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. 2022 Jan 3;12(1):32. doi: 10.1007/s13205-021-03091-1

Sugarcane cultivars manipulate rhizosphere bacterial communities’ structure and composition of agriculturally important keystone taxa

Muhammad Tayyab 1,2,6, Waqar Islam 3,4, Ali Noman 5, Ziqin Pang 1,2, Shiyan Li 1,2, Sheng Lin 6,7, Lin Wenxiong 2,6,7,✉, Zhang Hua 1,2,✉
PMCID: PMC8724486  PMID: 35070622

Abstract

Different sugarcane cultivars are grown to produce renewable energy and sugar in China. However, we have a limited awareness of the interactive influence of varying sugarcane cultivars on rhizosphere bacterial structure and diversity. Assessing cultivar choice impact on soil bacterial communities is vital since bacterial taxa are frequently impacted by planting performance. Employing high-throughput Illumina sequencing, we examined bacterial communities' assemblage in the rhizosphere of six Chinese sugarcane cultivars (Regan14-62, Guitang 08-120, Haizhe 22, Guitang 08-1180, Taitang 22 and Liucheng 05-136). Our results indicated that different sugarcane cultivars have no significant influence on the Shannon index; however, their impact on richness was substantial. There was a difference in the bacterial community structure that is also associated with a change in the community composition, as determined by the DESeq2 results, suggesting that “Haizhe 22 (HZ22)” had a completely different beta diversity as compared to other five cultivars by enriching abundance of Firmicutes, Proteobacteria, Gemmatimonadetes, Saccharibacteria and Bacteroidetes and reducing the quantity of Actinobacteria, Chloroflexi, Acidobacteria, and Planctomycetes, respectively. The HZ22 rhizosphere significantly enriched six genera (e.g., Devosia, Mizugakiibacter, Mycobacterium, Nakamurella, Rhizomicrobium, and Virgibacillus) relative to other varieties, suggesting an important role in plant disease tolerance and growth development, including soil nutrient cycling and bioremediation. Analysis of similarity (ANOSIM) and correlation analysis revealed that cultivars, soil organic matter, pH and soil moisture were central factors influencing bacterial composition. These findings may help in selection of plant cultivars capable of supporting highly abundant specific beneficial microbial groups, improving plant disease resistance, growth stimulation, and soil bioremediation capabilities, further leading to improvements in breeding strategies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-021-03091-1.

Keywords: Sugarcane cultivars, Bacterial interactions, Pyrosequencing, Rhizosphere communities, Breeding stratagies

Introduction

Predesigned breeding programs usually focus on improving crops’ agronomic features such as disease resistance, nutrient use efficiency, and productivity. However, they seldom take into account the impacts of plant-associated soil microbiome, which may hinder phytopathogens by competition, hyperparasitism and antagonism stimulate plant development by creating phytohormone and vitamins, including enhance plant’s resistance to various stresses (e.g., abiotic and abiotic) (Chen et al. 2021; Sarawaneeyaruk et al. 2019; Suresh et al. 2021; Wilson et al. 2020). Root phenotypic characteristics are significant determinants in transforming microbial assemblage and structure (Zhang et al. 2019). Adopting a breeding strategy directed toward genotype-associated-rhizospheric microbiome changes can enhance crops' agronomic attribures and preserve sustainability of soil ecosystem.

Various bacterial taxa (e.g., Paenibacillus spp., Bacillus cereus and Pseudomonas fluorescens) are the antagonists of the seed pathogens and Fusarium oxysporum (Adnan et al. 2019; Araújo Da Silva et al. 2003; Leeman et al. 1995) and plant-promoting strains (e.g., Azospirillum) are inhabitants in rhizospheric soil of different crop genotypes (Chamam et al. 2013; Hu et al. 2021). Crop genotypes represent an indispensable role in Pseudomonas fluorescens genetic makeup and its biological control potential in soilborne pathogens (Islam et al. 2021a; Raaijmakers et al. 2002). The Pseudomonas was much more abundant in the old native-breed wheat rhizosphere than other recent modern varities (Germida and Siciliano 2001). Particular wheat genotypes (e.g., Lewjain) can assist in developing resident soil groups, especially strains that produce 2,4-diacetyl phloroglucinol (e.g., Pseudomonas fluorescens “LR3-A28”). Recently, two newly phlD-based genotypes (PfY and PfZ) showed their colonization to two wheat genotype (“Lewjain” and “Penawawa”) rhizosphere (Mazzola et al. 2004). Sugarcane cultivar and Pseudomonas interaction effectively suppresses sugarcane diseases efficiently (root infestation or infection by Ustilago scitaminea, Fusarium spp. and Colletotricum spp.) (Patel et al. 2019). Besides, few findings have revealed inconsistent responses among different sugarcane cultivars to pathogens and biological control agents (Antunes et al. 2017; Qasim et al. 2020, 2021b). There are complex interactions among cultivar and plant-promoting bacteria that cycle nutrients in agricultural ecosystems (Aqeel et al. 2021; Beule et al. 2019). For example, the sugarcane rhizosphere with low atmospheric CO2 concentration was dominated by the Bacilli, Gammaproteobacteria and Clostridia classes, whereas the rhizosphere with high atmospheric CO2 mainly selected the Bacilli and Betaproteobacteria classes. This difference in diversity of Bacili class was attributed to transformations in root exudates of sugarcane (da Costa et al. 2018). In rhizospheres, sugarcane cultivar-specific alterations were observed for plant-promoting bacterial diversity and activity via using a multiphasic strategy (Antunes et al. 2017). The most operational taxonomic units (OTUs) show a substantial alteration in alpha diversity in the rhizosphere; the most critical rice cultivar influence on the microbiome was compared with the endosphere and rhizoplane compartments (Edwards et al. 2015). In addition, for field-grown potatoes, 40% of the OTU from the three cultivars was based on cultivar status (Weinert et al. 2011). Plants that have been genetically modified to produce different root exudates have diverse impacts on the makeup of root-related bacteria and fungal groups. Recently, variations in the core bacterial community in sugarcane cultivars are not associated with nitrogen fertilizer, while sugarcane cultivars (plant genotypes) have only subtle influences on bacterial community structure (Yeoh et al. 2016).

In the past span, plant host genetics has been used to understand better plant–microbe associations in rhizosphere soils. but, the trials were time-consuming, and the results are also tough to distinguish because of discrepancies in trial plans. Furthermore, conventional sequencing and cloning strategies might have ignored some responsive bacterial genera due to their prevalence. The most advanced Illumina sequencing technology has enabled us to properly understand microbial community structure and determine how the rhizosphere microbiome responds to multiple plant cultivars. Different sugarcane cultivars have been introduced in China to increase production, adaptability, or resistance to diseases (Table 1). Therefore, we aimed to distinguish the changes in soil bacterial communities associated with these sugarcane cultivars. We hypothesized that if cultivar-associated differences were identified, it could help develop plant germplasm derived from crop diversity and reserve soil ecosystem sustainability by reducing the employment of pesticides and fertilizers.

Table 1.

Parents and breeding institutions of the six sugarcane cultivars used in this study

Cultivars Breeding institutions Parents
Haizhe 22 SIRI, Guangdong Academy of Science YT93-159/ROC22
Guitang 08-120 SRI, AAS, Guangxi Province ROC24/Yunzhe 89-351
Guitang 08-1180 SRI, AAS, Guangxi Province ROC26/ROC22
Regan 14-62 SSCRI, Chinese Academy of Tropical Agricultural Sciences YT91-976/YT00-236
Liucheng 05-136 SRC, Liucheng County, Guangxi Province CP81-1254/ROC22
Taitang 22 TSRI, TSC, Taiwan Sugar Corporation, Tainan city ROC05/69463
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Breeding institutions: SRI Sugarcane Research Institute, AAS Academy of Agricultural Sciences, SRC Sugarcane Research Center, TSRI Taiwan Sugar Research Institute, TSC Taiwan Sugar Corporation, SSCRI South Subtropical Crops Research Institute

Materials and methods

Site description and sampling

The experimental site was located in Suixi County, Zhanjiang City, Guangdong Province (east longitude 21° 22′ 38″ N 110° 15′ 00″ E), which is the main sugarcane growing area. i.e., approximately 467 km2/annum. The soil and experimental composition of this study is mentioned in detail in our previous publication (Tayyab et al. 2021a). In 2018, six sugarcane cultivars were grown in a randomized block design under field condition (Table 1). Sugarcane seedlings were treated in a 50% carbendazim 800 times solution for 15 min before being planted with 85,000 ha−1 a density and 0.1 m plant spacing. Every sugarcane cultivar experimental place comprised three repetitions, with plot areas (30.6–50.4 m2) and sugarcane rows spaced 1.4 m apart. A wide (2 m) isolation band divided each plot, and a protection band (1 m) included each cultivar trial. Other on-site management practices are the same as previously mentioned (Tayyab et al. 2021a).

All soil samples were collected at the tasseling stage, and five rhizosphere soil samples were collected from each plot using the S sampling method and then mixed as a biological replicate. From each field, sugarcane plants with consistent growth were chosen and dugout. By shaking vigorously, the soil that adheres loosely to the plants' roots systems was thrown away. The rhizosphere soil was then brushed (firmly attached) to collect eaxh sample which was brought to the laboratory in an ice box and a part of which was stored at − 80 °C for DNA extraction (Arafat et al. 2019). The second part of soil was air dried, strained (< 2 mm) for analyzing pH (1:2 soil to H2O ratio), TN, OM, TC, and AP (Table S1) (Pang et al. 2019).

DNA extraction, PCR amplification and illumina sequencing

Total soil DNA was isolated from every sample that was quantified and used for PCR using the primers 341F and 805R (Klindworth et al. 2013) to amplify the V3–V4 bacterial region (16S rRNA gene). The AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, USA) was utilized to obtain amplicons, followed by their quantification through QuantiFluor™−ST (Promega, Madison, USA). Applying standard rules, the purified amplicons were pooled in equimolar and end-matched series (2 × 250) on the Illumina MiSeq platform (Majorbio, Shanghai).

Processing and analyzing of sequencing data

QIIME (version 1.17) was used to truncate the 250 bp reads. After removing ambiguous reads, overlapping sequences larger than 10 bp in size were assembled. OTUs were constructed using UPARSE software (version 7.1) with a cutoff rate of 97%, while chimeric sequences were deleted and UCHIME was used for classification. For each OTU, sample sequences were chosen, and the Ribosomal Database Project Classifier (RDP) was used to annotate each one with taxonomic information.

Statistical analysis

Using the Mothur pipeline, the richness (ACE and Chao1 indices) and diversity (Shannon indices) indices were analyzed. To investigate β-diversity (inter-community) across samples, a hierarchical tree (estimated using Bray–Curtis), principal coordinate and non-metric multidimensional scaling (NMDS) were carried out. To investigate the dissimilarity of bacterial community structures in sugarcane cultivars, an analysis of similarity (ANOSIM) was performed. The centroid of each variable was drawn according to the two-dimensional direction of the bacterial community determined by NMDS, and the “envfit” function in R (version 3.2.2) was applied to calculate the correlation and P value (https://cran.r-project.org/web/packages/vegan/index.html). The OTU-based Wilcoxon rank sum metric was used for OTU difference abundance and taxonomic analysis, and the corresponding P value was corrected for multiple tests using an FDR of 0.05.

Results

Overview of bacterial communities

A total of 20,389 (average 3398.16) clean reads and 2571 (shared 1558) OTUs were perceived in the rhizosphere of six sugarcane cultivars (GT08-120, RG14-62, GT08-1180, HZ22, LC05-136 and TT22, respectively) (Fig. S1). All the sugarcane cultivars were abundant in Actinobacteria, followed by Proteobacteria, Firmicutes, Chloroflexi, Acidobacteria, Planctomycetes, Gemmatimonadetes, Saccharibacteria, and Bacteroidetes, respectively. The “Haizhe 22” cultivar had a larger abundance of Proteobacteria, Firmicutes, Gemmatimonadetes, Saccharibacteria, and Bacteroidetes, but a smaller abundance of Actinobacteria, Chloroflexi, Acidobacteria, and Planctomycetes than other cultivars. These results also suggest that bacterial phyla abundance was different in “Haizhe 22” compared to other cultivars, indicating that it has recruited different bacterial phyla than the other five varieties (Fig. 1).

Fig. 1.

Fig. 1

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Rhizospheres’ bacterial phyla across six sugarcane cultivars. Lithograph shows the comparison of bacterial phyla among all sugarcane cultivars

Soil bacterial alpha diversity across different sugarcane cultivars

The bacterial α-diversity index, such as Shannon, did not significantly differ across all sugarcane varieties (P < 0.05). However, bacterial richness indexes (ACE and Chao1) in four varieties (GT08-1180, HZ22 and GT08-120) and TT22 were found lesser than those in the other two varieties (LC05-136 and RG14-62), while TT22 exhibited a significantly lower difference for bacterial richness (Table 2). These results indicate that we did not perceive differences in the Shannon index across sugarcane varieties regarding alpha diversity, but the bacterial richness was affected.

Table 2.

Alpha diversity indices across sugarcane variety rhizosphere

Cultivars ACE Chao1 Shannon
GT08-1180 2915.66 ± 408.51ab 2892.91 ± 430ab 6.12 ± 0.25a
GT08-120 2992.75 ± 84.43ab 2992.96 ± 167.25ab 6.11 ± 0.12a
HZ22 2864.75 ± 418.3ab 2817.71 ± 418.25ab 6.18 ± 0.32a
LC05-136 3102.04 ± 246.66a 3103.35 ± 224.13a 6.29 ± 0.14a
RG14-62 3309.23 ± 227.33a 3143.73 ± 232.18a 6.20 ± 0.31a
TT22 2507.46 ± 187.7b 2535.07 ± 211.02b 5.92 ± 0.27a
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Alpha diversity index, such as bacterial community diversity (Shannon) and richness (Chao and ACE) and the diversity of six different sugarcane varieties. Diverse lowercase characters in columns show significant differences among cultivars (LSD test; P < 0.05)

Soil bacterial beta diversity across different six sugarcane cultivars

Principal coordinate analysis (PCoA) revealed that the bacterial community was well separated in Haizhe 22 rhizosphere than the other five varieties, with both axes (first and second) representing the entire alteration in bacterial (58.05%) data (Fig. S2). Non-metric multidimensional scale (NMDS) analysis and hierarchical linkage clustering (UPGMA) also demonstrated that the separation of the bacterial communities in Haizhe 22 from other five varieties, which was confirmed by the dissimilarity test (R2 = 0.38, P = 0.05) using the ADONIS algorithm (Fig. 2; Fig. S2). Besides, soil OM, pH and moisture were abiotic parameters regulating bacterial community composition (Table S2).

Fig. 2.

Fig. 2

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The hierarchical tree of bacterial communities (calculated on Bray–Curtis) across six sugarcane cultivars

Differential abundance analysis and bacterial phyla related to sugarcane cultivars

The changes in the rhizosphere microbiota in all sugarcane cultivars were tested at the OTU level and Manhattan plots were drawn to perceive the OTUs enrichment with consideration of their taxonomy (Fig. 3, Fig. S3). The HZ22 rhizosphere enriched 21 OTUs related to various different bacterial phyla, such as Actinobacteria, Acidobacteria, Chloroflexi, Firmicutes, Bacteroidetes, Planctomycetes, Proteobacteria, and Saccharibacteria than GT08-120. However, GT08-120 roots enriched 5 OTUs associated with Proteobacteria, Chloroflexi, and Saccharibacteria than HZ22 rhizosphere (Fig. 3A; Fig. S4). The 57 OTUs associated with different bacterial phyla (Actinobacteria, Acidobacteria, Chloroflexi, Bacteroidetes, Firmicutes, Proteobacteria, Gemmatimonadetes, and Saccharibacteria) were enriched in HZ22 rhizosphere than GT08-1180. GT08-1180 roots increased 57 OTUs belonging to Actinobacteria, Chloroflexi, Firmicutes, and Proteobacteria (Fig. 3B; Fig. S4). The HZ22 rhizosphere was enhanced 66 OTUs associated with bacterial phyla, including Actinobacteria, Acidobacteria, Bacteroidetes, Firmicutes, Chloroflexi, Planctomycetes, Gemmatimonadetes, Proteobacteria, and Saccharibacteria than LC05-136. LC05-136 rhizosphere enriched 79 OTUs belonging to Acidobacteria, Actinobacteria, Chloroflexi, Firmicutes, and Planctomycetes than HZ22. In comparison with RG14-62, HZ22 rhizosphere enriched 60 OTUs associated with Acidobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Proteobacteria, and Saccharibacteria (Figs. S3, S4). RG14-62 rhizosphere enriched 25 OTUs related to Actinobacteria, Acidobacteria, Firmicutes, Chloroflexi, Planctomycetes, Gemmatimonadetes, and Proteobacteria than HZ22 rhizosphere (Fig. S3). The HZ22 rhizosphere enriched 65 OTUs related to different bacterial phyla, such as Actinobacteria, Acidobacteria, Chloroflexi, Bacteroidetes, Gemmatimonadetes, Firmicutes, Proteobacteria, and Saccharibacteria than TT22 (Figs. S3, S4). TT22 rhizosphere enriched 51 OTUs associated with Actinobacteria, Acidobacteria, Chloroflexi, Planctomycetes, Firmicutes, and Proteobacteria than HZ22 rhizosphere (Figs. S3, S4). Most of the six sugarcane cultivars have enriched/depleted OTU abundances.

Fig. 3.

Fig. 3

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Manhattan plot showing OTUs enriched in HZ22 and two cultivars (GT08-120 and GT08-1180). Each dot or triangle represents a single OTU. OTUs enriched in HZ22 or two cultivars (GT08-120 and GT08-1180) are represented by empty triangles or dots, respectively. OTUs are arranged in taxonomic order and colored according to the phylum. CPM counts per million

Differential abundance analysis and bacterial genera related to sugarcane cultivars

The HZ22 rhizosphere considerably depleted or enriched OTUs belonging to various bacterial genera compared to other cultivars, i.e., GT08-1180, GT08-120, RG 14-62, LC 05-136 and TT22 (Fig. 4; Figs. S4, S5). Specifically, the HZ22 rhizosphere significantly enriched OTUs related to nine bacterial genera (e.g., Devosia, Leifsonia, Nocardioides, Mycobacterium, Rhizomicrobium, Nakamurella, Virgibacillus, Mizugakiibacter, and Luteibacter) as compared to GT08-120. In contrast, OTUs belonging to Acidisphaera, Acidothermus, and Rummeliibacillus were enriched in GT08-120 rhizosphere than HZ22 (Fig. 4A). Similarly, the HZ22 rhizosphere significantly enriched OTUs assigned to ten bacterial genera (Devosia, Nitrolancea, Nakamurella, Rhizomicrobium, Agaricicola, Mycobacterium, Virgibacillus, Pseudomonas, Mizugakiibacter, and Gemmatimonas) than GT08-1180. However, GT08-1180 rhizosphere wasenriched with OTUs related to Catenulispora and Acidothermus than HZ22 (Fig. 4B).

Fig. 4.

Fig. 4

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DESeq2 analysis showing fold change of rhizosphere bacterial genera in HZ22 as compared to GT08-120 (A), GT08-1180 (B), LC 05-136 (C), RG 14-62 (D), and TT22 (E) (P < 0.05)

The HZ22 significantly augmented OTUs associated with 17 bacterial genera (Mycobacterium, Rhizomicrobium, Mizugakiibacter, Devosia, Pseudogracilibacillus, Nocardioides, Nitrolancea, Nakamurella, Agaricicola, Actinoplanes, Nitrococcus, Virgibacillus, Crenotalea, Pseudomonas, Candidatus Alysiosphaera, Gemmatimonas, and H16 RB41) than LC 05-136. In comparison with HZ22, OTUs allocated to Acidisphaera, Arthrobacter, Acidothermus, Catenulispora, Cohnella, Paenibacillus, Nocardia, Sorangium, Rummeliibacillus, and Singulisphaera were significantly greater in LC 05-136 rhizosphere (Fig. S5). Furthermore, HZ22 rhizosphere significantly augmented OTUs allotted to 17 bacterial taxa (Devosia, Nocardioides, Leifsonia, Mizugakiibacter, Dyella, Rhizomicrobium, Agaricicola, Burkholderia, Mycobacterium, Nakamurella, Nitrolancea, Rhodanobacter, Pseudogracilibacillus, Virgibacillus, Taibaiella, Alkanibacter, and Luteibacter) than RG 14-62. While the OTUs belonged to eight bacterial genera (e.g., Gemmatimonas, Acidisphaera, Luedemannella, Thermosporothrix, Acidothermus, Rummeliibacillus, Roseiarcus, and Bryobacter), they were considerably enriched in RG 14-62 rhizosphere as compared to HZ22 (Fig. S5).

Similarly, there were OTUs allocated to 25 bacterial taxa (Mycobacterium, Bacillus, Bryobacter, Leifsonia, Nakamurella, Devosia, Rhizomicrobium, Nitrolancea, Marmoricola, Agaricicola, Actinoplanes, Nocardioides, Oceanobacillus, Pseudogracilibacillus, Rhodanobacter, Gemmatirosa, Defluviicoccus, Taibaiella, Kroppenstedtia, Paenarthrobacter, Luteibacter, Pseudomonas, Virgibacillus, Mizugakiibacter, and Gemmatimonas) that were significantly augmented in HZ22 as compared to TT22. However, the OTUs apportioned to Acidisphaera, Catenulispora, Thermosporothrix, and Acidothermus genera were enriched considerably in TT22 rhizosphere than HZ22 (Fig. S5).

Shared sequences and OTUs associated with bacterial genera across cultivars

OTUs’ differential analysis suggested that the seven bacterial genera were found to be shared in six sugarcane cultivars. The HZ22 rhizosphere significantly enriched the OTUs associated with six genera across these bacterial genera (Devosia, Mizugakiibacter, Mycobacterium, Nakamurella, Rhizomicrobium, and Virgibacillus) as compared to five other varieties (Fig. 4; Fig. S5). On the other hand, the rhizosphere of five cultivars significantly enriched OTUs belonging to Acidothermus than HZ22, and these results were consistent with reads assigned to these genera (Fig. 5).

Fig. 5.

Fig. 5

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Comparison of genus-level bacterial taxa sequences detected in the rhizosphere of each sugarcane cultivar

Discussion

The rhizosphere-associated microbiome contributes to improving plant health, growth and productivity (Oladimeji et al. 2020; Tayyab et al. 2021b). Plant species or cultivars that excrete root exudates from bulk soils may attract specific microbial communities (Arafat et al. 2020). However, we have limited information about the interaction effects of different sugarcane cultivars in bacterial composition and diversity in the rhizosphere. To completely comprehend the hereditary principles governing rhizosphere bacterial composition and diversity trends, the rhizosphere bacterial communities of six sugarcane cultivars were characterized to assess the relative impact of edaphic characteristics and sugarcane cultivars.

Alpha diversity analysis revealed that different sugarcane cultivars had a discernible effect on bacterial richness. First, fluctuations in sugarcane root exudates could be ascribed to variations in diversity, as genotypes recruit specific microbiomes by releasing root exudates (Arafat et al. 2020). Variations in root exudate composition and different root structure considerably affect rhizosphere-associated microbiome. Secondly, the chemical composition of the rhizosphere is altered by different cultivars, thus affecting bacterial richness.

OTUs associated with Proteobacteria genera (Devosia, Mizugakiibacter, and Rhizomicrobium), Actinobacteria (e.g., Mycobacterium and Nakamurella), and Firmicutes genus (Virgibacillus) were statistically more abundant in HZ22 than the other five cultivars and their functions are given in (Table S3). Proteobacteria are usually fast-growing bacteria that adapt to the rhizosphere of different plant species. Owing to their response to labile carbon sources, their populations fluctuate opportunistically (Lauber et al. 2009). Members associated with Alphaproteobacteria (Devosia and Rhizomicrobium) have specific physiological characteristics, and most of the members contribute to biological nitrogen fixation (BNF). The genus Devosia was established when Pseudomonas riboflavia was recategorized as Devosia riboflavia and it contained eight well-known species. However, nitrogen-fixing (nif) and the nodulating (nodD) symbiotic genes are only found in D. neptuniae isolated from aquatic legumes. Likewise, Rhizomicrobium is a symbiotic mycorrhizal bacterium that facilitates NF. Virgibacillus inoculation promotes growth and resists stress in alfalfa grown under saline stress. The authors indicated a possible mechanism involving bacterial (Virgibacillus) binding of sodium ions and the development of volatile compounds, biofilm formation involving exopolysaccharides via signaling modulation, and stress-resistant gene expression. The genus Nakamurella is rare in Actinobacteria, with only four species having validly published names (Nouioui et al. 2017). Turning Mizugakiibacter growth from an aerobic to an anoxic environment enhances nitrate production and consumption (Khalid et al. 2021a, b), contributing to the total N levels. Besides, Mizugakiibacter potentially contributes to remediating heavy metal-contaminated soil and is commonly found in abundance at low pH in maize and rice fields (Jiao et al. 2019). Mycobacterium are pathogenic to humans and animals (Qasim et al. 2018, 2021a) and are abundant in acidic soils of the sugarcane cropping system (Pang et al. 2021). According to previous findings, low pH acceptance is a primary physiological ability of Mizugakiibacter and Mycobacterium (Jiao et al. 2019). The HZ22 rhizosphere acidity was lower than that of other five cultivars because of root-mediated pH corrections, i.e., proton extrusion to recompense for imbalanced anion and cation absorption (Arafat et al. 2020).

The composition of sugarcane root exudates shows fluctuations in quantity and quality according to the plant’s nutritional situation, growth stage, and even the status of the root system in space and time. Plant molecular signals vary by species and genotype and represent particular root exudate components which draw precise bacteria species (Haichar et al. 2008; Suvan et al. 2020). The difference between the two sugarcane genotypes is that the two genes encode the amount and type of sugar in the root, as well as the potential amount and type of sugar exuded to the rhizosphere, which changes the activity and composition of the rhizosphere microorganisms (Aira et al. 2010). Similarly, Arabidopsis variations or ecotypes with different root phytochemical secretions trigger a significant alteration in fungal and bacterial community composition in rhizosphere (Badri et al. 2009). This breakthrough reveals the possibility for showing plant genotypic features that can be used to choose definite functional strains for plant disease tolerance and growth, including soil bioremediation (Bell et al. 2014).

Analysis of similarity (ANOSIM) demonstrated that cultivar type enormously changed bacterial composition, indicating that genotypic aspects directly influenced the microbial community in the rhizospheric soil (Edwards et al. 2015). However, it was not possible to identify the crucial features causing the most significant distinctions in the rhizosphere bacterial community composition, owing to poor heredity and only six cultivars. These varieties have inadequate genetic modification and utilize an intense agricultural administration that relies profoundly on fertilization. Root colonization of disease-resistant rhizobia can be a hereditary characteristic, most likely linked to heterosis (Picard and Bosco 2006). Some key genes and special environmental factors of field are important for determining the composition and diversity of the rhizosphere microbiome (Islam et al. 2020, 2021b, c). Thus, effective sugarcane genotypes are needed to study functional alleles and genetic plant–microbe relationships under regular ecological settings. Through additional improvements in these areas, the rhizosphere bacteria may eventually support plant breeding.

Conclusion

Bacterial keystone taxa, their abundance, structure and composition are frequently manipulated by different sugarcane cultivars and their planting performance. Here, we utilized high-throughput Illumina sequencing to examine the assemblage of bacterial communities in the rhizosphere of six Chinese sugarcane cultivars. We found that sugarcane cultivars have no significant influence on the bacterial Shannon diversity; however they substantially influence the bacterial richness. Moreover, among all the cultivars, HZ22 exhibited a completely different bacterial community structure via enriching the abundance of Firmicutes, Proteobacteria, Gemmatimonadetes, Saccharibacteria and Bacteroidetes. We observed that the HZ22 rhizosphere significantly enriched six genera that were extremely important in plant disease tolerance and growth development. Furthermore, it was found that the cultivars, soil organic matter, pH and soil moisture were the central factors influencing bacterial composition. We think that these findings may help in selecting the plant cultivars capable of supporting beneficial microbial groups, improving plant disease resistance and growth stimulation, further leading to improvements in sugarcane breeding programs.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1252 KB) (1.2MB, docx)

Author contributions

MT, ZP, SL, and SL conceived the experiment, MT designed the sampling. MT, HZ, and ZP collected samples and conducted preparation in the laboratory. MT, ZP, WI, AN, and ZP conducted the statistical analysis, wrote, and finalized the manuscript. WL, SL, and HZ supervised and approved the manuscript for final publication.

Funding

This research was funded by the Earmarked Fund for the Modern Agriculture Technology of China via Grant number CARS-170401. This research was funded by the Science Foundation of Fujian Agriculture and Forestry University (Grant number: CXZX-2017556).

Data availability

The datasets generated and/or analyzed during the current study are available at www.datadryad.org via https://doi.org/10.5061/dryad.4m66m9999r that will be made fully available upon acceptance of the article.

Declarations

Conflict of interest

The authors have no competing interests.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Contributor Information

Lin Wenxiong, Email: lwx@fafu.edu.cn.

Zhang Hua, Email: zhanghua4553@sina.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 1252 KB) (1.2MB, docx)

Data Availability Statement

The datasets generated and/or analyzed during the current study are available at www.datadryad.org via https://doi.org/10.5061/dryad.4m66m9999r that will be made fully available upon acceptance of the article.

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