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Learning Evolution and Phylogeny Through Tree Building

Learning Evolution and Phylogeny Through Tree Building. Larry Aaronson, Utica College David Treves , Indiana Univ., Southeast Monica Trifas , Jacksonville State Univ. Audience (at Utica College). Junior and senior biology, biochemistry and pre-med students Have had genetics, cell biology

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Learning Evolution and Phylogeny Through Tree Building

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  1. Learning Evolution and Phylogeny Through Tree Building Larry Aaronson, Utica College David Treves, Indiana Univ., Southeast Monica Trifas, Jacksonville State Univ.

  2. Audience (at Utica College) • Junior and senior biology, biochemistry and pre-med students • Have had genetics, cell biology • May have had molecular biology and/or biochemistry • May have had evolution • Probably have never built a phylogenetic tree

  3. Objectives (Aaronson) • To help students understand that all viruses do not have have common evolutionary ancestry • We discuss the various hypotheses on viral origins in class, read articles about disparate origins of DNA and RNA viruses, look at evidence based on sequence similarity in capsid proteins and DNA/RNA polymerases • To introduce students to phylogenetic analysis through a tree-building exercise

  4. Background • Phylogenetic relationship between RNA viruses has been determined using sequence of RNA-dependent RNA polymerases (RdRp)

  5. Methods • Students will search NCBI and other databases for amino acid sequences for RdRp’s from the following RNA viruses: • (+) strand RNA: • Bacteriophage MS2 • Human Poliovirus • Hepatitis C Virus • Hepatitis A Virus • West Nile Virus • SARS Coronavirus • Sindbis Virus • Tobacco Mosaic Virus

  6. Methods • Students will search NCBI and other databases for amino acid sequences for RdRp’s from the following RNA viruses: • (-) strand RNA: • H1N1 Influenza A Virus • Rabies Virus • Ebola Virus • Measles Virus

  7. Methods • Students will search NCBI and other databases for amino acid sequences for RdRp’s from the following RNA viruses: • Double-stranded RNA: • Human Reovirus 3 • RNA Retroviruses • Human Immunodeficiency Virus 1

  8. Methods • Sequences will be converted to FASTA format • Alignment using MUSCLE • Tree building using aligned sequences

  9. Expected Outcomes • Students gain experience and proficiency in acquiring and processing sequence data from databases • Data will reveal that even viruses with RNA genomes are not all closely related with respect to amino acid sequence similarity of RdRp’s

  10. Problems • Complex data set • Some viruses have discrete ORFs encoding RdRp proteins • Many viruses produce polyprotein that is cleaved to produce functional proteins and enzymes • Database has partial amino acid sequences (< 100 aa) or polyprotein (>2000 aa) • Preliminary attempts at alignment of widely varied sequence resulted in huge alignment gaps and could not be used in BioNJ to create a tree

  11. Needed Resources • Modify polyprotein sequences to isolate portions that encode RdRp • Better chance of reasonable alignment • Possibly use MEGA software for alignment • Tara on speed dial!

  12. Seeing the light: Bioinformatics of fungal photoreceptors David Treves, IU Southeast Larry Aaronson, Utica College Monica Trifas, Jacksonville State College

  13. How does a fungus go blind? WhiteCollar-1 knockouts cannot produce UV protective carotenoids Normal and mutant strains of Neurosporacrassa, a fungus that normally produces orange carotenoids (thought to provide protection from ultraviolet rays) in response to light. Deletion of the photoreceptor White Collar-1 blinds the fungus so that it fails to produce carotenoids, resulting in a white appearance

  14. Should you take this lightly? • Light is a key environmental signal for many organisms (circadian rhythms) • In fungi, light regulates conidiation, spore release, sexual development, virulence.

  15. Why I’m excited • Microbotryumviolaceum (plant pathogen, recent genome seq. by Broad Institute)

  16. Class project for 300 level Micro • Mystery protein – given the WC-1 protein sequence, use PFAM to identify conserved regions, motifs • BLAST exercise –use BLAST to find WC-1-like proteins • Data set building and Tree making – download seqs, align and make a tree

  17. PFAM results – great talking points for students PFAM results: PAS PAS 9 domain- signal sensor GATA zinc finger - transcription factor

  18. Phylogenetic analysis of White collar-1 from five fungi Aligned with MUSCLE, tree created with TNT.

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