Comprehensive Notes on Molecular Evolution and Selection

End of Semester Schedule and Project Requirements

• Timeline and Key Milestones:     * Mar 23 – 26 (Phylogenetics): Focus on using trees to understand evolution. Includes a guest lecture on 3/25. Reading: Harmon 2019 Ch.1.     * Mar 30 – Apr 3 (Macroevolution): Reading: HF Ch. 18, EZ Ch. 14. Midterm Exam II is on Apr 2. Homework 3 is due Apr 2. Deadline to change to P/F is Mar 31.     * Apr 6 – 10 (Macroevolution continued): Reading: HF Ch. 18, EZ Ch. 14.     * Apr 13 – 17 (Development, contingency, and constraint): Reading: HF Ch. 19, EZ Ch. 9. Project topic selection is due by Apr 17.     * Apr 20 – 24 (Molecular evolution): Reading: HF Ch. 15, EZ Ch. 9. Homework 4 is due Apr 25.     * Apr 27 – May 1 (Human applications of evolution): Project presentations and project write-ups are both due on Apr 30.     * May 4 – 8 (Societal impacts): No class on May 7. Final exam is scheduled for May 8th from 10:05 AM – 12:05 PM. Project paragraph is due May 8.

• Course Logistics Details:     * Apr 28: Designated free time to work on projects in the classroom with instructor feedback.     * May 6: Review session at 5:00 PM in Derring 2084.

• Project Grading and Structure:     * Topic Selection (10%): 1-3 sentences explaining the topic. Due Apr 17th.     * Writeup (60%): 2-4 paragraphs. Must describe a problem/challenge, identify specific evolutionary principles used to address it, explain how they solve the problem, and provide examples. Due Apr 30th.     * Presentation: 5-minute oral presentation (no slides or visuals). Students are assigned to groups of 7-8 and present to their peers. Date: Apr 30th.     * Peer Writeup (30%): One paragraph summarizing a group member's work and why it was noteworthy. Due May 8th.

The Molecular Basis of Development and Regulation

• Complex Genetic Circuitry in Bacteria:     * Bacterial sporulation involves a complex regulatory network driven by environmental cues: Energy potential, Redox state, and impaired DNA replication or DNA damage (managed by $Sda$).     * Kinase Signaling: A cascade involving $KinA$, $KinB$, $KinC$, $KinD$, and $KinE$ feeds into the phosphorylation of $Spo0F$.     * Phosphorelay: Path follows $Spo0F-P \rightarrow Spo0B-P \rightarrow Spo0A-P$.     * Regulation Factors: Elements like $CodY-GTP$, $AbrB$, and various Rap proteins ($RapA, B, EH$) modulate the pathway.     * Cell Logic: High levels of $Spo0A-P$ lead to the formation of the septum and the differentiation into the Pre-spore and Mother cell.

• Hox Genes and Body Patterning:     * In animals like Drosophila, maternal effect genes ($bicoid$) establish the Anterior-Posterior axis.     * Antennapedia Complex: Includes genes like $labial (lab)$, $Pb$, $Deformed (Dfd)$, $Sex combs reduced (Scr)$, and $Antennapedia (Antp)$.     * Bithorax Complex: Includes $Ultrabithorax (Ubx)$, $abdominal A (abdA)$, and $Abdominal B (AbdB)$.

• Evolutionary Variation in Butterflies:     * Heliconius melpomene and Heliconius erato show convergent wing patterns.     * Variation is tied to the expression of the $optix$ gene in chrysalis wing tissue, which determines adult wing patterns (e.g., H. melpomene rosina vs. H. erato petiverana).

The Sequence Space and Mathematical Complexity

• Conceptual Walk through Sequence Space:     * The Metaphor: To mutate the word "WORD" into "GENE" via single steps where each intermediate is a valid word: $WORD \rightarrow WORE \rightarrow GORE \rightarrow GONE \rightarrow GENE$.     * Molecular evolution functions as a random walk along the space of possible sequences.

• Scaling of Dimensionality:     * The number of possible protein sequences increases exponentially with length.     * Dipeptides (2 amino acids): $20^2 = 400$ combinations in 2D space.     * Tripeptides (3 amino acids): $20^3 = 8,000$ combinations in 3D space.     * 10 amino acid proteins: $10^{13}$ combinations in 10D space.     * 50 amino acid proteins: $\approx 10^{65}$ combinations in 50D space.     * 100 amino acid proteins: $10^{130}$ combinations in 100D space.     * 300 amino acid proteins: $\approx 10^{390}$ combinations in 300D space.

• The Genetic Code and Mutation:     * Changes in the DNA sequence (e.g., $AAU \rightarrow CAU \rightarrow CUU$) result in different amino acids (e.g., Asparagine to Histidine to Leucine).     * Amino acids are classified by properties: nonpolar, polar, basic, and acidic.

Contingency and Evolutionary Predictability

• Case Study: Tetrodotoxin (TTX) Resistance in Garter Snakes:     * Species: Thamnophis sirtalis.     * Mechanism: Resistance is conferred by mutations in the skeletal muscle sodium channel ($Na_V1.4$) that prevent TTX binding.     * Convergent Evolution: Independent lineages (Intermountain, Northwest Coast, California) often arrive at the same phenotypic solution.     * The First Step: Research by Michael T. J. Hague et al. (2017) shows that adaptation proceeds through the same first-step mutation: $I1561V$ (Isoleucine to Valine) in the outer pore of $Na_V1.4$ (Domain IV).     * Progression: Greater resistance mutations accumulate later but almost always occur in the context of the initial $I1561V$ change.     * Constraint: Predictability stems from the fact that only a few mutational routes confer resistance while maintaining the channel's essential voltage-gated functions.

Fitness Landscapes and Mutation Distributions

• Sewall Wright’s Adaptive Landscape (1932):     * A diagrammatic representation of gene combinations where peaks represent high adaptiveness and valleys represent low fitness.     * Most mutations are deleterious.

• Distribution of Fitness Effects (DFE):     * In Yeast and Humans, most mutations are deleterious or neutral. Beneficial mutations are the rarest.     * Substitution types: Stop codons, Synonymous mutations, Nonsynonymous mutations.     * Safeguards: Life has evolved to avoid mutation because it is generally harmful. DNA polymerase includes a $3' \rightarrow 5'$ exonuclease activity to recognize and remove mismatched base pairs.

• Core Concepts:   The Neutral Theory of Molecular Evolution

  •   * Proposed as a null hypothesis where most observed genetic variation and differences between species are neutral, not driven by selection.     * Mathematical Model for Neutral Divergence:         $Divergence = \text{Number of mutations} \times P[\text{fixation}]$         $Div = 2Nμ \times \frac{1}{2N}$         $Div = μ$     * Under neutrality, differences accumulate gradually in a "clock-like" manner.

• Contrasting Views on Variation:     * Adaptationist View: Variation is maintained by selection (advantageous vs. deleterious).     * Neutral Theory: Most variation is neutral; deleterious mutations are purged, and advantageous ones are too rare to explain most polymorphism.     * Nearly Neutral Theory: Most variation is "neutral enough" to drift, depending on the effective population size ($N_e$).

• Evidence from Genomic Elements:     * Substitution rates vary by selective constraint. Elements under weaker selection accumulate substitutions faster:         * Highest rate: Pseudogenes.         * High rate: Intergenic regions and Introns.         * Lowest rate: Coding regions.

• Utility of Neutrality:     * Allows for complex mathematical modeling like coalescent theory.     * Expected Time to Most Recent Common Ancestor ($T_{MRCA}$): $E[T_{MRCA}] = 2 - \frac{2}{n}$.     * Expected Total Tree Length: $E[T_{Total}] = 2 \sum_{i=1}^{n-1} \frac{1}{i}$.     * Applications: Inferring human migrations (e.g., Out of Africa 55–65 kyr ago, Beringia crossing 15–23 kyr ago, Neolithic expansion 10 kyr ago).

Detecting Selection using $dN/dS$ and $π_N/π_S$

• Definitions:     * Synonymous variants: DNA changes that do not alter the amino acid.     * Non-synonymous variants: DNA changes that alter the amino acid.     * $π_N$: Average pairwise difference at non-synonymous sites.     * $π_S$: Average pairwise difference at synonymous sites.

• Calculation Adjustments:     * We cannot simply count differences because the number of possible non-synonymous sites typically outweighs synonymous sites in a codon.     * $d_N = \frac{N_d}{N_c}$ (Number of non-synonymous differences / possible non-synonymous sites).     * $d_S = \frac{S_d}{S_c}$ (Number of synonymous differences / possible synonymous sites).

• Interpretation of Ratios ($dN/dS$ or $π_N/π_S$):     * Ratio < 1: Purifying (Negative) selection; non-synonymous mutations are being purged.     * Ratio ≈ 1: Neutrality; selective pressures are similar between site classes.     * Ratio > 1: Positive selection (for $dN/dS$) or Balancing selection (for $π_N/π_S$).

The MacDonald-Kreitman (MK) Test

• Methodology: Compares the ratio of non-synonymous to synonymous variation within a species (polymorphism, $P$) to the ratio between species (divergence, $D$).

• The Matrix:     * Non-synonymous: $P_N, D_N$     * Synonymous: $P_S, D_S$

• Null Hypothesis (Neutrality):     $\frac{P_N}{D_N} = \frac{P_S}{D_S} \text{ or } \frac{D_N}{D_S} = \frac{P_N}{P_S}$

• Specific Interpretations:     * \frac{P_N}{P_S} < \frac{D_N}{D_S}: Positive selection; many non-synonymous changes have been fixed between groups.     * \frac{P_N}{P_S} > \frac{D_N}{D_S}: Negative selection; some non-synonymous polymorphism exists but none are being fixed.

Linked Selection and The Neutral Theory Debate

• Genetic Hitchhiking: Selection on a specific allele affects physically linked neutral alleles.     * Hard Sweep: A single new beneficial mutation rises quickly to fixation.     * Soft Sweep: Selection acts on standing genetic variation or multiple independent mutations.

• Martin Kreitman's Thesis:     * The Neutral Theory may not explain all features of protein evolution or codon bias, but it remains the critical benchmark/null hypothesis.     * "The neutral theory is dead. Long live the neutral theory."

• Summary of Molecular Evolution:     * Mutation generates "movement" in sequence space.     * Selection and drift define which "moves" persist.     * Surviving variation serves as a record of evolutionary history.