Effects of legume-based rotation on subsequent sorghum rhizosphere microbial communities and their drought tolerance-related genes.

Effects of legume-based rotation on subsequent sorghum rhizosphere microbial communities and their drought tolerance-related genes - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice Environ Microbiome . 2026 Jan 29;21:53. doi: 10.1186/s40793-025-00829-9 Search in PMC Search in PubMed View in NLM Catalog Add to search Effects of legume-based rotation on subsequent sorghum rhizosphere microbial communities and their drought tolerance-related genes Ben Jesuorsemwen Enagbonma Ben Jesuorsemwen Enagbonma 1 Food Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North‒West University, Private Mail Bag X2046, Mmabatho, 2735 South Africa Find articles by Ben Jesuorsemwen Enagbonma 1 , Rian E Pierneef Rian E Pierneef 2 Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa Find articles by Rian E Pierneef 2 , David Mxolisi Modise David Mxolisi Modise 1 Food Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North‒West University, Private Mail Bag X2046, Mmabatho, 2735 South Africa Find articles by David Mxolisi Modise 1 , Olubukola Oluranti Babalola Olubukola Oluranti Babalola 1 Food Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North‒West University, Private Mail Bag X2046, Mmabatho, 2735 South Africa 3 Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire, SL5 7PY UK Find articles by Olubukola Oluranti Babalola 1, 3, ✉ Author information Article notes Copyright and License information 1 Food Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North‒West University, Private Mail Bag X2046, Mmabatho, 2735 South Africa 2 Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa 3 Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire, SL5 7PY UK ✉ Corresponding author. Received 2025 May 1; Accepted 2025 Nov 23; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13077932  PMID: 41612514 Abstract Introduction The impacts of incorporating legumes into cereal crops on soil microbial structure, composition, functional genes involved in nitrogen, carbon and phosphorus cycling, signaling pathways and hydraulic conductivity adaptations have been well studied. However, the same cannot be said for functional genes that increase drought tolerance. Objectives Here, we examined the changes in microbial community structure and functional genes involved in drought tolerance in response to legume‒cereal rotation and cereal‒cereal rotation. This study provides a preliminary, exploratory characterization of microbial community and functional gene shifts, without direct evidence of functional impact on plant physiology or productivity. Methods DNA extracted from soil samples collected across cowpea-sorghum treatment (CS) or maize-sorghum treatment (MS) was sequenced via shotgun sequencing. Results Nonmetric multidimensional scaling analysis revealed that the microbial communities in the CS treatment significantly differed from those in the MS treatment. Compared with the MS rotation, the CS rotation increased the relative abundances of Pseudomonadota, Acidobacteriota, Chloroflexota, Gemmatimonadota, Euryarchaeota, and Candidatus Bathyarchaeota and reduced the abundances of Actinomycetota, Ascomycota, and Nitrososphaerota at the phylum level. Furthermore, the CS rotation increased the abundance of microbial genera such as Solirubrobacter, Sphingomonas, Nitrosocosmicus, Nitrosotenuis Aspergillus, and Metschnikowia when related to the MS rotation. STAMP analysis revealed that in the CS rotation, genes involved in trehalose biosynthesis, biofilm formation, oxidative stress mitigation (e.g., sodA, katG ), stress signaling (e.g., rpoS, ipdC ), nutrient provisioning (e.g., nifH, pqqC ), membrane fluidity ( desA, desB ), dormancy ( spo0A, spoVF ), and ion homeostasis ( nhaB, kup ) predominated. In the MS rotation, proline biosynthesis ( proA, proB, and proC ), glycine betaine synthesis ( betA and betB ), aquaporin ( aqpZ ), and structural integrity genes ( murA and murC ) were predominant. The RDA results revealed that crop rotation influenced the soil physicochemical parameters, which in turn impacted both the microbial communities and drought tolerance genes in both treatments, probably creating a favorable environment for resilience under drought. Conclusion These research findings provide insight into the relationships between cowpea cropping sequences and the soil microbiome and drought-tolerant functional genes fundamental for the productivity of successive crops and this understanding guides sustainable crop selection. Supplementary Information The online version contains supplementary material available at 10.1186/s40793-025-00829-9. Keywords: Cropping system, Food security, Metagenomics, Sustainable farming, Vigna unguiculata Introduction The yearly production of sorghum ( Sorghum bicolor L. Moench) is 61.4 million tons, and sorghum is the fifth most produced cereal crop globally [ 11 ]. Sorghum is progressively known as a climate-adaptable and resilient crop that holds substantial promise for increasing universal food security sustainably and has a more distinct nutritional profile than other common cereal grains do [ 96 ]. Sorghum grain is famous for its health benefits, which include anti-inflammatory, anticancer, antioxidative, and antimicrobial properties [ 22 ]. In addition, the highly resistant starch and low protein digestibility of sorghum provide potential benefits for handling health conditions such as obesity and diabetes [ 22 ]. As a result of these benefits, sorghum is reported as a desirable alternative to customarily staples such as rice and wheat for human and animal consumption [ 83 ]. To increase sorghum production, farming practices that increase plant yield, health and growth need to be promoted [ 35 ]. The continuous cropping style of sorghum production has significant problems. For example, Norwood [ 62 ] reported that continuous sorghum yielded significantly less sorghum than did fallow sorghum when they evaluated the impact of a cropping system on dryland sorghum in southwestern Kansas. Rotation cropping has been recommended as a potential alternative to help abate the adverse impacts of continuous cropping practices [ 92 ]. Crop rotation (the method of cultivating dissimilar crops in the same field during successive seeding or growth cycles) has been shown to shift the composition and structure of the soil microbiome, improve soil physical and chemical parameters, depress pathogens and promote plant development [ 98 ]. For example, Chen et al. [ 15 ] reported that maize–peanut rotation drastically augmented dissolved organic carbon in the rhizosphere by increasing maize yield and root growth by 10.2%. They further reported that the fungal structure shifted more under distinct agricultural methods. Yuan et al. [ 95 ] reported that the pineapple‒banana rotation system substantially reduced the incidence of next-stubble banana disease and the number of pathogens. Among all the crop rotational practices that impact soil microbial dynamics, crop rotation, specifically with legumes, has emerged as a sustainable approach to increase soil functional capacity [ 31 ]. Babalola et al. [ 8 ] and Babalola and Enagbonma [ 7 ] reported that legumes, through their unique ability to fix atmospheric nitrogen in association with rhizobia, contribute to improved soil nutrient content and create a favorable microenvironment for diverse microbial communities. These rotations are especially important in arid and semiarid regions, where soil functionality is critical for sustaining agricultural productivity under drought stress [ 74 ]. Drought poses a severe threat to global food security, reducing crop yields by limiting water availability and altering soil microbial activity [ 3 ]. Microbial communities play a central role in mitigating these impacts through their involvement in nutrient cycling, plant growth promotion, and stress tolerance [ 29 , 45 ]. Microbial functional genes linked to drought tolerance, such as those encoding osmolytes, stress-protective enzymes, and phytohormones, are essential for sustaining plant health under water-limited conditions [ 37 , 68 ]. Legume rhizodeposits, including organic acids, flavonoids, and root exudates [ 5 , 70 ], support the proliferation of beneficial microbes, such as nitrogen-fixing bacteria [ 70 ], phosphate-solubilizing bacteria [ 76 ], and other plant-growth-promoting rhizobacteria (PGPRs) [ 2 , 75 ]. These microbes often harbor genes associated with drought tolerance and contribute to improving soil structure, water retention, and nutrient availability [ 102 ]. Most studies on legume-based rotations have focused primarily on their effects on microbial functions related to carbon (C) [ 91 ] and nitrogen (N) metabolism and phosphorus (P) cycling in agricultural soils [ 90 , 94 ]. However, our understanding of how legume rotations influence functional genes and microbial communities associated with drought tolerance is limited. Additionally, plant species such as chickpea and mungbean [ 14 ], peanut and soybean [ 66 ], sainfoin [ 87 ], pea, and lentil [ 51 ] have been used in legume-based rotation studies, with little attention given to cowpea ( Vigna unguiculata ). While Alvey et al. [ 6 ] incorporated cowpea in a crop rotation study, their interest was focused only on the effects on rhizosphere ammonia-oxidizing communities by using 16S rDNA and denaturing gradient gel electrophoresis approaches, with no focus on functional genes. This study employs metagenomic sequencing to (1) evaluate the impact of cowpea-sorghum rotation and maize-sorghum rotation on the compositional shift and structure of the soil microbial community and (2) assess their effects on microbial functional genes that increase drought tolerance. We hypothesize that, compared with maize-sorghum rotation, cowpea-sorghum rotation can shift the soil microbiome to favor drought tolerance-related genes, improve soil physicochemical properties, and ultimately support better sorghum growth. This premise is centered on the fact that the rates of soil carbon and nitrogen intake and microbial biomass content are largely increased after rotation, with a significant increase in legume‒cereal rotation compared with cereal‒cereal rotation [ 87 ]. However, assessing cowpea–sorghum rotation over a single season cannot fully reveal the long-term ecological stability of microbial communities. Our focus, however, was to document the immediate microbial responses under seasonal conditions, offering a valuable reference point for future multiseason and long-term investigations. Materials and methods Description of the experimental sites The experimental field located at Molelwane Farm (25°49′22" S, 25°36′54" E), North-West province, South Africa, was used for this study. This area is characterized by annual rainfall averaging 540 mm, predominantly during summer. The site experiences summer temperatures ranging from 27 to 38 °C, whereas winter temperatures vary between 11 and 18 °C. The soil in the study area at an elevation of 1,286 m has a moisture content between 4.00 and 6.58% and a pH range of 5.41–6.69. The experimental field was divided into two plots, with individual plots measuring 5.2 m × 4 m (20.8 m 2 ). Eight rows of plants and a 1-m buffer zone were maintained between plots. The first plot had been previously used for cowpea cultivation and was left fallow for two weeks before the sorghum trial (CS) commenced in February 2024. The second plot (which served as the control) had been previously used for maize cultivation and was left fallow for two weeks before the sorghum trial (MS) commenced in February 2024. The two plots selected for the sorghum trial were strategically designed to assess the impact of preceding crops on the microbial community structure and drought tolerance genes in sorghum rhizospheres. The treatment groups were established with sorghum (cultivars: Avenger). Each treatment had three replicates (a minimum acceptable number and standard scientific practice that provides a balance between reliability and statistical power), with standardized crop management practices across all plots, including planting density, irrigation, and pest control. In June 2024, during the soft dough stage of sorghum (a stage when the grain can no longer contain a milky liquid and can be crushed between the index finger and thumb), soil samples were collected to analyze the soil physicochemical properties, microbial communities and metabolic activity. The purpose of sampling soil only at the soft dough stage, rather than across sorghum growth stages or seasons, was to provide a snapshot during a key developmental phase of the crop [ 7 ]. Rhizosphere soil was obtained by shaking and brushing the soil tightly adhering to the roots of 20–30 randomly selected plants per plot. In total, 3 rhizosphere soil samples (CS1, CS2, and CS3) for cowpea-sorghum rotations and (MS1, MS2, and MS3) for maize-sorghum rotations were collected. The samples were sieved through 2 mm meshes to remove debris, with a portion stored on ice and subsequently preserved at  −  80 °C [ 27 ] for DNA extraction and 4 °C [ 24 ] for physicochemical analysis. Soil chemical and physical analysis Soil samples (20 g) that had been preprocessed to eliminate debris and wooden material were used for soil physical and chemical property analysis. The soil pH in deionized H 2 O was estimated via a pH meter from 1 part of the soil to 2.5 parts of the water. An atomic absorption spectrophotometer (AAS) was used to read the exchangeable calcium (Ca) and magnesium (Mg) present in the extracts obtained from 1 M NH 4 CH 3 CO 2 at pH 7. A flame photometer was used to read exchangeable potassium (K). A spectrophotometer was used to calculate the accessible phosphorus (P), and organic carbon was estimated via the method previously described by Wakung’oli et al. [ 86 ]. The textural classes were determined on the basis of the particle size categories estimated by the USDA: sand, clay, and silt with particle sizes of 2.0–0.05, < 0.002 and 0.05–0.002 mm, respectively. The nitrate (NO 3 -N), moisture, Na, and ammonium (NH 4 + -N) contents were analyzed via methods outlined in previous studies [ 25 , 26 , 63 ]. The results are shown in Table S1. Soil total DNA isolation, library preparation and bioinformatic processing Total DNA isolation A Nucleospin Soil Kit (Macherey–Nagel, Germany) was used to mine whole DNA from 0.5 g of rhizosphere soil according to the manufacturer’s protocol. For the analysis of the rhizosphere microbiome, 6 DNA samples (3 replicates × 2 treatment groups) were subjected to shotgun metagenomic sequencing on the Illumina NovaSeq 6000 platform, which produced 150-base pair paired-end reads. Library preparation, quality assessment, and sequencing To initiate library preparation, 1 μg of genomic DNA was subjected to random fragmentation, which produced approximately 350 base pair (bp) segments. This was achieved via the Covaris system, an acoustic shearing technology that ensures unbiased fragmentation suitable for downstream applications. The fragmented DNA underwent end repair to convert overhangs into blunt ends, followed by the addition of a single adenine (A) nucleotide at the 3' end, a process known as A-tailing. This modification facilitates the ligation of sequencing adapters that possess a complementary thymine (T) overhang. Subsequently, adapters containing platform-specific sequences and unique index barcodes are ligated to the prepared DNA fragments. These adapters enable the identification and multiplexing of samples during sequencing. Postligation, the library was purified to remove excess adapters and other contaminants. The adapter-ligated fragments are then amplified via polymerase chain reaction (PCR) to enrich the library and generate sufficient material for sequencing.​ Quality control (QC) measures were implemented to assess the integrity and size distribution of the library fragments. Automated electrophoresis (Agilent Bioanalyzer) techniques were employed to verify that the fragment sizes aligned with the expected distribution. The quantification of the effective library concentration was performed via quantitative PCR (qPCR), ensuring that the library met the concentration threshold (e.g., > 3 nM) required for optimal sequencing performance. In addition to passing QC, libraries from different samples were pooled on the basis of their effective concentrations and the desired sequencing output. The pooled libraries are then subjected to paired-end 150 bp (PE150) sequencing, facilitating comprehensive analysis of the genomic content. Initial processing of sequencing data To prepare raw sequencing data obtained from the Illumina platform for downstream analyses, the tool fastp ( https://github.com/OpenGene/fastp ) was employed. This utility performs comprehensive quality control and preprocessing in a single pass, efficiently handling tasks such as adapter trimming, quality filtering, and per-read quality pruning. During preprocessing, paired-end reads are discarded under the following conditions: a) if either read contains adapter sequences, b) if either read has more than 10% ambiguous nucleotides (denoted as 'N'), and iii) if either read comprises over 50% low-quality bases (with a quality score below 5). To address potential contamination from host DNA, the cleaned reads are aligned against a host genome (with NCBI accession number ABXC00000000 ) reference using Bowtie2 ( http://bowtie-bio.sourceforge.net/bowtie2/index.shtml ). The alignment is performed with the following parameters: i) –end-to-end: ensures full-length alignment of reads; ii) –sensitive: applies a sensitivity preset for alignment; iii) -I 200: sets the minimum fragment length for valid paired-end alignments to 200 bp; and iv) -X 400: sets the maximum fragment length for valid paired-end alignments to 400 bp. Reads that align to the host genome are identified and removed to prevent interference in subsequent analyses [ 41 , 42 , 78 ]. Metagenome assembly The high-quality clean reads are assembled into longer contiguous sequences via MEGAHIT, a de novo assembler optimized for large and complex metagenomic datasets. The assembly is carried out with the –presets meta-large parameter configuration to ensure efficient handling of microbial communities. After scaffolding, sequences are split into ambiguous 'N' bases to generate contiguous fragments (scaftigs) free of undefined nucleotides. This step helps improve downstream analyses by excluding potentially low-confidence regions [ 49 , 71 ]. Open reading frame (ORF) prediction and gene abundance profiling To predict potential coding regions, MetaGeneMark was applied to all scaftigs equal to or greater than 500 base pairs via its standard settings. Sequences predicted to be shorter than 100 nucleotides are excluded to reduce noise and improve data reliability [ 58 , 72 ]. Following prediction, the CD-HIT algorithm was used to group and deduplicate similar ORFs, generating a nonredundant gene catalog. Clustering was performed with the following settings: -c 0.95, -G 0, -aS 0.9, -g 1, and -d 0, ensuring that only highly similar sequences were merged [ 34 , 53 ]. Next, the cleaned reads from each sample were aligned to this gene catalog via Bowtie2 with parameters such as –end-to-end, –sensitive, -I 200, and -X 400. This alignment step quantifies how many reads map to each gene per sample. Genes with read counts ≤ 2 were filtered out to establish a final list of representative genes (referred to as Unigenes) used in further analysis [ 71 , 97 ]. Gene abundance in each sample was calculated on the basis of the number of aligned reads (r) normalized by the gene length (L), following the following formula: Abundance = r/L. These abundance values are used for downstream statistical evaluations [ 16 , 85 ]. Taxonomic classification of unigenes To determine the taxonomic origins of the predicted unigenes, DIAMOND ( https://github.com/bbuchfink/diamond/ ) was employed for high-speed sequence alignment. The unigenes were aligned against the Micro_NR database, a curated subset of the NCBI nonredundant (NR) protein database, encompassing sequences from bacteria, archaea, fungi, and viruses. Alignments were performed via the BLASTP algorithm with an E value threshold of 1e −5 to ensure specificity; for each unigene, multiple alignment hits may be obtained. To assign a taxonomic classification, the lowest common ancestor (LCA) algorithm, as implemented in MEGAN software, was utilized. This method identifies the most specific taxonomic node that encompasses all significant hits, providing a conservative and reliable taxonomic assignment. Following taxonomic assignment, the abundance of each taxon at various hierarchical levels (e.g., phylum, genus, species) was calculated. This was achieved by summing the abundances of all unigenes assigned to a particular taxon. Additionally, the number of unique genes associated with each taxon was determined by counting unigenes with nonzero abundance values. Functional annotation via public databases To assign functional roles to the predicted unigenes, DIAMOND was utilized for fast and sensitive sequence alignment. The alignments were performed via the BLASTP mode with an E value cutoff of 1e −5 to identify potential homologs [ 32 , 52 ]. The functional database employed for annotation was the KEGG database [ 55 ]. For each unigene, the top alignment result was selected as its representative functional annotation [ 9 , 50 , 72 ]. On the basis of these annotations, the relative abundance of each functional category was calculated. This was done by summing the abundances of all unigenes annotated under a given functional classification [ 42 , 50 ]. A functional gene count table was generated for each sample at various annotation levels. The gene count for a specific function in a sample corresponds to the number of unigenes with nonzero abundance annotated to that function. The eggNOG database ( http://eggnogdb.embl.de/#/app/home ), accessed via eggNOG-mapper, was used to annotate metagenomic reads by assigning them to orthologous groups, with a sequence identity cutoff of 70% and a minimum alignment coverage of 80%. These thresholds and tools were chosen to balance sensitivity and specificity, allowing the confident identification of genes involved in the stress response under drought conditions. These annotations were then filtered for drought- and osmotic stress-related functions to identify candidate drought tolerance genes. Statistical analysis NMDS (nonmetric multidimensional scaling) analysis was performed on the analyzed sequenced data to examine the similarities and distances among the soil samples from the different cropping systems via Canoco 5 (Ithacha, USA) [ 27 ]. ANOSIM (analysis of similarity) was conducted with the R vegan package [ 64 ] to assess clustering differences between cowpea-sorghum and maize-sorghum rotation treatments. Version 2.1.3 of STAMP (statistical analysis of metagenomic profiles) software [ 65 ] was employed to identify significant differences in the microbial community and gene composition between sample groups. Biomarker identification across rotations was performed via linear discriminant analysis (LDA) effect size (LEfSe) with LEfSe software [ 79 ]. Redundancy analysis (RDA) was applied to explore relationships between microbiome structure, genes that code for drought tolerance, and soil physical and chemical properties, as visualized through Canoco 5. Taxonomic abundance data were visualized via Excel software [ 60 ]. Results Sequence analysis and soil microbial community among rotation treatments The raw sequence data before and after preprocessing (QC) are shown in Table S2. The assembly information is available in Table S3. Illumina sequencing yielded between 9.83 and 11.91 Gbp of raw bases per sample across both crop rotation treatments. After quality control (Phred ≥ Q20, adapter removal, and chimera filtering), between 9.81 and 11.88 Gbp of clean bases were retained, corresponding to an average retention rate of 99.7%. The mean Q20 and Q30 scores were 97.74% and 94.20%, respectively, indicating that high read quality was suitable for downstream metagenomic analyses. The GC content ranged from 66.75% to 68.04% across samples, which was consistent with the findings of the soil microbial metagenomes (Table S2). Rarefaction analysis (Fig. S1) revealed that species accumulation curves approached saturation for all samples, and Good’s coverage estimates exceeded 99% , demonstrating that sequencing depth was sufficient to capture the majority of the microbial diversity present. The mean Good’s coverage was 99.9% ± 0.3 for CS and 99.8% ± 0.4 for MS, with no significant difference ( p > 0.05) (Fig. S2). On average, 7,415,838 open reading frames were predicted (Table S4). Figure 1 shows the soil microbial community patterns. Compared with the cowpea-sorghum rotation treatments, the maize-sorghum rotation treatments clustered more closely. UniFrac distance analysis revealed significant differences between the cowpea-sorghum and maize-sorghum treatments (R = 0.592, p = 0.04); however, the effect size (R) was modest. Fig. 1. Open in a new tab NMDS analysis of cowpea-sorghum and maize-sorghum rotations . Both NMDS 1 and 2 account for 95.99% of the variations and this means that most of the variation (91.03%) among samples is captured along the first axis. With the stress value of 0.06, it means that the ordination provides a reliable representation. CS = cowpea-sorghum rotations, and MS = maize-sorghum rotations We used a metagenomic approach in this study, enabling the identification of 194 microbiomes belonging to viruses, archaea, fungi, and bacteria within the soil microbial community, with bacteria being the most prevalent (94.06% for CS and 93.71% for MS) in both cowpea-sorghum and maize-sorghum rotation treatments. This was followed by archaea (1.38% for CS and 1.62% for MS), Eukaryota (0.018% for CS and 0.019% for MS) and viruses (0.008% for CS and 0.014% for MS). Furthermore, the NR database assigned 4.54% (for CS) and 4.63% (for MS) of the sequences to other sequences (unknown) (Fig. S3). After a ≥ 80% prevalence threshold and a minimum relative abundance cutoff of 0.01% were applied, a total of 24 core microbial phyla were identified across both cropping systems (based on replicates from CS and MS). Three of these phyla, Candidatus Bathyarchaeota, Euryarchaeota, and Nitrososphaerota, belong to the Archaea domain. Twenty phyla belong to the Bacteria domain: Acidobacteriota, Actinomycetota, Armatimonadota, Bacillota, Bacteroidota, Candidatus Dormiibacterota, Candidatus Eisenbacteria, Candidatus Rokuibacteriota, Candidatus Tectomicrobia, Chloroflexota, Cyanobacteriota, Deinococcota, Gemmatimonadota, Myxococcota, Nitrospirota, Planctomycetota, Pseudomonadota, Thermodesulfobacteriota, Thermomicrobiota, and Verrucomicrobiota. Only one fungal phylum, Ascomycota, was detected. In the present study, Actinomycetota was the dominant bacterial phylum, accounting for 63.20% and 64.70% of the cowpea-sorghum and maize-sorghum rotations, respectively. This was followed by Pseudomonadota (10.69% for CS and 9.55% for MS), Acidobacteriota (5.81% for CS and 5.68% for MS), Chloroflexota (4.57% for CS and 4.44% for MS), Gemmatimonadota (2.10% for CS and 1.86% for MS), and others, whose abundances were less than 1% (Fig. 2 A). At the genus level, the bacterial communities were primarily composed of Nocardioides (18.50 and 22.40% in CS and MS rotations, respectively), Solirubrobacter (14.31% in CS and 14.24% in MS), Sphingomonas (4.51% in CS and 3.61% in MS), Streptomyces (4.37% in CS and 4.14% in MS), Conexibacter (2.85% in CS and 2.40% in MS), Arthrobacter (2.53% in CS and 2.48% in MS), and Bradyrhizobium (2.48% in CS and 2.07% in MS; Fig. 2 B). Fig. 2. Open in a new tab The soil bacterial community composition at the phylum ( A ) and genus ( B ) levels across the rotations. CS = cowpea-sorghum rotations; MS = maize-sorghum rotations The first four archaeal phyla were Nitrososphaerota (95.10% for CS and 95.68% for MS), Euryarchaeota (3.11% for CS and 2.77% for MS), Candidatus Bathyarchaeota (0.82% for CS and 0.74% for MS), and Thermoproteota (0.41% for CS and 0.34% for MS) (Fig. 3 A). The genera of archaea differed between cowpea-sorghum and maize-sorghum rotations. The relative abundances of Nitrosocosmicus , Nitrosotenuis, Nitrosopumilus, Methanoperedens, Haladaptatus, Methanosarcina, Methanoculleus, Methanothrix, Pyrococcus, Methanobacterium, Halorubrum, and Halomarina in cowpea-sorghum rotations were much greater than those in maize-sorghum rotations, whereas Candidatus Nitrosopolaris, Nitrososphaera, and Nitrosotalea were dramatically greater in maize-sorghum rotations than in cowpea-sorghum rotations (Fig. 3 B). Fig. 3. Open in a new tab The relative composition of soil archaea at the phylum ( A ) and genus ( B ) levels across rotations. CS = cowpea-sorghum rotations; MS = maize-sorghum rotations Ascomycota represented the predominant fungal phylum, with 0.011% for CS and 0.012% for MS, Mucoromycota (0.0027% for CS and 0.0026% for MS), Basidiomycota (0.001% for CS and 0.001% for MS), and Chytridiomycota (0.001% for CS and 0.001% for MS) (Fig. S4A). At the genus level, Aspergillus , Metschnikowia , Rhizopus , Fusarium, and Lizonia were the dominant soil fungi. The abundances of Aspergillus , Metschnikowia , Rhizopus , Glutinoglossum, Hyaloraphidium, Saitozyma, Hyaloscypha, and Pyrenophora in the cowpea-sorghum rotation treatment were significantly greater than those in the maize-sorghum treatment, whereas those of Fusarium, Lizonia, Quaeritorhiza, Rhizoctonia, Friedmanniomyces, and Penicillium were the opposite (Fig. S4B). STAMP analysis revealed that among the top 21 phyla with relative abundances greater than 1%, Pseudomonadota, Acidobacteriota, Chloroflexota, and Gemmatimonadota were enriched in cowpea-sorghum rotations, whereas Actinomycetota and Nitrososphaerota were enriched in maize-sorghum rotations (Fig. 4 A, p < 0.05). Ten genera with relative abundances greater than 1% differed markedly between the cowpea-sorghum and maize-sorghum rotations. Solirubrobacter , Sphingomonadaceae , Streptomyces , Conexibacter , Arthrobacter , Bradyrhizobium , and Baekduia were enriched in cowpea-sorghum rotations. Nocardioides and Blastococcus were enriched in the maize‒sorghum rotations (Fig. 4 B, p < 0.05). Fig. 4. Open in a new tab A: STAMP analysis of the soil bacterial community phyla between the CS and MS rotations. CS = cowpea-sorghum rotations; MS = maize-sorghum rotations. If the dot is blue. the phylum is more abundant in CS and If the dot is orange, the phylum is more abundant in MS. Corrected q-values (FDR-adjusted p-values) indicate statistical significance of the difference, % stands as the proportion of each microbial phylum in the two groups, and the horizontal lines show 95% confidence intervals for the difference. B : STAMP analysis of the soil bacterial community genera between the CS and MS rotations. CS = cowpea-sorghum rotations; MS = maize-sorghum rotations. If the dot is blue. the phylum is more abundant in CS and If the dot is orange, the phylum is more abundant in MS. Corrected q-values (FDR-adjusted p-values) indicate statistical significance of the difference, % stands as the proportion of each microbial phylum in the two groups, and the horizontal lines show 95% confidence intervals for the difference LEfSe was employed to assess which microbiome attributes vary significantly between rotations; enrichment of taxa from the Myxococcaceae family implies that this group may have ecological or functional roles relevant to the environmental conditions of cowpea-sorghum rotations. The enrichment of Intrasporangium sp. and Nocardioides under maize-sorghum rotation-specific conditions may be associated with different microbial functions or environmental factors (Fig. 5 A and Fig. 5 B). Fig. 5. Open in a new tab A and B : Linear discriminant analysis (LDA) effect size (LEfSE) circular diagram of bacterial taxa that differ among rotation treatments. CS = cowpea-sorghum rotations; MS = maize-sorghum rotations. The LDA (log10) scores quantify how strongly each taxon is associated with one group versus the other. The longer the bar, the more that taxon characterizes the group it’s colored for. Each node (circle) in the cladogram represents taxonomic level and the size of the node indicates the relative abundance of that taxon Redundancy analysis revealed significant positive and negative correlations between various microbial phyla and soil properties (Fig. 6 ). Notable positive correlations were found between Actinomycetota and K, Na, P, and N-NO 3, and strong negative correlations were observed with pH, moisture, Mg, Ca, and clay. Positive correlations were observed between Pseudomonadota and pH, moisture, Mg, Ca, silt, and sand and were negatively correlated with K, clay, Na, P, C, and N-NO 3 (Fig. 6 ). Fig. 6. Open in a new tab Redundancy analysis of the microbial community composition at the phylum level in relation to soil properties across both rotations Influences of rotations on drought tolerance genes STAMP analysis revealed that the treS genes encoding trehalose synthase (11.57% in CS and 11.28% in MS) dominated both the cowpea-sorghum and the maize-sorghum rotation treatments (Fig. 7 A). This was followed by murA genes coding for peptidoglycan biosynthesis (9.29% in CS and 9.41% in MS), betB genes coding for betaine aldehyde dehydrogenase (8.38% in CS and 8.34% in MS), and katG genes coding for catalase-peroxidase (7.58% in CS and 7.40% in MS). The predominant drought tolerance genes observed in cowpea-sorghum rotation include treS , otsA , and otsB (genes involved in trehalose biosynthesis) and pssA , wza , wzc , and gumD (genes that facilitate biofilm formation, help retain soil moisture and improve drought resilience). Genes such as sodA , katG , and prx , which mitigate oxidative stress caused by drought (except sodB , katE , or gpx ), rpoS (the general stress sigma factor that supports stationary phase survival), and trpB and ipdC , which are responsible for producing hormones and signaling molecules that enhance plant drought tolerance, are also predominant in cowpea-sorghum rotation. In addition, the nifH , nifD , nifK , gcd , pqqC , and sidA genes, which are involved in nutrient provisioning for plants under drought stress, and desA and desB , which alter membrane fluidity to maintain functionality under stress, were predominant in cowpea-sorghum rotation. Finally, spo0A and spoVF (which enable dormancy or resistance to prolonged drought) and nhaB and kup (except nhaA or trkH ) for ion transport and homeostasis under drought predominated cowpea-sorghum rotation. On the other hand, the key microbial genes involved in proline biosynthesis ( proA , proB , and proC ), glycine betaine synthesis ( betA and betB but not betT ), aquaporin genes ( aqpZ that support water transport), and both murA and murC (which contribute to enhanced structural integrity) were predominant in the maize‒sorghum rotation. Fig. 7. Open in a new tab A: STAMP analysis of genes between CS and MS rotations. CS = cowpea-sorghum rotations; MS = maize-sorghum rotations. B : Redundancy analysis of microbial functional genes involved in drought tolerance with respect to soil properties in both rotations. CS = cowpea-sorghum rotations; MS = maize-sorghum rotations. Axes 1 and 2 contributed a total of 76.29% We further performed RDA to verify the impacts of environmental variables in response to drought on genes involved in drought tolerance (Fig. 7 B). The results indicated that C, Silt, K, N-NO 3 and moisture contributed to the drought tolerance genes in the CS rotations, whereas with the exception of calcium, the other soil properties had a greater influence on the maize-sorghum rotations. Interplay between microbes, functional genes involved in drought tolerance, and soil physicochemical properties in shaping microbial diversity The PLS‒PM diagram reveals complex interactions among microbes, functional genes involved in drought tolerance, and soil physicochemical properties in shaping microbial diversity (Fig. S5). The analysis revealed that functional genes involved in drought tolerance and soil properties are strongly interconnected (weight = 1.67), indicating that they tend to vary closely together. Similarly, the microbiome and soil properties were positively related (weight = 0.86), indicating that shifts in microbial communities are generally associated with changes in soil conditions. In contrast, the link between genes and the microbiome is strongly negative (weight = –0.96), suggesting a trade-off in which an increase in one is often accompanied by a decrease in the other. Discussion Crop rotation has been reported to initiate shifts in the abundance and variety of soil microbiomes with subsequent effects [ 89 ]. Ordination analysis (NMDS) and similarity analysis revealed that legume-based rotations strongly affected the soil microbial community structure. The findings of this study were consistent with those of Yu et al. [ 94 ] and Siczek et al. [ 82 ]. Cowpea rotation impacted the compositional shift of the soil microbiome, which is consistent with the findings of Sánchez-Navarro et al. [ 77 ]. The cowpea rotation scheme could shift the structure of the rhizosphere microbiome via soil physical and chemical parameters, type of crop, or plant residue [ 88 ]. Our findings revealed that Actinomycetota, Pseudomonadota, Acidobacteriota, Chloroflexota, and Gemmatimonadota were the predominant bacterial phyla. This finding supported the findings of Shi et al. [ 81 ] and Babalola and Enagbonma [ 7 ]. The 24-core microbiome recorded in this study suggests that a stable community backbone potentially underpins functional resilience [ 30 ]. The positive correlations between Pseudomonadota and pH, moisture, Mg, Ca, silt, sand, and Actinomycetota with K, Na, P, and N-NO 3 contributed to a marked increase in abundance in the cowpea-sorghum and maize-sorghum treatments. Compared with the maize-sorghum rotation treatment, the cowpea-sorghum rotation treatment increased the relative abundances of Pseudomonadota, Acidobacteriota, Chloroflexota, and Gemmatimonadota; however, the relative abundances of Actinomycetota and Nitrososphaerota were lower than those of the maize-sorghum rotation in the present study. Pseudomonadota, Acidobacteriota, and Gemmatimonadota are prominent phyla in the cowpea rhizosphere [ 19 , 20 ]. This finding supported the findings of the Li et al. [ 54 ] study, which showed that the maize rhizosphere is dominated by Actinobacteria, which contribute largely to organic substance (such as cellulose) decomposition [ 67 ], with their richness in the maize-sorghum treatment linked to the maize residue in the soils. Additionally, root exudates such as isoflavonoids in cowpea may reduce Actinobacteria growth [ 18 , 23 ], which might explain the lower abundances of Actinobacteria in the cowpea-sorghum treatment. The enrichment of Arthrobacter, Sphingomonadaceae, Bradyrhizobium , Streptomyces and Baekduia in the CS rotation compared with the MS rotation occurred because they are crucial in the legume‒rhizosphere relationship. Owing to their role in biological nitrogen fixation [ 93 ], these bacteria ( Arthrobacter, Sphingomonadaceae, Bradyrhizobium , Streptomyces and Baekduia ) have been reported in the cowpea rhizosphere [ 48 ]. Compared with the bacteria and archaea in our study, the fungi had a lower relative abundance, which was greater in MS than in CS. The reference database and annotation process utilized in this study could explain the low fungal abundances detected [ 28 ]. In this study, we extracted DNA from the soil samples by using the Nucleospin Soil Kit (Macherey–Nagel, Germany), a direct extraction method that may favor bacteria over eukaryotes and microeukaryotes. Thus, the relatively low fungal abundance and high bacterial representation may stem from the DNA extraction method and reference database limitations. STAMP analysis revealed that crop rotation influenced the drought tolerance genes profiled in this study. Cowpea-sorghum rotation influences the genes that code for drought tolerance by fostering a dynamic rhizosphere environment that selects for stress-resistant microbial traits [ 10 ]. Cowpea enriches soil with nitrogen through symbiotic nitrogen fixation, promoting microbes (such as Pseudonocardia, Bradyrhizobium, and Methanosarcina, which were more abundant in the CS rotation) harboring the nifH , nifD , and nifK genes [ 21 , 36 ]. Sorghum’s extensive root system and carbon-rich exudates further attract microbes with genes such as pssA, wza, wzc , and gumD , which enhance biofilm formation, aiding in soil moisture retention [ 44 , 73 ]. Additionally, the alternating crop systems create variable nutrient and moisture conditions, selecting for microbes with treS, otsA, otsB (osmoprotection), and stress-mitigation genes such as sodA, katG, prx, and rpoS [ 47 , 80 ]. This rotation synergistically supports microbes that improve drought resilience through nutrient cycling ( gcd, pqqC, sidA ) and dormancy ( spo0A, spoVF ) [ 101 ], likely highlighting the influence of crop-specific root exudates and environmental pressures on microbial community composition and gene expression [ 103 ]. In the maize‒sorghum rotation, key microbial genes involved in drought tolerance were predominantly observed. These genes included proA, proB , and proC (proline biosynthesis), which enable osmotic adjustment and cellular protection under drought stress [ 33 ], and betA and betB (glycine betaine synthesis), which contribute to osmoprotection. The presence of aqpZ , an aquaporin gene, highlights microbial contributions to water transport and retention, which are critical for maintaining hydration. Furthermore, murA and murC , which support peptidoglycan biosynthesis, enhance microbial cell wall structural integrity, increasing resilience under abiotic stress. This gene profile reflects the synergistic selection pressures imposed by the maize‒sorghum rotation on microbial communities. On the other hand, maize-sorghum rotation fosters microbial adaptation, probably due to diverse root exudates, varying nutrient inputs, and environmental pressures [ 40 ]. These conditions select for microbes with genes such as proA, betA, and murA that increase osmotic adjustment, structural integrity, and water transport ( aqpZ ), ensuring microbial survival and improved plant‒microbe synergy under drought stress [ 46 , 99 ]. The differences in certain drought-related genes (e.g., treS , katG , betB ) between the two cropping systems are numerically small (~ 0.3%). While these shifts may tentatively suggest ecological responses to drought, their small magnitude warrants cautious interpretation. Such differences should not be considered definitive evidence of microbial contributions to drought adaptation without further functional validation through approaches such as metatranscriptomics [ 56 , 100 ]. The RDA results revealed that specific environmental variables, such as carbon, silt content, potassium, nitrate nitrogen, and moisture, play a key role in modulating the expression of drought tolerance genes in the CS rotation. These findings suggest that these factors create favorable environments that support the resilience of plants under drought conditions [ 39 ]. The presence of adequate soil moisture, for example, is essential for maintaining hydraulic conductivity and ensuring the activation of drought-responsive pathways [ 12 ]. Similarly, nutrients such as nitrogen and potassium are known to increase osmotic adjustment and enzyme functionality during stress conditions [ 4 , 38 ]. The high silt content likely influences the water retention capacity, further bolstering the plants' ability to mitigate drought effects [ 57 ]. On the other hand, in the maize-sorghum rotation system, pH, Mg, N-NH 4 , clay, sand, P, and Na, but not Ca, significantly contributed to drought tolerance. This broader influence may reflect the varying ecological requirements of maize and sorghum, both of which are adapted to thrive under water-limited conditions [ 59 ]. The limited role of Ca in this rotation could be attributed to its relatively stable presence in the soil and its lesser involvement in stress-signaling pathways than other nutrients. The network analysis highlights the central role of soil properties as a stabilizing factor, maintaining positive associations with both drought tolerance-related genes and the microbiome. However, the opposing relationship between drought tolerance-related genes and the microbiome points to potential functional trade-offs, competition for resources, or divergent adaptive strategies. Taken together, these findings suggest that soil properties may mediate the balance between genetic expression and microbial dynamics within the soil ecosystem [ 69 ]. The RDA illustrates that soil physicochemical properties strongly influence microbial community structure, while the PLS–PM model demonstrates that these microbial taxa mediate the effects of soil conditions on the expression of drought-tolerance genes. In this study, the key limitations are (i) single-season assessment limits long-term ecological stability conclusions, (ii) lack of plant-level physiological or agronomic measurements (e.g., biomass, yield, WUE), (iii) absence of direct functional activity validation (e.g., metatranscriptomics, qPCR, enzyme assays). (iv) no bulk soil control to distinguish root-specific versus field-wide microbial changes (v) no measurement of critical soil physical properties such as hydraulic conductivity, water-holding capacity, and soil texture heterogeneity and (vi) pooling of rhizosphere samples without microhabitat or vertical stratification masking fine-scale variability. Overall, this study employed DNA-based metagenomics to characterize the functional potential of microbial communities from legume-based rotation systems, offering a baseline of gene repertoires associated with drought resilience [ 1 , 61 ]. However, we recommend that future research incorporate direct activity assessments, such as metatranscriptomics, qPCR validation, or soil enzyme assays, to verify these functional contributions [ 17 , 84 ]. Conclusion This study provides a preliminary, exploratory characterization of microbial community and functional gene shifts, without direct evidence of functional impact on plant physiology or productivity. This study revealed that crop type in rotation had unique effects on the soil microbial structure, compositional shift, and genes coding for drought tolerance. This study emphasizes the critical role of crop selection in maintaining the sustainable functions of agroecosystems. A sound understanding of the interfaces of soil, microbiome structure and function, and crop management practices are important for choosing suitable crops to conserve the sustainable development of agriculture. To fully unveil the impact of legume-based rotation on subsequent sorghum rhizosphere microbial communities and their drought tolerance-related genes, future work should involve multi-season longitudinal studies, integration of plant-level agronomic and physiological measurements. application of metatranscriptomics, qPCR, enzyme assays for functional validation, soil physical property measurements (like hydraulic conductivity, water-holding capacity, and inclusion of bulk soil controls and microhabitat stratification. Supplementary Information supplementary material 1 (124.9KB, docx) Acknowledgements N/A Author contributions BJE: Writing—original draft, Formal analysis, Data curation, Investigation. OOB: Conceptualization, Formal analysis, Data curation, Investigation, Supervision, Project Administration, Writing-review & Editing, Resources, REP: Academic input, proofreading, review & editing. DMM: Supervision, Project administration, Writing—review & editing, Resources. Funding This research was supported by the ICGEB Research Project [CRP/ZAF22-03] awarded to O.O.B. Data availability The sequences obtained from this study have been submitted to the Sequence Read Archive (SRA) at the National Center for Biotechnology Information (NCBI) under the assigned bioproject numbers PRJNA1166242 (CS) and PRJNA1166458 (MS). Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Adebayo AA, Enagbonma BJ, Babalola OO. Comparative metagenomics on community structure and diversity of rhizomicrobiome associated with monoculture and soybean precedent carrot. Sci Rep. 2025;15(1):28161. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Agbodjato NA, Babalola OO. 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