Abstract

   Phenyllactic acid (PLA), a promising food preservative, is safe and
   effective against a broad spectrum of food-borne pathogens. However,
   its mechanisms against toxigenic fungi are still poorly understood. In
   this study, we applied physicochemical, morphological, metabolomics,
   and transcriptomics analyses to investigate the activity and mechanism
   of PLA inhibition of a typical food-contaminating mold, Aspergillus
   flavus. The results showed that PLA effectively inhibited the growth of
   A. flavus spores and reduced aflatoxin B[1] (AFB[1]) production by
   downregulating key genes associated with AFB[1] biosynthesis. Propidium
   iodide staining and transmission electron microscopy analysis
   demonstrated a dose-dependent disruption of the integrity and
   morphology of the A. flavus spore cell membrane by PLA. Multi-omics
   analyses showed that subinhibitory concentrations of PLA induced
   significant changes in A. flavus spores at the transcriptional and
   metabolic levels, as 980 genes and 30 metabolites were differentially
   expressed. Moreover, KEGG pathway enrichment analysis indicated
   PLA-induced cell membrane damage, energy-metabolism disruption, and
   central-dogma abnormality in A. flavus spores. The results provided new
   insights into the anti-A. flavus and -AFB[1] mechanisms of PLA.

   Keywords: phenyllactic acid, Aspergillus flavus, aflatoxin B[1],
   transcriptome, metabolome

1. Introduction

   Mycotoxin contamination in foodstuff threatens human health and causes
   great economic losses every year [[36]1]. According to the United
   Nations Food and Agricultural Organization (FAO), approximately
   one-quarter of agricultural products are affected by mycotoxins
   [[37]1]. Approximately 450 million people in developing countries
   experience long-term exposure to large doses of mycotoxins
   [[38]2,[39]3]. Multiple mycotoxins are present in the food chain, as
   contamination can occur at every stage (cultivation, storage,
   processing, and transportation) of food production [[40]4]. Numerous
   toxins, e.g., aflatoxins, ochratoxins, fumonisins, zearalenone,
   patulin, enniatins, Alternaria toxins, and trichothecenes, are
   frequently encountered in food systems [[41]4]. Among them, aflatoxin
   B[1] (AFB[1]), produced by Aspergillus flavus, is the most important
   and hazardous mycotoxin [[42]5]. The AFB[1] was classified as a group I
   human carcinogen because it was strongly teratogenic, carcinogenic,
   hepatotoxic, and immunosuppressive to the health of humans [[43]6].
   Therefore, efficient control of A. flavus infections and AFB[1]
   contamination has become an important research topic.

   Currently, AFB[1] contamination produced by A. flavus is controlled
   using physical, chemical, and biological methods [[44]7]. In
   particular, chemical fungicides are effective and economical tools to
   control A. flavus infections and AFB[1] contamination [[45]8]. However,
   excessive and non-selective use of chemical fungicides promotes drug
   resistance and may result in toxic residues that pose a serious threat
   to human health [[46]9]. Therefore, there is an urgent need to find
   efficient and non-toxic natural anti-fungal compounds to replace or
   complement traditional chemical fungicides to improve food safety and
   shelf life [[47]8]. Phenyllactic acid (PLA) is a natural phenolic acid
   commonly found in honey and traditional fermented foods that exhibits
   broad-spectrum and efficient inhibitory activity against food-borne
   bacterial and fungal pathogens [[48]10,[49]11]. In addition, PLA is
   safe and has good water solubility and no objectionable odor, making it
   a promising preservative for the food industry [[50]12,[51]13].
   Therefore, the anti-microbial mechanism of PLA has attracted attention.
   Ning et al. (2017) reported dual anti-bacterial targets of PLA were the
   cell membrane and genomic DNA. When Bacillus cereus was exposed to PLA,
   proteomic analysis showed that its K+ transport was inhibited, leading
   to the dissipation of the membrane potential and disruption of ribosome
   function [[52]14]. In a recent study, combining transcriptomics and
   metabolomics revealed that PLA acted against Rhizopus oryzae by
   inhibiting energy metabolism [[53]15]. Moreover, previous studies had
   demonstrated that PLA effectively inhibits the growth and AFB[1]
   biosynthesis of A. flavus [[54]16,[55]17]. However, the mechanism of
   inhibition has not been reported.

   In this study, we investigated the activity of PLA to inhibit A. flavus
   and AFB[1] and revealed its inhibition mechanism by physicochemical,
   morphological, metabolomic, and transcriptomic analyses. The results
   provided new insights into the effects of PLA against A. flavus and
   AFB[1].

2. Results

2.1. Anti-A. flavus and -AFB[1] Activity of PLA

   In a potato dextrose broth (PDB) medium, the growth of A. flavus spores
   was inhibited by 2.5 and 5 mg mL^−1 concentrations of PLA, and no
   growth was detected at PLA concentrations ≥7.5 mg mL^−1 ([56]Figure
   1a). In addition, no A. flavus spores were detected on the potato
   dextrose agar (PDA) with a PLA ≥ 10 mg mL^−1. Thus, the minimum
   inhibitory concentration (MIC) and minimum fungicidal concentration
   (MFC) of PLA against A. flavus spores were 7.5 and 10 mg mL^−1,
   respectively. In determining the time-dependent fungicidal activity of
   PLA, the number of viable spores remained relatively stable at a ≤2.5
   mg mL^−1 concentration of PLA ([57]Table 1). At 5 and 7.5 mg mL^−1 PLA,
   the numbers of viable spores decreased from 5 to 15 min and from 15 to
   30 min (p < 0.05). At 10, 15, and 20 mg mL^−1 PLA, the A. flavus spores
   were completely inactivated after 120, 15, and 5 min, respectively.

Figure 1.

   [58]Figure 1
   [59]Open in a new tab

   Effects of phenyllactic acid on Aspergillus flavus (a) growth, (b)
   aflatoxin B[1] production, and (c) relative expression levels of
   aflatoxin B[1] biosynthesis-related genes. The error bars indicate the
   standard deviation (n = 3). * represents statistical significance at p
   < 0.05.

Table 1.

   The numbers of viable Aspergillus flavus spores at different
   phenyllactic acid concentrations over time.
   PLA (mg mL^−1) Viable Spores (10^4 Spores mL^−1)
   5 min 15 min 30 min 60 min 120 min
   0 11.67 ± 0.58 ^a 12.00 ± 1.00 ^a 11.67 ± 0.58 ^a 11.67 ± 1.53 ^a 10.33
   ± 0.58 ^a
   2.5 11.33 ± 0.58 ^a 12.67 ± 0.58 ^a 12.00 ± 1.00 ^a 10.67 ± 2.52 ^a
   10.67 ± 1.15 ^a
   5 7.67 ± 0.58 ^a 4.67 ± 0.58 ^b 2.33 ± 0.58 ^c 1.33 ± 0.21 ^c 1.57 ±
   0.12 ^c
   7.5 7.33 ± 0.58 ^a 3.57 ± 0.38 ^b 1.23 ± 0.25 ^c 1.03 ± 0.06 ^c 0.53 ±
   0.06 ^c
   10 2.17 ± 0.15 ^a 1.50 ± 0.36 ^a 0.14 ± 0.03 ^c 0.02 ± 0.01 ^c ND
   15 0.18 ± 0.02 ND ND ND ND
   20 ND ND ND ND ND
   [60]Open in a new tab

   Values are mean ± SD (n = 3). Different letters within a row indicate
   statistically significant differences (p < 0.05). ND represents not
   detected.

   PLA effectively inhibited the production of AFB[1] by A. flavus, and
   the results showed that the concentration of AFB[1] in the supernatant
   of the treatment group (5 mg mL^−1 PLA) was 10.20 ± 0.03 μg L^−1, which
   was significantly lower (p < 0.05) than that of the control group
   (181.20 ± 1.08 μg L^−1) ([61]Figure 1b). The result of quantitative
   real-time polymerase chain reaction (qRT-PCR) analysis showed that the
   relative expressions of four key genes related to AFB[1] biosynthesis
   were also significantly (p < 0.05) downregulated after treatment with
   PLA ([62]Figure 1c).

2.2. Effects of PLA on Cell Membrane Integrity and Morphology of A. flavus
Spores

   After a 30-minute incubation in a PDB medium with 0, 5, or 10 mg mL^−1
   PLA, the cell membranes of 8.59 ± 0.62%, 54.26 ± 1.88%, and 98.02 ±
   0.01% of A. flavus spores were disrupted, respectively ([63]Figure 2a).
   In addition, TEM examination showed that PLA damaged the A. flavus
   spore structure in a dose-dependent manner, resulting in the loss of
   cytoplasm and abnormal cell morphology ([64]Figure 2b).

Figure 2.

   [65]Figure 2
   [66]Open in a new tab

   (a) Flow cytometry dot plots and (b) transmission electron microscopy
   images of Aspergillus flavus spores treated with 0 mg mL^−1, 5 mg mL^−1
   or 10 mg mL^−1 phenyllactic acid for 30 min.

2.3. Global Analysis of The Transcriptomic Response and qRT-PCR Validation

   In this study, approximately 42,000,000 clean reads were obtained from
   each treatment, and about 91% of clean reads were mapped to the A.
   flavus NRRL3357 genome ([67]Supplementary Table S1). The raw data have
   been deposited at the National Microbiology Data Center (NMDC) with the
   accession number NMDC40037319.

   Comparative transcriptomic results showed that the treatment of A.
   flavus spores with PLA resulted in changes at the mRNA level
   ([68]Supplementary Figure S1). In comparison to the control group, a
   total of 980 genes were differentially expressed (|log2 (fold change)|
   > 1, false discovery rate (FDR) < 0.05) in the PLA group, of which 711
   differentially expressed genes (DEGs) were upregulated and 269 DEGs
   were downregulated. The functional categories of DEGs were annotated
   through GO enrichment analysis and the results are shown in [69]Figure
   3a. DEGs are grouped into three categories: biological process,
   cellular component, and molecular function, consisting of 15, 13, and
   13 subcategories, respectively. Within the biological process category,
   metabolic processes, cellular processes, single-organism processes, and
   cellular component organization or biogenesis were the main
   subcategories distributed by DEGs. The cell, cell part, organelle, and
   membrane were the main subcategories within the cellular component
   categories. While for the molecular function category, the top four
   subcategories by frequency were binding, catalytic activity,
   transporter activity, and structural molecule activity.

Figure 3.

   [70]Figure 3
   [71]Open in a new tab

   Functional and pathway analysis of differentially expressed genes. (a)
   Functional categorization of differentially expressed genes in GO. KEGG
   enrichment analysis of (b) up- and (c) down-regulated differentially
   expressed genes.

   The functions of DEGs were further annotated using KEGG enrichment
   analysis. The results indicated that upregulated DEGs were mainly (FDR
   < 0.05) involved in the pathways of ribosome biogenesis in eukaryotes,
   carbon metabolism, RNA polymerase, 2-oxocarboxylic acid metabolism, and
   the citrate cycle (TCA cycle) ([72]Figure 3b). However, PLA treatment
   downregulated DEGs mainly (FDR < 0.05) associated with proteasome,
   glycolysis/gluconeogenesis, pyruvate metabolism, and carbon metabolism
   ([73]Figure 3c). To validate the results of the transcriptome, four
   DEGs (AFLA_130070, HK; AFLA_037480, ENO; AFLA_049290, CS; AFLA_098420,
   SDHA) were analyzed by qRT-PCR. Among them, HK and ENO were
   downregulated in the PLA treatment group, whereas CS, SDHA, Erg25,
   Erg5, TTDA, and ERCC3 were upregulated, which was consistent with the
   RNA-Seq results ([74]Supplementary Figure S2).

2.4. Global Analysis of The Metabolomic Response

   A total of 59 metabolites were detected, including lipids and
   lipid-like molecules (21), organic acids and derivatives (21), organic
   oxygen compounds (7), organoheterocyclic compounds (5), benzenoids (4),
   and organic nitrogen compounds (1) ([75]Table 2).

Table 2.

   Intracellular metabolites detected by GC-MS in Aspergillus flavus
   treated with or without phenyllactic acid.
   No. Compounds RT ^(1) (min) Peak Intensities (10^5) VIP ^(2)
   0 mg mL^−1 2.5 mg mL^−1
   Lipids and lipid-like molecules
   1 Hexanoic acid 5.67 1.54 ± 0.77 0.80 ± 0.56 0.70
   2 Heptanoic acid 6.91 0.45 ± 0.08 0.36 ± 0.06 0.74
   3 4-Hydroxybutanoic acid 7.85 1.32 ± 0.12 2.40 ± 0.19 1.30
   4 Octanoic acid 8.19 4.90 ± 1.20 2.45 ± 0.18 1.14
   5 Nonanoic acid 9.44 7.86 ± 1.79 4.02 ± 0.25 1.15
   6 Decanoic acid 10.66 10.07 ± 2.67 4.24 ± 0.35 1.16
   7 Undecanoic acid 11.81 2.48 ± 0.09 2.61 ± 0.09 0.85
   8 Dodecanoic acid 12.91 5.02 ± 0.92 3.15 ± 0.13 1.14
   9 Azelaic acid 14.51 0.38 ± 0.04 0.48 ± 0.09 0.83
   10 Myristic acid 14.99 5.57 ± 0.99 3.06 ± 0.12 1.20
   11 Palmitic acid 16.73 97.44 ± 9.12 90.34 ± 4.39 0.65
   12 Heptadecanoic acid 17.77 1.28 ± 0.04 1.64 ± 0.08 1.28
   13 1-Octadecanol 17.87 0.83 ± 0.03 1.19 ± 0.05 1.32
   14 Oleic acid 18.43 8.97 ± 0.54 12.99 ± 0.52 1.31
   15 Stearic acid 18.63 41.96 ± 24.41 51.04 ± 3.94 0.36
   16 Linoleic acid 18.72 2.81 ± 0.13 4.74 ± 0.14 1.33
   17 Arachidic acid 20.22 1.49 ± 0.19 1.04 ± 0.15 1.11
   18 2-Palmitoylglycerol 21.16 0.31 ± 0.01 0.42 ± 0.03 1.27
   19 1-Monopalmitin 21.41 68.53 ± 41.99 104.90 ± 1.86 0.79
   20 Glycerol monostearate 22.82 54.81 ± 1.76 53.95 ± 0.88 0.44
   21 Ergosterol 26.29 6.32 ± 0.81 4.02 ± 0.06 1.23
   Organic acids and derivatives
   22 Lactic acid 5.57 21.86 ± 5.75 29.14 ± 11.82 0.54
   23 Glycolic acid 5.74 2.29 ± 0.33 2.92 ± 0.14 1.09
   24 L-Alanine 6.09 48.18 ± 27.50 66.46 ± 39.96 0.38
   25 Oxalic acid 6.48 8.02 ± 1.38 16.97 ± 7.28 0.93
   26 L-2-Aminobutyric acid 6.97 1.44 ± 0.87 1.85 ± 1.17 0.32
   27 L-Proline 7.12 0.80 ± 0.42 0.88 ± 0.09 0.20
   28 L-Valine 7.62 3.49 ± 1.27 6.05 ± 1.81 0.91
   29 Urea 7.91 6.80 ± 0.35 5.64 ± 3.22 0.34
   30 L-Threonine 8.61 9.65 ± 0.28 8.88 ± 2.32 0.34
   31 Succinic acid 8.88 0.91 ± 0.08 0.75 ± 0.02 1.14
   32 Fumaric acid 9.29 2.57 ± 0.28 1.63 ± 0.02 1.26
   33 Serine 9.55 11.52 ± 1.69 8.88 ± 3.70 0.61
   34 L-Aspartic acid 10.30 31.77 ± 0.81 39.64 ± 6.97 0.89
   35 Malic acid 11.16 3.35 ± 0.24 2.47 ± 0.07 1.26
   36 L-5-Oxoproline 11.56 19.11 ± 1.01 17.99 ± 5.23 0.22
   37 4-Aminobutanoic acid 11.62 0.06 ± 0.14 2.61 ± 0.13 1.34
   38 L-Asparagine 12.40 7.03 ± 1.68 4.86 ± 2.76 0.64
   39 L-Glutamic acid 12.67 87.37 ± 4.71 72.29 ± 15.44 0.80
   40 L-Glutamine 14.37 25.26 ± 13.29 13.02 ± 7.21 0.72
   41 L-Ornithine 14.88 6.62 ± 3.40 2.22 ± 0.15 0.96
   42 Citric acid 14.91 107.46 ± 14.80 68.91 ± 1.01 1.21
   Organic oxygen compounds
   43 Diethylene glycol 7.96 1.03 ± 0.06 1.23 ± 0.13 0.99
   44 Erythritol 11.36 20.15 ± 0.44 26.85 ± 0.61 1.33
   45 D-Glucitol 16.11 7.98 ± 0.24 7.79 ± 0.75 0.24
   46 D-Mannitol 16.19 1.79 ± 1.04 1.24 ± 0.70 0.50
   47 Scyllo-Inositol 16.35 0.95 ± 0.02 1.38 ± 0.02 1.34
   48 D-Glucose 16.54 0.84 ± 0.12 3.84 ± 0.10 1.34
   49 Myo-Inositol 17.59 7.30 ± 0.15 10.37 ± 0.28 1.33
   Organoheterocyclic compounds
   50 3-Pyridinol 6.57 1.54 ± 0.15 3.13 ± 0.66 1.18
   51 4-Pyridinol 6.79 2.63 ± 0.13 3.52 ± 0.15 1.29
   52 Niacin 8.62 0.55 ± 0.06 0.87 ± 0.11 1.20
   53 2-Pyridinepropanol 9.62 3.69 ± 0.74 8.42 ± 0.80 1.29
   54 Levoglucosan 13.70 0.43 ± 0.25 1.00 ± 0.14 1.16
   Benzenoids
   55 Benzoic acid 8.01 3.05 ± 0.15 3.32 ± 1.87 0.24
   56 Benzeneacetic acid 8.66 ND 0.45 ± 0.07 1.31
   57 Terephthalic acid 14.49 12.33 ± 2.20 8.66 ± 1.49 0.99
   58 Bisphenol A 18.54 3.63 ± 0.50 3.01 ± 0.22 0.90
   Organic nitrogen compounds
   59 Ethanolamine 8.29 1.81 ± 0.29 2.38 ± 0.25 1.01
   [76]Open in a new tab

   Note: ^(1) RT: retention time; ^(2) This value is calculated with the
   PLS-DA model.

   In the principal component analysis (PCA; PC1: 56.9% and PC2: 15.5%),
   the samples treated with or without PLA were completely separated, with
   the five biological replicates grouped in the same cluster, suggesting
   differences between the control and PLA treatments ([77]Figure 4a).
   According to the partial least squares discrimination analysis (PLS-DA)
   model (Q2 = 0.949, R2 = 0.980), 30 out of the detected 59 metabolites
   were differential with variable importance of the projection (VIP) > 1
   ([78]Figure 4b). Compared to the control, the peak intensities of 19
   differential metabolites (DMs) were higher in the PLA treatment and
   those of 11 lower ([79]Figure 4c). The Kyoto Encyclopedia of Genes and
   Genome (KEGG) enrichment analysis was carried out to annotate the DMs
   into different metabolic pathways, mainly including the citrate cycle
   (TCA cycle), alanine, aspartate, and glutamate metabolism, the
   biosynthesis of unsaturated fatty acids, glyoxylate, and dicarboxylate
   metabolism, nicotinate and nicotinamide metabolism and butanoate
   metabolism ([80]Figure 4d and [81]Supplementary Table S2).

Figure 4.

   [82]Figure 4
   [83]Open in a new tab

   Influence of PLA on metabolomic profiles of Aspergillus flavus. (a) PCA
   and (b) PLS-DA of the metabolites detected by GC-MS in A. flavus
   treated with or without phenyllactic acid. (c) Variable importance of
   the projection (VIP) values of metabolites with VIP > 1 in the PLS-DA
   model. (d) KEGG pathway analysis of differential metabolites.

3. Discussion

   We studied applying PLA, a promising preservative for the food
   industry, against AFB[1] producing A. flavus CICC 2219. In line with
   the MFC of PLA against food-borne Aspergillus species [[84]16,[85]18],
   the MIC and MFC of PLA against A. flavus spores were 7.5 and 10 mg
   mL^−1. In addition, the inactivating activity of PLA against A. flavus
   spores was time- and dose-dependent. Compared to lactic acid, a
   traditional food-preserving organic acid, PLA had been reported to have
   a more rapid inactivation of Enterobacter cloacae and Listeria
   monocytogenes [[86]11,[87]19,[88]20]. AFB[1] is the most dangerous
   secondary metabolite biosynthesized by A. flavus and has caused serious
   health and economic problems [[89]2]. Previous studies have reported
   that PLA can effectively inhibit the biosynthesis of aflatoxins, and
   has stronger anti-aflatoxin activity compared to hydroxyphenyllactic
   acid, indole lactic acid, and lactic acid [[90]17]. In this study, the
   finding that PLA has been shown to have efficient inhibitory activity
   on the growth and AFB[1] biosynthesis of A. flavus was again confirmed
   in vitro. In addition, qRT-PCR was used to further investigate the
   molecular mechanism of AFB[1] reduction under PLA stress, and it was
   found that the expression of four key genes related to AFB[1]
   biosynthesis (aflD, aflM, aflQ, and aflR) was significantly
   downregulated (p < 0.05) after PLA treatment [[91]21]. These results
   demonstrated for the first time that PLA reduces the concentration of
   AFB[1] biosynthesis in A. flavus by downregulating the expression of
   genes related to AFB[1] biosynthesis, which is similar to the results
   of previous studies [[92]22,[93]23].

   The cell membrane plays an important role in the cell structure and
   physiological function [[94]24]. Based on the results of propidium
   iodide (PI) staining and transmission electron microscopy (TEM)
   analysis, the cell membrane was one of the anti-A. flavus spore targets
   of PLA. Previous studies had demonstrated that PLA is able to damage
   the cell membrane of bacteria, resulting in bacteriostatic activity
   [[95]11,[96]12,[97]19]. In this study, the cytoplasmic loss of A.
   flavus spores after PLA treatment may also be caused by damage to the
   cell membrane, similar to the mechanism of bacterial inhibition by PLA.
   In addition, changes related to the cell membrane have also been found
   at the level of transcription and metabolism. Firstly, ABC transporters
   are a superfamily of widely distributed membrane proteins that
   transport substances across the cell membrane [[98]25]. In this study,
   the genes encoding the ABC multidrug transporters (AFLA_064360, ABCB1;
   AFLA_036800, SNQ2) were upregulated in the PLA treatment group,
   suggesting that PLA may affect the normal transport function of the A.
   flavus spore cell membrane [[99]26]. Similarly, Lei et al. reported
   that trans-anethole inhibited A. flavus and upregulated the expression
   of its ABC multidrug transporters genes [[100]27]. Likewise, when
   Candida albicans was exposed to Huanglian Jiedu, which has antifungal
   activity, the transcriptome results showed a significant upregulation
   of ABC multidrug transporters gene expression [[101]28]. Secondly,
   fatty acids are important components of cell membranes with biological
   and structural functions, especially unsaturated fatty acids, which
   have an important impact on cell membrane permeability and fluidity
   [[102]29,[103]30]. Luz et al. (2014) reported that Origanum vulgare L.
   essential oil damaged the cell membranes of Salmonella typhimurium, and
   induced an increase in unsaturated fatty acids at sublethal
   concentrations [[104]31]. In this study, the KEGG pathway analysis of
   DMs showed that the biosynthesis of unsaturated fatty acids was
   enriched, and the peak intensities of unsaturated fatty acids (linoleic
   acid and oleic acid) were higher in the PLA treatment group. In
   addition, in the results of transcriptome analysis, the expression of
   genes (AFLA_004970, ELO3; AFLA_019690, ACAA1) associated with
   unsaturated fatty-acid synthesis were all increased in the PLA
   treatment group compared to the control group. Thirdly, ergosterol is
   an essential sterol component of fungal cell membranes and plays an
   important role in maintaining cell-membrane integrity and cell function
   [[105]32]. Therefore, the ergosterol biosynthesis pathway is an
   important target for many fungicides [[106]28]. For example, Xu et al.
   (2021) found that cuminaldehyde inhibited A. flavus by decreasing the
   relative content of ergosterol and downregulating the expression of
   genes related to the ergosterol synthesis pathway [[107]32]. Similarly,
   the peak intensity of ergosterol in A. flavus spores decreased after
   PLA treatment in this study. In contrast, the relative expression
   levels of all genes (AFLA_110370, Erg6; AFLA_115530, Erg25,
   AFLA_116730, FDFT1; AFLA_039610, Erg6; AFLA_028640, Erg5) associated
   with ergosterol biosynthesis were upregulated after PLA treatment. This
   may be a response to PLA damage to the cell membrane of A. flavus,
   which was similar to the findings of Yang et al. (2016) [[108]28].

   Energy metabolism is an essential metabolic pathway in microbial growth
   and development [[109]33]. In a recent study, a proteomic analysis
   showed that PLA could disrupt energy metabolism to inhibit R. oryzae
   [[110]15]. Similarly, KEGG enrichment analysis of DMs and DEGs in this
   study revealed enrichment of some metabolic pathways related to energy
   metabolism, including pyruvate metabolism, oxidative phosphorylation,
   glycolysis/gluconeogenesis, and the citrate cycle (TCA cycle),
   suggesting that PLA also influences energy metabolism in A. flavus
   spores. Glucose is one of the essential substrates of energy
   metabolism, which is converted to pyruvate via glycolysis and finally
   enters the TCA cycle [[111]34]. In this study, the peak intensity of
   glucose increased in the PLA treatment group, whereas the transcriptome
   results showed that the expression levels of most genes associated with
   glycolysis were downregulated ([112]Figure 5a). This indicated that PLA
   may inhibit the expression of glycolysis-related genes, resulting in
   the accumulation of glucose. Moreover, PLA decreased the peak
   intensities of citric acid, malic acid, succinic acid, and fumaric
   acid, significant intermediates in the TCA cycle, and the results were
   consistent with the findings of Fan et al. (2022) [[113]15]. However,
   the transcriptomic results were the opposite, with the expression
   levels of most genes related to the TCA cycle being upregulated,
   probably due to the different concentration and time of the PLA
   treatment as well as different inhibitory microorganisms ([114]Figure
   5a). In addition, oxidative phosphorylation is an important part of
   energy metabolism [[115]35]. In this study, the relative expression of
   all DEGs associated with oxidative phosphorylation was upregulated
   after treatment with PLA ([116]Figure 5b). Overall, PLA disrupted the
   energy metabolism in A. flavus spores.

Figure 5.

   [117]Figure 5
   [118]Open in a new tab

   KEGG enrichment analysis of differentially expressed genes and
   differential metabolites associated with (a) glycolysis and TCA cycle,
   and (b) oxidative phosphorylation. Squares and circles represent
   differentially expressed genes (DEGs) and differential metabolites
   (DMs), respectively. The red, green, and blue in the squares or circles
   represent the upregulation, downregulation, and up as well as
   downregulation of DEGs and DMs, respectively.

   Ling et al. (2017) reported that bacterial genomic DNA was able to
   interact with PLA in vitro; presumably, DNA was one of the inhibitory
   targets of PLA [[119]12]. In this study, the expression of a DNA
   replication-related gene (AFLA_045970, MCM4), encoding the DNA
   replication licensing factor MCM4, was found to be significantly
   downregulated after PLA treatment ([120]Table 3). MCM proteins are key
   components of the pre-replication complex and may play a role in the
   development of replication forks as well as the recruitment of other
   DNA replication-related proteins [[121]36]. Among them, MCM4 is a
   highly conserved mini-chromosome maintenance protein that is essential
   for the start of eukaryotic genome replication [[122]37]. This
   suggested that PLA may affect DNA replication. Similarly, Yao et al.
   (2019) reported that DNA replication in Zygosaccharomyces rouxii was
   inhibited by acids produced by Tetragenococcus halophilus [[123]35].

Table 3.

   Differentially expressed genes associated with cDNA replication, RNA
   transcription, and protein synthesis.
   Gene Description log2FC Gene ID
   DNA replication
   DNA replication licensing factor/MCM4 −1.39 AFLA_045970
   Basal transcription factors
   TFIIH basal transcription factor complex/TTDA 1.11 AFLA_060250
   DNA excision repair protein/ERCC3 1.14 AFLA_006070
   RNA polymerase
   DNA-directed RNA polymerase I subunit/RPA1 1.54 AFLA_017530
   DNA-directed RNA polymerases I, II, and III subunit/RPABC1 1.08
   AFLA_017150
   DNA-directed RNA polymerase I subunit/RPA49 1.24 AFLA_048730
   DNA-directed RNA polymerases I, II, and III subunit/RPABC2 1.13
   AFLA_030070
   DNA-directed RNA polymerase I subunit/RPA43 1.33 AFLA_071240
   DNA-directed RNA polymerase I subunit/RPA12 1.12 AFLA_129630
   DNA-directed RNA polymerases I and III subunit/RPAC2 1.27 AFLA_055410
   DNA-directed RNA polymerase I subunit/RPA2 1.28 AFLA_037100
   DNA-directed RNA polymerases I and III subunit/RPAC1 1.07 AFLA_137020
   RNA transport
   translation initiation factor/EIF4A 1.39 AFLA_086940
   translation initiation factor/EIF4E 1.09 AFLA_118010
   ribonuclease/rnz 1.12 AFLA_083090
   translation initiation factor/EIF5 1.25 AFLA_046010
   nuclear pore complex protein/NUP93 1.19 AFLA_018110
   Spliceosome
   heat shock 70 kDa protein/HSPA1s 2.22 AFLA_043390
   ATP-dependent RNA helicase/DDX5 1.42 AFLA_043840
   U4/U6 small nuclear ribonucleoprotein/SNU13 1.46 AFLA_130100
   pre-mRNA-splicing factor ATP-dependent RNA helicase/DHX16 1.52
   AFLA_006100
   pre-mRNA-splicing factor ATP-dependent RNA helicase/DHX16 1.68
   AFLA_047160
   pre-mRNA-splicing factor ATP-dependent RNA helicase/DHX15 1.87
   AFLA_132910
   heat shock 70 kDa protein/HSPA1s 1.31 AFLA_012200
   survival of motor neuron-related-splicing factor/SMNDC1 1.14
   AFLA_129350
   Aminoacyl-tRNA biosynthesis
   isoleucyl-tRNA synthetase/IARS −1.64 AFLA_117970
   tryptophanyl-tRNA synthetase/WARS 1.09 AFLA_054690
   aspartyl-tRNA(Asn)/glutamyl-tRNA(Gln) amidotransferase subunit/gatA 1.1
   AFLA_089240
   isoleucyl-tRNA synthetase/IARS 1.71 AFLA_073060
   Ribosome
   large subunit ribosomal protein/L24e 1.44 AFLA_133350
   large subunit ribosomal protein/L33 1.07 AFLA_129150
   large subunit ribosomal protein/L14 1.79
   Aspergillus_flavus_newGene_2055
   small subunit ribosomal protein/S5 1.06 AFLA_132790
   small subunit ribosomal protein/S17 1.17 AFLA_034020
   large subunit ribosomal protein/L5e 1.57 AFLA_018730
   large subunit ribosomal protein/L27 1.08 AFLA_006810
   large subunit ribosomal protein/L17 1.28 AFLA_021760
   small subunit ribosomal protein/S14 1.18 AFLA_043320
   small subunit ribosomal protein/S6 1.29 AFLA_067460
   large subunit ribosomal protein/L34 1.07 AFLA_112240
   large subunit ribosomal protein/L19 1.03 AFLA_098510
   Ribosome biogenesis in eukaryotes
   nuclear GTP-binding protein/NUG1 −1.09 AFLA_034320
   H/ACA ribonucleoprotein complex subunit/NOP10 1.26 AFLA_006780
   U3 small nucleolar RNA-associated protein/UTP18 1.28 AFLA_130080
   nuclear GTP-binding protein/NUG2 1.43 AFLA_110550
   nucleolar GTP-binding protein/NOG1 1.48 AFLA_134410
   N-acetyltransferase/NAT10 1.64 AFLA_029920
   small nucleolar RNA-associated protein/DIP2 1.16 AFLA_080740
   rRNA 2′-O-methyltransferase fibrillarin/NOP1 1.3 AFLA_016990
   nucleolar protein/NOP56 1.32 AFLA_085280
   5′–3′ exoribonuclease/XRN1 −1.51 AFLA_066480
   U4/U6 small nuclear ribonucleoprotein/SNU13 1.46 AFLA_130100
   H/ACA ribonucleoprotein complex subunit/DKC1 1.37 AFLA_048200
   U3 small nucleolar RNA-associated protein/UTP5 1.06 AFLA_026620
   nuclear GTP-binding protein/NUG1 1.31 AFLA_069120
   nucleolar protein/NOP4 1.42 AFLA_113720
   U3 small nucleolar ribonucleoprotein protein/IMP4 1.18 AFLA_018390
   H/ACA ribonucleoprotein complex subunit/NHP2 1.57 AFLA_017830
   U3 small nucleolar RNA-associated protein/UTP15 1.3 AFLA_042550
   nonsense-mediated mRNA decay protein/NMD3 1.03
   Aspergillus_flavus_newGene_82
   ribosome biogenesis protein/BMS1 1.15 AFLA_136700
   H/ACA ribonucleoprotein complex subunit/GAR1 1.52 AFLA_052540
   U3 small nucleolar RNA-associated protein/UTP13 1.19 AFLA_112310
   casein kinase II subunit/CSNK2B 1.29 AFLA_069840
   nucleolar protein/NOP58 1.46 AFLA_020450
   U3 small nucleolar ribonucleoprotein protein/IMP3 1.19 AFLA_046700
   Protein export
   YidC/Oxa1 family membrane protein insertase/yidC 1.19 AFLA_006440
   endoplasmic reticulum chaperone/BiP 1.07 AFLA_035620
   [124]Open in a new tab

   Moreover, 16 genes involved in transcription were detected as DEGs, and
   notably, their relative expression levels were all increased in the PLA
   treatment group ([125]Table 3). AFLA_060250 and AFLA_006070 encode
   TFIIH basal transcription factor complex TTDA subunit and DNA excision
   repair protein ERCC3, respectively, both of which are components of the
   multi-subunit transcription/repair factor IIH (TFIIH) complex and
   affect basal transcription [[126]38,[127]39]. RNA polymerase is the
   essential enzyme for transcription and its transfers of genetic
   information from DNA to mRNA, while the RNA transport process,
   transferring a variety of RNA from the nucleus to the cytoplasm, all
   have an important impact on the transcription process
   [[128]35,[129]40]. Notably, the relative expression levels of all DEGs
   related to RNA polymerase (AFLA_017530, RPA1; AFLA_017150, RPABC1;
   AFLA_048730, RPA49; AFLA_030070, RPABC2; AFLA_071240, RPA43;
   AFLA_129630, RPA12; AFLA_055410, RPAC2; AFLA_037100, RPA2; AFLA_137020,
   RPAC1) and RNA transport (AFLA_086940, EIF4A; AFLA_118010, EIF4E;
   AFLA_083090, rnz; AFLA_046010, EIF5; AFLA_018110, NUP93) were also
   increased in the PLA treatment group. Moreover, for protein synthesis
   (spliceosome, aminoacyl-tRNA biosynthesis, ribosome, ribosome
   biogenesis in eukaryotes, and protein export), a total of 51 genes were
   identified as DGEs. Among them, only 3 genes (AFLA_117970, IARS;
   AFLA_034320, NUG1; AFLA_066480, XRN1) showed downregulation of relative
   expression levels after PLA treatment, while the rest of the genes were
   upregulated. Overall, these results showed for the first time that the
   processes of central dogma may be an important target of the PLA effect
   in A. flavus, and further research on key genes will be conducted in
   the future.

4. Conclusions

   In this study, the anti-A. flavus and -AFB[1] activity and mechanism of
   PLA were investigated by physicochemical, morphological, metabolomic,
   and transcriptomic analyses. The results showed that PLA effectively
   inhibited and inactivated A. flavus, and decreased AFB[1] production by
   downregulating key genes associated with AFB[1] biosynthesis. In
   addition, combined transcriptomic and metabolomic analyses showed that
   PLA induced multiple effects such as cell-membrane damage,
   energy-metabolism disruption, and central-dogma abnormality. The
   results provide new insights into the activity and mechanisms of PLA in
   inhibiting toxigenic fungi.

5. Materials and Methods

5.1. Reagents, Media, and Fungal Strain

   PLA, isopropanol, acetonitrile, methoxyamine hydrochloride, pyridine,
   and N-methyl-N-(trimethylsilyl)trifluoroacetamide with 1%
   trimethylchlorosilane were purchased from Sigma-Aldrich Co. (Shanghai,
   China). Czapek-Dox agar, PDB, and PDA were purchased from Haibo Co.
   (Qingdao, China).

   A. flavus CICC 2219, a standard strain for the identification of
   aflatoxin-producing A. flavus, was obtained from the China Center of
   Industrial Culture Collection (CICC, Beijing, China). The strain was
   cultivated on Czapek-Dox agar at 28 °C for 7 days to prepare spore
   suspension in sterile water. The spore suspension was adjusted to 10^7
   spores mL^−1 using a hemocytometer.

5.2. Determination of Minimum Inhibitory and Minimum Fungicidal
Concentrations

   In a previous study, the MFC of PLA against A. flavus was 7.5–10 mg
   mL^−1, indicating that the inhibitory activity of PLA on A. flavus is
   strain-specific [[130]16]. Therefore, the MIC and MFC of PLA against A.
   flavus CICC 2219 were determined by a previously described method with
   slight modifications [[131]16].

   Briefly, 80 μL of the PDB medium, 20 μL of A. flavus spore suspension
   (10^7 spores mL^−1), and 100 μL of various concentrations of PLA
   solution were mixed in a 96-well microliter plate to make a final
   concentration of PLA at 0, 2.5, 5, 7.5, 10, 15 and 20 mg mL^−1,
   respectively. Then, the plate was incubated in the dark at 28 °C, and
   the growth of A. flavus was monitored at 12 h intervals for 3 days
   using a multimode plate reader (Synergy™ HTX, BioTek Instruments,
   Winooski, VT, USA). The MIC was defined as the lowest concentration of
   PLA that completely inhibited growth. To determine MFC, 50 μL of spore
   suspension was spread on PDA plates containing PLA at concentrations ≥
   MIC and incubated at 28 °C for 72 h. The MFC was defined as the lowest
   concentration of PLA that resulted in colony-free plates.

5.3. Fungicidal Activity of PLA over Time

   The 30 μL of A. flavus spore suspension (10^7 spores mL^−1) and 4970 μL
   of various concentrations of PLA solution were mixed in a 10 mL
   centrifuge tube to make the final concentration of PLA at 0, 2.5, 5,
   7.5, 10, 15, and 20 mg mL^−1, respectively. Then, at 5, 15, 30, 60, and
   120 min, 50 μL of the 0–10^5 fold diluted suspensions were spread on
   PDA plates. The plates were incubated at 28 °C for 3 days, and colonies
   were counted.

5.4. Effects of PLA on AFB1 Content and Biosynthesis-Related Genes

   The effects of PLA on AFB[1] content and biosynthesis-related genes
   were analyzed as described previously with a slight modification
   [[132]22]. Briefly, 30 μL of spore suspension (10^7 spores mL^−1) was
   inoculated into 30 mL of the PDB medium containing 0 and 5 mg mL^−1
   PLA, and incubated at 180 rpm and 28 °C for 4 days. Subsequently, the
   suspension was centrifuged (12,000 rpm, 10 min, 4 °C), and the
   supernatant was collected. The AFB^1 concentration in the supernatant
   was determined by isotope dilution liquid chromatography-tandem mass
   spectrometry (ID-LC-MS/MS) according to the Chinese national standards
   (GB 5009.22-2016).

   The total RNA of mycelia was extracted using an RNAprep Pure Plant Kit
   (Tiangen Biotech Co., Ltd., Beijing, China). The quality and quantity
   of RNA were determined using a NanoDrop 2000 spectrophotometer (Thermo
   Fisher Scientific, Wilmington, DE, USA) and an Agilent 2100 Bioanalyzer
   (Agilent Technologies, Santa Clara, CA, USA). Then, cDNA was
   synthesized using a PrimeScript RT reagent Kit (Takara Biotechnology
   Co., Ltd., Dalian, China). Four key genes (aflD, aflM, aflO, and aflQ)
   related to AFB[1] biosynthesis were amplified using the primers listed
   in [133]Supplementary Table S3. Twenty μL qRT-PCR contained 10 μL of
   2×Real PCR EasyTM Mix-SYBR, 0.8 μL of forward and reverse primer (10
   μM), 2 μL of cDNA, and 6.4 μL of ddH[2]O water. The qRT-PCR was carried
   out using a QuantStudio TM3 Real-Time PCR System (Thermo Fisher
   Technology Co., Ltd., Wilmington, DE, USA), and the procedure was
   consistent with the description of Xu et al. (2021) [[134]22]. Data
   were analyzed using the 2^−ΔΔCT method in Thermo Scientific PikoReal.

5.5. Cell Membrane and Spore Integrity

   Briefly, 100 μL of spore suspension (10^7 spores mL^−1) was inoculated
   into 30 mL of PDB with 0, 5, and 10 mg mL^−1 PLA. After a 30-minute
   incubation at 28 °C in the dark, the spores were collected by
   centrifugation at 1500 rpm and 4 °C for 5 min. The spore pellets were
   washed 3 times with PBS and resuspended in 500 μL of PBS.

   The integrity of the cell membrane of A. flavus spores was determined
   using PI staining. Briefly, 5 μL of PI and spore re-suspension were
   mixed and incubated in the dark for 15 min and then detected using a
   Beckman Coulter Cytoflex S flow cytometer (Beckman Coulter, CA, USA).
   The results were analyzed using CytExpert v 2.3 software.

   The integrity of A. flavus spores was determined using TEM. The
   suspensions were centrifuged at 1500 rpm for 10 min, the supernatant
   was removed, and spores were fixed with 2.5% (v/v) glutaraldehyde. The
   fixed spore specimens were dehydrated, embedded, sectioned, stained,
   and examined using a JEM-1400PLUS transmission electron microscope
   (JEOL, Tokyo, Japan).

5.6. Transcriptomic Analysis and qRT-PCR Validation

   Fifteen mL of the spore suspension (10^7 spores mL^−1) was inoculated
   into 15 mL of the PLA solution to make the final concentration of PLA
   in the treatment group. The concentration was 2.5 mg mL^−1, and
   incubated at 180 rpm and 28 °C for 30 min. Moreover, the treatment with
   sterile water instead of PLA solution was used as the control group.
   Subsequently, the spores of A. flavus were collected (8000 rpm, 10 min,
   4 °C) for transcriptomic and qRT-PCR analysis.

   The total RNA was extracted using an RNAprep Pure Plant Kit (Tiangen
   Biotech Co., Ltd., Beijing, China). The quality and quantity of RNA in
   the extracts were determined using a NanoDrop 2000 spectrophotometer
   (Thermo Fisher Scientific, Wilmington, DE, USA) and an Agilent 2100
   Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Subsequently,
   cDNA library preparation was carried out using the NEBNext Ultra TM RNA
   Library Prep Kit for Illumina (NEB, Ipswich, MA, USA), and sequencing
   by the Illumina platform in Biomarker Technologies Co. (Beijing,
   China). Post-analysis used the BMKCloud ([135]www.biocloud.net;
   accessed on 22 February 2022) online bioinformatics pipeline tool
   platform. Briefly, clean data was obtained by processing raw data using
   in-house perl scripts. Then, transcriptomic data was mapped and
   assembled based on the A. flavus NRRL3357 genome using HISAT2 (version
   2.2.1) and StringTie (version 2.0) software, respectively
   [[136]41,[137]42]. The FPKM (fragments per kilobase per transcript per
   million mapped reads) value was used to express the expression
   abundance of the corresponding genes. The calculation of the false
   discovery rate (FDR) was to correct the p-value. Differentially
   expressed genes (DEGs) were identified by DEseq2, with |log2 (fold
   change)| > 1 and FDR < 0.05 taken as conditions [[138]43]. Gene
   function was annotated using the KO (Kyoto Encyclopedia of Genes and
   Genome Orthologue) and GO (Gene Ontology) databases. The GO and the
   Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses were
   carried out using the GOseq R packages and KOBAS software, respectively
   [[139]44,[140]45].

   Furthermore, qRT-PCR was carried out to validate the results on the
   expression levels of key DEGs (HK, ENO, CS, SDHA, Erg25, Erg5, TTDA,
   ERCC3) associated with glycolysis and TCA cycle in the transcriptome
   ([141]Supplementary Table S3). The cDNA synthesis kit, qRT-PCR reaction
   system, and instrumentation were the same as in [142]Section 5.4. The
   procedure for qRT-PCR consisted of pre-denaturation at 95 °C for 30 s,
   45 cycles of denaturation at 95 °C for 5 s, annealing at 55 °C for 30
   s, elongation at 72 °C for 30 s, and final extension at 72 °C for 30 s,
   followed by an analysis of the data in Thermo Scientific PikoReal using
   the 2^−ΔΔCT method.

5.7. Metabolome Analysis

   The treatments of A. flavus spores were the same as in [143]Section
   5.6, and the extraction and derivatization of intracellular metabolites
   in A. flavus spores were analyzed as described previously [[144]46].
   Briefly, the spores were collected by centrifugation at 8000 rpm and 4
   °C for 2 min. The spore pellets were washed with sterile water,
   immediately frozen in liquid nitrogen, and ground into powder.
   Metabolites were extracted by dissolving 100 mg of spore powder into 1
   mL of 3:2:2 isopropanol: acetonitrile: water (v/v/v) solution and
   shaking the solution for 2.5 min in an MM400 mixer mill (Retsch
   Technology, Haan, Germany). The solution was centrifuged at 12,000 rpm
   for 5 min, 200 μL of the supernatant was lyophilized, and the residue
   was methoximated by dissolving into 100 μL of 20 mg mL^−1 methoxyamine
   hydrochloride in pyridine and incubating for 90 min in a 30 °C water
   bath. Silylation was carried out by adding 50 μL of
   N-methyl-N-(trimethylsilyl)trifluoroacetamide with 1%
   trimethylchlorosilane and incubating at 37 °C for 30 min.

   Metabolites were analyzed using an Agilent Intuvo 9000-5977 B gas
   chromatography-mass spectrometer (GC-MS) system (Agilent Corporation,
   Santa Clara, CA, USA). Metabolites were separated in a 30 m × 0.25 mm ×
   0.25 µm HP-5 MS column (Agilent Corporation, Santa Clara, CA, USA) with
   a 1.0 mL min^−1 helium flow rate. The injector, transfer line, and ion
   source temperatures were 250, 280, and 230 °C, respectively. The column
   temperature was 80 °C for 2 min, after which the temperature was
   increased from 10 °C min^−1 to 300 °C, which was held for 6 min. The
   metabolites were scanned at m/z 50–600.

   Peak detection and alignment were performed using MS-DIAL v 4.16
   [[145]47]. Compounds were identified using the National Institute of
   Standards and Technology (NIST) 17 database. The metabolites were
   classified by ClassyFire Batch ([146]https://cfb.fiehnlab.ucdavis.edu/;
   accessed on 15 March 2023) [[147]48]. PCA, PLS-DA, and KEGG pathway
   analyses were performed by the MetaboAnalyst
   ([148]https://www.metaboanalyst.ca; accessed on 15 March 2023)
   webserver [[149]49]. Metabolites with VIP > 1 in the PLS-DA model were
   considered as DMs.

5.8. Statistical Analysis

   All experiments were performed at least in triplicate. Differences were
   tested using a one-way ANOVA and Duncan’s multiple range test in SPSS
   22 (SPSS Inc., Chicago, IL, USA). Differences were taken as
   statistically significant at p < 0.05.

Supplementary Materials

   The following supporting information can be downloaded at:
   [150]https://www.mdpi.com/article/10.3390/toxins15060370/s1, Table S1:
   Summary of reads in A. flavus with or without PLA treatment; Table S2:
   KEGG enrichment of significantly differential metabolites; Table S3:
   Primers used for qRT-PCR; Table S4: Differentially expressed genes
   associated with cell membrane; Figure S1: Heat map of correlation
   between phenyllactic acid treatment group and control group; Figure S2:
   Expression of the eight selected genes in the phenyllactic acid
   treatment group and control group.
   [151]Click here for additional data file.^ (399.1KB, zip)

Author Contributions

   Conceptualization, C.Z.; methodology, C.Z.; software, C.Z. and Z.L.;
   formal analysis, C.Z.; re-sources, Z.L.; writing—original draft
   preparation, C.Z.; writing—review and editing, P.P., X.Z., L.D., F.Z.,
   L.Z. and Z.L.; supervision, Z.L. and X.Z.; funding acquisition, Z.L.
   and X.Z. All authors have read and agreed to the published version of
   the manuscript.

Institutional Review Board Statement

   Not applicable.

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The raw transcriptome data have been deposited at the National
   Microbiology Data Center (NMDC) under the registration number
   NMDC40037319 (accessed on 11 April 2023).

Conflicts of Interest

   The authors declare no conflict of interest.

Key Contribution

   Phenyllactic acid reduces AFB[1] production by downregulating the
   expression of genes crucial for AFB[1]. Combined transcriptomic and
   metabolomic analyses revealed that PLA-induced cell membrane damage;
   energy metabolism disruption and central dogma abnormality in A. flavus
   spore.

Funding Statement

   This work was funded by the National Key Research and Development
   Program of China (2021YFD1600804), Department of Science and Technology
   of Sichuan Province (2021JDJQ0038 and 2023YFS0399), and Funds of
   Sichuan Academy of Agricultural Sciences (2019LJRCC033, 2019QYXK013 and
   2021XKJS064).

Footnotes

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   referred to in the content.

References