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Abstract

Metatranscriptomic analysis of the soil microbiome has the potential to reveal molecular mechanisms that drive soil processes regulated by the microbial community. Therefore, RNA samples must be of sufficient yield and quality to robustly quantify differential gene expression. While short-read sequencing technology is often favoured for metatranscriptomics, long-read sequencing has the potential to provide several benefits over short-read technologies. The ability to resolve complete transcripts on a portable sequencing platform for a relatively low capital expenditure makes Oxford Nanopore Technology an attractive prospect for addressing many of the challenges of soil metatranscriptomics. To fully enable long-read metatranscriptomic analysis of the functional molecular pathways expressed in these diverse habitats, RNA purification methods from soil must be optimised for long-read sequencing. Here we compare RNA samples purified using five commercially available extraction kits designed for use with soil. We found that the Qiagen RNeasy PowerSoil Total RNA Kit performed the best across RNA yield, quality and purity and was robust across different soil types. We found that sufficient sequencing depth can be achieved to characterise the active community for total RNA samples using Oxford Nanopore Technology, and discuss its current limitations for differential gene expression analysis in soil studies.

Funding
This study was supported by the:
  • Shell United Kingdom (Award CW648947)
    • Principal Award Recipient: RichardK. Tennant
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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/content/journal/mgen/10.1099/mgen.0.001298
2024-09-19
2025-11-16

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References

  1. Kobras CM, Fenton AK, Sheppard SK. Next-generation microbiology: from comparative genomics to gene function. Genome Biol 2021; 22:123 [View Article] [PubMed]
    [Google Scholar]
  2. Zhang L, Chen F, Zeng Z, Xu M, Sun F et al. Advances in metagenomics and its application in environmental microorganisms. Front Microbiol 2021; 12:766364 [View Article] [PubMed]
    [Google Scholar]
  3. Acinas SG, Sánchez P, Salazar G, Cornejo-Castillo FM, Sebastián M et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun Biol 2021; 4:604 [View Article] [PubMed]
    [Google Scholar]
  4. De R, Mukhopadhyay AK, Dutta S. Metagenomic analysis of gut microbiome and resistome of diarrheal fecal samples from Kolkata, India, reveals the core and variable microbiota including signatures of microbial dark matter. Gut Pathog 2020; 12:32 [View Article] [PubMed]
    [Google Scholar]
  5. Alteio LV, Séneca J, Canarini A, Angel R, Jansa J et al. A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biol Biochem 2021; 160:108357 [View Article]
    [Google Scholar]
  6. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 2020; 38:685–688 [View Article] [PubMed]
    [Google Scholar]
  7. Sun S, Jones RB, Fodor AA. Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories. Microbiome 2020; 8:46 [View Article] [PubMed]
    [Google Scholar]
  8. Chivian D, Jungbluth SP, Dehal PS, Wood-Charlson EM, Canon RS et al. Metagenome-assembled genome extraction and analysis from microbiomes using KBase. Nat Protoc 2023; 18:208–238 [View Article] [PubMed]
    [Google Scholar]
  9. Shakya M, Lo CC, Chain PSG. Advances and challenges in metatranscriptomic analysis. Front Genet 2019; 10:904 [View Article] [PubMed]
    [Google Scholar]
  10. Bei Q, Moser G, Wu X, Müller C, Liesack W. Metatranscriptomics reveals climate change effects on the rhizosphere microbiomes in European grassland. Soil Biol Biochem 2019; 138:107604 [View Article]
    [Google Scholar]
  11. Bang-Andreasen T, Anwar MZ, Lanzén A, Kjøller R, Rønn R et al. Total RNA sequencing reveals multilevel microbial community changes and functional responses to wood ash application in agricultural and forest soil. FEMS Microbiol Ecol 2020; 96: [View Article]
    [Google Scholar]
  12. Sharma M, Sudheer S, Usmani Z, Rani R, Gupta P. Deciphering the Omics of plant-microbe interaction: perspectives and new Insights. Curr Genomics 2020; 21:343–362 [View Article] [PubMed]
    [Google Scholar]
  13. Overy DP, Bell MA, Habtewold J, Helgason BL, Gregorich EG. “Omics” technologies for the study of soil carbon stabilization: a review. Front Environ Sci 2021; 9: [View Article]
    [Google Scholar]
  14. Mettel C, Kim Y, Shrestha PM, Liesack W. Extraction of mRNA from soil. Appl Environ Microbiol 2010; 76:5995–6000 [View Article] [PubMed]
    [Google Scholar]
  15. Chuckran PF, Huntemann M, Clum A, Foster B, Foster B et al. Metagenomes and metatranscriptomes of a glucose-amended agricultural soil. Microbiol Resour Announc 2020; 9:e00895-20 [View Article] [PubMed]
    [Google Scholar]
  16. Sharma R, Sharma PK. Metatranscriptome sequencing and analysis of agriculture soil provided significant insights about the microbial community structure and function. Ecol Genet Genomics 2018; 6:9–15 [View Article]
    [Google Scholar]
  17. Liu L, Yang Y, Deng Y, Zhang T. Nanopore long-read-only metagenomics enables complete and high-quality genome reconstruction from mock and complex metagenomes. Microbiome 2022; 10:209 [View Article] [PubMed]
    [Google Scholar]
  18. Portik DM, Brown CT, Pierce-Ward NT. Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets. BMC Bioinformatics 2022; 23:541 [View Article] [PubMed]
    [Google Scholar]
  19. Tennant RK, Power AL, Burton SK, Sinclair N, Parker DA et al. In-situ sequencing reveals the effect of storage on lacustrine sediment microbiome demographics and functionality. Environ Microbiome 2022; 17:5 [View Article] [PubMed]
    [Google Scholar]
  20. Laver T, Harrison J, O’Neill PA, Moore K, Farbos A et al. Assessing the performance of the Oxford nanopore technologies MinION. Biomol Detect Quantif 2015; 3:1–8 [View Article] [PubMed]
    [Google Scholar]
  21. Poursalavati A, Javaran VJ, Laforest-Lapointe I, Fall ML. Soil metatranscriptomics: an improved rna extraction method toward functional analysis using nanopore direct RNA sequencing. Phytobiomes J 2023; 7:42–54 [View Article]
    [Google Scholar]
  22. Jacobsen CS, Nielsen TK, Vester JK, Stougaard P, Nielsen JL et al. Inter-laboratory testing of the effect of DNA blocking reagent G2 on DNA extraction from low-biomass clay samples. Sci Rep 2018; 8:5711 [View Article] [PubMed]
    [Google Scholar]
  23. Bonenfant Q, Noé L, Touzet H. Porechop_ABI: discovering unknown adapters in Oxford nanopore technology sequencing reads for downstream trimming. Bioinform Adv 2023; 3:vbac085 [View Article] [PubMed]
    [Google Scholar]
  24. Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 2016; 26:1721–1729 [View Article] [PubMed]
    [Google Scholar]
  25. Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012; 28:3211–3217 [View Article] [PubMed]
    [Google Scholar]
  26. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015; 12:59–60 [View Article] [PubMed]
    [Google Scholar]
  27. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28:27–30 [View Article] [PubMed]
    [Google Scholar]
  28. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res 2007; 17:377–386 [View Article] [PubMed]
    [Google Scholar]
  29. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci 2003; 14:927–930 [View Article]
    [Google Scholar]
  30. Wnuk E, Waśko A, Walkiewicz A, Bartmiński P, Bejger R et al. The effects of humic substances on DNA isolation from soils. PeerJ 2020; 8:e9378 [View Article] [PubMed]
    [Google Scholar]
  31. Sar A, Pal S, Dam B. Isolation of high molecular weight and humic acid-free metagenomic DNA from lignocellulose-rich samples compatible for direct fosmid cloning. Appl Microbiol Biotechnol 2018; 102:6207–6219 [View Article] [PubMed]
    [Google Scholar]
  32. Lim NYN, Roco CA, Frostegård Å. Transparent DNA/RNA co-extraction workflow protocol suitable for inhibitor-rich environmental samples that focuses on complete DNA removal for transcriptomic analyses. Front Microbiol 2016; 7:1588 [View Article] [PubMed]
    [Google Scholar]
  33. Zhao S, Zhang Y, Gamini R, Zhang B, von Schack D. Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion. Sci Rep 2018; 8:4781 [View Article]
    [Google Scholar]
  34. Petrova OE, Garcia-Alcalde F, Zampaloni C, Sauer K. Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Sci Rep 2017; 7:41114 [View Article] [PubMed]
    [Google Scholar]
  35. Herbert ZT, Kershner JP, Butty VL, Thimmapuram J, Choudhari S et al. Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction. BMC Genomics 2018; 19:199 [View Article] [PubMed]
    [Google Scholar]
  36. Nuccio EE, Nguyen NH, Nunes da Rocha U, Mayali X, Bougoure J et al. Community RNA-Seq: multi-kingdom responses to living versus decaying roots in soil. ISME Commun 2021; 1:72 [View Article] [PubMed]
    [Google Scholar]
  37. Tao F, Huang Y, Hungate BA, Manzoni S, Frey SD et al. 2023 https://www.nature.com/articles/s41586-023-06042-3 accessed 30 May 2023
  38. Culviner PH, Guegler CK, Laub MT. A simple, cost-effective, and robust method for rRNA depletion in RNA-sequencing studies. mBio 2020; 11:e00010-20 [View Article] [PubMed]
    [Google Scholar]
  39. McGee KM, Robinson CV, Hajibabaei M. Gaps in DNA-based biomonitoring across the globe. Front Ecol Evol 2019; 7:337 [View Article]
    [Google Scholar]
  40. Xu L, Chen H, Hu X, Zhang R, Zhang Z et al. Average gene length is highly conserved in prokaryotes and eukaryotes and diverges only between the two kingdoms. Mol Biol Evol 2006; 23:1107–1108 [View Article] [PubMed]
    [Google Scholar]
  41. Nip KM, Hafezqorani S, Gagalova KK, Chiu R, Yang C et al. Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2. Nat Commun 2023; 14:2940 [View Article] [PubMed]
    [Google Scholar]
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