Skip to content
1887

Abstract

Whole-transcriptome (long-read) RNA sequencing (Oxford Nanopore Technologies, ONT) holds promise for reference-agnostic analysis of differential gene expression in pathogenic bacteria, including for antimicrobial resistance genes (ARGs). However, direct cDNA ONT sequencing requires large concentrations of polyadenylated mRNA, and amplification protocols may introduce technical bias. Here we evaluated the impact of direct cDNA- and cDNA PCR-based ONT sequencing on transcriptomic analysis of clinical . Four bloodstream infection-associated isolates (=2 biological replicates per isolate) were sequenced using the ONT Direct cDNA Sequencing SQK-DCS109 and PCR-cDNA Barcoding SQK-PCB111.24 kits. Biological and technical replicates were distributed over eight flow cells using 16 barcodes to minimize batch/barcoding bias. Reads were mapped to a transcript reference and transcript abundance was quantified after depletion of low-abundance and rRNA genes. We found there were strong correlations between read counts using both kits and when restricting the analysis to include only ARGs. We highlighted that correlations were weaker for genes with a higher GC content. Read lengths were longer for the direct cDNA kit compared to the PCR-cDNA kit whereas total yield was higher for the PCR-cDNA kit. In this small but methodologically rigorous evaluation of biological and technical replicates of isolates sequenced with the direct cDNA and PCR-cDNA ONT sequencing kits, we demonstrated that PCR-based amplification substantially improves yield with largely unbiased assessment of core gene and ARG expression. However, users of PCR-based kits should be aware of a small risk of technical bias which appears greater for genes with an unusually high (>52%)/low (<44%) GC content.

Funding
This study was supported by the:
  • NIHR Oxford Biomedical Research Centre
    • Principle Award Recipient: NotApplicable
  • National Institute for Health Research Health Protection Research Unit (Award NIHR200915)
    • Principle Award Recipient: NotApplicable
  • John Fell Fund, University of Oxford (Award 0008776)
    • Principle Award Recipient: NicoleStoesser
  • 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.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.001296
2024-10-25
2025-02-17
Loading full text...

Full text loading...

/deliver/fulltext/mgen/10/10/mgen001296.html?itemId=/content/journal/mgen/10.1099/mgen.0.001296&mimeType=html&fmt=ahah

References

  1. Bayega A, Oikonomopoulos S, Wang YC, Ragoussis J. Improved nanopore full-length cDNA sequencing by PCR-suppression. Front Genet 2022; 13:1031355 [View Article] [PubMed]
    [Google Scholar]
  2. Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet 2019; 20:631–656 [View Article] [PubMed]
    [Google Scholar]
  3. Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM et al. A beginner’s guide to analysis of RNA sequencing data. Am J Respir Cell Mol Biol 2018; 59:145–157 [View Article] [PubMed]
    [Google Scholar]
  4. Hör J, Gorski SA, Vogel J. Bacterial RNA biology on a genome scale. Mol Cell 2018; 70:785–799 [View Article] [PubMed]
    [Google Scholar]
  5. Colgan AM, Cameron ADS, Kröger C. If it transcribes, we can sequence it: mining the complexities of host-pathogen-environment interactions using RNA-seq. Curr Opin Microbiol 2017; 36:37–46 [View Article] [PubMed]
    [Google Scholar]
  6. Creecy JP, Conway T. Quantitative bacterial transcriptomics with RNA-seq. Curr Opin Microbiol 2015; 23:133–140 [View Article] [PubMed]
    [Google Scholar]
  7. Grünberger F, Ferreira-Cerca S, Grohmann D. Nanopore sequencing of RNA and cDNA molecules in Escherichia coli. RNA 2022; 28:400–417 [View Article] [PubMed]
    [Google Scholar]
  8. Al Kadi M, Ishii E, Truong DT, Motooka D, Matsuda S et al. Direct RNA sequencing unfolds the complex transcriptome of Vibrio parahaemolyticus. mSystems 2021; 6:e0099621 [View Article] [PubMed]
    [Google Scholar]
  9. Bernard F, Dargère D, Rechavi O, Dupuy D. Quantitative analysis of C. elegans transcripts by nanopore direct-cDNA sequencing reveals terminal hairpins in non trans-spliced mRNAs. Nat Commun 2023; 14:1229 [View Article] [PubMed]
    [Google Scholar]
  10. Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol 2021; 39:1348–1365 [View Article] [PubMed]
    [Google Scholar]
  11. Wangsanuwat C, Heom KA, Liu E, O’Malley MA, Dey SS. Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion. BMC Genom 2020; 21:717 [View Article] [PubMed]
    [Google Scholar]
  12. Wahl A, Huptas C, Neuhaus K. Comparison of rRNA depletion methods for efficient bacterial mRNA sequencing. Sci Rep 2022; 12:5765 [View Article] [PubMed]
    [Google Scholar]
  13. Pitt ME, Nguyen SH, Duarte TPS, Teng H, Blaskovich MAT et al. Evaluating the genome and resistome of extensively drug-resistant Klebsiella pneumoniae using native DNA and RNA nanopore sequencing. Gigascience 2020; 9:giaa002 [View Article] [PubMed]
    [Google Scholar]
  14. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A et al. A survey of best practices for RNA-seq data analysis. Genome Biol 2016; 17:13 [View Article] [PubMed]
    [Google Scholar]
  15. Scholes AN, Lewis JA. Comparison of RNA isolation methods on RNA-Seq: implications for differential expression and meta-analyses. BMC Genom 2020; 21:249 [View Article] [PubMed]
    [Google Scholar]
  16. Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics 2010; 185:405–416 [View Article] [PubMed]
    [Google Scholar]
  17. Schurch NJ, Schofield P, Gierliński M, Cole C, Sherstnev A et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?. RNA 2016; 22:839–851 [View Article] [PubMed]
    [Google Scholar]
  18. Murray CJL, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399:629–655 [View Article]
    [Google Scholar]
  19. The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters; 2023 http://www.eucast.org
  20. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 2006; 7:3 [View Article] [PubMed]
    [Google Scholar]
  21. Lipworth S, Vihta K-D, Chau K, Barker L, George S et al. Ten-year longitudinal molecular epidemiology study of Escherichia coli and Klebsiella species bloodstream infections in Oxfordshire, UK. Genome Med 2021; 13:144 [View Article] [PubMed]
    [Google Scholar]
  22. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 2018; 34:3094–3100 [View Article] [PubMed]
    [Google Scholar]
  23. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 2017; 14:417–419 [View Article] [PubMed]
    [Google Scholar]
  24. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ et al. Validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates. Antimicrob Agents Chemother 2019; 63:e00483-19 [View Article] [PubMed]
    [Google Scholar]
  25. De Coster W, Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics 2023; 39:btad311 [View Article] [PubMed]
    [Google Scholar]
  26. Hall MB. Rasusa: randomly subsample sequencing reads to a specified coverage. JOSS 2022; 7:3941 [View Article]
    [Google Scholar]
  27. Seeman T. mlst: Github; 2022 https://github.com/tseemann/mlst
  28. This publication made use of the PubMLST website ( https://pubmlst.org/) developed by Keith Jolley (Jolley & Maiden 2010, BMC Bioinformatics, 11:595) and sited at the University of Oxford. The development of that website was funded by the Wellcome Trust; 2010
  29. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 2015; 25:1043–1055 [View Article] [PubMed]
    [Google Scholar]
  30. R Core Team R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2022 https://www.R-project.org
/content/journal/mgen/10.1099/mgen.0.001296
Loading
/content/journal/mgen/10.1099/mgen.0.001296
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error