Skip to main navigation menu Skip to main content Skip to site footer

Research Articles

Early Access

Comparative analysis of rhizosphere bacterial communities in tomato (Solanum lycopersicum L.) through 16S rRNA amplicon-based taxonomic and functional profiling

DOI
https://doi.org/10.14719/pst.11317
Submitted
19 August 2025
Published
10-11-2025
Versions

Abstract

Microorganisms in the soil play essential roles in the growth and development of plants. However, there is limited understanding of how the health status of plants influences the functions of these microorganisms. This study aims to analyze the composition of bacterial communities in the rhizosphere soil of tomato plants (Solanum lycopersicum L.) from the Beggli micro-watershed in Kolar, Karnataka using high throughput 16S rRNA amplicon sequencing. Four different rhizosphere soil samples of tomato were taken and subjected to microbial diversity analysis. Analysis of the soil showed differences in pH, Electrical conductivity (EC), percent organic carbon and macronutrients (N, P, K) that affected microbial composition. DNA sequencing, bioinformatic analysis and amplicon sequencing detected 350 amplicon sequence variants (ASVs) in all the rhizosphere soil samples. Among them, TSB2 showed the highest ASVs (210). Proteobacteria were the phylum with the highest abundance in all the samples, followed by Firmicutes, Actinobacteria, Acidobacteriota and Chloroflexi. Taxonomic analysis showed Gammaproteobacteria, Alphaproteobacteria and Bacilli to be the most dominant classes of bacteria while Bacillus, Acinetobacter, Sphingomonas and Flavobacterium were the most predominant genera. Alpha diversity indices showed remarkable diversity in microbial richness with the Shannon Index ranging from 8.25 to 9.11. Beta diversity analysis revealed a clear clustering of microbial communities based on soil characteristics. Functional annotation by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups of Proteins (COG) analysis revealed genes involved in nutrient cycling, oxidative stress response and plant-microbe interactions. These findings enhance our understanding of tomato rhizosphere bacterial community structure and guide sustainable soil management practices.

References

  1. 1. Bhardwaj A. Watershed hydrology and management. Watershed Hydrol Manag Model. 2019;1–17. https://doi.org/10.1201/9780429430633
  2. 2. Gupta R, Sharma PK. A review of groundwater-surface water interaction studies in India. J Hydrol. 2023;621:129592. https://doi.org/10.1016/j.jhydrol.2023.129592
  3. 3. Akbari F, Shourian M, Moridi A. Assessment of the climate change impacts on the watershed-scale optimal crop pattern using a surface-groundwater interaction hydro-agronomic model. Agric Water Manag. 2022;265:107508. https://doi.org/10.1016/j.agwat.2022.107508
  4. 4. Walia SS, Kaur K, Kaur T. Introduction, types and history of rainfed agriculture and watershed management in India. In: Rainfed Agriculture and Watershed Management. Singapore: Springer Nature. 2024. p. 95–102. https://doi.org/10.1007/978-981-99-8425-1_12
  5. 5. Srinivasarao C, Rakesh S, Kumar GR, Jagadesh M, Nataraj KC, Manasa R, et al. Improving water storage through effective soil organic matter management strategies under dryland farming in India. In: Soil and Drought. CRC Press; 2023. p. 256–84. https://doi.org/10.1201/b22954-10
  6. 6. Aydoner C. Development and application of a GIS tool in the design of surface water quality monitoring networks: a micro-watershed–based approach. Environ Monit Assess. 2024;196(10):985. https://doi.org/10.1007/s10661-024-13193-x
  7. 7. Megarsa RW. Characterization and Classification of Soils of Hate Leman Micro-Watershed in Dugda District, Central Ethiopia. [Doctoral dissertation]. Haramaya University; 2023. http://ir.haramaya.edu.et/hru/handle/123456789/6011
  8. 8. Hc H, S G, Srikanth L, Surendra HJ. Prioritization of sub-watersheds of the Kanakapura Watershed in the Arkavathi River Basin, Karnataka, India-using remote sensing and GIS. Geol Ecol Landscapes. 2021;5(2):149–60. https://doi.org/10.1080/24749508.2020.1846841
  9. 9. Lee SA, Kim Y, Kim JM, Chu B, Joa JH, Sang MK, et al. A preliminary examination of bacterial, archaeal and fungal communities inhabiting different rhizocompartments of tomato plants under real-world environments. Sci Rep. 2019;9(1):9300. https://doi.org/10.1038/s41598-019-45660-8
  10. 10. Babalola OO, Adedayo AA, Fadiji AE. Metagenomic survey of tomato rhizosphere microbiome using the shotgun approach. Microbiol Resour Announc. 2022;11(2):e01131-21. https://doi.org/10.1128/mra.01131-21
  11. 11. Ajilogba CF, Babalola OO, Ahmad F. Antagonistic effects of Bacillus species in biocontrol of tomato Fusarium wilt. Stud Ethno-Med. 2013;7(3):205–16. https://doi.org/10.1080/09735070.2013.11886462
  12. 12. French E, Kaplan I, Iyer-Pascuzzi A, Nakatsu CH, Enders L. Emerging strategies for precision microbiome management in diverse agroecosystems. Nat Plants. 2021;7(3):256–67. https://doi.org/10.1038/s41477-020-00830-9
  13. 13. Tarabih OM, Arias ME, Santos AL, Hua J, Cooper RZ, Khanal A, Dang TD, Khare YP, Charkhgard H, Rains MC, Zhang Q. Effects of the spatial distribution of best management practices for watershed wide nutrient load reduction. Ecol Eng. 2024;201:107211. https://doi.org/10.1016/j.ecoleng.2024.107211
  14. 14. Zhang X, Fa X, Ma W, Ba Z, Lu J, Ya J, et al. Toward to agricultural green development by multi-objective zoning and nitrogen nutrient management: a case study in the Baiyangdian Basin, China. Front Agric Sci Eng. 2024;11(1). https://doi.org/10.15302/J-FASE-2023533
  15. 15. Brown JD. Nutrient and Bacteria Management within the Lindsay Creek Watershed in Nez Perce County, Idaho. [Master's thesis]. University of Idaho; 2024. https://npshistory.com/publications/water/nrr-05-01.pdf
  16. 16. Adedayo AA, Fadiji AE, Babalola OO. The effects of plant health status on the community structure and metabolic pathways of rhizosphere microbial communities associated with Solanum lycopersicum. Horticulturae. 2022;8(5):404. https://doi.org/10.3390/horticulturae8050404
  17. 17. Sun H, Jiang S, Jiang C, Wu C, Gao M, Wang Q. A review of root exudates and rhizosphere microbiome for crop production. Environ Sci Pollut Res. 2021;28(39):54497–510. https://doi.org/10.1007/s11356-021-15838-7
  18. 18. Pascale A, Proietti S, Pantelides IS, Stringlis IA. Modulation of the root microbiome by plant molecules: the basis for targeted disease suppression and plant growth promotion. Front Plant Sci. 2020;10:1741. https://doi.org/10.3389/fpls.2019.01741
  19. 19. Jackson ML. Soil chemical analysis. New Delhi: Prentice Hall of India; 1973.
  20. 20. Walkley A, Black IA. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 1934;37(1):29–38. https://doi.org/10.1097/00010694-193401000-00003
  21. 21. Subbiah BV, Asija GL. A rapid procedure for the estimation of available nitrogen in soils.1956;25:259-60. https://www.cabidigitallibrary.org/doi/full/10.5555/19571900070
  22. 22. Dickman SR, Bray RH. Colorimetric determination of phosphate. Ind Eng Chem Anal Ed. 1940;12(11):665–68. https://doi.org/10.1021/ac50151a013
  23. 23. Pratt PF. Potassium. In: Methods of soil analysis: Part 2 chemical and microbiological properties. 1965. p. 1022–30. https://doi.org/10.2134/agronmonogr9.2.c20
  24. 24. Qiao Q, Wang F, Zhang J, Chen Y, Zhang C, Liu G, et al. The variation in the rhizosphere microbiome of cotton with soil type, genotype and developmental stage. Sci Rep. 2017;7(1):3940. https://doi.org/10.1038/s41598-017-04213-7
  25. 25. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41(1):e1. https://doi.org/10.1093/nar/gks808
  26. 26. Estaki M, Jiang L, Bokulich NA, McDonald D, González A, Kosciolek T et al. QIIME 2 enables comprehensive end-to-end analysis of diverse microbiome data and comparative studies with publicly available data. Curr Protoc Bioinformatics. 2020;70(1):e100. https://doi.org/10.1002/cpbi.100
  27. 27. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. https://doi.org/10.1093/bioinformatics/btu170
  28. 28. Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957–63. https://doi.org/10.1093/bioinformatics/btr507
  29. 29. Alishum A. DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea. 2019. https://doi.org/10.5281/zenodo.4735821
  30. 30. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41(D1):D590–6. https://doi.org/10.1093/nar/gks1219
  31. 31. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685–88. https://doi.org/10.1038/s41587-020-0548-6
  32. 32. 32. Edwards J, Johnson C, Santos-Medellín C, Lurie E, Podishetty NK, Bhatnagar S, et al. Structure, variation and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci USA. 2015;112(8):E911–20. https://doi.org/10.1073/pnas.1414592112
  33. 33. Mendes LW, Kuramae EE, Navarrete AA, Van Veen JA, Tsai SM. Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J. 2014;8(8):1577–87. https://doi.org/10.1038/ismej.2014.17
  34. 34. Prasannakumar MK, Netravathi LM, Mahesh HB, Buela Parivallal P, Puneeth ME, Sathish A, et al. Comparative metagenomic analysis of rice soil samples revealed the diverse microbial population and biocontrol organisms against plant pathogenic fungus Magnaporthe oryzae. 3 Biotech. 2021;11(5):245. https://doi.org/10.1007/s13205-021-02783-y
  35. 35. Resendiz-Nava CN, Alonso-Onofre F, Silva-Rojas HV, Rebollar-Alviter A, Rivera-Pastrana DM, Stasiewicz MJ, et al. Tomato plant microbiota under conventional and organic fertilization regimes in a soilless culture system. Microorganisms. 2023;11(7):1633. https://doi.org/10.3390/microorganisms11071633
  36. 36. Song X, Tao B, Guo J, Li J, Chen G. Changes in the microbial community structure and soil chemical properties of vertisols under different cropping systems in northern China. Front Environ Sci. 2018;6:132. https://doi.org/10.3389/fenvs.2018.00132
  37. 37. Tavares TC, Bezerra WM, Normando LR, Rosado AS, Melo VM. Brazilian semi-arid mangroves-associated microbiome as pools of richness and complexity in a changing world. Front Microbiol. 2021;12:715991. https://doi.org/10.3389/FMIB.2021.715991
  38. 38. Chaudhry V, Rehman A, Mishra A, Chauhan PS, Nautiyal CS. Changes in bacterial community structure of agricultural land due to long-term organic and chemical amendments. Microb Ecol. 2012;64(2):450–60. https://doi.org/10.3389/fmicb.2021.715991
  39. 39. Tkacz A, Hortala M, Poole PS. Absolute quantitation of microbiota abundance in environmental samples. Microbiome. 2018;6(1):110. https://doi.org/10.1186/s40168-018-0491-7
  40. 40. Ofek-Lalzar M, Sela N, Goldman-Voronov M, Green SJ, Hadar Y, Minz D. Niche and host-associated functional signatures of the root surface microbiome. Nat Commun. 2014;5(1):4950. https://doi.org/10.1038/ncomms5950
  41. 41. Berg G, Rybakova D, Fischer D, Cernava T, Vergès MC, Charles T, et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome. 2020;8(1):103. https://doi.org/10.1186/s40168-020-00875-0
  42. 42. Buyer JS, Teasdale JR, Roberts DP, Zasada IA, Maul JE. Factors affecting soil microbial community structure in tomato cropping systems. Soil Biol Biochem. 2010;42(5):831–41. https://doi.org/10.1016/j.soilbio.2010.01.020
  43. 43. Compant S, Samad A, Faist H, Sessitsch A. A review on the plant microbiome: ecology, functions and emerging trends in microbial application. J Adv Res. 2019;19:29–37. https://doi.org/10.1016/j.jare.2019.03.004
  44. 44. Kavamura VN, Robinson RJ, Hayat R, Clark IM, Hughes D, Rossmann M, et al. Land management and microbial seed load effect on rhizosphere and endosphere bacterial community assembly in wheat. Front Microbiol. 2019;10:2625. https://doi.org/10.3389/fmicb.2019.02625
  45. 45. Mendes R, Kruijt M, De Bruijn I, Dekkers E, Van Der Voort M, Schneider JH, et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science. 2011;332(6033):1097–100. https://doi.org/10.1126/science.1203980
  46. 46. Navarro-Noya YE, Chávez-Romero Y, Hereira-Pacheco S, de León Lorenzana AS, Govaerts B, Verhulst N et al. Bacterial communities in the rhizosphere at different growth stages of maize cultivated in soil under conventional and conservation agricultural practices. Microbiol Spectr. 2022;10(2):1834-21.
  47. 47. DeFord L, Yoon JY. Soil microbiome characterization and its future directions with biosensing. J Biol Eng. 2024;18(1):50. https://doi.org/10.1186/s13036-024-00444-1

Downloads

Download data is not yet available.