P​reactivity 7: Molecular Phylogenetics & Real-World Applications

Administrative Details

• Preactivity 7 due: Thursday, 11:15 AM (submit worksheet PDF/JPEG + Brightspace questions)
• Upcoming in-class session: Activity 7—additional real-world problem solving with phylogenies

Learning Objectives

• LO-1: Construct a data matrix from DNA sequence data taken from several taxa
• LO-2: Use a well-supported phylogenetic hypothesis to answer real-world biological questions by
  • Mapping known information (e.g., host, geography) onto the tree
  • Making evidence-based inferences about an unknown or novel taxon

Background & Conceptual Framework

• Phylogenetic accuracy matters: Practical decisions (crop protection, public health, conservation) rely on choosing the most accurate tree among competing hypotheses
• Previous units relied on morphological traits (phenotypic characters). This week shifts focus to molecular characters (DNA, RNA, proteins)
• Molecular data advantages
  • Most abundant, covering full genomes of thousands of taxa
  • Rapid, economical sequencing technology
• Genotype vs. phenotype refresher
  • DNA sequence = genotype
  • RNA & proteins = gene products and therefore part of phenotype

Using Molecular Sequences as Characters

• Alignment prerequisite
  • Arrange homologous positions in columns so that every taxon has the same positional numbering
  • Number each position (e.g., 1–17 for a protein fragment)
• Character vs. character state
  • Character: a position (site) in the aligned sequence
  • State: the nucleotide (DNA/RNA) or amino acid (protein) present at that position in a particular taxon
• Informative vs. uninformative sites
  • Only variable positions help resolve relationships
  • Invariant positions do not contribute and can be excluded from the parsimony count

Protein Example (5 taxa, 17 positions)

• Variable sites: 11 & 16
  • Position 11: 4× “M” (methionine), 1× “Q” (glutamine)
  • Position 16: 2× “N” (asparagine), 3× “H” (histidine)
• All other 15 sites are invariant → uninformative

DNA Example (5 taxa, 15 bp)

1. Identify variable sites: positions 4, 9, 13
2. Highlight differences relative to an arbitrary reference (e.g., Taxon A) to ease matrix construction
3. Build the data matrix
   • Rows = characters (3 variable positions)
   • Columns = taxa (A–E)
   • Binary coding (arbitrary but explicit choice of 0/1)
     • Position 4: A (0) vs. G (1)
     • Position 9: C (0) vs. T (1)
     • Position 13: G (0) vs. A (1)
4. Fill in matrix with 0s & 1s for each taxon—this becomes input for parsimony analysis

Parsimony & Quantitative Metrics

• Tree length (L): minimum number of evolutionary changes implied by the tree
• Consistency Index (CI) quantifies homoplasy CI = \frac{m}{s}
  • m: minimum possible number of changes (sum over characters of [number of states – 1])
  • s: observed total changes on the evaluated tree (tree length)
  • Range 0–1; higher = less homoplasy → more parsimonious

Virus Case Study (BIO 201 Review)

• Data set: 6 taxa (A–F) with a given matrix

• Three hypotheses tested → parsimony mapping performed

  • Winning tree: Hypothesis 2 with CI = 0.6

• Practical inference

  • Taxon A (novel virus) → closest relative = Taxon B
  • Known traits of B: hosted by fox in Mexico
  • Conclusions
  • Probable pre-human host: fox
  • Probable geographical origin: Mexico

  Ethical/Practical angle: informs surveillance, vaccination priorities, and public-health messaging

Real-World Problem for Preactivity 7

• Scenario: Florida orange grower reports crop failure due to a fungus; similar fungi documented on four Pacific islands
  • Islands & corresponding taxa: Saipan (B), Okinawa (C), Java (D), Guam (E)
  • New Florida strain = Taxon A
• Goal for students
  1. Create DNA data matrix from provided sequences (Taxa A–E)
  2. Compare two phylogenetic hypotheses (Tree 1 vs. Tree 2)
  • Map characters
  • Compute tree length and CI for each
  • Select most parsimonious / most accurate hypothesis
  1. Infer origin of Taxon A
  • Identify its closest relative on the chosen tree
  • Report island of origin to the Florida Dept. of Agriculture → informs which shipments to restrict

Step-by-Step Instructions (as per worksheet)

• Download PDF worksheet from Brightspace
• Either print or digitally annotate
• Tasks:
  1. Fill in DNA matrix (variable positions & binary coding)
  2. Map characters on both hypothesized trees
  3. Count changes; compute CI for each tree
  4. Decide which hypothesis is best (lowest tree length, highest CI)
  5. Mark inferred origin of Taxon A
• Save annotated worksheet as PDF or JPEG and upload
• Complete accompanying Brightspace quiz (Preactivity 7 Questions)

Connections & Broader Significance

• From morphology to molecules: reinforces that different data types can be integrated; molecular data now predominant due to availability
• Genotype→phenotype link: while DNA is genotypic, the method of parsimony and tree inference is identical to that used for morphological traits
• Application spectrum
  • Agriculture (crop disease tracking)
  • Epidemiology (virus spillover events)
  • Conservation genetics (source-population identification)
• Ethical & practical implications
  • Correctly identifying origins prevents unnecessary trade restrictions or misdirected control efforts
  • Phylogenetic misinterpretation can lead to economic loss or ineffective policy

Key Takeaways & Study Tips

• Memorize the workflow: Alignment → Variable sites → Data matrix → Tree mapping → Parsimony metrics → Inference
• Practice quickly spotting variable positions and coding them into 0/1 (or other scheme)
• Remember formulae and definitions (Tree length, CI)
• Keep real-world stakes in mind; they help cement why analytical rigor is essential