Throughout evolution, evolving objects (domains, genes, operons etc.) have continuously combined, forming new proteins, gene clusters, and genomes. Horizontal gene transfer, particularly among prokaryotes, has facilitated this combinatorial process. Thus, evolving objects that interact positively or synergistically with each other are expected to co-occur more often than by chance; conversely, evolving objects may avoid co-occurrence, indicating an antagonistic or redundant functionality between objects. In this work, we use methods adapted from graph theory to understand patterns of co-occurrence and exclusion in prokaryotes. We have implemented multi-level graph models in which each node (vertex) is a gene or species connected by an edge (relationship) to another node to display these coincidence relationships. Our method incorporates the phylogenetic distribution and synthenic distances of these genes, and we demonstrate how these concepts can be used to identify conserved clusters of vertical and horizontally inherited units of selection. We apply these multi-level graph models to a variety of datasets including prokaryotic pangenomes, and metagenomic sequencing datasets from human-associated microbial communities. We find evidence for genes that significantly co-occur with each other within each of these datasets; these genetic clusters include objects from characterized biological pathways but also include genes with unknown functions. Further, we identify genes that exclude each other, indicating evolving objects with antagonistic or redundant biological functions. This work represents a different approach to understanding the evolution of prokaryotes and allows us to draw novel hypotheses as to the potential role of these genetic clusters in prokaryote biology.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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