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Peroxidases are classified as oxidoreductases and are the second largest class of enzymes applied in biotechnological processes. These enzymes are used to catalyze various oxidative reactions using hydrogen peroxide and other substrates as electron donors. They are isolated from various sources such as plants, animals and microbes. Peroxidase enzymes have versatile applications in bioenergy, bioremediation, dye decolorization, humic acid degradation, paper and pulp, and textile industries. Besides, peroxidases from different sources have unique abilities to degrade a broad range of environmental pollutants such as petroleum hydrocarbons, dioxins, industrial dye effluents, herbicides and pesticides. Ironically, unlike most biological catalysts, the function of peroxidases varies according to their source. For instance, manganese peroxidase (MnP) of fungal origin is widely used for depolymerization and demethylation of lignin and bleaching of pulp. While, horseradish peroxidase of plant origin is used for removal of phenols and aromatic amines from waste waters. Microbial enzymes are believed to be more stable than enzymes of plant or animal origin. Thus, making microbially-derived peroxidases a well-sought-after biocatalysts for versatile industrial and environmental applications. Therefore, the current review article highlights on the recent breakthroughs in the discovery and use of peroxidase isoforms of microbial origin at a possible depth.

Aspergillus flavus colonisation of maize can produce mycotoxins that are detrimental to both human and animal health. Screening of maize lines, resistant to A. flavus infection, together with a biocontrol strategy, could help minimize subsequent aflatoxin contamination. We developed a qPCR assay to measure A. flavus biomass and showed that two African maize lines, GAF4 and KDV1, had different fungal loads for the aflatoxigenic isolate (KSM014), fourteen days after infection. The qPCR assay revealed no significant variation in A. flavus biomass between diseased and non-diseased maize tissues for GAF4, while KDV1 had a significantly higher A. flavus biomass (p < 0.05) in infected shoots and roots compared to the control. The biocontrol strategy using an atoxigenic isolate (KSM012) against the toxigenic isolate (KSM014), showed aflatoxin production inhibition at the co-infection ratio, 50:50 for both maize lines (KDV1 > 99.7% and GAF ≥ 69.4%), as confirmed by bioanalytical techniques. As far as we are aware, this is the first report in Kenya where the biomass of A. flavus from maize tissue was detected and quantified using a qPCR assay. Our results suggest that maize lines, which have adequate resistance to A. flavus, together with the appropriate biocontrol strategy, could limit outbreaks of aflatoxicoses.

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Some secondary metabolites produced by fungi are carcinogenic, hepatotoxic, and/or cause birth defects in humans and animals. We developed and optimised bio-analytical tools for detection of metabolites, aflatoxins and evaluated the effectiveness of the methods in co-infected maize tissues. Isolate KSM012 (atoxigenic) demonstrated no peaks and no blue fluorescence on HPLC and TLC plates respectively confirming non-toxicity. AFB1 and AFB2 were produced by Isolate KSM015 in addition to AFG1 and AFG2, which is an indication of possible S BG morphotype. The limits of quantification and detection ranged from 0.02 to 35.81 µg/mL and 0.01-6.8 µg/mL, respectively. The best mass spectrum with lowest noise was obtained at 100% ACN and sterile water spiked with 0.1% formic acid at a flow rate of 0.3 mL/min. The positive ion mode with electrospray ionisation application exhibited better fragmentation for mycotoxins. In total 17 metabolites were detected by targeted and formula mass. KDVI maize line exhibited high fungal colonisation in comparison to GAF4 at equal co-infection ratio 50:50. AFB1 and AFG2 were remarkably higher in GAF4 in comparison to sensitive KDV1 (p ˂ 0.05). The detection limits, linearity and sensitivity showed the method developed was suitable for the determination of mycotoxin in comparisons to the guidelines of European Commission 657/EC 2002.

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Molecular techniques and phenotypic characterisation have been used to differentiate aflatoxigenic and atoxigenic  Aspergillus flavus  strains. However, there is a lack of a consistent and reliable tool for discrimination between these strains of  A.  flavus .  Here we report, an optimised real-time qPCR-based tool for reliable differentiation between aflatoxigenic and atoxigenic strains of  A.  flavus . Accordingly, expression profiles and deletion patterns of genes responsible for aflatoxin production in five representative aflatoxigenic and atoxigenic  A.  flavus  strains (KSM012, KSM014, HB021, HB026 and HB027) were examined using the optimised real-time qPCR tool.  We observed that under induced conditions,  aflP ,  aflS ,  aflR and  aflO  transcripts were the most upregulated genes across the tested isolates while  aflS  and  aflO  were always expressed in both induced and uninduced isolates. However,  aflR  and  aflP  did not give clear distinctions between non-toxin and toxin producing isolates. The deletion patterns were prominent for  aflD  and  aflR  whereas  alfO ,  aflS  and  aflP  had no deletions among the isolates. Significant variation in transcript abundance for  aflD , aflR  and  aflS  were observed for aflatoxigenic isolate KSM014 under induced and uninduced states. False detection of  aflD  gene transcript in atoxigenic strain KSM012 was evident in both induced and uninduced conditions. With the exception of KSM012,  aflP  gene did not exhibit significant variation in expression in the isolates between induced and uninduced conditions. One-way ANOVA and Post-test analysis for linear trends revealed that aflatoxin biosynthetic cluster genes show significant (P ? 0.05) differences between atoxigenic and aflatoxigenic isolates. Our optimized qPCR-based tool reliably discriminated between aflatoxigenic and atoxigenic  A. flavus  isolates and could complement existing detection methods.