Optimising and applying RNA based approaches to identify active nitrifiers in coastal sediments

Cholet, Fabien (2021) Optimising and applying RNA based approaches to identify active nitrifiers in coastal sediments. PhD thesis, University of Glasgow.

Abstract

Nitrogen is an essential element for all forms of life on earth. Since the beginning of the industrial revolution, nitrogen has become a major pollutant of marine and coastal ecosystems due to the huge rise in the use of inorganic fertiliser. Like many other nutrients, the transformations of the nitrogen cycle are mainly controlled by the activity of microorganisms. Understanding the factors influencing the activity of microbes involved in the biochemical transformation of nitrogen in the environment is therefore crucial.
The aim of this thesis is to establish a robust workflow for the study of microbial activity in coastal sediment using transcriptomics. In particular, this work focuses on nitrification, the aerobic chemo-litho-autotrophic oxidation of ammonia to nitrite carried out by ammonia oxidizing bacteria and archaea (AOB and AOA respectively).
The First part of the thesis (Chapter I) will consist of a review of the literature on the nitrogen cycle, with a particular focus on nitrification. A review of the techniques used to measure microbial activity in natural environment will also be presented and the knowledge gap that exist in transcriptomic workflow in environmental microbiology identified alongside current understanding of active nitrifiers in coastal sediments.
The second part of the thesis (Chapters II and III) will present the first experimental work package which consists of the optimisation of reverse-transcription (RT)- based protocol for the study of microbial activity via transcriptomics. First, a new technique to evaluate RNA integrity, extracted from environmental samples, based mRNA will be developed and tested in a controlled-RNA degradation experiment. We show that this technique can provide a useful complement to the commercial approaches that evaluate RNA integrity mainly through the 16S/23S rRNA ratio. Then, the effect of the RT protocol itself on RT-Q-PCR and RT-PCR- sequencing results will be evaluated by testing a combination of four different RT enzymes and two priming strategies. We show that the choice of the correct protocol can greatly improve accuracy and precision of RT-based results.
The third part (Chapters IV) will present the application of the optimised protocol to study the effects of sedimentary structures (ridge/runnel) on microbial nitrification activity measured via reverse-transcriptase quantitative PCR (RT-Q-PCR) and reverse-transcriptase PCR- sequencing. Here, the work developed in part two to ensure RNA integrity and optimal RT-PCR protocols will be applied to ensure robust and reliable measure of nitrifier mRNA from within coastal sediments to inform ecological understanding of the active organisms and controls of nitrification. The study site chosen was the Montportail-Brouage intertidal mudflat, located along the French Atlantic coast. This site has been shown to display interesting characteristics in term of microbial dynamics, with the sedimentary structures (ridges/runnels) significantly influencing microbial nitrification rates. The hypothesis proposed previously to explain the differences in nitrification rates is that AOB are more abundant in the runnels, where the higher nitrification rates had been measured. Here, we will show that these differences are explained by the presence of low abundance but highly active AOB groups that drive ammonia oxidation. Furthermore, we show the inadequacy of DNA studies as stand alone methods to explore nitrification activity, with a negative correlation between abundance of AOB amoA genes and nitrification rates, due to the presence of a highly abundant but inactive AOB cluster.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: RNA, nitrogen, nitrification, ammonia, PCR sequencing.
• Subjects: Q Science > QR Microbiology
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Funder's Name: Royal Academy of Engineering
• Supervisor's Name: Smith, Professor Cindy J.
• Date of Award: 2021
• Depositing User: Mr Fabien Cholet
• Unique ID: glathesis:2021-82100
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 06 Apr 2021 08:56
• Last Modified: 07 Apr 2021 09:35
• Thesis DOI: 10.5525/gla.thesis.82100
• URI: https://theses.gla.ac.uk/id/eprint/82100