, Volume 143, Issue 2, pp 157–167 | Cite as

Technical challenges in metatranscriptomic studies applied to the bacterial communities of freshwater ecosystems

  • Noémie Pascault
  • Valentin Loux
  • Sandra Derozier
  • Véronique Martin
  • Didier Debroas
  • Selma Maloufi
  • Jean-François Humbert
  • Julie Leloup


Metatranscriptome analysis relates to the transcriptome of microbial communities directly sampled in the environment. Accessing the mRNA pool in natural bacterial communities presents some technical challenges such as the RNA extraction, rRNA depletion, and the choice of the high-throughput sequencing technique. The lack of technical details in scientific articles is a major problem to correctly obtained mRNA from a microbial community and thus the corresponding sequencing data. In our study, we present the methodological procedure that was developed in order to access to the metatranscriptome of the microbial communities during two cyanobacterial blooms successively occurring in a freshwater eutrophic lake. Each procedure step was detailed and discussed with regard to the choices and difficulties encountered and to the recent literature. Finally, the two major limits for metatranscriptomic approaches targeting bacterial communities from natural environments were (i) the removal of rRNA in order to increase the putative mRNA reads number after sequencing, and (ii) for most of the bacterial communities living in natural environments, the lack of reference genomes in databases that leads to the non-assignation of numerous reads. Once these challenges overcome, we managed to access putative mRNA of dominant species, i.e. cyanobacteria (from 6 to 72 % of mRNA assigned), and of the surrounding bacteria (from 1 to 5 % of mRNA assigned).


Metatranscriptome Messenger RNA High-throughput sequencing Illumina HiSeq 2000 Natural bacterial communities 



This study was funded by the PHYCOCYANO project in the Jeunes Chercheurs—Jeunes Chercheuses program of the French ANR (Agence Nationale de la Recherche; ANR-11-JSV7-014-01). C. Bernard and B. Marie (National Museum of Natural History, UMR MCAM 7245 MNHN-CNRS) are greatly thanked for their collaboration, and input to the data collection. We also would like to thank L. Albaric and A. Roullot for access to the Champ-sur-Marne recreational area. And we are grateful to the INRA MIGALE bioinformatics platform ( for providing computational resources. The anonymous reviewers are also thanked for the useful comments improving the quality of the manuscript.

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Noémie Pascault
    • 1
  • Valentin Loux
    • 2
  • Sandra Derozier
    • 2
  • Véronique Martin
    • 2
  • Didier Debroas
    • 3
    • 4
  • Selma Maloufi
    • 5
  • Jean-François Humbert
    • 1
  • Julie Leloup
    • 1
  1. 1.iEES Paris, UMR 7618 UPMC-CNRS-INRA-IRD-Paris 7-UPECParisFrance
  2. 2.UMR MIG, INRAJouy-en-JosasFrance
  3. 3.Laboratoire “Microorganismes: Génome et Environnement”Clermont Université, Université Blaise PascalClermont-FerrandFrance
  4. 4.UMR 6023, LMGECNRSAubiereFrance
  5. 5.UMR 7245 CNRS-MNHN Molécules de Communication et Adaptation des MicroorganismesParis Cedex 05France

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