3.1 Characterization of the soil from the locations
In this study, the soils were initially characterized to have a baseline concentration of glyphosate in relation to fungal distribution. All the locations had evidence of glyphosate contamination though the contamination levels varied depending on location (Fig. 1) and they were significant (p < 0.05). Location1 had the highest contamination comprising 319.1 mg/kg glyphosate and 194.2 mg/kg AMPA. Location 3 showed increased glyphosate level without transformation product (AMPA). Among the locations, the lower level of glyphosate was observed more rapid in location 4 with only traces of glyphosate present (6.98 mg/kg).
Correspondingly, the fungal density varied within the location (Fig. 2). There was high enumeration of fungal count in locations where AMPA concentration was high and with significant concentrations of glyphosate. However, other locations where AMPA concentration was low or absent had low fungal count. This implies active metabolic state of the fungi. As a result, this study evaluates the relationship between the fungal density, glyphosate and AMPA. The Pearson correlation shows that fungal load is significantly related to AMPA (r = 0.94965; p ≤ 0.05) in comparison to glyphosate.
3.2 Selection and isolation of glyphosate degraders
A total of 14 isolates were obtained from the farms (Table 1). The fungal inoculum from soils enrichment were plated on MSM agar plates to check for their ability to grow in presence of glyphosate on solid media. It was observed that S1b, S1c, S2.3, S3.2, S3.3, S4.1 and S4.4 isolates did not grow from any of the enriched soils on MSM agar when glyphosate was added externally, during the set incubation time. They displayed poor (+) clear zone, therefore, no further analysis was conducted on them. The isolates that showed good (++) clear zone (S1a, S1d, S2.1, S3.1, S4.1 and S4.3) were further analyzed for their ability to degrade glyphosate (Table 2). A total of six (6) potential degraders were obtained after successive sub-culturing from the soils.
3.3 Enrichment of glyphosate degraders
Six fungal isolates were stimulated to grow in the presence of glyphosate. The glyphosate mixed with MSM showed enhanced growth of some fungal isolates as shown by their optical density which ranged from 93.47% in P. simplicissimum SNB-VECD11G to 96.64% in T. gamsii P2-18 (Table 3). Furthermore, the growth of fungal isolates, A. fumigatus FJAT-31052 and A. flavus EFB01 were promoted and they tolerated the glyphosate as they continued growing till the Day 32 where the experiment ended. Whereas, growth promotion of other fungal isolates was inhibited after the Day 28. More so, the fungal growth in the presence of glyphosate caused a decrease in the pH of the environment from 6.0 to 4.7 in all the isolates (Table 4).
The changes were all significant (P < 0.05) with increasing incubation times but were not significant (P > 0.05) within the isolates. The change in pH was more obvious in A. flavus EFB01 (22.06%) and lowest in A. flavus JN-YG-3-5 (19.21%). Therefore, the fungal growth was inversely proportional with the pH.
The analysis of viable fungal count in all the samples at different days varied (Figure 3). Three major phases of growth were identified in all the isolates. These includes lag phase, exponential phase and death phase. These phases were similar in all the fungi. The lag phase lasted for 8 days (day 0 to day 8), thereafter a slight but non increase in growth from day 8 to day 16. The exponential phase started from day 16 to day 24 and death phase started after day 24. The peak of the growth was on the 24th day. On this day, the maximum fungal count was 1.09+E06 CFU by A. fumigatus FJAT-31052 and the lowest was 6.20+E05 CFU by T. gamsii P2-18. All other isolates growths were significant.
3.4 Degradation of glyphosate
The potential ability of the six fungal strains for glyphosate biodegradation were observed for 32 days (Fig. 4). The strain T. gamsii P2-18 sp. degraded 91.45% of glyphosate leaving 930.81 mg/kg of AMPA. In addition, it was observed that there was 92.07% glyphosate degradation when inoculated with A. niger APBSDSF96 leaving 113.53 mg/kg AMPA (Fig. 5). Interestingly, A. flavus JN-YG-3-5 utilized 92.86% without accumulation of AMPA; this had the highest extent of degradation.
Overall, an analysis of the degradation efficiency of the fungi strains in glyphosate degradation showed that the isolates were efficient degraders with percentage degradation above 90% (Fig. 6). However, A. flavus EFB01 had the poorest percentage degradation (27.17%) indicating poor metabolism of glyphosate. The degradation efficiency of A. flavus JN-YG-3-5 was the most efficient fungi (85.6%) (Fig. 6).
3.5 Molecular characteristics
The molecular characteristics of these promising isolates are shown in Table 5. BLAST analysis (ITS gene sequence) carried out through NCBI GenBank showed that the first two bacterial sequences were identified as strains of T. gamsii P2-18 (94.57% similarity) and A. flavus JN-YG-3-5 (99.28% similarity), respectively. Other isolates were identified as Aspergillus niger APBSDSF96 (95.22%) similarity, A. fumigatus FJAT-31052 (99.30%) similarity, A. flavus EFB01 (99.29%) similarity and P. simplicissimum SNB-VECD11G (89.91) similarity. The isolates had high level of GC contents ranging from 53.54% in P. simplicissimum SNB-VECD11G to 58.66% in Aspergillus flavus JN-YG-3-5 suggesting their potential for environmental management.
The ITS gene sequence showed that all the six isolates clustered into three group (Penicillum sp., Trichoderma sp. and Aspergillus sp.) (Fig. 7) for phylogeny analyses of the isolates. Aspergillus flavus JN-YG-3-5 clustered with genus Aspergillus flavus EFB01 showing similarity, they distantly clustered with Aspergillus niger APBSDSF96 and Aspergillus fumigatus FJAT-31052. However, Trichoderma gamsii P2-18 and Penicillium simplicissimum SNB-VECD11G out clustered.
3.6 Bacteria genome annotation
Automated annotation identified several genes using a statistical significance threshold (Table 6). The genome sequences of the fungi were compared to those of several organisms (Archae generic, C. pefringes, B. subtilis and P. putida) known to function in metabolic processes. Validation of the sequence annotation using the FGENESB database yielded the following result: Rhizobium huautlense comprises 5 potential protein coding genes, 1 operon and 4 transcription units. Pseudomonas aeruginosa strain MZ4A contains 11 protein genes, 1 operon and 7 transcriptional units. Pseudomonas aeruginosa strain 22ABUH7 had 5 protein genes, 1 operon and 3 transcriptional units. Bacillus subtilis strain VBN01 had 8 protein genes, 1 operon and 5 transcriptional units. Pseudomonas aeruginosa strain HS-38 sequence was made up of 6 potential protein coding genes, 1 operon and 5 transcriptional units. Pseudomonas aeruginosa strain MZ4A and Pseudomonas aeruginosa strain HS-38 had potential protein coding genes similar to Pseudomonas putida while others did not. A search of the identified proteins for specific functions revealed that the genes are distributed in different functional categories majorly protein metabolism and respiration (Table 7). Numerous genes associated with pesticide degradation were identified.