Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.

Phylogenetic trees are composed of tips, branches, and nodes. The tips represent extant or sampled species, while the branches trace evolutionary trajectories. Branch lengths are proportional to the number of genetic changes inferred between nodes. Rooted trees highlight a common ancestor and directional divergence, whereas unrooted trees simply show the relatedness without assuming an origin point. Internal nodes represent hypothetical ancestral forms and can mark speciation or divergence events.

In microbial phylogenetics, highly conserved genes—particularly small subunit ribosomal RNA (SSU rRNA)—are commonly used due to their presence across all domains of life and slow evolutionary rate. These universal genetic markers enable comparisons across broad phylogenetic distances. Homologous, and preferably orthologous, genes are selected for tree construction to ensure that the sequences share a common ancestry and function. The orthologs are typically identified using reciprocal best-hit searches (e.g., BLAST) or orthology pipelines, then amplified via locus-specific primers (or targeted enrichment) across taxa. The resulting amplicons or enriched fragments are then sequenced (often using high-throughput platforms) and quality-filtered. These sequences are aligned to detect substitutions, insertions, or deletions at homologous positions. Evolutionary distances are quantified by the degree of sequence divergence.

While phylogenetic trees offer critical insights, they may not always reflect true evolutionary histories. Horizontal gene transfers can lead to misleading similarities. To address this, methods like maximum parsimony evaluate sequence alignments to infer trees that require the fewest evolutionary changes. Other approaches, such as maximum likelihood and Bayesian inference, are also commonly used for tree construction and often provide more statistically robust models of evolution.