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Influenza

Influenza. The Blame Game.

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Influenza

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  1. Influenza

  2. The Blame Game The greatly feared pandemic flu virus has finally broken out. Millions are sick and thousands have already died. It is almost impossible for the Centers for Disease Control (CDC) to keep track of the new cases reported each day. Contrary to everyone's expectations, the first reported cases appeared in San Francisco and not in Asia or Eastern Europe. From an anonymous source the New York Times is reporting that there was mishandling of the recently reconstituted and extremely dangerous 1918 influenza virus at several labs. Apparently, there was unauthorized shipping of the virus to a Biosafety Level 3 (BSL-3) lab at UC San Francisco and it …

  3. The Blame Game appears that the package might have been damaged en route to the lab or potentially mishandled onsite at UCSF. In immediate reaction to the newspaper's report all related parties at UCSF have been arrested for the illegal dissemination of a biological agent to the public. Several of the arrested parties are researchers without US citizenship (but with appropriate visas) and some members of congress are calling for immediate deportation or even reclassification of their status to 'Enemy Combatants' and trying them as terrorists. In other related news, the virus strain from San Francisco has been fully sequenced and, just today, released to the public.

  4. PBS video • http://www.pbs.org/wgbh/nova/body/1918-flu.html

  5. Influenza Virus (flu) • Small genome—8 RNA molecules • Antigenic glycoproteins • 16 Hemagglutinins: • Attachement to host • 9 Neuraminidases: Passage through mucin, budding • E.g., H1N1

  6. Influenza A, B, and C • A: the one that can cause pandemics, broad host range (humans, birds, swine, horses…) • B: infects only seals and humans, ~ 1/3 of all influenza cases in US • C: infects humans and swine, causes only mild infections

  7. Influenza Virus (flu) • Sequencing Reverse Transcriptase Sequencing DNA Genomic Nucleotide Sequence

  8. Influenza Pandemics • 1918 Flu • Killed from 50-100 Mil. people worldwide • Considered to be one of the most deadly pandemics • Killed many of the young and healthy • Influenza A, Type H1N1 • Thought to have derived from Avian Influenza • Recently reconstituted from recovered human samples • Considerable ethical debate

  9. Avian Influenza • Fear of pandemic • High mortality rate (including young and healthy) • Current concern is Influenza A, Type H5N1 • Limited human-human transmission (2 cases as of 2009) in avian flu

  10. Confirmed cases

  11. Swine virus, the fear of viral reassortment • HHMI animation

  12. Post-pandemic stage of swine flu

  13. Antiviral drugs Amantadine + Rimantadineinhibit one of the matrix proteins and thus passage into the cytosol. By 2008-2009 season, virtually all H3N2 were resistant. . Kimball’s Biology Pages

  14. Antiviral drugs Relenza and Tamiflublock neraminidase and thus inhibit the attachment of virions. By 2008-2009, all H1N1 strains circulating in the US were resistant. . Kimball’s Biology Pages

  15. Introduction to Bioinformatics Alexandra M Schnoes Univ. California San Francisco Alexandra.Schnoes@ucsf.edu

  16. What is Bioinformatics? • Intersection of Biology and Computers • Broad field • Often means different things to different people • Personal Definition: • The utilization of computation for biological investigation and discovery—the process by which you unlock the biological world through the use of computers.

  17. What does one do in Bioinformatics?(a small sample) • Our Lab: Understanding Protein (Enzyme) Function • dsafd • dsafd ?

  18. What does one do in Bioinformatics?(a small sample) • Discover new drug targets—computational docking Atreya, C. E. et al. J. Biol. Chem. 2003;278:14092-14100 Shoichet, B. K. Nature. 2004;432:862-865

  19. What does one do in Bioinformatics?(a small sample) • Systems Biology sbw.kgi.edu/ www.sbi.uni-rostock.de/ research.html

  20. This lab: Nucleotide & Protein Informatics • Sequence analysis • Finding similar sequences • Multiple sequence alignment • Phylogenetic analysis

  21. SequenceStructureFunction

  22. Process of Evolution • Sequences change due to • Mutation • Insertion • Deletion

  23. Use Evolutionary Principles to Analyze Sequences • If sequence A and sequence B are similar • A and B evolutionarily related • If sequence A, B and C are all similar but A and B are more similar than A and C and B and C. • A and B are more closely evolutionarily related to each other than to C

  24. Extremely Powerful Idea • Start with unknown sequence • Find what the unknown is similar to • Use information about the known to make predictions about the unknown

  25. How do you know when sequences are similar? • Align two sequences together and score their similarity TASSWSYIVE TATSFSYLVG • Use substitution matrices to score the alignment

  26. Substitution Matrices Give a Score for Each Mutation • Many different matrices available • Blosum matrices standard in the field Blosum 62 Scoring matrix http://www.carverlab.org/images/

  27. Scoring: Add up the positional Scores • Score of 30 TASSWSYIVE TATSFSYLVG TASSWSYIVE TATSFSYLVG • Score of 1

  28. Additional issues… • Gaps (insertions/deletions) • Have scoring penalties for opening and continuing a gap TASSWSYIVETASSWSYIVE TATSFLVGTATSF--LVG

  29. How do we find similar sequences? • Start at the National Center for Biotechnology Information • http://www.ncbi.nlm.nih.gov/

  30. How do we find similar sequences? • Nucleotide Sequence Databases

  31. How do we find similar sequences? • Protein Sequence Databases

  32. How do we find similar sequences? • Similarity Search: BLAST • Basic Local Alignment Search Tool

  33. BLAST is very quick but … • Only local alignments • Alignments aren’t great • Only pair-wise alignments

  34. Want better alignments … • Multiple alignment • Multiple sequences • Better signal to noise • More Sequences = Better alignment • More accurate reflection of evolution • ClustalW • Commonly used • Easy to use

  35. Visualize the Multiple Alignment

  36. Use the Alignment to Calculate Evolutionary Distances • See ‘how close’ sequences are to each other • Best way to tell what is ‘most similar’ • Can calculate simple tree from clustalW Taubenberger et al., Nature: 437, 889-893, 2005

  37. Caveats! • In reality • Sequences (even parts of sequences) can evolve at different rates • Don’t have a good understanding of sequence and function • High sequence identity does not always mean the same function • Getting good alignments and good trees can be very hard

  38. Bioinformatics: Sequence Analysis • Start with unknown sequence • Find similar sequences • Create alignment • Create phylogenetic tree • Use information about knowns to make predictions about unknown

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