Integrated multi-omic analyses of mobile genetic elements within a mixed microbial community

Microbial communities are ubiquitous, complex and dynamic systems that constantly adapt to
changing environmental conditions, while playing important roles in natural environments, human
health and biotechnological processes. Invasive mobile genetic elements (iMGE) are considered as
important biotic components of microbial communities, in particular (bacterio)-phages and plasmids
are some of the most abundant and diverse biological entities, which may influence community
structure and dynamics. Microbial populations within naturally occurring communities are
constantly interacting with each other. Ecological interactions between those populations can be
generally classified as competitive and cooperative relationships. To date, extensive studies on biotic
interactions, i.e. relationships between microbial hosts with iMGEs and between microbial
populations, have been somewhat limited, thus restricting our understanding of microbial community
dynamics. Fortunately, high-throughput multi-omics derived from microbiomes, i.e. metagenomics
and metatranscriptomics, enables access to both functional -potential and -expression information
of those biotic components. Combining longitudinal multi-omics data with mathematical
frameworks allows us to model microbial community interactions and dynamics, unlike ever before.
Here, I present a longitudinal integrated multi-omics analysis of biotic components within
foaming activated sludge, spanning ~1.5 years to unravel i) iMGE-host dynamics and ii) ecological
interactome. In the first part of this work, empirical host-iMGE CRISPR-based links in combination
with mathematical modelling highlighted the importance of plasmids, relative to phages,
in shaping community structure, while also showing that plasmids vastly outnumbered, and were
more targeted via CRISPR-Cas systems, compared to their phage counterparts. In the second part
of this work, mathematical modelling is used to provide ecological contexts for the relationships
between microbial community members. In general, we observed a dynamic interactome, with
higher cooperative interactions, despite these populations encoding highly similar functional potential.
In summary, this work demonstrates the potential of longitudinal multi-omics in expanding
our understanding of microbial community dynamics, which could be expanded to other microbial
ecosystems and potentially lead to applications in human health and biotechnological processes.