International Journal of Agronomy

International Journal of Agronomy / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 5468602 |

Richard Moise Alansou Dieme, Issa Faye, Yedomon Ange Bovys Zoclanclounon, Daniel Fonceka, Ousmane Ndoye, Papa Madiallacke Diedhiou, "Identification of Sources of Resistance for Peanut Aspergillus flavus Colonization and Aflatoxin Contamination", International Journal of Agronomy, vol. 2018, Article ID 5468602, 7 pages, 2018.

Identification of Sources of Resistance for Peanut Aspergillus flavus Colonization and Aflatoxin Contamination

Academic Editor: Glaciela Kaschuk
Received25 May 2018
Revised07 Aug 2018
Accepted13 Sep 2018
Published01 Oct 2018


Peanut aflatoxin contamination caused by Aspergillus flavus is a serious constraint for food safety and human health in Senegal. The present study aimed to identify sources of resistance for A. flavus colonization and aflatoxin contamination. Thus, seeds from 67 peanut genotypes were tested under laboratory conditions. Aqueous conidial suspension of an aflatoxinogenic strain of A. flavus was used for inoculation in Petri dishes containing ten seeds of each genotype, and data on incidence and severity were recorded. Total aflatoxin concentration in seeds was determined on 15th day after inoculation using mReader® method. Results showed a significant () variation of aflatoxin, incidence and severity among the tested peanut genotypes. Incidence ranged from 0 to 70% with a mean of 20.36 ± 0.8%. Out of the 67 genotypes, eight showed incidence less than 10%. Severity ranged from 0 to 44% with a mean value of 8.82 ± 0.45%. The genotype 12CS_104 showed aflatoxin concentration level in conformity with the European standard (4 ppb). Out of three clusters revealed by hierarchical classification based on disease incidence and severity, the cluster 1 contained 33 genotypes characterised by low incidence and severity values. These genotypes can be tested under field conditions to confirm their resistance to A flavus.

1. Introduction

Peanut (Arachis hypogaea L.) is an important staple crop in Senegal. The national peanut production was estimated at 1,050,042 tons during the rainy season of 2016 [1]. This crop is mainly produced in Fatick, Kaolack, Kaffrine, Louga, and Thies regions, with more than 60% of the national peanut production [1]. Peanut seeds are widely used for food consumption and play a significant economic role for small-scale farmers and food industries in Senegal [2]. However, pre- and postharvest aflatoxin contamination in peanut is a serious threat for food safety and human health in Senegal [3]. It is one of the major constraints limiting sustainable and good quality seed production in the world [4]. Aflatoxin contamination is due to Aspergillus flavus (Link ex Fries, Teleomorph: Petromyces flavus) [5]. Damages caused by this facultative plant pathogen in maize, peanut, and sesame were reported in Senegal [6]. Considerable economic losses caused by this bacterium are mainly due to crop quality value and international trade restrictions on food stuffs charged in aflatoxin [7].

Aflatoxin is the name of a group of toxin known as G1, G2, B1, B2, M1, and M2 that produced the plant pathogen [8]. These toxins occur naturally and have been found in a wide range of commodities, including peanuts used for animal and human consumption [9]. Aflatoxins are toxic, mutagenic, and carcinogenic compounds [10]. Depending on their levels, toxins can severely affect the liver and induce immune-suppressing effects [9].

To handle this issue, a wide range of preharvest aflatoxin contamination management methods were developed. Application of atoxinogenic isolates of A. flavus [11] and host genetic resistance were tested [12]. In Senegal, previous studies reported that varieties 55-437 and 73-3 were resistant to A. flavus [13]. Identification of new sources of resistance merits to be investigated for efficient peanut breeding program. First step of host genetic resistance is the seed colonization test. Therefore, the present study was undertaken to identify promising peanut genotypes under laboratory conditions.

2. Materials and Methods

2.1. Plant Materials

The plant material consisted of 67 genotypes including 58 chromosomal substitutions lines [14] and nine national released varieties. The chromosomal substitution lines belong to a cross between Fleur 11 and a synthetic amphidiploid parent (Table 1).

NoGenotypesDescriptionCountry of origin

6155-437Resistant controlSenegal
6373-33Resistant controlSenegal
65Fleur11Susceptible controlSenegal

Chromosomal substitution lines.
2.2. Isolation of Aspergillus flavus, Sporangial Suspension Preparation, and Inoculation

Aflatoxinogenic strain provided from peanut seeds were purified by successive cultures on 5/2 agar medium. The aflatoxin concentration level was checked using the Reveal® Q+ Aflatoxin test kit (accesso peanut enterprise corporation, USA). The spore suspension of A. flavus was obtained by soaking colonized seeds in 50 ml of sterile distilled water. Then, one drop of Tween 20 was added to the solution and thoroughly mixed for 10 minutes. Inoculation was carried out by introducing 100 μl of the supernatant of the spore suspension into each Petri dish.

2.3. Seed Colonization Test

The seed colonization test was conducted following a modified Mehan and McDonald procedure. For each genotype, 50 seeds were sterilized and rinsed properly in sterile distilled water. Then, the seeds were hydrated to about 20% moisture content. The 50 seeds of each genotype were placed in 5 Petri dishes containing 10 seeds, and each Petri dish was considered as a replication. The seeds were inoculated with a conidial suspension (60 µL containing approximately 1 × 108 mL−1 conidia of the aflatoxigenic strain of A. flavus). This preparation was kept at laboratory conditions (25 ± 0.12°C and 82 ± 0.42% relative humidity) for fifteen days.

2.4. Data Collection

The seeds’ colonization was observed during two weeks, and aflatoxin concentration was measured using the Reveal® Q+ Aflatoxin test kit (accesso peanut enterprise corporation, USA). The incidence was calculated using the following formula:

The severity scale of aflatoxin on seeds was estimated using a modified Tonapi et al. [15] scale. It was defined as follows: 0, noninfected seeds; 1, seeds whose surface covered by the fungus is less than 20%; 2, 20%–40% seed surface covered by the fungus; 3, 40%–60% seed surface covered by the fungus; 4, 60%–80% seed surface covered by the fungus; and 5, 80%–100% seed surface covered by the fungus. The severity calculation based on Tonapi et al. [15] formula was as follows:where i is severity scale from 0 to 5 and is the number of seed corresponding to scale of severity.

2.5. Data Analysis

Data analysis was performed with the open-source statistical software R version 3.4.5 [16]. Descriptive statistics of recorded data were generated with pastecs package [17]. In order to find out variability of incidence and severity according to tested genotypes, data were subjected to Poisson regression analysis using glm (generalized linear model) function of package stats implemented in the R. Spearman’s rank correlation test was performed to highlight relationship between incidence, severity, and aflatoxin concentration levels using correlation test function of package stats. Identification of different groups of genotypes based on incidence and severity was performed based on a principal component analysis and a hierarchical clustering with the functions PCA and HCPC of package FactoMineR [18], respectively. The Euclidean distance and Ward classification method were used to classify tested genotypes. The function fviz_pca_biplot [19] was used to plot the principal components analysis biplot in different clusters based on hierarchical classification.

3. Results

3.1. Reaction of Peanut Genotypes to Aspergillus flavus

Analysis of variance revealed highly significant () variation of aflatoxin incidence and severity among the tested peanut genotypes (Table 2).

Source of variationDegree of freedomIncidenceSeverity

Replication433.9 (ns)2.04 (ns)

Significant chi-squared test at 0.001 level of probability; ns = not significant.

The severity ranged between 0 and 44%, respectively, with a mean of 8.82 ± 0.45%. The recorded incidence ranged from 0 to 70% with an average value of 20.36 ± 0.80% (Table 3).

MeanStd deviationMeanStd deviation


Standard error0.800.45

One genotype (12CS_104) showed aflatoxin concentration level less than 4 ppb. A total of 34 genotypes presented aflatoxin concentration level up to 2000 ppb (Figure 1).

Out of the 67 genotypes, eight showed incidence less than 10% while 33 showed incidences between 10 and 20% and 16 with incidences ranged from 20 to 30% (Figure 2).

3.2. Correlation between Incidence, Severity, and Aflatoxin Concentration Level

Spearman’s rank correlation test revealed a strong relationship (, ) between incidence and severity of peanut genotypes. Positive and significant correlations were detected between aflatoxin concentration levels and disease incidence (, ) and aflatoxin concentration levels and disease severity (, ) (Table 4).

IncidenceSeverityAflatoxin concentration levels

Aflatoxin concentration levels0.280.351.00

Significant Spearman’s rank correlation test at 0.05 level of probability. Significant Spearman’s rank correlation test at 0.01 level of probability. Significant Spearman’s rank correlation test at 0.001 level of probability.
3.3. Classification of the Tested Genotypes according to Sensibility and Aflatoxin Concentration Level

The factorial axes 1 and 2 explained 60.5 and 39.5% of overall variability, respectively (Figure 3). Hierarchical classification performed on principal component analysis revealed three clusters of genotypes based on disease incidence and aflatoxin concentration levels (Figure 3). The clusters 1, 2, and 3 grouped 33, 20, and 14 genotypes, respectively. The incidence and aflatoxin concentration are significantly () associated to cluster 1 (Table 4).

Mean values of these two variables in this cluster are less than the overall mean. Therefore, cluster 1 is characterized by desirable genotypes which combine low incidence values and aflatoxin concentration levels. Cluster 2 is significantly () related to the aflatoxin concentration level (Table 5).

Cluster 1Cluster 2Cluster 3
MeanOverall mean valueMeanOverall mean valueMeanOverall mean value

Description of clusters by variables

Description of clusters by distances
Top 3 representative genotypes††12CS_039 (0.11)12CS_090 (0.16)73-33 (0.29)12CS_010 (0.12)12CS_079 (0.22)12CS_027 (0.24)12CS_050 (0.15)12CS_066 (0.41)12CS_016 (0.42)
Top 3 characteristic genotypes†††12CS_104 (2.82)55-33 (2.75)12CS_111 (2.74)78-936 (3.68)12CS_028 (2.39)12CS_031 (2.28)12CS_021 (3.45)12CS_001 (3.00)12CS_100 (2.59)

Correlation signification of variables with a cluster. †† Based on the closest distance between each genotype and the respective cluster centres. ††† Based on the farthest distance from a genotype projected point in a cluster to the centres of the two others.

The mean value of aflatoxin concentration in cluster 2 (4075.5 ppb) is 190% which is higher than the overall mean (2143.8 ppb). Thus, this second cluster is characterized by genotypes with high level of aflatoxin. Incidence is linked to cluster 3 (Table 5). Mean value of this variable (35%) in cluster 3 is superior to overall mean (20.35%). Thus, the cluster 3 encompasses the most susceptible genotypes to A. flavus.

Based on the closest distance between each genotype and the respective cluster centres, 12CS_039, 12CS_010, and 12CS_050 were the first representative genotypes (paragon) of cluster 1, 2, and 3, respectively (Table 4). Based on the farthest distance from a genotype projected point in a cluster to the centres of the two others, clustering revealed that cluster 1, 2, and 3 were characterised by the genotypes 12CS_104, 78-936, and 12CS_021, respectively (Figure 3, Table 5). Based on results, out of 67 genotypes, 33 promising genotypes (cluster 1) were noted (Figure 3).

4. Discussion

In the present study, a wide phenotypic variation was observed among the tested genotypes for incidence, severity, and aflatoxin concentrations. This variation can be explained by the variability of seed coat structure of the tested genotypes. In fact, the seed coat can constitute a barrier to A. flavus seed invasion depending on its thickness and/or permeability [20], and Zhou and Liang [21] studies showed that genotypes seed coat with smaller hilum, more compact arrangement and thicker testa showed more resistance to A. flavus. In addition, implication of wax and cutin layers of seed coat was demonstrated to be related to genotypes resistance [22]. Another explanation of this wide variation in incidence, severity, and aflatoxin rate can be biochemical compounds’ differential variability in the tested seeds. Lindsey and Turner [23] demonstrated that the presence of polyphenol compounds, specifically, tannins in seed can have inhibitor effect against A. flavus. Amaya et al. [24] and Liang et al. [25] showed the difference among seed coat biochemical compounds to determine sensibility to A. flavus. Liang [22] demonstrated that the presence of trypsin in seeds can also be related to resistance to A. flavus. Turner et al. [26] isolated and identified the 5,7-dimethoxyisoflavone as an inhibitor for A. flavus invasion in peanut seed.

12CS_104 was the most resistant genotype to aflatoxin contamination with an aflatoxin level lower than the European Union standards (4 ppb). However, except 12CS_104, all the genotypes have their aflatoxin concentration level higher than the Chinese (20 ppb) standards. Indeed, the highest aflatoxin concentration level was observed with genotype 78-936. The contrasting genotypes observed in this study can be used as positive and negative checks, respectively, for accurate field experiment. Furthermore, these contrasted genotypes can be used to develop mapping population for genetic study such as inheritance of aflatoxin and identification of quantitative trait loci (QTL). The varieties 55-437 and 73-30 showed incidence less than 15% as reported by the previous study realized 30 years ago by Zambettakis et al. [13], but their aflatoxin concentration levels were largely up to the European Union standards.

The correlation test showed a positive relationship between A. flavus colonization and aflatoxin contamination. This confirmed that the presence of A. flavus induced aflatoxin production in seeds. Hierarchical classification highlighted three clusters according to incidence, severity, and aflatoxin concentration levels. The relatively low values of incidence observed on the 33 genotypes belonged to cluster 1 should be confirmed under field conditions. These genotypes can be evaluated in different locations on infested fields.

5. Conclusion

This study uncovered that the lines 12CS_104 exhibited low values of incidence and severity. Furthermore, its aflatoxin concentration level was smaller than standards. This genotype represents a relevant tool for the breeding program for resistance to A. flavus as a potentially resistant gene donor.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.


The authors sincerely thank the West Africa Agricultural Productivity Program (WAAPP) for financial support and facilities help.


  1. ANSD, Bulletin Mensuel des Statistiques Économiques, ANSD, Dakar, Sénégal, 2017.
  2. D. Clavel, A. Da Sylva, O. Ndoye, and A. Mayeux, “Amélioration de la qualité sanitaire de l'arachide au Sénégal: un challenge pour une opération de recherche dévelopement participative,” Cahiers Agricultures, vol. 22, no. 13, pp. 174–81, 2013. View at: Google Scholar
  3. P. M. Diedhiou, F. Ba, A. Kane, and N. Mbaye, “Effect of different cooking methods on aflatoxin fate in peanut products,” African Journal of Food Science and Technology, vol. 3, no. 12, pp. 53–58, 2012. View at: Google Scholar
  4. W. A. Korani, Y. Chu, C. Holbrook, J. Clevenger, and P. Osias Akins, “Genotypic regulation of aflatoxin accumulation but not Aspergillus fungal growth upon post-harvest infection of peanut (Arachis hypogaea L.) seeds,” Toxins, vol. 9, no. 7, p. E218, 2017. View at: Publisher Site | Google Scholar
  5. J. C. Fountain, J. Koh, L. Yang et al., “Proteome analysis of Aspergillus flavus isoate-specific responses to oxidative stress in relationship to aflatoxin production capability,” Scientic Reports, vol. 8, no. 1, p. 3430, 2018. View at: Publisher Site | Google Scholar
  6. P. M. Diedhiou, R. Bandyopadhyay, J. Atehnkeng, and P. S. Ojiambo, “Aspergillus colonization and aflatoxin contamination of maize and sesame kernels in two agro-ecoloical zones in Senegal,” Journal of Phytopathology, vol. 159, no. 4, pp. 268–275, 2011. View at: Publisher Site | Google Scholar
  7. X. Q. Liang, M. Luo, and B. Z. Guo, “Resistance mechanisms to Aspergillus flavus infection and aflatoxin contamination in peanut (Arachis hypogea L.),” Plant Pathology Journal, vol. 5, no. 11, pp. 115–124, 2006. View at: Publisher Site | Google Scholar
  8. S. Amaike and N. P. Keller, “Aspergillus flavus,” Annual Review of Phytopathology, vol. 49, no. 1, pp. 107–133, 2011. View at: Publisher Site | Google Scholar
  9. J. H. Williams, T. D. Phillips, P. Jolly, J. K. Styles, C. M. Jolly, and D. Aggarwal, “Human aflatoxicosis in developing countries: a review of toxicology, exposure, potential health consequences, and interventions,” American Journal of Clinical Nutrition, vol. 80, no. 5, pp. 1106–1122, 2004. View at: Publisher Site | Google Scholar
  10. Y. C. Chen, C. D. Liao, H. Y. Lin, L. C. Chiueh, and D. Y. C. Shih, “Survey of aflatoxin contamination in peanut proucts in Taiwan from 1997 to 2011,” Journal of Food and Drug Analysis, vol. 21, no. 3, pp. 247–252, 2013. View at: Publisher Site | Google Scholar
  11. R. Y. Kelley, W. P. Williams, J. E. Mylroie et al., “Identification of maize genes associated with host plant resistance or susceptibility to Aspergillus flavus infection and aflatoxin accumulation,” PLoS ONE, vol. 7, no. 5, Article ID e36892, 2012. View at: Publisher Site | Google Scholar
  12. H. Q. Xue, T. G. Isleib, G. A. Payne, and G. OBrian, “Evaluation of post-harvest aflatoxin production in peanut germplasm with resistance to seed colonization and pre-harvest aflatoxin contamination,” Peanut Science, vol. 31, no. 2, pp. 124–134, 2004. View at: Publisher Site | Google Scholar
  13. C. Zambettakis, F. Waliyar, A. Bockelee-Morvan, and O. de Pins, “Results of four years of research on resistance of groundnut varieties to Aspergillus flavus,” Oleagineux, vol. 36, pp. 377–385, 1981. View at: Google Scholar
  14. D. Fonceka, H. A. Tossim, R. Rivallan et al., “Construction of chromosome segment substitution lines in peanut (Arachis hypogea L.) using a wild synthetic and QTL mapping for plant morphology,” Plos ONE, vol. 7, no. 11, Article ID e48642, 2012. View at: Publisher Site | Google Scholar
  15. V. K. Mehan and D. McDonald, Screening for Resistance to Aspergillus Invasion and Aflatoxin Production in Groundnuts, ICRISAT Groundnut Improvement Program Occasional Paper2, Patancheru, India, 1980.
  16. V. A. Tonapi, R. R. Mundada, S. S. Navi et al., “Effect of temperature and humidity regimes on grain mold sporulation and seed quality in sorghum (Sorghum bicolor (L.) Moench),” Archives of Phytopathology and Plant Protection, vol. 40, no. 2, pp. 113–127, 2007. View at: Publisher Site | Google Scholar
  17. R Core Team, A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2018.
  18. P. Grosjean and F. Ibanez, PASTECS: Package for Analysis of Space Time Ecological Series, R package version 1.3-18, 2014.
  19. S. Lê, J. Josse, and F. Husson, “FactoMineR: an R package for multivariate analysis,” Journal of Statistical Software, vol. 25, no. 1, pp. 1–18, 2008. View at: Publisher Site | Google Scholar
  20. A. Kassambara and F. Mundt, factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.4, 2017.
  21. R. A. Taber, R. E. Pettit, C. R. Benedict, J. W. Dieckert, and D. L. Kertrin, “Comparison of Aspergillus flavus tolerant and susceptible lines. I. Light microscope investigation,” in Proceedings of the American Peanut Research Education Association, vol. 5, pp. 206-207, Oklahoma, USA, 1973. View at: Google Scholar
  22. G. Y. Zhou and X. Q. Liang, “Studies on the ultramicroscopic structure of seed coats between resistant and susceptible to Aspergillus flavus invasion in peanut,” Chinese Journal of Oil Crop Sciences, vol. 20, pp. 32–35, 1999. View at: Google Scholar
  23. X. Q. Liang, G. Y. Zhou, and R. Z. Pan, “Study on the relationship of wax and cutin layers in peanut seeds and resistance to invasion and aflatoxin production by Aspergillus flavus,” Journal Of Tropical And Subtropical Botany, vol. 11, pp. 11–14, 2003. View at: Google Scholar
  24. D. L. Lindsey and R. B. Turner, “Inhibition of growth of Aspergillus flavus and Trichoderma viride by peanut embryos,” Mycopathologia, vol. 55, no. 3, pp. 149–152, 1975. View at: Publisher Site | Google Scholar
  25. F. J. Amaya, C. T. Young, A. J. Norden, and A. C. Mixon, “Chemical screening for Aspergillus flavus resistance in peanut,” Oleagineux, vol. 35, no. 5, pp. 255–259, 1980. View at: Google Scholar
  26. X. Q. Liang, G. Y. Zhou, and R. Z. Pan, “Changes of some biochemical substances in peanut seeds under infection of Aspergillus flavus and their role in resistance to seed invasion,” Chinese Journal of Oil Crop Sciences, vol. 23, no. 2, pp. 26–31, 2001. View at: Google Scholar

Copyright © 2018 Richard Moise Alansou Dieme et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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