Name Kyle P Strickenberger Section BIOL 201 601 Forensic Tutorial Evolution relies on the existence of trait variation within populations Mutations errors in genome replication are one way for new variation to be brought into a population However it is often difficult to observe those mutations at the phenotypic level Even without being selected upon at the phenotypic level frequencies of mutant variants can change from generation to generation via genetic drift Molecular data such as DNA sequence data allows us to observe frequency changes on a much finer scale than phenotypic data We can use that molecular data to build phylogenies that show us the relationships among many organisms In molecular phylogenies sequences that are similar are grouped together Branches which represent change in a lineage over time are connected by nodes which represent the splitting of one lineage into two separate lineages Divergence happens along both branches after splitting at a node The data from the extant organisms used to generate the phylogeny are represented at the tips of the branches By examining branching patterns phylogenies allow us to see the relationships among organisms and follow the path of evolution In this week s recitation we will use a simulation program called ForensicEA Lite to model the accumulation of mutations in populations of virions over time and then create phylogenies to trace relationships among populations Evolution within an individual A viral infection is a population of individual virus particles When mutations occur during viral reproduction the population becomes genetically variable To see how viral populations might evolve under selection by the host s immune system we will examine change over time in the composition of a model population Launch ForensicEA Lite You will see a window titled Divergence The box on the upper left contains our population of virus particles inside a patient This is Patient Zero because he or she will start the epidemic Click on a virus particle and hold the mouse button down A window will pop up showing you a picture of the virion plus the nucleotide sequence from part of its genome Virus particles with the same color are genetically identical to each other From the starting population most of the particles are black and any that are a different color are mutants You can compare the sequences of two virions by dragging them to the small boxes on the lower left 1 Drag a black virion from Patient Zero to the first small box Leave it there for the rest of the simulation so you can use it to compare against later virions Drag a virion that is a different color to the second box How many differences are there between the 2 DNA sequences 1 Difference During replication a virus is chosen at random to produce an offspring virus However this replication is not perfect and sometimes there is a mistake when the genome is being copied These mistakes or mutations add genetic variation to our population When a mutation creates a new nucleotide sequence the virion containing it gets a new color To incorporate selection by the host s immune system we imagine that the immune system has learned to recognize the proteins encoded by the virions that were present in previous generations It has not however learned to recognize the proteins encoded by new mutants We therefore give new mutants a somewhat better chance of reproducing than the rest of the virons in the population 2 We will now let the virus population grow and see how it changes In the box next to the Fast Fwd button enter 50 and then press the Fast Fwd button This will allow you to run the simulation 50 generations at a time How has the population changed from Generation 0 to Generation 50 The population exhibits a greater amount of colors Comparison of the genomes shows that there are 4 differences between the original specimen and the specimen used for comparison from Generation 50 3 Drag a virion into the second small box not the one that has your original black virion and record difference number in the table below Then run the simulation up to 500 generations stopping every 50 generations to compare viruses and fill in the table below Generation 50 100 150 200 250 300 350 400 450 500 Number of differences 4 4 7 9 12 17 17 18 20 20 4 On the graph below draw a scatterplot of number of sequence differences y axis against the number of generations that have passed x axis and label it Within 1 patient Within 1 Patient s n o ti a t u M f o r e b m u N 25 20 15 10 5 0 0 50 100 150 200 350 400 450 500 550 250 300 Generation 5 What can you say about how the virus population evolves in this model The virus population seems to develop mutations in a linear manner 6 Why might evolutionary biologists think of this graph as a molecular clock Because it expresses a linear relationship between the number of mutations and the number of generations that have passed the number of mutations can give an estimate of the number of generations passed or vice versa thus a clock Divergence between patients Click on the File menu button and then on Reset then click Okay Having examined sequence evolution in our population of virions in a single individual we can consider what will happen when our patient infects another individual 7 What do you predict will happen to the virus populations when you infect a new patient with the virus Will the original population and the new one remain similar or become more distinct The two populations will most likely become more distinct as they will be developing mutations from a different original population Use the Fast Fwd button to let the simulation run in Patient Zero for 100 generations Infect Patient One by dragging one virion from Patient Zero into the large box at the upper right 8 Compare a virion in Patient Zero to a virion in Patient One How many differences are there Fill in the table below by comparing virions between Patient Zero and Patient One 50 generations at a time You can compare new virions from each patient each time you stop i e you don t have to leave any in the small boxes Generation 50 100 150 200 250 300 350 400 450 500 Number of differences 11 12 20 21 33 36 37 38 42 44 Between 2 Patients s n o ti a t u M f o r e b m u N 50 30 20 10 0 0 9 Plot the number of differences against generations on the same graph in number 4 and 40 label it Between 2 patients 50 100 150 200 350 400 450 500 550 250 300 Generation 10 If you had sequences of virions