Introduction

Pomegranate (Punica granatum) is a very important fruit crop belonging to the Punicaceae family that is widely farmed around the world and is prominent in India’s dry regions (Melgarejo-Sánchez et al. 2021). Karnataka is the second largest producer of pomegranate after Maharashtra (Saxena et al. 2018). In Karnataka, India, major varieties grown are Bhagwa, Ganesh, Mridula (Vasanth Kumar 2009). Apart from its agricultural value, the health advantages of pomegranate have been investigated in relation to illness states such as diabetes, high blood pressure, and inflammatory activities (Asgary et al. 2014). The antioxidant activity, nutritional status, and total phenol content of pomegranate peel extracts in water and methanol were investigated previously by Middha et al. (Middha et al. 2013). Various metabolites, such as steroids, were discovered, as well as other components like flavonoids, anthraquinone, proanthocyanidins, and phenolic compounds like gallic acid. Usha et al (2020) critically reviewed anti-cancerous property of pomegranate (Usha et al. 2020). Bhagwa is a highly commercially viable variety of pomegranate, found to be the most suitable and promising variety of pomegranate for dry regions of Karnataka. Compared to other elite varieties like Ganesh, fruit quality is outstanding fetching an overall acceptance score of 90.8/100 compared to Ganesh with the score of 57.8/100 (Prasad et al. 2013). The variety is commonly used as it is also suitable for high-density planting in pomegranate orchards (Saroj and Sharma 2017). Our lab also annotated the whole genome of pomegranate (Bhagwa var.) and provided new insights into the pharmacological properties of the secondary metabolites (Usha et al. 2022). All these factors gave a justification for the inclusion of Bhagwa variety of pomegranate in our experimental study.

Microorganisms serve a critical part in a variety of sectors, including medical, engineering, industry, and agriculture (Sloan et al. 2006). As a result, using biotechnological tools to investigate the genetic and biological variety of these bacteria will aid in the discovery of novel genes, enzymes, and chemicals of interest. Many microorganisms cannot be cultivated using traditional microbiological methods. To address these issues, DNA-based molecular approaches that can characterize microorganisms present in any material without the need for culturing have been developed (McSweeney et al. 2006). As described by Streit and Schmitz (2004), methodologies such as 16S rRNA gene analysis have the limitation of only disclosing the taxonomic features of bacteria and not their functional role (Streit and Schmitz 2004). With the advancement of modern technology, such as ‘metagenomics,’ taxonomic diversity as well as functional characterization of microbes in environments like soil, water, ancient animal remains, or the digestive system of animals and humans (Ghazanfar and Azim 2009) can be investigated without the need for microbes to be cultured in lab conditions. Metagenomics aids in the isolation of species like Verrucomicrobia, Acidobacteria, Armatimonadetes, and methanogenic Archaea that are not cultivable using traditional culture methods (Cardenas and Tiedje 2008). 16S rRNA sequencing using NGS platforms have helped to unravel the microbial diversity of any ecosystem which is otherwise a challenge to explore through wet lab studies. The advantage of nanopore over other NGS platforms is the generation of long reads facilitating the sequencing of entire 16S rRNA region from V1-V9 which in turn facilitates higher resolution for bacterial identification unlike Illumina platform where short reads limit the sequencing to V1-V2 or V3-V4 (Santos et al. 2020).

The interplay between microbes and host–microbe interaction results in rhizosphere microbial community differentiation that can be explored using 16S rRNA-targeted sequencing and shotgun metagenome as demonstrated in experiment with barley having abundance of Comamonadaceae, Flavobacteriaceae, and Rhizobiaceae in the rhizosphere (Bulgarelli et al. 2015). Citlali et al. (2018) discovered that, in addition to soil type, host plant and species had a significant impact on microbial diversity reporting the dominance of Proteobacteria, Firmicutes, Actinobacteria, Acidobacteria, and Bacteroidetes associated with Crassulacean Acid Metabolism plants (Citlali et al. 2018). Pathogenesis, secretion, phage interactions, and nutrition mobilization were all linked to the functional annotation of features associated with microbial diversity.

The metagenomics approaches like pyrosequencing have also facilitated the discovery and utilization of new industrially important enzymes from shift in the microbial community abundance to bacteria involved in chitin breakdown, including phyla such as actinobacteria, proteobacteria, and proteobacteria (Jacquiod et al. 2013). The impact of physico-chemical properties of soil on the microbial community, especially the soil depth, has been investigated (Yan et al. 2019) wherein 16S rRNA targeted sequencing using Illumina MiSeq platform revealed a decrease in alpha diversity across a depth of 0–300cms in legume crop cultivation (Robinia pseudoacacia and Trifolium repens) with an abundance of Nitrospira in top soil. Phosphorus concentration decreased across the depth. Similar results were reported by Kuramae et al. employing real-time PCR and microarray methodologies based on phylogeny targeting 16S rRNA (Kuramae et al. 2012). The C:N ratio and soil phosphorus level were more important factors in defining microbial diversity. Firmicutes were determined to be the most prevalent phylum in deciduous forest soils of Netherlands. Bacillus and Clostridium OTUs were shown to be associated with high phosphate and pH, implying that the bacterial population can be used as a proxy for soil properties. There are reports of availability of soil resources having profound influence on functional diversity of microbes in spruce plantations with the organic horizon revealing an abundance of sequences for glycoside hydrolases in comparison with enriched glycoside transferases in mineral horizon (Uroz et al. 2013). Although there are few publications on traditional screening of microbial flora using normal microbiological techniques, there have been no reports using metagenomics strategy to examine microbial diversity in the rhizosphere of pomegranate (Shahaby et al. 2016). Our lab previously reported the adaptations of Fusarium in the rhizosphere from wilt-infected pomegranate field (Das et al. 2021). Therefore, the current study on microbial diversity in the rhizosphere of Punica granatum using amplicon targeted sequencing on a nanopore sequencing platform was an attempt to correlate soil characteristics based on depth with the selective microbial community associated in the rhizosphere compared to bulk sample, as well as to identify the prominent and abundant bacterial species associated with the rhizosphere.

Materials and methods

Site description and sampling

Collection of soil sample

Soil samples were collected from Doddamarali, Chikkaballapur Taluk of Karnataka (Chikkaballapur, Doddamarali, 13.3669°N, 77.7373°E), where the pomegranate variety, Bhagwa is cultivated (Fig. 1a, b). The farming practice followed an integrated approach of conventional and organic farming. The irrigation practice followed in the field was drip irrigation. The total numbers of pomegranate plants in the field were 429 in an area of 1 acre. The plants were in 13 columns and 33 rows. The average yield recorded was 9 t/acre. The average temperature recorded was 28–30 °C, humidity of 43%, wind speed 13 kmp, wind direction NE. Monocropping was practiced with no tillage. Samples were collected from all the corners and centre of the plot randomly from the one-acre land of pomegranate crop. The soil samples were collected from the rhizosphere of the plant and the bulk soil away from the root zone (two feet away from root). For each type of sample, the depths covered for sample collection were 0–5 cms, 5–10 cms, 10–15 cms.

Fig. 1
figure 1

a Study area map showing the site of soil sample collection in Chikkaballapur district of Karnataka. b Pomegranate field in Doddamaralli, Chikkaballapur where Bhagwa variety is grown

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Physiochemical characterization and total microbial count estimation

Since the soil characteristics play a very important role in shaping the microbial diversity, the study of soil characteristics with respect to the physico-chemical parameters was very crucial in this study. These parameters should be well defined and recorded before exploring the microbial diversity. Thorough analysis of physico-chemical properties of soil including pH, electrical conductivity, NPK, organic carbon, micronutrients such as Zn, B, Mn, Fe, Cl, Cu, bacterial and fungal count were carried out for samples drawn from bulk and rhizosphere soil in the pomegranate field. The analysis was done for soil samples taken from various depths, 0–5 cm, 5–10, and 10–15 cm (Fig. 2).

Fig. 2
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Experimental design

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The pH and electrical conductivity were measured using electrometric method. Total nitrogen was estimated using Kjeldahl method, phosphorus using spectrophotometer and potassium using flamephotometer. Titration method was followed for organic carbon and chlorine. Atomic absorption spectrometry (AAS) was used for Fe, Cu, Mn, B, Zn. Total bacterial count was enumerated using microbiology of food and animal feeding stuffs, horizontal method for the enumeration of micro-organisms colony-count technique at 30 °C; Bureau of Indian Standards (IS 5401:2002) and total fungal count using method for yeast and mold count of foodstuffs & animal feeds; Bureau of Indian standards (IS 5403:1999).

Sample preparation, microbial community, DNA extraction and sequencing

The 16S targeted metagenomics sequencing was carried out to explore the microbial diversity of the soil samples from rhizosphere and bulk regions at various depths of 0–5 cms, 5–10 cms, 10–15 cms.

DNA extraction, QC and amplification of 16S rRNA gene

DNA extraction was done for all the six samples through commercially available kits such as QIAGEN, ZYMO RESEARCH DNA extraction as per the manufacturer’s recommendation. Extracted DNA from all the samples were subjected to NanoDrop 1000 spectrophotometer (Thermoscientific) and GEL Check before being taken for PCR amplification: The NanoDrop readings of 260/280 at an approximate value of 1.8–2 was used to determine the quality of DNA.

40 ng of extracted DNA was used for amplification along with 10 pM of each primer. Universal primers 16F: 5′ AGAGTTTGATCMTGGCTCAG 3′ and 16R: 5′ TACGGYTACCTTGTTACGACTT 3′ were used. 25 cycles of PCR were carried out maintaining the following conditions: Denaturation at 95 °C for 15 s, annealing at 60 °C for 15 s, elongation at 72 °C for 2 min, final extension at 72 °C for 10 min and hold at 4 °C. The amplified 16S PCR product was purified and subjected to GEL check and NanoDrop QC. The NanoDrop readings of 260/280 at an approximate value 1.8–2 is used to determine the DNA’s quality. Sequencing was performed taking 1 μg of DNA template using Oxford Nanopore MinION platform.

Bioinformatics workflow

EPI2ME 16S analysis workflow followed by Quantitative Insights into Microbial Ecology (QIIME) was used to perform genus-level identification from single reads; with access to basecalled files for detailed investigations at the species and sub-species level. The phylogeny analysis of query sequence with the closely related sequence of blast results was performed followed by multiple sequence alignment. The workflow was designed to BLAST basecalled sequence against the NCBI 16S bacterial database, which contains 16S sequences from different organisms. Each read was classified based on percentage coverage and identity. The quality check for the sequences obtained was also subjected to QIIME workflow. KEGG pathway prediction was adopted to find the major pathway hits in rhizosphere soil.

Data analysis

Microbiome analyst, the web-based tool was used for data processing and statistical analysis. A total of six samples (B2, B4, B6, R2, R4, R6) and 1270 features or taxa were present in the current study. The sample data contained a total of six samples and one sample variables. The OTUs were annotated as SILVA label. Data Integrity Check was carried out before data analysis and Library Size was determined for the inspection of each sample. Data filtering was carried out after integrity check. A total of 579 low abundance features were removed based on prevalence and 25 low variance features were removed based on interquartile range. The number of features that remained after the data filtering step was 217.

For data normalization, the data were stored as a table with one sample per column and one variable (taxa or OTU) per row. Normalization was done using data rare faction and total sum normalization. Following that, no data transformation was performed.

Visualization of taxonomic composition in bulk and rhizosphere samples were done using Stacked bar/area plot and pie chart. Rarefaction curve analysis, used to present relationship between number of OTUs and number of sequences was performed using the modified function ggrare originated from ranacapa package2. Heat tree method was adopted to compare abundance of different taxonomic levels present in various depths of bulk and rhizosphere. Heat tree analysis was performed using R package metacoder package3. The prominent taxa in each sample was analyzed using this method.

Community profiling

Alpha diversity, Beta Diversity and Core microbiome analysis were carried out for the samples for community profiling.

Alpha diversity analysis

This method was used to measure the diversity present within rhizosphere and bulk soil samples for each depth individually as well as for bulk and rhizosphere in general. Alpha diversity analysis was performed using the phyloseq package4. The results were plotted across bulk and rhizosphere samples and reviewed as box plots for each group or experimental factor. Alpha diversity measures across all the samples of bulk and rhizosphere for given diversity index as well as the diversity distribution using box plot for the given group/ experimental factor was calculated.

Beta diversity analysis

This method was adopted to compare the microbial diversity between bulk and rhizosphere soil samples to arrive at the variance and similarities between samples. Each sample in bulk and rhizosphere soil were compared to every other five samples. Similarity or distance between samples was measured using Bray–Curtis distance method. Ordination-based Principle Coordinate Analysis (PCoA) plot was prepared. Beta diversity analysis was performed using the phyloseq package5. Ordination plot was represented in 2-D, Statistical significance was estimated using PERMANOVA.

Hierarchical clustering

The rhizosphere and bulk soil data were subjected to hierarchical cluster analysis. Hierarchical clustering was performed with the hclust function in package stat and depicted as a heatmap.

Core microbiome analysis

To identify the core taxa that remain unchanged in their composition across the rhizosphere and bulk samples, Core Microbiome Analysis was carried out. Sample prevalence and relative abundance (fractions) of taxa was identified using R package microbiome. The result was represented in the form of heatmap of core taxa or features where Y-axis represents the prevalence level of core features across the detection threshold (Relative abundance) range on X-axis.

Wet laboratory validation

Wet laboratory analysis was carried out to confirm the organisms identified through 16S rRNA targeted sequencing. Soil samples from R2 and B2 layers were serially diluted and plated on nutrient agar media for estimating the CFU (Colony Forming Units). Selective media were prepared for E.coli, S. typhi and Pseudomonas aeruginosa. Mac Conkey agar was used to culture E.coli and S. typhi whereas Centrimide Agar was used for culturing Pseudomonas aeruginosa.

Isolation of organisms from soil using selective media

Media preparation

Ready to use Mac Conkey agar media (SRL, 76875) was prepared for isolation of E.coli and S. typhi, ready to use Cetrimide agar media (Himedia, MH024) was prepared for culturing Pseudomonas aeruginosa from the soil sample. Media was autoclaved at 15 psi pressure at 121 °C for 15 min.

Procedure of plating

Test samples (1 ml) were tenfold diluted in 9 ml of water (10–1). This was further serially diluted to (10–2) and (10–3). All three dilutions were plated on selective media by spread plate technique. The plates were incubated in both aerobic chamber at 37 °C for 24–48 h. After the incubation period colonies were observed and colony characterization was carried out.

Results

Physicochemical characterization of the rhizosphere and bulk soil

The pH value showed significant variation between bulk and rhizosphere (p < 0.05). The rhizosphere soil was found to be more alkaline (7.61–7.73) than bulk soil (6.85–7.1). Within each sample, considerable variation was not present among the layers. The soil was found to be neutral to alkaline with appreciable amount of macronutrients and micronutrients. There were differences in the nutrient status between bulk and rhizosphere soil, rhizosphere soil having better nutrient status than bulk. Electrical conductivity did not show significant variation between rhizosphere and bulk soil (p > 0.05) (Table 1).

Table 1 Physiochemical characterization of soil and total microbial count estimation
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With respect to total nitrogen content, the rhizosphere layers showed significant difference with the top layer, R2 having highest (0.2%) followed by R6 the bottom layer (0.18%) in the study (p < 0.05), whereas there was no significant difference with respect to bulk sample layers. Rhizosphere sample had significantly higher total nitrogen content than bulk (p < 0.05). The total phosphorous and potassium content did not show any variation between the samples and within the layers in both the cases. Organic carbon content did not show any significant difference between bulk and rhizosphere samples, nor among the layers. Fe content and Cu content did not show significant variation between rhizosphere sample and bulk sample (p > 0.05) as well as among the layers. Mn and Zn content also did not differ between rhizosphere and bulk samples. In case of Zn, within bulk sample the mean value showed significant difference (p < 0.05), between B4 and B6, B4 with maximum Zn content (25.2 ppm) whereas there was no significant variation within the rhizosphere layers (Table 1).

Total bacterial count did not show any variation between bulk and rhizosphere sample as well as within the layers. Total fungal count showed variation between bulk and rhizosphere with rhizosphere sample having increased CFU (176) compared to bulk (122.67), p < 0.05 (one tail t test). Within the sample, layer wise variation was not significant (Table 1).

To conclude, the rhizosphere sample showed significant variation in physio-chemical parameters across soil depth for pH, N, and total fungal content compared to bulk sample. No significant variation was reported in case of Mn, Cu, Cl, Zn, P & K levels. The P and K content remained the same across the soil depth in both the samples (0.01%). With respect to the soil physicochemical properties in the rhizosphere sample, the highest pH and nitrogen content were reported in R2, and fungal CFU in R6 layer (Table 1).

Soil depth and microbial diversity

The rhizosphere and bulk soil samples from various depths of 0–15 cms of pomegranate field (Bhagwa variety) collected and processed for 16S rRNA gene high-throughput sequencing using nanopore platform gave maximum read count for R6 (6805) and minimum read count for B6 (5408) with a total read count of 34,252 (Table S1). The DNA extracted from all the six layers of both rhizosphere and bulk samples were subjected to quality check using NanoDrop (Table S2). The alpha diversity did not show significant difference among rhizosphere soil layers based on the Chao 1 index (p > 0.05). Within bulk sample, there was significant variation between B2 and B4, with B2 showing the highest alpha diversity Cha0 1 index in terms of species richness (207.06 and 190.93), p < 0.05 (Table 2).

Table 2 Alpha diversity analysis
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Alpha-diversity measure was estimated using Chao1index at OTU level represented in the form of boxplots. The maximum species richness in terms of OTU was associated with bulk compared to rhizosphere (Fig. 3b).

Fig. 3
figure 3

a Rarefaction curves b Alpha diversity index/ box plot c Beta diversity analysis using Bray Curtis distance method. Rare faction curve denoting the species richness and the plateau denoting optimal sequencing depth. Alpha diversity measure using Chao1 at OTU level represented as boxplot. Each boxplot represents the diversity distribution of a group present within Sample Type class [Statistical significance: p-value: 0.52514; [t-test] statistic: 0.70804]. Beta diversity analysis was performed using the phyloseq package5. Statistical significance was calculated using [PERMANOVA] F-value: 14.505; R-squared: 0.78384; p-value < 0.01

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The rarefaction curves for both the bulk and rhizosphere sample revealed that the sequencing depth was adequate to explore the maximum number of species in terms of OTU (approximately 101 species) (Fig. 3a). Principal coordinates analysis (PCoA) was used to visualize the beta diversity between the rhizosphere and bulk sample with respect to the various layers in each sample. Bray Curtis distance represented the distance between the samples that reflected the dissimilarities between the samples and layers in terms of microbial diversity (Fig. 3c). Rhizosphere samples were more scattered showing higher microbial diversity than the bulk samples.

Hierarchical clustering was performed with the hclust function in package stat and was represented in the form of Heat Map depicting the clustering of organisms in both bulk and rhizosphere. The most abundant phylum in both the samples were proteobacteria (15%), followed by Firmicutes (10%), Planctomycetota (10%), Chloroflexi (9%), Acidobacteriota (7%) and Actinobacteriota (5%) comprising more than 50% of the total (Fig. 4, Figure S1).

Fig. 4
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Heat map representing the abundance at phylum level with proteobacteria showing maximum abundance

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Similarly, core microbiome analysis to identify the taxa that remain unchanged in the bulk and rhizosphere samples at OTU level was also analyzed and represented in the form of heat map where the Proteobacteria, Firmicutes, Planctomycetota, Chloroflexi, Acidobacteriota and Actinobacteriota represented the core microbiome (Figure S2).

Correlation analysis to establish the relationship among the organisms and pattern search to identify the Phylum correlated with Firmicutes to know the trend of association were depicted in the form of heat maps. Fermicutes showed strong negative correlation with Acidobacteriota, Planctomycetota, Verrucomicrobiota, Abditibacteriota and Gemmatimonadota whereas strong positive correlation was shown with Chloroflexi, Patescibacteria and Cyanobacteria (p < 0.05) (Figure S3).

Univariate analysis, RNA seq method, metagenomeSeq were compared for differential abundance analysis. Linear discriminant analysis (LDA) effect size was also calculated and error estimation was done through Random Forest method. The STAMP software was used for statistical analysis to prepare the Bar plots as well as the scatter plots for layer-wise comparison between the top, mid and bottom layers of rhizosphere and bulk as well as between rhizosphere sample and bulk sample in general. The analysis gave a detailed account of the most abundant and prominent organisms in each layer of rhizosphere compared to bulk. The most abundant organism in R2 was found to be Pirellula staleyi (50%) compared to B2 (15%). The abundant organism in B2 was Candidatus solibacter usitatus (80%) as clearly reflected in the profile bar compared to R2 (38%). Similar results were reported for B4 Vs R4 and B6 Vs R6. The same trend reflected for rhizosphere and bulk sample layers in general (Figs. 5a, b, 6a, b, Table 3).

Fig. 5
figure 5

a, b Differential abundance Analysis of bacterial species. The figure depicts extended error bar plots denoting the samples (a) mid-layer (b) bottom layer of the corresponding bulk B (in brown) and rhizosphere sample R (in green). The axes denote the corrected p-values against the proportion of reads and difference between the proportions with 95% confidence intervals (colour figure online)

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Fig. 6
figure 6

a, b Differential abundance Analysis of bacterial species. The figure depicts extended error bar plots denoting the samples (a) mid-layer (b) bottom layer of the corresponding bulk B (in brown) and rhizosphere sample R (in green). The axes denote the corrected p-values against the proportion of reads and difference between the proportions with 95% confidence intervals (colour figure online)

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Table 3 List of organisms at species level in the order of abundance (OTU) present in various layers
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Pathway predictions

KEGG Pathway prediction gave the first hit as arginine and proline metabolism (gamma-glutamyl putrescine oxidase) followed by purine metabolism, monobactam biosynthesis, sulfur metabolism, secondary metabolite synthesis and microbial metabolism in diverse environments in case of rhizosphere soil (p < 0.05) (Table 4, Fig. 7).

Table 4 Pathway prediction
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Fig. 7
figure 7

Pathway Prediction. The figure depicts the various significant (p < 0.05) pathway hits and their respective KEGG Ids from the pooled samples Bpool representing the Bulk layer and the Rpool representing the Rhizosphere. The sequenced data from various soil depths of rhizosphere and bulk samples were deposited in SRA repository (Table S7)

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KEGG Pathway prediction of rhizosphere soil samples revealed the predominance of gamma-glutamyl putrescine oxidase activity responsible for argine and proline metabolism followed by cysNC; bifunctional enzyme involved in sulphur metabolism.

Wet laboratory validation: cultural characteristics of isolated organisms in selective media

In the wet lab studies, Rhizosphere soil sample reported more bacterial microbial load in terms of CFU in Nutrient Agar media (723 × 102 ± 4.35) compared to the Bulk Sample 554 × 102 ± 2 (Table S3 and Figure S4). The bacterial species that could be cultured in selective media were E coli, Salmonella, Pseudomonas aeruginosa and Enterobacter aerogenes. Colony characterization of E coli in Mac Conkey agar revealed Pink colonies with entire margin and microscopic observation revealed rod shaped organisms, whereas Salmonella colonies appeared colourless, smooth and transparent. The CFU was more for Salmonella compared to E coli with Bulk sample reporting more CFU (Table S4, Figure S5).

Colony characterization of Pseudomonas aeruginosa in Cetrimide agar revealed yellowish green colonies with curled margin and microscopic observation revealed rod shaped organisms, whereas Enterobacter aerogenes colonies appeared blue, smooth and mucoid. The CFU was more for Enterobacter aerogenes compared to Pseudomonas aeruginosa with bulk sample reporting more CFU (Table S5, Figure S6).

Discussion

Physicochemical properties of soil and microbial diversity

There was significant variation in certain parameters of rhizosphere soil compared to Bulk soil such as alkalinity of rhizosphere soil compared to bulk though the significant difference in pH was not detected among the layers in rhizosphere and bulk soil. In general, soil of pomegranate filed was found to be neutral to alkaline in nature. The soil pH has profound influence in shaping the microbial diversity as already reported (Tan et al. 2020) especially with respect to alpha diversity. Tan and his coworkers reported that in the pH range of 5.23–8.86, there was a positive correlation with the OTU in maize cropping system. This also supports the rhizosphere specific microbial diversity with the prevalence of Pirellula staleyi and Flavisolibacter obtained in our study compared to bulk soil. Similar findings establishing the correlation of soil pH and microbial community were reported in the permafrost region of North Eastern China (Ren et al. 2018).

Electrical conductivity did not show significant variation between rhizosphere and bulk soil (p > 0.05). The significant increase in nitrogen content in the rhizosphere sample compared to bulk could be attributed to the plant secretions and other exudates along with the presence of litter in the rhizosphere region. The finding that the abundance of Planctomycetes member Pirellula staleyi which can degrade putrescine exudate from plant roots could also be the reason for augmentation of nitrogen content in the rhizosphere (Lu et al. 2022). The higher nitrogen content in rhizosphere would have contributed to higher number of organisms/biomass in rhizosphere reflected in terms of higher read counts obtained in sequencing than in bulk. The absence of the root effect was reflected in bulk sample with relatively lower nitrogen content. Similar results of relatively higher nitrogen content in the top soil (Zhou et al. 2008; Tang et al. 2018).

The total P, K, Fe, Cu and Mn and organic carbon content did not vary between the samples and within the layers in rhizosphere and bulk soil. The increased fungal CFU in rhizosphere could also be attributed to the plant root effect compared to bulk soil. Another reason could be the zero-tillage practice adopted in the farming system of pomegranate (Bhattarai 2015).

Community profiling

The phyloseq package employed to calculate the alpha diversity revealed only significant difference between B2 and B4, with B2 showing the highest alpha diversity Cha0 1 index in terms of species richness (207.06 and 190.93) which could be also correlated with the highest read count reported in B2 sample (5810). Similar decrease in alpha diversity across soil depth was also reported in legume fields of China (Yan et al. 2019). Candidatus solibacter usitatus dominated in all the bulk layers.

The bacterial community among the rhizosphere soil layers almost remained the same with Pirelulla staleyi and Flavisolibacter sp LCS9 being the most abundant in all the three layers. The absence of change in microbial diversity could be due to the negligible variations in physicochemical properties of rhizosphere soil across the depth.

Beta diversity

Beta diversity analyzed through Phyloseq package 5 and visualized through PCoA adopting Bray- Curtis distance method showed higher diversity for rhizosphere samples, all the three layers well separated, R6 showing the maximum diversity in terms of highest dimension of variation (79.2%) and second highest dimension of variation for B6 (5.9%). B2 and B4 samples were clustered together showing similarity in the microbial diversity. The higher F value and R2 value (14.505 and 0.78384) gives a clear indication that the root zone effect can play a major role in shaping the microbial diversity supporting the higher beta diversity in rhizosphere soil. Similar findings were reported in the rhizosphere and bulk samples of Ranunculus glacialis along a high alpine altitudinal gradient (Praeg et al. 2019).

The hierarchical clustering and core microbiome analysis through R package microbiome depicted in the form of heat map revealed higher prevalence of Proteobacteria, Firmicutes, Planctomycetota, Chloroflexi, Gammaproteobacteria and Actinobacteriota. Similar findings of abundance of these phyla were reported in biofuel cropping system (Zhang et al. 2017), fallow field (Ko et al. 2017) and unfertilized aerable soil (Wang et al. 2018). These findings lead to the conclusion that the common phyla remain unaltered irrespective of farming practices.

Fermicutes showed strong negative correlation with Acidobacteriota, Planctomycetota, Verrucomicrobiota, Abditibacteriota and Gemmatimonadota whereas strong positive correlation was shown with Chloroflexi, Patescibacteria and Cyanobacteria though Fermicutes shared dominance with Planctomycetota which is an interesting finding.

Microbial abundance and soil depth

Phylum level, Proteobacteria dominated followed by Firmicutes, Planctomycetota, Chloroflexi, Gammaproteobacteria and Actinobacteriota. Pirellula staleyi, belonging to Phylum Planctomycetes was the most abundant species discovered in the study in the rhizosphere region compared to Bulk soil. Similar group of phyla were reported by Li et al 2021 (Li et al. 2021). It is very difficult to get the pure cultures of this phylum, only metagenomics approaches have been successful in unveiling the various species of Plantomycetes (Elshahed et al. 2007). Following Pirellula, Flavisolibacter LC59, Niastella koreensis dominated the Rhizosphere layers from R2 to R6 compared to Bulk soil. Another species of Flavisolibacter, Flavisolibacter nicotianae was also reported in Nicotiana tabacum rhizosphere (Li et al. 2019). Candidatus solibacter usitatus followed by Pseudomonas aueruginosa were more abundant across the bulk soil layers of Pomegranate Variety Bhagwa. The rhizosphere layers had few organisms which were unique to only pomegranate root zone such as Flammeovirgaceae bacterium 311 (R2, R4, R6) and Halobacteriovorax marinus (R2, R4, R6) (Table 3).

The role of Halobacteriovorax in predating the soil pathogens reported could be linked to our study with the possible role of the organism in helping the plant growth by controlling the soil pathogens in the rhizosphere zone (Oyedara et al. 2016). The presence of Flammeovirgaceae bacterium was also reported in the roots of Salicornia europaea in Central Poland (Furtado et al. 2019).

Bulk soil had more number of organisms which were very unique to Pomegranate such as Methylotenera versatilis, Methylotenera mobilis, Massilia sp WG5, Pseudobacter phenanthrenivorans, Pseudomonas pseudoalcaligenes (B2, B4, B6) and Mucilaginibacter sp PAMC 26640 (Figs. 5a, b, 6a, b, Table 3, Table S6). This leads to the finding that the rhizosphere of pomegranate field had a subset of microorganism found in Bulk soil. This finding is also in line with the reports earlier where the microbial community in the rhizosphere was analyzed through Shot Gun Metagenomics (Mendes et al. 2014).

Pathway prediction

The first hit which showed significant increase in the rhizosphere sample is arginine and proline metabolism pathway involving gamma-glutamylputrescine oxidase for putrescene catabolism. This pathway is involved in stress response as well as regulation of putrescene (Schneider et al. 2013). The second hit was related to bifunctional enzyme CysN/CysC involved in sulfur metabolism, hydrogen sulfide biosynthesis. This protein is involved in step 1 and 2 of the subpathway that synthesizes sulfite from sulfate. Proteins known to be involved in the 3 steps of the subpathway are Sulfate adenylyltransferase subunit 2 (cysD), Bifunctional enzyme CysN/CysC (cysNC) Bifunctional enzyme CysN/CysC (cysNC) and phosphoadenosine phosphosulfate reductase (cysH). This sub pathway is part of the hydrogen sulfide biosynthesis pathway, which is itself is an integral part of Sulfur metabolism. This finding is also in line with the study done by Elshahed (Elshahed et al. 2007) where the sulfur reduction mechanism of the organism is described as a strategy of survival under anaerobic conditions, i.e., the metabolic functions assigned to planctomycetes like Pirellula which showed highest abundance in rhizosphere.

H2S has great positive impact on plant root development and ABA-mediated abiotic stress response through stomatal closure (Li et al. 2022). Since Pirellula, belonging to planctomycetes has the ability to reduce sulfur and convert to H2S, it can positively influence the growth and development of pomegranate variety Bhagwa. Another major pathway hit was with respect to the one associated with xenobiotic compound degradation involving salicylate hydroxylase which could be the reason for the bioremediation of xenobiotic compounds used in the pomegranate field thereby protecting the plant and the soil ecosystem from toxicity.

The fact that the rhizosphere community selection of microbes is influenced by the functional attributes of the microorganism was also reported in a similar study where in comparative analysis was carried out in terms of the taxonomical composition and functional pathways of the rhizosphere and bulk soil using 16S rRNA amplicon based sequencing (Yan et al. 2019).

Wet lab studies

The major drawback in exploring the microbial diversity in various ecosystems is the fact that only 1–2% of these microorganisms can be cultured in the lab. Metagenomics approaches can efficiently unveil the diversity of bacterial community (Cardenas and Tiedje 2008). The validation of 16S rRNA targeted amplicon sequencing was done through wet lab studies which could provide the data regarding presence of only four species out of 101 species obtained through metagenomics approach. E coli, Salmonella, Pseudomonas aeruginosa and Enterobacter aerogenes were the species that could be cultured. This further emphasize the importance of culture independent techniques in exploring the microbial diversity.

The rhizosphere soil reported higher CFU/ml than the bulk soil. The number of reads obtained through metagenomics approach was also higher in rhizosphere soil highlighting the root effect in enhancing the bacterial population compared to bulk soil. The existing studies in pomegranate have only employed conventional culture techniques to isolate the microbes from rhizosphere where only few organisms could be cultured (Shahaby et al. 2016). Only 13 bacterial species could be isolated out of which Pseudomonas aeruginosa, Bacillus subtilis., Streptomyces species were common to both the studies. The role of Pseudomonas aeruginosa could be conferring systemic resistance to pathogens in plants like beans through salicylic acid production (de Meyer et al. 1999; Audenaert et al. 2002).

Conclusion

This study involving metagenomics approach is the pioneer attempt in exploring microbial diversity of pomegranate rhizosphere. Our study also reports the presence of Pirellula staleyi for the first time in the pomegranate field. Being a planctomycetes member, it can enhance the plant growth by the production of H2S influencing root growth and ABA mediated abiotic stress response positively. The ability to degrade putrescine can also facilitate the nitrogen content enhancement in the root zone and better association of beneficial bacteria like Pseudomonas fluorescens (Kuiper et al. 2001). Present study is a systematic approach in understanding the microbial diversity of rhizosphere and the influence of the physicochemical properties of pomegranate soil on microbial diversity of pomegranate. The study has revealed the existence of specific set of microorganisms unique to rhizosphere and has significantly contributed to the enhancement of database of microorganisms in the rhizosphere of pomegranate variety Bhagwa.

The findings from the study leads to the fact that the insignificant variations within the topsoil of rhizosphere contributed to consistent microbial diversity across the soil depth. The study has confirmed the role played by the host in defining the distinct microbial diversity through the root activity which is a subset of bulk microbiome. The presence of beneficial organisms in the rhizosphere such as Pirellula staleyi, Flavisolibacter LC59, Niastella koreensis and the unique organisms such as flammeovirgaceae bacterium 311 and Halobacteriovorax marinus already proven to have functional role in plant protection which could not be explored through conventional culture techniques makes our study very relevant and interesting. The plant growth promoting activity of Flavisolibacter has already been explored (Xiao et al. 2017). Niastella koreensis was earlier isolated from the Ginseng soil in Korea and proven to have alkaline phosphatase activity that makes it a good candidate for biofertilizer having phosphate solubilization trait (Weon et al. 2006). These organisms can be used as effective plant growth promoting rhizobacteria and biocontrol agents in pomegranate fields.

Presently shotgun metagenomics are ongoing, and the data generated through whole genome sequencing would go one step ahead in adding pertinent data with respect to the functional genes of the microbes revealed through 16S rRNA sequencing involved in enhancement of plant productivity and plant protection as biocontrol agents.

The pot studies would be used for the validation of Insilco data generated. The outcome would be the development of biofertilizers, and biocontrol agents identified through metagenomics approach. The thorough understanding of the functions of the gene with respect to the metabolic pathways prevalent in the farming system and the impact on plant growth and productivity would in turn help the farmer to adopt ecofriendly and sustainable farming practices.