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What forces constrain/drive protein evolution?

Looking at all coding sequences across multiple genomescan shed considerable light on

which forces contribute how much to the rates of protein evolution.

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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 transcriptexplains 30 - 50% of the variation in protein evo. rates!

Insights from Genomics:

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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 thesefeatures makes multiple regression inappropriate… use principal component analysis instead

Principal Component Analysis (PCA)

Takes complex (perhaps related) measurements for each item* and identifiesindependent ‘components’ (= abstract summaries of the data points) that best distinguish youritems 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.

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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

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From Pal et al. Integrated View of Protein Evolution

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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

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i.e. When, Where, How much, and in what context a protein is present

ORF

TF

TF

RNAP

AAAAAAA

RNAP

anti-sense RNA

RNAiAffect translation rates,

RNA decay, RNA localization(some affect splice sites)

Of course, phenotypes can also evolve through regulatory changes

RBP

RBP

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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

TF

RNAP

AAAAAAA

RNAP

anti-sense RNA

RNAiAffect translation rates,

RNA decay, RNA localization

Some effectors are encoded at the gene affected (local or cis effectors)

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i.e. When, Where, How much, and in what context a protein is present

ORF

TF

TF

RNAP

AAAAAAA

RNAP

anti-sense RNA

RNAiAffect 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

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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.

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EVE regulatory elements in D. melanogaster: a model of modularity

From Developmental Biology, 6th Edition

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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 characterizehow gene expression varies within and between species.

What evolutionary forces act on gene expression regulation?

The Coding vs. Noncoding Debate

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Next-generation (‘deep’) sequencing can also be applied toquantify mRNA (or other RNA) levels

ORF

AAAAAAA

DNA

RNA

cDNA

Seq reads

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What facilitates regulatory evolution?

* Gene dispensibilityGenes with variable expression within species are heavily enriched for non-essential genes

* Genes with upstream TATA elementsTATA regulation in yeast (and other organisms?) is associatedwith variable expression

* RedundancyEither gene or regulatory redundancy

* Modularity in regulationGenes 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:

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 (Vm) = how much expression of a given gene varies in response to mutation but in the ABSENCE of selection?

Genetic variance (Vg) = how much expression of a given gene variesin natural populations (i.e. influenced by mutation + selection)

Vg/Vm = 1 means no constraint (expression variation in nature is the sameas 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

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 Vm

Measured whole-genome expression differences in each of 5 natural isolates- calculated Vm

All genes had Vg/Vm < 1 … pervasive purifying selection on expressionGenes with the lowest Vg/Vm: enriched for signaling proteins and TFsGenes with the highest Vg/Vm: enriched for carbon and amino acid metabolism

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

ORFsupstream

TF

TFTF

TF

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