Transcriptome and metabolome profiling reveal the inhibitory effects of food preservatives on pathogenic fungi

Materials

Aspergillus flavus (1-b2-1.3), Alternaria alternata (2-c2-4.2), and Talaromyces funiculosus (4-a3-2.1) were obtained from the Biotechnology Research Laboratory of Langfang Normal University.

Spore suspension preparation and plate culture

The mold preserved on the PDA slant medium was inoculated on the PDA plate (Lin et al., 2018) and incubated at 30 °C during seven days. Sterile water was added with a sterile triangular glass rod to gently scrape the spores and mycelium on the medium to make part of the mycelium suspended in the water layer. The water layer was then transferred to a 50 ml triangular conical flask and 15∼25 glass beads of about six mm in diameter were added. The flask was shaken at 30 °C for about 3 mins, and then the mold containing spores and mycelium suspension was filtered off the mycelium with three layers of sterilizing gauze. The resulting spore suspension was counted by hemocytic technique plate, and the spore suspension concentration was controlled in the range of 10⁵∼10⁶ CFU/mL (Dantigny & Nanguy, 2009). 0.2 ml of the above spore suspension was evenly coated onto PDA medium and incubated in a constant temperature incubator at 28 °C during 3 days. Fungus with a diameter of six mm were punched out on the Petri dishes with a sterile punch and set aside.

The effect of preservatives on the growth of the test strain

Two ml of different concentrations of food preservative solution were mixed with 100 ml of PDA medium and poured into sterile Petri dishes, the medium was retained for naturally cooling and put in the incubator, 28 °C during 24 h to ensure sterility of plate. The uncontaminated medium was taken and placed a piece of beaten fungal mass in the center of the Petri dish. Three parallel experiments were conducted for each treatment, with equal amount of sterile water in the medium as the control group. The culture was incubated in a constant temperature incubator at 28 °C during 36 h. The colony diameter was measured by the crossover method and the inhibition rate was calculated (Schutt & Netzly, 1991; Lederer, Lorenz & Seemüller, 1992). ${\displaystyle \text{Inhabition rate}\left(\text{\%}\right)=\left(1-\text{net growth of treatment}/\text{net growth of control}\right)\times 100}$ where, net growth = colony diameter (mm)−disc diameter (6 mm).

Regression equation of the inhibition of the preservative to the test strains

The mycelial growth rate method (Raimbault & Alazard, 1980; Smith & Walker, 1981) was used to determine the magnitude of inhibition of the three conservatives against each of the three pathogenic fungi. According to the biostatistical chance value conversion table, the inhibition rates were converted into chance values and the logarithm of the concentrations was taken. The vertical coordinate was the chance value and the horizontal coordinate was the logarithm of the concentration, and the regression equation and value of the virulence of the three preservatives against each of the three pathogenic fungi were derived using an Excel sheet (McClelland, Bernhardt & Casadevall, 2006).

Scanning electron microscope observation

After collected fresh mycelium and fixed it in 2.5% glutaraldehyde at 4 °C during 12 h, the samples were dehydrated in 70% ethanol during 15 mins, dehydrated in 90% and anhydrous ethanol during 20 mins, respectively. The samples were then transferred to 25%, 50%, 75%, and 100% concentrations of acetone during 10 mins each (Lima, Colombo & de Almeida Junior, 2019). After that, the samples were successively transferred to an elution buffer containing isoamyl acetate and acetone during 10 mins. Finally, the samples were dried and fixed, and the sample was observed by scanning electron microscope (SEM) after the ion sputtering sprayer sprayed the samples with gold.

Transcriptome analysis

Three biological replicates of sec-butylamine treatment of A. flavus and control under the same growth conditions were used for transcriptome sequencing. Z1, Z2, and Z3 represented treatment samples, and ZC1, ZC2, and ZC3 represented control samples. Total RNA was extracted using the Ezup Column Fungi Genomic DNA Purification Kit (Sangon Biotech, Shanghai, China). The RNA concentration was measured using Nanodrop, and RNA purity and integrity were assessed by 1% agarose gel electrophoresis. 28S/18S and RNA integrity number (RIN) were analyzed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). Agilent 2100 and qPCR were used for quality control. The libraries were subjected to transcriptome sequencing using the Illumina Novaseq platform.

Raw data (raw reads) of fastq format were firstly processed through fastp software, the raw data were filtered for reads with junctions and all-A bases and for more than 10% N- or low-quality reads. Hisat2 v2.0.5 was used to compare the filtered reads with the NRRL 3357 (NCBI accession number: EQ963471) reference genome for analysis. Feature Counts v1.5.0-p3 was used to count the reads numbers mapped to each gene. The Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) of each gene was calculated based on the length of the gene and reads count mapped to this gene (Trapnell et al., 2012), and differential analysis of mRNAs across samples were performed using DESeq2 R package (1.20.0) (Robinson, Mccarthy & Smyth, 2010), with a screening condition of FDR (false discovery rate) < 0.05 and —log2FC (fold change)— > 1.

The clusterProfiler R package was used to analyze GO (Gene Ontology) enrichment of differentially expressed genes. We used clusterProfiler R package to test the statistical enrichment of differential expression genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Reverse transcription of RNA and q RT-PCR

After extraction of RNA using the SUPERspin Plant RNA Rapid Extraction Kit (Vazyme, Nanjing, China), cDNA was synthesized using the HiScript III All-in-One RT SuperMix Perfect for qPCR (Vazyme, Nanjing, China) reverse transcription kit. Primers for candidate genes were added to cDNA and Taq pro Universal SYBR qPCR Master Mix Kit for qRT-PCR analysis, which was performed in a fluorescent quantitative PCR instrument (Bio-Rad, Hercules, CA, USA) according to the manufacturer’s instructions. For each sample, we employed three biological replicates using AFLA_nor as the reference gene. Conditions for PCR amplification were as follows: each cycle consisted of 90 s of pre-denaturation at 95 °C, 5 s of denaturation at 95 °C, 15 s of annealing at 60 °C, and 20 s of extension at 72 °C. After every cycle, which totaled 35 cycles and three replicates of each reaction, we obtained fluorescence signals. For the primer design, Primer Express 2.0 was used (Table S1). The 2^−ΔΔC approach was employed for data analysis (Livak & Schmittgen, 2001).

Metabolome analysis

The samples (one mL) were freeze-dried and resuspended with prechilled 80% methanol by well vortex. D1, D2, D3, D4, D5, and D6 represented treatment samples, and DC1, DC2, DC3, DC4, DC5, and DC6 represented control samples. Then the samples were incubated on ice during 5 mins and centrifuged at 15,000 g, 4 °C during 15 mins. Some of supernatant was diluted to final concentration containing 53% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube and then were centrifuged at 15,000 g, 4 °C during 15 mins. Finally, the supernatant was injected into the LC-MS/MS system analysis. UHPLC-MS/MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA) coupled with an Orbitrap Q Exactive TM HF mass spectrometer or Orbitrap Q Exactive TMHF-X mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) in Novogene Co., Ltd. (Beijing, China). Samples were injected onto a Hypersil Goldcolumn (100 × 2.1 mm, 1.9 µm) using a 12-min linear gradient at a flow rate of 0.2 mL/min. Q Exactive TM HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.5 kV, capillary temperature of 320 °C, sheath gas flow rate of 35 psi and aux gas flow rate of 10 L/min, S-lens RF level of 60, Aux gas heater temperature of 350 °C. UHPLC-MS/MS generated the raw data files, and perform peak alignment, peak picking, and quantitation for each metabolite were processed by the Compound Discoverer 3.3 (CD3.3, Thermo Fisher Scientific, Waltham, MA, USA). The KEGG database, HMDB database and LIPIDMaps database were used to annotate these metabolites. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed at metaX (Wen et al., 2017). The univariate analysis (t-test) was used to calculate the statistical significance (P-value). The difference criterion of metabolites was VIP > 1 and P-value < 0.05 and fold change ≥ 2 or FC ≤ 0.5.

Effect of three preservatives on the inhibition effect of the test strains

The effects of sec-butylamine, citric acid, and potassium sorbate on the inhibition of A. flavus, A. alternata, and T. funiculosus were shown in Fig. 1. It could be seen that the three preservatives have a strong inhibitory effect on the three pathogenic fungi, and the inhibition effects became more obvious as the concentration increases. Under the conditions of this experiment, six mg/ml of sec-butylamine, 12 mg/ml of citric acid, and two mg/ml of potassium sorbate had a significant inhibitory effect on the growth of A. flavus. Four mg/ml of sec-butylamine, 10 mg/ml of citric acid, and six mg/ml of potassium sorbate had a significant inhibitory effect on the growth of A. alternata. While 10 mg/ml of sec-butylamine, 12 mg/ml of citric acid, and eight mg/ml of potassium sorbate significantly inhibited the growth of T. funiculosus.

Sec-butylamine was an alkaline preservative that creates an alkaline growth environment for the strain, which affected the growth of A. flavus, A. alternata, and T. funiculosus. The higher the concentration of sec-butylamine, the stronger the alkalinity, and the more significant the inhibition effect (Fig. 1). Under the treatment of citric acid, the strain was in an acidic growth environment, which inhibited the growth and reproduction of A. flavus, A. alternata, and T. funiculosus. The higher the concentration of citric acid, the stronger the acidity, and the more significant the inhibition effect (Fig. 1). Potassium sorbate could inhibit the activity of dehydrogenase in pathogenic fungi and destroy various important enzyme systems of A. flavus, A. alternata, and T. funiculosus, effectively inhibiting their growth and reproduction, thus achieving the purpose of fungus inhibition and antisepsis. It could be seen that the three preservatives have a significant inhibitory effect on the three pathogenic fungi, respectively, and within a certain concentration range, the inhibitory effects of single preservatives with different concentrations and different preservatives with the same concentration on the pathogenic fungi are significant. The inhibition of mycelial growth of pathogenic fungi was enhanced with the increase of concentration (Fig. S1).

Inhibition equations of the three preservatives against the test strains

Table S2 displaied the inhibition equations and EC ₅₀ (effective concentrations) values of sec-butylamine, citric acid, and potassium sorbate against A. flavus, A. alternata, and T. funiculosus, respectively. It could be seen that the three preservatives have good growth inhibition effects on the three pathogenic fungi within the range of test concentrations. Under isolated conditions, the growth inhibition rate increased with the increase of the preservative concentration. The smaller the EC ₅₀ value of the preservative to the pathogenic fungi, the stronger the inhibition effect on the mycelial growth of the pathogenic fungi. Through calculation and comparison, it was found that the EC ₅₀ value of potassium sorbate to A. flavus was the smallest at 0.4819 mg/ml, indicating the strongest inhibition effect, which was consistent with the experimental results of the strain growth effect. The EC ₅₀ value of sec-butylamine against A. alternata was the smallest at 1.0709 mg/ml, indicating the strongest inhibitory effect, which was consistent with the experimental results of the strain growth effect. The EC ₅₀ value of potassium sorbate against T. funiculosus was the smallest at 1.9216 mg/ml, indicating the strongest inhibitory effect, which was consistent with the experimental results of the strain growth effect (Table S2).

Microstructure of A. flavus

In order to explore the change of the microscopic structure of A. flavus mycelium under the action of sec-butylamine, the microscopic structure of A. flavus cultured on solid medium during 7 days were observed by scanning electron microscopy. The microstructural changes of A. mycelia after treatment with sec-butylamine reflected the degree of inhibition of A. flavus mycelia growth by sec-butylamine. In the control group, the form of A. flavus mycelium was normal, and the mycelium was smooth and full. In the sec-butylamine treatment group, the morphology of A. flavus mycelia was changed, and spines appeared on the surface and some mycelia were dented (Fig. 2).

Transcriptome analysis of A. flavus

The transcriptome and metabolome analysis of A. flavus mycelia before and aftersec-butylamine treatment were performed to further explore the inhibitory mechanism of sec-butylamine on A. flavus in this study.

Transcriptome sequencing identified 273,675,516 raw reads and 41.05G raw bases, 270,194,450 clean reads and 40.52G clean bases after filtering out inferior reads. The Q30 ranged from 94.64% to 95.28%, while the GC content varied from 51.18% to 53.05% for all samples, and these results demonstrate the high quality of the sequencing data obtained (Table S3). Furthermore, evaluating the sequencing data against the A. flavus genome revealed that the mapped reads ranged from 91.99% to 93.21%, and the Uniq mapped reads ranged from 91.43% to 92.79% (Table S4). PCA analysis of the samples showed that samples of the same treatments aggregated together while different treatments among different varieties could be differentiated, and there were differences between Z1, Z2, Z3 and ZC1, ZC2, ZC3, implying the reliability of our sequencing data (Fig. S2). We detected 967 DEGs (differentially expressed genes) in Z vs. ZC groups, including 638 with up-regulation and 329 with down-regulation (Fig. S3).

The identified DEGs were broadly categorized according to their biological process, and molecular function. Within biological processes, DEGs were mainly associated with cellular response to chemical stimulus, response to chemical, cellular response to toxic substance, and cellular oxidant detoxification. Within cellular components, DEGs were categorized in terms of ribosomal subunit. In molecular function, DEGs were generally associated with anion transmembrane transporter activity and organic acid transmembrane transporter activity (Fig. S4). KEGG enrichment analysis demonstrated that DEGs in Z vs. ZC were mostly related to metabolic pathway, biosynthesis of secondary metabolites and peroxisome (Fig. S5).

Key genes involved in sec-butylamine inhibits A. flavus

To study the key genes of sec-butylamine inhibits A. flavus, the differentially expressed genes were analyzed. AFLA_053390 encoded beta-1,3-endoglucanase, after sec-butylamine treatment, the expression decreased. AFLA_121370 encoded 1,3-beta-glucanosyltransferase, and the expression was down-regulated after treatment in A. flavus. AFLA_024930 encoded extracellular endoglucanase, after treatment with sec-butylamine, the expression was down-regulated in A. flavus. AFLA_041970 encoded chitin binding protein, the expression was down-regulated after treatment. AFLA_002830 encoded alpha-trehalose-phosphate synthase subunit and AFLA_030450 encoded trehalose synthase, both of them were treated with sec-butylamine and their expression levels decreased in A. flavus (Table 1). Q RT-PCR was used to confirm key genes associated with sec-butylamine inhibits A. flavus, and the results were in line with the transcriptome result. They showed the same trend in different samples (Fig. 3).

Metabolome analysis of A. flavus

PCA analysis classified overall variation as PC1 and PC2, contributing 23.56/22.61%, respectively (Fig. 4). The results of metabolome analysis revealed that metabolites of A. flavus treated with sec-butylamine were significantly different from control samples (Fig. 5).

A total of 108 differential metabolites were detected in the D and DC samples based on the UPLC-MS/MS detection platform and the local metabolic database (Biomarker Technologies, Beijing, China) (Table S5). Furthermore, the KEGG pathway enrichment analysis showed that the significantly enriched pathways were metabolic pathway, biosynthesis of antibiotics, purine metabolism, starch and sucrose metabolism (Fig. 6).

Key metabolites involved in sec-butylamine inhibits A. flavus

We further analyzed metabolites associated with sec-butylamine inhibits A. flavus. (3R)-4,4-Dimethyl-2-oxotetrahydro-3-furanyl beta-D-glucopyranoside (Com_5857_neg), Trehalose (Com_3182_neg), D-Glucosamine 6-phosphate (Com_4401_neg), as well as Sucrose (Com_494_neg) present at high levels after sec-butylamine treatment in A. flavus. In contrast, D-Gluconic acid (Com_9540_neg), D-Glucose 6-phosphate (Com_723_neg), Verbascose (Com_11501_neg), and D-(-)-Fructose (Com_285_neg) present at low levels after sec-butylamine treatment in A. flavus (Fig. 7).