Bayesian methods using genomic data to gain insights into the history of photosynthesis

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

Abstract

Photosynthesis is one of the most important metabolisms on Earth, because it allows living organisms to harvest the most abundant source of energy available on the planet: light from the Sun. The ability to perform photosynthesis originated within bacteria and it can now be found in a number of different bacterial groups, each with its own peculiar characteristics. A particularly interesting group of phototrophs are the Cyanobacteria: unlike other photosynthetic bacteria, Cyanobacteria use water as an electron donor for photosynthesis. As a result, they release oxygen in the atmosphere, and it is likely that they are singlehandedly responsible for the oxygen we now breathe.

In this thesis, I explore the origin of photosynthesis, aiming to determine who was the first photosynthetic bacterium and what kind of photosynthetic metabolism it could perform. I also look at the ancestral environment of the first Cyanobacteria, studying whether they lived in high-salinity environments, such as seas and oceans, or in freshwater lakes and rivers. To do this, I developed a number of tools and software libraries to visualise and analyse biological data in novel ways. These include sMap, a new application and extension of the stochastic mapping algorithm to study the evolution of morphological characters, and TreeViewer, a new software to plot phylogenetic trees.

My analyses demonstrate that the first bacterium to ever appear on the Earth was already capable of an advanced form of photosynthesis, suggesting the existence of a direct lineage from this organism to the ancestors of modern Cyanobacteria. This group, in particular, appears to have originated in a high-salinity environment, possibly a coastal region, and then to have spread on the rest of the planet. These analyses also serve as a demonstration of the potentialities of my new programs and methods, which will find application in many different fields.
Date of Award12 May 2022
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorPatricia Sanchez-Baracaldo (Supervisor) & Fanny M Monteiro (Supervisor)

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