1. 1 What forces constrain/drive protein evolution? Looking at all coding sequences across multiple genomes can shed considerable light on which forces contribute how much to the rates of protein evolution.

2. 2 What features explain the variation in rates of protein evolution? 1.Rate of mutation/recombination of the locus (more recombination = more efficient selection = easier to select adaptive alleles) 2.Number of constrained residues (‘functional density’) 3.Protein fold (structure, stability, folding) 4.Protein essentiality (i.e. essential proteins evolve slower) …. explains very little of the variation 4. Number of protein-protein interactions (‘connectivity) Initially reported, but now largely refuted as a global constraint 5. Pleiotropy (i.e. number of processes in which protein is involved) … explains only 1% of variation in evo. rates 6. And the # 1 best predictor is …… Expression Level of the underlying transcript explains 30 - 50% of the variation in protein evo. rates! Insights from Genomics:

3. 3 Assessed the %variation explained by: * expression level * dispensibility * protein abundance * codon bias * gene length * # protein-protein interactions * centrality in protein-protein networks Previous studies: linear and multiple regressions Here: They argue the inter-dependence of these features makes multiple regression inappropriate … use principal component analysis instead

4. Principal Component Analysis (PCA) Takes complex (perhaps related) measurements for each item* and identifies independent ‘components’ (= abstract summaries of the data points) that best distinguish your items into subgroups. The first component (PC1) is the plane that explains the most of the variance in your groups (i.e. is the best predictor of subgroups). * Each item (e.g. gene, protein, dog skull) can be plotted as a point in PC space. 4

5. 5 Gene expression/Codon Bias/Protein Abundance (*all related) explain 43% of variation in Ka and 52% variation in Ks! Same holds for Ka and Ks, but less so for Ka/Ks … because selection is likely acting on BOTH Ka AND Ks % var in Ka explained by 7 Principal Components Their model: selection is acting on translation to minimize protein unfolding

6. 6 From Pal et al. Integrated View of Protein Evolution

7. 7 Seminal paper by King & Wilson: # of genes can’t be the only answer … must involve regulatory differences Of course, phenotypes can also evolve through regulatory changes

8. 8 i.e. When, Where, How much, and in what context a protein is present ORF TF RNAP AAAAAAA RNAP anti-sense RNA RNAi Affect translation rates, RNA decay, RNA localization (some affect splice sites) Of course, phenotypes can also evolve through regulatory changes RBP

9. 9 Of course, phenotypes can also evolve through regulatory changes i.e. When, Where, How much, and in what context is a protein is present ORF TF RNAP AAAAAAA RNAP anti-sense RNA RNAi Affect translation rates, RNA decay, RNA localization Some effectors are encoded at the gene affected (local or cis effectors)

10. 10 i.e. When, Where, How much, and in what context a protein is present ORF TF RNAP AAAAAAA RNAP anti-sense RNA RNAi Affect translation rates, RNA decay, RNA localization Other effectors are encoded far from the gene affected (trans effectors) Of course, phenotypes can also evolve through regulatory changes

11. 11 The Coding vs. Noncoding Debate Which type of change is ‘more important’ in evolution? Are some genes/processes/functions more likely to evolve by one or the other? What are the features that dictate coding vs. noncoding evolution? A major advantage of non-coding regulatory changes: Minimizing Pleiotropic Effects Because cis-regulatory information is often modular.

12. 12 EVE regulatory elements in D. melanogaster: a model of modularity From Developmental Biology, 6th Edition

13. 13 Which type of change is ‘more important’ in evolution? Are some genes/processes/functions more likely to evolve by one or the other? What are the features that dictate coding vs. noncoding evolution? Before considering selection, it’s important to characterize how gene expression varies within and between species. What evolutionary forces act on gene expression regulation? The Coding vs. Noncoding Debate

14. 14 Next-generation (‘deep’) sequencing can also be applied to quantify mRNA (or other RNA) levels ORF AAAAAAA DNA RNA cDNA Seq reads

15. 15 What facilitates regulatory evolution? * Gene dispensibility Genes with variable expression within species are heavily enriched for non-essential genes * Genes with upstream TATA elements TATA regulation in yeast (and other organisms?) is associated with variable expression * Redundancy Either gene or regulatory redundancy * Modularity in regulation Genes with more upstream elements or greater environmental responsiveness By now, many studies have looked at natural variation in transcript abundance, simply to look qualitatively at which genes vary more/less. Features that influence how variable a gene’s expression is across individuals:

16. What facilitates regulatory evolution? But some genes may not vary in expression because of constraint (i.e. purifying selection) while others may not vary in expression due to low rates of mutation/change These cases can be distinguished by measuring the: Mutational variance (V m ) = how much expression of a given gene varies in response to mutation but in the ABSENCE of selection? Genetic variance (V g ) = how much expression of a given gene varies in natural populations (i.e. influenced by mutation + selection) Vg/Vm = 1 means no constraint (expression variation in nature is the same as in lab-derived ‘mutation lines’ … must be little selection in nature) Vg/Vm <<1 means much less variation in natural population than mutation lines … this must mean there has been purifying selection to reduce Vg 16

17. Generated ‘mutation accumulation’ lines in C. elegans For each line: - grew cells 280 generations - each generation randomly picked 1 individual to generate next gen. Measured whole-genome expression differences in each MA line - calculated V m Measured whole-genome expression differences in each of 5 natural isolates - calculated V m All genes had Vg/Vm < 1 … pervasive purifying selection on expression Genes with the lowest Vg/Vm: enriched for signaling proteins and TFs Genes with the highest Vg/Vm: enriched for carbon and amino acid metabolism 17

18. Expression can vary by the single gene (due to cis polymorphisms) or for modules of coregulated genes (due to trans-acting effects) ORFsupstream TF 18