Tight control of mycotoxin biosynthesis gene expression in Aspergillus flavus by temperature as revealed by RNA-Seq

Authors


  • Editor: Michael Galperin

Correspondence: Jiujiang Yu, US Department of Agriculture (USDA), Agricultural Research Service (ARS), Southern Regional Research Center, 1100 Robert E. Lee Boulevard, New Orleans, LA 70124, USA. Tel.: +1 504 286 4405; fax: +1 504 286 4419; e-mail: jiujiang.yu@ars.usda.gov

Abstract

To better understand the effect of temperature on mycotoxin biosynthesis, RNA-Seq technology was used to profile the Aspergillus flavus transcriptome under different temperature conditions. This approach allowed us to quantify transcript abundance for over 80% of fungal genes including 1153 genes that were differentially expressed at 30 and 37 °C. Eleven of the 55 secondary metabolite clusters were upregulated at the lower temperature, including aflatoxin biosynthesis genes, which were among the most highly upexpressed genes. On average, transcript abundance for the 30 aflatoxin biosynthesis genes was 3300 times greater at 30 °C as compared with 37 °C. The results are consistent with the view that high temperature negatively affects aflatoxin production by turning down transcription of the two key transcriptional regulators, aflR and aflS. Subtle changes in the expression levels of aflS to aflR appear to control transcription activation of the aflatoxin cluster.

Introduction

Aspergillus flavus produces aflatoxins B1 and B2 and causes aflatoxin contamination of preharvest crops such as corn, cotton, peanuts and tree nuts, and postharvest grains during storage (Bhatnagar et al., 1987; Bennett & Klich, 2003). The discovery of the first stable aflatoxin precursor, norsolorinic acid (Bennett, 1981), paved the way for the elucidation of the aflatoxin biosynthetic pathway, including its intermediates and biosynthetic gene clusters in A. flavus, Aspergillus parasiticus, Aspergillus nidulans (sterigmatocystin as end product), Aspergillus sojae and Aspergillus oryzae (nonfunctional gene cluster) (Brown et al., 1996; Yu et al., 2004a, b). Aflatoxin biosynthesis is affected by many biotic and abiotic factors (Payne & Brown, 1998; Yu et al., 2010). The influence of temperature on aflatoxin formation has been reported previously (Schroeder & Hein, 1968; Ogundero, 1987). The optimum temperature for biosynthesis of aflatoxin and other secondary metabolites is at 30 °C; while the optimum temperature for fungal growth is at about 37 °C but it is less optimal for mycotoxin production. Sequencing of the A. flavus genome facilitated the construction of microarrays, which have been used to study transcriptional regulation of aflatoxin biosynthesis at different temperatures (OBrian et al., 2007; Georgianna et al., 2008, 2010; Payne et al., 2008; Schmidt-Heydt et al., 2009). These studies identified a large number of genes expressed at high level under low temperature. The effect of temperature on natural antisense transcript expression was also reported (Smith et al., 2008).

While microarrays are a robust tool for genome-wide gene expression analysis, they have been plagued by high background and low sensitivity problems. For regulatory genes with low level of expression, microarrays often fail to provide meaningful information about their expression levels. Thus, no published microarray experiments have provided an accurate estimate of the aflR and aflS expression levels. RNA-Seq technology has been successful for transcriptome profiling in a closely related species, A. oryzae (Wang et al., 2010). In this study, to overcome limitations associated with microarrays, we used the RNA-Seq approach to reveal high-resolution expression signatures associated with aflatoxin regulation in A. flavus. This approach allowed us to comprehensively identify most genes differentially expressed under temperature conditions conducive and nonconducive to aflatoxin production.

Materials and methods

Wild-type A. flavus strain NRRL 3357 (ATCC# 20026) was used in this study. Fungal cultures were maintained on potato dextrose agar (Difco, Detroit, MI). Conidial spores were inoculated into glucose minimal salts growth media, which support aflatoxin production. Two cultural conditions were used for gene expression comparison: (1) 30 °C, which supports aflatoxin formation, and (2) 37 °C, which does not support aflatoxin formation. Mycelia were harvested at 24 h after inoculation. Mycelia were collected, fresh frozen with liquid nitrogen and ground to a fine powder in liquid nitrogen. Total RNA was extracted from 100 mg of fungal tissue using TRIzol® Reagent (Invitrogen) according to manufacturer's instructions.

Library construction was performed according to the Illumina protocol (http://www.illumina.com). Briefly, each total RNA sample (20–50 μg) was treated with DNase and enriched for mRNA using oligo(dT) tags. Samples of poly(A) RNA (0.2–1 μg) were fragmented into smaller pieces (200–500 bp) and used to synthesize cDNA. The cDNA library construction involved end repair, A-tailing, adapter ligation, and library amplification followed by cluster generation and sequencing. All cDNA libraries were sequenced (one sample per lane) using the Illumina Genome Analyzer II (GA II) instrument (http://www.illumina.com), which generated over 1 million reads (100 bp each) for each lane.

Raw sequence data generated by GA II were processed, filtered and normalized using the Illumina pipeline (http://www.illumina.com) to generate fast-q files, which were analyzed using the RNA-Seq module of CLC Genomic Workbench (http://www.clcbio.com). All reads were mapped to A. flavus coding sequences to calculate expression values for every gene in RPMK (Reads Per Kilobase exon Model per million mapped reads) units. These values were normalized for total exon length and the total number of matches in an experiment, to allow for cross-sample comparisons. A gene was considered to be expressed if it had at least one sequence read aligned with it. Log2 ratios were used to measure relative changes in expression level between two growth conditions. Genes were considered differentially expressed if the corresponding log2 ratios were >2 or <−2. Genes were considered highly differentially expressed if log2 ratios were >5 or <−5. Analysis results were submitted to the NCBI's GEO database (accession number GSE30031).

Results and discussion

Total RNA samples collected from A. flavus mycelia grown under different temperature conditions were converted into cDNA and sequenced by the RNA-Seq technology. A total of 10.8 and 9.4 million Illumina reads were detected at 30 and 37 °C, respectively (Supporting Information, Table S1). Among them 23–29% reads mapped to genes, 8–9% mapped to intergenic regions, while the rest mapped to rRNA genes and mitochondria. This large number of intergenic transcripts suggests that noncoding RNA may play a significant role in transcriptional regulation. The results also indicate that almost 50% more rRNA transcripts are generated at the lower temperature consistent with high levels of aflatoxin production.

Among the 13 487 known genes in the A. flavus genome, 72% were expressed under both conditions. Overall, 8626 genes were not significantly affected by the growth temperature, while 1153 were differentially expressed. Among the latter, 551 genes had higher expression levels, while 602 genes had lower expression levels at lower temperature. Notably, six times more genes were highly upexpressed at 30 °C. Thus, 77 genes were highly upexpressed, while only 12 were highly downexpressed at that temperature. Most of the highly upexpressed genes were involved in aflatoxin biosynthesis as discussed below.

To evaluate the effect of temperature on the regulation of secondary metabolite biosynthesis, we used the smurf program (http://www.jcvi.org/smurf) (Khaldi et al., 2010) to identify putative secondary metabolite gene clusters (Table S2). Among the 55 clusters identified in the A. flavus genome, 11 clusters were upregulated (clusters #1, 11, 13, 23, 20, 21, 30, 43, 45, 54 and 55), while only two clusters were downregulated (cluster #2 and 3) at lower temperature. Among upregulated clusters three were associated with known products: conidial pigment (cluster #10), aflatoxin (cluster #54) and cyclopiazonic acid (CPA) (cluster #55).

Further analysis of the aflatoxin biosynthesis cluster quantitatively demonstrated that aflatoxin production is one of the most tightly regulated processes in a fungal cell. Most genes in the aflatoxin cluster were highly upexpressed at 30 °C, while not expressed at 37 °C (Table 1). The five most highly expressed genes encoded the following enzymes: reductase AflD, ketoreductase AflM, alcohol dehydrogenase AflH, O-methyltransferase AflO and VERB synthase AflK. Notably, adjacent sugar utilization genes (nadA, hxtA, glcA and sugR) (Yu et al., 2000), had higher expression levels under conditions nonconducive to aflatoxin production. This suggests that they are not controlled by the aflatoxin pathway regulatory genes and not directly involved in aflatoxin biosynthesis contrary to previous reports (Yu et al., 2000, 2004a, b).

Table 1.   Expression profiling of Aspergillus flavus genes involved in aflatoxin biosynthesis and sugar utilization at 30 and 37°C
NCBI's RefSeq
gene locus IDs
Expression values (RPKM)*Gene
symbol
Old gene
symbol
Gene product name
30°C37°CLog2 ratios
  • *

    These values are normalized for the total exon length and the total number of matches in an experiment. For each gene, log2 ratios of expression values at 30 and 37°C were used to estimate relative changes in expression level between the two growth conditions.

  • Gene names were adopted in 2004 based on international convention of fungal nomenclature.

  • Old gene names were given initially when the gene was first cloned.

AFLA_1394404.320NAaflFnorBAldo-keto reductase
AFLA_1394306.930NAaflUcypACytochrome P450 monooxygenase
AFLA_139420133.842.635.67aflTaflTMFS transporter
AFLA_139410275.494.395.97aflCpksA, pksL1Polyketide synthase
AFLA_139400542.981.838.21aflCahypCDUF1772 domain protein
AFLA_1393901689.794.668.50aflDnor-1Norsolorinic acid reductase
AFLA_139380111.723.55.00aflAfas-2, hexAFatty acid synthase α-subunit
AFLA_139370121.562.665.51aflBfas-1Fatty acid synthase β-subunit
AFLA_13936076.3316.272.23aflRapa-2, afl-2Zn2Cys6 transcription activator
AFLA_139340281.0911.874.57aflSaflJTranscription coactivator
AFLA_1393301483.700.6311.20aflHadhAShort-chain alcohol dehydrogenase
AFLA_139320462.410.3110.54aflJestAAflatoxin biosynthesis lipase/esterase
AFLA_139310738.530NAaflEnorA, aad, adh-2Norsolorinic acid reductase
AFLA_1393001613.350NAaflMver-1Aflatoxin biosynthesis short-chain alcohol dehydrogenase
AFLA_139290335.840NAaflMahypEEthD family protein
AFLA_139280164.440NAaflNverACytochrome P450 monooxygenase
AFLA_139270400.061.488.08aflNahypDIntegral membrane protein
AFLA_139260216.860NAaflGavnA, ord-1Cytochrome P450 monooxygenase
AFLA_139250479.950NAaflLverBCytochrome P450 monooxygenase
AFLA_139240387.910NAhypBhypBDUF1772 domain protein
AFLA_139230258.920NAaflIavfAAverufin oxidase
AFLA_1392201313.610NAaflOomtB, dmtAO-Methyltransferase B
AFLA_139210760.990NAaflPomtA, omt-1O-Methyltransferase A
AFLA_139200136.590NAaflQordA, ord-1Cytochrome P450 monooxygenase
AFLA_1391901167.690NAaflKvbsVersicolorin B synthase
AFLA_139180538.970NAaflVcypXCytochrome P450 monooxygenase
AFLA_139170458.470NAaflWmoxYFlavin-binding monooxygenase-like
AFLA_139160919.650.3611.32aflXordBNAD-binding dehydrogenase
AFLA_139150527.850NAhypAhypPConserved hypothetical protein
AFLA_13914030.290NAnadAnadAFAD/FMN-binding oxidoreductase
AFLA_1391301.283.3−1.37hxtAhxtAMFS hexose transporter
AFLA_1391203.668.64−1.24glcAglcAα-Glucosidase
AFLA_1391102.3811.58−2.28sugRsugRZn2Cys6 transcription factor involved in hexose utilization

Intriguingly, aflR and aflS (formerly designated aflJ), the two transcriptional regulators of the aflatoxin biosynthesis pathway, were expressed at both temperature conditions. Their expression levels were five and 24 times higher, respectively, at the lower temperature. They were among the three most expressed genes in the cluster at the higher temperature. It was hypothesized previously that AflS binds to AflR to prevent inhibitor binding and to allow for the aflatoxin pathway transcription (Chang, 2004). Our results imply that, at 37 °C, aflS is still expressed, but apparently at a level that is not sufficient to prevent inhibitor binding. The downregulation of the aflatoxin cluster at higher temperatures may be explained by the low levels of AflR as well as by inhibitor binding due to reduced levels of AflS. This is in contrast to previous microarray studies (OBrian et al., 2007), which reported that the aflR and aflS transcripts were expressed at about the same level under both temperature conditions. This discrepancy may be due to the lower sensitivity associated with microarray gene expression studies.

Unlike the aflatoxin cluster, cluster #55, which controls the biosynthesis of CPA (Chang et al., 2009), was expressed under both conditions, although the expression levels were much higher at the lower temperature (Table 2). This indicates that the two adjacent clusters are regulated by slightly different mechanisms. No putative transcription factor genes have been found in this cluster. CPA is typically produced under the same conditions that favor aflatoxin production. CPA is known to be produced at both high and low growth temperatures, although the 24-h time point may not be its peak production time. Further studies with multiple time points may be needed to elucidate the mechanism of transcriptional regulation of this cluster.

Table 2.   Expression profiling of Aspergillus flavus genes involved in CPA transport and biosynthesis at 30 and 37°C
Gene locus ID30°C (RPKM)37°C (RPKM)Log2 ratio (30°C/37°C)Gene symbolGene product name
AFLA_139460412.585.246.30NAMFS transporter
AFLA_139470308.480.429.52maoAFAD-dependent oxidoreductase
AFLA_139480249.740.449.15dmaTDimethylallyl tryptophan synthase
AFLA_1394909.070.057.50pks-nrpsHybrid PKS/NRPS enzyme

Traditionally, researchers relied on microarray technology to reveal genes required for toxin biosynthesis and regulation in Aspergillus species (OBrian et al., 2007; Wilkinson et al., 2007a, b). However, due to the sensitivity problem, microarrays are not the best technology to detect expression levels of regulatory genes, such as aflR and aflS. This study demonstrates that the RNA-Seq approach can profile a cell's entire transcriptome with almost infinite resolution. The obtained data defined conclusively the complete aflatoxin cluster consisting of 30 genes, which are coordinately regulated. Having the accurate measurement of the aflR and aflS transcript abundance levels allowed us to conclude that high temperature negatively affects aflatoxin production by turning down transcription of aflR and aflS.

Acknowledgements

We would like to thank Yan Yu, Sana Scherbakova and Karen Beeson from JCVI for their superb technical assistance during library preparation and sequencing.

Authors' contribution

J.Y. and N.D.F. contributed equally to this work.

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