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/lp/springer-journals/structural-diversity-of-bacterial-communities-in-two-divergent-ZgdlilztHD
- Publisher
- Springer Journals
- Copyright
- Copyright © The Author(s) 2023
- ISSN
- 1590-4261
- eISSN
- 1869-2044
- DOI
- 10.1186/s13213-023-01713-y
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- See Article on Publisher Site
Abstract
Purpose Farming practices on farmlands aim to improve nutrients in the fields or crops, soil quality and functions, as well as boost and sustain crop yield; however, the effect of loss of ecological diversity and degradation have impacted ecosystem functions. The beneficial rhizosphere-microorganism network and crop rotation may enhance a stable ecosystem. The use of next-generation sequencing technique will help characterize the entire bacterial species in the sunflower rhizosphere compared with the nearby bulk soils. We investigated the potential of the bacterial community structure of sunflower rhizosphere and bulk soils cultivated under different agricultural practices at two geographical locations in the North West Province of South Africa. Methods DNA was extracted from rhizosphere and bulk soils associated with sunflower plants from the crop rotation (rhizosphere soils from Lichtenburg (LTR) and bulk soils from Lichtenburg (LTB) and mono-cropping (rhizosphere soils from Krayburg (KRPR) and bulk soils from Krayburg (KRPB) sites, and sequenced employing 16S amplicon sequencing. Bioinformatics tools were used to analyse the sequenced dataset. Results Proteobacteria and Planctomycetes dominated the rhizosphere, while Firmicutes and Actinobacteria were predominant in bulk soils. Significant differences in bacterial structure at phyla and family levels and predicted func- tional categories between soils (P < 0.05) across the sites were revealed. The effect of physicochemical parameters was observed to influence bacterial dispersal across the sites. Conclusion This study provides information on the predominant bacterial community structure in sunflower soils and their predictive functional attributes at the growing stage, which suggests their future study for imminent crop production and management for enhanced agricultural yields. Keywords Bacterial diversity, Helianthus annuus, Soil metagenomics, Sustainable agriculture, 16S rRNA gene sequencing each with the ability to induce maximal adaptive Introduction responses in the plant via specific metabolic pathways. Comprehending the rhizosphere’s geographical distri- The rhizosphere is the area near the plant’s roots where bution of microbial communities has opened up several exudates containing various metabolites are discharged, possibilities for exploiting their agricultural potential. as well as a variety of microorganisms (Agomoh et al. Various microbial communities inhabit the rhizosphere, 2020; Ai et al. 2012). Roots are engaged in the release of exudates of various chemical components into the rhizo- *Correspondence: sphere, in addition to providing nutrients and anchoring Olubukola Oluranti Babalola olubukola.babalola@nwu.ac.za the entire plant. Through the secreted root exudates, the Food Security and Safety Focus Area, Faculty of Natural and Agricultural rhizosphere plays an important role in the modification Science, North-West University, Private Mail Bag X2046, Mmabatho 2735, of its microbiome component. South Africa © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 2 of 18 Plant-microbial interactions are complicated and can the rhizosphere zone and controls particular bacterial enhance plant growth and development (Igiehon and enhancement. Babalola 2018; Berlanas et al. 2019). Bacteria are the predominant microorganisms in the rhizosphere and Materials and methods are indicators of soil quality, health and fertility due Site location, sampling, and climatic conditions to their responses to biotic and abiotic pressures (Igie- In March 2020, the rhizosphere and bulk replicate soil hon et al. 2019). The actions of bacteria are dynamic samples from the two commercial sunflower fields (at the because they accelerate most biogeochemical pro- growing stage) of different cultivars, PAN 7160 CLP and cesses, thus inducing mineral nutrient availability in PAN 7011 Pannar, were collected from Lichtenburg (LT) soil (Nwachukwu and Babalola 2021). Bacterial com- (S26°4′31.266′′ E25°58′44.442) and Krayburg/Kraaipan munities in the rhizosphere can resist pathogens and (KRP) (S26°17′24.186′′ E25°13′33.258), North West Prov- stimulate tolerance to abiotic stressors, hence promot- ince, South Africa. A total of 12 samples each for the ing plant growth, health and yield (Li et al. 2020; Meena rhizosphere and bulk soil were collected from 4 points et al. 2014). Bacterial communities that colonize the of sunflower plant and 15–20 cm depth from the two rhizosphere could be valuable, however, most do not farms and pooled into labelled zip lock bags and were affect plant health (Nwachukwu et al. 2021; Maquia homogenized to get a composite sample as described et al. 2020). by (Oberholster et al. 2018). The soils were immediately Although, various researchers have explored the micro- transported to the Microbial Biotechnology Research biome of oil food crops such as sunflower root microbi - Laboratory, North-West University, South Africa. The ome, studies on the impact of plants on microorganisms soils were placed separately, sieved, and stored in zip lock are still ongoing; thus, necessitating this study. The sun - bags in the dark at -80 °C for DNA extraction and high flower (Helianthus annuus ), a major oilseed crop in mod- throughput sequencing. ern agriculture, is used for various food and industrial Usually, North West Province has a summer tem- purposes (Majeed et al. 2018). Owing to its growing perature ranging from 17 °C to 31 °C and a winter tem- agricultural importance, some continents, such as South perature ranging from 3 °C to 21 °C. The annual rainfall America, Europe, and Africa especially, South Africa, ranges between 300 and 600 mm. According to the farm have exploited the potential for its usage (Pandey et al. owner, the farmland in Lichtenburg has been cultivated 2013; Majeed et al. 2018). for over 40 years. Sunflower has been rotationally cul - Reports on the plant growth-promoting bacteria asso- tivated with other agricultural crops, such as soybean ciated with sunflower plants for improved productivity in and maize. Water supply is mainly by rainfall during the South Africa are limited, perhaps due to the inadequate summer while irrigation during winter. The main farm studies on sunflower plants using the next-generation activities are clearing, tilling, plowing, and ridging. Also, sequencing techniques. Hence, it is imperative to deter- the application of chemical fertilizers (NPK 15:8:4), pre- mine bacterial community structures that are resident in emergence and post-emergence herbicides (Metagon the sunflower rhizosphere soils using the 16S rRNA gene Gold and Judo 50EC) the soil before and after planting. and their associated predictive functions (Yadav et al. Foliar insecticide spray (Max-Foliar) was applied to the 2017; Lu et al. 2020). Given this, to distinguish the effects leaves after plant germination. In Krayburg, the farm- of plants, we evaluated bacterial communities in the yard size is 24.711 Acres with 24.711 Acres of sunflower rhizopheric soil of sunflower and the corresponding bulk plantation landscape coverage. Maize was the only crop soils. Furthermore, we explored the dissimilarities in the previously cultivated on the farmland. Soil amendments associated predicted functional compositions of the soils. include the application of urea and organic manure. We postulated that the soil properties and agricultural practices, such as the use of chemical fertilizer, cropping Soil physicochemical analysis type (mono-cropping and mixed cropping) and organic The analyses of rhizosphere and bulk soil samples for manure would influence the structure and metabolic physicochemical parameters were performed using potential of sunflower rhizosphere bacterial communi - standard procedures, and 30 g of pulverized and sieved ties compared to the bulk soil. A good knowledge of the soil was taken from each sample. The soil pH in distilled predicted metabolic pathways of bacterial communities water was measured using a pH meter (ratio 1:2.5, soil to in the rhizosphere region is essential since functional water), the organic matter (OM) present in the soils was heterogeneity is a delicate signal of the quality elements determined using the Walkley–Black method (Walak- of the soil management. It also speeds up the amplifi - ley and Black 1934), while phosphorus (P) was extracted cation of bacterial community functions as a compre- from the samples according to the method of (Bray and hension of biochemical and molecular components in Kurtz 1945). Potassium (K) was evaluated using 1 M N wachukwu et al. Annals of Microbiology (2023) 73:9 Page 3 of 18 acetate at pH 7.0 (Gutierrez Boem et al. 2011). The soil’s the bacterial community between the sunflower rhizos - total carbon (C) and total nitrogen (N) were determined phere and bulk soil was plotted using the Shinyheatmap using the dry combusting technique as described by (version 0.12.2) online tool (www1. heatm apper. ca/ expre (Craft et al. 1991). The nitrate (N–NO ) and ammonium- ssion/) (Khomtchouk et al. 2017). The alpha diversity N (N–NH ) were determined using the KCl extraction (diversity within the samples) of the bacterial commu- method by (Nelson et al. 1996). nity structure across each sampling sites, diversity indi- ces (Simpson, Evenness, and Shannon_H) and bacterial DNA extraction and 16S rRNA amplicon sequencing richness were assessed using a Kruskal–Wallis test in the The DNA was extracted from 5 g of each sieved rhizos - paleontological statistics software package (PAST version phere and bulk soil samples using a Zymo DNA isolation 4.0) (Hammer et al. 2001). These indices were also com - kit (Zymo Research, Irvine, USA) following the manu- pared the rhizosphere and bulk soils. facturer’s instructions. All the data are products of 16S The beta diversity was determined using the principal amplicon sequencing at the Molecular Research Labora- coordinate analysis (PCoA) on a Bray–Curtis dissimilar- tory (MR DNA, Shallowater, TX. USA). The polymerase ity matrix and the one-way analysis of similarities (ANO- chain reactions (PCRs) were performed in a single-step SIM) was used to determine the variances in community PCR using the HotStarTaq Plus Master Mix Kit (Qiagen, structure and composition among the sites (Clarke and USA) primer pairs 515F (5′- AAT GAT ACG GCG ACC Green 1988). Principal component analysis (PCA) using ACC ACC GAG ATC TAC AC TAT GGT AATT GT GTG the Euclidean matrix was employed to identify the dis- CCA GCMGCC GCG GTAA-3′) and 806R (5′-CAA GCA tribution of bacteria across the sunflower sites. Also, GAA GAC GGC ATA C GAGAT TCC CTT GTC TCC AGT PCA was used to evaluate the environmental variables CAG TCAG CC GGA CTACHVGGG TWT CTAAT-3′). that best described the composition of the obtained bac- The PCR products from the DNA samples were quan - teria and we assessed the possible correlations between tified using PicoGreen dsDNA assay. The samples were bacterial communities and the measured environmental pooled together in an equimolar concentration. Then, variables. calibrated Ampure XP beads (Agencourt Bioscience Cor- We employed a forward selection of environmental poration, MA, USA) was used for purification. The Illu - variables to conduct a significance test. The PCoA and mina DNA library was prepared from the pooled and PCA plots were designed using CANOCO version 5 purified PCR products. Sequencing was performed on (Microcomputer Power, Ithaca, NY, USA) software. The an Illumina MiSeq 2000 using a paired-end approach to predictive functional annotation of the bacterial catego- obtain 312 bp paired-ends reads. ries in the sampling site was assessed on Phylogenetic The sequence read processing was performed using Investigation of Communities by Reconstruction of Quantitative Insights Into Microbial Ecology (QIIME Unobserved States (PICRUSt); the predicted functional 2) 16S pipeline (version 2020.11) (Caporaso et al. 2010) classifications at the different levels (i.e. first, second, and performed on Nephele microbial bioinformatics platform third) were obtained. (version 1.8) (https:// nephe le. niaid. nih. gov/) (Weber et al. 2018). Preprocessing steps involve read pair joining Results using default parameters (a minimum overlap of 10, and Physical and chemical analysis of sunflower rhizosphere percentage maximum difference of 25), an average Phred and bulk soils score of ≤ 20 was removed, while chimeras were removed Soil analysis showed that OM, N-NH and total N were using VSEARCH (Edgar et al. 2011), while clustering was higher in rhizosphere soils from Lichtenburg (LTR) than done using Open Reference Method and SILVA 99 ver- in rhizosphere soils from Krayburg (KRPR) as shown in sion 132 (Wang et al. 2007). SILVA version 132 was used Table 1. We observed that the pH values of the soil sam- to assign taxa, with a sequence similarity of 0.99, and ples from the LT site had low pH values (acidic) com- then chimeric sequences, including mitochondria, single- pared to the pH values of the soils from the KRP site. ton, and chloroplast reads were eliminated. Sequence data and beta analyses of the rhizosphere Statistical analysis and bulk soil samples Microsoft excel sheet was used to derive the mean and The taxonomic groups were assigned using the SILVA standard errors of the soil physicochemical properties. reference database. The total number of uploaded Soil physicochemical data were transferred to the Statis- sequences varied between samples and across the tical Package for the Social Sciences (SPSS), where one- sites. Sequence base pair count of 87,446 (LTR), 80,404 way analysis of variance (ANOVA) and Duncan multiple (KRPR), 100,988 (bulk soils from Lichtenburg- LTB) tests were performed. The relative abundance graph of Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 4 of 18 Table 1 Mean _ standard error values of the physical and chemical properties of the sunflower rhizosphere soils Site LTR LTB KRPR KRPB a a,b b c Organic matter (OM) (%) 1.85 ± 0.1 1.81 ± 0.0 1.19 ± 0.0 1.27 ± 0.0 ab b,c b,c a Nitrate (N-NO ) (mg/kg) 11.54 ± 2.5 9.3 ± 0.0 9.695 ± 0.3 13.14 ± 0.0 a b b,c c Ammonium (N-NH ) (mg/kg) 9.875 ± 0.1 8.723 ± 0.1 6.255 ± 1.1 5.01 ± 0.0 b b a a pH (N/A) 6.92 ± 0.1 6.91 ± 0.0 6.94 ± 0.2 6.93 ± 0.0 a a b b Resistivity conductivity (ohm) 2365 ± 135.0 2120 ± 21.0 855 ± 85.0 890 ± 80.0 b c c a Phosphorus (P) (mg/kg) 23.095 ± 1.2 7.84 ± 3.1 6.315 ± 0.9 72.88 ± 2.3 b b a b Calcium (Ca) (mg/kg) 1752.5 ± 3.5 781 ± 1.5 1680 ± 250.0 659.5 ± 17.5 a c b c Magnesium (Mg) (mg/kg) 350 ± 7.0 145.5 ± 3.5 311 ± 12.0 148.5 ± 3.5 a b,c a,b c Potassium (K) (mg/kg) 230 ± 7.0 183 ± 1.0 228 ± 18.0 179.5 ± 2.5 a a,b b a Sodium (Na) (mg/kg) 72.85 ± 2.4 69.25 ± 3.3 7.52 ± 0.18 73.85 ± 0.9 a a,b b b Total carbon (%) 0.685 ± 0.0 0.6 ± 0.0 0.5975 ± 0.0 0.589 ± 0.1 a b b b Total nitrogen (%) 0.057 ± 0.0 0.055 ± 0.0 0.056 ± 0.0 0.0535 ± 0.0 a b b b Sand (%) 85 ± 1.0 87 ± 0.0 76 ± 2.0 77 ± 1.0 a a a a Silt (%) 5 ± 0.0 4 ± 1.0 3 ± 1.0 2 ± 0.0 a a a a Clay (%) 20 ± 1.0 19 ± 1.0 21 ± 1.0 21 ± 1.0 Legend: % - percentage, LTR- Rhizosphere soils from Lichtenburg, LTB- Bulk soils from Lichtenburg, KRPR- Rhizosphere soils from Krayburg, KRPB- Bulk soil from Krayburg. Data represent mean ±SE. Mean values having different alphabets are considered statistically significant (P≥ 0.05), while mean values having the same alphabets are considered not statistically significant (P ≥ 0.05), following Duncan’s multiple range test and 74,956 (bulk soils from Krayburg- KRPB) sequence Structural composition of the bacterial community reads for the soil samples. Consequently, quality control At the phylum level, the dominant rhizospheric bacte- (QC) check revealed the sequence read count for LTR— ria in LTR were Proteobacteria, Planctomycetes, Gem- 47,471, LTB—9,628, KRPR- 16,621, and KRPB – 19,050 matimonadetes, Acidobacteria, Armatimonadetes, and between the samples and across the sites. Sequences Cyanobacteria, while Actinobacteria, Nitrospirae and were clustered at 97% similarity according to their con- Elusimicrobia predominated LTB. Interestingly, unclassi- nection to one another by Operational Taxonomic Units fied bacteria dominated KRPR, while Firmicutes, Bateroi - (OTUs) and the different OTU abundances in all sam - detes, Verrucomicrobia and unclassified sequences, and ples were obtained based on the similarity threshold.The Spirochaetes were abundant in KRPB (Fig. 3). PCoA graph of the bacterial diversity at the phyla level At the family level (Fig S1), Moraxellaceae, Caulobac- in the soil samples across the sites is presented in Fig. 1, teraceae, Geodermatophilaceae, Solirubrobacteraceae, which indicated that samples from LTB differ signifi - Streptomycetaceae, Acetabacteraceae, Bradrhizobiaceae, cantly from LTR, KRPR, and KRPB samples. The vector Comamonadaceae, Micromonosporaceae, and Chitin- length of the PCA graph revealed the most dominant ophagaceae were predominant in LTR. Unknown bacte- bacterial phyla in each soil niche. Specifically, this is the ria, Micrococcaceae, Nocardioidaceae, Rhodospirillaceae, bacterial phyla having the longest vector length of PCA. Pseudonocardiaceae, Sphingomonadaceae, Thermomono - The vector length was used as an indicator, notably, in sporaceae, and Microbacteriaceae were dominant in LTB. LTR Acidobacteria, Planctomycetes, Chloroflexi, Arma - Pseudomonadaceae, Streptomycetaceae, Paenibacillaceae timonadetes, Gemmatimonadetes and Cyanobacteria and Planococcaceae influenced KRPR, while Baccilaceae, dominated, whereas Actinobacteria, Elusimicrobia and Rubrobacteraceae, Oxalobacteraceae, and Clostridiaceae Nitrospirae were predominant in LTB, whereas Proteo- dominated KRPB. bacteria, Verrucomicrobia, Bacteroidetes and unclas- sified sequences were prevalent in KRP, and the main Influence of environmental factors on the bacterial bacterial phyla in KRPB were Spirochaetes and unclas- community structure sified bacteria (Fig. 2). The bacterial phyla selected for The PCA (Fig. 4) was used to determine the correla- PCoA and PCA plots were established on the level of tion between the soil physical and chemical proper- significance. Analysis of similarities (ANOSIM) revealed ties (Table 1) on the bacterial community distribution that the differences in the beta diversity of the bacte - at the phylum level. The six best explained soil physical rial communities across the sites differed significantly and chemical properties (Table 1) were considered for (P = 0.01 and R = 0.58). the PCA plot (Fig. 4). The PCA plot indicated that the N wachukwu et al. Annals of Microbiology (2023) 73:9 Page 5 of 18 Fig. 1 A Rarefaction curves used to determine the bacterial species richness sequences across the cropping sites. LTR, rhizosphere soil from Lichtenburg site; LTB, bulk soil from Lichtenburg site. KRPR, rhizosphere soil from Krayburg site; KRPB, bulk soil from Krayburg site. B Venn diagram of the distributed operation taxonomic units between the bacterial communities (at the phyla level) of the rhizosphere and bulk soils obtained from sunflower farms in Lichtenburg and Krayburg. LTR- Lichtenburg rhizosphere soil; LTB- Lichtenburg bulk soil; ` KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg. C Principal coordinate analysis (PCoA) of shared OTUs between the rhizosphere and bulk soils from Lichtenburg and Krayburg at phylum level. (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg) bacterial community structure was influenced by the the vector length as an indicator, it is obvious that OM soil physicochemical properties. The total variation was was at the mid-point. The vector lengths of total C, total 0.14385 and explanatory variable account for 100%. Using N, and N-NH positively correlated with Planctomycetes, 4 Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 6 of 18 Fig. 2 Principal component analysis (PCA) of shared OTUs between the rhizosphere and bulk soils from Lichtenburg and Krayburg at phylum level. (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg) Armatimonadetes, Cyanobacteria, and Gemmatimona- processing, genetic information processing, human dis- detes from LTR. The vector length of N-NO and pH was eases, metabolism, and organismal systems (Figs. 5a and positively correlated with Verrucomicrobia and unclassi- 5b). Also, unclassified predicted functions were catego - fied sequences from KRPR. rized (Fig. 5b). Furthermore, the predicted functions revealed at sec- Predictive functional information analysis associated ond-level classification (Figs. 5a and 5b), 16 predicted with the bacterial community in the rhizosphere and bulk functions including cell communication, cell growth soils and death, replication and repair, immune system dis- The predictive functional categories of bacterial com - eases, metabolic diseases, amino acid metabolism, bio- munity composition with differences in their relative synthesis of other secondary metabolites, carbohydrate abundances across the sunflower farms at three differ - metabolism, lipid metabolism, metabolism of cofactors ent levels were analyzed employing PICRUSt. At level 1 and vitamins, metabolism of amino acids, xenobiotic bio- functional classification, the bacterial predictive func - degradation and metabolism, environmental adaptation tions were categorized into 6 major predicted func- and immune system were more predominant in LTR, tions in both rhizosphere and bulk soils of the farms, whereas the predicted functions including cell motility, including cellular processes, environmental information signal transduction, signal molecules and interaction, (See figure on next page.) Fig. 3 A Taxonomic classification of the relative abundance of bacterial phylum from rhizosphere and bulk soils from Lichtenburg and Krayburg (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg). The colour permeation gradient is designated as the scale bar based on the relative abundances; with a row z-score of the bacterial communities transformed relative abundance. B Taxonomic classification of the relative abundance of bacterial family from rhizosphere and bulk soils from Lichtenburg and Krayburg (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg). The colour permeation gradient is designated as the scale bar based on the relative abundances; with a row z-score of the bacterial communities transformed relative abundance. C Taxonomic classification of the relative abundance of bacterial genus from rhizosphere and bulk soils from Lichtenburg and Krayburg (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg). The colour permeation gradient is designated as the scale bar based on the relative abundances; with a row z-score of the bacterial communities transformed relative abundance N wachukwu et al. Annals of Microbiology (2023) 73:9 Page 7 of 18 Fig. 3 (See legend on previous page.) Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 8 of 18 Fig. 4 Principal Component Analysis (PCA) plot of the bacterial phyla distribution and soil environmental variables of both rhizosphere and bulk soils from Lichtenburg and Krayburg. (OM = Organic matter, N-NH = Ammonium-N, N-NO = Nitrate, Total C = Total carbon, Total N = Total 4 3 nitrogen) cancers, infectious diseases, neurodegenerative disease, KRPR. Beta-Lactam resistance was the same (0.07) across N-Glycan biosynthesis and metabolism, circulatory sys- all sites and samples. tem and nervous systems predominated the KRPR. The We found that amino acids and derivatives pathways abundance of enzyme families in the soils across the sites including alanine, aspartate and glutamate metabolism, were the same (1.86), except for LTB whose enzyme fam- phenylalanine metabolism, tryptophan, cyanoamino ily’s relative abundance was 1.85. N-Glycan biosynthesis acid metabolism and taurine and hypotaurine metabo- and metabolism relative abundance (1.47) were the same lism, were more abundant in LTR than in other samples in LTR and LTB whereas KRPR and KRPB were 1.52 and (Fig S2). Alternatively, the relative abundances of amino 1.41 respectively. acid related enzymes, arginine and proline metabolism, The predicted functions revealed that at third-level cysteine and methionine metabolism, glycine, serine and selection (Fig. 6), the highest predicted functional pro- threonine metabolism, histidine metabolism, lysine deg- filing of bacteria was in KRPR. The abundance of bacte - radation, tyrosine metabolism, D-alanine metabolism, rial motility proteins was predominant followed by ABC D-arginine and D-ornithine metabolism, D-glutamine transporters (KRPR) whereas the least predicted function and D-glutamate metabolism, glutathione metabolism, was the biosynthesis of steroid hormone (0.04) recorded and phosphonate and phosphinate metabolism were in both the rhizosphere and bulk soils from Krayburg. more in KRPR than in other samples (Fig S2). Nitrogen (N) and sulfur (S) metabolism were higher in (See figure on next page.) Fig. 5 a Major metabolisms of bacterial communities in the sunflower rhizosphere and bulk soils from Lichtenburg and Krayburg at level 1 and 2. (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg). b Major metabolisms of bacterial communities in the sunflower rhizosphere and bulk soils from Lichtenburg and Krayburg at level 1 and 2. (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg) N wachukwu et al. Annals of Microbiology (2023) 73:9 Page 9 of 18 Fig. 5 (See legend on previous page.) Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 10 of 18 Fig. 6 Selected predictive metabolic pathways of bacterial communities in the rhizosphere and bulk soils of sunflower from Lichtenburg and Krayburg at level 3. (LTR = Rhizosphere soils from Lichtenburg, LTB = Bulk soils from Lichtenburg, KRPR = Rhizosphere soils from Krayburg, KRPB = Bulk soils from Krayburg) The predictive functions and bacterial community 20.5%. The vector length of environmental information distribution in the rhizosphere and bulk samples processing positively correlated with Spirochaetes, Verru- The PCA (Fig. 7) was used to illustrate the correlation comicrobia, Firmucutes, unclassified sequences and unclas - between the predictive functional categories (Level 1) on sified bacterial community structure. The vector length of the bacterial community distribution at the phylum level. organismal systems positively correlated with Elusimicro- The PCA plot indicated that axis 1 had 94.17% and axis bia, Nitrospirae, Acidobacteria and Actinobacteria. Fig. 7 Principal Component Analysis (PCA) of major predictive functional information (Level 1) of bacterial communities in the rhizosphere and bulk soils from Lichtenburg and Krayburg. The vector lengths depict the strength of the dominance of the bacterial metagenomes N wachukwu et al. Annals of Microbiology (2023) 73:9 Page 11 of 18 The impact of soil physical and chemical properties categories was statistically significant (Table 2). The on bacterial predictive functions total variation was 0.00093 and the explanatory varia- To determine the relationship between the predic- ble account for 100%. The results revealed that OM had tive functional categories of bacterial communities in the most explained variable and contribution of 93.4% the samples from LT and KRP and soil physical and at the structural classification, whereas pH had the chemical properties, we used PCA (Fig S3). The for - most explained variable and contribution of 75.7% at ward selection result of environmental factors that best the predictive functional categories, which is depicted explain the variations in the bacterial structural com- by the length of the vector arrows, as shown in Fig S3 position and predictive functional categories revealed and Table 2. that only the p-value of N-NH at the structural Alpha diversity assessment of bacterial communities and predictive functions in the rhizosphere and bulk soil Table 2 The forward selection results of environmental variables The Simpson, Shannon_H and Evenness diversity index that best explains the variations in bacterial structure and values within the samples were used to describe the predictive functions from rhizosphere and bulk soil samples alpha diversity of the bacterial communities at the taxo- using the canonical correspondence analysis nomic level presented in Table 3 across the sites. At the Soil property Explains % Contribution F P phylum and family levels, high Shannon_H diversity index values were obtained between the samples com- Bacterial OM (%) 93.4 93.4 28.3 0.102 pared with other diversity indices measured across the structure farm sites (Table 3). These diversity indices at phylum N-NH (mg/ 85.7 85.7 12.0 0.048 and family levels demonstrated that there were no sig- kg) nificant differences (p > 0.05) in the alpha diversity of pH 75.0 75.0 6.0 0.292 the bacterial composition. Based on Shannon_H diver- Total N (%) 28.7 28.7 0.8 0.354 sity indices, LTR had the highest alpha diversity index Total C (%) 42.0 42.0 1.4 0.054 observed at the family level, and the least Shannon_H N-NO 11.2 11.2 0.3 0.522 diversity index values were obtained LTB at the phylum Predictive OM (%) 74.9 74.9 6.0 0.31 level (Table 3). functional Also, the result from the predictive functional cat- category egories analysis (Kruskal–Wallis, p-value = 0.51) N-NH (mg/kg) 53.8 53.8 2.3 0.35 (Table 3) showed that Shannon_H in LTR had a higher pH 75.7 75.7 6.2 0.338 alpha diversity index compared to other samples. The Total N (%) 7.7 7.7 0.2 1 alpha diversity showed that bacterial diversity and Total C (%) 27.3 27.3 0.8 0.696 predictive functions were not significantly differ - N-NO 14.0 14.0 0.3 0.842 ent (p-value > 0.05) between the LTR, LTB, KRPR and Legend: Organic matter, %—percentage, p – probability value, OM = Organic matter, N-NH = Ammonium-N, N-NO = Nitrate, Total C = Total carbon, Total KRPB (Table 3). 4 3 N = Total nitrogen. Table 3 Alpha diversity indices of bacterial community and predictive functions of the sunflower rhizosphere and bulk soils from the sites Diversity indices LTR LTB KRPR KRPB p-value Bacterial taxonomic level Simpson_1-D 0.7376 0.5639 0.7364 0.7201 0.50 Shannon_H 1.67 1.333 1.488 1.471 Evenness_e^H/S 0.3542 0.2529 0.3163 0.3109 Family Simpson_1-D 0.7798 0.736 0.7794 0.7576 0.51 Shannon_H 2.286 2.105 1.887 1.954 Evenness_e^H/S 0.3172 0.2736 0.2276 0.2433 Predictive functional categories Simpson_1-D 0.9368 0.9346 0.9373 0.9368 0.50 Shannon_H 3.028 3.003 3.026 3.015 Evenness_e^H/S 0.4998 0.4916 0.5027 0.4975 Legend: p– probability value, LTR- Rhizosphere soil from Lichtenburg, LTB- Bulk soil from Lichtenburg, KRPR- Rhizosphere soil from Krayburg, KRPB- Bulk soil from Krayburg Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 12 of 18 Discussion The abundance of the identified bacterial phyla in sun - Sunflower is an important oil-seed crop, hence, increas - flower soils from Lichtenburg has been reported to be ing its production is a major step toward ensuring food important in improving soil health, plant growth and availability and sustainable agriculture. Improving disease suppression (Kielak et al. 2016; Naumoff and sunflower yield requires a better understanding of the Dedysh 2012; Li et al. 2017b). The phylum Armatimona - structural, functional and metabolic potentials of the deteswas among the less abundant bacteria community diverse bacterial communities abundant in their rhizo- identified in LT and it is relatively novel and was formerly sphere, especially those involved in biogeochemical recognized as a member phylum OP10. (Hu et al. 2014; cycles, plant-growth promotion, conservation of eco- Jiménez et al. 2020). There is limited information on its system function and sustainable agriculture (Li et al. function in the rhizosphere or of the phylum (Jiménez 2022). In a bid to comprehend the activities in the plant et al. 2020). The unclassified bacterial phyla and identi - rhizosphere, we employed a next-generation sequencing fied unclassified sequences may create insights for fur - technique to evaluate the bacterial community struc- ther research in determining their novel distinctiveness. ture in the rhizosphere and bulk soil of sunflower at the The dominance the bacterial community in LTR com - growing stage. pared to other samples may indicate the agricultural According to the information on the farm history, we relevance of this bacterial family, whereas the bacterial postulated that soil physicochemical properties and agri- community dominant in the KRPR site has been reported cultural practices, including the use of organic manure, to be important plant growth-promoting bacteria. Simi- chemical fertilizer, and cropping type (mono-cropping, larly, the majority of these families have been shown to and mixed cropping) may influence the bacterial diversity positively influence sunflower growth in the past (Tseng and their functions in the sunflower rhizosphere, which et al. 2021; WEN et al. 2016; Majeed et al. 2018). compelled the choice of the sampling sites. The applica - Furthermore, differences in the relative abundance of tion of organic manure and chemical fertilizer to enhance the majority of bacterial community compositions in the soil nutrients and plant growth, in reverse, may incite a rhizosphere soil of sorghum, maize, mustard, and cucum- shift in the bacterial community structure and soil prop- ber plants have been shown to improve agrobiodiversity erties (Li et al. 2017a). Our results demonstrated that (Agomoh et al. 2020; Ali et al. 2019; Wang et al. 2012). agricultural practices altered both the structure and func- Moreover, the effect of climatic conditions and soil man - tional traits of the rhizosphere bacterial communities. agement practices impact the distribution of bacterial The use of next-generation sequencing technique communities in the rhizosphere (Igiehon and Babalola (amplicon-based approach) has been employed in stud- 2018). Interestingly, the variation in the bacterial com- ies to evaluate the diversity of the bacterial communi- munity observed in the rhizosphere soil of Lichtenburg ties in the rhizosphere soil of maize, soybean, as well as compared to the rhizosphere soil of Krayburg supports sunflower with success (Kielak et al. 2016; Naumoff and the study’s hypothesis on the influence of mixed and crop Dedysh 2012). In the present study, predominant bacte- rotational farming systems on the diversity of bacterial rial phyla were identified in the rhizosphere and bulk communities under diverse agricultural practices. soil of sunflower at the growing stage. The presence of In the present study, the bacterial diversity indices at these bacterial phyla might be due to their attraction the phylum and family level showed a significant differ - to form a community within the rhizosphere. Most of ence in the bacterial distribution across sites and this fur- the identified rhizosphere bacterial phyla have been ther explained how mixed cropping and crop rotational previously reported in the rhizosphere of sunflower, practice showed greater bacterial diversity in Lichten- soybean, wheat and maize (Alawiye and Babalola 2021; burg than the mono-cropping system in Krayburg. The Igiehon et al. 2021; WEN et al. 2016). Firmicutes con- use of crop rotation systems in maintaining stable biodi- tributes a significant quantity of nitrogen to plant versity and bacterial activities has been documented by nutrition resulting to increase in agricultural crop yield (Gentsch et al. 2020). The series of mixed cropping sys - (Ichihashi et al. 2020). Acidobacteria have previously tem can increase nutrient acquisition and nutrient bio- reported to play a significant role in carbon cycling availability, which directly increases rhizospheric bacteria because of their potential to degrade complex plant tis- and selectively attracts diverse plant growth-promoting sues, as well as lignin and cellulose, though, their role bacteria into the region (Tyler 2021; Couëdel et al. 2018). in the rhizosphere is not well recorded (Ward et al. The bacterial phylum identified in this study, such as 2009), whereas Bacteroidetes contain some species that Elusimicrobia predominantly in soils from Lichtenburg, are involved in nitrogen cycling through denitrification has not been reported in the soil of any oilseed crops, (Chaparro et al. 2014). thus revealing its bioprospecting potential in agriculture. However, a study by (Gkarmiri et al. 2017) revealed an N wachukwu et al. Annals of Microbiology (2023) 73:9 Page 13 of 18 abundance of Verrucomicrobia, Gemmatimonadetes, difference (p-value > 0.05) between bacterial diversity and Planctomycetes, Proteobacteria, Acidobacteria, Act- predictive functions of the soils from LTR, LTB, KRPR inobacteria, and Chloroflexi in the rhizosphere soil of and KRPB. In this study, the sunflower rhizosphere effect oilseed rape has been documented. The most active phy - is the major driving force of alpha diversity. lum Proteobacteria from the KRPR corroborates with Sunflower root exudates can influence bacterial diver - that of (Saleem et al. 2016), who reported a similar bac- sity and functions in the rhizosphere and bulk soils after terial phylum from the rhizosphere and roots of burley secreting different profiles of bioactive compounds and tobacco plants. nutrients into the rhizosphere (Reavy et al. 2015; Wei Intriguingly, the different families of rhizospheric bac - et al. 2019). The alpha diversity indices (Shannon_H) also teria in the LTR and KRPR can highlight their impor- indicated that only the predictive functional diversity tance in agriculture in improving plant growth and represented by the bacterial metagenomes of the rhizo- health. Because of the large number of unclassified bacte - sphere and bulk soils passed its hypothetical limit of 2.81 ria phyla found in sunflower using amplicon sequencing, (Dinsdale et al. 2008; Rygaard et al. 2017), suggesting that the findings of this study can be used as a model in future bacterial metagenomes were most characterized in both studies of plant growth-promoting rhizospheric bacteria soils from LTR, LTB, KRPR, and KRPB. The Simpson and associated with oilseed crops, including sunflower. Con - Evenness diversity indices for the metagenomes across all tinuous fertilization of farming soils may alter the bacte- samples were < 1, indicating that there are a few predomi- rial diversity and nutritional profile (Zhang et al. 2020; nant bacterial taxa, (e.g. Moraxellaceae, Solirubrobacte- Xiong et al. 2021). raceae, Chitinophagaceae, and Streptomycetaceae) and Correspondingly, researchers have documented that the predictive functional categories (At level 1, cell pro- OM is an important factor determining the diversity cessing, environmental information processing, genetic of bacterial in different soils, including sunflower and information processing, organismal systems, metabo- other agricultural soils (Cordero et al. 2020; WEN et al. lisms, and human diseases) in each soil samples. 2016). The diversity, abundance, and richness of bacte- The relative abundance of bacterial predictive func - rial communities are largely dependent on the soil OM tional categories at the second-level was used to dis- content. This study revealed that the soil OM influenced tinguish the particular predictive functions that are of the relative abundances of the major phyla differently greater benefit to the bacteria present in a given habitat. across the sites. Bacteria from soil use carbons as a source of energy for The vector lengths of the environmental variables in metabolism and growth, so they rely on diverse carbon the PCA plot revealed that OM is not the only factor sources such as maltose, inositol, glucose, and mannose that influences the modelling of the bacterial communi - for their growth and survival. Evidently, this is seen in the ties and their functional diversity. The pH of the soil is a abundance of predictive functional categories involved in fundamental driver of the bacterial community structure carbon dioxide, di- and oligosaccharides fixation and car - (Qu et al. 2020). The pH values of sunflower rhizosphere bohydrate metabolism at level 2, as well as the presence and bulk soils ranged from 6.91 to 6.94 and this validates of metabolic pathways related to sugar usage, galactose the findings of (Alawiye and Babalola 2021), who docu- metabolism, fructose and mannose metabolism, starch mented pH values, ranging from 5.8 to 6.6 on rhizos- and sucrose metabolism, pentose phosphate pathway and phere soils collected from four sunflower farms in South TCA cycle in our samples. Africa. The effect of these factors on rhizospheric bacte - Also, bacteria use amino acids as an energy source for rial structure diversity and their functional potentials has survival in environments with poor nutrient and in envi- been reported (Chen et al. 2021), though, may form the ronments with little OM content (Gianoulis et al. 2009). bacterial community structure and selection of soil for This is in agreement with the results obtained for the soil agricultural purposes. physicochemical properties of our samples, where we In accordance to (Jacoby et al. 2017), phosphorus, found higher amounts of OM, N-NO , P, and Na in the sodium and potassium available in the rhizosphere also LTR samples than in the samples from the Krayburg site. contribute to the soil microbial community structure and The reason for the decline in soil nutrients in KRPR soil participate in the mineralization processes critical for samples may be because of cropping system and land-use plant nutrition in natural ecosystems. The secretion of practices, this substantiate the report from previous stud- root exudates released by plants is linked to the modu- ies that continuous cropping of a particular crop depletes lation of microbial communities and their functions in soil nutrients (Kumar Behera et al. 2009; Dhaliwal et al. the rhizosphere (Bargaz et al. 2018). Also, root exudates 2019; Foley et al. 2005; El-Fouly et al. 2015; Chen et al. initiate’s connections between the plant roots and soil 2018). Therefore, as a response mechanism for bacte - microbes. The alpha diversity revealed no significant rial survival in nutrient-poor soils, the richness of genes Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 14 of 18 linked with cell motility (bacterial mobility proteins and chemical resistance, siderophores, plant hormones, oxi- bacterial chemotaxis) is necessary. dative stress, and virulence regulation at level 3 support Our findings demonstrated that the bacterial com - these findings. munities found in soil samples assist plants in acquir- The metabolic pathways involved in indole alkaloid bio - ing carbon through various metabolic pathways (Wang synthesis, flavonoid biosynthesis, clavulanic acid biosyn - et al. 2019; Xu et al. 2020; Prabha et al. 2019). In addition, thesis, steroid hormone biosynthesis, inositol phosphate sequences related to the metabolism of important min- metabolism, linoleic acid metabolism, and N-Glycan eral nutrients like S and N were discovered in soils. LTR biosynthesis were all shown to be abundant at level 3. and KRPR had larger relative abundances of these pre- Sequences associated to streptomycin biosynthesis and dictive functions than LTB and KRPB. Mineral nutrients antibiosis resistance, particularly beta-lactam resistance, are required for plant growth and health. As a result, our were discovered once more. Secondary metabolism, findings suggest that the bacterial communities found in which includes the bacterial community’s biosynthesis of our samples assist sunflower plants in obtaining essential several metabolites (low molecular-weight compounds) nutrients for growth and development. as a sign of metabolic complexity, is an important feature Similarly, at level 3, the presence of selected predicted of bacteria from soil (Berdy 2005; Barka et al. 2016). Bac- metabolic functions such as ABC transporters, oxida- teria use this defense mechanism to defend themselves tive phosphorylation, glycerophospholipid, and nitrogen against pathogens. As a result, they are important in and sulfur metabolism in our samples indicate that bac- clinical practice, serving as antimicrobials and antibiotics terial communities inhabiting these soils play an impor- (Newman and Cragg 2016). tant role in promoting nutrient cycling and plant growth Many of the bacterial phyla identified, such as Pro - (Tang et al. 2016; Reed et al. 2011; Lindsay et al. 2010). teobacteria, Actinobacteria, Firmicutes, and Bacteroi- The metabolism of sulfur in level 3 revealed that the sam - detes, produce a variety of bioactive compounds, such as ples were also dominated by S. Studies have documented siderophores, which act as antibacterial, antifungal, and many critical roles played by bacteria found in various biosurfactant (Hwang et al. 2014; O’Connor 2015; Lud- environments, including sunflower soil in the enzymatic wig-Müller 2015; Gómez Expósito et al. 2017). Because sulfur metabolism (Wrighton et al. 2012; Yin et al. 2014; secondary metabolism is so important to plant growth, Zhang et al. 2016). Hence, the presence of genes involved competent culturing methods must isolate and identify in sulfur metabolism in our samples suggests that the bacterial strains from the soil bacterial community that bacteria present in our soil samples contribute to sustain- can perform secondary metabolic functions efficiently ing a balanced sulfur metabolism in their environments. for increased crop yield. As a result, examining the soil Moreover, we observed the presence of sequences bacterial community for bacteria that have these differ - involved in the biosynthesis of chelating and iron com- ent genes can lead to the classification of novel second - pounds such as siderophore in our results. Iron is a ary metabolic traits that can be used as biofertilizers in micronutrient that is important in the initiation of meta- soils and plants to enhance resistance against pathogenic bolic pathways and a constituent of the prosthetic group attacks. Our findings are in accordance with previous in living organisms (Dimkpa et al. 2009). Abiotic stresses studies that show the diversity and abundance of genes in plants caused by iron can be alleviated by bacteria linked to antibiotic resistance (Wang et al. 2013; Enag- through high-affinity transport systems linking the bio - bonma and Babalola 2020). synthesis of siderophores. The transport systems play The metabolic pathways of amino acids at level 3 critical roles in many soil environments, including aid- revealed the samples were also dominated by amino ing the competitive acquisition of iron for plant usage acids and derivatives. Our results indicate that the bac- (Prabha et al. 2019; Mohapatra et al. 2021). terial communities inhabiting the fields can produce The sequences associated with metabolic activity, ABC amino acids such as glutathione and Lysine involved transporters, were discovered at level 3. Most of these in the protection against oxidative stress in the crops metabolic genes were found to be abundant in the Kray- such as sunflower plants (Takagi and Ohtsu 2016). The brug samples. As a result, we predict that our samples high richness of the unclassified predicted functions will be dominated by bacteria that aid in the acquisition and poorly characterized predicted functions at level 1 of minute iron, hence increasing iron bioavailability in and level 2 respectively in our samples, show that there our soil samples. At level 3, we observed the predicted are many bacterial genes whose predicted functions metabolic processes, such as those involved in second- in the soils are still uncharacterized. Though, the fact ary metabolism, virulence, disease, and defense, stress that they are present indicates that they contribute to response, and aromatic chemical metabolism. The abun - significant functions in the soils that can be useful to dance of sequences relevant to antibiotic and hazardous the plants’ growth and health. N wachukwu et al. Annals of Microbiology (2023) 73:9 Page 15 of 18 Another hypothesis of this study was that the phys- documented between the rhizosphere and bulk soil icochemical parameters would influence the predic- across the sites. The dominance of unclassified bacteria tive functional attributes of the bacterial communities and sequences in the samples proposes further studies in the samples. The aggregation of various bacterial in developing culturable approaches for their classifica - communities occurs because of the pressure of selec- tion and discovery of new genes that can be harnessed tion of sunflower roots that constantly release exu- as bioinoculants in developing environmentally friendly dates containing amino acids and carbohydrates into agriculture. the rhizosphere. The effect of host plants on the bacte- Across the sites, bacterial diversity was positively and rial diversity in the rhizosphere has been observed in negatively influenced by environmental variables. The various agricultural plants including, maize, wheat and predicted functional attributes of these bacteria propose peas (Mohammadi et al. 2011; Gentsch et al. 2020). their agricultural significance, which can be discovered in (Hamel et al. 2006), documented that in a study of emerging biofertilizers as a substitute to chemical ferti- high-frequency pea production, there was a rise in the lizer. Because of the economic importance of sunflower, it diversity of bacteria in pea rhizosphere soil with min- is recommended to employ culture-dependent methods, eral nitrogen levels compared to the bulk soil. invitro inoculation of seeds, and planting in the fields and Furthermore, metagenomic analysis of wheat rhizo- greenhouse to further study the potential of rhizospheric sphere and bulk soil found that the rhizosphere soil bacteria on sunflower crops. Also, mining the metagen - has higher bacterial diversity than the bulk soil (Priya omes using more advanced techniques is important to et al. 2018; Velázquez-Sepúlveda et al. 2012). More- identify novel genes that encode valuable metabolic path- over, several research focused mostly on selected ways with numerous essential functions crucial for plant rhizospheric isolates have found that the rhizosphere development and enhancement of sustainable agriculture region of young plants is a more unpredictable envi- [Klimek et al., 2016, Kumar and Dubey 2020]. ronment than the rhizosphere region of mature plants Likewise, understanding plant-associated microorgan- during the developing phases of maize (Tiemann et al. isms under different cropping systems will help deter - 2015). This is consistent with the findings of (García‐ mine their functional roles in nutrient cycling, plant Salamanca et al. 2013), who reported that the rhizo- nutrition, development, and health. Interestingly, this sphere is a more nutrient-dense ecosystem than bulk study offers clear proof of the effect of agricultural prac - soil, and that the activity levels of some enzymes from tices, crop rotation and physicochemical properties on bacterial cells in the maize rhizosphere, such as dehy- the bacterial diversity in sunflower soils from the two drogenase, β-glucosidase, and alkaline phosphatase, sites (Lichtenburg- LT and Krayburg- KRP). Conclusively, were higher than similar enzymatic activity tested in this study will enable the agricultural industry to enhance bulk soil. economic, agricultural and environmental sustainability We also discovered that physicochemical parameters by making critical soil management decisions. influenced the predicted functionalities. The primary factors to the distinctiveness exhibited in soil bacterial Supplementary Information structural diversity have been identified as soil physic- The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13213- 023- 01713-y. ochemical characteristics (Shi et al. 2011; Hanson et al. 2012). The functional diversity of bacterial communi- Additional file 1. ties is driven by soil characteristics, according to stud- ies (Shi et al. 2011; Hanson et al. 2012). The present Acknowledgements study’s findings show that the physical and chemical BCN thanks the National Research Foundation (NRF), South Africa/The World properties of the soil impacted the relative abundance Academy of Science African Renaissance Ph.D. scholarship (Ref: UID: 121772) for giving her a stipend. A. S. A. is grateful to the North-West University for of bacterial predicted functions in the two study sites. postdoctoral bursary and research support. OOB. want to thank the National Research Foundation of South Africa for grants (Grant Refs: UID123634; UID Conclusion 132595 granted to OOB) that have supported work in our laboratory. Understanding on the roles of various rhizospheric Authors’ contributions bacterial communities in the promotion of plant BCN handled the literature findings, carried out the laboratory work, per - growth and health using 16S rRNA gene sequencing formed all necessary analyses, interpreted the results, wrote the first draft and corrected the manuscript. ASA provided technical input and proofread the creates novel prospects for enhancing effective and manuscript. OOB supervised all co-authors, provided academic and technical eco-friendly methods for improving agricultural yield inputs, intensively critiqued the manuscript, and funded the research. All through the manipulation of microorganisms. Dissimi- authors agreed that the manuscript is published. larities in predominant bacterial communities were Nwachukwu et al. Annals of Microbiology (2023) 73:9 Page 16 of 18 Funding Chen S, Qi G, Luo T, Zhang H, Jiang Q, Wang R, Zhao X (2018) Continuous- Open access funding provided by North-West University. This work was cropping tobacco caused variance of chemical properties and structure supported by the National Research Foundation of South Africa [Grant Refs: of bacterial network in soils. Land Degrad Dev 29(11):4106–4120 UID123634; UID 132595 OOB]. Chen WC, Ko CH, Su YS, Lai WA, Shen FT (2021) Metabolic potential and com- munity structure of bacteria in an organic tea plantation. Appl Soil Ecol Availability of data and materials 157:103762 Sequence data obtained in this work have been deposited in the NCBI Clarke K, Green R (1988) Statistical design and analysis for a’biological effects’ Sequence Read Archive under Accession Number PRJNA782103 and study. 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BMC Genet 17(1):1–12 Zhang BH, Hong JP, Zhang Q, Jin DS, Gao CH (2020) Contrast in soil microbial metabolic functional diversity to fertilization and crop rotation under Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : rhizosphere and non-rhizosphere in the coal gangue landfill reclamation area of Loess Hills. PLoS ONE 15(3):e0229341 fast, convenient online submission thorough peer review by experienced researchers in your field Publisher’s Note rapid publication on acceptance Springer Nature remains neutral with regard to jurisdictional claims in pub- support for research data, including large and complex data types lished maps and institutional affiliations. • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions
Journal
Annals of Microbiology – Springer Journals
Published: Feb 17, 2023
Keywords: Bacterial diversity; Helianthus annuus; Soil metagenomics; Sustainable agriculture; 16S rRNA gene sequencing