Antagonistic interactions, such as the production of bacteriocins and antibiotics, are common in bacteria. In Streptococcus pneumoniae, the genes responsible for competence and bacteriocin production are controlled by quorum sensing. In quorum sensing, cells constantly release low levels of a small signal molecule; when concentrations of the signal molecule reach a high enough concentration, then all cells up-regulate genes in a coordinated manner.
One key bacteriocin system in S. pneumoniae is the blp (bacteriocin-like peptides) operon, which controls at least 12 different bacteriocin molecules. The method of action of these bacteriocins is unknown; as these bacteriocins specifically kill other S. pneumoniae cells, it is also unknown how a cell producing bacteriocins prevents unintentional suicide. We aim to understand the blp operon on a mechanistic level (how do the proteins interact with each other?) as well as on the evolutionary level (which evolutionary forces create and maintain this diversity?) and on the ecological level (how does this operon affect interactions between other S. pneumoniae strains and other species?).
Antagonistic interactions also may act to maintain diversity if there is no best strategy for outcompeting neighbours, exactly like how there is no best strategy in a paper-rock-scissors game. Using agent-based computer simulations, what are the conditions required to maintain, or even create, diversity through these antagonistic interactions?
Competence systems (i.e. the ability to pick up DNA outside of the cell) are common within the Streptococcus viridans group, which are a set of related species that range from free-living to opportunistic pathogens of mammals. Horizontal gene transfer is rampant between these species, as one of these related operons is the com operon, which controls bacteriocin production followed by DNA uptake. Competence systems are also controlled through quorum sensing in the Streptococcus viridians group.
How do the unique quorum sensing signals in S. mutans affect their competence, (i.e. their ability to pick up DNA outside of the cell)? These patterns will then be compared to S. pneumoniae, which has a different quorum sensing pathway that activates the same genes.
Most species in the Streptococcus genus can pick up DNA from the environment (i.e. competence) and recombine it into their own genome. Through bioinformatic analysis of recombination across species, we can examine which types of genes are more likely to be carried across species. Additionally, are species more likely to recombine with evolutionarily-distant species found in the same ecological habitat, or closely related species in which genes are more likely to maintain their function?
Our world is composed of communities – collections of multiple different species that cooperate, compete, or (more generally) interact with each other. Just as the millions of plants, animals, and microorganisms have been changing or evolving as individuals over billions of years, so have the communities that they are a part of. Community-level selection is defined as the differential success and replication of entire communities composed of multiple species and their interspecies interactions. This selection of whole communities (rather than individuals within the community) of microbes has been used to develop unique traits, such as flavors in cheese or kombucha for human benefit. Further, “plant probiotics” – the inoculation of plants with beneficial microbial communities to improve plant health – is a growing field in the face of agricultural uncertainty due to climate change.
Using experimental evolution on a plant-microbiome system, we can illuminate patterns in evolutionary and ecological processes in real time. How do different communities and different combinations of microbes interact with each other and the host plant? Can bacterial communities be selected to have significant effects on plant traits, such as growth? How do the species and genetic makeups of communities change over this time and selection process? Using culture-based and culture-independent methods, we explore the ecology and evolution of the rhizosphere (dirt and root associated microbial communities) and the less-examined phyllosphere (leaf associated microbial communities) using the weed Arabidopsis thaliana as our plant host.
Bacteriophage are viruses that are only capable of targeting bacteria. Due to the rise of antibiotic resistance, research has turned to these bacteria-specific viruses as a potential solution to “superbugs”. However, in order to efficiently employ bacteriophage into medicinal treatments (Phage Therapy), it is necessary to collect more data about their infection and evolutionary dynamics. Additionally, there is a lack of focus on understanding infectivity in nature, and how bacteriophage may evolve when encountering bacterial colonies in the harsher environments outside of the laboratory.
Escherichia virus T4 (often called T4 or T4 bacteriophage) is a bacteriophage that infects E. coli cells and reproduces most prominently through the lytic cycle – using the bacterial host’s cellular machinery to build new phages and releasing them when the host cell bursts. However, T4 bacteriophage have been observed in a state called “pseudolysogeny” where the phage is stalled and not actively replicating in the host. It is thought that this stage is a response to resource deprivation or starvation in the host, allowing the virus to “wait” for better conditions to reproduce.
How do bacteriophages change and evolve when facing a combination of healthy and starving bacteria, just as in nature? How does this effect the replication cycle and/or pseudolysogeny in T4? By creating E. coli mutants able to metabolize distinct sugars, we aim to experimentally evolve T4, measuring evolving viral activity under varying conditions of starving/healthy hosts.
What differentiates species of bacteria? The standard definition of species — organisms reproductively isolated from each other — fails when describing bacteria species. Likewise, bacteria are given species designations based on pairwise percent identity, which has little evolutionary (and no ecological) basis. Genomic analysis allows us to examine the similarities and differences between organisms to better understand species in terms of unique clusters of genes that always co-occur. Through examining core genes (i.e. genes present in all members of a species), can we discover what makes a species unique when compared to closely-related species?