Gene Expression Analysis of Litter-Associated Fungi Using RNA-Seq



Studies of RNA expression levels indicate which genes and organisms are active in a particular sample and therefore in particular environmental conditions or life-history stages. The study of gene expression in aquatic fungi has the potential to provide an in-depth understanding of the molecular basis of leaf litter decomposition, including how the physiological pathways involved vary among fungal species and substrate types. This chapter describes a method to extract and purify RNA from cultures or mixed environmental samples, prepare the RNA for next-generation sequencing (NGS) and carry out bioinformatic analyses of the resulting sequence data. The latter quantifies gene expression by aligning to a transcriptome that is derived from an annotated genome or a de novo RNA-seq assembly, and aggregates counts at the gene level using a pseudoalignment algorithm. The use of transcript-level quantification allows for both model and non-model organisms to be analysed using the same framework, and the bioinformatic analysis can be performed on a standard computer. The method has been successfully applied to cultivated Clavariopsis aquatica and Helicodendron triglitziense, and can yield insight into how physiological pathways are differentially regulated on different substrates, and may ultimately lead to the discovery of new physiological pathways.


Fungal community analysis Fungal transcriptional activity mRNA Next-generation sequencing RNA extraction RNA sequencing RNA-Seq counts Sequence database Transcript-level quantification Transcriptome 

Supplementary material

114771_2_En_39_MOESM1_ESM.pdf (247 kb)
39_supplemental_code_s1 (PDF 247 kb)


  1. Allan, J. D., & Castillo, M. M. (2007) Stream ecology: Structure and function of running waters (436 pp.). Dordrecht: Springer.Google Scholar
  2. Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq – A python framework to work with high-throughput sequencing data. Bioinformatics, 311, 166–169.Google Scholar
  3. Bärlocher, F., & Boddy, L. (2016). Aquatic fungal ecology – How does it differ from terrestrial? Fungal Ecology, 19, 5–13.CrossRefGoogle Scholar
  4. Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34, 525.CrossRefGoogle Scholar
  5. Buck, U., Babenzien, H.-D., & Zwirnmann, E. (2008). Extracellular peroxidase activity in an experimentally divided lake (Große Fuchskuhle, northern Germany). Aquatic Microbial Ecology, 51, 97–103.Google Scholar
  6. Floudas, D., Binder, M., Riley, R., Barry, K., Blanchette, R. A., Henrissat, B., Martínez, A. T., Otillar, R., Spatafora, J. W., Yadav, J. S., Aerts, A., Benoit, I., Boyd, A., Carlson, A., Copeland, A., Coutinho, P. M., de Vries, R. P., Ferreira, P., Findley, K., Foster, B., Gaskell, J., Glotzer, D., Górecki, P., Heitman, J., Hesse, C., Hori, C., Igarashi, K., Jurgens, J. A., Kallen, N., Kersten, P., Kohler, A., Kües, U., Kumar, T. K. A., Kuo, A., LaButti, K., Larrondo, L. F., Lindquist, E., Ling, A., Lombard, V., Lucas, S., Lundell, T., Martin, R., McLaughlin, D. J., Morgenstern, I., Morin, E., Murat, C., Nagy, L. G., Nolan, M., Ohm, R. A., Patyshakuliyeva, A., Rokas, A., Ruiz-Dueñas, F. J., Sabat, G., Salamov, A., Samejima, M., Schmutz, J., Slot, J. C., St. John, F., Stenlid, J., Sun, H., Sun, S., Syed, K., Tsang, A., Wiebenga, A., Young, D., Pisabarro, A., Eastwood, D. C., Martin, F., Cullen, D., Grigoriev, I. V., & Hibbett, D. S. (2012). The Paleozoic origin of enzymatic lignin decomposition reconstructed from 31 fungal genomes. Science, 336, 1715–1719.CrossRefGoogle Scholar
  7. Heeger, F. (2018). Genomics approaches to the study of diversity and function of aquatic fungi. Doctoral thesis, Freie Universität Berlin, Berlin, Germany ( Scholar
  8. Johnson, M. T. J., Carpenter, E. J., Tian, Z., Bruskiewich, R., Burris, J. N., Carrigan, C. T., Chase, M. W., Clarke, N. D., Covshoff, S., de Pamphilis, C. W., Edger, P. P., Goh, F., Graham, S., Greiner, S., Hibberd, J. M., Jordon-Thaden, I., Kutchan, T. M., Leebens-Mack, J., Melkonian, M., Miles, N., Myburg, H., Patterson, J., Pires, J. C., Ralph, P., Rolf, M., Sage, R. F., Soltis, D., Soltis, P., Stevenson, D., Stewart, C. N., Jr., Surek, B., Thomsen, C. J. M., Villarreal, J. C., Wu, X., Zhang, Y., Deyholos, M. K., & Wong, G. K.-S. (2012). Evaluating methods for isolating total RNA and predicting the success of sequencing phylogenetically diverse plant transcriptomes. PLoS One, 7, e50226.CrossRefGoogle Scholar
  9. Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 15, R29.CrossRefGoogle Scholar
  10. Li, B., & Dewey, C. N. (2011). RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12, 323.CrossRefGoogle Scholar
  11. Liao, Y., Smyth, G. K., & Shi, W. (2013). featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30, 923–930.CrossRefGoogle Scholar
  12. Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550.CrossRefGoogle Scholar
  13. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 14, 417.CrossRefGoogle Scholar
  14. R Core Team (2018). R: A language and environment for statistical computing. R foundation for statistical computing. Vienna, Austria (
  15. Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140.CrossRefGoogle Scholar
  16. Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., & Ragg, T. (2006). The RIN: An RNA integrity number for assigning integrity values to RNA measurements. BMC Molecular Biology, 7, 3.Google Scholar
  17. Soneson, C., Love, M. I., & Robinson, M. D. (2016). Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research, 4, 1521.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Ecosystem ResearchLeibniz Institute of Freshwater Ecology and Inland Fisheries (IGB)BerlinGermany
  2. 2.Berlin Center for Genomics in Biodiversity ResearchBerlinGermany
  3. 3.Institut für BiologieFreie Universität BerlinBerlinGermany

Personalised recommendations