A computational biology approach to studying algae-bacterial interactions
Microalgae have a profound effect on the world due to their large contribution to net carbon fixation. Although they are phototrophic, more than 50% of microalgae are thought to depend on external supply of metabolites such as B-vitamins. In oceans, algae are therefore often found together with a community of bacteria and form intricate networks where metabolites are exchanged. Currently, only a fraction of the related mechanisms and metabolite exchanges between algae and bacteria have been uncovered and many more are likely to exist. The work presented in this thesis is based on a model system for algae-bacterial interactions made up of the green alga, Lobomonas rostrata and the alpha-proteobacterium Mesorhizobium loti. In the model system, it is known that the bacterium provides vitamin B12 to the alga and itself, whilst the alga provides fixed carbon. I have applied methods from the field of computational biology to study the interactions between these organisms and other similar partnerships, with the aim of uncovering new insights. The thesis is made up of three research chapters, each focused on using a specific method to study algae-bacterial interactions. I developed a genome scale metabolic model of metabolism of M. loti that enabled simulation of growth. The model simulates 1908 enzymatic reactions and takes 1804 metabolites into account. Using the model, I simulated growth of the bacterium on 1018 different substrates with the aim of identifying substrates supplied by L. rostrata when the two organisms are co-cultured. In addition, I carried out a set of simulations studying the bacterium’s ability to produce B12 from 1368 different substrates. The modelling efforts in this project was successful in enabling simulations, but it was not possible to validate the simulations with experimental data. A transcriptomics experiment was undertaken with the aim of identifying genes related to the interaction between L. rostrata and M. loti. In the experiment, the partners from the model system was grown in axenic and co-culture conditions and RNA samples were taken from each state. Using RNA-seq, the RNA samples were sequenced and from this a candidate transcriptome was created. The expression of each putative gene was then quantified and differentially expressed genes were identified. Based on sequence similarity, candidate functions were assigned where possible. In the analysis of differentially expressed genes, it was found that there appears to be an increased expression of a transporter responsible for uptake of the plant hormone, auxin. Currently, only a small fraction of all bacteria has been shown to produce B12 and it is not clear in which phylogenetic groups this is a common trait. I therefore applied methods from comparative genomics to study the synthesis of this metabolite in more than 8000 bacterial species. This involved developing a computational framework that allowed me to search for the presence of more than 50 genes in more than 8000 genomes in a rapid manner. I found that 37.2% of bacteria can synthesis B12 and that this capability is very common in some phylogenetic groups such as Cyanobacteria, but extremely rare in others such as Lactobacillus. I was also able to confirm that cyanobacteria are not able to make cobalamin, a variant of B12 used by eukaryotic algae, and thus they are unlikely to support algal growth in the photic zone. In the final section of the thesis, I discuss the application of computational biology methods in this field and summarise my experience from applying genome scale modelling, comparative genomics and transcriptomics to study algae-bacterial interactions.
Computational Biology, Algae, Bacteria, Vitamin B12
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