Deciphering crop-specific rhizobacteriome assembly in cotton, sorghum, and soybean under hot semi-arid field conditions in Texas

Deciphering crop-specific rhizobacteriome assembly in cotton, sorghum, and soybean under hot semi-arid field conditions in Texas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deciphering crop-specific rhizobacteriome assembly in cotton, sorghum, and soybean under hot semi-arid field conditions in Texas Mostafa Abdelrahman, Sudish Jogaiaha, Mohamed Abdelmoteleb, Mohamed Foker, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5727917/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Different crops may recruit specific rhizosphere microbiomes that support their survival under unfavorable conditions, including hot semi-arid climates. However, the processes driving microbiome assembly within different crops and their adaptation to such extreme environmental conditions remain poorly understood. This study investigates whether upland cotton (Gossypium hirsutum), sorghum ( Sorghum bicolor ), and soybean ( Glycine max ) recruit distinct or overlapping rhizospheric bacterial communities under hot semi-arid conditions in Lubbock, Texas, United States, with a focus on their potential role in enhancing crop resilience. By exploring rhizobacterial recruitment strategies and differential microbial associations in these crops, this study addresses critical gaps in plant-microbiome interactions and paves the way for practical applications in hot semi-arid agricultural systems. Results We found that the abundances and structures of rhizospheric bacterial communities differed among sorghum, soybean, and cotton, with the differences being closely linked to their predicted functional roles in stress adaptation and nutrient assimilation. Alpha and beta diversity analyses revealed that soybean rhizosphere exhibited the highest bacterial richness and diversity followed by cotton. In contrast, sorghum rhizobacteriome showed the lowest richness and less even distribution of rhizobacterial taxa compared with the other two crops, emphasizing crop-specific rhizobacterial associations. Actinobacteriota and Firmicutes phyla were significantly enriched in sorghum rhizosphere, whereas Proteobacteria , Bacteroidota , and Acidobacteriota phyla were significantly enriched in soybean and cotton rhizospeheres under hot semi-arid conditions. Functional prediction analysis demonstrated that sorghum-associated rhizobacteriome was significantly enriched in pathways related to stress adaptation, while soybean and cotton rhizobacteriomes exhibited more diverse pathways, primarily associated with nitrogen and sulfur assimilation. Conclusions These findings underscore the influence of crop-specific factors in shaping rhizobacteriome composition and function to ensure their behavior and performance under hot semi-arid conditions in Lubbock, Texas, United States, with sorghum favoring stress adaptation, soybean being linked to nitrogen and sulfur assimilation, and cotton displaying intermediate traits. Our results highlight the potential for leveraging rhizobacteriome in developing innovative cultivation strategies to enhance crop resilience and productivity under challenging environmental conditions. Bacteriome cotton hot semi-arid environments rhizosphere sorghum soybean Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Soil microbiota play a pivotal role in ecosystem biology by shaping developmental, physiological, and reproductive traits, while driving essential processes, such as nutrient cycling, soil structure formation, hormone synthesis to promote plant growth, and pathogen suppression, all of which are crucial for sustainable agricultural productivity [1, 2]. The diversity and abundance of soil microbial communities are influenced by several environmental factors, including soil pH, oxygen availability, temperatures, moisture, and the quantity and quality of carbon sources [3]. These factors vary across spatial scales, fluctuating from centimeter-level macro-scale to millimeter-level micro-scale within the soil profile [4]. This spatial heterogeneity creates localized hotspots of microbial diversity and activity, with the rhizosphere standing out as a particularly active zone [5]. The rhizosphere, the narrow soil region directly influenced by plant roots and extending about 1 mm from the root surface, hosts a highly diverse microbial communities [6]. The rhizosphere is characterized by significantly higher microbial biomass and activity compared with the surrounding bulk soil, and represents one of the most intriguing and yet poorly understood areas in the field of microbial ecology [7]. This microbial hotspot is shaped by host plant root activities, including nutrient and water uptake and the release of carbon compounds, known as rhizodeposits [8]. Rhizodeposits, with their varying bioavailability, selectively promote the growth of specific microbiota, while inhibiting others, contributing to the dynamic composition and functional diversity of the rhizosphere microbiomes, hereafter referred to as rhizobiomes [7]. Plants rely on their rhizobiomes to facilitate the acquisition of essential nutrients, including minerals that are present in non-bioavailable forms [9]. In turn, plants actively influence the abundance and composition of their rhizobiomes by modifying the soil environment. They adjust soil pH through the release of protons (H⁺) and bicarbonate (HCO₃⁻), reduce competition by releasing antimicrobial compounds that inhibit pathogens while promoting beneficial microbes, and supply carbon-rich rhizodeposits as energy sources to support microbial growth [10]. The composition of microbial communities in plant-associated environments, such as the rhizosphere, varies across plant species and even among genotypes within a species [11]. Increasing evidence points to a complex interplay between host genetic traits and the assembly of associated microbiome. For instance, a study using model systems, such as Arabidopsis thaliana grown under controlled conditions, revealed that plant genotype has a subtle but measurable effect on the rhizobiome composition and abundance [12]. Using broad-sense heritability and genome-wide association studies across 200 sorghum ( Sorghum bicolor ) accessions, several host genetic loci associated with rhizobiome variation have been identified, revealing reproducible associations with specific microbial taxa and highlighting the potential to predict microbiome structure based on the host’s genetics [13]. In soybean ( Glycine max ), rhizospheric bacterial (rhizobacteriome) structures differed between soybean landraces and cultivars based on seed oil content, with seven bacterial families enriched in high-oil cultivars. Among these, Oxalobacteraceae family was specifically assembled by root exudates rich in phenolic acids and flavonoids, impacting auxin signaling, oxidative metabolism, and glycolysis in host plants to promote seed oil accumulation [14]. A study of the rhizobacteriome and rhizospheric fungi (rhizofungiome) of four upland cotton ( Gossypium hirsutum ) and four sea-island cotton ( G. barbadense ) cultivars grown in three different soil types demonstrated that soil origin was the primary factor driving microbial divergence, with cotton genotype serving as a secondary influence [15]. Despite significant progress, the mechanisms governing microbiome acquisition and assembly, along with the host genetic variants that regulate these processes, remain poorly understood. Thus, deciphering the complex interactions between plants and their associated microbiota, as well as identifying the host genetic determinants of these relationships, is critical for advancing plant breeding and biotechnological innovations. In the context of global warming, the frequency and intensity of extreme weather events, including heatwaves and droughts, have significantly increased, leading to a significant reduction in crop yields [16-18]. Over the past decades, numerous studies have explored the impacts of climate change on rhizosphere microbiomes [19]. Nevertheless, knowledge regarding the responses of rhizosphere microbiomes and their functionality under naturally occurring extreme weather events remains limited, despite their importance in forecasting global environmental changes. Texas, known for its diverse and often extreme weather conditions, plays a pivotal role in the United States’ (US) agriculture. The state climate ranges from arid and semi-arid in the west to humid subtropical in the east, with high temperatures and frequent droughts presenting challenges for crop production (www.epa.gov). Texas leads the nation in cotton production, with upland cotton thriving in the dry, hot conditions of the High Plains and the Blackland Prairie. However, many cotton-growing regions in southwest US experienced a significant reduction in cotton yields due to heat stress [20]. Sorghum, another key crop, is well-suited to Texas’s drought-prone areas due to its resilience and low water requirements (www.nrcs.usda.gov). Both crops rely on innovative cultivation practices, such as no-till farming and efficient irrigation systems, to optimize yields, while conserving resources in the face of Texas’ variable climate. In contrast, soybean, as a nitrogen-fixing legume model, is primarily cultivated in the temperate climate of the Midwest US and is less frequently grown in arid regions [21]. We hypothesize that cultivating cotton and sorghum in their native hot semi-arid environments may promote the development and/or enrichment of specialized rhizobiomes that enhance these crops' survival and adaptation to extreme climatic conditions, in contrast to soybean. This study focuses on understanding how these three cropping systems recruit rhizobiomes to ensure their growth and performance under hot semi-arid conditions. Thus, we investigate whether cotton, sorghum, and soybean cultivated under hot semi-arid climatic conditions in Lubbock, Texas recruit distinct rhizobacteriomes that contribute to their behavior and performance in natural field, or whether the rhizobacteriomes remain consistent across these cropping systems. This research aims to identify rhizobacteriomes adapted to hot semi-arid climates, leading to the discovery of rhizobacteria that can be used as biofertilizers to sustainably enhance crop resilience and productivity under challenging climatic conditions. By exploring rhizobacteriome recruitment strategies and differential microbial associations in these crops, this study addresses critical gaps in plant-microbiome interactions and paves the way for practical applications in hot semi-arid agricultural systems. Materials and methods Plant materials and growth conditions Sorghum ‘BTx’, cotton ‘Fibermax’ and soybean ‘William 82’ cultivars were grown in Quaker Ave. research farm operated by Texas Tech University, Lubbock, Texas (33° 41' 36.4596" N, -101° 54' 18.612"W, and 992 m elevation above sea level). The soil at the site is sandy loam in the upper 0–30 cm, containing 0.8% organic matter and a pH of approximately 7.8 [22]. This semi-arid region receives an average annual rainfall of 470 mm, with average evapotranspiration of 1500 mm. During July and August, the farm experienced exceptionally high temperatures, with daily highs ranging from 36.6°C to 40.6°C (www.weather.gov). The farm is mainly cultivated with cotton, sorghum, maize and turfgrass. In this study, each crop was cultivated in a 50-meter plot with 10 rows, with plants spaced 20 cm apart within the rows, following the farm standard cultivation practices. The three crops were separated by 1–2 meters distance between plots. Rhizosphere soil was collected during July and August when all the plants reached their maturity stage. Plants were manually uprooted, and the roots were gently shaken to remove loose soil. The soil remaining attached to the roots was collected as rhizosphere soil in sterilized Falcon tubes stored in an ice box, and promptly transferred to −20°C for storage. For each crop, five biological replicates were analyzed, with each replicate consisting of 3 to 4 individual plants pooled together. All samples were collected from the center of the plot to minimize border effects. Soil microbial DNA was extracted 24 h after sample collection to avoid changes in microbial communities. Preparation, sequencing, and library construction of 16S rRNA gene samples A total of 15 rhizosphere soil samples, collected from sorghum, cotton and soybean plants (3 crops × 5 biological replicates), were subjected to microbial DNA extraction using DNeasy PowerSoil Pro Kit (Qiagen, ML, US). The V4-V5 hypervariable region of the bacterial 16S rRNA gene was amplified with 515F (5’-GTGCCAGCMGCCGCGGTAA-3’) and 907R (5’-CCGTCAATTCCTTTGAGTTT-3’) primers using PCR conditions of 3 minutes at 95°C, followed by 35 cycles of 30 seconds at 95°C, 30 seconds at 55°C, 30 seconds at 72°C, and a final extension at 72°C for 5 minutes. The amplified PCR products were purified with 20 uL AMPure XP magnetic beads per sample (Beckman Coulter Life Sciences, CA, US). Next, purified DNA from each sample was attached with indices and Illumina sequencing adaptors using Nextra XT index kit (Illumina, CA, US), through PCR conditions of 95°C for 3 minutes, followed by 8 cycles of 95°C for 30 seconds, 55°C for 30 seconds, 72°C for 30 seconds, and a final extension at 72°C for 5 minutes. The integrity and size of the DNA fragments in the library were assessed using Fragment Analyzer (AATI, IO, US). Effective concentration of the library was detected by QPCR. The quantified libraries were pooled and sequenced on the Illumina NovaSeq platform at Novogene (Sacramento, CA, USA). 16S rRNA abundance, taxonomy, and statistical evaluation The raw read quality and sequencing depth were assessed using the qiime tools peek function in QIIME2 [23]. Data processing, including denoising, chimera removal, error model and abundance estimation, was systematically performed using the DADA2 plugin in QIIME2. The taxonomy of each amplicon sequence variant (ASV) was assigned using the classifier generated from the Silva 138.1 (www.arb-silva.de/documentation/release-1381/) 16S rRNA bacteria database. Next, sequence alignment was performed using qiime alignment mafft function, followed by the construction of an unrooted phylogenetic tree with qiime phylogeny fasttree . The phylogenetic tree was then rooted using qiime phylogeny midpoint-root function for further analysis. After removing mitochondrial and chloroplast reads, abundance, taxonomy, metadata, and phylogenetic tree objects were merged for statistical analysis. Alpha and beta diversity analyses and visualizations were performed using a range of packages, including ‘phyloseq’, ‘seqinr’, ‘ANCOMBC’, ‘MicrobiomeStat’, ‘microeco’, ‘MASS’, ‘GUniFrac’, ‘ggh4x’, ‘Tax4Fun2’ ‘ggplot2’, and ‘pheatmap’ in R.4.3.2. A false discovery rate-corrected P value ≤ 0.05 was considered indicative of statistical significance. Alpha and beta diversity comparisons among and between different crops were conducted in R.4.3.2using analysis of variance (ANOVA), the Kruskal-Wallis rank sum test, pairwise Wilcoxon rank-sum test, permutational multivariate analysis of variance (PERMANOVA), analysis of similarities (ANOSIM), and beta dispersion tests. Differential microbial abundance (DMA) was determined using ‘DESeq2’ package in R.4.3.2, while significant crop-specific associated microbiomes were identified using Linear Discriminant Analysis Effect Size (LEfSe). Functional predictions were performed using Functional Annotation of Prokaryotic Taxa (FAPROTAX) [24] and PICRUSt2 [25] tools. Significant pathways and principal component analysis (PCA) of PICRUSt2 results were carried out using STAMP (https://beikolab.cs.dal.ca/software/STAMP). Microbial network was generated using trans_network function in ‘microeco’ package in R.4.3.2. Spearman correlation analysis was conducted using the WGCNA package in R 4.3.2, with a filtering threshold of 0.001 and a correlation coefficient cutoff 0.7 to optimize the coefficient threshold. The resulting network object was converted to GEXF format for visualization using Gephi (https://gephi.org/). Results Alpha and beta diversity reveal distinct rhizobacteriome composition across sorghum, cotton, and soybean cropping systems Rarefaction curves show species richness (Chao1 and observed) as a function of sequencing depth across sorghum, cotton, and soybean samples (Figure 1A). The flattening of the curves confirms that the sequencing depth was sufficient to capture the majority of rhizobacteriome present, ensuring comprehensive coverage of the microbial diversity within each sample (Figure 1A). The alpha diversity metrics such as Observed richness, Chao1 richness, and Shannon diversity index revealed significant differences ( p -adjusted ≤ 0.0055, 0.0081 and 0.0032, respectively) among sorghum, cotton, and soybean rhizobacteriome based on the Kruskal-Wallis rank sum test (Table 1). Additionally, pairwise Wilcoxon rank sum test indicated that soybean was significantly different from sorghum and cotton, while no significant difference was observed between sorghum and cotton (Table 1). Soybean rhizobacteriome exhibited the highest observed richness, indicated by higher abundance of rhizobacterial taxa compared with cotton and sorghum (Table1; Figure 1B). Similarly, Chao1 index followed a similar trend, with soybean having the highest richness, indicating that it supports a broader range of both common and rare rhizobacterial taxa (Table1; Figure 1B). Cotton and sorghum exhibited comparable observed and Chao1 richness, with no significant difference between them, but both were significantly lower than soybean (Table 1; Figure 1B). Soybean exhibited the highest Shannon diversity, reflecting its combination of high richness and a relatively even distribution of rhizobacteriome (Table 1; Figure 1B). Cotton exhibited intermediate Shannon diversity, indicating moderate richness with a reasonably balanced community structure, while sorghum displayed the lowest Shannon diversity, suggesting lower richness, less even distribution of rhizobacterial taxa, and potential dominance by specific taxa compared with the other two crops (Table1; Figure 1B). Next, we performed an interaction size analysis to visualize the extent of rhizobacterial interactions that are either shared among the three crops or unique to each individual crop (Figure 1C). Soybean exhibited the largest number of 3168 unique rhizobacterial taxa, followed by cotton with 1375 taxa and sorghum with 1196 taxa (Figure 1C). On the other hand, 415 shared rhizobacterial taxa were identified among sorghum, cotton, and soybean, emphasizing crop-specific microbial associations (Figure 1C). Beta diversity analysis using Bray-Curtis dissimilarity revealed significant differences in rhizobacterial community structure across the three cropping systems (Table 1, Figure 1D and E). The PERMANOVA test using Bray-Curtis dissimilarity method showed that the cropping system, as categorical variable, explained a significant portion of the variation in rhizobacterial community ( R² = 0.81, p -adj = 0.001; Table 1). Additionally, ANOSIM results, based on rank-based comparisons, supported the PERMANOVA findings, revealing significant differences in rhizobacterial composition among sorghum, cotton and soybean, with an R statistic =1, indicating highly distinct rhizobacterial communities influenced predominantly by crop-specific factor (Table 1). On the other hand, the overall beta dispersion test, which evaluates differences in rhizobacteriome variability, did not show significant differences among the three cropping systems ( p ≤ 0.09) (Table 1). Nevertheless, beta dispersion pairwise comparison of ‘sorghum vs soybean’ exhibited significant differences (Table 1). Non-metric multidimensional scaling (NMDS) revealed distinct clusters of rhizobacterial variability across sorghum, cotton, and soybean, indicating highly differentiated rhizobacterial communities (Figure 1E). The low stress value of 0.000088 indicates a reliable representation of the data (Figure 1E). NMDS of dominant taxa at phyla level indicated that Proteobacteria , Firmicutes , and Actinobacteria phyla showed distinct clustering patterns (Figure 1F). Sorghum appears enriched with Proteobacteria and Firmicutes phyla, while cotton and soybean harbor unique distributions of Actinobacteria (Figure 1F). These findings suggest crop-specific recruitment of rhizobacterial taxa, potentially influenced by root exudate profiles or environmental adaptations. Table 1. Statistical analysis of alpha and beta diversity metrics across sorghum, cotton, and soybean rhizobacterial communities. Cropping Observe Mean ± SD Chao1 Mean ± SD Shannon Mean ± SD Sorghum 946.4 ± 42.6 1012.8 ± 47.9 5.3 ± 0.06 Cotton 999.2 ± 20.1 1032.1 ± 30.7 5.8 ± 0.04 Soybean 1537.8 ± 124.3 1626.3 ± 191.7 6.5 ± 0.08 Kruskal-Wallis rank sum test P -adj P -adj P -adj Sorghum-Cotton-Soybean 0.0055 ** 0.0081 ** 0.0032 ** Wilcoxon rank sum test P -adj P -adj P -adj Sorghum vs Cotton 0.1003 ns 0.5476 ns 0.0108 * Sorghum vs Soybean 0.0108 * 0.0108 * 0.0108 * Soybean vs Cotton 0.0137 * 0.0108 * 0.0108 * PERMANOVA (Bray-Curtis) Sums of Sqs R 2 p -adj Sorghum-Cotton-Soybean 2.1083927 0.8097167 0.001 ** Sorghum vs Cotton 0.7981479 0.014 * Sorghum vs Soybean 0.7916142 0.014 * Soybean vs Cotton 0.6888733 0.014 * ANOSIM Permutations R p -adj ANOSIM for all groups 999 1 0.001 ** Sorghum vs Cotton 1 0.009 ** Sorghum vs Soybean 1 0.015 * Soybean vs Cotton 1 0.009 ** Beta dispersion Sum of Sqs Permutations Pr(>F) 0.016867 999 0.09 ns Observed p -value Permuted p -value Sorghum vs Cotton 0.075 0.095 Sorghum vs Soybean 0.046 0.050 Soybean vs Cotton 0.372 0.365 Taxonomic insights revealed crop-specific rhizobacterial taxa in sorghum, cotton, and soybean Alpha diversity metrics are presented as mean ± standard deviation (SD), with significance determined using the Kruskal-Wallis rank sum test followed by pairwise Wilcoxon rank sum tests. Significant p -values are indicated as follows: ** p ≤ 0.01, * p ≤0.05, and ns = not significant. Sum of Sqs, sum of squares; R 2 , coefficient of determination; R , statistic for rank similarity. To gain deeper insights into rhizobacterial taxonomy across the investigated crops, we assessed the relative abundance of rhizobacteriome at multiple taxonomic levels (Figure 2A-D). A total of 32 distinct phyla were identified across the three cropping systems, and their relative abundances in each crop are summarized in Table S1. The relative abundance and distribution of top 10 rhizobacterial phyla in sorghum, cotton, and soybean are visualized in a ternary plot (Figure 2A). The most relative abundant phyla across all cropping systems, in descending order, were Actinobacteriota , followed by Proteobacteria , Bacteroidota , Chloroflexi , Firmicutes , Acidobacteriota , Planctomycetota , Verrucomicrobiota , Gemmatimonadota , and Myxococcota (Figure 2A, Table S1). The Acidobacteriota phylum showed the highest relative abundance in sorghum rhizospheres, followed by cotton and soybean (Figure 2A, Table S1). In contrast, the Proteobacteria phylum displayed greater relative abundance in soybean rhizospheres compared with sorghum and cotton (Figure 2A, Table S1). Similarly, Bacteroidota exhibited elevated relative abundance in soybean, whereas Firmicutes were more abundant in sorghum rhizospheres, highlighting crop-specific rhizobacterial associations (Figure 2A, Table S1). Within Actinobacteria phylum, the dominant class was Actinobacteria , followed by Acidimicrobiia , Thermoleophilia , and Rubrobacteria across the three crops (Figure 2B). The Actinobacteria class showed higher relative abundance in sorghum, followed by cotton and soybean (Figure 2B). In contrast, the Acidimicrobiia class showed higher relative abundance in cotton compared with sorghum and soybean (Figure 2B). Within the Proteobacteria phylum, Alphaproteobacteria and Gammaproteobacteria were the dominant classes, showing the highest relative abundance in soybean, followed by cotton, with the lowest levels observed in sorghum (Figure 2B). Within the Bacteroidota phylum, the Bacteroidia class was detected across all crops, exhibiting the highest relative abundance in soybean, followed by cotton and sorghum (Figure 2B). Within the Actinobacteriota phylum, Micrococcales and Streptomycetales emerged as the most dominant orders, particularly enriched in sorghum and cotton, whereas soybean displayed relatively lower abundance (Figure 2C). Additionally, the Microtrichales order exhibited higher enrichment in cotton compared with sorghum and soybean (Figure 2B). Within the Proteobacteria phylum, Rhizobiales and Burkholderiales were the predominant orders across all crops (Figure 2B). Rhizobiales showed higher relative abundance in soybean, followed by cotton and sorghum, reflecting its importance in nitrogen-fixing processes in legume crops (Figure 2B). Burkholderiales also exhibited a similar trend, being most abundant in soybean and less in cotton and sorghum (Figure 2B). In the Bacteroidota phylum, the dominant orders were Cytophagales and Chitinophagales , with higher abundance observed in soybean and lower abundance in cotton and sorghum. (Figure 2B). Within the Firmicutes phylum, Bacillales emerged as the key order, predominantly enriched in sorghum compared with cotton and soybean (Figure 2B). -*-+--Micrococcaceae , Streptomycetaceae , and Nocardioidaceae families, were predominantly enriched in sorghum rhizospheres compared with cotton and soybean (Figure 2C). Illumatobacteraceae , Pseudonocardiaceae and Sphingomonadaceae families were predominantly enriched in cotton rhizospheres compared with soybean and sorghum (Figure 2C). On the other hand, soybean rhizosphere was enriched with Rhizobiaceae , Microscillaceae and Chitinophagaceae families (Figure 2C). At genus level, Streptomyces , Pseudarthrobacter and Nocardioides were predominantly enriched in sorghum, whereas CL500-29 (unclassified), Illumatobacter and Pseudonocardia were enriched in cotton. Meanwhile, Ensifer, Steroidobacter, and Allo/NeoRhizobium genera were specifically enriched in soybean (Figure 2C). The remaining taxa and their relative abundances across all cropping systems are illustrated in Figure 2A-C. The above results highlight the distinct rhizobacterial recruitment strategies driven by each crop, which may reflect functional adaptations to their respective cropping systems. Distinct rhizobacteriome, phylogenetic relationships, and differential microbial abundance across sorghum, cotton, and soybean rhizospheres Pairwise correlations using scatterplots of log-transformed rhizobacterial abundance highlight increasing correlation coefficients in ‘sorghum vs soybean ( r = 0.38)’, ‘cotton vs soybean ( r = 0.43),’ and ‘sorghum vs cotton ( r = 0.57)’ suggesting progressively greater similarity in rhizobacterial composition (Figure 3A). The higher correlation between cotton and sorghum indicates shared rhizobacterial taxa, likely driven by comparable ecological niches. In contrast, the lower correlation between sorghum and soybean (Figure 3A), reflects distinct rhizobacterial community structure, potentially influenced by soybean leguminous nature. The relative abundance heatmap, combined with LEfSe-derived LDA scores, highlights crop-specific microbial association and significant taxa that differentiate each cropping system (Table S2). LEfSe-derived LDA scores indicated that Actinobacteriota and Firmicutes phyla, Actinobacteria and Bacilli classes, Micrococcales , Propionibacteriales , and Streptomycetales orders, Nocardiaceae and Streptomycetaceae families, as well as Streptomyces , Pseudarthrobacter , and Nocardiodes genera were significantly enriched in sorghum rhizosphere (Figure 3B). In turn, the Acidimicrobiia class, Microtrichales order, Illumatobacteraceae family, and CL500-19 (unclassified) and Illumatobacter genera were significantly associated with the cotton rhizosphere (Figure 3B). With respect to the soybean rhizobacteriome, Proteobacteria , Bacteroidota , and Acidobacteriota phyla; Alphaproteobacteria , Gammaproteobacteria , and Bacteroidia classes; Rhizobiales , Cytophagales , and Burkholderiales orders; as well as Rhizobiaceae and Microscillaceae families were significantly enriched (Figure 3B). To further explore the phylogenetic relationships among the rhizobacterial taxa and their significant associations with different cropping systems, the phylogenetic cladogram illustrates the evolutionary relationships and differential abundances of rhizobacterial taxa within the rhizospheres of sorghum, cotton, and soybean (Figure 3C). Branch lengths and clustering patterns within the cladogram highlight evolutionary divergence and conservation among taxa, while differential abundance is indicated by unique color and marker sizes corresponding to specific cropping systems (Figure 3C). Sorghum-associated taxa were predominantly from the Actinobacteriota phylum, specifically within Streptomycetaceae , Streptomycetales and Streptomyces were phylogenetically grouped in closely related lineages with Nocardioidaceae and Nocardioides (Figure 3C). The enriched lineages, such as CL500 , Illumatobacter and Microtrichales in cotton rhizospheres are positioned on the cladogram diverging from other Actinobacteriota- associated taxa, highlighting their evolutionary adaptation to the soil conditions of cotton cultivation. In contrast, soybean-associated taxa were predominantly represented by Proteobacteria , with a notable abundance of nitrogen-fixing groups like Rhizobiales . The family Rhizobiaceae was phylogenetically clustered within the Alphaproteobacteria lineage, reflecting its specialized symbiotic association with leguminous crops such as soybean (Figure 3C). The volcano plots were used to identify DMA across pairwise comparisons of sorghum, soybean, and cotton rhizospheres, using enriched (log 2 fold-change ≥ 1; p -value ≤ 0.05) and depleted (log 2 fold-change ≤ -1; p -value ≤ 0.05) cutoff (Figure 3D; Tables S3-5). In the ‘sorghum vs. soybean’ comparison, 106 rhizobacterial taxa were enriched, while 236 rhizobacterial taxa were depleted (Figure 3D). In ‘soybean vs. cotton’ comparison, 183 and 69 rhizobacterial taxa were significantly enriched and depleted, respectively (Figure 3D). In ‘cotton vs sorghum’ comparison, 70 and 56 rhizobacterial taxa were significantly enriched and depleted, respectively (Figure 3D). Our results demonstrated greater DMA in the ‘sorghum vs soybean’ comparison, whereas the ‘cotton vs sorghum’ comparison showed lower DMA (Figure 3D). These findings underscore distinct rhizobacterial associations among these crops, with the soybean rhizosphere exhibiting the most unique microbial assemblage and significant differences compared both sorghum and cotton. Functional prediction and microbial network of sorghum, soybean and cotton rhizobacteriome We conducted functional predictions using FAPROTAX and PICRUSt2 to unravel the functional roles of the rhizobacteriome associated with sorghum, cotton, and soybean (Figure 4 A and B; Table S6 and Figure S1). FABROTAX functional predictions revealed that sorghum-associated rhizobacterial pathways were significantly enriched in processes such as ‘fermentation,’ ‘manganese_oxidation’, ‘xylanolysis’, and ‘cellulolysis’ (Figure S1). In contrast, soybean-associated rhizobiomes exhibited significant enrichment in pathways related to ‘nitrogen_fixation’, ‘dark_hydrogen_oxidation’, ‘methylotrophy’, and ‘methanolotrophy’ (Figure S1). Cotton-associated rhizobacteriome pathways exhibited intermediate characteristics between those of sorghum and soybean, with significant enrichment in ‘nitrogen_fixation’ and ‘methalotrohy’ pathways compared with sorghum, and ‘fermentation’, ‘xylanolysis’, and ‘cellulolysis’ compared with soybean (Figure S1). PCA revealed distinct clustering patterns of functional pathways predicted through PICRUSt2, demonstrating crop-specific variations in rhizobacterial functional potential (Figure 4A). PC1 explains the largest proportion of variation (79.9%), followed by PC2 (18.5%) and PC3 (0.8%) (Figure 4A). Sorghum form a separate cluster from soybean and cotton, highlighting crop-specific functional traits (Figure 4A). Key pathways significantly enriched in sorghum rhizobacteriome include ‘Adenosine nucleotides degradation II’, ‘TCA cycle IV (2-oxoglutrate decarboxylase)’, ‘Superpathway for glycolysis’, ‘Peptidoglycan biosynthesis V’, ‘Superpathway of sulfur oxidation’, ‘, ‘Superpathway of polyamine biosynthesis’, ‘ myo -inositol degradation’, ‘Arginine, ornithine, and proline interconversion’, ‘Superpathway of S -adenosyl-methionine biosynthesis’, and ‘Teichoic acid (polyglycerol) biosynthesis’, among others, highlighting the specific roles of sorghum rhizobacteriome in stress adaption (Figure 4B; Table S6). In contrast, soybean rhizosphere samples display a more dispersed distribution, reflecting functional diversity likely linked to ‘ cis - Vaccenate biosynthesis’, ‘Gondoate biosynthesis’, ‘Fatty acid elongation-saturated’, ‘Nitrate reduction VI (Assimilatory)’, ‘NAD biosynthesis’, and ‘Sulfate reduction I (Assimilatory)’, and other symbiotic processes facilitated by its leguminous nature (Figure 4B; Table S6). Cotton rhizobacteriome falls between sorghum and soybean in functional clustering, suggesting shared pathways with both crops while maintaining distinct profiles related to stress tolerance and assimilation (Figure 4B; Table S6). The microbial network analysis provides a holistic view of the co-occurrence relationships among rhizobacteriome across sorghum, cotton, and soybean. The network, built from significant correlations ( p ≤ 0.01) correlation coefficient ( r = 0.7), offers insights into microbial interactions such as cooperation, competition, and niche overlap (Figure 4C). Proteobacteria and Actinobacteriota phyla dominate the network, highlighting their functional importance in all three cropping systems (Figure 4C). Bacteroidota and Acidobacteriota contribute to the network as secondary players, whereas Planctomycetota , represented by green nodes, exhibits distinct clustering, suggesting its specialized role and niche differentiation within the rhizosphere microbial community. In suammry, the functional predictions using FAPROTAX and PICRUSt2 revealed crop-specific variations in rhizobacterial functional roles, with sorghum enriched in stress-adaptation pathways, soybean in nitrogen fixation and symbiotic processes, and cotton displaying intermediate characteristics. Microbial network analysis further highlighted Proteobacteria and Actinobacteriota as dominant contributors across all cropping systems and probably play important roles in rhizobacterial community stability under hot semi-arid conditions. Discussion For millennia, farmers have improved agriculture by steering the evolution of crops, selectively saving seeds from plants that demonstrated resilience to both biotic and abiotic challenges, grew quickly, or produced the largest yields [26]. However, as climate change rapidly alters agricultural environments and promotes the spread of diseases, conventional breeding techniques struggle to keep pace with these changes. On the other hand, the emergence of microbiome strategy is expected to yield results much more rapidly [27]. Researchers and farmers have long acknowledged the benefits that microbes offer to crop production, such as the application of rhizobia in fields to facilitate nitrogen fixation, [28], and arbuscular mycorrhizal fungi to assist plants in absorbing various nutrients [29]. Typically, these and other microbial applications depend on the use of one microbe. However, microbial ecologist recently proposed that a varied assortment of microbiota may improve the functional capabilities of microbial community members [30]. This diversity can offer mutual assistance, aid in survival on plant surfaces or roots, and enhance competition against existing microbial communities in agricultural environments [31]. For instance, the rise in Actinobacteria populations during drought conditions was associated with enhanced sorghum root colonization, which positively increased sorghum root growth and overall growth performance under drought stress conditions [32]. The microbiome, which includes both the microbiota and their genetic material, forms intricate and dynamic relationships with the host plant [33]. These relationships are significantly shaped by environmental factors and can enhance the plant resilience to various environmental stresses [34]. Several laboratory and greenhouse experiments demonstrated that host genetics can influence microbial assembly in the rhizosphere [35]. This raises the question of whether these host genetic influences can be observed in natural environments, where organisms simultaneously encounter both abiotic and biotic stressors. Thus, this research aims to explore the interactions between three different cropping systems and their rhizobacteriome assembly under hot semi-arid environments. Specifically, we examine whether cotton, sorghum, and soybean, when cultivated in the hot semi-arid climate of Lubbock, Texas, attract different rhizobacteriome that aid in their survival during field cultivation, or if the rhizobacteriome remain uniform across these crops. The goal of this study is to identify rhizobacteriome that are well-suited to hot semi-arid climates, with the intention of revealing the potential of these rhizobacteriome as biofertilizers to sustainably improve crop resilience in challenging climatic conditions. Alpha and beta diversity analyses revealed distinct rhizobacteriome structures across sorghum, cotton, and soybean (Figure 1B; Table 1). Additionally, Bray-Curtis dissimilarity highlighted crop-specific microbial communities, with PERMANOVA and ANOSIM indicating significant differences driven by crop type (Figure 1D; Table 1). These results underscore the role of crop-specific factors in shaping rhizobacterial diversity and composition. Alpha diversity metrics indicated that soybean had significantly higher richness and diversity compared with cotton and sorghum (Figure 1B; Table 1). Soybean, being a legume, supports broad range of unique rhizobacteriome due to its ability to establish symbiotic relationships with nitrogen-fixing bacteria, particularly rhizobia [36]. Beyond nitrogen-fixing bacteria, the symbiosis facilitated by specialized soybean root nodules creates an enriched niche that supports a diverse array of rhizobacterial species [37]. This environment fosters complex and mutually beneficial interactions between the plant and its associated microbial community, which can partially explain the high richness and diversity observed in soybean rhizobacteriome (Figure 1B; Table 1). On the other hand, sorghum had the lowest diversity, potentially due to the dominance of specific taxa (Figure 1B; Table 1). The low richness and diversity in sorghum can be partially explained by the nature of sorghum, as a C4 grass known for its remarkable adaptation to hot semi-arid environments, a feat partly attributed to its unique root exudates, particularly the sorgoleone [38]. Sorgoleone delayed the establishment of bacterial and archaeal networks during the early stages of plant development. Additionally, Sorgoleone has been shown to selectively inhibit or promote the growth of specific bacterial isolates [38]. Sorghum rhizobacteriome exhibited significant enrichment in Actinobacteriota and Firmicutes phyla, and significant depletion in Proteobacteria , Bacteroidota , and Acidobacteriota phyla under hot semi-arid conditions (Figures 2B and 3B). In contrast, soybean exhibited significant enrichment in Proteobacteria , Bacteroidota , and Acidobacteriota and depletion in Actinobacteriota and Firmicutes under hot semi-arid conditions (Figures 2B and 3B). Cotton exhibited intermediate abundance compared with sorghum and soybean, with higher enrichment in the Acidimicrobiia class and Microtrichales order (Figures 2B and 3B). Our results align with [32], showing significant shifts in the relative abundance of dominant phyla such as Actinobacteria , Firmicutes , and Proteobacteria in the sorghum rhizosphere under drought conditions. Drought delays the early development of the sorghum root microbiome, increasing the abundance of monoderm bacteria such as Actinobacteria and Firmicutes phyla with thick cell walls, while reducing the abundance of diderm bacteria like Proteobacteria [32]. These changes were associated with altered plant metabolism and increased activity of bacterial ABC transporter genes under drought conditions [32]. The differences in rhizobacterial taxa among soybean, cotton, and sorghum may reflect their distinct functional roles in crop stress adaptation. sorghum, as a crop adapted to hot and semi-arid environments [39], recruits specialized rhizobacterial taxa such as Streptomyces and Pseudarthrobacter genera, that play critical roles in enhancing drought stress resilience, distinguishing its rhizobiome composition from that of soybean and cotton (Figures 2C and 3B). Streptomyces employ various mechanisms, including phytohormone modulation, enhancement of antioxidant enzyme activity, improved water and nutrient uptake, and other strategies to mitigate water deficit stress in crops [40]. The Pseudarthrobacter genus, a well-known plant growth-promoting microorganism, is characterized by high indole acetic acid production and antibacterial activity, which positively influence plant growth and enhance antioxidant activity [41]. In contrast, the leguminous nature of soybean likely provides an advantage in recruiting rhizobacterial taxa such as Burkholderiales order and Ensifer and Rhizobium genera (Figures 2C and 3B), specialized in nitrogen fixation reflecting its symbiotic relationships and unique functional adaptations [42]. The enrichment and depletion of specialized rhizobacterial taxa was consistence with their functional analysis in the different crops. For example, FABROTAX and PICRUSt2 pathway prediction analyses revealed higher activity in ‘xylanolysis’, and ‘cellulolysis’, ‘adenosine nucleotides degradation II’, ‘TCA cycle IV (2-oxoglutrate decarboxylase)’, ‘peptidoglycan biosynthesis V’, ‘superpathway of sulfur oxidation’, ‘, ‘superpathway of polyamine biosynthesis’, ‘ myo -inositol degradation’, ‘arginine, ornithine, and proline interconversion’, ‘superpathway of S -adenosyl-methionine biosynthesis’, and ‘teichoic acid (polyglycerol) biosynthesis’ in sorghum (Figure 4B). Xylanolysis and cellulolysis microbial activities aid sorghum by breaking down complex plant polysaccharides, improving soil nutrient availability, and recycling organic matter into accessible nutrients [43]. Similarly, bacterial activity in the adenosine nucleotide degradation pathway supports sorghum’s energy metabolism and stress recovery by salvaging nucleotides essential for cellular repair and stress responses [44]. Peptidoglycan and teichoic acid biosynthesis pathways are associated with strengthening bacterial cell walls, enabling rhizobacteria to withstand environmental stress and support plant health through robust microbial colonization [45]. Polyamine biosynthesis and myo -inositol degradation pathways enable bacteria to contribute to osmotic adjustment, stabilize cellular structures, and facilitate signaling under drought stress [46]. Additionally, the arginine, ornithine, and proline interconversion rhizobacterial activity enhances sorghum’s drought resilience by producing or metabolizing osmoprotectants critical during abiotic stresses [47]. The high bacterial activity in the S -adenosyl-methionine biosynthesis pathway, a precursor for ethylene and polyamine production, supports sorghum by enhancing stress signaling and adaptation mechanism [48]. Overall, the enrichment of specialized rhizobacterial communities underscores the critical role of rhizobacteria in supporting sorghum's adaptation to environmental stress. Under hot semi-arid conditions, soybean activates several key enzymatic pathways, contributing to nodulation efficiency and nutrient assimilation. Nitrate reduction pathway converts nitrate into ammonium, which is critical for nitrogen assimilation [49]. Sulfate reduction is essential for the biosynthesis of sulfur-containing amino acids and coenzymes required for nodule function. Thus, enhanced sulfate reduction ensures the availability of sulfur for nodulation processes and supports the synthesis of leghemoglobin, which facilitates oxygen transport within nodules [50]. Nitrifier-denitrification pathway plays a key role in regulating nitrogen cycling and reducing nitrogen loss from the soil, thereby increasing nitrogen availability for nodulating bacteria and promoting effective symbiosis [51]. Palmitate contributes to the formation of lipids essential for membrane stability under stress conditions [52]. NAD is vital for redox reactions and energy transfer during nitrogen fixation in nodules [53]. Enhanced NAD biosynthesis supports the metabolic processes required for nodulation and nitrogen assimilation. Soybean leverages its leguminous nature to activate key enzymatic pathways, such as nitrate reduction, sulfate reduction, and nitrifier-denitrification, which enhance nitrogen and sulfur assimilation while promoting effective symbiosis with nodulating bacteria. These pathways, along with increased NAD biosynthesis and palmitate production, support nodulation efficiency, nutrient acquisition, and stress resilience, ensuring soybean's adaptability and productivity in challenging environments. Conclusions This study highlights the pivotal role of crop-specific rhizobacterial communities in shaping the resilience of sorghum, cotton, and soybean under hot semi-arid conditions in Lubbock, Texas. The rhizobacteriomes of these crops demonstrated distinct taxonomic and functional profiles, reflecting their unique adaptations to environmental stress and nutrient assimilation requirements. Specifically, Actinobacteriota and Firmicutes phyla were significantly enriched in sorghum rhizosphere, whereas Proteobacteria , Bacteroidota , and Acidobacteriota phyla were significantly enriched in soybean and cotton rhizospeheres under hot semi-arid conditions. Soybean exhibited the highest microbial richness and diversity, attributed to its leguminous nature and symbiotic relationships with nitrogen-fixing bacteria, such as Ensifer and Rhizobium . These associations enhanced pathways related to nitrogen and sulfur assimilation, contributing to improved nodulation and nutrient acquisition. Cotton displayed intermediate traits, with microbial communities enriched in pathways supporting both stress adaptation and nutrient cycling. In contrast, sorghum recruited specialized rhizobacterial taxa, including Streptomyces and Pseudarthrobacter genera, which play critical roles in drought resilience by modulating phytohormones, enhancing antioxidant activity, and improving nutrient uptake. By elucidating the interplay between crops and their rhizobacteriomes, this study provides valuable insights into the potential of leveraging rhizobacterial communities as biostimulants to enhance crop resilience and sustainability in challenging climatic conditions. As a future direction, we aim to conduct a large-scale germplasm collection for sorghum and isolate Streptomyces and other stress-associated microbial taxa from the sorghum rhizosphere to develop a biofertilizer consortium. Additionally, we will focus on unraveling the genetic and biochemical mechanisms underpinning these plant-microbe interactions to develop targeted microbial solutions for sustainable agriculture. Declarations Authors’ contributions MA and LSPT designed research; MA collected rhizosphere soil materials; MF performed library preparation; MA performed the research and analyzed data; MA, SJ, Mohamed A, MF, HTN, and LSPT wrote the main manuscript text. All authors read and approved the final manuscript. Data availability All raw sequencing data has been successfully submitted to the Sequence Read Archive (SRA) under NCBI BioProject ID PRJNA1203912. All data supporting the findings of the present study are available within the paper and in the Supplementary Information . Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Fierer, N., Embracing the unknown: disentangling the complexities of the soil microbiome. Nature Reviews Microbiology, 2017. 15 (10): p. 579-590. Coban, O., G.B. De Deyn, and M. van der Ploeg, Soil microbiota as game-changers in restoration of degraded lands. Science, 2022. 375 (6584): p. abe0725. Domeignoz-Horta, L., et al., Microbial diversity drives carbon use efficiency in a model soil. Nat Commun 11: 3684 . 2020. 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Frontiers in plant science, 2018. 9 : p. 1434. Klimasmith, I.M. and A.D. Kent, Micromanaging the nitrogen cycle in agroecosystems. Trends in microbiology, 2022. 30 (11): p. 1045-1055. Sharma, P., et al., Drought and heat stress mediated activation of lipid signaling in plants: a critical review. Frontiers in Plant Science, 2023. 14 : p. 1216835. Baslam, M., et al., Recent advances in carbon and nitrogen metabolism in C3 plants. International Journal of Molecular Sciences, 2020. 22 (1): p. 318. Additional Declarations No competing interests reported. Supplementary Files TableS1.csv FigureS1.docx TableS3.csv TableS2.csv TableS6.tsv.csv TableS5.csv TableS4.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5727917","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":395831296,"identity":"ccb1bf48-c0b2-44c7-aa5c-909da9627ff3","order_by":0,"name":"Mostafa Abdelrahman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYJACZgY2Bhl+GJtoLTySbcykajE4RqwW3fb2h58Lymx4jO/3H5NgqLBObCCkxezMGWPpGefSeMyOMbNJMJxJJ0LLjRwGad62wxAtjG2HidGS/vg3b9t/HuM2kJZ/RGlJMAPacoDHgA2kpYEYLWfOmFnPOJfMI3Es2dgi4Vi6MWEtx9sf3y4os5Pjbz748MaHGmtZglpQQQJpykfBKBgFo2AU4AIAtEk38/dcx+0AAAAASUVORK5CYII=","orcid":"","institution":"Texas Tech University","correspondingAuthor":true,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Abdelrahman","suffix":""},{"id":395831297,"identity":"9dbf7c4f-9535-4da7-895b-d6e5c90208fc","order_by":1,"name":"Sudish Jogaiaha","email":"","orcid":"","institution":"Central University of Kerala, Kasaragod (DT)","correspondingAuthor":false,"prefix":"","firstName":"Sudish","middleName":"","lastName":"Jogaiaha","suffix":""},{"id":395831299,"identity":"d97ca8a3-ae01-4ce8-b2a2-c8557e5942b0","order_by":2,"name":"Mohamed Abdelmoteleb","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Abdelmoteleb","suffix":""},{"id":395831301,"identity":"0ce6a8e0-1c81-4c42-a0f9-e8cec048ddab","order_by":3,"name":"Mohamed Foker","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Foker","suffix":""},{"id":395831303,"identity":"308dd8b9-1e01-4702-a2e4-f615d97d0dd3","order_by":4,"name":"Henry T. Nguyen","email":"","orcid":"","institution":"University of Missouri","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"T.","lastName":"Nguyen","suffix":""},{"id":395831304,"identity":"940e0f1f-7afe-4c2b-87b9-979773844b75","order_by":5,"name":"Lam-Son Phan Tran","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Lam-Son","middleName":"Phan","lastName":"Tran","suffix":""}],"badges":[],"createdAt":"2024-12-28 23:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5727917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5727917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72735322,"identity":"ae9608d2-ab37-41f0-b695-ffe782fa5e73","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10098247,"visible":true,"origin":"","legend":"\u003cp\u003eComparative alpha and beta diversity metrices as well as the evaluation of shared and unique rhizobacterial communities in sorghum, cotton, and soybean. (A) Rarefaction curves displaying species richness (Observed and Chao1 indices) as a function of sequencing depth and count of rhizobacterial species. (B) Boxplots of alpha diversity indices, including Observed and Chao1 richness, and Shannon diversity index. Letters indicate statistical significance at \u003cem\u003ep\u003c/em\u003e ≤ 0.05, using ANOVA and Dunn’s post hoc test. (C) Upset plot illustrating the number of shared and unique rhizobacterial taxa. (D) Violin plots showing Bray-Curtis dissimilarity among rhizobacterial communities associated with sorghum, cotton, and soybean. Significant differences between all pairwise comparisons (**\u003cem\u003ep \u003c/em\u003e≤ 0.01, *\u003cem\u003ep\u003c/em\u003e ≤ 0.05) were identified using permutational multivariate analysis of variance (PERMANOVA) test. (E) Non-metric multidimensional scaling (NMDS) plot based on Bray-Curtis dissimilarity, showing distinct clustering of rhizobacterial communities. (F) NMDS ordination of microbial taxa colored by dominant phyla.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/1678dd2eb0d7910f75ffac96.png"},{"id":72735332,"identity":"a065865d-f92d-432a-a3b5-651a22b4550f","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8511619,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomy and relative abundance (%) of rhizobacterial taxa in the rhizospheres of sorghum, cotton, and soybean. (A) The ternary plot illustrates the relative abundance and distribution of the top 10 rhizobacterial phyla across the three cropping systems. Each point represents a phylum, and its position indicates the proportional contribution to the rhizobacterial community within each crop. The size of the points corresponds to the relative abundance of the phylum. (B) Stacked bar plots display the relative abundance of rhizobacteriome at the class and order levels. (C) The heatmaps illustrate the relative abundance (RA%) of rhizobacteriome within each crop at family and genus levels.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/da11ae61b5dbc78f0976316a.png"},{"id":72735508,"identity":"d9c81305-23ee-4c93-8c6b-f5964394cadb","added_by":"auto","created_at":"2025-01-01 08:19:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7302216,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of differential microbial abundance and phylogenetic relationships across sorghum, cotton, and soybean rhizobacteriome. (A) Scatterplots show pairwise correlations of log\u003csub\u003e10\u003c/sub\u003e-transformed rhizobacterial abundance in different crop comparisons (B) Heatmap of relative abundances (RA) and linear discriminant analysis effect size (LEfSe) derived scores of significant rhizobacterial taxa enriched in the rhizospheres of sorghum, cotton, and soybean. (C) Phylogenetic cladogram illustrates the microbial taxa present in the rhizospheres of the three crops and highlights taxa with significant differential enrichment. The outer rings represent specific microbial taxa, such as phylum (p), class (c), order (o), family (f), and genus (g). Branch lengths and clustering patterns reflect evolutionary relationships, while marker sizes represent differential abundance. Green, blue, and orange segments indicate taxa enriched in sorghum, soybean, and cotton rhizospheres, respectively. (D) Volcano plots illustrate the differential rhizobacterial abundance [log\u003csub\u003e2 \u003c/sub\u003e(fold-changes) ≥ 1 and ≤ −1; \u003cem\u003eq\u003c/em\u003e-values ≤ 0.05] in pairwise comparisons among sorghum, cotton, and soybean, highlighting the significant enriched (red) or depleted (blue) rhizobacterial taxa. The shame and color of the rhizobacterial taxa based on phylum.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/8757ed58441ed787c1d52e87.png"},{"id":72735339,"identity":"a1e4387c-8d56-4c7f-82a5-8442fd457a60","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10374122,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional profiling and Co-occurrence rhizobacterial network associated with sorghum, cotton, and soybean rhizospheres. (A) Principal component analysis (PCA) plot displays the predicted functional differentiation of rhizobacterial communities in sorghum, cotton, and soybean rhizospheres based on PICRUSt2 predictions. (B) The heatmap shows the relative frequences of predicted functional pathways in rhizobacterial communities across sorghum, cotton, and soybean based on Metacyc database. (C) The Co-occurrence network depicts interactions among rhizobacterial taxa in the rhizospheres of sorghum, cotton, and soybean. Nodes represent microbial taxa, color-coded by phylum. Larger nodes indicate taxa with higher relative correlation, emphasizing their dominance or ecological significance within the rhizobacterial community.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/25a8d2cabd29d9592b09e02a.png"},{"id":73397679,"identity":"07583efb-a9ef-49d6-9935-a6e2dc06bed0","added_by":"auto","created_at":"2025-01-09 14:02:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36944139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/96f8cb74-92e7-492a-a7b3-b4f51878c2a1.pdf"},{"id":72735328,"identity":"5a24df1a-3dce-4682-bb7c-7e3b8512df29","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5521,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/7464c878c77136b02330e097.csv"},{"id":72735320,"identity":"f54e1941-b1ef-4995-8fb2-f7c04e03d8fe","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":49258,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/a2b78c3cdddc6a91fccd29d2.docx"},{"id":72735506,"identity":"a7fe63b5-b419-4aec-b48b-1def6802f41a","added_by":"auto","created_at":"2025-01-01 08:19:16","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":67010,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.csv","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/693a384747c570511a4cf27c.csv"},{"id":72735323,"identity":"1dc796e3-bd2a-4dd3-b338-95e50e5f7d49","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":210720,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/4c210ab80388a9d5e47da966.csv"},{"id":72735340,"identity":"1255a598-9b6f-4829-b972-b954c79b9653","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":293275,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.tsv.csv","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/c44d706c969865a3f8e13498.csv"},{"id":72735325,"identity":"bbe39665-12a2-4666-81eb-eb0be89a1316","added_by":"auto","created_at":"2025-01-01 08:11:16","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1179732,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.csv","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/9551652487641a4bd68074be.csv"},{"id":72735351,"identity":"bf6b1eaa-b339-45cd-b1ad-fb76880b0447","added_by":"auto","created_at":"2025-01-01 08:11:17","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1124517,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.csv","url":"https://assets-eu.researchsquare.com/files/rs-5727917/v1/dd1b437b8205fc69f52e747a.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering crop-specific rhizobacteriome assembly in cotton, sorghum, and soybean under hot semi-arid field conditions in Texas","fulltext":[{"header":"Background","content":"\u003cp\u003eSoil microbiota play a pivotal role in ecosystem biology by shaping developmental, physiological, and reproductive traits, while driving essential processes, such as nutrient cycling, soil structure formation, hormone synthesis to promote plant growth, and pathogen suppression, all of which are crucial for sustainable agricultural productivity\u0026nbsp;[1, 2]. The diversity and abundance of soil microbial communities are influenced by several environmental factors, including soil pH, oxygen availability, temperatures, moisture, and the quantity and quality of carbon sources\u0026nbsp;[3]. These factors vary across spatial scales, fluctuating from centimeter-level macro-scale to millimeter-level micro-scale within the soil profile\u0026nbsp;[4]. This spatial heterogeneity creates localized hotspots of microbial diversity and activity, with the rhizosphere standing out as a particularly active zone\u0026nbsp;[5].\u0026nbsp;The rhizosphere, the narrow soil region directly influenced by plant roots and extending about 1 mm from the root surface, hosts a highly diverse microbial communities [6]. The rhizosphere is characterized by significantly higher microbial biomass and activity compared with the surrounding bulk soil, and represents one of the most intriguing and yet poorly understood areas in the field of microbial ecology\u0026nbsp;[7]. This microbial hotspot is shaped by host plant root activities, including nutrient and water uptake and the release of carbon compounds, known as rhizodeposits\u0026nbsp;[8].\u0026nbsp;Rhizodeposits, with their varying bioavailability, selectively promote the growth of specific microbiota, while inhibiting others, contributing to the dynamic composition and functional diversity of the rhizosphere microbiomes, hereafter referred to as rhizobiomes\u0026nbsp;[7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlants rely on their rhizobiomes to facilitate the acquisition of essential nutrients, including minerals that are present in non-bioavailable forms\u0026nbsp;[9]. In turn, plants actively influence the abundance and composition of their rhizobiomes by modifying the soil environment. They adjust soil pH through the release of protons (H⁺) and bicarbonate (HCO₃⁻), reduce competition by releasing antimicrobial compounds that inhibit pathogens while promoting beneficial microbes, and supply carbon-rich rhizodeposits as energy sources to support microbial growth\u0026nbsp;[10]. The composition of microbial communities in plant-associated environments, such as the rhizosphere, varies across plant species and even among genotypes within a species\u0026nbsp;[11]. Increasing evidence points to a complex interplay between host genetic traits and the assembly of associated microbiome. For instance, a study using model systems, such as \u003cem\u003eArabidopsis thaliana\u003c/em\u003e grown under controlled conditions, revealed that plant genotype has a subtle but measurable effect on the rhizobiome composition and abundance\u0026nbsp;[12]. Using broad-sense heritability and genome-wide association studies across 200 sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e) accessions, several host genetic loci associated with rhizobiome variation have been identified, revealing reproducible associations with specific microbial taxa and highlighting the potential to predict microbiome structure based on the host\u0026rsquo;s genetics [13]. In soybean (\u003cem\u003eGlycine max\u003c/em\u003e), rhizospheric bacterial (rhizobacteriome) structures differed between soybean landraces and cultivars based on seed oil content, with seven bacterial families enriched in high-oil cultivars. Among these, \u003cem\u003eOxalobacteraceae\u0026nbsp;\u003c/em\u003efamily was specifically assembled by root exudates rich in phenolic acids and flavonoids, impacting auxin signaling, oxidative metabolism, and glycolysis in host plants to promote seed oil accumulation\u0026nbsp;[14].\u0026nbsp;A study of the rhizobacteriome and rhizospheric fungi (rhizofungiome) of four upland cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e) and four sea-island cotton (\u003cem\u003eG. barbadense\u003c/em\u003e) cultivars grown in three different soil types demonstrated that soil origin was the primary factor driving microbial divergence, with cotton genotype serving as a secondary influence\u0026nbsp;[15]. Despite significant progress, the mechanisms governing microbiome acquisition and assembly, along with the host genetic variants that regulate these processes, remain poorly understood. Thus, deciphering the complex interactions between plants and their associated microbiota, as well as identifying the host genetic determinants of these relationships, is critical for advancing plant breeding and biotechnological innovations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the context of global warming, the frequency and intensity of extreme weather events, including heatwaves and droughts, have significantly increased, leading to a significant reduction in crop yields [16-18]. Over the past decades, numerous studies have explored the impacts of climate change on rhizosphere microbiomes [19]. Nevertheless, knowledge regarding the responses of rhizosphere microbiomes and their functionality under naturally occurring extreme weather events remains limited, despite their importance in forecasting global environmental changes. Texas, known for its diverse and often extreme weather conditions, plays a pivotal role in the United States\u0026rsquo; (US) agriculture. The state climate ranges from arid and semi-arid in the west to humid subtropical in the east, with high temperatures and frequent droughts presenting challenges for crop production (www.epa.gov). Texas leads the nation in cotton production, with upland cotton thriving in the dry, hot conditions of the High Plains and the Blackland Prairie. However, many cotton-growing regions in southwest US experienced a significant reduction in cotton yields due to heat stress [20]. Sorghum, another key crop, is well-suited to Texas\u0026rsquo;s drought-prone areas due to its resilience and low water requirements (www.nrcs.usda.gov). Both crops rely on innovative cultivation practices, such as no-till farming and efficient irrigation systems, to optimize yields, while conserving resources in the face of Texas\u0026rsquo; variable climate. In contrast, soybean, as a nitrogen-fixing legume model, is primarily cultivated in the temperate climate of the Midwest US and is less frequently grown in arid regions [21]. We hypothesize that cultivating cotton and sorghum in their native hot semi-arid environments may promote the development and/or enrichment of specialized rhizobiomes that enhance these crops\u0026apos; survival and adaptation to extreme climatic conditions, in contrast to soybean. This study focuses on understanding how these three cropping systems recruit rhizobiomes to ensure their growth and performance under hot semi-arid conditions. Thus, we investigate whether cotton, sorghum, and soybean cultivated under hot semi-arid climatic conditions in Lubbock, Texas recruit distinct rhizobacteriomes that contribute to their behavior and performance in natural field, or whether the rhizobacteriomes remain consistent across these cropping systems. This research aims to identify rhizobacteriomes adapted to hot semi-arid climates, leading to the discovery of rhizobacteria that can be used as biofertilizers to sustainably enhance crop resilience and productivity under challenging climatic conditions. By exploring rhizobacteriome recruitment strategies and differential microbial associations in these crops, this study addresses critical gaps in plant-microbiome interactions and paves the way for practical applications in hot semi-arid agricultural systems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePlant materials and growth conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSorghum \u0026lsquo;BTx\u0026rsquo;, cotton \u0026lsquo;Fibermax\u0026rsquo; and soybean \u0026lsquo;William 82\u0026rsquo; cultivars were grown in Quaker Ave. research farm operated by Texas Tech University, Lubbock, Texas (33\u0026deg; 41\u0026apos; 36.4596\u0026quot; N, -101\u0026deg; 54\u0026apos; 18.612\u0026quot;W, and 992 m elevation above sea level). The soil at the site is sandy loam in the upper 0\u0026ndash;30 cm, containing 0.8% organic matter and a pH of approximately 7.8\u0026nbsp;[22]. This semi-arid region receives an average annual rainfall of 470 mm, with average evapotranspiration of 1500 mm. During July and August, the farm experienced exceptionally high temperatures, with daily highs ranging from 36.6\u0026deg;C to 40.6\u0026deg;C (www.weather.gov). The farm is mainly cultivated with cotton, sorghum, maize and turfgrass. In this study, each crop was cultivated in a 50-meter plot with 10 rows, with plants spaced 20 cm apart within the rows, following the farm standard cultivation practices. The three crops were separated by 1\u0026ndash;2 meters distance between plots. Rhizosphere soil was collected during July and August when all the plants reached their maturity stage. Plants were manually uprooted, and the roots were gently shaken to remove loose soil. The soil remaining attached to the roots was collected as rhizosphere soil in sterilized Falcon tubes stored in an ice box, and promptly transferred to \u0026minus;20\u0026deg;C for storage. For each crop, five biological replicates were analyzed, with each replicate consisting of 3 to 4 individual plants pooled together. All samples were collected from the center of the plot to minimize border effects. Soil microbial DNA was extracted 24 h after sample collection to avoid changes in microbial communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation, sequencing, and library construction of 16S rRNA gene samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 15 rhizosphere soil samples, collected from sorghum, cotton and soybean plants (3 crops \u0026times; 5 biological replicates), were subjected to microbial DNA extraction using DNeasy PowerSoil Pro Kit (Qiagen, ML, US). The V4-V5 hypervariable region of the bacterial 16S rRNA gene was amplified with 515F (5\u0026rsquo;-GTGCCAGCMGCCGCGGTAA-3\u0026rsquo;) and 907R (5\u0026rsquo;-CCGTCAATTCCTTTGAGTTT-3\u0026rsquo;) primers using PCR conditions of 3 minutes at 95\u0026deg;C, followed by 35 cycles of 30 seconds at 95\u0026deg;C, 30 seconds at 55\u0026deg;C, 30 seconds at 72\u0026deg;C, and a final extension at 72\u0026deg;C for 5 minutes. The amplified PCR products were purified with 20 uL AMPure XP magnetic beads per sample (Beckman Coulter Life Sciences, CA, US). Next, purified DNA from each sample was attached with indices and Illumina sequencing adaptors using Nextra XT index kit (Illumina, CA, US), through PCR conditions of 95\u0026deg;C for 3 minutes, followed by 8 cycles of 95\u0026deg;C for 30 seconds, 55\u0026deg;C for 30 seconds, 72\u0026deg;C for 30 seconds, and a final extension at 72\u0026deg;C for 5 minutes. The integrity and size of the DNA fragments in the library were assessed using Fragment Analyzer (AATI, IO, US). Effective concentration of the library was detected by QPCR. The quantified libraries were pooled and sequenced on the Illumina NovaSeq platform at Novogene (Sacramento, CA, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e16S rRNA abundance, taxonomy, and statistical evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw read quality and sequencing depth were assessed using the \u003cem\u003eqiime tools peek\u003c/em\u003e function in QIIME2 [23]. Data processing, including denoising, chimera removal, error model and abundance estimation, was systematically performed using the DADA2 plugin in QIIME2. The taxonomy of each amplicon sequence variant (ASV) was assigned using the classifier generated from the Silva 138.1 (www.arb-silva.de/documentation/release-1381/) 16S rRNA bacteria database. Next, sequence alignment was performed using \u003cem\u003eqiime alignment mafft\u003c/em\u003e function, followed by the construction of an unrooted phylogenetic tree with \u003cem\u003eqiime phylogeny fasttree\u003c/em\u003e. The phylogenetic tree was then rooted using qiime phylogeny midpoint-root function for further analysis. After removing mitochondrial and chloroplast reads, abundance, taxonomy, metadata, and phylogenetic tree objects were merged for statistical analysis. Alpha and beta diversity analyses and visualizations were performed using a range of packages, including \u0026lsquo;phyloseq\u0026rsquo;, \u0026lsquo;seqinr\u0026rsquo;, \u0026lsquo;ANCOMBC\u0026rsquo;, \u0026lsquo;MicrobiomeStat\u0026rsquo;, \u0026lsquo;microeco\u0026rsquo;, \u0026lsquo;MASS\u0026rsquo;, \u0026lsquo;GUniFrac\u0026rsquo;, \u0026lsquo;ggh4x\u0026rsquo;, \u0026lsquo;Tax4Fun2\u0026rsquo; \u0026lsquo;ggplot2\u0026rsquo;, and \u0026lsquo;pheatmap\u0026rsquo; in R.4.3.2. A false discovery rate-corrected \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026le; 0.05 was considered indicative of statistical significance. Alpha and beta diversity comparisons among and between different crops were conducted in R.4.3.2using analysis of variance (ANOVA), the Kruskal-Wallis rank sum test, pairwise Wilcoxon rank-sum test, permutational multivariate analysis of variance (PERMANOVA), analysis of similarities (ANOSIM), and beta dispersion tests. Differential microbial abundance (DMA) was determined using \u0026lsquo;DESeq2\u0026rsquo; package in R.4.3.2, while significant crop-specific associated microbiomes were identified using Linear Discriminant Analysis Effect Size (LEfSe). Functional predictions were performed using Functional Annotation of Prokaryotic Taxa (FAPROTAX)\u0026nbsp;[24]\u0026nbsp;and PICRUSt2\u0026nbsp;[25]\u0026nbsp;tools. Significant pathways and principal component analysis (PCA) of PICRUSt2 results were carried out using STAMP (https://beikolab.cs.dal.ca/software/STAMP). Microbial network was generated using \u003cem\u003etrans_network\u003c/em\u003e function in \u0026lsquo;microeco\u0026rsquo; package in R.4.3.2. Spearman correlation analysis was conducted using the WGCNA package in R 4.3.2, with a filtering threshold of 0.001 and a correlation coefficient cutoff 0.7 to optimize the coefficient threshold. The resulting network object was converted to GEXF format for visualization using Gephi (https://gephi.org/).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAlpha and beta diversity reveal distinct rhizobacteriome composition across sorghum, cotton, and soybean cropping systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRarefaction curves show species richness (Chao1 and observed) as a function of sequencing depth across sorghum, cotton, and soybean samples (Figure 1A). The flattening of the curves confirms that the sequencing depth was sufficient to capture the majority of rhizobacteriome present, ensuring comprehensive coverage of the microbial diversity within each sample (Figure 1A). The alpha diversity metrics such as Observed richness, Chao1 richness, and Shannon diversity index revealed significant differences (\u003cem\u003ep\u003c/em\u003e-adjusted \u0026le; 0.0055, 0.0081 and 0.0032, respectively) among sorghum, cotton, and soybean rhizobacteriome based on the Kruskal-Wallis rank sum test (Table 1). Additionally, pairwise Wilcoxon rank sum test indicated that soybean was significantly different from sorghum and cotton, while no significant difference was observed between sorghum and cotton (Table 1). Soybean rhizobacteriome exhibited the highest observed richness, indicated by higher abundance of rhizobacterial taxa compared with cotton and sorghum (Table1; Figure 1B). Similarly, Chao1 index followed a similar trend, with soybean having the highest richness, indicating that it supports a broader range of both common and rare rhizobacterial taxa (Table1; Figure 1B). Cotton and sorghum exhibited comparable observed and Chao1 richness, with no significant difference between them, but both were significantly lower than soybean (Table 1; Figure 1B). Soybean exhibited the highest Shannon diversity, reflecting its combination of high richness and a relatively even distribution of rhizobacteriome (Table 1; Figure 1B). Cotton exhibited intermediate Shannon diversity, indicating moderate richness with a reasonably balanced community structure, while sorghum displayed the lowest Shannon diversity, suggesting lower richness, less even distribution of rhizobacterial taxa, and potential dominance by specific taxa compared with the other two crops (Table1; Figure 1B). Next, we performed an interaction size analysis to visualize the extent of rhizobacterial interactions that are either shared among the three crops or unique to each individual crop (Figure 1C). Soybean exhibited the largest number of 3168 unique rhizobacterial taxa, followed by cotton with 1375 taxa and sorghum with 1196 taxa (Figure 1C). On the other hand, 415 shared rhizobacterial taxa were identified among sorghum, cotton, and soybean, emphasizing crop-specific microbial associations (Figure 1C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeta diversity analysis using Bray-Curtis dissimilarity revealed significant differences in rhizobacterial community structure across the three cropping systems (Table 1, Figure 1D and E). The PERMANOVA test using Bray-Curtis dissimilarity method showed that the cropping system, as categorical variable, explained a significant portion of the variation in rhizobacterial community (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.81, \u003cem\u003ep\u003c/em\u003e-adj = 0.001; Table 1). Additionally, ANOSIM results, based on rank-based comparisons, supported the PERMANOVA findings, revealing significant differences in rhizobacterial composition among sorghum, cotton and soybean, with an \u003cem\u003eR\u003c/em\u003e statistic =1, indicating highly distinct rhizobacterial communities influenced predominantly by crop-specific factor (Table 1). On the other hand, the overall beta dispersion test, which evaluates differences in rhizobacteriome variability, did not show significant differences among the three cropping systems (\u003cem\u003ep\u003c/em\u003e \u0026le; 0.09) (Table 1). Nevertheless, beta dispersion pairwise comparison of \u0026lsquo;sorghum vs soybean\u0026rsquo; exhibited significant differences (Table 1). Non-metric multidimensional scaling (NMDS) revealed distinct clusters of rhizobacterial variability across sorghum, cotton, and soybean, indicating highly differentiated rhizobacterial communities (Figure 1E). The low stress value of 0.000088 indicates a reliable representation of the data (Figure 1E). NMDS of dominant taxa at phyla level indicated that \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e phyla showed distinct clustering patterns (Figure 1F). Sorghum appears enriched with \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e phyla, while cotton and soybean harbor unique distributions of \u003cem\u003eActinobacteria\u003c/em\u003e (Figure 1F). These findings suggest crop-specific recruitment of rhizobacterial taxa, potentially influenced by root exudate profiles or environmental adaptations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 1.\u003c/strong\u003e Statistical analysis of alpha and beta diversity metrics across sorghum, cotton, and soybean rhizobacterial communities.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCropping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObserve\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChao1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e946.4 \u0026plusmn; 42.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1012.8 \u0026plusmn; 47.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eCotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e999.2 \u0026plusmn; 20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1032.1 \u0026plusmn; 30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSoybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1537.8 \u0026plusmn; 124.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1626.3 \u0026plusmn; 191.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.5 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKruskal-Wallis rank sum test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum-Cotton-Soybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.0055\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.0081\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.0032\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWilcoxon rank sum test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Cotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.1003\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.5476\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.0108\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Soybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.0108\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.0108\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.0108\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSoybean vs Cotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.0137\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.0108\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.0108\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePERMANOVA (Bray-Curtis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSums of Sqs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum-Cotton-Soybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.1083927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.8097167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Cotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.7981479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Soybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.7916142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSoybean vs Cotton\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.6888733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eANOSIM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePermutations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eR\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eANOSIM for all groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Cotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Soybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSoybean vs Cotton\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta dispersion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Sqs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePermutations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePr(\u0026gt;F)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.016867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.09\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eObserved \u003cem\u003ep\u003c/em\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Permuted \u003cem\u003ep\u003c/em\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Cotton \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSorghum vs Soybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSoybean vs Cotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTaxonomic insights revealed crop-specific rhizobacterial taxa in sorghum, cotton, and soybean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlpha diversity metrics are presented as mean \u0026plusmn; standard deviation (SD), with significance determined using the Kruskal-Wallis rank sum test followed by pairwise Wilcoxon rank sum tests. Significant \u003cem\u003ep\u003c/em\u003e-values are indicated as follows: **\u003cem\u003ep\u003c/em\u003e \u0026le; 0.01, *\u003cem\u003ep\u003c/em\u003e \u0026le;0.05, and ns = not significant. Sum of Sqs, sum of squares; \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e, coefficient of determination; \u003cem\u003eR\u003c/em\u003e, statistic for rank similarity.\u003c/p\u003e\n\u003cp\u003eTo gain deeper insights into rhizobacterial taxonomy across the investigated crops, we assessed the relative abundance of rhizobacteriome at multiple taxonomic levels (Figure 2A-D). A total of 32 distinct phyla were identified across the three cropping systems, and their relative abundances in each crop are summarized in Table S1. The relative abundance and distribution of top 10 rhizobacterial phyla in sorghum, cotton, and soybean are visualized in a ternary plot (Figure 2A). The most relative abundant phyla across all cropping systems, in descending order, were \u003cem\u003eActinobacteriota\u003c/em\u003e, followed by \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003ePlanctomycetota\u003c/em\u003e, \u003cem\u003eVerrucomicrobiota\u003c/em\u003e, \u003cem\u003eGemmatimonadota\u003c/em\u003e, and \u003cem\u003eMyxococcota\u003c/em\u003e (Figure 2A, Table S1). The \u003cem\u003eAcidobacteriota\u003c/em\u003e phylum showed the highest relative abundance in sorghum rhizospheres, followed by cotton and soybean (Figure 2A, Table S1). In contrast, the \u003cem\u003eProteobacteria\u003c/em\u003e phylum displayed greater relative abundance in soybean rhizospheres compared with sorghum and cotton (Figure 2A, Table S1). Similarly, \u003cem\u003eBacteroidota\u003c/em\u003e exhibited elevated relative abundance in soybean, whereas \u003cem\u003eFirmicutes\u003c/em\u003e were more abundant in sorghum rhizospheres, highlighting crop-specific rhizobacterial associations (Figure 2A, Table S1). Within \u003cem\u003eActinobacteria\u003c/em\u003e phylum, the dominant class was \u003cem\u003eActinobacteria\u003c/em\u003e, followed by \u003cem\u003eAcidimicrobiia\u003c/em\u003e, \u003cem\u003eThermoleophilia\u003c/em\u003e, and \u003cem\u003eRubrobacteria\u003c/em\u003e across the three crops (Figure 2B). The \u003cem\u003eActinobacteria\u003c/em\u003e class showed higher relative abundance in sorghum, followed by cotton and soybean (Figure 2B). In contrast, the \u003cem\u003eAcidimicrobiia\u003c/em\u003e class showed higher relative abundance in cotton compared with sorghum and soybean (Figure 2B). Within the \u003cem\u003eProteobacteria\u003c/em\u003e phylum, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e and \u003cem\u003eGammaproteobacteria\u003c/em\u003e were the dominant classes, showing the highest relative abundance in soybean, followed by cotton, with the lowest levels observed in sorghum (Figure 2B). Within the \u003cem\u003eBacteroidota\u003c/em\u003e phylum, the \u003cem\u003eBacteroidia\u003c/em\u003e class was detected across all crops, exhibiting the highest relative abundance in soybean, followed by cotton and sorghum (Figure 2B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin the \u003cem\u003eActinobacteriota\u003c/em\u003e phylum, \u003cem\u003eMicrococcales\u003c/em\u003e and \u003cem\u003eStreptomycetales\u0026nbsp;\u003c/em\u003eemerged as the most dominant orders, particularly enriched in sorghum and cotton, whereas soybean displayed relatively lower abundance (Figure 2C). Additionally, the \u003cem\u003eMicrotrichales\u0026nbsp;\u003c/em\u003eorder exhibited higher enrichment in cotton compared with sorghum and soybean (Figure 2B). Within the \u003cem\u003eProteobacteria\u003c/em\u003e phylum, \u003cem\u003eRhizobiales\u003c/em\u003e and \u003cem\u003eBurkholderiales\u0026nbsp;\u003c/em\u003ewere the predominant orders across all crops (Figure 2B). \u003cem\u003eRhizobiales\u003c/em\u003e showed higher relative abundance in soybean, followed by cotton and sorghum, reflecting its importance in nitrogen-fixing processes in legume crops (Figure 2B). \u003cem\u003eBurkholderiales\u003c/em\u003e also exhibited a similar trend, being most abundant in soybean and less in cotton and sorghum (Figure 2B). In the \u003cem\u003eBacteroidota\u003c/em\u003e phylum, the dominant orders were \u003cem\u003eCytophagales\u0026nbsp;\u003c/em\u003eand \u003cem\u003eChitinophagales\u003c/em\u003e, with higher abundance observed in soybean and lower abundance in cotton and sorghum. (Figure 2B). Within the \u003cem\u003eFirmicutes\u003c/em\u003e phylum, \u003cem\u003eBacillales\u003c/em\u003e emerged as the key order, predominantly enriched in sorghum compared with cotton and soybean (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e-*-+--Micrococcaceae\u003c/em\u003e, \u003cem\u003eStreptomycetaceae\u003c/em\u003e, and \u003cem\u003eNocardioidaceae\u003c/em\u003e families, were predominantly enriched in sorghum rhizospheres compared with cotton and soybean (Figure 2C). \u003cem\u003eIllumatobacteraceae\u003c/em\u003e, \u003cem\u003ePseudonocardiaceae\u003c/em\u003e and \u003cem\u003eSphingomonadaceae\u003c/em\u003e families were predominantly enriched in cotton rhizospheres compared with soybean and sorghum (Figure 2C). On the other hand, soybean rhizosphere was enriched with \u003cem\u003eRhizobiaceae\u003c/em\u003e, \u003cem\u003eMicroscillaceae\u003c/em\u003e and \u003cem\u003eChitinophagaceae\u0026nbsp;\u003c/em\u003efamilies (Figure 2C). At genus level, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e and \u003cem\u003eNocardioides\u003c/em\u003e were predominantly enriched in sorghum, whereas \u003cem\u003eCL500-29\u003c/em\u003e (unclassified), \u003cem\u003eIllumatobacter\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePseudonocardia\u003c/em\u003e were enriched in cotton. Meanwhile, \u003cem\u003eEnsifer,\u003c/em\u003e \u003cem\u003eSteroidobacter,\u003c/em\u003e and \u003cem\u003eAllo/NeoRhizobium\u0026nbsp;\u003c/em\u003egenera were\u003cem\u003e\u0026nbsp;\u003c/em\u003especifically enriched in soybean (Figure 2C). The remaining taxa and their relative abundances across all cropping systems are illustrated in Figure 2A-C. The above results highlight the distinct rhizobacterial recruitment strategies driven by each crop, which may reflect functional adaptations to their respective cropping systems. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistinct rhizobacteriome, phylogenetic relationships, and differential microbial abundance across sorghum, cotton, and soybean rhizospheres\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePairwise correlations using scatterplots of log-transformed rhizobacterial abundance highlight increasing correlation coefficients in \u0026lsquo;sorghum vs soybean (\u003cem\u003er\u003c/em\u003e = 0.38)\u0026rsquo;, \u0026lsquo;cotton vs soybean (\u003cem\u003er\u003c/em\u003e = 0.43),\u0026rsquo; and \u0026lsquo;sorghum vs cotton (\u003cem\u003er\u003c/em\u003e = 0.57)\u0026rsquo; suggesting progressively greater similarity in rhizobacterial composition (Figure 3A). The higher correlation between cotton and sorghum indicates shared rhizobacterial taxa, likely driven by comparable ecological niches. In contrast, the lower correlation between sorghum and soybean (Figure 3A), reflects distinct rhizobacterial community structure, potentially influenced by soybean leguminous nature. The relative abundance heatmap, combined with LEfSe-derived LDA scores, highlights crop-specific microbial association and significant taxa that differentiate each cropping system (Table S2). LEfSe-derived LDA scores indicated that \u003cem\u003eActinobacteriota\u003c/em\u003e and \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003ephyla, \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eBacilli\u003c/em\u003e classes, \u003cem\u003eMicrococcales\u003c/em\u003e, \u003cem\u003ePropionibacteriales\u003c/em\u003e, and \u003cem\u003eStreptomycetales\u0026nbsp;\u003c/em\u003eorders, \u003cem\u003eNocardiaceae\u003c/em\u003e and \u003cem\u003eStreptomycetaceae\u003c/em\u003e families, as well as \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e, and \u003cem\u003eNocardiodes\u0026nbsp;\u003c/em\u003egenera were significantly enriched in sorghum rhizosphere (Figure 3B). In turn, the \u003cem\u003eAcidimicrobiia\u003c/em\u003e class, \u003cem\u003eMicrotrichales\u003c/em\u003e order, \u003cem\u003eIllumatobacteraceae\u0026nbsp;\u003c/em\u003efamily, and \u003cem\u003eCL500-19\u003c/em\u003e (unclassified) and \u003cem\u003eIllumatobacter\u0026nbsp;\u003c/em\u003egenera were significantly associated with the cotton rhizosphere (Figure 3B). With respect to the soybean rhizobacteriome, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e phyla; \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eGammaproteobacteria\u003c/em\u003e, and \u003cem\u003eBacteroidia\u003c/em\u003e classes; \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eCytophagales\u003c/em\u003e, and \u003cem\u003eBurkholderiales\u003c/em\u003e orders; as well as \u003cem\u003eRhizobiaceae\u003c/em\u003e and \u003cem\u003eMicroscillaceae\u003c/em\u003e families were significantly enriched (Figure 3B). To further explore the phylogenetic relationships among the rhizobacterial taxa and their significant associations with different cropping systems, the phylogenetic cladogram illustrates the evolutionary relationships and differential abundances of rhizobacterial taxa within the rhizospheres of sorghum, cotton, and soybean (Figure 3C). Branch lengths and clustering patterns within the cladogram highlight evolutionary divergence and conservation among taxa, while differential abundance is indicated by unique color and marker sizes corresponding to specific cropping systems (Figure 3C). Sorghum-associated taxa were predominantly from the \u003cem\u003eActinobacteriota\u0026nbsp;\u003c/em\u003ephylum, specifically within \u003cem\u003eStreptomycetaceae\u003c/em\u003e, \u003cem\u003eStreptomycetales\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e were phylogenetically grouped in closely related lineages with \u003cem\u003eNocardioidaceae\u0026nbsp;\u003c/em\u003eand \u003cem\u003eNocardioides\u003c/em\u003e (Figure 3C). The enriched lineages, such as \u003cem\u003eCL500\u003c/em\u003e, \u003cem\u003eIllumatobacter\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Microtrichales\u003c/em\u003e in cotton rhizospheres are positioned on the cladogram diverging from other \u003cem\u003eActinobacteriota-\u003c/em\u003eassociated taxa, highlighting their evolutionary adaptation to the soil conditions of cotton cultivation. In contrast, soybean-associated taxa were predominantly represented by \u003cem\u003eProteobacteria\u003c/em\u003e, with a notable abundance of nitrogen-fixing groups like \u003cem\u003eRhizobiales\u003c/em\u003e. The family \u003cem\u003eRhizobiaceae\u003c/em\u003e was phylogenetically clustered within the \u003cem\u003eAlphaproteobacteria\u0026nbsp;\u003c/em\u003elineage, reflecting its specialized symbiotic association with leguminous crops such as soybean (Figure 3C). The volcano plots were used to identify DMA across pairwise comparisons of sorghum, soybean, and cotton rhizospheres, using enriched (log\u003csub\u003e2\u003c/sub\u003e fold-change \u0026ge; 1; \u003cem\u003ep\u003c/em\u003e-value \u0026le; 0.05) and depleted (log\u003csub\u003e2\u003c/sub\u003e fold-change \u0026le; -1; \u003cem\u003ep\u003c/em\u003e-value \u0026le; 0.05) cutoff (Figure 3D; Tables S3-5). In the \u0026lsquo;sorghum vs. soybean\u0026rsquo; comparison, 106 rhizobacterial taxa were enriched, while 236 rhizobacterial taxa were depleted (Figure 3D). In \u0026lsquo;soybean vs. cotton\u0026rsquo; comparison, 183 and 69 rhizobacterial taxa were significantly enriched and depleted, respectively (Figure 3D). In \u0026lsquo;cotton vs sorghum\u0026rsquo; comparison, 70 and 56 rhizobacterial taxa were significantly enriched and depleted, respectively (Figure 3D). Our results demonstrated greater DMA in the \u0026lsquo;sorghum vs soybean\u0026rsquo; comparison, whereas the \u0026lsquo;cotton vs sorghum\u0026rsquo; comparison showed lower DMA (Figure 3D). These findings underscore distinct rhizobacterial associations among these crops, with the soybean rhizosphere exhibiting the most unique microbial assemblage and significant differences compared both sorghum and cotton.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional prediction and microbial network of sorghum, soybean and cotton rhizobacteriome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted functional predictions using FAPROTAX and PICRUSt2 to unravel the functional roles of the rhizobacteriome associated with sorghum, cotton, and soybean (Figure 4 A and B; Table S6 and Figure S1). FABROTAX functional predictions revealed that sorghum-associated rhizobacterial pathways were significantly enriched in processes such as \u0026lsquo;fermentation,\u0026rsquo; \u0026lsquo;manganese_oxidation\u0026rsquo;, \u0026lsquo;xylanolysis\u0026rsquo;, and \u0026lsquo;cellulolysis\u0026rsquo; (Figure S1). In contrast, soybean-associated rhizobiomes exhibited significant enrichment in pathways related to \u0026lsquo;nitrogen_fixation\u0026rsquo;, \u0026lsquo;dark_hydrogen_oxidation\u0026rsquo;, \u0026lsquo;methylotrophy\u0026rsquo;, and \u0026lsquo;methanolotrophy\u0026rsquo; (Figure S1). Cotton-associated rhizobacteriome pathways exhibited intermediate characteristics between those of sorghum and soybean, with significant enrichment in \u0026lsquo;nitrogen_fixation\u0026rsquo; and \u0026lsquo;methalotrohy\u0026rsquo; pathways compared with sorghum, and \u0026lsquo;fermentation\u0026rsquo;, \u0026lsquo;xylanolysis\u0026rsquo;, and \u0026lsquo;cellulolysis\u0026rsquo; compared with soybean (Figure S1). PCA revealed distinct clustering patterns of functional pathways predicted through PICRUSt2, demonstrating crop-specific variations in rhizobacterial functional potential (Figure 4A). PC1 explains the largest proportion of variation (79.9%), followed by PC2 (18.5%) and PC3 (0.8%) (Figure 4A). Sorghum form a separate cluster from soybean and cotton, highlighting crop-specific functional traits (Figure 4A). Key pathways significantly enriched in sorghum rhizobacteriome include \u0026lsquo;Adenosine nucleotides degradation II\u0026rsquo;, \u0026lsquo;TCA cycle IV (2-oxoglutrate decarboxylase)\u0026rsquo;, \u0026lsquo;Superpathway for glycolysis\u0026rsquo;, \u0026lsquo;Peptidoglycan biosynthesis V\u0026rsquo;, \u0026lsquo;Superpathway of sulfur oxidation\u0026rsquo;, \u0026lsquo;, \u0026lsquo;Superpathway of polyamine biosynthesis\u0026rsquo;, \u0026lsquo;\u003cem\u003emyo\u003c/em\u003e-inositol degradation\u0026rsquo;, \u0026lsquo;Arginine, ornithine, and proline interconversion\u0026rsquo;, \u0026lsquo;Superpathway of \u003cem\u003eS\u003c/em\u003e-adenosyl-methionine biosynthesis\u0026rsquo;, and \u0026lsquo;Teichoic acid (polyglycerol) biosynthesis\u0026rsquo;, among others, highlighting the specific roles of sorghum rhizobacteriome in stress adaption (Figure 4B; Table S6). In contrast, soybean rhizosphere samples display a more dispersed distribution, reflecting functional diversity likely linked to \u0026lsquo;\u003cem\u003ecis\u003c/em\u003e- Vaccenate biosynthesis\u0026rsquo;, \u0026lsquo;Gondoate biosynthesis\u0026rsquo;, \u0026lsquo;Fatty acid elongation-saturated\u0026rsquo;, \u0026lsquo;Nitrate reduction VI (Assimilatory)\u0026rsquo;, \u0026lsquo;NAD biosynthesis\u0026rsquo;, and \u0026lsquo;Sulfate reduction I (Assimilatory)\u0026rsquo;, and other symbiotic processes facilitated by its leguminous nature (Figure 4B; Table S6). Cotton rhizobacteriome falls between sorghum and soybean in functional clustering, suggesting shared pathways with both crops while maintaining distinct profiles related to stress tolerance and assimilation (Figure 4B; Table S6). The microbial network analysis provides a holistic view of the co-occurrence relationships among rhizobacteriome across sorghum, cotton, and soybean. The network, built from significant correlations (\u003cem\u003ep\u003c/em\u003e \u0026le; 0.01) correlation coefficient (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.7), offers insights into microbial interactions such as cooperation, competition, and niche overlap (Figure 4C). \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteriota\u003c/em\u003e phyla dominate the network, highlighting their functional importance in all three cropping systems (Figure 4C). \u003cem\u003eBacteroidota\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAcidobacteriota\u003c/em\u003e contribute to the network as secondary players, whereas \u003cem\u003ePlanctomycetota\u003c/em\u003e, represented by green nodes, exhibits distinct clustering, suggesting its specialized role and niche differentiation within the rhizosphere microbial community. In suammry, the functional predictions using FAPROTAX and PICRUSt2 revealed crop-specific variations in rhizobacterial functional roles, with sorghum enriched in stress-adaptation pathways, soybean in nitrogen fixation and symbiotic processes, and cotton displaying intermediate characteristics. Microbial network analysis further highlighted \u003cem\u003eProteobacteria\u0026nbsp;\u003c/em\u003eand \u003cem\u003eActinobacteriota\u003c/em\u003e as dominant contributors across all cropping systems and probably play important roles in rhizobacterial community stability under hot semi-arid conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFor millennia, farmers have improved agriculture by steering the evolution of crops, selectively saving seeds from plants that demonstrated resilience to both biotic and abiotic challenges, grew quickly, or produced the largest yields\u0026nbsp;[26]. However, as climate change rapidly alters agricultural environments and promotes the spread of diseases, conventional breeding techniques struggle to keep pace with these changes. On the other hand, the emergence of microbiome strategy is expected to yield results much more rapidly\u0026nbsp;[27]. Researchers and farmers have long acknowledged the benefits that microbes offer to crop production, such as the application of rhizobia in fields to facilitate nitrogen fixation,\u0026nbsp;[28], and\u0026nbsp;arbuscular mycorrhizal fungi to assist plants in absorbing various nutrients\u0026nbsp;[29]. Typically, these and other microbial applications depend on the use of one microbe. However, microbial ecologist recently proposed that a varied assortment of microbiota may improve the functional capabilities of microbial community members\u0026nbsp;[30]. This diversity can offer mutual assistance, aid in survival on plant surfaces or roots, and enhance competition against existing microbial communities in agricultural environments\u0026nbsp;[31]. For instance, the rise in \u003cem\u003eActinobacteria\u003c/em\u003e populations during drought conditions was associated with enhanced sorghum root colonization, which positively increased sorghum root growth and overall growth performance under drought stress conditions\u0026nbsp;[32]. The microbiome, which includes both the microbiota and their genetic material, forms intricate and dynamic relationships with the host plant\u0026nbsp;[33]. These relationships are significantly shaped by environmental factors and can enhance the plant resilience to various environmental stresses\u0026nbsp;[34]. Several laboratory and greenhouse experiments demonstrated that host genetics can influence microbial assembly in the rhizosphere\u0026nbsp;[35]. This raises the question of whether these host genetic influences can be observed in natural environments, where organisms simultaneously encounter both abiotic and biotic stressors. Thus, this research aims to explore the interactions between three different cropping systems and their rhizobacteriome assembly under hot semi-arid environments. Specifically, we examine whether cotton, sorghum, and soybean, when cultivated in the hot semi-arid climate of Lubbock, Texas, attract different rhizobacteriome that aid in their survival during field cultivation, or if the rhizobacteriome remain uniform across these crops. The goal of this study is to identify rhizobacteriome that are well-suited to hot semi-arid climates, with the intention of revealing the potential of these rhizobacteriome as biofertilizers to sustainably improve crop resilience in challenging climatic conditions.\u003c/p\u003e\n\u003cp\u003eAlpha and beta diversity analyses revealed distinct rhizobacteriome structures across sorghum, cotton, and soybean (Figure 1B; Table 1). Additionally, Bray-Curtis dissimilarity highlighted crop-specific microbial communities, with PERMANOVA and ANOSIM indicating significant differences driven by crop type (Figure 1D; Table 1). These results underscore the role of crop-specific factors in shaping rhizobacterial diversity and composition. Alpha diversity metrics indicated that soybean had significantly higher richness and diversity compared with cotton and sorghum (Figure 1B; Table 1). Soybean, being a legume, supports broad range of unique rhizobacteriome due to its ability to establish symbiotic relationships with nitrogen-fixing bacteria, particularly rhizobia\u0026nbsp;[36]. Beyond nitrogen-fixing bacteria, the symbiosis facilitated by specialized soybean root nodules creates an enriched niche that supports a diverse array of rhizobacterial species\u0026nbsp;[37]. This environment fosters complex and mutually beneficial interactions between the plant and its associated microbial community, which can partially explain the high richness and diversity observed in soybean rhizobacteriome (Figure 1B; Table 1). On the other hand, sorghum had the lowest diversity, potentially due to the dominance of specific taxa (Figure 1B; Table 1). The low richness and diversity in sorghum can be partially explained by the nature of sorghum, as a C4 grass known for its remarkable adaptation to hot semi-arid environments, a feat partly attributed to its unique root exudates, particularly the sorgoleone\u0026nbsp;[38].\u0026nbsp;Sorgoleone delayed the establishment of bacterial and archaeal networks during the early stages of plant development. Additionally, Sorgoleone has been shown to selectively inhibit or promote the growth of specific bacterial isolates\u0026nbsp;[38]. \u0026nbsp;Sorghum rhizobacteriome exhibited significant enrichment in \u003cem\u003eActinobacteriota\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFirmicutes\u003c/em\u003e phyla, and significant depletion in \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e phyla under hot semi-arid conditions (Figures 2B and 3B). In contrast, soybean exhibited significant enrichment in \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e and depletion in \u003cem\u003eActinobacteriota\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e under hot semi-arid conditions (Figures 2B and 3B). Cotton exhibited intermediate abundance compared with sorghum and soybean, with higher enrichment in the \u003cem\u003eAcidimicrobiia\u003c/em\u003e class and \u003cem\u003eMicrotrichales\u003c/em\u003e order (Figures 2B and 3B). Our results align with [32], showing significant shifts in the relative abundance of dominant phyla such as \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e in the sorghum rhizosphere under drought conditions. Drought delays the early development of the sorghum root microbiome, increasing the abundance of monoderm bacteria such as \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e phyla with thick cell walls, while reducing the abundance of diderm bacteria like \u003cem\u003eProteobacteria\u003c/em\u003e [32]. These changes were associated with altered plant metabolism and increased activity of bacterial ABC transporter genes under drought conditions [32]. The differences in rhizobacterial taxa among soybean, cotton, and sorghum may reflect their distinct functional roles in crop stress adaptation. sorghum, as a crop adapted to hot and semi-arid environments\u0026nbsp;[39], recruits specialized rhizobacterial taxa such as \u003cem\u003eStreptomyces\u003c/em\u003e and \u003cem\u003ePseudarthrobacter\u0026nbsp;\u003c/em\u003egenera, that play critical roles in enhancing drought stress resilience, distinguishing its rhizobiome composition from that of soybean and cotton (Figures 2C and 3B). \u003cem\u003eStreptomyces\u003c/em\u003e employ various mechanisms, including phytohormone modulation, enhancement of antioxidant enzyme activity, improved water and nutrient uptake, and other strategies to mitigate water deficit stress in crops\u0026nbsp;[40]. The \u003cem\u003ePseudarthrobacter\u0026nbsp;\u003c/em\u003egenus, a well-known plant growth-promoting microorganism, is characterized by high indole acetic acid production and antibacterial activity, which positively influence plant growth and enhance antioxidant activity\u0026nbsp;[41]. In contrast, the leguminous nature of soybean likely provides an advantage in recruiting rhizobacterial taxa such as \u003cem\u003eBurkholderiales\u0026nbsp;\u003c/em\u003eorder and \u003cem\u003eEnsifer\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRhizobium\u0026nbsp;\u003c/em\u003egenera (Figures 2C and 3B), specialized in nitrogen fixation reflecting its symbiotic relationships and unique functional adaptations\u0026nbsp;[42].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe enrichment and depletion of specialized rhizobacterial taxa was consistence with their functional analysis in the different crops. For example, FABROTAX and PICRUSt2 pathway prediction analyses revealed higher activity in \u0026lsquo;xylanolysis\u0026rsquo;, and \u0026lsquo;cellulolysis\u0026rsquo;, \u0026lsquo;adenosine nucleotides degradation II\u0026rsquo;, \u0026lsquo;TCA cycle IV (2-oxoglutrate decarboxylase)\u0026rsquo;, \u0026lsquo;peptidoglycan biosynthesis V\u0026rsquo;, \u0026lsquo;superpathway of sulfur oxidation\u0026rsquo;, \u0026lsquo;, \u0026lsquo;superpathway of polyamine biosynthesis\u0026rsquo;, \u0026lsquo;\u003cem\u003emyo\u003c/em\u003e-inositol degradation\u0026rsquo;, \u0026lsquo;arginine, ornithine, and proline interconversion\u0026rsquo;, \u0026lsquo;superpathway of \u003cem\u003eS\u003c/em\u003e-adenosyl-methionine biosynthesis\u0026rsquo;, and \u0026lsquo;teichoic acid (polyglycerol) biosynthesis\u0026rsquo; in sorghum (Figure 4B). Xylanolysis and cellulolysis microbial activities aid sorghum by breaking down complex plant polysaccharides, improving soil nutrient availability, and recycling organic matter into accessible nutrients\u0026nbsp;[43]. Similarly, bacterial activity in the adenosine nucleotide degradation pathway supports sorghum\u0026rsquo;s energy metabolism and stress recovery by salvaging nucleotides essential for cellular repair and stress responses\u0026nbsp;[44]. Peptidoglycan and teichoic acid biosynthesis pathways are associated with strengthening bacterial cell walls, enabling rhizobacteria to withstand environmental stress and support plant health through robust microbial colonization\u0026nbsp;[45]. Polyamine biosynthesis and \u003cem\u003emyo\u003c/em\u003e-inositol degradation pathways enable bacteria to contribute to osmotic adjustment, stabilize cellular structures, and facilitate signaling under drought stress\u0026nbsp;[46]. Additionally, the arginine, ornithine, and proline interconversion rhizobacterial activity enhances sorghum\u0026rsquo;s drought resilience by producing or metabolizing osmoprotectants critical during abiotic stresses\u0026nbsp;[47]. The high bacterial activity in the \u003cem\u003eS\u003c/em\u003e-adenosyl-methionine biosynthesis pathway, a precursor for ethylene and polyamine production, supports sorghum by enhancing stress signaling and adaptation mechanism\u0026nbsp;[48]. Overall, the enrichment of specialized rhizobacterial communities underscores the critical role of rhizobacteria in supporting sorghum\u0026apos;s adaptation to environmental stress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder hot semi-arid conditions, soybean activates several key enzymatic pathways, contributing to nodulation efficiency and nutrient assimilation. Nitrate reduction pathway converts nitrate into ammonium, which is critical for nitrogen assimilation [49]. Sulfate reduction is essential for the biosynthesis of sulfur-containing amino acids and coenzymes required for nodule function. Thus, enhanced sulfate reduction ensures the availability of sulfur for nodulation processes and supports the synthesis of leghemoglobin, which facilitates oxygen transport within nodules [50]. Nitrifier-denitrification pathway plays a key role in regulating nitrogen cycling and reducing nitrogen loss from the soil, thereby increasing nitrogen availability for nodulating bacteria and promoting effective symbiosis [51]. Palmitate contributes to the formation of lipids essential for membrane stability under stress conditions [52]. NAD is vital for redox reactions and energy transfer during nitrogen fixation in nodules [53]. Enhanced NAD biosynthesis supports the metabolic processes required for nodulation and nitrogen assimilation. Soybean leverages its leguminous nature to activate key enzymatic pathways, such as nitrate reduction, sulfate reduction, and nitrifier-denitrification, which enhance nitrogen and sulfur assimilation while promoting effective symbiosis with nodulating bacteria. These pathways, along with increased NAD biosynthesis and palmitate production, support nodulation efficiency, nutrient acquisition, and stress resilience, ensuring soybean\u0026apos;s adaptability and productivity in challenging environments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the pivotal role of crop-specific rhizobacterial communities in shaping the resilience of sorghum, cotton, and soybean under hot semi-arid conditions in Lubbock, Texas. The rhizobacteriomes of these crops demonstrated distinct taxonomic and functional profiles, reflecting their unique adaptations to environmental stress and nutrient assimilation requirements. Specifically, \u003cem\u003eActinobacteriota\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Firmicutes\u003c/em\u003e phyla were significantly enriched in sorghum rhizosphere, whereas \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;Acidobacteriota\u003c/em\u003e phyla were significantly enriched in soybean and cotton rhizospeheres under hot semi-arid conditions. Soybean exhibited the highest microbial richness and diversity, attributed to its leguminous nature and symbiotic relationships with nitrogen-fixing bacteria, such as \u003cem\u003eEnsifer\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e. These associations enhanced pathways related to nitrogen and sulfur assimilation, contributing to improved nodulation and nutrient acquisition. Cotton displayed intermediate traits, with microbial communities enriched in pathways supporting both stress adaptation and nutrient cycling. In contrast, sorghum recruited specialized rhizobacterial taxa, including \u003cem\u003eStreptomyces\u003c/em\u003e and \u003cem\u003ePseudarthrobacter\u0026nbsp;\u003c/em\u003egenera, which play critical roles in drought resilience by modulating phytohormones, enhancing antioxidant activity, and improving nutrient uptake. By elucidating the interplay between crops and their rhizobacteriomes, this study provides valuable insights into the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epotential of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eleveraging rhizobacterial communities as biostimulants to enhance crop resilience and sustainability in challenging climatic conditions. As a future direction, we aim to conduct a large-scale germplasm collection for sorghum and isolate Streptomyces and other stress-associated microbial taxa from the sorghum rhizosphere to develop a biofertilizer consortium. Additionally, we will focus on unraveling the genetic and biochemical mechanisms underpinning these plant-microbe interactions to develop targeted microbial solutions for sustainable agriculture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003eMA and LSPT designed research; MA collected rhizosphere soil materials; MF performed library preparation; MA performed the research and analyzed data; MA, SJ, Mohamed A, MF, HTN, and LSPT wrote the main manuscript text. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw sequencing data has been successfully submitted to the Sequence Read Archive (SRA) under NCBI BioProject ID PRJNA1203912. All data supporting the findings of the present study are available within the paper and in the Supplementary Information\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFierer, N., \u003cem\u003eEmbracing the unknown: disentangling the complexities of the soil microbiome.\u003c/em\u003e Nature Reviews Microbiology, 2017. \u003cstrong\u003e15\u003c/strong\u003e(10): p. 579-590.\u003c/li\u003e\n\u003cli\u003eCoban, O., G.B. 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Kent, \u003cem\u003eMicromanaging the nitrogen cycle in agroecosystems.\u003c/em\u003e Trends in microbiology, 2022. \u003cstrong\u003e30\u003c/strong\u003e(11): p. 1045-1055.\u003c/li\u003e\n\u003cli\u003eSharma, P., et al., \u003cem\u003eDrought and heat stress mediated activation of lipid signaling in plants: a critical review.\u003c/em\u003e Frontiers in Plant Science, 2023. \u003cstrong\u003e14\u003c/strong\u003e: p. 1216835.\u003c/li\u003e\n\u003cli\u003eBaslam, M., et al., \u003cem\u003eRecent advances in carbon and nitrogen metabolism in C3 plants.\u003c/em\u003e International Journal of Molecular Sciences, 2020. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 318.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bacteriome, cotton, hot semi-arid environments, rhizosphere, sorghum, soybean ","lastPublishedDoi":"10.21203/rs.3.rs-5727917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5727917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferent crops may recruit specific rhizosphere microbiomes that support their survival under unfavorable conditions, including hot semi-arid climates. However, the processes driving microbiome assembly within different crops and their adaptation to such extreme environmental conditions remain poorly understood. This study investigates whether upland cotton (Gossypium hirsutum), sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e), and soybean (\u003cem\u003eGlycine max\u003c/em\u003e) recruit distinct or overlapping rhizospheric bacterial communities under hot semi-arid conditions in Lubbock, Texas, United States, with a focus on their potential role in enhancing crop resilience. By exploring rhizobacterial recruitment strategies and differential microbial associations in these crops, this study addresses critical gaps in plant-microbiome interactions and paves the way for practical applications in hot semi-arid agricultural systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found that the abundances and structures of rhizospheric bacterial communities differed among sorghum, soybean, and cotton, with the differences being closely linked to their predicted functional roles in stress adaptation and nutrient assimilation. Alpha and beta diversity analyses revealed that soybean rhizosphere exhibited the highest bacterial richness and diversity followed by cotton. In contrast, sorghum rhizobacteriome showed the lowest richness and less even distribution of rhizobacterial taxa compared with the other two crops, emphasizing crop-specific rhizobacterial associations. \u003cem\u003eActinobacteriota\u003c/em\u003eand \u003cem\u003eFirmicutes\u003c/em\u003e phyla were significantly enriched in sorghum rhizosphere, whereas \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003ephyla were significantly enriched in soybean and cotton rhizospeheres under hot semi-arid conditions. Functional prediction analysis demonstrated that sorghum-associated rhizobacteriome was significantly enriched in pathways related to stress adaptation, while soybean and cotton rhizobacteriomes exhibited more diverse pathways, primarily associated with nitrogen and sulfur assimilation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings underscore the influence of crop-specific factors in shaping rhizobacteriome composition and function to ensure their behavior and performance under hot semi-arid conditions in Lubbock, Texas, United States, with sorghum favoring stress adaptation, soybean being linked to nitrogen and sulfur assimilation, and cotton displaying intermediate traits. Our results highlight the potential for leveraging rhizobacteriome in developing innovative cultivation strategies to enhance crop resilience and productivity under challenging environmental conditions.\u003c/p\u003e","manuscriptTitle":"Deciphering crop-specific rhizobacteriome assembly in cotton, sorghum, and soybean under hot semi-arid field conditions in Texas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 08:11:11","doi":"10.21203/rs.3.rs-5727917/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d392cd4f-9f69-4559-b822-d961c081a24e","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[{"value":"featured","date":"2025-01-02 18:52:31"}],"updatedAt":"2025-01-18T17:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 08:11:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5727917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5727917","identity":"rs-5727917","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}