PDP2021-10

High-throughput detection of marine diazotrophs

Host laboratory and collaborators

Abstract

Dinitrogen (N2) fixing microbes (diazotrophs) provide the main source of nitrogen in the ocean, sustaining ecosystem productivity. Thus, quantifying their abundance, diversity and spatiotemporal distribution is of utmost relevance. Current methods to quantify and taxonomically identify diazotrophs are low-throughput and highly biased toward several organisms. This project aims at developing a high-throughput method combining small molecular probes able to cross cell membranes rapidly. We will design molecular beacons (MB) and nanobodies (NAb) to discriminate diazotrophs as non-active (nucleotide detection by the MB) vs active (protein expression detected by Nab). The molecular probes will be delivered into the cell by acoustic permeation of the cell wall (sonoporation). Coupling this technique with bioinformatics for the design of a highly inclusive MB database and the deconvolution of the results, we will be capable of quantifying and identifying diazotrophs ~50 times faster than all other methods available today.

Keywords

Marine Diazotrophs, Nitrogenase, high-throughput, Molecular beacons, Nanobodies

Objectives

This project requires combining bioinformatics, immunofluorescence and acoustics methods:
Obj1) Mining of genomic databases (EMBL-EBI, NCBI, TARA Oceans) to design molecular probes able to
target most if not all the diversity of marine diazotrophs.
Obj2) Design of a NAb product that gives fluorescent only when detecting its protein target.
Obj3) Optimize molecular probes delivery into diazotrophic cells by sonoporation.

In the case of an existing project, please explain the links between the two projects

The existing project (SCAN-IRD) was a starting grant aimed at getting an ERC. It allowed us to find that it is possible to generate an intracellular staining on living cyanobacteria for some strains tested. With this project we aim at unveiling a universal protocol for high-throughput detection of marine diazotrophs. Ultimately this project will lead to the development of a new flow cytometry module adapted for the Cytosense, enabling continuous on site detection of the desired targets.

2 to 5 references related to the project

  1. Lin, S., Henze, S., Lundgren, P., Bergman, B. & Carpenter, E. J. Whole-Cell Immunolocalization of
    Nitrogenase in Marine Diazotrophic Cyanobacteria, Trichodesmium spp. Appl. Environ. Microbiol. 64,
    3052–3058 (1998).
  2. Tillett, D. & Neilan, B. A. Xanthogenate nucleic acid isolation from cultured and environmental
    cyanobacteria. J. Phycol. 36, 251–258 (2000).
  3. Benavides, M. & Robidart, J. Bridging the Spatiotemporal Gap in Diazotroph Activity and Diversity with
    High-Resolution Measurements. Front. Mar. Sci. 7, (2020).
  4. Dumoulin, M. et al. Single-domain antibody fragments with high conformational stability. Protein Sci.
    Publ. Protein Soc. 11, 500–515 (2002).
  5. Tyagi, S. & Kramer, F. R. Molecular beacons: probes that fluoresce upon hybridization. Nat. Biotechnol.
    14, 303–308 (1996).

3 main publications from each PI over the last 5 years

Mar Benavides

  1. Benavides, M., Conradt, L., Bonnet, S., Berman-Frank, I., Barrillon, S., Petrenko, A., Doglioli, A.M. Fine-scale sampling unveils diazotroph patchiness in the South Pacific Ocean. Accepted ISME Communications.
  2. Benavides M, Berthelot H, Duhamel S et al. Dissolved organic matter uptake by Trichodesmium in the Southwest Pacific. Sci Rep 2017;7:1–6.
  3. Benavides M, Bonnet S, Hernández N et al. Basin-wide N2 fixation in the deep waters of the Mediterranean Sea. Global Biogeochem Cycles 2016a, DOI: 10.1002/2015GB005326.

Bianca Habermann

  1. de Boissier P., Habermann B.H. (2020) A Practical Guide to Orthology Resources. In: Pontarotti P. (eds)
    Evolutionary Biology—A Transdisciplinary Approach. Springer, Cham. https://doi.org/10.1007/978-3-030-57246-4_3
  2. Prytuliak R, Pfeiffer F, Habermann BH. SLALOM, a flexible method for the identification and statistical
    analysis of overlapping continuous sequence elements in sequence- and time-series data. BMC
    Bioinformatics. 2018 Jan 26;19(1):24. doi: 10.1186/s12859-018-2020-x.
  3. Prytuliak R, Volkmer M, Meier M, Habermann BH. HH-MOTiF: de novo detection of short linear motifs in
    proteins by Hidden Markov Model comparisons. Nucleic Acids Res. 2017 Jul 3;45(W1):W470-W477. doi: 10.1093/nar/gkx341. PubMed PMID: 28460141;2
Application form