Lab 4.31 Lab 4.3: Molecular Evolution Jennifer Gardy Molecular Biology & Biochemistry Simon Fraser University

Lab

Lab 4.33 Goals Perform an MSA for a family of related proteins Create & view a phylogenetic tree showing the relationships between these proteins Use the tree to determine the evolutionary history of the genes/proteins Complete the phylogeny assignment

Lab 4.34 Outline Research Question Creating a tree: ClustalX –An edited alignment –How Neighbour-Joining works –Bootstrapping Viewing a tree:NJPlot

Lab 4.35 A Handy Tip Glutamine = Gln Glutamic Acid = Glu

Lab 4.36 Research Question Protein translation involves tRNAs A tRNA has an anticodon that recognizes a specific codon on mRNA, and is “charged” with a particular amino acid

Lab 4.37 Research Question How is a tRNA with a particular anticodon charged with the correct amino acid? A tRNA synthetase tRNA = red, tRNA synthetase = blue aaRS grips the anticodon (for recognition) and then adds the correct amino acid at the active site

Lab 4.38 Research Question There are 20 amino acids, therefore there ought to be 20 tRNA synethetases, no? Many bacterial genomes do not contain a Glutaminyl-tRNA-synethetase (GlnRS) No! So how do bacteria charge their tRNA with Glutamine?!?!?!

Lab 4.39 Research Question These bacteria mis-acylate their GlnRS with Glutamic Acid (Glu) The GatABC enzyme complex then converts the Glutamic Acid to Glutamine CAA CAG Glu CAA CAG Gln GatABC

Lab Research Question However, some bacteria, like E. coli, have a functioning GlnRS Bacterial GlnRS (rarer) and GluRS (more common) are derived from a common ancestor What is the evolutionary history of the bacterial GlnRS and GluRS genes?

Lab Hypotheses 1.Gene loss The common ancestor of life on earth had a GlnRS gene, which was subsequently lost in most bacteria 2.Gene duplication GlnRS evolved independently in some bacteria after a GluRS underwent a gene duplication event in a bacterial ancestor 3.Horizontal gene transfer The GlnRS gene was introduced into a bacteria from another lineage

Lab Hypotheses Each hypothesis can be described by a unique phylogenetic tree We will build a tree, and you will determine which hypothesis it best supports

Lab Step 1: The MSA MSAs are the basis of phylogenetic analyses, therefore… make sure it’s a good one! Select appropriate sequences: –11 GlnRS and GluRS sequences Human, fly, nematode, yeast, E. coli, archaea

Lab Step 1: The MSA Edit the sequences to remove “uninformative” regions –N-terminal signal peptides, frayed ends –Non-homologous regions –Poorly aligned regions For enzymes (like GlnRS and GluRS), often best to use only the catalytic core

Lab Step 1: A 30 second MSA - Day 4 website tRNAsynthetases.txt - tRNAsynthetases.txt - ClustalX with default parameters - Leave it open on your screen

Lab Our Alignment ? Outgroup: Slightly similar protein descended from the same long-long- long ago common ancestor, but not a member of the group of interest. Used to root tree.

Lab Step 2: Drawing a Tree Distance matrix method –% difference between all pairwise combinations of sequences measured –Distances assembled into one tree Works better for related sequences Fast THIS IS A BAD REASON but at least you will not feel lonely if you use it!Commonly used: THIS IS A BAD REASON but at least you will not feel lonely if you use it! Discrete data method –Creates all possible trees that describe the sequences –Looks at each column in the MSA to count up # of evolutionary change events –Picks the simplest tree that describes the events observed Works better for highly divergent sequences Slower Allows you to trace the evolution of specific sites within the molecule Neighbour-Joining Maximum Parsimony

Lab Building an NJ Tree - An Example CatRatBatMatt Cat- Rat0.7- Bat Matt Compare all sequences to each other. 2.Assign divergence values to each pair 3.Assemble the values in a distance matrix Cbw protein from cat, rat, bat and Matt

Lab Building an NJ Tree 4. Arrange the subjects in a “star” phylogeny

Lab Building an NJ Tree 5. Fuse the two subjects with the least divergence between them 6. Create a new distance matrix, replacing Rat and Bat with the fusion of the two, repeat step 4… CatRatBatMatt Cat- Rat0.7- Bat Matt CatRatBatMatt Cat- RatBat0.75- Matt

Lab A Word about Bootstrapping 1001 definitions, none of which have to do with boots. Or straps. – In phylogenetic analysis, bootstrapping is a simple test of phylogenetic accuracy Does my whole dataset strongly support my tree? Or was this tree just marginally better than the other alternatives?

Lab Bootstrapping – The Wordy Version Original dataset is “randomly sampled with replacement” Multiple (N=100, 1000, etc…) “pseudo- datasets” of the same size as the original are created Each of the N pseudo-datasets is used to create a tree If a specific branching order is found in X of the N trees, that node is given the bootstrap support value X X values of 70% or more = very reliable groupings

Lab Bootstrapping – The Picture Version 1.Slice original MSA of Y residues into Y columns, put the columns into a hat 2.Pull out a random column, place it in column #1 of your new test set 3. Put the column back in the hat 4. Pull another column from the hat, place it in column #2 in the test set, put it back 5. Repeat until a pseudo-dataset of Y columns has been made “random sampling” “with replacement”

Lab Bootstrapping –Repeat N number of times to generate N pseudo-datasets –For each pseudo-dataset, draw a tree (yields N trees) –Compare your tree to all N trees. How often do the branching orders in your trees appear in the N pseudo-trees? 2 - On branch of your tree, write # of times that branch appeared in your test set Rat and Bat branch together in 2 of the 3 pseudo-trees 2

Lab Step 2. Our Neighbour-Joining Tree Trees > –“Exclude positions w/ gaps” Any column with 1+ gaps Deletes uninformative regions Not good for gappy MSAs –“Correct for multiple subs.” M -> V -> L -> V 3, not 1, mutations Correction formula makes distances proportional to time since divergence Trees > Output Format Opt –“node”, not “branch”

Lab Draw an Bootstrapped NJ Tree Trees > Bootstrap NJ Tree What does this file look like? ( GluRS_Human: , GluRS_Nematode: ) : , GluRS_FruitFly: , ( GluRS_Yeast: , ( GluRS_E.coli: , GluRS_Methanococcus: ) : , TrpRS_Geobacillus: ) : , ( GlnRS_Human: , ( GlnRS_FruitFly: , GlnRS_Nematode: ) : ) : , GlnRS_Yeast: ) : , GlnRS_E.coli: ) : ) : ) : ); Not very tree-like

Lab View the Tree with NJPlot NJPlot – very basic njplot at prompt Turn on bootstrap value display Can swap nodes Many other tree viewing programs available –Treeview, 3 different views:

Lab Remainder of lab time/Evening open lab Play with the NJPlot options –until you like the look of your tree, can clearly see the branching order, and bootstrap labels are displayed Print your tree (Q3 of phylogeny assignment) Complete the phylogeny assignment! –Q4: Bootstrapping –Q5 & 6: Which hypothesis does your tree support? (slide 11) With your team, sketch the trees you’d expect from the different hypotheses Fiona’s slides show some examples of different evolutionary scenarios Use your resource sites! NCBI Bookshelf, Google, etc.. Attention biologists! Karma alert! Help your teammates to understand evolution today, and they’ll help you understand programming tomorrow! Module 3 of the Integrated Assignment –Again, there is LOTS of time available for the IA – you don’t need to finish it tonight. –Won’t begin Module 4 until Monday