Proteomic analysis of the soil filamentous fungus Aspergillus nidulans exposed to a Roundup formulation at a dose causing no macroscopic effect: a functional study

  • Florence Poirier
  • Céline Boursier
  • Robin Mesnage
  • Nathalie Oestreicher
  • Valérie Nicolas
  • Christian Vélot
Research Article

Abstract

Roundup® is a glyphosate-based herbicide (GBH) used worldwide both in agriculture and private gardens. Thus, it constitutes a substantial source of environmental contaminations, especially for water and soil, and may impact a number of non-target organisms essential for ecosystem balance. The soil filamentous fungus Aspergillus nidulans has been shown to be highly affected by a commercial formulation of Roundup® (R450), containing 450 g/L of glyphosate (GLY), at doses far below recommended agricultural application rate. In the present study, we used two-dimensional gel electrophoresis combined to mass spectrometry to analyze proteomic pattern changes in A. nidulans exposed to R450 at a dose corresponding to the no-observed-adverse-effect level (NOAEL) for macroscopic parameters (31.5 mg/L GLY among adjuvants). Comparative analysis revealed a total of 82 differentially expressed proteins between control and R450-treated samples, and 85% of them (70) were unambiguously identified. Their molecular functions were mainly assigned to cell detoxification and stress response (16%), protein synthesis (14%), amino acid metabolism (13%), glycolysis/gluconeogenesis/glycerol metabolism/pentose phosphate pathway (13%) and Krebs TCA cycle/acetyl-CoA synthesis/ATP metabolism (10%). These results bring new insights into the understanding of the toxicity induced by higher doses of this herbicide in the soil model organism A. nidulans. To our knowledge, this study represents the first evidence of protein expression modulation and, thus, possible metabolic disturbance, in response to an herbicide treatment at a dose that does not cause any visible effect. These data are likely to challenge the concept of “substantial equivalence” when applied to herbicide-tolerant plants.

Keywords

Roundup® NOAEL Aspergillus nidulans Proteomics Metabolism Herbicide tolerance Substantial equivalence 

Introduction

The effects of intensive agricultural practices on biogeochemical flows have been defined as critical for earth-system functioning (Steffen et al. 2015). A common feature of biochemical cycles is that soil microorganisms are key agents in the maintenance of soil quality and resilience. Sustainability at this level can be affected by pesticide application, such as by glyphosate-based herbicides (GBH), which are currently the most widely used pesticides worldwide. GBH are used exponentially, especially since 80% of genetically modified (GM) plants commercially grown are designed at least to tolerate Roundup® (James 2011): glyphosate (GLY) represented 3.7% of the mass of total herbicide active ingredient applied in 1996 in the United States, but 53.5% in 2009 (Coupe and Capel 2015). Such intensive use of GBH aggravates soil erosion, undermining soil quality by increasing leaching of banned remnant pesticides (Sabatier et al. 2014) and of nitrate and phosphate into the environment (Gaupp-Berghausen et al. 2015). In plants, GLY disrupts the shikimate pathway through inhibition of the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) enzyme, blocking the synthesis of essential aromatic amino acids and precursors of other critical aromatic compounds including plant growth regulators and phytoalexins (Duke et al. 2003). Besides the fact that shikimate pathway is also found in some microorganisms (Bentley 1990), some of the GBH toxic effects may also be due to other ingredients mixed with GLY in the commercial formulations since these latter are much more toxic than GLY alone, whether it is for human cells or tissues (Mesnage et al. 2015) or microorganisms (Braconi et al. 2006; Clair et al. 2012; Lipok et al. 2010; Nicolas et al. 2016; Qiu et al. 2013). Thus, GBH use is also suggested to affect soil quality through toxic effects on soil microorganisms such as symbiotic mycorrhizal fungi (Zaller et al. 2014) as well as some Aspergillus species (Carranza et al. 2014; Nicolas et al. 2016).

In a previous study (Nicolas et al. 2016), we evaluated the toxicity of a GBH commercial formulation, Roundup “GT plus” containing 450 g/L of GLY (R450), on the soil filamentous fungus Aspergillus nidulans. This ascomycete fungus is an experimental model organism used for decades in basic and industrial microbiology research (Martinelli and Kinghorn 1994). Because it has been extensively studied, it is a well-characterized organism that provides a relevant marker for agricultural soil health. We found that R450 causes multiple effects affecting various biological processes at doses far below agricultural dilution.

The objective of the present work is to further investigate these toxicological effects using proteomics, in order to get new insights into the toxic mechanisms caused by GBH application. Proteomic differential display is a powerful tool for identifying proteins and studying global cellular responses to a specific environment (de Arruda Grossklaus et al. 2013; Gillardin et al. 2009; Poirier et al. 2001; Sacheti et al. 2014; Shimizu et al. 2009). Not only this approach based on two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) can reveal alterations for genes that are not regulated at the transcriptional level, but it also allows to detect a number of protein isoforms with specific activities or functions due to posttranslational modifications (PTMs). Thus, it is probably the most informative approach to evidence the direct and indirect effects of particular environmental variations on ecologically relevant species (Lemos et al. 2010), and especially on a soil fungus such as A. nidulans (Kniemeyer 2011). In several recent studies, 2-DE has been used to elucidate the effects of several pesticides such as atrazine, butachlor, 2,4-dichlorophenoxyacetic acid, pentachlorophenol or glyphosate alone (Ahsan et al. 2008; Fang et al. 2010; Kumari et al. 2009; Teixeira et al. 2005; Thornton et al. 2010). To date, only two proteomic analyses were conducted to understand the effects of a commercial formulation of Roundup®: one concerned a maize (Mesnage et al. 2016), and the other was about the liver of rats chronically exposed to an ultra-low dose of Roundup® herbicide (Mesnage et al. 2017). Not only is the present study related to an entirely different type of eukaryotic cells, but it also focuses on the effects of a direct exposure under conditions of apparent herbicide tolerance (i.e. in the absence of general toxic effects at the macroscopic level). It is the reason why we chose to perform this proteomic analysis at a dose corresponding to a no-observed-adverse-effect level (NOAEL) associated to these macroscopic parameters. This choice was all the more relevant since we previously showed that some of the cellular and metabolic effects caused by R450 were still evident at this concentration (Nicolas et al. 2016).

Materials and methods

Chemicals

The herbicide Roundup used in this work (R450) was the commercial formulation called “GT plus” (450 g/L of GLY, corresponding to 100%), available on the market (homologation 2020448, Monsanto, Anvers, Belgium).

Protease inhibitor cocktail tablets were from Roche (Mannheim, Germany). Linear 18 cm IPG strips pH 4–7, IPG buffer pH 4–7, 2D Quant kit, urea and thiourea were from GE Healthcare (Uppsala, Sweden). The acrylamide/bisacrylamide solution was from Euromedex (Mundolsheim, France). The SYPRO Ruby solution was from BioRad (Hercules, CA, USA). CHAPS and EZblue were from Sigma-Aldrich (St Louis, USA). Trypsin Porcine was from Promega (Madison, WI, USA). Acetonitrile was from Merck (Darmstadt, Germany).

A. nidulans strain, growth conditions and protein extraction

A. nidulans strain used in this study was CV125 (pabaA1) (Nicolas et al. 2016). Media composition, supplements and basic growth conditions were as described by Cove (1966). Solid media contained 1.2 or 3% agar. Plates were incubated at 37 °C, and liquid cultures were carried out at 30 °C in an orbital shaker at 150 rpm. Mycelia for protein extraction were grown at 30 °C for 15 h in 400 mL minimal medium with fructose (0.1%) as the carbon source and urea (5 mM) as the nitrogen source, in the absence (“Control”) or presence (“R450”) of 0.007% R450 (i.e. containing 31.5 mg/L GLY among adjuvants). For each treatment (absence and presence of R450), duplicate cultures were carried out (two cultures separately inoculated and grown in parallel). Proteins were extracted as previously described (Shimizu et al. 2009) with minor modifications. Briefly, mycelia were harvested by filtration, washed with chilled sterile water, wrung dry and ground under liquid nitrogen into a fine powder using a mortar and a pestle. The ground powder was suspended in four volumes of cold acetone (− 20 °C) containing 10% (w/v) tricarboxylic acid (TCA) and stored overnight at − 20 °C. After centrifugation at 15,000×g for 15 min, the precipitate was washed twice with cold acetone containing 1% (v/v) ß-mercaptoethanol and air-dried for 5 min at room temperature. The pellet was then dissolved in a solution containing 7 M urea, 2 M thiourea, 4% (w/v) CHAPS, 0.5% (v/v) Nonidet NP-40 and a protease inhibitor (Roche, Mannheim, Germany) (1 tablet for 10 mL). After incubation for 2 h, the lysate was centrifuged at 150,000×g for 25 min at 4 °C, and the supernatant (protein sample) was stored at − 20 °C. Protein concentrations were determined using the 2D Quant kit (GE Healthcare).

Two-dimensional gel electrophoresis

The protein samples (100 μg) were added to a solution containing 7 M urea, 2 M thiourea, 3% (w/v) CHAPS, 1% (w/v) NP-40, 0.5% (v/v) IPG buffer pH 4–7 and bromophenol blue, to a final volume of 350 μL. After swelling of dry IPG strips with samples (9 h at 19 °C), focusing was carried out for a total of 47,260 Vh (Protean IEF Cell focusing system, BioRad). Prior to second-dimension separation, strips were incubated for 15 min in 50 mM Tris-HCl, pH 8.6, containing 6 M urea, 2% (w/v) SDS, 1% (w/v) DTT, 30% (v/v) glycerol and bromophenol blue, then re-incubated for 15 min in the same buffer containing 2.5% (w/v) iodoacetamide instead of DTT. Using home-made gels (12%, 1 mm × 200 mm × 250 mm), electrophoresis was carried out for 30 min at 80 V, 30 min at 160 V, then for 3 h and 40 min at 600 V (Ettan DALT6 Separation unit, GE Healthcare). After protein fixation (50% ethanol, 8% acetic acid) for 1 h, gels were stained using SYPRO Ruby for comparative image analysis and Coomassie blue for mass spectrometry analysis. SYPRO Ruby-stained gels were digitized with the Typhoon™ 9400 (GE Healthcare). Coomassie blue-stained gels were digitized with the ImageScannerII (GE Healthcare).

Comparative image analysis

SYPRO Ruby images were analyzed with the ImageMaster® 2D Platinum v5.0 software (GE Healthcare). Quantitative differential analysis was performed on a set of four gels per sample type (“Control” or “R450”). After automatic spot detection, manual checking and correction, approximately 1170 spots were detected per gel. Groups of spots were automatically created by matching a set of gels with a reference gel. Variations in staining intensity between gels were subjected to scatter plot analysis. Scatter plots indicate the relationship between the spot values (by searching for the linear dependence between them) from two gels. The goodness-of-fit is given by a correlation coefficient value near 1. Only gels showing a correlation coefficient value > 0.85 were kept for the comparative 2-DE image analysis leading to three gels per condition. Only groups of spots present in at least three gels from the same class were considered for differential analysis.

Mass spectrometry analysis

Spots were excised from the Coomassie blue-stained 2-DE gels, and proteins were in-gel-digested with sequence-grade trypsin (Promega, Madison, WI, USA) as previously described (Lescuyer et al. 2003). NanoLC-MS/MS analysis was performed using an Agilent 1200 Series capillary LC system (Agilent Technologies, Palo Alto, USA) coupled to a 6330 Ion Trap equipped with the nanospray Chip Cube ion source (Agilent Technologies). Chromatographic separation of 7 μL injection volume per sample was carried out on a C18 reverse-phase column (Zorbax 300SB-C18, 75 μm, Agilent Technologies). Gradient elution (Froidevaux-Klipfel et al. 2011) was applied for solvents at a flow rate of 200 nL/min. MS/MS spectra were analyzed using the Data Analysis software v6.1 (Agilent Technologies). The peptide mass profiles were analyzed using the MASCOT software (Matrix Science, London, UK). The search parameters used were as follows: NCBI database; fungi taxonomy; fixed carbamidomethylation for cysteine residues; variable oxidation for methionine residues; variable phosphorylation for serine, tyrosine and threonine residues; peptide mass tolerance of 100 ppm (MS) and 0.5 Da (MS/MS) maximum was allowed; only one enzymatic missed cleavage; trypsin as digestion compound. Validated proteins had at least three different matched peptides and a minimum Mascot score of 60. Results leading to “hypothetical proteins” were then submitted to the Broad Institute database (https://www.broadinstitute.org) as well as to the Aspergillus genome database (http://www.aspergillusgenome.org; Cerqueira et al. 2014) to find the name of the gene product.

Microscopy imaging

The A. nidulans strain CV125 was grown on an agar-rich medium surface in the absence or presence of the indicated R450 dose, and plates were inoculated at 37 °C for 3 days. Imaging was performed according to the inverted agar method as previously described (Hickey et al. 2005). Differential interferential contrast (DIC) images of hyphae apices were obtained with an inverted confocal laser scanning microscope LSM 510-Meta (Carl Zeiss, Germany), using fluorescent channel (543 nm laser wavelength) and a transmission detector.

Statistical analysis

For each treatment (“Control” and “R450”), protein extracts from each one of the duplicate cultures were analyzed in triplicate (3 different 2D gel electrophoresis experiments were made per culture material). Variations in protein abundance were calculated as the ratio of average values (intensities) for a given spot between the two classes (“Control” and “R450”). Differential expression was considered significant for spots with ratio > 1.5. The minimal ratio 1.5, corresponding to experimental variations, was defined after quantitative image analysis performed between 2-DE gels from the same class. Statistically significant differences were estimated by comparing the empirical distribution functions of these two classes. Data points were sorted in ascending order, and the differences were considered significant when the empirical distributions of the two classes had no overlap at all. This corresponds to a Kolmogorov–Smirnov test with a D value of 1.

Results and discussion

As part of our previous study on the toxicity of R450 on A. nidulans, we determined the lowest- and no-observed-adverse-effect levels (LOAEL and NOAEL) associated to macroscopic parameters (Nicolas et al. 2016). The determination of these specific doses was based on growth characteristics (growth rate, germination lag time and ratio) and morphology (mycelial organization, pigmentation) both in solid and liquid media (Fig. 1a, b). Results obtained at the median lethal dose (LD50) are also shown as a positive control of the growth and morphology effects of R450. Given that mycelial disorganization visible at the macroscopic level (Fig. 1b, LD50) was due to abnormal hyphal branch formation (observed at the microscopic level), we also carried out DIC images (Fig. 1c) to ensure that the hyphal branching was normal or aberrant. All the proteomic analyses described in the present study were performed at the NOAEL dose (0.007%, i.e. containing 31.5 mg/L GLY among adjuvants) associated to macroscopic parameters.
Fig. 1

Behavior of the fungus A. nidulans when exposed to R450 at the NOAEL, LOAEL and LD50 doses. These doses were 0.007, 0.0075 and 0.025% (i.e. containing 31.5, 33.75 and 112.5 mg/L GLY among adjuvants, respectively). a Plates containing rich medium with the indicated R450 concentration were inoculated with spore of the CV125 strain at the central point. b Enlarged view of a sector of the colony shown in (a) to visualize the mycelial organization. c Effects of R450 on hyphae branching (cell polarity). Images were obtained by differential interferential contrast (DIC) microscopy of hyphae apices of the CV125 A. nidulans strain grown for 3 days at 37 °C on solid rich media in the absence or presence of the indicated R450 dose. For each dose, the image was representative of observations made on 10 hyphae from each one of three independent experiments (n = 30)

Protein-pattern changes following R450 exposure

Exposure of A. nidulans to the GBH R450 at a concentration causing no morphology and growth effect changed the protein expression profile, as revealed by a proteomics approach based on 2-DE and mass spectrometry to identify proteins that are differentially regulated. The average numbers of protein spots detected in untreated and R450-treated samples were 1207 and 1167, respectively (Fig. 2a, b). In order to analyze gel similarities or experimental problems (SYPRO Ruby staining, differences in protein loading or image acquisition problems), variations in staining intensity between gels were subjected to scatter plot analysis (Fig. 2c). Fifty spots were modulated by R450: 32 increased in abundance, while 18 decreased. Two examples of protein spots affected by the herbicide are presented on Fig. 3. Spots of interest were then submitted to LC-MS/MS analysis. Fifty up-regulated proteins (including 14 with a ratio of 100, i.e. detected only in the treated sample) and 32 repressed proteins (including 7 with a ratio of 100, i.e. detected only in the control sample) were unambiguously identified (Table 1 and Table 2, respectively). The functional classification of the putative proteins showed that R450 affected several cell metabolic pathways, mainly detoxification, protein synthesis, amino acid metabolism, Krebs TCA cycle and acetyl-CoA synthesis, glycolysis/neoglucogenesis, pentose phosphate pathway and glycerol metabolism (Tables 1 and 2 and Fig. 4).
Fig. 2

2-DE gels of proteins extracted from A. nidulans grown in the absence (a) or presence (b) of 0.007% R450. Proteins were separated on a linear pH 4–7 IPG strip, followed by a 12% SDS polyacrylamide gel, as stated in the “Materials and methods” section. The gels were stained with SYPRO Ruby. c Scatter plot result between the spot values (intensities) from two gels (control and R450). Regression line equation is given at the bottom right; Corr: correlation coefficient value; Count: number of identical spots between the two gels

Fig. 3

Example of differential analysis for down-regulated (a) and up-regulated (b) polypeptides following A. nidulans exposure to 0.007% R450. Histograms on the left, imported from ImageMaster 2D Platinum 5.0, show the intensity values of spots considered for analysis: spot 2010 (a) corresponding to subunit α of translation initiation factor eIF-1 (AN3156) and to transaldolase (AN0240) (see Table 2), and spot 2036 (b) corresponding to glyceraldehyde 3-phosphate dehydrogenase (AN8041) and to an unknown function protein (AN9194) (see Table 1). Three gels were analyzed for “Control” (histogram columns a–c) and “R450-treated” (histogram columns d–f) samples. The horizontal purple line indicates in each case the median measure. Partial 2-DE images from a “Control” (top) and a “R450” (down) gel are shown for corresponding spots. Arrows indicate the spots modulated by the herbicide exposure

Table 1

Proteins up-regulated in A. nidulans after exposure to 0.007% R450 (NOAEL dose)

Gene codea

Protein (EC number)

FCb

M. scorec

Mat. pepd

Spot number

Amino acid biosynthesis

 AN5886 (luA)

 AN6521 (lysF)

 AN2526

 AN4376 (gdhA)

 AN4290

3-Isopropylmalate dehydratase (4.2.1.33)

Homoaconitase (4.2.1.36)

Ketol-acid reductoisomerase (1.1.1.86)

NADP-glutamate dehydrogenase (1.4.1.4)

Methylthioribose-1-P isomerase (5.3.1.23)

100

100

1.86

2.71

100

180

150

93

122

74

7

6

5

5

3

1016

1016

1823

3019

2111

Protein biosynthesis

 AN4550 (dps1)

 AN1913

 AN6563

 AN6563

 AN1084

 AN6700

 AN6700

 AN1954

Aspartyl-tRNA synthetase (6.1.1.12)

Lysil-tRNA synthetase (6.1.1.6)

Translation elongation factor eEF-1 subunit γ

Translation elongation factor eEF-1 subunit γ

Mitochondrial elongation factor Tu

Translation elongation factor eEF-3

Translation elongation factor eEF-3

Mitoch. ribosomal protein (small subunit)

100

1.85

2.04

100

100

2.04

1.78

1.95

110

86

81

99

162

153

896

269

6

9

6

4

13

5

19

7

1250

993

1333

1353

2111

692

695

1560

Glycolysis/gluconeogenesis

 AN5746 (acuN)

 AN8041 (gpdA)

 AN2875 (fbaA)

 AN5604 (acuG)

 AN4462 (pycA)

 AN4462 (pycA)

Enolase (4.2.1.11)

Glyceraldehyde 3-P dehydrogenase (1.2.1.12)

Fructose bisphosphate aldolase (4.1.2.13)

Fructose 1,6-bisphosphatase (3.1.3.11)

Pyruvate carboxylase (6.4.1.1)

Pyruvate carboxylase (6.4.1.1)

1.95

1.76

100

1.86

1.65

1.79

112

77

115

105

178

171

3

3

5

9

18

17

1560

2036

2234

1823

666

668

Krebs TCA cycle

 AN5571 (kgdA)

 AN5525 (acoA)

α-Ketoglutarate dehydrogenase (1.2.4.2)

Aconitase (4.2.1.3)

2.18

1.87

80

73

4

3

2925

3022

Detoxification/stress response

 AN6840

 AN6840

 AN5831

 AN8637 (catA)

 AN5129 (hsp70)

 AN12473 (hscA)

 AN9124

Hydroxyacyl glutathione hydrolase (3.1.2.6)

Hydroxyacyl glutathione hydrolase (3.1.2.6)

Glutathione S-transferase (2.5.1.18)

Catalase A (1.11.1.6)

Heat shock protein 70

Heat shock protein 70

Heat shock protein

100

100

3.45

1.97

2.18

2.18

1.63

131

191

217

186

300

459

134

3

5

13

8

20

25

13

2628

3476

3056

884

1057

1057

2903

Proteolysis

 AN5121

 AN4557

26S proteasome regulatory particle subunit

Mitochondrial inner membrane AAA protease

1.86

1.87

130

86

7

10

1823

3022

Glycerol metabolism

 AN6792 (gfdB)

Glycerol-3-phosphate dehydrogenase (1.1.1.8)

5.00

179

11

2959

Purine/pyrimidine biosynthesis

 AN6157 (pyrG)

 AN3626 (adD/ad3)

Orotidine 5′-P decarboxylase (4.1.1.23)

P-Ribosylaminoimidazole carboxylase (4.1.1.21)

100

3.59

177

71

11

3

2628

3055

ATP metabolism

 AN1211

 AN8674

Vacuolar ATP synthase subunit H

V-type H+-transporting ATPase subunit E

2.88

100

296

320

6

4

2942

3476

Nitrogen/amino acid catabolism

 AN10079 (ureB)

 AN7641

 AN1808

Urease (3.5.1.5)

Peroxisomal copper amine oxidase (1.4.3.6)

l-Amino acid oxidase (1.4.3.2)

1.74

100

100

362

111

224

6

10

14

755

1152

1250

Protein trafficking

 AN0922

 AN7687

 AN3594 (sogA)

Coatomer subunit delta

Mitoch. outer membrane translocase receptor

Vacuolar protein sorting-associated protein

2.18

1.77

1.63

84

261

186

6

10

8

1057

936

2903

Cell cycle/signaling/morphogenesis

 AN1911

 AN4163 (cpcB)

 AN2412 (cmkA)

 AN9085

 AN3739 (snxA)

GDP-mannose phosphorylase (2.7.7.22)

G-Protein beta subunit

Calmodulin-dependent protein kinase

U5 snRNP complex subunit

RNP domain protein

1.84

2.88

2.88

100

2.71

233

302

385

114

121

12

5

6

9

8

1493

2942

2942

2234

3019

Uncharacterized/poorly characterized

 AN2572

 AN2731

 AN2939

 AN2343

 AN4171

 AN8764

 AN4848

 AN9194 (cetL)

Dipeptidyl-peptidase (3.4.14.-)

Heat shock protein

Mitochondrial inner membrane protein

Nitroreductase family protein

Unknown function protein

Unknown function protein

Unknown function protein

Unknown function protein

2.55

5.00

3.59

2.91

3.59

100

5

1.76

165

67

191

156

58

101

121

154

10

6

11

9

3

4

3

14

790

2959

3055

2985

3055

2628

2959

2036

aThe corresponding current A. nidulans gene name is indicated between brackets

bFold change

cMascot score

dNumber of matched peptides

Table 2

Proteins down-regulated in A. nidulans after exposure to 0.007% R450 (NOAEL dose)

Gene codea

Protein (EC number)

FCb

M. scorec

Mat. pepd

Spot number

Amino acid metabolism

 AN8770

 AN8866

 AN3591

 AN3593

Acetylglutamate kinase (2.7.2.8)

D-3-Phosphoglycerate dehydrogenase (1.1.1.95)

Methylmalonate semialdehyde DHe (1.2.1.27)

Methylthioribulose-1-P-dehydratase (4.2.1.109)

100

100

2.09

2.01

161

87

93

96

14

5

5

4

1498

1498

1352

2258

Protein biosynthesis

 AN6330

 AN3156

 AN2734

 AN2734

 AN3172

Translation elongation factor eEF-2

Translation initiation factor eIF-1 subunit α

60S acidic ribosomal protein P0

60S acidic ribosomal protein P0

40S ribosomal protein S0

11.43

5.83

3.23

2.10

1.71

84

115

258

63

96

3

9

6

3

3

1503

2010

2070

2082

2237

Acetyl-CoA biosynthesis

 AN2435 (aclA)

 AN9403 (pdhC)

ATP citrate lyase (2.3.3.8)

Pyruvate dehydrogenase E1 β subunit (1.2.4.1)

100

2.10

97

148

7

6

1498

2082

Detoxification/stress response

 AN9339 (catB)

 AN7388 (cpeA)

 AN10220 (ccp1)

 AN0858 (hsp104)

 AN4616 (ssz1)

Catalase B (1.11.1.6)

Catalase-peroxidase

Mitochondrial cytochrome c peroxidase

Heat shock protein

Heat shock protein 70

3.47

100

1.67

100

1.57

122

114

116

265

232

4

7

6

16

16

818

967

2196

734

1109

Glycerol metabolism

 AN5563 (gldB)

 AN5563 (gldB)

NADP(+)-dependent glycerol DHe (1.1.1.72)

NADP(+)-dependent glycerol DHe (1.1.1.72)

2.09

3.47

108

98

3

3

2049

818

Pentose phosphate pathway

 AN0240 (pppA)

 AN0285

Transaldolase (2.2.1.2)

6-Phosphogluconolactonase (3.1.1.31)

5.83

1.71

89

67

6

3

2010

2237

ATP metabolism

 AN2315

Mitochondrial ATP-synthase beta chain

11.43

313

7

1503

Cytoskeleton/morphogenesis/cell cycle

 AN7570 (tubB)

 AN0316 (tubA)

 AN2126

 AN5686 (tpmA)

 AN0410 (bimG)

Tubulin alpha-2 chain

Tubulin alpha-1 chain

F-actin capping protein subunit α

Tropomyosin

Serine/threonine protein phosphatase

1.71

1.71

2.10

100

3.23

725

478

118

182

78

11

12

3

8

3

1371

1371

2082

2516

2070

Carbon catabolism

 AN7590

NADP-dependent mannitol DHe (1.1.1.138)

2.01

149

10

2258

Vesicle trafficking

 AN3416 (ssoA)

SNARE-domain containing protein

2.10

96

5

2082

NAD biosynthesis

 AN1745

Nicotinamide mononucleotide adenylyl transferase (2.7.7.1)

3.23

86

3

2070

Splicing

 AN4978

Pre-RNA splicing factor

100

120

3

3057

Uncharacterized/poorly characterized

 AN3996

 AN8228

 AN7587

 AN9194 (cetL)

Methyltransferases family protein

UBX domain-containing protein

Unknown function protein

Unknown function protein

2.18

11.43

1.67

2.09

77

61

80

121

3

4

5

9

2363

1503

2196

2049

aThe corresponding current A. nidulans gene name is indicated between brackets

bFold change

cMascot score

dNumber of matched peptides

eDehydrogenase

Fig. 4

Assignment of identified proteins to cellular processes. Spots down- and up-regulated were subjected to tryptic digestion and LC-MS/MS analysis. From 50 modulated spots, 85% of the proteins (70/82) were unambiguously identified and assigned to a cellular process or metabolic pathway

Main cellular processes modulated during the Roundup exposure

Intermediary metabolism and related pathways

The main intermediary metabolic pathways affected by R450 exposure, and their interconnection, are summarized in Fig. 5. The herbicide Roundup was previously shown to promote a strong decrease in the mitochondrial transmembrane potential (Δψ), resulting into an uncoupling of oxidative phosphorylations (Peixoto 2005). We have recently shown that exposure to R450 resulted into a stimulation of TCA cycle enzyme activities in A. nidulans (Nicolas et al. 2016), likely as a result of the Δψ collapse, the uncoupling effect causing an acceleration of respiration and Krebs TCA cycle. Our present proteomic data confirmed this observation, since levels of at least two TCA cycle enzymes (aconitase and α-ketoglutarate dehydrogenase) were increased under R450 exposure (Table 1). Such a metabolic stimulation may seem inconsistent with the down-regulation of all the acetyl-CoA-producing pathways. However, pyruvate dehydrogenase and the putative methylmalonate-semialdehyde dehydrogenase (MSDH) were down-regulated by a factor 2 only, while ATP citrate lyase, whose activity would result into a depletion of mitochondrial citrate, and consequently into a block of the Krebs TCA cycle, was suppressed (Table 2). In A. nidulans, loss of ATP citrate lyase greatly affects growth on carbon sources that do not directly result in cytoplasmic acetyl-CoA, such as sugars (Hynes and Murray 2010). This severe phenotype indicates an absence of compensation by the MSDH pathway, possibly due to the fact that this branched-chain amino acid (BCAA) degradation pathway would be localized in mitochondria and/or peroxisomes as it seems to be the case for plants (Binder 2010). Now, the fact that the R450 concentration used in this study did not lead to any growth effect (NOAEL dose associated to growth and morphology), including with fructose as the carbon source (Nicolas et al. 2016), suggests either the presence of residual ATP citrate lyase activity (i.e. low levels of the enzyme under the detection limit on the 2D gels) or an induction of acetyl-CoA synthetase activity, although this enzyme was not identified as up-regulated protein under R450 exposure at this NOAEL dose (Table 1). Indeed, the other way of producing cytoplasmic acetyl-CoA in A. nidulans is acetyl-CoA synthetase from acetate. However, in the presence of fructose as the sole carbon source, the only source of acetate is pyruvate via pyruvate decarboxylase, which was not detected as up-regulated protein.
Fig. 5

Compilation of the main intermediary metabolism and related pathways affected by R450 exposure, and their interconnections. Up-regulated (red arrows) and down-regulated (blue arrows) proteins are outlined in red and blue, respectively. Green frames indicate metabolic pathways whose cell location is not well-defined and/or some enzyme activities have not been characterized in A. nidulans (the putative functionality of the cognate modulated proteins is based solely on sequence homologies). MSDH: methylmalonate-semialdehyde dehydrogenase

The up-regulation of both ketol-acid reductoisomerase and 3-isopropylmalate dehydratase combined with the down-regulation of the MSDH pathway would result in an accumulation of the three BCAA, valine, leucine and isoleucine. A previous study (Shimizu et al. 2010) demonstrated that up-regulated BCCA synthesis in A. nidulans cells functions as an electron sink for regeneration of NAD+ and NADP+ under conditions that result in higher ratios of NAD(P)H/NAD(P)+, such as hypoxia. However, the stimulation in A. nidulans of the TCA cycle, resulting from R450 exposure (Nicolas et al. 2016), together with the Δψ collapse (i.e. the uncoupling effect) promoted by this herbicide (Peixoto 2005), would also result in an intracellular accumulation of NADH and NADPH. Then, the up-regulation of the BCCA synthesis would be a cellular response in A. nidulans to offset the effects of Roundup, thus preventing an unbalanced NAD(P)H/NAD(P)+ ratio that in turn impairs cellular metabolism.

In A. nidulans, gluconeogenesis is subjected to carbon catabolite repression (Hynes et al. 2007), which is ultimately mediated by the transcriptional repressor CreA (Félenbok and Kelly 1996). This repression exerts optimally in the presence of glucose and to a lower extent in the presence of fructose (Flipphi et al. 2003). In this study, mycelia for protein extraction were grown (in the absence or presence of R450) with fructose as the sole carbon source. In such conditions, it is therefore not surprising to detect the presence of the gluconeogenesis-specific enzymes. This was the case for, at least, two of them since pyruvate carboxylase and fructose 1,6-bisphosphatase proved to be up-regulated in protein expression abundance (with a ratio about 2) under R450 exposure relative to the control (Fig. 5 and Table 1). This up-regulation was also observed for enolase (Fig. 5 and Table 1), one of the reversible enzymes that are essential for both glycolysis and gluconeogenesis. A. nidulans enolase is encoded by acuN, whose expression is transcriptionally activated in response to both gluconeogenic and glycolytic carbon sources, but according to two different and independent ways (Hynes et al. 2007). Now, the two other gluconeogenesis-specific enzymes, phosphoenolpyruvate carboxykinase (PEPCK) and putative glucose-6 phosphatase, were not found to be up-regulated. However, the up-regulation of NADP-glutamate dehydrogenase combined with down-regulation of N-acetylglutamate kinase (Fig. 5) suggests a possible accumulation of glutamate, which has been shown, in A. nidulans, to be an inducer of the PEPCK activity (Kelly and Hynes 1981). The absence of up-regulation of a putative glucose-6 phosphatase implies that such a stimulation of gluconeogenesis would lead to production of glucose-6 phosphate, likely to fuel the pentose phosphate pathway (PPP) (Fig. 5), in order to supply the cell with ribose-5-phosphate for nucleotide synthesis and to produce NADPH. This cofactor is especially required for glutathione reductase to form reduced glutathione (GSH) that is essential to maintain the redox balance of the cell and to eliminate the xenobiotics (see the “Detoxification pathways” section). The global down-regulation of the PPP (Fig. 5) would avoid a too strong stimulation of the gluconeogenesis which, when running simultaneously with glycolysis, would lead to a total dissipation of energy (without possibility that a pool of pyruvate produced by glycolysis can be used to fuel the Krebs TCA cycle). However, the fact that the reversible non-oxidative phase of the PPP would be more severely down-regulated than the reversible oxidative one (ratio of 5.83 for abundance of transaldolase versus ratio of 1.71 for 6-phosphogluconolactonase; Table 2) would ensure the simultaneous synthesis of a sufficient pool of ribose and NADPH while avoiding the withdrawal of two intermediates from glycolysis/gluconeogenesis (fructose-6-phosphate and glyceraldehyde-3-phosphate) to produce only ribose-5-phosphate (Fig. 5) (such a metabolic pattern occurs when the cellular requirements are greater in ribose than in NADPH).

R450 exposure resulted into the modulation of the abundance of the cytosolic glycerol-3-phosphate dehydrogenase and the NADP-dependent glycerol dehydrogenase, two key enzymes regulating the metabolism of glycerol, an important by-product of glycolysis (Gancedo et al. 1986). While glycerol-3-phosphate dehydrogenase that catalyzes the first step of the glycerol synthesis pathway was up-regulated, the NADP-dependent glycerol dehydrogenase involved in the first step of the glycerol catabolism was down-regulated, suggesting that R450 exposure resulted into an increase in glycerol production rate and its accumulation in A. nidulans (Fig. 5). In yeast and A. nidulans, such a glycerol accumulation has been shown to be the main response to a hyperosmotic stress, in order to counterbalance the external osmotic pressure (Blomberg and Adler 1989; Nevoigt and Stahl 1997; Redkar et al. 1995). Consistently, we previously demonstrated that exposure of A. nidulans to R450 (at doses higher than the NOAEL one) resulted in a slight increase of the spore diameter as well as of the hyphae width, suggesting a modification of the wall structure affecting osmoregulation (Nicolas et al. 2016). Moreover, we also observed that R450 exposure at these doses resulted into a high degree of hyphae branching with multiple, random secondary germ tube emergence at the tip, giving a “stump” appearance. Such a disruption of hyphal polarity could be also a consequence of osmoregulation disturbance due, for instance, to a side effect of the surfactant ethoxylated etheralkylamine, present in some Roundup formulations, including R450, and necessary for an effective uptake of GLY in plants (Riechers et al. 1994). Interestingly, no variation in glycerol metabolism enzyme levels was reported in the yeast Saccharomyces cerevisiae when exposed to another GBH such as Silglif (Braconi et al. 2008). This supports the hypothesis that the observed osmotic stress in A. nidulans in response to R450 would be due to one or more adjuvant(s) rather than to GLY itself.

Protein synthesis

About 14% of A. nidulans proteins that are modulated under R450 exposure are relative to protein synthesis (Fig. 4). This is the case for the translation elongation factors EF1 and EF3 and some aminoacyl-tRNA synthetases corresponding altogether to six distinct spots up-regulated by R450 (Table 1). These results suggest that protein translation was significantly stimulated when A. nidulans was exposed to the herbicide, although some other spots corresponding to ribosomal proteins 40S and 60S and elongation factor EF2 were slightly down-regulated by R450 (Table 2). In plants, the mode of action of GBH is quite clear since GLY annihilates protein synthesis by disrupting the shikimic acid pathway through inhibition of the EPSPS enzyme, thus blocking the synthesis of essential aromatic amino acids (Duke et al. 2003). However, in other organisms, that may not have the shikimate pathway, mechanisms responsible for Roundup toxicity appear multiple and may vary from one organism or cell type to another. In A. nidulans, as in other fungi and bacteria, the shikimate pathway is also present, but that does not necessarily imply that GBH operate as in plants. Indeed, there are two classes of EPSPS: GLY-sensitive, as in plants (class I) and GLY-tolerant (class II). It is not really clear whether EPSPS from A. nidulans belongs to class I or class II, but our previous data (Nicolas et al. 2016) indicate that GLY does not inhibit (or only partially) the A. nidulans EPSPS enzyme, and that the multiple cellular effects of R450 in this fungus are likely due to various targets of GLY and/or additives present in the formulation. Indeed, we have shown that these additives are not inert since the formulation R450 proved to be much more active than GLY alone (Nicolas et al. 2016). Similarly, the antimicrobial effect of glyphosate formulations on some protozoa (patent US7771736 B2) is not necessarily due to an (exclusive) inhibition of the shikimate pathway in these parasites. We already demonstrated that the mode of action of Roundup on energetic metabolism in A. nidulans (Nicolas et al. 2016) was different of that previously observed for human cells or tissues (Mesnage et al. 2015; Peixoto 2005). Obviously, the data reported here indicate that the mode of action of Roundup on protein synthesis in A. nidulans is totally different, even diametrically opposed to that observed in plants. Such a possible stimulation of protein synthesis is consistent with the apparent up-regulation of some amino acid biosynthesis, such as valine, leucine, isoleucine, lysine and glutamate (Table 1 and Fig. 5).

Detoxification pathways

The three detoxification pathways modulated in response to R450 exposure are described in Fig. 6. Methylglyoxal is mainly a by-product of glycolysis. Its high cytotoxicity implies that it does not accumulate into the cell. In A. nidulans, methylglyoxal has been shown to be a substrate of NADP-glycerol dehydrogenase that can reduce it while simultaneously oxidizing NADPH (Schuurink et al. 1990). The down-regulation of this enzyme under R450 exposure (Fig. 5 and Table 2) suggests a possible accumulation of methylglyoxal in the presence of the herbicide. Another methylglyoxal detoxification pathway involves glyoxalase enzymes (I and II) and reduced GSH to generate pyruvate (Fig. 6a). This pathway proved to be strongly stimulated in A. nidulans exposed to R450, since herbicide treatment highly increased abundance of glyoxalase II: the two spots corresponding to this enzyme were not detected in the control 2-DE gels (ratio of 100 for spots 2628 and 3476; Table 1).
Fig. 6

Detoxification pathways modulated in response to R450 treatment. Methylglyoxal (a), xenobiotics (b) and hydrogen peroxide (c) detoxifying systems. Variation in abundance of R450-responsive proteins is indicated by the direction of the gray arrows (up or down)

Our data reveal that two other detoxification pathways are stimulated in A. nidulans under R450 exposure: one, involving again GSH and a glutathione S-transferase (GST), contributes to xenobiotic resistance (Fig. 6b), and the other, involving catalase and peroxidase activities, is required to transform the harmful oxygen compound H2O2 by reducing it to water (Fig. 6c). Interestingly, in A. nidulans, the fungal glutathione system has been shown to function as an antioxidant process and to interplay with the hydrogen peroxide defense mechanism (Sato et al. 2009). Thus, these two pathways are essential in maintaining cellular redox homeostasis in response to oxidative stress. Many studies have shown that GLY induces oxidative stress (Ahsan et al. 2008; de Aguiar et al. 2016; de Melo Tarouco et al. 2016; Gomes et al. 2017; Gomes and Juneau 2016; Martini et al. 2016; Mesnage et al. 2015; Murussi et al. 2016; Salvio et al. 2016; Wu et al. 2016). The fact that GLY acts as a protonophore (Olorunsogo 1990) increasing mitochondrial membrane permeability to protons and Ca2+ can explain such a cellular effect of GLY. Indeed, Ca2+ is considered to be one of the major stimulators of mitochondrial reactive oxygen species (ROS) accumulation because it promotes structural alterations of the inner mitochondrial membrane (Kowaltowski and Vercesi 1999). ROS are highly reactive and could impair cellular molecules such as lipids, proteins or DNA, leading to cell damages. Such damages are reflected by aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT) increased activities in laboratory animals exposed to GLY (Mesnage et al. 2015).

GST abundance was increased by 3.45-fold under R450 treatment (Table 1). GSTs are ubiquitous enzymes, which play a key role in the cell detoxification of a wide range of compounds. GSTs catalyze the conjugation of reduced GSH, via its thiol group, to electrophilic centers of a wide variety of xenobiotic substrates (X), in order to make these compounds more hydrophilic (GS-X), thus facilitating their breakdown and elimination (Fig. 6b).

In A. nidulans, three catalase and/or peroxidase activities have been identified: catalase A, catalase B and catalase-peroxidase. Only catalase A proved to be up-regulated in response to R450 exposure (Table 1, Fig. 6c), while the two other activities were down-regulated (Table 2, Fig. 6c), especially catalase-peroxidase whose corresponding spot was not detected in the R450 2-DE gels (Table 2). However, this enzyme would be required mainly during sexual development (Scherer et al. 2002). Now, the difference observed between the two catalases might be due to the fact that the gene catA (encoding catalase A) but not catB (encoding catalase B) is induced by osmotic stress (Kawasaki et al. 1997; Navarro and Aguirre 1998), consistent with the modulation of glycerol metabolism as described in the previous texts.

Conclusions

Consistent with our previous toxicological data (Nicolas et al. 2016), this proteomic analysis evidenced that R450 affects oxidative metabolism, and especially stimulates Krebs TCA cycle, thus confirming the unexpected impact of this herbicide on the energetic metabolism in this fungus, since Roundup was previously shown to damage basic mitochondrial functions, including the TCA cycle, in mammalian cells (Mesnage et al. 2015; Peixoto 2005). Based on enzyme activity measurements, such a TCA cycle stimulation was not only observed at higher doses, but also, to a lesser extent, at the concentration used here, i.e. the NOAEL dose associated to macroscopic parameters (Nicolas et al. 2016). Moreover, the present study revealed an alteration of translation functions and amino acid metabolism that suggested an enhancement of protein synthesis. Such data indicate again a different mode of action of Roundup, when compared to plants or other organisms with a class I EPSPS enzyme. Indeed, in such organisms, protein synthesis is inhibited and finally abolished by blocking the synthesis of essential aromatic amino acids (Duke et al. 2003).

Thus, mechanisms responsible for Roundup toxicity appear multiple and can vary from one organism or cell type to another. This broad spectrum of effects of Roundup, not only within a specific organism but also according to the exposed organism, is likely due to various targets of GLY and/or adjuvants present in the formulations. Indeed, the additives cannot be considered to be inert since many studies have shown that the GBH formulations are much more toxic than GLY alone (Braconi et al. 2006; Clair et al. 2012; Cuhra et al. 2013; Lipok et al. 2010; Mesnage et al. 2013, Mesnage et al. 2014; Mottier et al. 2013; Nicolas et al. 2016; Piola et al. 2013; Qiu et al. 2013).

Our data also evidenced the up-regulation of three detoxification pathways, accounting for both osmotic and oxidative stresses. One is necessary for the elimination of methylglyoxal, the accumulation of which may result, at least in part, from the modulation of the glycerol metabolism, which was also revealed in this analysis. The two others are required to maintain the redox balance of the cell, and their stimulation was consistent with the known oxidative stress induced by GBH, especially because of its uncoupling effect.

Altogether, this proteomic analysis revealed multiple molecular and metabolic effects of a commercial formulation of Roundup (R450) in A. nidulans, at a dose that does not affect the macroscopic parameters. All these metabolic modulations would be a cellular response to offset the effects of the herbicide. Such metabolic adaptations would therefore be sufficient at this dose (0.007%) to prevent the appearance of specific phenotypes (growth and morphology), while they would be only partial or even inoperative at higher doses (Nicolas et al. 2016). Indeed, it has been shown in particular that the uncoupling effect of Roundup is dose-dependent (Peixoto 2005). These data imply that metabolic disturbances due to pesticide residues may occur at exposure doses for which there is no visible toxic effect, regardless of the exposed organism. This is especially the case of agricultural doses for GM plants designed to tolerate herbicides. The assessment of these plants is based on the principle of “substantial equivalence” (i.e. close nutritional and compositional similarity between two crop-derived foods) used to claim that GM crops are as safe and nutritious as currently consumed conventional plant-derived foods (Aumaitre et al. 2002). Surprisingly, the presence of herbicide residues in such plants is ignored (Cuhra 2015). The data reported here confirm, however, the importance of taking into account the impact of these herbicide residues in the determination process of substantial equivalence, since metabolic disturbances due to these residues may add toxic properties to the final plant product.

Notes

Acknowledgments

This work was supported by the non-governmental organization “Générations Futures” and the Committee for Independent Research and Information on Genetic Engineering (CRIIGEN), in the framework of a participatory research project.

References

  1. Ahsan N, Lee D-G, Lee K-W, Alam I, Lee S-H, Bahk JD, Lee B-H (2008) Glyphosate-induced oxidative stress in rice leaves revealed by proteomic approach. Plant Physiol Biochem 46:1062–1070CrossRefGoogle Scholar
  2. Aumaitre A, Aulrich K, Chesson A, Flachowsky G, Piva G (2002) New feeds from genetically modified plants: substantial equivalence, nutritional equivalence and safety for animals and animal products. Livest Prod Sci 74:223–238CrossRefGoogle Scholar
  3. Bentley R (1990) The shikimate pathway—a metabolic tree with many branches. Crit Rev Biochem Mol Biol 25:307–384CrossRefGoogle Scholar
  4. Binder S (2010) Branched-chain amino acid metabolism in Arabidopsis thaliana. Arabidopsis Book. https://doi.org/10.1199/tab.0137
  5. Blomberg A, Adler L (1989) Roles of glycerol and glycerol-3-phosphate dehydrogenase (NAD+) in acquired osmotolerance of Saccharomyces cerevisiae. J Bacteriol 171:1087–1092CrossRefGoogle Scholar
  6. Braconi D, Possenti S, Laschi M, Geminiani M, Lusini P, Bernardini G, Santucci A (2008) Oxidative damage mediated by herbicides on yeast cells. J Agric Food Chem 56:3836–3845CrossRefGoogle Scholar
  7. Braconi D, Sotgiu M, Millucci L, Paffetti A, Tasso F, Alisi C, Martini S, Rappuoli R, Lusini P, Sprocati AR, Rossi C, Santucci A (2006) Comparative analysis of the effects of locally used herbicides and their active ingredients on a wild-type wine Saccharomyces cerevisiae strain. J Agric Food Chem 54:3163–3172CrossRefGoogle Scholar
  8. Carranza CS, Barberis CL, Chiacchiera SM, Magnoli CE (2014) Influence of the pesticides glyphosate, chlorpyrifos and atrazine on growth parameters of nonochratoxigenic Aspergillus section Nigri strains isolated from agricultural soils. J Environ Sci Health 49:747–755CrossRefGoogle Scholar
  9. Cerqueira GC, Arnaud MB, Inglis DO, Skrzypek MS, Binkley G, Simison M, Miyasato SR, Binkley J, Orvis J, Shah P, Wymore F, Sherlock G, Wortman JR (2014) The Aspergillus genome database: multispecies curation and incorporation of RNA-Seq data to improve structural gene annotations. Nucleic Acids Res 42:705–710CrossRefGoogle Scholar
  10. Clair E, Linn L, Travert C, Amiel C, Séralini G-E, Panoff J-M (2012) Effects of Roundup(®) and glyphosate on three food microorganisms: Geotrichum candidum, Lactococcus lactis subsp. cremoris and Lactobacillus delbrueckii subsp. bulgaricus. Curr Microbiol 64:486–491CrossRefGoogle Scholar
  11. Coupe RH, Capel PD (2015) Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States. Pest Manag Sci 72:1013–1022CrossRefGoogle Scholar
  12. Cove DJ (1966) The induction and repression of nitrate reductase in the fungus Aspergillus nidulans. Biochim Biophys Acta 113:51–56CrossRefGoogle Scholar
  13. Cuhra M (2015) Review of GMO safety assessment studies: glyphosate residues in Roundup Ready crops is an ignored issue. Env Sci Eur 27:20. https://doi.org/10.1186/s12302-015-0052-7 CrossRefGoogle Scholar
  14. Cuhra M, Traavik T, Bøhn T (2013) Clone- and age-dependent toxicity of a glyphosate commercial formulation and its active ingredient in Daphnia magna. Ecotoxicology 22:251–262CrossRefGoogle Scholar
  15. de Aguiar LM, Figueira FH, Gottschalk MS, da Rosa CE (2016) Glyphosate-based herbicide exposure causes antioxidant defence responses in the fruit fly Drosophila melanogaster. Comp Biochem Physiol C Toxicol Pharmacol 185-186:94–101CrossRefGoogle Scholar
  16. de Arruda Grossklaus D, Bailão AM, Vieira Rezende TC, Borges CL, de Oliveira MA, Parente JA, de Almeida Soares CM (2013) Response to oxidative stress in Paracoccidioides yeast cells as determined by proteomic analysis. Microbes Infect 15:347–364CrossRefGoogle Scholar
  17. de Melo Tarouco F, de Godoi FG, Velasques RR, da Silveira GA, Geihs MA, da Rosa CE (2016) Effects of the herbicide Roundup on the polychaeta Laeonereis acuta: cholinesterases and oxidative stress. Ecotoxicol Environ Saf 135:259–266CrossRefGoogle Scholar
  18. Duke SO, Baerson SR, Rimando AM (2003) Herbicides: glyphosate. In: Plimmer JR, Gammon DW, Ragsdale NN (eds) Encyclopedia of agrochemicals. John Wiley & Sons, New York, pp 708–869Google Scholar
  19. Fang Y, Gao X, Zha NB, Li X, Gao Z, Chao F (2010) Identification of differential hepatic proteins in rare minnow (Gobiocypris rarus) exposed to pentachlorophenol (PCP) by proteomic analysis. Toxicol Lett 199:69–79CrossRefGoogle Scholar
  20. Félenbok B, Kelly JM (1996) Regulation of carbon metabolism in mycelial fungi. In: Marzluf G, Beambl R (eds) The Mycota III: biochemistry and molecular biology, 1st edn. Springer, Berlin Heidelberg New York, pp 369–380CrossRefGoogle Scholar
  21. Flipphi M, van de Vondervoort PJ, Ruijter GJ, Visser J, Arst HN Jr, Felenbok B (2003) Onset of carbon catabolite repression in Aspergillus nidulans. Parallel involvement of hexokinase and glucokinase in sugar signaling. J Biol Chem 278:11849–11857CrossRefGoogle Scholar
  22. Froidevaux-Klipfel L, Poirier F, Boursier C, Crépin R, Poüs C, Baudin B, Baillet A (2011) Modulation of septin and molecular motor recruitment in the microtubule environment of the Taxol-resistant human breast cancer cell line MDA-MB-231. Proteomics 11:3877–3886CrossRefGoogle Scholar
  23. Gancedo C, Llobell A, Ribas J-C, Luchi F (1986) Isolation and characterization of mutants from Schyzosaccharomyces pombe defective in glycerol catabolism. Eur J Biochem 159:171–174CrossRefGoogle Scholar
  24. Gaupp-Berghausen M, Hofer M, Rewald B, Zaller JG (2015) Glyphosate-based herbicides reduce the activity and reproduction of earthworms and lead to increased soil nutrient concentrations. Sci Rep 5:12886CrossRefGoogle Scholar
  25. Gillardin V, Silvestre F, Dieu M, Delaive E, Raes M, Thome J-P, Kestemont P (2009) Protein expression profiling in the African clawed frog Xenopus laevis tadpoles exposed to the polychlorinated biphenyl mixture Aroclor 1254. Mol Cell Proteomics 8:596–611CrossRefGoogle Scholar
  26. Gomes MP, Bicalho EM, Smedbol É, Cruz FV, Lucotte M, Garcia QS (2017) Glyphosate can decrease germination of glyphosate-resistant soybeans. J Agric Food Chem 65:2279–2286CrossRefGoogle Scholar
  27. Gomes MP, Juneau P (2016) Oxidative stress in duckweed (Lemna minor L.) induced by glyphosate: is the mitochondrial electron transport chain a target of this herbicide? Environ Pollut 218:402–409CrossRefGoogle Scholar
  28. Hickey PC, Swift SR, Roca MG, Read ND (2005) Live-cell imaging of filamentous fungi using vital fluorescent dyes and confocal microscopy. In: Savidge T, Pothoulakis C (eds) Methods in microbiology, Microbial Imaging, vol 35. Elsevier, London, pp 63–87Google Scholar
  29. Hynes MJ, Murray SL (2010) ATP-citrate lyase is required for production of cytosolic acetyl coenzyme A and development in Aspergillus nidulans. Eukaryot Cell 9:1039–1048CrossRefGoogle Scholar
  30. Hynes MJ, Szewczyk E, Murray SL, Suzuki Y, Davis MA, Sealy-Lewis HM (2007) Transcriptional control of gluconeogenesis in Aspergillus nidulans. Genetics 176:139–150CrossRefGoogle Scholar
  31. James C (2011) Global status of commercialized biotech/GM crops. ISAAA brief no 43. ISAAA, Ithaca, NYGoogle Scholar
  32. Kawasaki L, Wysong D, Diamond R, Aguirre J (1997) Two divergent catalase genes are differentially regulated during Aspergillus nidulans development and oxidative stress. J Bacteriol 179:3284–3292CrossRefGoogle Scholar
  33. Kelly JM, Hynes MJ (1981) The regulation of phosphoenolpyruvate carboxykinase and the NADP-linked malic enzyme in Aspergillus nidulans. J Gen Microbiol 123:371–375Google Scholar
  34. Kniemeyer O (2011) Proteomics of eukaryotic microorganisms: the medically and biotechnologically important fungal genus Aspergillus. Proteomics 11:3232–3243CrossRefGoogle Scholar
  35. Kowaltowski AJ, Vercesi AE (1999) Mitochondrial damage induced by conditions of oxidative stress. Free Radic Biol Med 26:463–471CrossRefGoogle Scholar
  36. Kumari N, Narayan OP, Rai LC (2009) Understanding butachlor toxicity in Aulosira fertilissima using physiological, biochemical and proteomic approaches. Chemosphere 77:1501–1507CrossRefGoogle Scholar
  37. Lemos MF, Soares AM, Correia AC, Esteves AC (2010) Proteins in ecotoxicology—how, why and why not? Proteomics 10:873–887CrossRefGoogle Scholar
  38. Lescuyer P, Strub JM, Luche S, Diemer H, Martinez P, Van Dorsselaer A, Lunardi J, Rabilloud T (2003) Progress in the definition of a reference human mitochondrial proteome. Proteomics 3:157–167CrossRefGoogle Scholar
  39. Lipok J, Studnik H, Gruyaert S (2010) The toxicity of Roundup® 360 SL formulation and its main constituents: glyphosate and isopropylamine towards non-target water photoautotrophs. Ecotoxicol Environ Saf 73:1861–1868CrossRefGoogle Scholar
  40. Martinelli SD, Kinghorn JR (1994) Aspergillus: 50 years on—progress in industrial microbiology. Volume 29. Elsevier, Amsterdam—London—New York—TokyoGoogle Scholar
  41. Martini CN, Gabrielli M, Brandani JN, Vila Mdel C (2016) Glyphosate inhibits PPAR gamma induction and differentiation of preadipocytes and is able to induce oxidative stress. J Biochem Mol Toxicol 30:404–413CrossRefGoogle Scholar
  42. Mesnage R, Agapito-Tenfen SZ, Vilperte V, Renney G, Ward M, Séralini GE, Nodari RO, Antoniou MN (2016) An integrated multi-omics analysis of the NK603 Roundup-tolerant GM maize reveals metabolism disturbances caused by the transformation process. Sci Rep 19:37855CrossRefGoogle Scholar
  43. Mesnage R, Bernay B, Séralini GE (2013) Ethoxylated adjuvants of glyphosate-based herbicides are active principles of human cell toxicity. Toxicology 313:122–128CrossRefGoogle Scholar
  44. Mesnage R, Defarge N, Spiroux de Vendômois J, Séralini GE (2014) Major pesticides are more toxic to human cells than their declared active principles. Biomed Res Int. https://doi.org/10.1155/2014/179691
  45. Mesnage R, Defarge N, Spiroux de Vendomois J, Séralini G-E (2015) Potential toxic effects of glyphosate and its commercial formulations below regulatory limits. Food Chem Toxicol 84:133–153CrossRefGoogle Scholar
  46. Mesnage R, Renney G, Séralini GE, Ward M, Antoniou MN (2017) Multiomics reveal non-alcoholic fatty liver disease in rats following chronic exposure to an ultra-low dose of Roundup herbicide. Sci Rep 9:39328CrossRefGoogle Scholar
  47. Mottier A, Kientz-Bouchart V, Serpentini A, Lebel JM, Jha AN, Costil K (2013) Effects of glyphosate-based herbicides on embryo-larval development and metamorphosis in the Pacific oyster, Crassostrea gigas. Aquat Toxicol 128-129:67–78CrossRefGoogle Scholar
  48. Murussi CR, Costa MD, Leitemperger JW, Guerra L, Rodrigues CC, Menezes CC, Severo ES, Flores-Lopes F, Salbego J, Loro VL (2016) Exposure to different glyphosate formulations on the oxidative and histological status of Rhamdia quelen. Fish Physiol Biochem 42:445–455CrossRefGoogle Scholar
  49. Navarro RE, Aguirre J (1998) Posttranscriptional control mediates cell type-specific localization of catalase A during Aspergillus nidulans development. J Bacteriol 180:5733–5738Google Scholar
  50. Nevoigt E, Stahl U (1997) Osmoregulation and glycerol metabolism in the yeast Saccharomyces cerevisiae. FEMS Microbiol Rev 21:231–241CrossRefGoogle Scholar
  51. Nicolas V, Oestreicher N, Vélot C (2016) Multiple effects of a commercial Roundup® formulation on the soil filamentous fungus Aspergillus nidulans at low doses: evidence of an unexpected impact on energetic metabolism. Environ Sci Pollut Res 23:14393–14404CrossRefGoogle Scholar
  52. Olorunsogo OO (1990) Modification of the transport of protons and Ca2+ ions across mitochondrial coupling membrane by N-(phosphonomethyl)glycine. Toxicology 61:205–209CrossRefGoogle Scholar
  53. Peixoto F (2005) Comparative effects of the Roundup and glyphosate on mitochondrial oxidative phosphorylation. Chemosphere 61:1115–1122CrossRefGoogle Scholar
  54. Piola L, Fuchs J, Oneto ML, Basack S, Kesten E, Casabé N (2013) Comparative toxicity of two glyphosate-based formulations to Eisenia andrei under laboratory conditions. Chemosphere 91:545–551CrossRefGoogle Scholar
  55. Poirier F, Pontet M, Labas V, le Caër JP, Sghiouar-Imam N, Raphaël M, Caron M, Joubert-Caron R (2001) Two-dimensional database of a Burkitt lymphoma cell line (DG 75) proteins: protein pattern changes following treatment with 5′-azycytidine. Electrophoresis 22:1867–1877CrossRefGoogle Scholar
  56. Qiu H, Gen J, Ren H, Xia X, Wang X, Yu Y (2013) Physiological and biochemical responses of Microcystis aeruginosa to glyphosate and its Roundup® formulation. J Hazard Mater 248-249:172–176CrossRefGoogle Scholar
  57. Redkar RJ, Locy RD, Singh (1995) Biosynthetic pathways of glycerol accumulation under salt stress in Aspergillus nidulans. Exp Mycol 19:241–246CrossRefGoogle Scholar
  58. Riechers DE, Wax LM, Liebl RA, Bush DR (1994) Surfactant-increased glyphosate uptake into plasma membrane vesicles isolated from common lambsquarters leaves. Plant Physiol 105:1419–1425CrossRefGoogle Scholar
  59. Sabatier P, Poulenard J, Fanget B, Reyss JL, Develle AL, Wilhelm B, Ployon E, Pignol C, Naffrechoux E, Dorioz JM, Montuelle B, Arnaud F (2014) Long-term relationships among pesticide applications, mobility, and soil erosion in a vineyard watershed. Proc Natl Acad Sci U S A 111:15647–15652CrossRefGoogle Scholar
  60. Sacheti P, Patil R, Dube A, Bhonsle H, Thombre D, Marathe S, Vidhate R, Wagh P, Kulkarni M, Rapole S, Gade W (2014) Proteomics of arsenic stress in the gram-positive organism Exiguobacterium sp. PS NCIM 5463. Appl Microbiol Biotechnol 98:6761–6773CrossRefGoogle Scholar
  61. Salvio C, Menone ML, Rafael S, Iturburu FG, Manetti PL (2016) Survival, reproduction, avoidance behavior and oxidative stress biomarkers in the earthworm Octolasion cyaneum exposed to glyphosate. Bull Environ Contam Toxicol 96:14–319CrossRefGoogle Scholar
  62. Sato I, Shimizu M, Hoshino T, Takaya N (2009) The glutathione system of Aspergillus nidulans involves a fungus-specific glutathione S-transferase. J Biol Chem 284:8042–8053CrossRefGoogle Scholar
  63. Scherer M, Wei H, Liese R, Fischer R (2002) Aspergillus nidulans catalase-peroxidase gene (cpeA) is transcriptionally induced during sexual development through the transcription factor StuA. Eukaryot Cell 1:725–735CrossRefGoogle Scholar
  64. Schuurink R, Busink R, Hondmann DH, Witteveen CF, Visser J (1990) Purification and properties of NADP(+)-dependent glycerol dehydrogenases from Aspergillus nidulans and A. niger. J Gen Microbiol 136:1043–1050CrossRefGoogle Scholar
  65. Shimizu M, Fujii T, Masuo S, Fujita K, Takaya N (2009) Proteomic analysis of Aspergillus nidulans cultured under hypoxic conditions. Proteomics 9:7–19CrossRefGoogle Scholar
  66. Shimizu M, Fujii T, Masuo S, Takaya N (2010) Mechanism of de novo branched-chain amino acid synthesis as an alternative electron sink in hypoxic Aspergillus nidulans cells. Appl Environ Microbiol 76:1507–1515CrossRefGoogle Scholar
  67. Steffen W, Richardson K, Rockström J, Cornell SE, Fetzer I, Bennett EM, Biggs R, Carpenter SR, de Vries W, de Wit CA, Folke C, Gerten D, Heinke J, Mace GM, Persson LM, Ramanathan V, Reyers B, Sörlin S (2015) Sustainability. Planetary boundaries: guiding human development on a changing planet. Science 347:1259855CrossRefGoogle Scholar
  68. Teixeira MC, Santos PM, Fernandes AR, Sá-Correia I (2005) A proteome analysis of the yeast response to the herbicide 2,4-dichlorophenoxyacetic acid. Proteomics 5:1889–1901CrossRefGoogle Scholar
  69. Thornton BJ, Elthon TE, Cerny RL, Siegfried BD (2010) Proteomic analysis of atrazine exposure in Drosophila melanogaster (Diptera: Drosophilidae). Chemosphere 81:235–241CrossRefGoogle Scholar
  70. Wu L, Qiu Z, Zhou Y, Du Y, Liu C, Ye J, Hu X (2016) Physiological effects of the herbicide glyphosate on the cyanobacterium Microcystis aeruginosa. Aquat Toxicol 178:72–79CrossRefGoogle Scholar
  71. Zaller JG, Heigl F, Ruess L, Grabmaier A (2014) Glyphosate herbicide affects belowground interactions between earthworms and symbiotic mycorrhizal fungi in a model ecosystem. Sci Rep 4:5634CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Université Paris 13, UFR SMBH, Plateforme PPUP13Bobigny cedexFrance
  2. 2.UMS-IPSIT, US31 Inserm-UMS3679 CNRS, Plateformes Trans-Prot et d’Imagerie Cellulaire, Université Paris-Sud, Faculté de Pharmacie, Tour E1Châtenay-MalabryFrance
  3. 3.Gene Expression and Therapy Group, King’s College London, Faculty of Life Sciences & Medicine, Department of Medical and Molecular GeneticsLondonUK
  4. 4.CRIIGENParisFrance
  5. 5.Laboratoire VEAC, Université Paris-Sud, Faculté des SciencesOrsayFrance
  6. 6.Pôle Risques MRSH-CNRS, Université de Caen, Esplanade de la PaixCaenFrance

Personalised recommendations