Metagenomic analysis of the rhizospheric soil of Piper longum L. established in D.E.I, Agra, India

Metagenomic analysis of the rhizospheric soil of Piper longum L. established in D.E.I, Agra, India | 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 Metagenomic analysis of the rhizospheric soil of Piper longum L. established in D.E.I, Agra, India Shivangi Mathur, Mrinalini Prasad, Sunil Kumar, Anurag Chaurasia, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7995958/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2026 Read the published version in World Journal of Microbiology and Biotechnology → Version 1 posted 9 You are reading this latest preprint version Abstract The rhizosphere of medicinal plants contains a variety of microbial populations that influence plant health, nutrient uptake, and the synthesis of bioactive compounds. In this study, we used a culture-independent metagenomic approach to characterize the rhizospheric microbiota of Piper longum , a plant with significant medicinal potential in traditional medicine. High-throughput sequencing using the Illumina technology yielded 19.94 million paired-end reads (~ 5.92 Gb), which were assembled into 97,432 scaffolds, resulting in a genomic length of 52.26 Mb. In all, 45,876 genes were predicted and functionally identified across multiple databases. The phylum Proteobacteria is dominant in terms of taxonomic classification, with Nitrososphaera and Candidatus Nitrososphaera gargensis being the most common genus and species, respectively. A metabolically diverse and ecologically significant microbial population was indicated by functional profiling, which also showed enrichment in genes linked to metabolism, amino acid transport, and environmental adaptability. This work highlights the value of using microbial diversity for sustainable agriculture and enhanced phytochemical output while providing basic insights into the P . longum rhizosphere microbiome. Piper longum Rhizosphere microbiome Metagenomics Functional annotation Medicinal plants Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction One of the nature's most dynamic environments is the rhizosphere, which is the microecological interface between plant roots and soil. It is created by complex interactions between plants, soil, and a broad range of microorganisms, including bacteria, fungi, protozoa, and archaea (Philippot et al. 2013 ; Vetterlein et al. 2020 ). These microbial communities play a crucial role in plant health by affecting growth regulation, nutrient uptake, stress tolerance, and disease defense. Consequently, the rhizospheric microbiota is preferentially shaped by the root exudates that plants release, which are made up of sugars, amino acids, organic acids, and secondary metabolites (Ali and Glick 2024 ; Chen and Liu 2024 ). As a result, the rhizosphere is a closely related plant-microbe system that is essential to the sustainability of agriculture and ecosystems. In recent decades, the possibility of using rhizospheric microbial populations to improve soil health, decrease the usage of chemical fertilizers, and increase plant growth and resistance has come to light (Wei et al. 2024 ). Studies on model plants and key crops like maize, wheat, and rice have demonstrated that the rhizosphere is home to beneficial microbes like phosphate solubilizers, nitrogen-fixing bacteria, and biocontrol agents (Kour et al. 2019 ; Imade and Babalola 2021 ; Di Benedetto et al. 2017 ; Ngalimat et al. 2021 ). Although many therapeutic plants are valued for their ability to produce secondary metabolites and are often cultivated in low-input systems, little is known about their rhizosphere microbiomes. Long pepper, or Piper longum L., is a perennial climbing vine that is indigenous to South Asia and is a member of the Piperaceae family. Due to its medicinal qualities, which include antibacterial, anti-inflammatory, hepatoprotective, and anticancer actions, it is highly valued in ancient medical systems such as Ayurveda, Siddha, and Unani (Biswas et al. 2022 ). Its medical effectiveness is attributed to bioactive substances such piperine, piperdardine, and piperlongumine. There is a growing need for Piper longum to be grown using sustainable methods that preserve or improve its phytochemical content due to the increased interest in natural products and herbal remedies around the world (Prasad et al. 2025 ). However, little is understood about the microbial communities in the rhizosphere that could affect its metabolite production, growth, and health. Historically, the variety of soil microbiomes has been difficult for culture-based methods to fully capture because a large number of soil microorganisms are not culturable in standard laboratory settings. Microbial ecology has undergone a radical transformation thanks to the direct study of DNA made possible by developments in next-generation sequencing (NGS) technology, particularly metagenomic sequencing (Dubey et al. 2022 ; Raghav et al. 2024 ). Metagenomics provides information about the functional potential of microbial communities in addition to their taxonomic composition by identifying genes linked to the nutrient cycle, stress adaptation, and plant–microbe interactions (Kumar and Yadav 2024 ). In this study, we used high-throughput Illumina sequencing to conduct a comprehensive metagenomic analysis of the P . longum rhizosphere. Through gene annotation across major functional databases, this study sought to clarify the functional potential of the microbial communities residing in the rhizosphere of P . longum while also characterizing their taxonomic diversity. Finding dominant microbial taxa that may contribute to improving soil fertility, bolstering plant health, and maintaining ecosystem stability received special attention. This work lays the groundwork for future functional studies centered on microbiome-assisted strategies for sustainable cultivation, increased plant productivity, and improved phytochemical quality in medicinal plants by offering an integrated overview of the P . longum rhizosphere microbiome. 2. Material and Methods 2.1 Soil sample collection for analysis The rhizospheric soil of the plants during the spike stage (November) was selected for the metagenomic study of the P . longum established in Herbal Garden, D.E.I, Agra. A total of three random replicates were considered for the study. The plants were uprooted, and the loosely attached soil was removed by vigorous shaking. The soil adhered tightly with the root surface was collected in a pre-sterilized container. A pooled sample was prepared for metagenomic analysis by mixing the rhizospheric soils of three replicates (Hou and Williams 2013 ; Znój et al. 2022). The samples for estimation of soil properties were collected and transported at room temperature whereas the samples for metagenomic analysis were collected and transported at 4℃ and further kept at -20℃. 2.2 Soil parameter analysis These soil samples were mixed to form a composite of all collected samples. Samples were allowed to equilibrate with air for 2 hours in trays. The soil samples were dried and sieved with 2mm sieve. The physiochemical examination of the rhizospheric soil for its physical properties (pH, EC) and macronutrients (NPK and % organic carbon) was conducted according to the (Laishram et al. 2013 ; Nyoki et al. 2018). 2.3 Sample Preparation and DNA Extraction The rhizospheric soil samples of P . longum were pooled and we sent to Xcleris Labs Ltd, Ahmedabad for further analysis. From the samples, the genomics DNA was isolated using the Xcelgen soil gDNA kit, following the manufacturer’s protocol. The quality of the genomic DNA was assessed using 0.8% agarose gel electrophoresis. The gel was run for 30 mins at 110V. The DNA quantification was carried out using the Qubit® 2.0 Fluorometer. Samples exhibiting a single intact band and an A260/280 ratio between 1.8–2.0 were selected for library preparation. 2.4 Library Preparation and Sequencing Paired-end sequencing libraries were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina. Briefly, 200 ng of high-quality DNA was fragmented using a Covaris, end-repaired, and ligated with platform-specific adapters. Dual-indexed libraries were PCR-amplified using HiFi PCR Master Mix to ensure sufficient yield. The quality and average size distribution of the final libraries were assessed using an Agilent 2100 Bioanalyzer with a High Sensitivity DNA chip. Sequencing was performed on the Illumina platform (2 × 150 bp chemistry) generating high-quality paired-end reads. 2.5 Bioinformatics Analysis The raw reads were subjected to quality control to remove adapter sequences, ambiguous bases, and low-quality reads. High-quality paired-end reads were assembled de novo using the metaSPAdes assembler with default parameters optimized for metagenomic datasets. The assembly statistics, including the number of scaffolds, total scaffold length, N50, and maximum scaffold length, were calculated using in-house Perl scripts. Gene prediction was carried out using Prodigal (v2.6.3) in metagenome mode, which is designed to handle anonymous contigs from mixed microbial communities. These predicted gene sequences were used as input for taxonomic and functional analyses. Taxonomic annotation was performed using Kaiju, a fast and sensitive metagenomic classifier that assigns taxonomy based on maximum exact matches at the protein level using the Burrows–Wheeler transform algorithm (Menzel et al., 2016 ). Predicted gene sequences were uploaded to the Kaiju web server ( http://kaiju.binf.ku.dk ) and analyzed with the following parameters: run mode = greedy, minimum match length = 11, minimum match score = 75, allowed mismatches = 5, and SEQ low-complexity filter = yes. Kaiju classified the sequences against the NCBI RefSeq microbial protein database, optionally including fungi and microbial eukaryotes. Functional annotation of predicted genes from Piper longum was performed using COGNIZER (v0.9b), a comprehensive standalone pipeline that simultaneously assigns functional terms across multiple databases. The predicted gene sequences were analyzed to obtain Clusters of Orthologous Groups (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG), Pfam, Gene Ontology (GO), and FIGfams annotations. COG analysis classified genes into functional categories involved in essential cellular processes such as amino acid transport and metabolism, transcription, replication, and energy production. KEGG mapping associated the genes with pathway modules using KO identifiers, enabling the reconstruction of metabolic and signal transduction pathways. Pfam analysis identified conserved protein families and domains, providing insight into potential structural and functional properties. GO annotation categorized genes into biological process, molecular function, and cellular component classes, thereby facilitating the interpretation of gene roles in the microbial community. FIGfams annotation grouped genes into sets of functionally homologous proteins, allowing the identification of conserved metabolic capabilities across diverse taxa (Galperin et al. 2015 ). Collectively, these annotations provided a comprehensive understanding of the metabolic potential and functional diversity of the microbial community associated with Piper longum . 3. Results The fertility status and suitability for microbial activity of the rhizospheric soil of Piper longum were evaluated by analyzing its physicochemical properties as shown in Table 1 . With a pH of 7.52 ± 0.02 and a neutral to slightly alkaline reaction, the soil showed ideal conditions for microbial growth and nutrient availability. A moderate amount of soluble salts that is within the permissible range for healthy plant growth is indicated by the electrical conductivity (EC), which was measured at 639.21 ± 0.04 dS/m. While the levels of potassium (22.36 ± 0.007 kg/ha) and phosphorus (24.06 ± 0.10 kg/ha) demonstrated balanced nutrient availability necessary for plant metabolism and microbial functioning in the rhizosphere, the macronutrient composition showed a nitrogen content of 310.19 ± 0.03 kg/ha, indicating moderate fertility. Low to medium organic matter was indicated by the organic carbon content of 0.44 ± 0.008%. These soil parameters collectively show a biologically active, fairly fertile rhizosphere that supports the functional potential revealed by metagenomic profiling and is favorable for plant growth as well as the development of a metabolically diverse microbial community. Table 1 Physio-chemical properties of the rhizospheric soil of Piper longum . Soil Properties pH 7.52 ± 0.02 EC (dS/m) 639.21 ± 0.04 Nitrogen (kg/ha) 310.19 ± 0.03 Phosphorus (kg/ha) 24.06 ± 0.10 Potassium (kg/ha) 22.36 ± 0.007 Organic carbon (%) 0.44 ± 0.008 To explore the rhizospheric bacterial community of P . longum metagenomic analysis was performed which yielded high-quality paired-end sequencing genomic DNA on the Illumina platform which generated 19,942,187 read pairs, yielding approximately (~ 5.92 Gb) of data with Q30 values exceeding 90%, which ensured high-confidence base calls. The quality-filtered reads were assembled de novo using metaSPAdes, resulting in the 97,432 scaffolds with a total length of 52.26 Mb, an average scaffold size of 536 bp, an N50 of 489 bp, and a maximum scaffold size of 44,108 bp as provided in Table 2 . The assembly statistics provided indicated a comprehensive as well as well-represented metagenomic dataset with a sufficient coverage for downstream analysis. Further, genes were predicted using Prodigal (v2.6.3) which identified 45,876 protein-coding genes, with an average gene length of 334 bp and a total gene length of 15.36 Mb, providing a robust catalog of coding sequences for taxonomic and functional annotation as shown in Table 3 . Table 2 De novo Assembly Statistics Assembly Elements #Scaffolds 97432 Total scaffold length (bp) 52264418 Average Scaffold size (bp) 536 Scaffold N50 489 Maximum scaffold size (bp) 44108 Table 3 Predicted Genes Statistics Description #Genes 45876 Total gene size (bp) 15362571 Average gene size (bp) 334 Max scaffold size (bp) 3699 Taxonomic classification was performed using Kaiju software, unraveling the microbial community associated with Piper longum which was found to be quite diverse; however, it was strongly dominated by the phylum Proteobacteria, that accounted for 14,015 hits (~ 42% of classified genes). Other major phyla that constituted the majority of the bacterial population in the P . longum rhizosphere with includes, Actinobacteria (4555 hits) which accounted for 13.9% of the total classified genes, Thaumarchaeota (13.16%), Chloroflexi (8.21%), Acidobacteria (7.28%), Candidatus rokubacteria (2.47%), and Nitrospirae (1.92%) whereas the least found phylum was that of Candidatus Fraserbacteria, Candidatus Roizmanbacteria and Thermodesulfobacteria (4 hits or 0.012% of relative abundance) as shown in Fig. 1 . By digging into the finer taxonomic resolution, the most abundant class was found to be of Gammaproteobacteria, with relative abundance of 26.01%, followed by Betaproteobacteria with 17.09%, Actinobacteria with 12.02% and Nitrososphaeria 11.03%, respectively ( Fig. 2 ) . Similarly, the order level of classification revealed that Nitrososphaerales have a relative abundance of 15.82% was the dominant order, which was mirrored by the dominance of Nitrososphaeraceae (20.28%) at the family level ( Figs. 3 & 4 ) . At the genus level, Nitrososphaera was found to be the most abundant with relative abundance of 23.58% followed by Acinetobacter (13.11%), Rubrobacter (6.36%) and Nitrospira (5.06%), while the most abundant species detected was Candidatus Nitrososphaera gargensis with relative abundance of 11.21%, followed by Acidobacteria bacterium (9.74%) ( Figs. 5 & 6 ) . The preponderance of nitrifying archaea, such as Nitrososphaera , is indicative of the existence of an active ammonia-oxidizing microbial community. This community may be responsible for nitrogen cycling in the rhizosphere of Piper longum , indicating a community structure that is not only very diverse but also functionally relevant. A thorough understanding of the metabolic capacity of the Piper longum microbiome was obtained by the functional analysis of the predicted genes using COGNIZER (v0.9b). The COG analysis identified 23,705 terms, with amino acid transport and metabolism accounting for the highest prevalence (2649 terms) which was followed by prediction of general function (2488 terms), carbohydrate transport and metabolism (2100 terms), and energy generation and conversion (1900 terms) ( Fig. 7 ) . This distribution emphasizes the microbial community's metabolic activity, especially in the areas of energy metabolism and nutrient cycling. The metagenome sequencing from the current work has been submitted to the NCBI website under the accession number PRJNA600362. A variety of protein databases, including KEGG, Pfam, GO, COG, and FIG, were used to annotate the datasets with functional hierarchy information for the functional predictions ( Fig. 8 ) . The GO database produced the greatest number of hits, followed by the KEGG and Pfam databases. The COG functional analysis identified 23705 terms, and the KEGG annotation assigned 27,446 terms to 3594 KO classes, with the majority (54.6%) pertaining to metabolic pathways such as amino acid biosynthesis, carbohydrate metabolism, and energy production pathways. The next most prevalent functional category was environmental information processing (25.5%), followed by genetic information processing (12.7%), showing the existence of genes involved in two-component systems, ABC transporters, and transcriptional control as shown in ( Fig. 9 ) . The annotation of Pfam domains revealed 24,583 functional domains in 2930 distinct Pfam families, indicating a great variety of structural and catalytic proteins. Further clustering of 15,148 words into 4518 functionally homologous groupings using FIGfam analysis suggests conserved functionality across several taxa. The Gene Ontology (GO) annotation generated 33,522 words over 952 functional categories, with the largest representation in biological processes such as metabolic activity, stimulus response, and cellular functions. A substantial number of genes were classified as molecular function categories such as catalytic activity and binding, as well as cellular component categories related to membrane proteins and intracellular activities. These findings collectively indicate that the microbiome associated with Piper longum is physiologically flexible, with a focus on nutrition mobilization, energy metabolism, and environmental adaptation. These findings collectively show that the microbiome of P . longum is rich and functionally diversified, dominated by ammonia-oxidizing archaea and Proteobacteria, and that metabolic pathways linked to nitrogen, carbohydrate, and amino acid cycling are strongly enriched. This functional potential highlights the ecological relevance of the P . longum microbiome in maintaining plant growth, improving the availability of nutrients, and possibly affecting the manufacture of secondary metabolites. Discussion A strong relationship between soil fertility parameters and the functional competence of the microbial community in the Piper longum rhizosphere is highlighted by the combination of metagenomic insights and soil nutrient profiling. Plant-microbe symbiosis and microbially mediated nutrient cycling are supported by the moderate organic carbon content and balanced nutrient composition. The ecological and biotechnological significance of the P. longum microbiome is highlighted by these interactions, which not only maintain soil health but may also have an impact on the biosynthesis of pharmacologically significant compounds like piperine. The present research offers novel insights into the taxonomic constitution, functional potential, and ecological importance of the related microbial community by conducting a thorough metagenomic analysis of the Piper longum microbiome. The number of scaffolds and a N50 value of 489 bp demonstrate the high-quality and outstanding coverage of the dataset produced by high-throughput Illumina sequencing in tandem with de novo assembly. The assembly and gene prediction outcomes were similar to those reported for other medicinal plants, including Tribulus terrestris , Piper nigrum , Calatropis procera , where plant-associated microbial communities have been investigated using comparable sequencing depths (Rahimlou et al. 2025 ; Vaz et al. 2025 ; Li et al. 2016 ; Ramadan et al. 2021 ). As demonstrated by the taxonomic profiling, Proteobacteria clearly dominated the rhizosphere and endophytic communities in medicinal plants. Proteobacteria are often the most abundant phylum because of their diverse metabolic capacities and capacity to colonize plant tissues (Vincze et al. 2024 ). The high prevalence of betaproteobacteria and gammaproteobacteria among the Proteobacteria indicates a metabolically active community that can react quickly to changes in nutrient levels in the rhizosphere (Manapure et al. 2022 ). Given that these ammonia-oxidizing archaea are important participants in the nitrification process and aid in the conversion of ammonia to nitrite, a crucial stage in the nitrogen cycle, the identification of Nitrososphaerales and the dominance of Candidatus Nitrososphaera gargensis seems particularly significant (Xiang et al. 2023 ; Clark et al. 2021 ). Their presence strengthens the possibility that the rhizospheric zone of P . longum is a nitrogen cycling hotspot, which could improve the intake and growth of nitrogen by plant. The robust vegetative development and the generation of bioactive metabolites under natural conditions may be partially explained by these microbial interactions. The metabolic adaptability of the P . longum microbiome was further highlighted by functional annotation. A microbiome that is actively involved in nutrient mobilization is indicated by the enrichment of COG categories linked to energy production, carbohydrate metabolism, and amino acid transport and metabolism (Sefrji et al. 2025 ; Xu et al. 2021). Through the synthesis of precursors for secondary metabolites, such as alkaloids like piperine, amino acid metabolism affects plant health in addition to being essential for microbial proliferation (Narayanan et al. 2022). With notable presence in pathways related to nitrogen, sulfur, and carbon cycle, the KEGG pathway analysis showed that over half of the annotated genes fell under the metabolic category. These results are consistent with recent research on various microbiomes of medicinal plants, which emphasize the role of environmental information processing and nutrient cycling in promoting plant development and metabolite biosynthesis (Semenzato and Fani 2024 ; Singh et al. 2022 ). Remarkably, genes linked to stress response and xenobiotic degradation were also found, indicating that the Piper longum microbiome is capable of assisting the plant in overcoming abiotic stress and possibly enhancing resistance in harsh environmental circumstances (Pandey et al. 2023 ; Chaudhary et al. 2022 ). Additionally, our findings offer a useful resource for comprehending interactions between microbes and plants in relation to the generation of secondary metabolites. Since the availability of nitrogen is a known regulator of alkaloid production, the presence of microbial genes involved in nitrogen cycling, phenylpropanoid metabolism, and amino acid biosynthesis may have an indirect effect on piperine biosynthesis (Gao et al. 2022 ). It may be possible to determine whether particular microbial species actively control the piperine biosynthesis pathway by future functional investigations, such as transcriptomics and metabolomics. Targeted enrichment of these species may increase the efficiency of nitrogen utilization in Piper longum culture, and the discovery of nitrogen-transforming archaea like Nitrososphaera opens up possibilities for the development of biofertilizer (Li et al. 2023 ). Overall, this study creates a baseline microbiome profile for Piper longum , exposing a metabolically active and taxonomically varied microbial community that may play roles in secondary metabolism, stress adaption, and nutrient cycling. These discoveries broaden our knowledge of the ecological functions of microorganisms linked to plants and point to potential uses in precision farming, where piperine productivity could be increased through microbiome engineering. Conclusion Plant health and soil nutrient dynamics depend on the taxonomically diverse and functionally rich microbial community found in the rhizosphere of Piper longum , according to metagenomic research. The abundance of Proteobacteria and ammonia-oxidizing archaea such as Nitrososphaera and Candidatus Nitrososphaera gargensis indicates that the rhizosphere is likely to be an active site of nitrogen transformation. Functional gene annotation demonstrated a significant representation of metabolic pathways associated with energy synthesis, amino acid transport, and environmental adaptation, as well as a microbiome that is precisely regulated to support plant development in a variety of soil conditions. Together, the data suggest that the rhizospheric microbiota of P. longum is important for the cycling of nutrients and may indirectly affect the production of compounds that are relevant to pharmacology, such as piperine. This study highlights the importance of using beneficial microbial consortia for sustainable cultivation and increased phytochemical production, while also offering a basic understanding of the P. longum microbiome. Future multi-omics methods that combine transcriptome and metabolomic studies will be essential to identify possible taxa for the development of bioinoculants in medicinal plant agriculture as well as to pinpoint the exact microbiological processes underlying the production of secondary metabolites. Declarations Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Consent to participate Not applicable to this manuscript. Consent for publication We give our consent and ensure that the publisher has the author’s permission to publish the research article in their Journal. Funding The Authors received NO FUNDING for this work Author Contribution **M.P.** Conceptualization **S. M.** Writing original draft **R. R.** Review and Supervision, S.K. and A.C. Editing. All authors reviewed the results and the manuscript. Acknowledgement The authors sincerely thank the Director, of Dayalbagh Educational Institute, Dayalbagh, Agra, for providing the necessary infrastructure and support for this research. Data Availability The metagenome sequencing from the current work has been submitted to the NCBI website under the accession number PRJNA600362 References Ali S, Glick BR (2024) Root exudate metabolites alter food crops microbiomes, impacting plant biocontrol and growth. 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Cite Share Download PDF Status: Published Journal Publication published 23 Mar, 2026 Read the published version in World Journal of Microbiology and Biotechnology → Version 1 posted Editorial decision: Revision requested 03 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 12 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 02 Nov, 2025 Submission checks completed at journal 01 Nov, 2025 First submitted to journal 31 Oct, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7995958","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544039119,"identity":"74bca926-2fc3-4160-88f2-3e18638997e0","order_by":0,"name":"Shivangi Mathur","email":"","orcid":"","institution":"Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra-282005, India","correspondingAuthor":false,"prefix":"","firstName":"Shivangi","middleName":"","lastName":"Mathur","suffix":""},{"id":544039120,"identity":"2df55cd9-1fcb-43b2-8abf-961be7c2dbfe","order_by":1,"name":"Mrinalini Prasad","email":"","orcid":"","institution":"Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra-282005, India","correspondingAuthor":false,"prefix":"","firstName":"Mrinalini","middleName":"","lastName":"Prasad","suffix":""},{"id":544039121,"identity":"45aaa715-4309-4143-bf1f-6ded0a3f7025","order_by":2,"name":"Sunil Kumar","email":"","orcid":"","institution":"ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India-110012","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Kumar","suffix":""},{"id":544039122,"identity":"af2761e5-3720-4cda-9025-6761334d2ede","order_by":3,"name":"Anurag Chaurasia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYDADAwglwcDPDBdjbCBOi2QziVqAjAME3MM/u8fsw49fDPLm7GeAjD8W8sbHmR9+rmCwyZd3YG57gEWLxJ0zxjN7+xgMd/bkABltEobbDrMZS55hSLPceICx3QCLFoYbOcYMvD0MCQYHQIwGCcZth3kYJBsYDhsYNjC2SWDRIQ/UwvgXpOX8G2PGP38k7Dc38zD/xKfFAKiFmecHUAuYwSaRuAFIgm2RZ8CuxfBGWjGzbIOE4YYbz4CMNonkGYfZzCwbDNIMDJixa5G7kbyZ8c0fG3mD82BGnW1//+HHNxsqbAzk29ufYdMCBlhMAwaVwWFc6kHgDzZB+QZ8WkbBKBgFo2AEAQB4bFujgZyXCAAAAABJRU5ErkJggg==","orcid":"","institution":"ICAR-Indian Institute of Vegetable Research, Varanasi, Uttar Pradesh, India-221305","correspondingAuthor":true,"prefix":"","firstName":"Anurag","middleName":"","lastName":"Chaurasia","suffix":""},{"id":544039123,"identity":"3679be32-0ebb-4fc3-b0f3-e16ee9cb3dba","order_by":4,"name":"Rajiv Ranjan","email":"","orcid":"","institution":"Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra-282005, India","correspondingAuthor":false,"prefix":"","firstName":"Rajiv","middleName":"","lastName":"Ranjan","suffix":""}],"badges":[],"createdAt":"2025-10-31 08:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7995958/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7995958/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11274-026-04825-0","type":"published","date":"2026-03-23T16:11:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96240785,"identity":"47764ec2-be7c-4226-9a9f-c3c461e66fc5","added_by":"auto","created_at":"2025-11-19 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18:58:51","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97532,"visible":true,"origin":"","legend":"","description":"","filename":"613e13c05f4045b6b5d096103e9f9b3d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/d3b0acbbda55ebe371f31d84.xml"},{"id":95865086,"identity":"c15d89c5-e7a1-49b7-ae82-cd7f45e3151f","added_by":"auto","created_at":"2025-11-13 18:58:51","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105548,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/5be916c19f8a6315e56fa6be.html"},{"id":95865068,"identity":"38ec14e4-e987-4a64-819d-1db206a76a99","added_by":"auto","created_at":"2025-11-13 18:58:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart showing the taxonomic abundance of sample \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at phylum level. From the figure, it can be inferred that Proteobacteria is the most abundant phylum followed by Actinobacteria.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/3c0d5a07f13cec4ea7b3a8a9.png"},{"id":96242194,"identity":"9e0586a0-38ae-4a0c-b6e6-e0ce144b04d4","added_by":"auto","created_at":"2025-11-19 07:12:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58215,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart showing the taxonomic abundance of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Sample at class level. From the figure it can be inferred that Gammaproteobacteria is the most abundant class followed by Betaproteobacteria.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/3f34f23f4da5cdd5178d048f.png"},{"id":96240518,"identity":"57de4e8e-15f7-4d0d-b90a-093ce8191c77","added_by":"auto","created_at":"2025-11-19 07:09:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart showing the taxonomic abundance of sample \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at order level. From the figure, it can be inferred that Nitrososphaerales is the most abundant order followed by Nevskiales.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/df5815552b4ce814cfdc9e4a.png"},{"id":95865072,"identity":"7d34d15c-d67a-4c53-9633-b7f892d93c6b","added_by":"auto","created_at":"2025-11-13 18:58:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart showing the taxonomic abundance of sample \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at family level. From the figure, it can be inferred that Nitrososphaeraceae is the most abundant family followed by Sinobacteraceae.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/e7f772b60d5ea101cee4e2ad.png"},{"id":96241522,"identity":"f14c1bcd-8f88-481f-8155-f898504a507b","added_by":"auto","created_at":"2025-11-19 07:10:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56188,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart showing the taxonomic abundance of sample \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at genus level. From the figure, it can be inferred that Nitrososphaera is the most abundant genus followed by Acinetobacter.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/e425ae4b07591799c3f3d32d.png"},{"id":95865074,"identity":"207a84f1-a66a-46e2-aff7-aa7a2f37ac61","added_by":"auto","created_at":"2025-11-13 18:58:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":114470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart showing the taxonomic abundance of sample \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at species level. From the figure, it can be inferred that \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCandidatus Nitrososphaera gargensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e is the most abundant species followed by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAcidobacteria bacterium\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/4f5e2e0ea12244a8f2049ac5.png"},{"id":95865075,"identity":"ab0d019b-a958-4904-b3ae-a4f954fbb54c","added_by":"auto","created_at":"2025-11-13 18:58:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":258491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCOG Functional Category Hits Distribution of Sample \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/078a964be02202709103848b.png"},{"id":96241539,"identity":"3afda696-7c56-476e-9c5d-35f3dbdff4d8","added_by":"auto","created_at":"2025-11-19 07:10:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":149975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSource hits distribution of Sample \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. Each number indicated represents the number of terms annotated against the respective databases. Maximum number of hits was observed in GO, followed by KEGG and PFAM.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/088410b2e593fcceb0248359.png"},{"id":96241469,"identity":"68141566-60f6-41f0-bf36-43bffb36c6ff","added_by":"auto","created_at":"2025-11-19 07:10:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":51470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKO Functional Category Hits Distribution of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePiper longum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/ab4d37efc4b9fd33aa7df8ad.png"},{"id":105754986,"identity":"4a77ae38-539d-41fb-98e1-75ffe990dbd5","added_by":"auto","created_at":"2026-03-30 16:23:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2214804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7995958/v1/0d1e5e16-90c2-4b48-b8a0-860dcc606b5b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic analysis of the rhizospheric soil of Piper longum L. established in D.E.I, Agra, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOne of the nature's most dynamic environments is the rhizosphere, which is the microecological interface between plant roots and soil. It is created by complex interactions between plants, soil, and a broad range of microorganisms, including bacteria, fungi, protozoa, and archaea (Philippot et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vetterlein et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These microbial communities play a crucial role in plant health by affecting growth regulation, nutrient uptake, stress tolerance, and disease defense. Consequently, the rhizospheric microbiota is preferentially shaped by the root exudates that plants release, which are made up of sugars, amino acids, organic acids, and secondary metabolites (Ali and Glick \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chen and Liu \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, the rhizosphere is a closely related plant-microbe system that is essential to the sustainability of agriculture and ecosystems. In recent decades, the possibility of using rhizospheric microbial populations to improve soil health, decrease the usage of chemical fertilizers, and increase plant growth and resistance has come to light (Wei et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies on model plants and key crops like maize, wheat, and rice have demonstrated that the rhizosphere is home to beneficial microbes like phosphate solubilizers, nitrogen-fixing bacteria, and biocontrol agents (Kour et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Imade and Babalola \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Di Benedetto et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ngalimat et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although many therapeutic plants are valued for their ability to produce secondary metabolites and are often cultivated in low-input systems, little is known about their rhizosphere microbiomes.\u003c/p\u003e\u003cp\u003eLong pepper, or \u003cem\u003ePiper longum\u003c/em\u003e L., is a perennial climbing vine that is indigenous to South Asia and is a member of the Piperaceae family. Due to its medicinal qualities, which include antibacterial, anti-inflammatory, hepatoprotective, and anticancer actions, it is highly valued in ancient medical systems such as Ayurveda, Siddha, and Unani (Biswas et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Its medical effectiveness is attributed to bioactive substances such piperine, piperdardine, and piperlongumine. There is a growing need for \u003cem\u003ePiper longum\u003c/em\u003e to be grown using sustainable methods that preserve or improve its phytochemical content due to the increased interest in natural products and herbal remedies around the world (Prasad et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, little is understood about the microbial communities in the rhizosphere that could affect its metabolite production, growth, and health.\u003c/p\u003e\u003cp\u003eHistorically, the variety of soil microbiomes has been difficult for culture-based methods to fully capture because a large number of soil microorganisms are not culturable in standard laboratory settings. Microbial ecology has undergone a radical transformation thanks to the direct study of DNA made possible by developments in next-generation sequencing (NGS) technology, particularly metagenomic sequencing (Dubey et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Raghav et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Metagenomics provides information about the functional potential of microbial communities in addition to their taxonomic composition by identifying genes linked to the nutrient cycle, stress adaptation, and plant\u0026ndash;microbe interactions (Kumar and Yadav \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, we used high-throughput Illumina sequencing to conduct a comprehensive metagenomic analysis of the \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e rhizosphere. Through gene annotation across major functional databases, this study sought to clarify the functional potential of the microbial communities residing in the rhizosphere of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e while also characterizing their taxonomic diversity. Finding dominant microbial taxa that may contribute to improving soil fertility, bolstering plant health, and maintaining ecosystem stability received special attention. This work lays the groundwork for future functional studies centered on microbiome-assisted strategies for sustainable cultivation, increased plant productivity, and improved phytochemical quality in medicinal plants by offering an integrated overview of the \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e rhizosphere microbiome.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Soil sample collection for analysis\u003c/h2\u003e\u003cp\u003eThe rhizospheric soil of the plants during the spike stage (November) was selected for the metagenomic study of the \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e established in Herbal Garden, D.E.I, Agra. A total of three random replicates were considered for the study. The plants were uprooted, and the loosely attached soil was removed by vigorous shaking. The soil adhered tightly with the root surface was collected in a pre-sterilized container. A pooled sample was prepared for metagenomic analysis by mixing the rhizospheric soils of three replicates (Hou and Williams \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zn\u0026oacute;j et al. 2022). The samples for estimation of soil properties were collected and transported at room temperature whereas the samples for metagenomic analysis were collected and transported at 4℃ and further kept at -20℃.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Soil parameter analysis\u003c/h2\u003e\u003cp\u003eThese soil samples were mixed to form a composite of all collected samples. Samples were allowed to equilibrate with air for 2 hours in trays. The soil samples were dried and sieved with 2mm sieve. The physiochemical examination of the rhizospheric soil for its physical properties (pH, EC) and macronutrients (NPK and % organic carbon) was conducted according to the (Laishram et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nyoki et al. 2018).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sample Preparation and DNA Extraction\u003c/h2\u003e\u003cp\u003eThe rhizospheric soil samples of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e were pooled and we sent to Xcleris Labs Ltd, Ahmedabad for further analysis. From the samples, the genomics DNA was isolated using the Xcelgen soil gDNA kit, following the manufacturer\u0026rsquo;s protocol. The quality of the genomic DNA was assessed using 0.8% agarose gel electrophoresis. The gel was run for 30 mins at 110V. The DNA quantification was carried out using the Qubit\u0026reg; 2.0 Fluorometer. Samples exhibiting a single intact band and an A260/280 ratio between 1.8\u0026ndash;2.0 were selected for library preparation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Library Preparation and Sequencing\u003c/h2\u003e\u003cp\u003ePaired-end sequencing libraries were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina. Briefly, 200 ng of high-quality DNA was fragmented using a Covaris, end-repaired, and ligated with platform-specific adapters. Dual-indexed libraries were PCR-amplified using HiFi PCR Master Mix to ensure sufficient yield. The quality and average size distribution of the final libraries were assessed using an Agilent 2100 Bioanalyzer with a High Sensitivity DNA chip. Sequencing was performed on the Illumina platform (2 \u0026times; 150 bp chemistry) generating high-quality paired-end reads.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Bioinformatics Analysis\u003c/h2\u003e\u003cp\u003eThe raw reads were subjected to quality control to remove adapter sequences, ambiguous bases, and low-quality reads. High-quality paired-end reads were assembled \u003cem\u003ede novo\u003c/em\u003e using the metaSPAdes assembler with default parameters optimized for metagenomic datasets. The assembly statistics, including the number of scaffolds, total scaffold length, N50, and maximum scaffold length, were calculated using in-house Perl scripts. Gene prediction was carried out using Prodigal (v2.6.3) in metagenome mode, which is designed to handle anonymous contigs from mixed microbial communities. These predicted gene sequences were used as input for taxonomic and functional analyses.\u003c/p\u003e\u003cp\u003eTaxonomic annotation was performed using Kaiju, a fast and sensitive metagenomic classifier that assigns taxonomy based on maximum exact matches at the protein level using the Burrows\u0026ndash;Wheeler transform algorithm (Menzel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Predicted gene sequences were uploaded to the Kaiju web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kaiju.binf.ku.dk\u003c/span\u003e\u003cspan address=\"http://kaiju.binf.ku.dk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and analyzed with the following parameters: run mode\u0026thinsp;=\u0026thinsp;greedy, minimum match length\u0026thinsp;=\u0026thinsp;11, minimum match score\u0026thinsp;=\u0026thinsp;75, allowed mismatches\u0026thinsp;=\u0026thinsp;5, and SEQ low-complexity filter\u0026thinsp;=\u0026thinsp;yes. Kaiju classified the sequences against the NCBI RefSeq microbial protein database, optionally including fungi and microbial eukaryotes.\u003c/p\u003e\u003cp\u003eFunctional annotation of predicted genes from \u003cem\u003ePiper longum\u003c/em\u003e was performed using COGNIZER (v0.9b), a comprehensive standalone pipeline that simultaneously assigns functional terms across multiple databases. The predicted gene sequences were analyzed to obtain Clusters of Orthologous Groups (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG), Pfam, Gene Ontology (GO), and FIGfams annotations. COG analysis classified genes into functional categories involved in essential cellular processes such as amino acid transport and metabolism, transcription, replication, and energy production. KEGG mapping associated the genes with pathway modules using KO identifiers, enabling the reconstruction of metabolic and signal transduction pathways. Pfam analysis identified conserved protein families and domains, providing insight into potential structural and functional properties. GO annotation categorized genes into biological process, molecular function, and cellular component classes, thereby facilitating the interpretation of gene roles in the microbial community. FIGfams annotation grouped genes into sets of functionally homologous proteins, allowing the identification of conserved metabolic capabilities across diverse taxa (Galperin et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Collectively, these annotations provided a comprehensive understanding of the metabolic potential and functional diversity of the microbial community associated with \u003cem\u003ePiper longum\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe fertility status and suitability for microbial activity of the rhizospheric soil of \u003cem\u003ePiper longum\u003c/em\u003e were evaluated by analyzing its physicochemical properties as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. With a pH of 7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 and a neutral to slightly alkaline reaction, the soil showed ideal conditions for microbial growth and nutrient availability. A moderate amount of soluble salts that is within the permissible range for healthy plant growth is indicated by the electrical conductivity (EC), which was measured at 639.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 dS/m. While the levels of potassium (22.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007 kg/ha) and phosphorus (24.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 kg/ha) demonstrated balanced nutrient availability necessary for plant metabolism and microbial functioning in the rhizosphere, the macronutrient composition showed a nitrogen content of 310.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 kg/ha, indicating moderate fertility. Low to medium organic matter was indicated by the organic carbon content of 0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008%. These soil parameters collectively show a biologically active, fairly fertile rhizosphere that supports the functional potential revealed by metagenomic profiling and is favorable for plant growth as well as the development of a metabolically diverse microbial community.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhysio-chemical properties of the rhizospheric soil of \u003cem\u003ePiper longum\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Properties\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC (dS/m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e639.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrogen (kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e310.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphorus (kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e24.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e22.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic carbon (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo explore the rhizospheric bacterial community of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e metagenomic analysis was performed which yielded high-quality paired-end sequencing genomic DNA on the Illumina platform which generated 19,942,187 read pairs, yielding approximately (~\u0026thinsp;5.92 Gb) of data with Q30 values exceeding 90%, which ensured high-confidence base calls. The quality-filtered reads were assembled \u003cem\u003ede novo\u003c/em\u003e using metaSPAdes, resulting in the 97,432 scaffolds with a total length of 52.26 Mb, an average scaffold size of 536 bp, an N50 of 489 bp, and a maximum scaffold size of 44,108 bp as provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The assembly statistics provided indicated a comprehensive as well as well-represented metagenomic dataset with a sufficient coverage for downstream analysis. Further, genes were predicted using Prodigal (v2.6.3) which identified 45,876 protein-coding genes, with an average gene length of 334 bp and a total gene length of 15.36 Mb, providing a robust catalog of coding sequences for taxonomic and functional annotation as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDe novo\u003c/em\u003e Assembly Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssembly Elements\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#Scaffolds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal scaffold length (bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52264418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage Scaffold size (bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScaffold N50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum scaffold size (bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredicted Genes Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal gene size (bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15362571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage gene size (bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax scaffold size (bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTaxonomic classification was performed using Kaiju software, unraveling the microbial community associated with \u003cem\u003ePiper longum\u003c/em\u003e which was found to be quite diverse; however, it was strongly dominated by the phylum Proteobacteria, that accounted for 14,015 hits (~\u0026thinsp;42% of classified genes). Other major phyla that constituted the majority of the bacterial population in the \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e rhizosphere with includes, Actinobacteria (4555 hits) which accounted for 13.9% of the total classified genes, Thaumarchaeota (13.16%), Chloroflexi (8.21%), Acidobacteria (7.28%), Candidatus rokubacteria (2.47%), and Nitrospirae (1.92%) whereas the least found phylum was that of Candidatus Fraserbacteria, Candidatus Roizmanbacteria and Thermodesulfobacteria (4 hits or 0.012% of relative abundance) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. By digging into the finer taxonomic resolution, the most abundant class was found to be of Gammaproteobacteria, with relative abundance of 26.01%, followed by Betaproteobacteria with 17.09%, Actinobacteria with 12.02% and Nitrososphaeria 11.03%, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Similarly, the order level of classification revealed that Nitrososphaerales have a relative abundance of 15.82% was the dominant order, which was mirrored by the dominance of Nitrososphaeraceae (20.28%) at the family level \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. At the genus level, \u003cem\u003eNitrososphaera\u003c/em\u003e was found to be the most abundant with relative abundance of 23.58% followed by \u003cem\u003eAcinetobacter\u003c/em\u003e (13.11%), \u003cem\u003eRubrobacter\u003c/em\u003e (6.36%) and \u003cem\u003eNitrospira\u003c/em\u003e (5.06%), while the most abundant species detected was \u003cem\u003eCandidatus Nitrososphaera gargensis\u003c/em\u003e with relative abundance of 11.21%, followed by \u003cem\u003eAcidobacteria bacterium\u003c/em\u003e (9.74%) \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The preponderance of nitrifying archaea, such as \u003cem\u003eNitrososphaera\u003c/em\u003e, is indicative of the existence of an active ammonia-oxidizing microbial community. This community may be responsible for nitrogen cycling in the rhizosphere of \u003cem\u003ePiper longum\u003c/em\u003e, indicating a community structure that is not only very diverse but also functionally relevant.\u003c/p\u003e\u003cp\u003eA thorough understanding of the metabolic capacity of the Piper \u003cem\u003elongum\u003c/em\u003e microbiome was obtained by the functional analysis of the predicted genes using COGNIZER (v0.9b). The COG analysis identified 23,705 terms, with amino acid transport and metabolism accounting for the highest prevalence (2649 terms) which was followed by prediction of general function (2488 terms), carbohydrate transport and metabolism (2100 terms), and energy generation and conversion (1900 terms) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This distribution emphasizes the microbial community's metabolic activity, especially in the areas of energy metabolism and nutrient cycling.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe metagenome sequencing from the current work has been submitted to the NCBI website under the accession number PRJNA600362. A variety of protein databases, including KEGG, Pfam, GO, COG, and FIG, were used to annotate the datasets with functional hierarchy information for the functional predictions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The GO database produced the greatest number of hits, followed by the KEGG and Pfam databases. The COG functional analysis identified 23705 terms, and the KEGG annotation assigned 27,446 terms to 3594 KO classes, with the majority (54.6%) pertaining to metabolic pathways such as amino acid biosynthesis, carbohydrate metabolism, and energy production pathways. The next most prevalent functional category was environmental information processing (25.5%), followed by genetic information processing (12.7%), showing the existence of genes involved in two-component systems, ABC transporters, and transcriptional control as shown in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The annotation of Pfam domains revealed 24,583 functional domains in 2930 distinct Pfam families, indicating a great variety of structural and catalytic proteins. Further clustering of 15,148 words into 4518 functionally homologous groupings using FIGfam analysis suggests conserved functionality across several taxa.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Gene Ontology (GO) annotation generated 33,522 words over 952 functional categories, with the largest representation in biological processes such as metabolic activity, stimulus response, and cellular functions. A substantial number of genes were classified as molecular function categories such as catalytic activity and binding, as well as cellular component categories related to membrane proteins and intracellular activities. These findings collectively indicate that the microbiome associated with \u003cem\u003ePiper longum\u003c/em\u003e is physiologically flexible, with a focus on nutrition mobilization, energy metabolism, and environmental adaptation. These findings collectively show that the microbiome of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e is rich and functionally diversified, dominated by ammonia-oxidizing archaea and Proteobacteria, and that metabolic pathways linked to nitrogen, carbohydrate, and amino acid cycling are strongly enriched. This functional potential highlights the ecological relevance of the \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e microbiome in maintaining plant growth, improving the availability of nutrients, and possibly affecting the manufacture of secondary metabolites.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA strong relationship between soil fertility parameters and the functional competence of the microbial community in the \u003cem\u003ePiper longum\u003c/em\u003e rhizosphere is highlighted by the combination of metagenomic insights and soil nutrient profiling. Plant-microbe symbiosis and microbially mediated nutrient cycling are supported by the moderate organic carbon content and balanced nutrient composition. The ecological and biotechnological significance of the \u003cem\u003eP. longum\u003c/em\u003e microbiome is highlighted by these interactions, which not only maintain soil health but may also have an impact on the biosynthesis of pharmacologically significant compounds like piperine. The present research offers novel insights into the taxonomic constitution, functional potential, and ecological importance of the related microbial community by conducting a thorough metagenomic analysis of the \u003cem\u003ePiper longum\u003c/em\u003e microbiome. The number of scaffolds and a N50 value of 489 bp demonstrate the high-quality and outstanding coverage of the dataset produced by high-throughput Illumina sequencing in tandem with \u003cem\u003ede novo\u003c/em\u003e assembly. The assembly and gene prediction outcomes were similar to those reported for other medicinal plants, including \u003cem\u003eTribulus terrestris\u003c/em\u003e, \u003cem\u003ePiper nigrum\u003c/em\u003e, \u003cem\u003eCalatropis procera\u003c/em\u003e, where plant-associated microbial communities have been investigated using comparable sequencing depths (Rahimlou et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Vaz et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ramadan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs demonstrated by the taxonomic profiling, Proteobacteria clearly dominated the rhizosphere and endophytic communities in medicinal plants. Proteobacteria are often the most abundant phylum because of their diverse metabolic capacities and capacity to colonize plant tissues (Vincze et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The high prevalence of betaproteobacteria and gammaproteobacteria among the Proteobacteria indicates a metabolically active community that can react quickly to changes in nutrient levels in the rhizosphere (Manapure et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given that these ammonia-oxidizing archaea are important participants in the nitrification process and aid in the conversion of ammonia to nitrite, a crucial stage in the nitrogen cycle, the identification of Nitrososphaerales and the dominance of \u003cem\u003eCandidatus Nitrososphaera gargensis\u003c/em\u003e seems particularly significant (Xiang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Clark et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their presence strengthens the possibility that the rhizospheric zone of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e is a nitrogen cycling hotspot, which could improve the intake and growth of nitrogen by plant. The robust vegetative development and the generation of bioactive metabolites under natural conditions may be partially explained by these microbial interactions. The metabolic adaptability of the \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e microbiome was further highlighted by functional annotation. A microbiome that is actively involved in nutrient mobilization is indicated by the enrichment of COG categories linked to energy production, carbohydrate metabolism, and amino acid transport and metabolism (Sefrji et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xu et al. 2021). Through the synthesis of precursors for secondary metabolites, such as alkaloids like piperine, amino acid metabolism affects plant health in addition to being essential for microbial proliferation (Narayanan et al. 2022).\u003c/p\u003e\u003cp\u003eWith notable presence in pathways related to nitrogen, sulfur, and carbon cycle, the KEGG pathway analysis showed that over half of the annotated genes fell under the metabolic category. These results are consistent with recent research on various microbiomes of medicinal plants, which emphasize the role of environmental information processing and nutrient cycling in promoting plant development and metabolite biosynthesis (Semenzato and Fani \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Remarkably, genes linked to stress response and xenobiotic degradation were also found, indicating that the \u003cem\u003ePiper longum\u003c/em\u003e microbiome is capable of assisting the plant in overcoming abiotic stress and possibly enhancing resistance in harsh environmental circumstances (Pandey et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chaudhary et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, our findings offer a useful resource for comprehending interactions between microbes and plants in relation to the generation of secondary metabolites. Since the availability of nitrogen is a known regulator of alkaloid production, the presence of microbial genes involved in nitrogen cycling, phenylpropanoid metabolism, and amino acid biosynthesis may have an indirect effect on piperine biosynthesis (Gao et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It may be possible to determine whether particular microbial species actively control the piperine biosynthesis pathway by future functional investigations, such as transcriptomics and metabolomics. Targeted enrichment of these species may increase the efficiency of nitrogen utilization in \u003cem\u003ePiper longum\u003c/em\u003e culture, and the discovery of nitrogen-transforming archaea like \u003cem\u003eNitrososphaera\u003c/em\u003e opens up possibilities for the development of biofertilizer (Li et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, this study creates a baseline microbiome profile for \u003cem\u003ePiper longum\u003c/em\u003e, exposing a metabolically active and taxonomically varied microbial community that may play roles in secondary metabolism, stress adaption, and nutrient cycling. These discoveries broaden our knowledge of the ecological functions of microorganisms linked to plants and point to potential uses in precision farming, where piperine productivity could be increased through microbiome engineering.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePlant health and soil nutrient dynamics depend on the taxonomically diverse and functionally rich microbial community found in the rhizosphere of \u003cem\u003ePiper longum\u003c/em\u003e, according to metagenomic research. The abundance of Proteobacteria and ammonia-oxidizing archaea such as \u003cem\u003eNitrososphaera\u003c/em\u003e and \u003cem\u003eCandidatus Nitrososphaera gargensis\u003c/em\u003e indicates that the rhizosphere is likely to be an active site of nitrogen transformation. Functional gene annotation demonstrated a significant representation of metabolic pathways associated with energy synthesis, amino acid transport, and environmental adaptation, as well as a microbiome that is precisely regulated to support plant development in a variety of soil conditions. Together, the data suggest that the rhizospheric microbiota of \u003cem\u003eP. longum\u003c/em\u003e is important for the cycling of nutrients and may indirectly affect the production of compounds that are relevant to pharmacology, such as piperine. This study highlights the importance of using beneficial microbial consortia for sustainable cultivation and increased phytochemical production, while also offering a basic understanding of the \u003cem\u003eP. longum\u003c/em\u003e microbiome. Future multi-omics methods that combine transcriptome and metabolomic studies will be essential to identify possible taxa for the development of bioinoculants in medicinal plant agriculture as well as to pinpoint the exact microbiological processes underlying the production of secondary metabolites.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003eNot applicable to this manuscript.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eWe give our consent and ensure that the publisher has the author\u0026rsquo;s permission to publish the research article in their Journal.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe Authors received NO FUNDING for this work\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**M.P.** Conceptualization **S. M.** Writing original draft **R. R.** Review and Supervision, S.K. and A.C. Editing. All authors reviewed the results and the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors sincerely thank the Director, of Dayalbagh Educational Institute, Dayalbagh, Agra, for providing the necessary infrastructure and support for this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe metagenome sequencing from the current work has been submitted to the NCBI website under the accession number PRJNA600362\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli S, Glick BR (2024) Root exudate metabolites alter food crops microbiomes, impacting plant biocontrol and growth. 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Water Res X 21:100204\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Piper longum, Rhizosphere microbiome, Metagenomics, Functional annotation, Medicinal plants","lastPublishedDoi":"10.21203/rs.3.rs-7995958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7995958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rhizosphere of medicinal plants contains a variety of microbial populations that influence plant health, nutrient uptake, and the synthesis of bioactive compounds. In this study, we used a culture-independent metagenomic approach to characterize the rhizospheric microbiota of \u003cem\u003ePiper longum\u003c/em\u003e, a plant with significant medicinal potential in traditional medicine. High-throughput sequencing using the Illumina technology yielded 19.94\u0026nbsp;million paired-end reads (~\u0026thinsp;5.92 Gb), which were assembled into 97,432 scaffolds, resulting in a genomic length of 52.26 Mb. In all, 45,876 genes were predicted and functionally identified across multiple databases. The phylum Proteobacteria is dominant in terms of taxonomic classification, with Nitrososphaera and \u003cem\u003eCandidatus Nitrososphaera gargensis\u003c/em\u003e being the most common genus and species, respectively. A metabolically diverse and ecologically significant microbial population was indicated by functional profiling, which also showed enrichment in genes linked to metabolism, amino acid transport, and environmental adaptability. This work highlights the value of using microbial diversity for sustainable agriculture and enhanced phytochemical output while providing basic insights into the \u003cem\u003eP\u003c/em\u003e. \u003cem\u003elongum\u003c/em\u003e rhizosphere microbiome.\u003c/p\u003e","manuscriptTitle":"Metagenomic analysis of the rhizospheric soil of Piper longum L. established in D.E.I, Agra, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 18:58:46","doi":"10.21203/rs.3.rs-7995958/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T08:39:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T12:11:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T15:34:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21227602699207463266468656878167802127","date":"2025-11-04T04:24:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194940922776958175439479967940597039600","date":"2025-11-03T10:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T09:59:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-02T06:00:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-01T05:49:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Microbiology and Biotechnology","date":"2025-10-31T07:52:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"55f47c14-ee45-4333-8d06-f4bd53c95c75","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:18:50+00:00","versionOfRecord":{"articleIdentity":"rs-7995958","link":"https://doi.org/10.1007/s11274-026-04825-0","journal":{"identity":"world-journal-of-microbiology-and-biotechnology","isVorOnly":false,"title":"World Journal of Microbiology and Biotechnology"},"publishedOn":"2026-03-23 16:11:56","publishedOnDateReadable":"March 23rd, 2026"},"versionCreatedAt":"2025-11-13 18:58:46","video":"","vorDoi":"10.1007/s11274-026-04825-0","vorDoiUrl":"https://doi.org/10.1007/s11274-026-04825-0","workflowStages":[]},"version":"v1","identity":"rs-7995958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7995958","identity":"rs-7995958","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}