Studying microbiomes presents an ever evolving and complex set of analytical challenges (Berges and Franklin, 2001). Microbiomes, regardless of ecosystem origin, typically comprise hundreds of bacterial species (Flemming et al., 2016). This complexity makes it difficult to simultaneously analyze one species' physiology in detail while understanding its broader context and co-occurrence with other microbiome members (Galloway-Peña and Hanson, 2020). Consequently, researchers face two main methodological strategies: isolate one bacterial member in a controlled lab setting or analyze the network of microbes at a coarse level (Galloway-Peña and Hanson, 2020). The first strategy, species isolation and culturing, offers a high-resolution understanding of a specific species' functions. However, this approach loses context for how that bacterium interacts within the broader microbiome (Sarhan et al., 2019). Additionally, less than 1% of all bacterial species on Earth have been cultured (Lewis et al., 2021) because many bacteria have complex interactions with other microbiome members requiring metabolite exchange, which researchers are currently unaware of (Flemming et al., 2016). The second strategy, quantifying compositional changes of microbiome members, allows researchers to understand the context of co-occurrences and taxa proportionality in each member of the microbiome (Berg et al., 2020; Galloway-Peña and Hanson, 2020; Weinroth et al., 2022). However, this approach lacks detailed information about any individual species' function (Weinroth et al., 2022). This analytical weakness is problematic because it obscures each bacterium's specific contributions to the microbiome's functionality and formation processes.
Fortunately, next-generation sequencing is now cost-effective enough to sequence deeply and construct metagenome-assembled genomes (MAGs), bridging the gap between these two experimental strategies (Van Nimwegen et al., 2016). MAGs enable the assessment of compositional changes in microbiomes using read information while also predicting the metabolic capabilities of each MAG representing a bacterium (Forbes et al., 2017; Laudadio et al., 2019). Although not empirically validated with wet-lab tests, the predictive power of MAGs has been immensely beneficial in guiding wet-lab research efforts efficiently (Laudadio et al., 2019). For example, studying MAGs has led to the discovery of natural product resistance mechanisms in pathogens, allowing scientists to develop new clinical strategies for treating pathogenic infections (Ma et al., 2023).
For my Ph.D., I set out to use metagenomics to analyze microbiomes exposed to multiple environmental conditions simultaneously. Specifically, I aimed to understand how microbiomes develop under differing environmental conditions. To achieve this, I selected study systems where microbiomes of the same host genotype were exposed to different environmental conditions, reducing the variation of host genotype on microbiome composition (Wagner et al., 2016; Sanders-Smith et al., 2020; Chaudhry et al., 2021). I settled on the giant kelp forest, which allowed me to assess surface and subsurface microbiomes in juvenile and mature samples (Chapter 1) and characterize microbiome member carbohydrate metabolic capabilities (Chapter 2). Simultaneously, I collaborated with other members of the Wilbanks lab on the pink berry system, where geographically isolated bacterial aggregates are exposed to marshes with different environmental conditions. This collaboration provided additional insights into the influence of environmental factors on microbial biosynthetic gene cluster diversity and strain-level variation (Chapter 3).
