CH8.3 – Genomics & Evolutionary History

Genomics allows biologists to ask and answer macro-evolutionary questions:
- How did life reach its present diversity?
- How do new species evolve?
- How are all living things related to one another and to extinct ancestors?
By comparing complete genomes we can reconstruct a phylogenetic tree stretching from LUCA (Last Universal Common Ancestor) to every living species.
The lecture builds on earlier concepts (e.g.
orthologs, haplotypes) and links them to practical/ethical issues such as tracking antibiotic-resistance genes.

Strategy: compile the list of genes present in every sequenced organism.
- Estimated size of the universal gene set: between $80$ (strict orthologs) and $500$ (includes genes with conserved function but less-obvious sequence similarity).
Functions represented in this core set:
- Fundamental transcription machinery (e.g.
  RNA polymerase subunits).
- Fundamental translation machinery (e.g.
  ribosomal proteins, tRNA synthetases).
Logical inference: if a gene is present in every lineage today, it was almost certainly present in LUCA.

Complementary approach: start with modern organisms that have exceptionally small genomes.
Case study – Mycoplasma genitalium:
- Genome size: $580{,}000$ base pairs.
- Encodes $521$ genes in total.
- $482$ of those genes produce proteins.
- Knock-out experiments show $385$ protein-coding genes are absolutely essential for survival.
Ongoing goal: construct an "artificial cell" that contains precisely this minimal gene complement, testing our definition of “cellular life.”

Phylogeny = evolutionary relationship among species, populations, or individual genes.
Phylogenetics = discipline that reconstructs and analyzes those relationships.
Phylogenetic tree anatomy:
- Taxa: extant groups placed at the tips (labeled A–E in the lecture figure).
- Node/branch point: hypothetical extinct ancestor common to descendant branches; basal node represents LUCA for the whole tree.
- Branch length (when scaled) can encode evolutionary time or amount of change (e.g.
  number of mutations).
- Multiple visual layouts (rooted, radial, rectangular, etc.) are topologically equivalent as long as connectivity remains intact.

Potential over- or under-estimation issues:
- Variable mutation rates – environmental pressure can accelerate or decelerate substitution rates.
- Back (reversion) mutations – e.g.
  $A \rightarrow G \rightarrow A$ masks two events, creating an apparent shorter distance.
Practical consequence: branch length estimates require correction models and multi-gene corroboration.

Not all DNA descends vertically (parent → offspring).
Bacteria & Archaea often exchange genes laterally, obscuring tree-like patterns.
Classic example:
- Enterococcus faecalis passes a vancomycin-resistance gene to Staphylococcus aureus.
- Result: a gene “jumps” into a lineage where it appears novel and unrelated to surrounding taxa.
Detecting HGT:
- Gene suddenly present in a taxon but absent from immediate sister groups.
- Phylogenetic incongruence when single-gene tree disagrees with species tree.

Approx. $50{,}000$ years ago, a small population of anatomically modern humans left Africa.
Method used in the lecture map:
- Compare haplotypes on the Y-chromosome (non-recombining, paternal line).
- Haplotypes = clusters of neighboring SNPs inherited together because recombination rarely separates them.
Observations:
- Greatest haplotype diversity occurs in African populations (the origin).
- Each outward migration carried only a subset of the original haplotypes (founder effect).
- Serial bottlenecks predict that the farthest populations (e.g.
  at the tip of South America) should show the lowest overall haplotype diversity, plus a few novel mutations that arose locally.
Significance:
- Haplotypic gradients provide a genomic “breadcrumb trail” of ancient human routes without relying on sparse fossil records.

Genome-wide comparisons allow us to:
- Diagnose and monitor the spread of antibiotic resistance (public-health urgency).
- Infer common ancestry, challenging or confirming historical migration narratives.
- Understand minimal life requirements, informing synthetic biology and origin-of-life research.
Phylogenetic misinterpretations (e.g.
ignoring HGT) can mislead evolutionary conclusions or medical decisions.

Universal gene set size: $80 - 500$ genes.
Mycoplasma genitalium metrics:
- Genome length: $580{,}000\ \text{bp}$ .
- Total genes: $521$ .
- Protein-coding: $482$ .
- Essential protein-coding: $385$ .
Out-of-Africa migration: $\sim 50{,}000\ \text{years}$ ago.

LUCA – Last Universal Common Ancestor.
Orthologs – homologous genes in different species that diverged after a speciation event and retain the same function.
Taxa – the operational units (species, genera, etc.) in a phylogenetic tree.
Node – branching point signifying a common ancestor.
HGT – Horizontal Gene Transfer; non-vertical gene movement.
Haplotypes – linked clusters of polymorphisms inherited together.
Founder effect – loss of genetic variation when a new population is established by a small number of individuals.

Transcriptomes & proteomes (covered earlier) complement genomic data in detecting functional consequences of mutations identified via phylogenetics.
Ortholog vs. paralog distinction (Lecture 2) underlies accurate universal gene set calculations.

[ ] Explain how shared genes indicate LUCA’s genome.
[ ] Contrast universal gene set vs. minimal gene set methodologies.
[ ] Draw and interpret phylogenetic trees, including branch length logic.
[ ] Identify sources of error in distance estimation (rate variation, back mutations, HGT).
[ ] Describe how haplotype diversity patterns reconstruct human migration.
[ ] Apply concepts to real-world scenarios such as antibiotic resistance tracking and synthetic cell design.