating sclerotia production in A. flavus.

Figure 3.

Analysis of sclerotia production in the WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains. (a) The phenotype of WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains cultured on the CM medium for 7 days at 37 °C in dark conditions. (b) The phenotype of WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains cultured on the YGT medium for 7 days at 37 °C in dark conditions. (c) Quantification of sclerotia in the WT and all mutant strains. The abbreviation ND means “not detected”. (d, e) Expression levels of sclerotial-related genes (nsdC and nsdD) from the aforementioned strains. The expression levels were calculated by $2{ }^{-\Delta \mathrm{\Delta }\mathit{CT}}$ method, and β-actin gene was used as a reference gene. The asterisks indicate significant difference levels when mutant strains were compared with WT (i.e., **p ≤ 0.01, and ***p ≤ 0.001). ND indicates no detection. Standard deviation is indicated by error bars.

3.4. Effects of pdc1 deletion and Ksucc on stress response in A. flavus

To investigate the potential functions of PDC1 in A. flavus under extracellular stimuli responses, we analysed the sensitivity of WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains to different types of stresses. The stress stimuli included cell wall stress induced by 100 µg/mL CFW or 200 µg/mL CR agents, cell membrane stress induced by 100 µg/mL SDS, osmotic stress induced by 0.6 mol/L D-Sorbitol, 1.2 mol/L NaCl or 1.2 mol/L KCl agents, oxidative stress induced by 5 mmol/L H₂O₂ or 0.5 mmol/L tBooH and genotoxic stress induced by 0.02% MMS or 0.02% HU. An MMG agar medium supplemented with/without the aforementioned stress agents was inoculated with WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains. The cultures were grown at 37 °C for 4 days in dark condition (Figure 4a), then the diameter of each colony was measured, and the growth inhibition rate was calculated (Figure 4b–e). We observed that both the Δpdc1 and K258R mutants were more sensitive than the WT and ∆pdc1.com strains on the media supplemented with cell membrane agent (SDS), cell wall stress agents (CFW, CR), oxidative stress (H₂O₂, tBooH) and genotoxic stress (MMS, HU) (Figure 4b–e). In the osmotic stress analysis, all the strains enhanced the growth of A. flavus. However, compared to the WT and ∆pdc1.com strains, both the Δpdc1 and K258R strains showed significantly less stress resistance (Figure 4a,c). The aforementioned findings suggest that PDC1 influences the stress responses of A. flavus, and succinylation modification could be involved in the function of PDC1 in response to oxidative, osmotic, genotoxic, cell membrane and cell wall stresses in A. flavus.

Figure 4.

The PDC1 is involved in stress response in Aspergillus flavus. (a) Morphology of A. flavus WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains grown on MMG media added with the indicated agents. (b) Growth inhibition rate of all strains under cell membrane and cell wall stresses as in (a). (c) Growth inhibition rate of all strains under osmotic stress as in (a). (d) Growth inhibition rate of all strains under oxidative stress as in (a). (e) Growth inhibition rate of all strains under genotoxic stress as in (a). Growth inhibition rate = (the diameter of untreated strain − the diameter of treated strain)/(the diameter of untreated strain) × 100%. The asterisks indicate significant difference levels when mutant strains were compared with WT (i.e., *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001). Standard deviation is indicated by error bars.

3.5. Effect of PDC1 and Ksucc on aflatoxin biosynthesis in A. flavus

AFB₁ is the foremost toxic and carcinogenic secondary metabolite mainly produced by A. flavus (Qin et al. 2021). Thus, to investigate the effect of pdc1 on AFB₁ production, WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains were cultured in YES and PDB liquid media with or without 1% (wt/vol) glucose as a carbon source and grown for 4 days at 29 °C in the dark. The produced AFB₁ was analysed by TLC analysis, and the results demonstrate that in YES and PDB media with or without glucose, the production of AFB₁ was significantly increased in the ∆pdc1 and K258R strains compared to that in the WT and ∆pdc1.com strains. However, AFB₁ production was significantly decreased in the K258E compared to the WT and ∆pdc1.com strains (Figure 5a,b). To confirm the above results, the expression levels of eight genes (AflC, HypC, AflD, AflH, AflK, AflO, AflP, and AflQ) involved in aflatoxin biosynthesis were further analysed by qRT-PCR. The results revealed that in YES and PDB media with or without glucose, the expression levels of all above eight candidates were considerably increased in the Δpdc1 and K258R strains while significantly decreased in the K258E, compared to that in the WT and ∆pdc1.com strains (Figure 5c). All the above findings showed that PDC1 and K258 succinylation reduce aflatoxin biosynthesis by suppressing expression of genes in the aflatoxin cluster in A. flavus.

Figure 5.

Detection of AFB₁ production in the WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains of Aspergillus flavus. (a) TLC analysis of AFB₁ produced by the WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains after 4 days of incubation at 29 °C in the dark. The abbreviation Std means “AFB₁ standard”. (b) Computation of AFB₁ produced by the strains as in (a). (c) Relative expression levels of eight aflatoxin biosynthesis-related genes from all A. flavus. The two asterisks in (b) and (c) represent significant difference levels when Δpdc1, ∆pdc1.com, K258R, and K258E strains were compared with WT (i.e., *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001). Standard deviation is indicated by error bars.

3.6. PDC1 and Ksucc are involved in A. flavus virulence to crop seeds

As mentioned early, A. flavus can infect a variety of crop seeds leading to a significant loss of agricultural commodities and result in economic losses. To examine the function of PDC1 in A. flavus virulence to crop seeds, maize kernels and peanut seeds were infected with A. flavus WT, Δpdc1, ∆pdc1.com, K258R, and K258E strains and incubated at 29 °C for 3 days and 6 days in the dark (Figure 6a,b). All mutant strains produced significantly fewer conidia on either maize kernel or peanut seeds compared to WT, and ∆pdc1.com strain after 6 days, and the same results were obtained when peanut seeds were used for 3 days (Figure 6c,d). The AFB₁ was extracted from the A. flavus infected maize kernels and peanut seeds and detected by TLC analysis. We observed that in contrast to WT and ∆pdc1.com stains, both Δpdc1 and K258R mutants significantly increased AFB₁ production on the infected peanut seeds and maize kernels, while the K258E strain decreased AFB₁ production (Figure 6e,f). Together, these results suggested that PDC1 influences the virulence of A. flavus to crop seeds, and K258 succinylation may play an important role in the function of PDC1 in the pathogenicity of A. flavus to crop seeds.

Figure 6.

Crops infection analysis of the WT and all mutant strains of Aspergillus flavus. (a) Growth of A. flavus WT, Δpdc1, ∆pdc1.com, K258E, and K258R strains on maize kernels after 3 days and 6 days of inoculation. (b) Growth of A. flavus WT, Δpdc1, ∆pdc1.com, K258E, and K258R strains on peanut seeds after 3 days and 6 days of inoculation. (c, d) Quantitative analysis of conidia produced on the infected peanut seeds and maize kernels. (e) TLC analysis of AFB₁ extracted from maize kernels and peanut seeds infected by the WT and mutant strains. The abbreviation Std indicates “AFB₁ standard”. (f) Quantitative analysis of AFB₁ produced by the strains as in (e). The asterisks indicate significant difference levels when Δpdc1, ∆pdc1.com, K258E, and K258R strains were compared with WT (i.e., *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001). ND indicates no detection. Standard deviation is indicated by error bars.

3.7. Effect of PDC1 and Ksucc on the secondary metabolites in A. flavus by HPLC

To find out whether secondary metabolites (SMs) such as malic acid, pyruvic acid, acetic acid, lactic acid, and succinic acid (organic acids) are produced specifically in the presence of carbon source and reliant on PDC1, HPLC analysis was carried out on culture supernatants from all A. flavus grown for 5 days in MM media supplemented with 1% (wt/vol) glucose. After optimisation, Xtimate Sugar-H column was adopted for separation, and 1 g/L KH₂PO₄ solution was utilised as the mobile phase A at pH 2.5 and 100% methanol as mobile phase B, in a binary gradient of 97% A/3% B. The flow rate, sample volume, column temperature and detection wavelength were 0.8 mL/min, 10 μL, 30 °C and 220 nm respectively. Each standard compound was initially analysed at a concentration of 250 ppm, and one peak was obtained from each standard (Figure S3a). The HPLC chromatography of the analysed standard solutions is shown in Figure S3b–f. The identification of these organic acids in A. flavus strains was achieved by comparing the retention time obtained for each peak with those obtained for the standards. Figure 7 shows the chromatographic profile of organic acid compounds in the WT, ∆pdc1.com, and mutant strains. HPLC results indicated that pyruvic acid (1), malic acid (2), lactic acid (3), and succinic acid (5) were the major components in the WT, ∆pdc1.com, K258R, and ∆pdc1 strains, whereas acetic acid (4) was only detected in K258E strain (Figure 7a–c). Our results also revealed that in both mutant strains ∆pdc1 and K258R, the amounts of pyruvic acid, malic acid, lactic acid, and succinic acid were drastically increased compared to the WT and ∆pdc1.com strains (Figure 7a,b). Remarkably, lactic acid and succinic acid were not identified in the K258E strain, whereas the amount of pyruvic acid and malic acid were decreased in the K258E strain (Figure 7c). The above results suggest that PDC1 and K258 succinylation play a regulatory function in fungal secondary metabolism, particularly organic acid production, whereby pdc1 is a prerequisite for organic acid production in A. flavus.

Figure 7.

The HPLC-based chromatogram of organic acids in the Aspergillus flavus strain. (a) Chromatogram of organic acids in the WT, Δpdc1, and ∆pdc1.com. (b) Chromatogram of organic acids in the WT, K258R, and ∆pdc1.com. (c) Chromatogram of organic acids in the WT, K258E, and ∆pdc1.com. 1 is pyruvic acid, 2 is malic acid, 3 is lactic acid, 4 is acetic acid, and 5 is succinic acid.

3.8. Effect of PDC1 and Ksucc on the production of the volatile metabolites by GC-MS

The fungal volatiles are the types of cellular metabolites that hold a significant portion of the microbial metabolome. In this study, to analyse the variety of volatile metabolites within the WT, Δpdc1, and ∆pdc1.com strains, GC-MS analysis was carried out to detect the volatile compounds emitted from all strains grown for 22 days in PDB liquid media supplemented with 1% (wt/vol) sodium acetate (PDB + A) or with an additional 1% (wt/vol) glucose (PDB + G) in the dark. GC-MS analysis of compounds from each strain was performed in dichloromethane extract of A. flavus, and the identified compounds in the WT, Δpdc1, and ∆pdc1.com strains under PDB + G are shown in Table 1. Whereas Table S3 shows the detected compounds in the WT, Δpdc1, and ∆pdc1.com strains under PDB + A. The obtained total ion chromatogram (TIC) is shown in Figure 8a, illustrating various volatile profiles among WT, Δpdc1, and ∆pdc1.com strains under different conditions. Δpdc1 was further found to produce several significantly decreased compounds. Whereas 9-octadecenamide and methoxyacetic acid, 4-tridecyl ester were significantly increased in Δpdc1 compared to the WT and Δpdc1.com (Table 1 and Figure 8a). The heat map of various volatile profiles among WT, Δpdc1, and ∆pdc1.com strains under PDB + G is shown in Figure 8b.

Table 1.

Selected common volatile compounds and their relative quantities (expressed in peak area) produced by the WT, Δpdc1, and ∆pdc1.com strains grown on the PDB media with an addition of 1% (wt/vol) glucose.

Sr. No	Name of the compound	Formula	Molecular weight (gm/mol)	RT (min)	WT				Δpdc1				∆pdc1.com
					QI (m/z)	Peak Area	SI (%)	S/N	QI (m/z)	Peak Area	SI (%)	S/N	QI (m/z)	Peak Area	SI (%)	S/N
1	Propanoic acid, 2-methyl-, 2-ethylhexyl ester	C12H24O2	200.3178	5.342	71	2,935	87	0.62	–	–	–	–	71	2,899	91	0.62
2	1-Iododecane	C10H21I	268.1782	5.398	57	6,660	93	1.44	–	–	–	–	57	6,352	94	1.2
3	2,3,4-trimethyl-hexane	C9H20	128.2551	5.672	71	7,218	91	1.33	71	5,101	93	1.77	71	7,119	87	1.33
4	2-propyl-1-Heptanol	C10H22O	158.2811	6.796	57	12,051	96	2.26	57	5,813	95	1.23	57	12,253	94	2.26
5	2,6,11-trimethyl,dodecane	C15H32	212.4146	6.817	57	21,588	99	7.52	57	22,633	100	2.37	57	21,256	99	2.28
6	2,3,5,8-tetramethyl-decane	C14H30	198.3880	7.007	57	8,985	98	0.89	57	8,917	92	1.21	57	9,069	100	1.19
7	4,6,8-trimethyl-,1-nonene	C12H24	168.319	7.596	57	14,993	93	1.01	57	11,427	98	1.72	57	15,752	89	1.36
8	octyl-,oxirane	C10H20O	156.2652	7.73	55	9,894	89	0.82	55	7,674	100	1.1	55	9,196	96	0.82
9	4,8-dimethyl-undecane	C13H28	184.3614	7.921	71	15,098	92	2.56	71	6,957	88	1.69	57	14,994	91	0.82
10	4-methyl-heptadecane	C18H38	254.4943	8.208	71	11,916	92	2.25	71	10,804	95	1.89	71	12,120	89	1.17
11	6-methyl-ctadecane	C19H40	268.5209	8.662	57	18,111	89	1.1	57	8,509	88	0.89	57	17,854	90	1.1
12	Hexadecane	C16H34	226.4412	8.836	57	23,983	91	1.58	57	8,829	89	1.56	57	24,602	99	6.84
13	Carbonic acid, nonyl vinyl ester	C12H22O3	214.3013	8.956	71	18,238	94	1.8	71	11,897	95	1.58	71	18,792	92	0.79
14	2-Bromotetradecane	C14H29Br	277.28	9.012	57	36,105	100	2.42	57	29,388	95	1.89	57	34,371	98	1.42
15	7,9-Di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione	C17H24O3	276.3707	9.238	57	63,496	97	5.31	57	31,756	95	3.51	57	62,683	99	3.88
16	N-hexadecanoic acid	C16H32O2	256.4241	9.451	60	45,093	96	9.12	60	22,485	86	11.99	60	45,982	93	9.12
17	Palmitic acid, TMS derivative	C19H40O2Si	328.6052	9.59	73	23,637	95	8.14	73	16,917	91	2.76	73	25,816	90	6.75
18	Methoxyacetic acid, 4-tridecyl ester	C16H32O3	272.42	10.18	57	8,685	89	1.36	57	21,695	88	1.72	57	8,847	94	1.36
19	Tetracosane	C24H50	338.6538	10.509	57	8,398	94	1.81	57	6,888	94	1.16	57	8,454	87	1.45
20	2-butyl-,1-Octanol	C12H26O	186.3342	10.825	55	5,340	96	1.76	55	3,110	95	1.05	55	4,972	98	1.08
21	n,n-dimethyloctanamide	C10H21NO	171.2798	11.402	87	7,576	87	11.6	–	–	–	–	87	7,113	90	16.42
22	2,6,10,15-tetramethyl-, heptadecane	C21H44	296.5741	11.767	57	13,402	89	1.69	57	3,791	87	0.82	57	12,495	92	2.77
23	n,n-dimethyl-7-Octynamide	C10H17NO	167.25	12.828	87	5,436	92	11.91	–	–	–	–	87	5,452	95	13.21
24	Eicosane	C20H42	282.5475	13.092	57	7,307	93	1.43	57	5,483	91	0.56	57	7,701	86	0.83
25	1-(2,2,2-trifluoroacetylamino)cyclopropylmethyl ester, trifluoroacetic acid	C8H7F6NO3	279.14	14.741	69	6,857	87	1.54	–	–	–	–	85	6,141	91	0.65
26	9-Octadecenamide, (Z)-	C18H35NO	281.4766	14.841	59	6,828	98	32.96	59	12,557	87	4.89	59	6,235	89	3.65
27	Benzenepropanoic acid, 3,5-bis(1,1-dimethylethyl)-4-hydroxy-, octadecyl ester	C35H62O3	530.8650	15.37	57	72,592	97	4.11	57	55,226	93	4.48	57	71,646	90	1.06
28	Heptacosane	C27H56	380.7335	16.22	57	3,926	90	1.36	57	3,164	98	1.02	57	3,358	95	1.21

RT = Retention time, QI = Quantification ion, m/z = Mass/charge, SI = Similarity index, S/N = Signal to noise, (-) = Compound not detected.

Figure 8.

GC-MS Chromatogram of dichloromethane extract for Aspergillus flavus strains. (a) The GC-MS-based chromatogram of dichloromethane extract of the WT, Δpdc1, and ∆pdc1.com strains. (b) The heat map visualisation of the top 28 volatile metabolites produced by the WT, Δpdc1, and ∆pdc1.com strains. The abbreviation TIC stands for total ion chromatogram. (c) GC-MS Chromatogram of dichloromethane extract of WT, K258R, and K258E strains. (d) Heat map visualisation of the top 28 volatile metabolites produced by the WT, K258R, and K258E strains. The abbreviation TIC stands for total ion chromatogram.

The GC-MS identified compounds in the WT, K258E, and K258R strains are shown in Table 2. The GC-MS chromatogram of the peaks of the volatile compounds from each strain is shown in Figure 8c. A widely used stabiliser in polymers, tris (2,4-di-tert-butylphenyl) phosphate, was also detected in the K258E strain but could not be detected in the WT and K258R strains (Table 2 and Figure 8c). The heat map of various volatile profiles among WT, K258R, and K258E strains under PDB + A is shown in Figure 8d.

Table 2.

Selected common volatile compounds and their relative quantities (expressed in peak area) produced by Aspergillus flavus WT, K258R, and K258E strains grown on PDB supplemented with 1% (wt/vol) sodium acetate.

Sr. No	Name of the compound	Formula	Molecular weight (gm/mol)	RT (min)	WT				K258R				K258E
					QI (m/z)	Peak Area	SI (%)	S/N	QI (m/z)	Peak Area	SI (%)	S/N	QI (m/z)	Peak Area	SI (%)	S/N
1	3,3-dimethyl-Hexane	C8H18	114.2285	3.026	57	13444	98	4.16	–	–	–	–	57	10910	97	3.88
2	2,4-Dimethyldodecane	C14H30	198.3880	4.481	57	14575	97	3.65	57	5956	97	1.91	57	15982	94	2.45
3	2,6,11-trimethyl-dodecane	C15H32	212.4146	4.534	57	20244	100	3.42	71	19554	99	6.87	57	20695	98	6.18
4	4-methyl-dodecane	C13H28	184.3614	4.906	57	8526	97	2.47	71	6663	97	2.36	71	10697	98	3.26
5	Heptadecane	C17H36	240.4677	5.469	57	15976	99	0.41	57	14613	99	0.35	57	13171	98	0.38
6	Pentadecane	C15H32	212.4146	5.557	57	17768	98	0.86	57	18632	97	1.73	57	11786	85	1.19
7	Eicosane	C20H42	282.5475	5.962	57	114674	100	5.83	71	89262	99	7.38	71	71530	99	7.42
8	2,6,10-Trimethyltridecane	C16H34	226.4412	6.296	57	50858	89	1.69	57	33715	93	2.44	57	26028	94	2.14
9	Heneicosane	C21H44	296.5741	6.78	57	31044	94	0.49	57	14034	100	0.58	57	19677	97	0.51
10	Octadecane	C18H38	254.4943	6.85	57	32179	96	2.19	57	18385	95	1.69	57	15310	98	1.78
11	Hexadecane, 2,6,11,15-tetramethyl–	C20H42	282.5475	7.091	57	54808	85	1.84	57	18541	86	0.94	57	54197	88	1.32
12	13-Methylheptacosane	C28H58	394.7601	9.55	57	50206	81	2.34	73	1857	86	1.19	71	16223	85	1.05
13	N-hexadecanoic acid	C16H32O2	256.4241	9.773	57	67488	98	3.66	57	25347	98	3.01	71	12741	99	2.21
14	7,9-Di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione	C17H24O3	276.3707	10.524	57	25767	96	1.95	57	16498	97	1.65	57	9519	96	1.38
15	Tetrapentacontane	C54H110	759.4512	10.93	71	56811	99	4.4	71	33244	100	3.88	57	16247	95	0.08
16	Dotriacontane	C32H66	450.8664	11.015	71	62911	98	4	71	42215	100	2.57	57	11718	93	0.91
17	3,4,5-Tri-t-butyl-1-methyl-4,5-dihydro-1H-[1,2,4]diazaphosphole	C15H31N2P	270.39	11.417	57	212873	92	1.55	57	333827	99	18.76	57	1217562	100	124.11
18	Heptasiloxane, hexadecamethyl-	C16H48O6Si7	533.1472	11.797	73	3684	88	4.02	221	4270	90	17.78	73	4873	93	6.98
19	N,N-dimethyldodecanamide	C14H29NO	227.3862	12.675	87	116336	96	232.51	87	76388	94	161.17	87	73667	96	160.11
20	Tetracosane	C24H50	338.6538	12.763	57	9568	93	0.93	85	19829	95	1.93	57	19666	95	1.05
21	Docosyl propyl ether	C25H52O	368.6798	12.912	57	32730	98	2.82	57	25906	98	1.41	57	19698	98	2.43
22	Tributyl acetylcitrate	C20H34O8	402.4792	12.955	185	75870	95	258.11	185	34055	89	107.92	185	45626	93	158.59
23	9-Octadecenamide, (Z)-	C18H35NO	281.4766	13.981	59	95696	92	116.88	59	57777	79	34.19	59	59965	91	66.02
24	1,4-Benzenediamine,N-(1,3-dimethylbutyl)-N’-phenyl–	C18H24N2	268.4	14.702	211	19829	88	14.19	211	11313	90	5.09	211	14634	92	13
25	Bis(2-ethylhexyl) phthalate	C24H38O4	390.5561	14.907	149	8842735	100	1874.08	149	6095762	100	2398.09	149	5766494	100	4543.63
26	Tris(2,4-di-tert-butylphenyl) phosphate	C42H63O4P	662.9210	14.981	–	–	–	–	–	–	–	–	57	217114	100	17.62
27	Cyclononasiloxane, octadecamethyl-	C18H54O9Si9	667.3855	15.172	73	49335	96	1.75	73	12930	84	4.55	73	11363	85	6.47
28	1,3-Benzenedicarboxylic acid, bis(2-ethylhexyl) ester	C24H38O4	390.5561	15.595	70	13414	92	4.09	70	18032	97	6.24	70	11704	88	7.16

RT = Retention time, QI = Quantification ion, m/z = Mass/charge, SI = Similarity index, S/N = Signal to noise, (-) = Compound not detected.

3.9. Transcriptome analysis of WT and mutants in A. flavus

In this study, both Δpdc1 and K258R strains were found to be more effective in inhibiting sclerotia production, growth and conidia formation in A. flavus compared to WT, and they were also found to increase AFB₁ production in the presence or absence of a carbon source. Therefore, WT, Δpdc1, and K258R of A. flavus were further sequenced using RNA-Seq technology. After checking GC content distribution, sequencing error rate and filtering out low-quality reads, clean reads for subsequent analysis were obtained, and the sequencing data for each sample are summarised in Table S4. The results showed that the Q30 and Q20 quality levels of each biological repeat were above 97% and 94%, respectively. The statistical findings of the expression characteristics of all the genes in each sample are shown in Figure S4a,b. Principal component analysis (PCA) based on the gene expression levels revealed that replicates belonging to different samples were well gathered into three groups (WT, Δpdc1, and K258R) (Figure S4c). Further results of sample correlation tests also demonstrated a clear heterogeneity between the three groups of sequencing samples (Figure S4d). All the above results showed that the high-throughput sequencing data of the three groups are of high quality and meet the requirements for subsequent transcriptomics analysis.

To analyse the DEGs between WT, Δpdc1, and K258R strains, all assembled unigenes were examined using the DESeq2 R package (1.20.0). The specifically expressed DEGs and overlapped DEGs across the three samples (WT, K258R, and Δpdc1) are shown in Figure 9a. Statistics on the number of DEGs (both up- and down-regulated) for each comparison combination, and the criteria for screening differences are shown in Table S5 and Figure S4. Furthermore, volcano plots were obtained using log2 of fold change (x-axis) and log10 of Padj (y-axis) and used to visualise the common DEGs (Figure 9c,d). The results showed that 3,817 DEGs including 1,904 down-regulated and 1,913 up-regulated genes were found in Δpdc1 vs. WT (Figure 9b,c). In K258R vs. WT, among 2,950 DEGs, 1,584 genes were down-regulated and 1,366 were up-regulated (Figure 9b,d). To observe the gene expression patterns, hierarchical clustering of all the DEGs was performed based on the log10 RPKM values for the nine samples (red colour reveals genes that were relatively up-regulated, while green indicates down-regulated genes and black means not statistically significant) when K258R/Δpdc1 groups were compared to the WT group (Figure 9e).

Figure 9.

Analysis of differentially expressed genes (DEGs) in Aspergillus flavus. (a) Venn diagram of differentially expressed genes in the Δpdc1 (KOpdc1), K258R, and WT strain. The number in each circle represents the amount of differentially expressed genes between the different comparisons. (b) Statistical histogram of the DEGs number in different comparison combinations (test versus control). Gray and blue indicate down-regulated and up-regulated genes, respectively, and the numbers on the column indicate the number of differentially expressed genes. (c, d) Volcano map of the DEGs in WT, Δpdc1, and K258R strains. The abscissa in the figure is the log2FoldChange value, the ordinate is −log10Padj, and the blue dotted line represents the threshold line of the DEG screening criteria. (e) Clustering/heat map of DEGs. The abscissa in the figure is the sample name, and the ordinate is the normalised value of the different gene FPKM. The redder the colour, the higher the expression; the greener, the lower the expression.

3.10. GO enrichment and KEGG pathways analysis of DEGs in strains

To analyse the function of the obtained DEGs and to further understand the regulation of PDC1 and K258 in A. flavus secondary metabolites, growth and development, these DEGs were classified and analysed for both GO (Gene Ontology) function enrichment analysis and KEGG pathway enrichment analysis by using clusterProfiler software (3.8.1). GO and KEGG enrichment analyses of the DEG in the Δpdc1 versus WT were first carried out. GO analysis of DEGs was divided into three aspects including molecular function (MF), cellular component (CC), and biological process (BP). The top 10 (up-regulated and down-regulated) gene-enriched GO terms in the CC, MF, and BP are identified in Figure S5a,b. Enrichment analyses of the up-regulated DGEs showed that ribosome, aflatoxin biosynthesis and valine, leucine and isoleucine biosynthesis were significantly enriched (Figure S5c). In the down-regulated DEGs, starch and sucrose metabolism were significantly enriched (Figure S5d). We also found that in the Δpdc1 vs. WT, most DEGs related to the ribosome biogenesis (Figure S6) were up-regulated, indicating that PDC1 can affect ribosomal protein gene expression, thereby affecting cell metabolism.

GO and KEGG enrichment analyses of the DEG in the K258R versus WT were also conducted. The top 30 most enriched GO terms were found in the analysis of up-regulated DEGs (Figure 10a) and down-regulated DEGs (Figure 10b) in K258 vs. WT. The most significantly enriched GO terms of down-regulated DEGs in BP include the organophosphate catabolic process and carbohydrate metabolic process. Whereas MF analysis includes transporter activity, hydrolase activity, hydrolase activity, oxidoreductase activity, iron ion binding, haem binding, tetrapyrrole binding, primary active transmembrane transporter activity, P–P-bond-hydrolysis-driven transmembrane transporter activity, ATPase activity, transmembrane transporter activity, organic acid transmembrane transporter activity, amino acid transmembrane transporter activity, carboxylic acid transmembrane transporter activity, organic anion transmembrane transporter activity, flavin adenine dinucleotide binding, active transmembrane transporter activity, FAD binding, serine-type peptidase activity, serine hydrolase activity, and anion transmembrane transporter activity (Figure 10b). KEGG pathway analysis of up-regulated DEGs in K258 vs. WT also showed that pentose and glucuronate interconversions and valine, leucine and isoleucine degradation were significantly enriched (Figure 10c). Whereas the analysis of down-regulated DGEs in K258 vs. WT demonstrated that cyano-amino acid metabolism, starch, sucrose metabolism, and nitrogen metabolism were significantly enriched (Figure 10d). Additionally, the top one enriched KEGG pathway for up-regulated DEGs was obtained in both K258 vs. WT and Δpdc1 vs. WT was valine, leucine and isoleucine degradation (Figures 10c and S5c), while the top one for down-regulated DEGs was starch and sucrose metabolism (Figures 10d and S5d). Collectively, these findings and observations suggest that PDC1 and K258 succinylation are involved in regulating several metabolic pathways in A. flavus.

Figure 10.

Gene ontology (GO) and KEGG pathway enrichment analysis of differentially expressed genes (DEGs). (a) The top 30 most enriched GO terms found in the analysis of up-regulated DEGs in K258 vs. WT. Different colors represent biological processes (BP), cellular components (CC), and molecular functions (MF). (b) The top 30 most enriched GO terms found in the analysis of down-regulated DEGs in K258 vs. WT. (c) The top 20 functionally enriched KEGG pathways were found in the analysis of up-regulated DEGs in K258 vs. WT. (d) The top 20 functionally enriched KEGG pathways were found in the analysis of down-regulated DEGs in K258 vs. WT.

Then, networks were visualised by Cytoscape 3.9.1 software and analysed by Network Analyzer, and all the topological parameters are shown in Table S6. From the analysis of the up-regulated PPI network in the Δpdc1 vs. WT, top nodes were highly connected for degree ≥ 86 (Figure S7a and Table S7). Similarly, for the down-regulation, the top most connected nodes were identified for degree ≥ 9 (Figure S7b and Table S7). In addition, from the analysis of the up-regulated PPI network in the K258R vs. WT, top nodes were highly connected for degree ≥ 12 (Figure S7c and Table S7). Similarly, for the down-regulation, the top most connected nodes were identified for degree ≥ 8 (Figure S7d and Table S7).

3.11. Effect of Δpdc1 on genes expression related to metabolites production

Through GC-MS analysis, succinic acid 2-ethoxyl pentadecyl ester was observed to be high in Δpdc1 compared to WT and Δpdc1.com (Figure 11a and Table S3). HPLC results also showed that pyruvic acid, malic acid and succinic acid were significantly increased in both the Δpdc1 and K258R in contrast to the WT and Δpdc1.com strains (Figure 11b). These results hypothesised that the up-regulation of mdh1 and idhA genes in the TCA cycle could be attributed to the increased production of both succinic acid and malic acid, which further enhances the conversion to oxaloacetate, citrate, acetyl-CoA, and pyruvate, then pyruvate further converted to oxaloacetate. According to MacDonald et al. (1989) and Parker et al. (2021), ester of carboxylic acids is permeable to cells and then hydrolysed to carboxylic acids once inside the cell, affirming the entry of succinic acid 2-ethoxyl pentadecyl ester into the TCA cycle where it is able to modulate the expression of the genes in A. flavus (Figure 11c).

Figure 11.

Illustration of metabolites implication on the regulatory genes. (a) Heat map for succinic acid 2-ethoxyl pentadecyl. (b) Organic acid metabolites in Aspergillus flavus strains. Pyruvic acid (1), malic acid (2), lactic acid (3), and succinic acid (5) are shown in the figure. (c) Novel regulation and metabolic trade-off mechanism in interlinked pathways of fungal secondary metabolism and central metabolism in response to the lack of pdc1 gene in A. flavus. [abbreviation: dihydrosterigmatocystin (DHST), dihydro-Omethylsterigmatocystin (DHOMST), aflatoxin B₁ (AFB₁), aflatoxin B₂ (AFB₂), aflatoxin G₁ (AFG₁), aflatoxin G₂ (AFG₂), sterigmatocystin (St), versicolorin B (VERB), versicolorin a (VERA), oxoaverantin (OAVN), hydroxyversicolorone (HVN), versiconal hemiacetal acetate (VHA), versiconol acetate (VOAc), versiconol (VOH), norsolorinic acid (NOR), hydroxyaverantin (HAVN), and averufin (AVF)]. pkiA: encoding for pyruvate kinase, enoA: enolase, pgkA: phosphoglycerate mutase, gpmA: 3-phosphoglycerate kinase, gapdh: glyceraldehyde-3-phosphate dehydrogenase, gpdA: glyceraldehyde-3-phosphate dehydrogenase, fbpA: fructose-1,6-bisphosphate aldolase, pfkA: 6-phosphofructokinase 1, pgiA: glucose-6-phosphate isomerase, and glkA: glucokinase. mdhA: NAD-dependent malate dehydrogenase, fumA: fumarase, sdhC: succinate dehydrogenase, ygrA: succinyl-CoA synthetase, kdhA: 2-oxoglutarate dehydrogenase, idhA: NADP-dependent isocitrate dehydrogenase, acoA: aconitate hydratase, citA: ATP-citrate lyase, and pdhA: pyruvate dehydrogenase.

In addition, HPLC results also demonstrated that the production of both pyruvic acid and lactic acid were increased in Δpdc1, while only pyruvic acid was increased in the K258R strain compared to the WT and Δpdc1.com strains (Figure 11b). It is important to note that acetyl-CoA is known as the important precursor for aflatoxin biosynthesis (Yabe and Nakajima 2004). Thus, the metabolic pathways illustrated in Figure 11c including the glycolysis, TCA cycle, and aflatoxin biosynthesis are related to acetyl-CoA. The up-regulated genes and the increase in the AFB₁ production in Δpdc1 and K258R strains might be due to the increase in pyruvate as well as lactic acid which enhanced the conversion to acetyl-CoA then to malonyl-CoA. The arrows in Figure 11c indicate differentially expressed genes through comparative RNA-seq analysis, while the graphs represent qRT-PCR results of the indicated genes. The two results (RNA-seq and qRT-PCR) supported each other, indicating that the findings of this experiment are credible.

4. Discussion

Pyruvate produced by the glycolysis pathway is an essential metabolite at the branch point of numerous metabolic pathways. It is converted to CO₂ and acetaldehyde, a reaction catalysed by pyruvate decarboxylase (PDC) in several organisms (Gao et al. 2016). The pyruvate decarboxylase gene (pdc) encoding the PDC enzyme has been studied in some organisms (Alharbi et al. 2022), but its function in A. flavus is not yet known. In the present study, our main interest was to identify the essential role of PDC and its K258 in development and metabolite production in A. flavus. During the identification process of pdc1 in A. flavus, we found that it is conserved among different species. The protein sequence alignment of the analysed species demonstrated that numerous amino acid residues in the active site are conserved in PDC1 proteins from various species (Figure 1). Other studies further confirmed that the protein sequence of PDC1 is conserved in several species, such as Saccharomyces cerevisiae, Acetobacter pasteurianus, Sarcina ventriculi, Zea mays, Zymomonas mobilis, Zymobacter palmae, Kluyveromyces lactis, and Kluyveromyces marxianus (Alharbi et al. 2022; Parker et al. 2021). In this study, phylogenetic tree analysis indicated that PDC1 is highly conserved across a variety of species, and domain analysis showed the presence of the conserved TPP_enzyme_M, TPP_enzyme_N, and TPP_enzyme_C domains in the PDC1 protein in all the analysed fungal species. These results were supported by findings from Choo et al. (2018), who reported that the PDC1 protein possesses several domains that are well conserved among different species.

During growth analysis, we observed that the growth patterns of A. flavus were similar across all strains in MM and MMA media. However, Δpdc1 and K258R mutant strains grew slowly on MMGlu and MMGly media compared to WT and ∆pdc1.com strains (Figure 4). These findings were corroborated by the findings of Son et al. (2012) who previously reported that deletion of pdc1 reduces mycelial growth in Gibberella zeae. Similarly, a previous study in Saccharomyces cerevisiae found a growth defect in the pdc negative strain on MM media with glucose as the sole carbon source (Flikweert et al. 1996). Conidiophores are recognised as multicellular structures that can produce conidia during vegetative growth under favourable conditions (Waterhouse et al. 2009). Our findings on succinylation were corroborated by Wang et al. (2023), who tested the effect of succinylation on A. flavus by mutating the vbs succinylation modification site (K135R). The authors reiterated that, compared to WT, the K135R mutant strain exhibited decreased growth, conidiophores, and conidia production on PDA media. A previous study demonstrated that the lack of Pdc1 activity generally induces excessive accumulation of pyruvate, leading to subtle growth retardation and abnormal colony morphology (Choo et al. 2018). These results suggest that PDC1 and its succinylation play important functions in vegetative growth and conidiation in A. flavus.

Site mutation and knockout strains of the pdc1 gene were also performed to explore the role of PDC1 and its Ksucc in sclerotia production in A. flavus. Our data showed that the K258E strain exhibited an identical number of sclerotia to the WT and Δpdc1.com strains. However, the sclerotia production in the Δpdc1 and K258R strains was significantly decreased compared to that in the WT and Δpdc1.com strains. Further, qRT-PCR results demonstrated that the expression levels of the two genes related to sclerotia development (nsdC and nsdD) were significantly down-regulated in Δpdc1 and K258R compared to that in WT and Δpdc1.com (Figure 6). A recent study indicated that Ksucc of VBS could affect the development and production of sclerotia in A. flavus (Wang et al. 2023), and an earlier study carried out in our laboratory also found that the Ksucc of AflE is involved in regulating sclerotia development in A. flavus (Ren et al. 2018). Taken together, these results suggest that Δpdc1 and succinylation may delay, prevent, or inhibit sclerotia formation, confirming that both PDC1 and its Ksucc regulate sexual development in A. flavus.

The role of PDC1 and K258 in response to different stresses was also examined. Compared to WT and ∆pdc1.com, both ∆pdc1 and K258R mutants were shown to be more sensitive to SDS, D-Sorbitol, CFW, CR, H₂O₂, tBooH, MMS, and HU. However, the K258E mutant was highly sensitive to SDS and could resist the cell wall stress induced by CFW. Surprisingly, all the strains enhanced the growth of A. flavus under osmotic stress analysis. Yet, both Δpdc1 and K258R strains revealed significantly less resistance in contrast to the WT and ∆pdc1.com strains. As a result, we suspect that pdc1 involves in several mechanisms in the regulation of oxidative, genotoxic, osmotic, cell membrane, and cell wall stresses in A. flavus. We also propose that a post-translational modification (PTM), Ksuss, could be involved in the function of PDC1 in response to the aforementioned stresses. The study conducted by Retanal et al. (2021) reported that several fungi, such as A. fumigatus, Candida albicans, and Cryptococcus neoformans, use PTMs mechanisms to regulate gene expression during various stresses, including osmotic and heat stress. In addition, post-translational modifications (PTMs), including lysine succinylation (Ksucc), are critical for enabling fungal cells to adapt to high-stress environments, such as host infection, by regulating signalling cascades, protein degradation and activation (Adejor et al. 2024). Comparative studies on A. flavus further demonstrate that environmental conditions modulate Ksucc levels, influencing aflatoxin biosynthesis and metabolic adaptation, with succinylation of AflE at Lys370 enhancing aflatoxin and sclerotial production under osmotic stress (0.5 mol/L and 1 mol/L NaCl), thereby reinforcing its positive regulatory role (Ren et al. 2018; Wang et al. 2023), confirming that both PDC1 and its Ksucc regulate environmental stress.

To further explore the regulatory function and the mechanism of Ksucc in aflatoxin biosynthesis, we evaluated the function of PDC1 and its Ksucc in AFB₁ production. Obtained data proved that AFB₁ production was considerably increased in Δpdc1 and K258R strains while decreased in K258E compared to that in WT and ∆pdc1.com. Further, qRT-PCR results demonstrated that the expression levels of the genes involved in the biosynthesis of aflatoxins were significantly higher in both Δpdc1 and K258R strains, while significantly lower in K258E compared to those in WT and ∆pdc1.com strains (Figure 5). Taken together, it is conceivable that differences in AFB₁ production between WT and mutant strains may have been caused by abnormal expression of aflatoxin-related genes. In this study, we also examined the role of PDC1 and its Ksucc in pathogenicity by infecting both the peanut seeds and maize kernels. Results revealed that ∆pdc1 and K258R strains on the infected crop seeds appeared to produce a lower number of conidia than the WT and ∆pdc1.com strains of A. flavus. In addition, ∆pdc1 and K258R strains increased the production of AFB₁ on the infected crop seeds, while it was decreased in the K258E strain compared to WT and ∆pdc1.com. A previous study (Retanal et al. 2021) reaffirmed that the occurrence of PTMs in fungal pathogens such as Aspergillus fumigatus, Candida albicans, Cryptococcus gattii, and Cryptococcus neoformans could influence the production of virulence factors and the outcome of infections. These findings suggested that PDC1 and its Ksucc may influence the virulence of A. flavus in crop seeds.

In addition to aflatoxins and related biosynthetic precursors, A. flavus can produce many other secondary metabolites (Yabe and Nakajima 2004; Gao et al. 2016). Therefore, to better understand the effect of PDC1 and its Ksucc on A. flavus secondary metabolites, comparative metabolomics using HPLC and GC-MS were employed to detect and identify differentially expressed secondary metabolites in the extracts of mutant and Wild-Type (WT) strains (Flikweert et al. 1996; Raj et al. 2002). In the present study, we found that deletion of pdc1 and mutation of K258 in A. flavus affected the production of several volatile metabolites (Tables 1, 2 and S3). We also found that deletion of pdc1 and mutation of K258 in A. flavus influenced the production of various organic acids, including succinic acid, malic acid, pyruvic acid and lactic acid. A study by Liu et al. (2020) on metabolic changes following Pdc1 depletion in Zymomonas mobilis revealed an increase in pyruvate, glycerol and lactate production. In contrast, the authors observed a decrease in the production of ethanol, succinate and several amino acids including serine, tyrosine, glutamate, citrulline, ornithine and acetyl-ornithine upon pdc depletion.

RNA-Seq technology has been widely used in microbiology research to investigate dynamic changes in RNA expression, including mycotoxin biosynthesis, environmental stress response, nutrient metabolism and conidia germination (Liu et al. 2015; Tamano et al. 2015; Chai et al. 2016). RNA-Seq enables the examination of differentially expressed genes (DEGs) and their relevant pathways as well as their cellular processes under specific conditions or time points (Stark et al. 2019). To gain further insight into how PDC1 and Ksucc regulate secondary metabolite production, growth and development in A. flavus, we selected two mutants, ∆pdc1 and K258R, for transcriptomic analysis using RNA-seq technology, compared to the WT. When ∆pdc1 was compared to WT, 3,817 DEGs were identified including 1,904 down-regulated and 1,913 up-regulated genes. In contrast, 2,950 DEGs were found in the K258R compared to WT, with 1,584 genes down-regulated and 1,366 up-regulated genes (Figure 9). After analyzing the up-regulated protein-protein interaction (PPI) network of DEGs in the Δpdc1 vs. WT comparison, we observed that highly connected nodes (degree ≥ 86) correspond to genes involved in the ribosome biogenesis pathway (Table S7). Furthermore, in the Δpdc1 vs. WT comparison, ribosome biogenesis-related DEGs were predominantly up-regulated (Figure S6), suggesting PDC1 modulates ribosomal protein gene expression to influence cellular metabolism.

5. Conclusions

In conclusion, this paper presents the characterisation of the functions of PDC1 and its Ksucc in A. flavus relevant to the development of the fungus and the production of secondary metabolites. The results indicated that both Δpdc1 and K258R decreased the production of conidia and conidiophores and did not produce any sclerotia. Additionally, PDC1 and its Ksucc play important roles in stress response, secondary metabolite biosynthesis and virulence of A. flavus. The results further demonstrated that the lack of PDC1 and mutation of K258 in A. flavus affected the production of several volatile metabolites and organic acids. Through a comparative RNA-seq analysis, our study identified the up- and down-regulated differentially expressed genes (DEGs) in the ∆pdc1 and K258R strains, highlighting the hub genes, enriched GO terms, and pathways of the DEGs. We believe that the results obtained will promote further studies to enhance our understanding of the regulatory mechanisms involved in the development of A. flavus and the production of its secondary metabolites. Furthermore, the genes identified in this study may serve as potential targets for future research on metabolite production and the molecular mechanisms for controlling this pathogenic fungus.

Funding Statement

This research was supported by the National Natural Science Foundation of China [31972214].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21501203.2025.2522081