4.1. Structural Analysis
From the alpha diversity analysis, no significant difference was seen in the microbial community structure within the environments (
p > 0.05—Kruskal–Wallis test). As it is represented in
Table 2, the Shannon diversity index (used to characterize species diversity) is greater than 1 in all samples, and this depicts the high diversity of the microbial communities. The evenness index of the microbial community showed a good distribution of the metagenomes, especially at the order level [
38]. The principal component analysis showed that each of the sites has distinctive microbial phyla, which accounts for the combined 86.9% variation between the samples (
Figure 3). The vector arrows in the PCA depict the phyla that dominate each site. Thus, the microbial group that is more prevalent in each of the sampling sites can be deduced.
The PCoA plot (
Figure 5) showed variations in the microbial community structure of the two rhizosphere soil samples, with clear differences being noted between the rhizosphere samples and their corresponding bulk soil samples. This was buttressed by the conducted analysis of similarities (ANOSIM) which indicated a significant difference (
p = 0.01) and separation (R = 0.58) between the samples. This agrees with our assumption that there would be variation in the microbial community structure of the examined soil environments. The separation observed between F1 and R1 in the PCoA plot denotes disparity in the microbial communities, which could be linked to differences in the soil properties [
39,
40,
41].
The major microbial phyla observed in this study included Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Chloroflexi, Planctomycetes, Verrucomicrobia, Cyanobacteria, Crenarchaeota, Thaumarchaeota and Ascomycota, which were earlier reported in similar studies to be actively involved in various activities in the rhizosphere of maize [
42,
43,
44,
45,
46].
Proteobacteria are involved in a broad range of activities such as nitrogen, carbon and sulfur cycling necessary for nutrient cycling in plants [
47]. Alphaproteobacteria can survive in harsh environments such as those with a low level of nutrients while carrying out their activities, with Caulobacterales, which belongs to this phylum, being capable of inducing nitrogen fixation despite the poor nutrient conditions [
48]. Similarly, myxobacteria/Myxococcales, which belong to the Proteobacteria phylum, are capable of surviving in low-nutrient soil by aggregating into fruiting bodies, and until adequate nutrients are available, they can thrive and carry out activities such as protecting the plants from phagocytosis and toxins [
49].
Firmicutes are an important bacteria phylum, which can be used as biofertilizers due to their vital role in plant growth promotion, phytoremediation of heavy metals and biocontrol of plant pathogens. These important traits exhibited by the members of this phylum are relevant in crop production and position the organisms in this phylum as those that can be exploited as microbial inoculants and biofertilizers for sustainable agriculture [
50]. Actinobacteria also possess attributes that make them important for use as bioinoculants/biofertilizers, and this includes their ability to solubilize phosphorus, zinc and potassium, and their production of iron-chelating compounds and phytohormones such as indole acetic acid. These plant growth-promoting and soil health-enhancing attributes exhibited by Actinobacteria contribute to their relevance for use as biofertilizers [
51].
Observed in the metagenomes from this study is the presence of
Rhizobium, Bacillus, Burkholderia, Pseudomonas, Haloferax and Aquificales, which occurred more in F1 (maize rhizosphere soil from the first site). These microorganisms are important in instilling a certain degree of tolerance into plants towards drought and other abiotic stresses, such as high temperature, metal toxicity, salinity and chilling injury, which makes them exploitable as microbial inoculants [
52,
53].
The differences observed in the concentration of total carbon and moisture in the samples could be a major factor that influenced the variation in the microbial structure, as these soil properties have been shown to influence soil microbial abundance and diversity [
54,
55]. In their study, Liu et al. [
54] found total soil carbon and soil water content as part of the main contributors to variations in bacteria and fungi (
Actinomycetes and arbuscular mycorrhizal fungi) diversity in the soil. Similarly, Li et al. [
55] discovered the significant impact of soil carbon on the diversity of soil bacteria (
Deltaproteobacteria and
Gammaproteobacteria) and other microbes.
Adeboye et al. [
56] noted that soil organic carbon and pH influence the abundance of soil microorganisms, and
Table 1 shows that a significant difference (
p < 0.05) occurred in the pH of F1 and R1, and between R1 and R2. This difference is capable of influencing the variation in the relative abundance of microbes in the samples according to the report of Zheng et al. [
57]. The role of soil carbon in stability and soil fertility is crucial and cannot be overlooked [
58]. Carbon has been known to influence the microbial community structure [
54], and the higher carbon content observed in the rhizosphere soils compared to their corresponding bulk soils could be linked to the presence of more beneficial microbial phyla such as Firmicutes, Planctomycetes, Verrucomicrobia, Crenarchaeota and Chlorobi observed in these samples.
The canonical correspondence analysis (
Figure 6,
Table 4) showed that phosphorus explained 56.9% of the microbial diversity in the samples, and organic matter explained 26.20%, while N-NO
3 contributed about 16.9%. The result of the CCA showed that phosphorus (P), organic matter and N-NO
3 mostly shaped the microbial communities in the samples. Phosphorus (P) is an essential component of plants’ “energy unit”, being important for the general health and vigor of plants as it converts other nutrients into building blocks with which plants grow. P is also a vital nutrient for plants and the organisms in the soil and is usually correlated with pH, while maintenance of the soil organic matter is important to phosphorus availability. The high pH in F1 and the higher concentration of phosphorus in this sample confirms that pH influences the P availability for soil microorganisms [
46]. The high pH in F1 can be linked to the higher phosphorus concentration present in this sample and, hence, its increased abundance of microbes specifically. This study is in line with that of Wang et al. [
59], who stated that the addition of phosphorus to soil impacts the abundance and diversity of soil organisms. Similarly, the higher organic matter content in F1 can also influence the abundance of microorganisms in this sample as organic matter allows an increased water storage capacity which provides the level of higher humidity required for the expansion of certain microorganisms [
60,
61].
4.2. Functional Potentials in Maize Rhizosphere
Understanding microbial functions in biogeochemical processes is important in developing and advancing environmentally safe approaches to enhance production in agriculture/maize production for food security by manipulating the soil organisms. In this study, metabolic/functional processes in maize rhizosphere and bulk soils were examined. No significant difference was observed in the alpha diversity indices of the functional categories in the habitats (
p > 0.05). As it can be seen in
Table 4, the Shannon–Wiener and evenness indices of the rhizosphere and bulk soils, which ranged between 2.80 and 2.83, show high functional diversity in the samples, while the evenness index (0.59–0.61) shows that in all samples, the functional categories were well distributed [
62].
The principal coordinate analysis conducted showed distinct differences between the habitats, which was illustrated by the position of the samples on the PCoA plot (
Figure 10). A significant difference was noted in the rhizosphere soils (F1 and R1) and the bulk soils (F2 and R2). The large distance between locations of the rhizosphere samples (F1 and R1) illustrated dissimilarities in their functional groups, with variation also being indicated in the bulk soil samples (F2 and R2), which was represented by the distance between them. Axes 1 and 2 of the PCoA explained 82.85% and 10.72% of the variation across the samples, respectively.
Similarly, the beta diversity analysis conducted using one-way analysis of similarity (ANOSIM) showed significant differences between the samples (p = 0.01), and the R value of 0.58 which depicts the strength of separation between the samples showed a strong dissimilarity between them, which agrees with our assumption that there will be differences in the functional categories across each environment.
Principal component analysis was performed to test the variations in the microbial functions from each site, the result showing that each sample possesses a distinct functional/metabolic profile (
Figure 8). PCA axes 1 and 2 explained 98.9% and 0.49% of the variation, respectively, in the functional categories. The length of the vector arrows showed the strength of dominance of the functions in the samples (the functions on the longest vector length are those which are majorly performed by the microbes in each environment). On the PCA plot, functional categories such as disease virulence and defense, nitrogen metabolism, secondary metabolism, sulfur metabolism and potassium and phosphorus metabolism placed sample F1’s microbiome as distinct from the microbiome of F2, R1 and R2 (
Figure 8). This implies that its associated microbiome majorly helps with these functions. This is supported by the prevalence of
Aquificales,
Bacillales,
Myxococcales,
Pseudomonadales,
Sordariales and
Bdellovibrionales in the sample [
63] (
Figure 4A). These organisms are noted for the above-mentioned functions and other significant contributions to plant growth [
47,
64].
The functional groups involved in R1 included motility and chemotaxis, respiration and metabolism of aromatic compounds, which can be linked to the abundance of
Acidobacteriales,
Flavobacteriales and
Methanomicrobiales and occurrence of
Pseudomonadales in the samples [
65,
66,
67,
68,
69]. Functional groups such as cell wall and cell capsule, photosynthesis, cofactors, vitamins, prosthetic groups, pigments and DNA metabolism distinguished F2 from other samples (
Figure 8), and this is evident in the higher composition of
Bacillales, Caulobacterales and
Clostridales in this sample. RNA metabolism, clustering-based subsystems and amino acids and derivatives distinguished R2 from other samples in the PCA, which can be linked with the higher composition of
Chromatiales,
Ktedonobacteriales,
Nostocales and
Thermotogales in these samples [
70,
71,
72,
73,
74,
75].
Higher functional diversity was seen in microbes in the rhizosphere, which is in line with the study of Garcia-Fraile et al. [
76], in which bacteria cells obtained from the rhizosphere contained a significant higher level of alkaline phosphatase, β-glucosidase and dehydrogenase activities than those isolated from the bulk soil. This is because plants alter the diversity of microbes within the rhizosphere to provide a suitable and healthy environment for sprouting and growth. The larger amount of carbon immobilized in microbial biomass indicates that soil organic matter, which is observed to be higher in rhizosphere soils, provides greater levels of more labile carbon in these environments than in the bulk soils [
77].
In order to determine the relationship between the functional diversities of microbes in the habitats and the soil properties/environmental variables, canonical correspondence analysis was performed (
Figure 11). The CCA result shows that K, P and N-NO
3 were the soil properties that best explain the diversities in the functional categories in the samples, where K and P explained 38.9% and 34.5% of the variation, respectively, while N-NO
3 explained 26.6% of the variation (
Table 5,
Figure 11). Soil properties are major factors that drive the diversity and structure of soil microbes, thus influencing the functional potentials in the soil ecosystem [
8,
15].
The results from the soil property analysis show heterogeneity in the physical and chemical attributes of the soil samples (
Table 1). The pH of samples R1 and R2 was below the ideal range of 6–7.5 [
78], while that of samples F1 and F2 lies within the normal range, thus indicating the availability and balance of other important soil nutrients [
79]. The higher pH observed in F1 in our study can be linked to the higher abundance of other nutrients (such as phosphorus, potassium and sulfur) observed in this sample as compared to others, hence the dominance of more functional categories in F1 than other samples.
Other soil properties such as N-NO
3, K and N-NH
4, as observed in the samples, were within the range needed for sufficient plant growth, with these findings being in line with those of Babalola et al. [
80]. The higher abundance of organisms such as
Pseudomonales, a phosphate-solubilizing bacteria, in F1 could be linked with the raised phosphorus content in the sample [
81,
82].
The larger amount of carbon immobilized in the microbial biomass indicates that soil organic matter, which was observed to be higher in the rhizosphere soils, provides greater levels of more labile carbon in this environment than in the bulk soils [
77]. From this study, higher functional categories were shown in the rhizosphere samples (
Figure 7), which is in line with our hypothesis. This is expected due to the higher concentration of important soil nutrients in this region and the root exudates being released into the rhizosphere, which serve as a source of energy for soil microbes to carry out diverse anabolic and catabolic processes.