Aspergillus species and aflatoxins production are more prevalent during times of high heat and drought. In South Africa, there is frequent occurrence of drought as a result of climate change. The aim of this study was to investigate the biodiversity and distribution of Aspergillus species with their corresponding toxins in maize from main maize producing regions of South Africa; [Western Regions (WR) and Eastern Regions (ER)]. One hundred and twenty-three (64 from WR and 59 from ER) maize samples from the two agro-climatic regions in South Africa were analyzed using cultural, molecular and analytical methods. Across agro-climatic regions, Aspergillus species contaminated about 62% of the maize samples, while Aspergillus flavus was the most prevalent (47.15%) followed by Aspergillus fumigatus (4.69%) while the least was Aspergillus parasiticus (0.81%). The Western Regions showed a higher distribution of varieties of Aspergillus species compared to the Eastern Regions. Aflatoxins contaminated only 27.64% of the maize samples with a mean total aflatoxin concentration of 2.40 μg/kg which is below the South Africa’s set standard for total aflatoxin in food (5 μg/kg). About 10.57% of the samples produce aflatoxins above the 5 μg/kg permissible limit for total aflatoxin in foods. The ratio of toxigenic to atoxigenic strains of Aspergillus flavus was generally low in all the regions of South Africa. This study could aid policy makers to make informed decisions in developing remediation strategies for Aspergillus mycotoxins.
The filamentous fungus; Aspergillus flavus is a cosmopolitan soil-borne saprophytic organism with opportunistic parasitic behaviors to plants, animals and humans. Aflatoxins are metabolites produced mainly by toxigenic strains of Aspergillus flavus and A. parasiticus, which grow in soil, hay, decaying vegetation and grains [
Climate change plays a major role in the production of AFs in crops by Aspergillus spp. [
Mycotoxin toxicity occurs at very low concentrations, therefore sensitive and reliable methods for their detection are required. Different qualitative and quantitative analytical methods having different sensitivity, specificity and accuracy have been developed such as thin layer chromatography (TLC), high performance liquid chromatography (HPLC) liquid chromatography mass spectrometry tandem (LCMS/MS) and many other techniques.
Literature is depleted in information on the distribution and biodiversity of Aspergillus species and their toxins in silo stored maize cultivated in different climate regions of the country. Hence, this study aimed at filling the information gap by investigating the biodiversity and distribution of Aspergillus species with their corresponding toxins in maize samples from the different agro-climatic areas of SA. Molecular biomarkers to differentiate the ambiguity between the Aspergillus species were employed to identify the different Aspergillus species present in SA maize. The presence of the five most important genes in the aflatoxin biosynthesis pathway among Aspergillus isolates was also evaluated.
This study will aid policy makers to make informed decisions in developing strategies based on monitoring and characterization of risks, prevention, intervention and remediation strategies for Aspergillus mycotoxins, which start from critical points along the production chain such as field, storage, processing and transportation. It will also provide useful information that will enhance global efforts in ensuring production of quality food/feed as well as food security.
maize producing areas of the different agro-climatic regions of South Africa; Free State (45%), North West (17%) and Guateng (5%) [
One hundred and twenty three (123) maize samples were randomly collected from selected silo sites from different agricultural regions of South Africa (Gauteng, North West and Free State). A structured sampling model was used for sampling where 10 kg of maize was taken from different points of the silos while the maize was routinely moved. The collected samples were mixed and 5 kg subsequently collected. The samples were collected separately, finely milled using a warring blender (IKA, Model M20, Germany) and put into sterile zip lock polythene bags, labeled and stored at 4˚C prior analysis to arrest any formation of mycotoxins before analysis. Fifty nine samples (59) were from the Eastern region (ER) while sixty four (64) were from the Western region (WR).
The moisture content of samples was determined using the AOAC method [
Moisturecontent ( % ) = ( Initialweight − Finalweight ) Initialweight × 100
The mycobiota population in the maize samples was enumerated using the dilution plate techniques as described by [
cfu = Numberofcolonies × reciprocalofthedilutionfactor Platingvolume ( 1 ml )
The percentage occurrence of each of the isolates was calculated by comparing the ratio of the number of individual organism to the total number of organism present in each sample.
Percentage of appearance (PA) was calculated according to the following formula:
PA = Numberofsampleswiththesamegeneraorspp . Totalnumberofsamplesofgeneraofspp .
The previously cultured plates were returned to the incubator for additional 2 - 4 days for isolation of surface sample mycoflora and purification [
Extraction of the genomic DNA of fungal isolates was done using the Zymo Research kit (Zymo-Research fungal/Bacterial Soil Microbe DNA, D6005, USA), following the manufacture’s protocols. A reaction volume of 25 μl was used containing 7 μl nuclease-free water, 12 μl PCR Master Mix (Biolabs), 1.5 μl each of oligonucleotide forward and reverse primers (3 μm) and 3 μL template DNA mixed in the PCR tubes [
Sequencing was conducted on an ABI Prism 3500 XL DNA Sequencer (Applied Biosystems, Foster City, California) at the Inqaba Biotechnical Industrial, Pretoria, South Africa. Finch TV software version 1.4.0 was used to analyse chromatograms, resulting from sequencing reaction for good quality sequence assurance. The resulting chromatographs were blasted in the NCBI database (http://www.ncbi.nlm.nih.gov) with the Basic Local Alignment Search Tool (BLASTn) for homology in order to identify probable organisms in question [
Yeast Extract Sucrose (YES) Agar (yeast extract; 20 g, sucrose; 150 g, agar; 20 g and MgSO4; 0.5 g) was used as described by Criseo, Bagnara [
Extraction of aflatoxin from the isolates was done in accordance with Midorikawa, Pinheiro [
The DNA of suspected Aspergilla were examined for the presence of five important aflatoxin-producing genes (aflR, aflJ, aflM, aflQ, aflD and omt-A) present in the aflatoxin biosynthesis pathway by PCR method, using previously reported primer sets [
The following primers were used for the detection of the aflatoxin producing gene: Nor primer (Nor 1-5-ACC GCT ACG CCG GCA CTC TCG GCA C-3 and Nor 2-5-GTT GGC CGC CAG CTT CGA CAC TCC G-3 with 400 bp amplicon with the Nor (AflD) gene); Ver primer (Ver 1-5-GCC GCA GGC CGC GGA GAA AGT GGT-3 and Ver 2-5-GGG GAT ATA CTC CCG CGA CAC AGC C-3 with 537 bp amplicon with Ver (AflM) gene); Omt primer (Omt 1-5-GTG GAC GGA CCT AGT CCG ACA TCA C-3 and Omt 2-5-GTC GGC GCC ACG CAC TGG GTT GGG G-3 with a 797 amplicon size having the gene OmtA); AflR primer (AflR 1-5-TAT CTC CCC CCG GGC ATC TCC CGG-3 and AflR 2-5-CCG TCA GAC AGC CAC TGG ACA CGG-3 with 1032 bp amplicon size having the AflR gene); and the AflJ primer (AflJ F-5-TGA ATC CGT ACC CTT TGA GG-3 and AflJ R-5-GGA ATG GGA TGG AGA TGA GA-3 with 737 bp amplicon size having the gene AflJ gene). The PCR optimization conditions for the primers are presented in
An LC-MS/MS method was designed and validated for the analysis of 123 analytes. A Waters Acquity UPLC system coupled to a Xevo TQS mass spectrometer
Primer code (gene) | Pre-denaturation | Denaturation | Annealing | Elongation | ||
---|---|---|---|---|---|---|
Nor 1&2 (Nor(aflD) | 94˚C—10 min | 94˚C—1 min | 65˚C—1 min | 72˚C—2 min (33 cycles) | 72˚C—5 min (1 cycle | |
Ver 1&2 (Ver(aflM) | 95˚C—4 min | 95˚C—1 min | 58˚C—1 min | 72˚C—30 sec (30 cycles) | 72˚C—10 min (1 cycle | |
Omt 1&2 (omt) | 94˚C—5min | 94˚C—1 min | 72˚C—2 min | 72˚C—2 min (33 cycles) | 72˚C—10 min (1 cycle | |
AflR 1&2 (aflR) | 95˚C—4 min | 95˚C—1 min | 60˚C—1 min | 72˚C—30 sec (30 cycles) | 72˚C—10 min (1 cycle | |
AflJ F&R (aflJ) | 95˚C—10 min | 95˚C—50 sec | 58˚C—50 sec | 72˚C—2 min (30 cycles) | 72˚C—10 min (1 cycle | |
(Waters, Milford, MA, USA) was used to analyse the samples, equipped with Mass LynxR (version 4.1) and Quan Lynx R (version 4.1) software (Waters, Manchester, UK) for data acquisition and processing. A ZORBAX Eclipse XDB C18-column (1.8 μm, 100 × 2.1 mm) was applied (Agilent Technologies, Diegem, Belgium). The mobile phase consisted of water/methanol (55/45, v/v), adjusted with 9mg potassium bromide and 1ml 65% nitric acid at a flow rate of 0.4 mL/min. The gradient elution programme started at 100% mobile phase. Then, the mobile phase increased with a linear increase to 99% in the 20th minute. Duration of each LC run was 30 minutes including re-equilibration. The capillary voltage was 3 kV and nitrogen applied as spray gas. Source and desolvation temperatures were set at 120˚C and 400˚C respectively. The argon collision gas pressure was 9 × 10−6 bars, cone gas flow was 35 L/h and desolvation as flow was 800 L/h. For increased sensitivity and selectivity, the instrument was operated in the selected reaction monitoring (SRM) mode and two SRM transitions monitored for each analyte. Matrix-matched calibration plots were constructed for the determination of AFB1, AFB2, AFG1 and AFG2. The limit of detection (LOD) was calculated as three times the standard error of the intercept divided by the slope of the standard curve; the limit of quantification (LOQ) was computed in a similar way except for the standard error, which was by a factor of six. The calculated LOD and LOQ were verified by the signal-to-noise ratio (s/n), which was more than 3 and 10 respectively in accordance with the IUPAC guidelines [
This is a quantitative determination method for the extraction of aflatoxins. Extraction of aflatoxins from the samples was done using the Easi-Extract Aflatoxin (R-Biopharm Rhone LTD) immuno-affinity columns kits in conjunction with HPLC or LC-MS/MS as described in the product’s extraction kit. The total aflatoxin content of the samples was determined and quantified from maize samples with a high performance liquid chromatography column (HPLC) using the Shimadzu corporation model (Kyoto, Japan). LC-20AB liquid chromatography equipped with CBM-20A communication bus module, LC-20AB degasser, CTO-20A column oven, SIL-20A auto sampler, RF-10AxL fluorescence detector, RID-10A refractive index detector and SPD-M20A photodiode array detector linked to LC solutions version 1.22 software release. The chromatographic separation of analytes and standards was performed by passing through a reverse phase Symmetry column C18 (Waters). The oven temperature was maintained at 30˚C. Peak areas and retention times of mycotoxins were used to determine the amount of specific mycotoxins per sample based on those of standard mycotoxins using a calibration curve. To determine AFs, the fluorescence detector RF 10AXL was coupled with a Coring cell (CoBrA cell) (DR Weber Consulting, Germany) as electrochemical cell for the derivatisation of AFs. The mobile phase consisted of water/methanol (55/45, v/v), adjusted with 9 mg potassium bromide and 1 ml 65% nitric acid. The HPLC method used was validated by determining its linearity, accuracy and sensitivity. Linearity was determined by constructing calibration curves from standards of AFB1, AFB2, AFG2, AFG2, total aflatoxin (AFtot) and from extracts of blank samples of previously analyzed maize samples that did not contain any of the aflatoxins. Linear range was examined at 3 different concentrations (0.025 µg/ml, 0.25 µg/ml and 2,5 µg/ml). The matrix-matched calibration curves were built by spiking blank samples with selected aflatoxin standards after the extraction process. Calibration curves were constructed by plotting peak areas against concentration and linear functions applied to the calibration curves. Matrix effect (ME) was calculated for each analyte by comparing the slope of the standard calibration curve with the matrix-matched calibration curve for the same levels of concentration. Sensitivity of the methodology or system used was evaluated by limit of detection (LOD) and limit of quantification (LOQ), estimated for a signal-to-noise ratio (S/N) ×3 and ×10 respectively from chromatograms of samples spiked at the lowest level validated. Accuracy was evaluated through recovery studies and determined by calculating the ratio of the peak areas for each aflatoxin by analyzing the samples spiked before and after extraction at three additional levels of 25, 50, and 100 µg/kg for all aflatoxins analyzed (AFB1, AFB2, AFG1, AFG2 AFtot). Quantification of the toxins was performed by measuring peak areas, retention time and comparing them with the relevant standard calibration curves.
The moisture content of the maize samples ranged from 7.38% - 8.84% across both regions, with ranges of 7.38% - 8.81% and 7.64% - 8.84% for ER and WR respectively. The mean moisture content for maize in both regions (8.30% and 8.26%) is not significantly different (p > 0.05) from one another (
Mycobiota count in the maize ranged from 0 - 48 cfu/g and 0 - 23 cfu/g for WR and ER respectively, with mean count in WR (9.75 cfu/g) higher than those obtained in the ER (5.93 cfu/g). Sample 045/M/2015 from ER had the highest Aspergillus spp. load (9 cfu/g), with fungal load ranging from 0 - 7.33 cfu/g and 0 - 9 cfu/g for WR and ER respectively (
Regions | Range | Mean ± SD | |
---|---|---|---|
Moisture (%) | WR (n = 64) | 7.64 - 8.84 | 8.30 ± 0.27 |
ER (n = 59) | 7.38 - 8.81 | 8.26 ± 0.26 | |
Fungi (cfu/g) | WR (n = 64) | 0 - 48.00 | 9.75 ± 9.55 |
ER (n = 59) | 0 - 23.00 | 5.93 ± 14.76 | |
Aspergillus (cfu/g) | WR (n = 64) | 0 - 7.33 | 0.82 ± 6.05 |
ER (n = 59) | 0 - 9.00 | 1.09 ± 6.42 | |
SD = Standard deviation |
A total of 123 maize samples were analyzed (WR; 64 and ER; 59). Fungi spp. belonging to eight genera; Fusarium, Aspergillus, Penicillium, Rasamsonia, Talaromyces, Paecilomyces, Byssochlamys and Verticillium were isolated from the maize and four distinct genera Talaromyces, Paecilomyces, Byssochlamys, Verticillium found in maize were from the ER (
Fungi isolate | No. of infected samples from each Region | Frequency of isolation (%) from each Regions | Total No. of infected samples | Total frequency of isolation (%) | ||
---|---|---|---|---|---|---|
Western Region (n = 64) | Eastern Region (n = 59) | Western Region (64) | Eastern Region (59) | (n = 123) | (n = 123) | |
Aspergillus spp. | 47 | 29 | 73.44 | 49.15 | 76 | 61.79 |
Fusarium spp. | 52 | 44 | 81.25 | 74.58 | 96 | 78.05 |
Penicillium spp. | 12 | 3 | 18.75 | 5.08 | 15 | 12.2 |
Other Fungi | 31 | 10 | 48.44 | 16.95 | 41 | 33.33 |
Out of the total 123 samples, 76 (29 from ER and 47 from WR) were morphologically identified as Aspergillus isolates and these represented 11 Operational Taxonomic Unit (OTU) and four different Aspergilli sections; Aspergillus flavi section (A. flavus, A. parasiticus, A. oryzae); Aspergillus nigri section (A. niger, A. awamori, A. brasiliensis, A. luchuensis, A. welwitschiae); Aspergillus fumigate section (A. fumigatus) and Aspergillus usti section (A. insuetus, A. ustus, A. aff. ustus, A. mnutus) by matching the sequences of the isolates with ITS sequences in the database, as well as those of partial benA and caM genes of closely related type strains in the GenBank (
Group | Isolates | Descrition | Fungal ID (ITS) | Fungal ID (CMD) | Fungal ID (BT2) |
---|---|---|---|---|---|
1 | 22, G275, 76, 100, 24, 40, 15, 55, 74, 66, 68, 50, 17, 51, 108, 58, 57, 117, 49, 27, 16, 29, 105, 107, 87, 102, 91, 5 | Greenish colony without scerotia | A. flavus A. oryzae | A. flavus | A. flavus |
2 | 108, 59, 36, 111, 113, 110, 1, 192, 48, 32, 6, 54, 37, 67, 109, 72, 51, 94, 84, 95, 62, 2, | Greenish yellow colony with scerotia | A. oryzae | A. flavus | A. flavus |
3 | G208, 47, 88, 26, 13, 35, 31, 21, 118, 46, 119, 114, 53, 99, 40, 107, 10, 70, 6, 101, 45, 16, 64, 80, 82, 104 | Greenish brown colony with timidly visible scerotia | A. flavus | A. flavus | A. flavus |
4 | 38 | Dark green | A. parasiticus | A. parasiticus | A. parasiticus |
5 | 108, 16 | Greenish thallus structure | A. aff. ustus A. ustus A. insuetus | A. insuetus A. mnutus | A. insuetus |
6 | 27, 119 | Blue grey | A. fumigatus | A. fumigatus | A. fumigatus |
7 | 119, 64, 19, 6, 87, G218, 103, 111, 70 | Dark green to grey | A. fumigatus | A. fumigatus | A. niger A. brasiliensis |
8 | 111, 12, 42 | Dark with yellow tones on the reverse side | A. niger | A. niger | A. niger A. awamori |
9 | 108, 1, 70 | Black | N/A | A. luchuensis | N/A |
10 | 115, 45, 80, 84, 2 | Black | A. niger | A. welwitschiae | A. niger |
11 | 58, 95 | Dark brown to black | A. brasiliensis Aspergillus sp. | A. brasiliensis | Rasamsonia columbiensis |
Thirteen different Aspergillus spp. were isolated from the 11 OTUs group out of which only six were found in maize from the ER while all the spp. were found in maize from the WR in varying loads, spp. such as A. parasiticus, A. insuetus, A. awamori, A. mnutus, A. ustus as well as A. aff. ustus were not found in Eastern regions (
The result of screening for aflatoxin producing genes in the Aspergillus section flavi revealed that none of the isolates from both regions possessed the AflR gene (
Aspergillus isolate | Total number sample (n = 123) | Frequency of isolation (%) | Eastern Region | Western Region | ||
---|---|---|---|---|---|---|
Sample (n = 59) | Frequency of isolation (%) | Sample (n = 64) | Frequency of isolation (%) | |||
A. flavus (A. oryzae) | 58 | 47.15 | 21 | 35.59 | 37 | 57.81 |
A. fumigatus | 8 | 6.5 | 5 | 8.47 | 3 | 4.69 |
A. niger | 3 | 2.44 | 0 | 0 | 3 | 4.69 |
A. parasiticus | 1 | 0.81 | 0 | 0 | 1 | 1.56 |
A. brasiliensis | 2 | 1.63 | 1 | 1.69 | 1 | 1.56 |
A. luchuensis | 2 | 1.63 | 2 | 3.39 | 1 | 1.56 |
A. aff. ustus | 2 | 1.63 | 0 | 0 | 2 | 3.13 |
A. awamori | 3 | 2.44 | 0 | 0 | 3 | 2.42 |
A. insuetus A. mnutus | 2 | 1.63 | 0 | 0 | 2 | 1.63 |
2 | 1.63 | 0 | 0 | 2 | 1.63 | |
A. ustus | 2 | 1.63 | 0 | 0 | 2 | 1.63 |
A. welwitschiae | 5 | 4.07 | 4 | 3.25 | 1 | 0.81 |
Aflatoxin was not detected in any of the maize samples analyzed using the LCM-S/MS. However, other detected metabolitesviz: 3-Nitropropionic acid; Sterigmatocystin; seco-sterigmatocystin; averufin; Kojic acid; and orsellinic acid. The limit of detection (LOD) ranged between 0.04 µg/kg (averufin and seco-sterigmatocystin) and 21 µg/kg (kojic acid), while limit of quantification (LOQ) ranged from 0.13 µg/kg (averufin) to 68 µg/kg (kojic acid). Percentage recovery ranged from 75.5% (seco-sterigmatocystin) to 168.8% (orsellinic acid) (
In all the regions, orsellinic acid had the highest percentage of occurrence (29.27%; concentration range between 235.76 and 829.60 µg/kg), followed by kojic acid (5.69%; concentration range of 65.61 to 587.51 µg/kg) while the rest were found in only one sample (0.81%). Fifty percent (50%) of maize samples from WR contained at least, one of these metabolites while about twenty-two percent (22.03%) of maize samples from ER had these metabolites (
Analytes | LOQ (µg/kg) | LOD (µg/kg) | Recovery (%) | Frequency of occurrence | |
---|---|---|---|---|---|
WR (n = 64) | ER (n = 59) | ||||
32 (50%) | 13 (22.03%) | ||||
Kojic acid | 68 | 21 | 78.6 | 4 (6.25%) | 3 (5.08%) |
3-Nitropropionic acid | 2.5 | 0.75 | 87.8 | 1 (1.56%) | 0 |
Sterigmatocystin | 0.25 | 0.08 | 91.3 | 0 | 1 (1.69%) |
Seco-sterigmatocystin | 0.14 | 0.04 | 75.5 | 0 | 1 (1.69%) |
Averufin | 0.13 | 0.04 | 104.1 | 0 | 1 (1.69%) |
Orsellinic acid | 60 | 20 | 168.8 | 27 (42.19%) | 9 (15.25%) |
Aflatoxin G1 | - | - | - | - | - |
Aflatoxin G2 | - | - | - | - | - |
Aflatoxin B1 | - | - | - | - | - |
Aflatoxin B2 | - | - | - | - | - |
Western Region | Eastern Region | ||||||
---|---|---|---|---|---|---|---|
Sample | B2 | B1 | Total Aflatoxin (µg/kg) | Sample | B2 | B1 | Total Aflatoxin (µg/kg) |
Mean | 0.45 | 3.02 | 3.53 | Mean | 0.25 | 19.01 | 19.26 |
Range | 0 - 17 | 0 - 65 | 0 - 65 | Range | 0 - 12.9 | 0 - 1069.8 | 0 - 1082.7 |
Frequency of distribution | |||||
---|---|---|---|---|---|
G2 | G1 | B2 | B1 | Total | |
WR (n = 64) | 0 | 0 | 9 (14.06%) | 16 (25%) | 22 (34.38%) |
ER (n = 59) | 0 | 0 | 3 (5.08%) | 11 (18.64%) | 12 (20.34%) |
SA (n = 123) | 0 | 0 | 12 (9.76%) | 27 (21.95%) | 34 (27.64%) |
Standard curves were constructed by calculating the ratio of the peak areas for each aflatoxin standard samples spiked before and after extraction at three levels of 25, 50 and 100 µg/ kg for all aflatoxins analyzed (Suppl.
Aflatoxins G1 and G2 were absent in all analyzed samples as revealed by HPLC technique. However, aflatoxins B1 and B2 were found in both regions in varying amounts. The ranges for aflatoxin B1 concentration (0 - 65 and 0 - 10.70 µg/kg) was higher than AFB2 (0 - 17 µg/kg to 0 - 12.9 µg/kg) across WR and ER. The ER had a mean value of aflatoxins B1 of 1.04 µg/kg compared to the WR with a mean value of 3.11 µg/kg. The same trend was observed for AFB2 with WR having a higher mean value of 0.05 µg/kg and ER with a mean value of 0.98 µg/kg. Thus, the mean total aflatoxin was higher in WR (3.59 µg/kg) compared to ER (1.09 µg/kg) (
Moisture content of grain at post-harvest and during storage is critical in the quality of the product after storage. A moisture content of 13% is the maximum moisture content required for grain storage [
The three main genera of fungi known for producing mycotoxins; Aspergillus, Fusarium, and Penicillium [
Out of the thirteen Aspergillus species isolated in this study, A. flavus (47.15%) was the most predominant followed by A. niger (4.69%) and A. fumigatus (4.69%), which contradicts the reports of Egbuta [
In this study, 27.64% of the A. flavus isolates were aflatoxins producers where 10.57 of the isolates produced aflatoxins concentration above the South Africa’s set standard (5 µg/kg). Gruber-Dorninger, Jenkins [
Aspergillus flavus were found in 61% of the maize samples from both regions where about 28% did produce aflatoxins. Fungal growth and AFs production in cereals depends on temperature, moisture, etc. [
Though there are regulatory limits for aflatoxins in only 15 African countries, the regulations vary widely among these countries. Exposure to aflatoxin in food is a significant risk factor [
Maize from both regions in SA contained AFs and traceable amounts of other metabolites using HPLC and LCMS/MS techniques. HPLC detected AFs while LCMS/MS did not detect any aflatoxins in the same sample rather detected a number of other metabolites, some of which are precursors of aflatoxins. The reason might be that, while HPLC is vastly used for chromatography fingerprinting of the constituents, where standards (AFB1, AFB2, AFG1 and AFG2) solvent calibration thereby reprogramming the HPLC for the targeted aflatoxins. Unfortunately, these standards did not exist for the co-metabolites, therefore could not be picked up by the HPLC method. On the other hand, LC-MS/MS is the most successful interface and a powerful approach for identification of the unknown constituents [
Among the metabolites found in the maize samples, averufin, sterigmatocystin and seco-sterigmatocystin were found only in one sample from the ER. Averufin, sterigmatocystin and seco-sterigmatocystin metabolites are active precursors for AFB1 formation [
Sterigmatocystin is a metabolite produced mainly by Aspergillus fungi, and is an intermediate in the biosynthesis of aflatoxin B1. Sterigmatocystin is a potentially health hazardous mycotoxin, produced mainly by Aspergillus species and is usually detected in food and feed as a natural contaminant [
The 3-Nitropropionic acid, one of the regulated metabolites detected by the LCMS/MS method, was found only in one sample (074/M/2015) from the WR contaminated with A. flavus. 3NPA is a natural potent environmental toxin and mitochondrial inhibitor [
Kojic acid (5-hydroxy-2-(hydrxymethyl)-4-pyrone; KA), a non-regulated metabolite, is an organic acid secreted by several species of Aspergillus, especially A. oryzae [
Orsellinic acid is a common salicylic acid unit in the biosynthesis of secondary metabolites in actinomycetes, fungi and lichens, formally isolated from chaetomium cochliodes in 1959 [
Among the 58 Aspergillus flavus isolates tested if any of the five aflatoxigenic genes were present (aflD, aflM, omtA, aflJ and aflR), no Aspergillus flavus isolate had the regulatory aflR gene, three isolates possessed the omt-A gene (two isolates from WR and one from ER) eleven isolates possessed the aflD gene (seven from WR and four from ER) while sixteen isolates possessed the aflJ gene (twelve from WR and four from ER). Majority of the isolates that tested positive, possessed the aflM gene (twenty-one from WR and ten from ER). One isolate (from ER) possessed up to three of the five genes tested, fifteen isolates possessed two out of the five genes tested (thirteen from WR and two from ER) while twenty-eight isolates possessed, at least, one of the genes tested. 50% and 25.86% of tested isolates from WR and ER respectively tested positive for aflatoxigenicity. This study has shown that there is a clear similarity between A. flavus and aflatoxicity in both regions, which, agrees to earlier studies conducted using RT-PCR targeting aflD, aflQ, aflO, aflP, aflR and aflS genes [
Climate change is clear and has been increasing over the past few years and this could join a threat to food security and safety. Even though the findings of this study show that irrespective of the climatic variations of the different regions in South Africa, Aspergillus species and aflatoxin production is not yet a threat to South Africa’s commercial food/feed industry, the frequent occurrence of drought in South Africa as experienced in 2015 is a clear sign that climatic zones that appear safe now might later lead to risk of disease and/or loss in crop production as climatic conditions change. There is, therefore, a need for long-term and continuous monitoring of Aspergillus species and their toxins in maize across different agro-climatic areas of South Africa (4 to 5 years) as well as other suppliers of agricultural maize and maize grown and stored by small-scale farmers. This will help simulate and model a trend that can clearly predict the long-term effects of climate change on aflatoxins in South African maize. Extensive studies will be carried out on strategies such as competitive exclusion and touch inhibition and real application be done on South African fields comparatively in the different agro-climatic regions.
This research was funded by North West University, Mafikeng and The APC was funded by North West University, Mafikeng.
The authors wish to acknowledge the North-west University for the privilege to carry out this study and support to publish the findings.
The authors declare no conflict of interest.
Nji, N.Q., Christianah, A.M., Njie, A.C. and Mulunda, M. (2022) Biodiversity and Distribution of Aspergillus and Their Toxins in Maize from Western and Eastern Regions of South Africa. Advances in Microbiology, 12, 121-149. https://doi.org/10.4236/aim.2022.123011