**3.2 Colonization of plant tissues by** *Aspergillus flavus*

*Biotechnological Applications of Biomass*

**2.9 Statistical analysis**

**3. Results and discussion**

more commonly used.

determination.

**3.1 Gene specificity and qPCR assays**

conditions: for 10 min at 95°C; 35 cycles for 3 s at 95°C, for 20 s at 64°C, for 1 s at 72°C for *MEP* and at annealing temperature 62°C for both *Ef1ɑ* and β-tubulin.

To our knowledge, a qPCR assay for the detection and quantification of *A. flavus* biomass using extracted fungal DNA from control or infected maize tissues has not been previously reported. Since this is the first report, our discussion will be in comparison with reports for *Fusarium* spp. and related fungi where this assay is

In this study, the qPCR assay was developed to specifically detect and quantify *A. flavus* gDNA in maize tissues. Primers were designed, and their specificity was confirmed by testing against control and infected tissues (**Figure 1**). The fungal biomass in the co-infected shoots differed from the fungal biomass in the roots

Amplification of the MEP gene (203 bp) was used to detect maize DNA, while amplification of *β*-tubulin (118 bp) and *Ef1ɑ* (102 bp) were used to detect *A. flavus* DNA (**Table 1**; **Figure 1**). The specificity of the primer pairs was determined by conventional PCR (**Figure 1**) after *A. flavus* KSM014 infection of GAF4 and KDV1 maize lines. *A. flavus* DNA extracted from infected maize plant tissues, for both lines, gave an amplification product for both *β*-tubulin (118 bp) and *Ef1a* (102 bp) (**Figure 1**). However, there was amplification product for *Ef1a* than there was for to *β*-tubulin (**Figure 1**), especially in the roots. The *MEP* gene (203 bp) was amplified in both control and infected maize plants for both lines (**Figure 1**). *MEP* amplification was plant specific and *β*-Tub and *Ef1ɑ* were fungal specific. Based on these results, *β*-Tub is a better marker for detecting *A. flavus* in infected maize tissues than was *Ef1ɑ* (**Figure 1**) and was used for fungal biomass

*Gel images of the quantitative polymerase chain reaction amplicon sizes for maize maker gene (*MEP*) and*  A. flavus *maker genes (*Ef1ɑ, β-*tub) assessed on 2% agarose/EtBr gel run at 80 v for 45 min. M. 100 bp ladder; 1. NTC; 2. Pooled samples (maize gDNA and pure fungal gDNA); 3. GAF4 (control roots); 4. GAF4 (infected roots) 5. GAF4 (control shoots); 6. GAF4 (infected shoots); 7. KDV1 (control shoots); 8. KDV1 (infected shoots);* 

*9. KDV1 (control roots); 10. KDV1 (infected roots); 11. KSM014 (positive control).*

according to 1-way ANOVA analysis and TMCT test (P < 0.05).

The statistical analysis was performed as previously described [14].

**516**

**Figure 1.**

*Aspergillus flavus* KSM014 infection of both maize lines resulted in changes in maize phenotype with the KDV1 showing more severe symptoms that GAF4 (**Figure 2**). After 3–14 days post infection, the infected kernels for both maize lines showed stunted growth compared to control kernels (**Figure 2**). Additionally, the shoots and roots exhibited minimal growth with the *A. flavus* fungi colonizing the kernels and this could possibly explain the reason for stunted growth or germination. The phenotypic observations suggest that KDV1 maize line grown in Makeuni is more susceptible to fungal infection (*A. flavus*), whereas GAF4, grown in Kisumu and Homa bay appeared more resistant to the infection (**Figure 2**).

The observed phenotypic characteristics were further supported by the detection and quantification of fungal biomass load in gDNA extracted from infected and control plant tissues as revealed by the qPCR assay (**Figure 3**).

Insignificant difference was observed in biomass of fungi between infected plant tissues for the GAF4 and the control maize line (**Figure 3a**). In contrast, significant differences in biomass of fungi for the KDV1 maize line was exhibited upon infection (p < 0.05) for both the shoot and root tissue (**Figure 3b**). Fungal gDNA level was observed to be lower in the infected GAF4 maize line tissues compared to KDV1 suggesting that GAF4 was more resistant to *A. flavus* KSM014 infection than KDV1 (**Figure 3**).

The fungal biomass of *Alternaria dauci* was observed to be equivalent in two carrot cultivars between 1 and 15 days of post-inoculation, whereas it was found to be four-fold higher in the more susceptible cultivar between 21 and 25 days postinoculation [21]. This suggests that fungal pathogens may colonize both susceptible and resistant cultivars in a similar manner during the first stages of the interaction, but fungal development is subsequently restricted in the partially resistant cultivar due to putative plant defense mechanisms [21].

It must be noted that we measured fungal biomass 14 days after infection when symptoms of the infection was phenotypically visible. However, other fungal biomass studies have shown that specific fungi could be identified even before the development of the symptoms. The presence of *Colletotrichum acutatum* by qPCR in

#### **Figure 2.**

*The GAF4 and KDV1 maize lines after 14 days of growth with and without* Aspergillus flavus *KSM014 infection. The red sticker shows infected maize plants while the white stickers are the control, uninfected maize plants.*

**Figure 3.**

*Quantitative polymerase chain reaction analysis indicating fungal load of* A. flavus *KSM014 in the shoot and root tissues of KDV1 and GAF4 maize* lines respectively. Biomass of fungi was measured in infected and non-infected *(control) GAF4 (a) and KDV1 (b) maize lines after 14 days where the A. flavus β-tub gene was used for quantification of fungi against the maize* MEP *gene. Tukey's multiple comparison test and one-way ANOVA (P ˂ 0.05), was done where the asterisks indicate significance and the error bars shows standard mean deviation.*

strawberry leaves was detected by Debode et al. [22] 2 h post-inoculation whereas the initial symptoms of the disease appeared only after 96 h. Similarly, *Fusarium langsethiae* gDNA was accurately measured by Divon and Razzaghian [23] in oats independently from symptoms of the disease. These findings show the specificity and efficiency of the qPCR assay for the detection and quantification of fungal pathogens upon infection at early stages, before symptomatic appearances.

GAF4 is a *Striga* spp. resistant maize line cultivated in Kisumu, Kibos, Homa Bay and some parts of Nandi, while KDV1 is an open pollinated maize variety cultivated in Makueni and the neighboring counties. The observation that KDV1 maize line as more susceptible to aflatoxigenic *A. flavus* (KSM014) infection could be one of the contributing factors to why Makueni and the neighboring regions are more prone to frequent aflatoxicosis outbreak and high levels of aflatoxin contamination of the maize used for consumption.

The current study relates to the previous findings on Makueni maize samples [18] where they screened the strains of *A. flavus* isolated from maize kernels obtained from Makueni region on CAM media and found that there was significant variation in production of blue (toxigenic) and green (atoxigenic) fluorescence by most isolates. Seventy eight percent of the isolates from Makueni were observed to produce high amounts of aflatoxin AFB1, AFB2, the most potent carcinogen compared to other regions under study [18]. Additionally, studies conducted by Probst et al. [24] in eastern Kenya, revealed a similar result where they performed culturebased methods to monitor and describe the population structures of aflatoxigenic fungi and its closely associated strains on maize kernels. Moreover, a related study by Lewis et al. [25] and Klich [26] observed that in sub-Saharan Africa, products from subsistence farmers may reach the final consumer without the appropriated monitoring, resulting in critical risks for human health.

Moreover, the current study developed a qPCR assay using *A. flavus* gDNA and the *β*-tubulin gene for the quantification of *A. flavus* in maize tissue. Due to its high sensitivity and specificity, qPCR has been incorporated in official protocols of the European Plant Protection Organization (http://archives.eppo. org/index.htm) for the production, certification and assessment of healthy plant

**519**

**4. Conclusion**

interaction studies.

**Acknowledgements**

*Fungal Biomass Load and* Aspergillus flavus *in a Controlled Environment*

materials [27, 28]. This could therefore, in future, provide a screening strategy for finding African maize cultivars that are resistant to *A. flavus* infection or as an assessment of healthy maize plants. Zhao et al. [29] developed a qPCR assay for the detection of *Magnaporthe poae* resistant *Poa pratensis* (Kentucky bluegrass turf), which typically needed 3 weeks to detect using conventional culture-based methods. Further, Montes-Borrego et al. [30] demonstrated that fungal presence can be detected earlier, enabling the selection of resistant plants even when samples are indistinguishable based on visual assessment. Lastly, the early detection of latent infections of rust on leaves of cereals was used to estimate infection

The genomic DNA extracted from the co-infected shoots of both maize lines showed varied concentrations of fungal biomass load compared to the roots according to analysis using 1-way ANOVA and TMCT test (p < 0.05). The quantification of *Verticillium dahliae* gDNA in different tomato cultivars also revealed the concentration of pathogen DNA in plant tissues increased and decreased in susceptible and resistant cultivars, respectively [31]. Similarly, significant differences were found in the amount of *F. oxysporum* DNA in roots of different chickpea cultivars [32], while the detection of *Phomopsis sclerotioides* in pumpkin, melon, cucumber and watermelon showed that infection and rate of disease development of this polyphagous pathogen may vary according to the host [33]. In general, Vandemark and Barker [34], concluded that low levels of pathogen DNA in resistant plants is indicative of a mechanism that inhibits pathogen growth, whereas, the presence of a relatively high amount of pathogen DNA in asymptomatic plants indicates a resistance

The study demonstrated that KDV1 maize line was more susceptible to *A. flavus* infection when compared to GAF4. This also implies that a possible reason for the frequent cases of aflatoxicosis in Makeuni county is the fact that the KDV1 maize

The *β-*tubulin gene is a potential marker for quantification of the *A. flavus* biomass load in maize plants compared to *Ef1ɑ*. The *MEP* gene for maize gDNA was also found to be plant specific by the absence of cross-reaction with fungal gDNA. The specificity of the qPCR assay for *A. flavus* biomass quantification makes it a useful tool in other areas such as screening of *A. flavus* resistant maize lines for breeding, determining possible asymptomatic infection and in plant-pathogen

The next chapter will focus on in vitro biocontrol approach in aflatoxin mitigation and bio-analytical approaches to detect and quantify aflatoxins. The aim is to determine whether biocontrol can minimize aflatoxin production and to find

The work was supported by the University Science, Humanities and Engineering Partnerships in Africa (USHEPiA) Fund and South African Bio-Design Initiative (SABDI) grant number 420/01 SABDI 16/1021. Also, the authors acknowledge the University of Nairobi, Kenya and Vaal University of Technology, Vanderbijlpark,

*DOI: http://dx.doi.org/10.5772/intechopen.93307*

levels before the appearance of the disease [2].

mechanism based on tolerance rather than on true resistance.

line is grown in that region is more susceptible to *A. flavus* infection.

important metabolites that are produced by specific *A. flavus* isolates.

South Africa for providing laboratory space and funding.

#### *Fungal Biomass Load and* Aspergillus flavus *in a Controlled Environment DOI: http://dx.doi.org/10.5772/intechopen.93307*

materials [27, 28]. This could therefore, in future, provide a screening strategy for finding African maize cultivars that are resistant to *A. flavus* infection or as an assessment of healthy maize plants. Zhao et al. [29] developed a qPCR assay for the detection of *Magnaporthe poae* resistant *Poa pratensis* (Kentucky bluegrass turf), which typically needed 3 weeks to detect using conventional culture-based methods. Further, Montes-Borrego et al. [30] demonstrated that fungal presence can be detected earlier, enabling the selection of resistant plants even when samples are indistinguishable based on visual assessment. Lastly, the early detection of latent infections of rust on leaves of cereals was used to estimate infection levels before the appearance of the disease [2].

The genomic DNA extracted from the co-infected shoots of both maize lines showed varied concentrations of fungal biomass load compared to the roots according to analysis using 1-way ANOVA and TMCT test (p < 0.05). The quantification of *Verticillium dahliae* gDNA in different tomato cultivars also revealed the concentration of pathogen DNA in plant tissues increased and decreased in susceptible and resistant cultivars, respectively [31]. Similarly, significant differences were found in the amount of *F. oxysporum* DNA in roots of different chickpea cultivars [32], while the detection of *Phomopsis sclerotioides* in pumpkin, melon, cucumber and watermelon showed that infection and rate of disease development of this polyphagous pathogen may vary according to the host [33]. In general, Vandemark and Barker [34], concluded that low levels of pathogen DNA in resistant plants is indicative of a mechanism that inhibits pathogen growth, whereas, the presence of a relatively high amount of pathogen DNA in asymptomatic plants indicates a resistance mechanism based on tolerance rather than on true resistance.

### **4. Conclusion**

*Biotechnological Applications of Biomass*

maize used for consumption.

**Figure 3.**

*deviation.*

monitoring, resulting in critical risks for human health.

strawberry leaves was detected by Debode et al. [22] 2 h post-inoculation whereas the initial symptoms of the disease appeared only after 96 h. Similarly, *Fusarium langsethiae* gDNA was accurately measured by Divon and Razzaghian [23] in oats independently from symptoms of the disease. These findings show the specificity and efficiency of the qPCR assay for the detection and quantification of fungal pathogens upon infection at early stages, before symptomatic appearances.

*Quantitative polymerase chain reaction analysis indicating fungal load of* A. flavus *KSM014 in the shoot and root tissues of KDV1 and GAF4 maize* lines respectively. Biomass of fungi was measured in infected and non-infected *(control) GAF4 (a) and KDV1 (b) maize lines after 14 days where the A. flavus β-tub gene was used for quantification of fungi against the maize* MEP *gene. Tukey's multiple comparison test and one-way ANOVA (P ˂ 0.05), was done where the asterisks indicate significance and the error bars shows standard mean* 

GAF4 is a *Striga* spp. resistant maize line cultivated in Kisumu, Kibos, Homa Bay and some parts of Nandi, while KDV1 is an open pollinated maize variety cultivated in Makueni and the neighboring counties. The observation that KDV1 maize line as more susceptible to aflatoxigenic *A. flavus* (KSM014) infection could be one of the contributing factors to why Makueni and the neighboring regions are more prone to frequent aflatoxicosis outbreak and high levels of aflatoxin contamination of the

The current study relates to the previous findings on Makueni maize samples [18] where they screened the strains of *A. flavus* isolated from maize kernels obtained from Makueni region on CAM media and found that there was significant variation in production of blue (toxigenic) and green (atoxigenic) fluorescence by most isolates. Seventy eight percent of the isolates from Makueni were observed to produce high amounts of aflatoxin AFB1, AFB2, the most potent carcinogen compared to other regions under study [18]. Additionally, studies conducted by Probst et al. [24] in eastern Kenya, revealed a similar result where they performed culturebased methods to monitor and describe the population structures of aflatoxigenic fungi and its closely associated strains on maize kernels. Moreover, a related study by Lewis et al. [25] and Klich [26] observed that in sub-Saharan Africa, products from subsistence farmers may reach the final consumer without the appropriated

Moreover, the current study developed a qPCR assay using *A. flavus* gDNA and the *β*-tubulin gene for the quantification of *A. flavus* in maize tissue. Due to its high sensitivity and specificity, qPCR has been incorporated in official protocols of the European Plant Protection Organization (http://archives.eppo. org/index.htm) for the production, certification and assessment of healthy plant

**518**

The study demonstrated that KDV1 maize line was more susceptible to *A. flavus* infection when compared to GAF4. This also implies that a possible reason for the frequent cases of aflatoxicosis in Makeuni county is the fact that the KDV1 maize line is grown in that region is more susceptible to *A. flavus* infection.

The *β-*tubulin gene is a potential marker for quantification of the *A. flavus* biomass load in maize plants compared to *Ef1ɑ*. The *MEP* gene for maize gDNA was also found to be plant specific by the absence of cross-reaction with fungal gDNA. The specificity of the qPCR assay for *A. flavus* biomass quantification makes it a useful tool in other areas such as screening of *A. flavus* resistant maize lines for breeding, determining possible asymptomatic infection and in plant-pathogen interaction studies.

The next chapter will focus on in vitro biocontrol approach in aflatoxin mitigation and bio-analytical approaches to detect and quantify aflatoxins. The aim is to determine whether biocontrol can minimize aflatoxin production and to find important metabolites that are produced by specific *A. flavus* isolates.

#### **Acknowledgements**

The work was supported by the University Science, Humanities and Engineering Partnerships in Africa (USHEPiA) Fund and South African Bio-Design Initiative (SABDI) grant number 420/01 SABDI 16/1021. Also, the authors acknowledge the University of Nairobi, Kenya and Vaal University of Technology, Vanderbijlpark, South Africa for providing laboratory space and funding.
