BIO 290/BIO 599: Microbial Ecology background (NAU Spring 2015)

BIO 290/BIO 599: Microbial Ecology background (NAU Spring 2015)

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Greg Caporaso

March 24, 2015
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  1. BIO/CS  290,  BIO  599   Microbial  Ecology  Background  and  Microbial

     Life   in  “Extreme  Environments”   J.  Gregory  Caporaso   Department  of  Biological  Sciences   Center  for  Microbial  GeneKcs  and  Genomics   Northern  Arizona  University   Photo  by  John  Spear  
  2. Image  credit:  Norman  Pace  

  3. Microbial Ecology of the Gastrointestinal Tract Annual Review of Microbiology

    31: 107–33. Savage, D. C. (1977).
  4. Microbes  rarely  live  or   act  alone.  

  5. (b) Layer 1 (1 mm) Layer 2 (2 mm) Layer

    3 (3 mm) Layer 4 (4 mm) Layer 5 (5 mm) Layer 6 (6 mm) Layer 7 (10 mm) Layer 8 (22 mm) Layer 9 (34 mm) Layer 10 (49mm) (a) 6 10 15 24 27 30 32 6 11 26 27 30 (1) Acidobacteria Actinobacteria Armatimonadetes BRC1 (5) Bacteroidetes Caldiserica Caldithrix Chlorobi (10) Cyanobacteria Firmicutes GN01 GN02 GN04 (15) Gemmatimonadetes Hyd24-12 KSB3 Lentisphaerae NKB19 (20) OD1 OP11 OP3 OP8 OP9 (25) Planctomycetes Proteobacteria SAR406 SR1 Spirochaetes (30) Synergistes TG3 TM6 Tenericutes Thermi (35) Thermotogae Verrucomicrobia WS1 WS3 WS4 (40) WS6 ZB3 A A Zone B C Image  source:     PhylogeneKc  straKgraphy  in  the  Guerrero  Negro  hypersaline  microbial  mat.   Harris,  Caporaso  et  al.  (2012)   InternaKonal  Society  for  Microbial  Ecology  Journal   Microbes  rarely  live  or   act  alone.  
  6. Photo  credit:  John  Spear  

  7. Culturing  microbes  is  hard   Bacillus  anthracis  in  culture  

    Back  of  the  envelope   calculaKon:  less  than  13%   of  bacterial  species*  have   a  representaKve  that  has   been  grown  in  culture.     Many  recent  advances     are  based  on     culture-­‐independent     approaches  for  studying   microbial  communiKes.     *  Defined  as  97%  OTUs  in  the  Greengenes  13_5  reference  database.  
  8. Culture-­‐independent  invesKgaKon  of   microbial  communiKes     All  cellular

     life  has  a  shared   evoluKonary  history,  and   some  genes  are  shared  by  all   organisms.       The  sequence  of  those   genes  can  be  used  as  a   gene3c  fingerprint  for   different  organisms.    
  9. ACCAGGTT The  random  accumulaKon  of   muta3ons  (changes  to  gene

      sequences  over  evoluKonary   Kme)  gives  us  informaKon   for  idenKfying  and   comparing  organisms.   Time
  10. ACCAGGTT ACCAGGTT ACCATGTT ACTAGGAT Time The  random  accumulaKon  of  

    muta3ons  (changes  to  gene   sequences  over  evoluKonary   Kme)  gives  us  informaKon   for  idenKfying  and   comparing  organisms.  
  11. ACCAGGTT ACCAGGTT ACCATGTT ACTAGGAT TCCATGTT ACCATATT ACTAGCAT ACTAGTAT ACCAGGTT Time

    The  random  accumulaKon  of   muta3ons  (changes  to  gene   sequences  over  evoluKonary   Kme)  gives  us  informaKon   for  idenKfying  and   comparing  organisms.  
  12. ACCAGGTT ACCAGGTT ACCATGTT ACTAGGAT TCCATGTT ACCATATT ACTAGCAT ACTAGTAT ACCAGGTT ACCATATT

    ACTAGCAT ACTAGTAT TCAATGTT TCCATGTT ACCAGGTT Time The  random  accumulaKon  of   muta3ons  (changes  to  gene   sequences  over  evoluKonary   Kme)  gives  us  informaKon   for  idenKfying  and   comparing  organisms.  
  13. Collect  samples  

  14. Image  source  and  instrucKons:   hbp://learn.geneKcs.utah.edu/content/labs/extracKon/howto/   Extract  DNA  

    (you  can  do  this  at  home!)  
  15. Isolate  the  small  subunit  ribosomal   RNA  gene  to  “fingerprint”

     different   microbial  organisms.   Why  this  gene?   •  It’s  ubiquitous.   •  Contains  regions  that   idenKcal  across  organisms,   and  regions  that  are   variable  across  organisms.  
  16. Sequence  the  rRNA  from  all  samples  on  a  “high-­‐ throughput”

     DNA  sequencer   Pool  samples   and  sequence   Micah  Hamady,  et  al.,  Nature  Methods,  2008.   Error-­‐correcKng  barcodes  for  pyrosequencing  hundreds  of  samples  in  mulKplex.   Per-­‐sample  rRNA   >GCACCTGAGGACAGGCATGAGGAA…   >GCACCTGAGGACAGGGGAGGAGGA…   >TCACATGAACCTAGGCAGGACGAA…   >CTACCGGAGGACAGGCATGAGGAT…   >TCACATGAACCTAGGCAGGAGGAA…   >GCACCTGAGGACACGCAGGACGAC…   >CTACCGGAGGACAGGCAGGAGGAA…   >CTACCGGAGGACACACAGGAGGAA…   >GAACCTTCACATAGGCAGGAGGAT…   >TCACATGAACCTAGGGGCAAGGAA…   >GCACCTGAGGACAGGCAGGAGGAA…    
  17. Which  microbial  organisms  are   represented  by  the  rRNA  gene

      sequences  in  each  sample?   >PC.634_1 FLP3FBN01ELBSX CTGGGCCGTGTCTCAGTCCCAATGTGGCCGTTTACCCTCTCAGGCCGG CTACGCATCATCGCCTTGGTGGGCCGTTACCTCACCAACTAGCTAATG CGCCGCAGGTCCATCCATGTTCACGCCTTGATGGGCGCTTTAATATAC TGAGCATGCGCTCTGTATACCTATCCGGTTTTAGCTACCGTTTCCAGC AGTTATCCCGGACACATGGGCTAGG! >PC.634_2 FLP3FBN01EG8AX! TTGGACCGTGTCTCAGTTCCAATGTGGGGGCCTTCCTCTCAGAACCCC TATCCATCGAAGGCTTGGTGGGCCGTTACCCCGCCAACAACCTAATGG AACGCATCCCCATCGATGACCGAAGTTCTTTAATAGTTCTACCATGCG GAAGAACTATGCCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCC CGAGTCATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGT! >PC.354_3 FLP3FBN01EEWKD! TTGGGCCGTGTCTCAGTCCCAATGTGGCCGATCAGTCTCTTAACTCGG CTATGCATCATTGCCTTGGTAAGCCGTTACCTTACCAACTAGCTAATG CACCGCAGGTCCATCCAAGAGTGATAGCAGAACCATCTTTCAAACTCT AGACATGCGTCTAGTGTTGTTATCCGGTATTAGCATCTGTTTCCAGGT GTTATCCCAGTCTCTTGGG   RefSeq 1 RefSeq 2 RefSeq 3 RefSeq 4 RefSeq 5 RefSeq 6 RefSeq 7 RefSeq 8 RefSeq 9 RefSeq 10 rRNA  reference  database   Search  against   reference   sequences  
  18. Search  against   reference   sequences   RefSeq 1 RefSeq

    2 RefSeq 3 RefSeq 4 RefSeq 5 RefSeq 6 RefSeq 7 RefSeq 8 RefSeq 9 RefSeq 10 >PC.634_1 FLP3FBN01ELBSX CTGGGCCGTGTCTCAGTCCCAATGTGGCCGTTTACCCTCTCAGGCCGG CTACGCATCATCGCCTTGGTGGGCCGTTACCTCACCAACTAGCTAATG CGCCGCAGGTCCATCCATGTTCACGCCTTGATGGGCGCTTTAATATAC TGAGCATGCGCTCTGTATACCTATCCGGTTTTAGCTACCGTTTCCAGC AGTTATCCCGGACACATGGGCTAGG! >PC.634_2 FLP3FBN01EG8AX! TTGGACCGTGTCTCAGTTCCAATGTGGGGGCCTTCCTCTCAGAACCCC TATCCATCGAAGGCTTGGTGGGCCGTTACCCCGCCAACAACCTAATGG AACGCATCCCCATCGATGACCGAAGTTCTTTAATAGTTCTACCATGCG GAAGAACTATGCCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCC CGAGTCATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGT! >PC.354_3 FLP3FBN01EEWKD! TTGGGCCGTGTCTCAGTCCCAATGTGGCCGATCAGTCTCTTAACTCGG CTATGCATCATTGCCTTGGTAAGCCGTTACCTTACCAACTAGCTAATG CACCGCAGGTCCATCCAAGAGTGATAGCAGAACCATCTTTCAAACTCT AGACATGCGTCTAGTGTTGTTATCCGGTATTAGCATCTGTTTCCAGGT GTTATCCCAGTCTCTTGGG   Which  microbial  organisms  are   represented  by  the  rRNA  gene   sequences  in  each  sample?  
  19. Comparing  microbial  communiKes   Who  is  there?      

    How  many  “species”  are  there?       How  similar  are  pairs  of  samples?    
  20. Faith  DP  (1992)  ConservaKon  evaluaKon  and  phylogeneKc  diversity.  Biological  ConservaKon.

     61:1-­‐10.   PhylogeneKc  Diversity  (PD):          a  qualitaKve,  phylogeneKc  α-­‐diversity  metric   Sum  of  branch  length  covered  by  a  sample  
  21. IdenKcal  communiKes   D  =  0.0   Related  communiKes  

    D  ~  0.5   Unrelated  communiKes   D  =  1.0   Lozupone  and  Knight,  2005,  Appl  Environ  Microbiol  71:8228   Unweighted  UniFrac:        a  qualitaKve,  phylogeneKc  β-­‐diversity  metric   Percent  of  observed  branch  length  that  is  unique  to   either  sample  
  22. Clustering  by  UniFrac  distance  

  23. None
  24. Reprints This copy is for your personal, noncommercial use only.

    You can order presentation-ready copies for distribution to your colleagues, clients or customers here or use the "Reprints" tool that appears next to any article. Visit www.nytreprints.com for samples and additional information. Order a reprint of this article now. November 30, 2011 DNA Sequencing Caught in Deluge of Data By ANDREW POLLACK BGI, based in China, is the world’s largest genomics research institute, with 167 DNA sequencers producing the equivalent of 2,000 human genomes a day. BGI churns out so much data that it often cannot transmit its results to clients or collaborators over the Internet or other communications lines because that would take weeks. Instead, it sends computer disks containing the data, via FedEx. “It sounds like an analog solution in a digital age,” conceded Sifei He, the head of cloud computing for BGI, formerly known as the Beijing Genomics Institute. But for now, he said, there is no better way. The field of genomics is caught in a data deluge. DNA sequencing is becoming faster and cheaper at a pace far outstripping Moore’s law, which describes the rate at which computing gets faster and cheaper. The result is that the ability to determine DNA sequences is starting to outrun the ability of researchers to store, transmit and especially to analyze the data. “Data handling is now the bottleneck,” said David Haussler, director of the center for biomolecular science and engineering at the University of California, Santa Cruz. “It costs more to analyze a genome than to sequence a genome.” That could delay the day when DNA sequencing is routinely used in medicine. In only a year or N   November  30,  2011   N   “’Data  handling  is  now  the  bobleneck,’  said  David  Haussler,   director  of  the  center  for  biomolecular  science  and  engineering   at  the  University  of  California,  Santa  Cruz.  ‘It  costs  more  to   analyze  a  genome  than  to  sequence  a  genome.’”   “…  the  ability  to  determine  DNA  sequences  is  starKng  to   outrun  the  ability  of  researchers  to  store,  transmit  and   especially  to  analyze  the  data.”  
  25. Local  installaKon   Laptops  or  compute   clusters    

    Cloud  installaKon   Amazon  Web  Services,  iPlant   CollaboraKve,  among  others     *Hopper  (hbp://i.top500.org/system/176952)     www.qiime.org  
  26. Science  (2012)   PNAS  (2011)   Gastroenterology  (2011)   A

     few  of  the  ~2440  arKcles  ciKng  QIIME     (Google  Scholar,  23  Mar  2015)   Cell  (2012)   Applied  and   Environmental   Microbiology  (2011)   Current  InfecKous  Disease   Reports  (2011)   PLoS  One  (2011)   Microbiology  and  Molecular   Biology  Reviews  (2013)  
  27. Temperature gradients in Yellowstone Hot Springs 89ºC 95+ºC 63ºC 73ºC

    1.8 m transect Steep  Cone,  Yellowstone  NaKonal  Park  
  28. None
  29. None
  30. Insufficient Data Temperature (C) Bacterial  (phylum-­‐level)  composiKon  by  temperature  in

      ouulow  channels  of  Steep  Cone,  Yellowstone  NaKonal  Park    
  31. Insufficient Data Temperature (C) Phylum:  Thermi   Genus:  Thermus  

    Phylum:  Chlorobi   UnidenKfied  genus   Bacterial  (phylum-­‐level)  composiKon  by  temperature  in   ouulow  channels  of  Steep  Cone,  Yellowstone  NaKonal  Park    
  32. Thermus  aqua3cus:  a  heat  resistant  organism  (or   hyperthermophile)  

    Image  source:  hbp://en.wikipedia.org/wiki/File:Thermus_aquaKcus.JPG   Heat  resistance   Enzymes  have  evolved  to  use  many  more   strong  bonds  (covalent  and  ionic)  than  in   related,  non-­‐heat-­‐resistant  organisms,  among   other  adaptaKons.    
  33. Thermus  aqua3cus:  a  heat  resistant  organism  (or   hyperthermophile)  

    Image  source:  hbp://en.wikipedia.org/wiki/File:Thermus_aquaKcus.JPG   Heat  resistance   Enzymes  have  evolved  to  use  many  more   strong  bonds  (covalent  and  ionic)  than  in   related,  non-­‐heat-­‐resistant  organisms.       Biotechnological  relevance   T.  aqua3cus  enzymes  can  withstand  high   temperature,  and  have  therefore  been   invaluable  in  biotechnology.       The  Taq  polymerase  is  central  to  the   Polymerase  Chain  ReacKon  (PCR).  
  34. PC1 (25%) PC2 (24%) PC3 (20%) OrdinaKon:  another  view  of

     the  same  data   Insufficient Data Temperature (C)
  35. None
  36. Image  source:  hbp://www.space.com/21872-­‐mars-­‐life-­‐zoe-­‐rover-­‐atacama.html  

  37. Photo  by  Julie  Neilson  

  38. Photo  by  Julie  Neilson  

  39. Photo  by  Julie  Neilson  

  40. Bacterial  (phylum-­‐level)  composiKon  by  site  in  arid   and  hyperarid

     regions  of  the  Atacama  Desert   Baquedano transect Yungay transect Arid Hyperarid Arid Hyperarid
  41. Baquedano transect Yungay transect Arid Hyperarid Arid Hyperarid Phylum:  AcKnobacteria

      Genus:  Rubrobacter   Bacterial  (phylum-­‐level)  composiKon  by  site  in  arid   and  hyperarid  regions  of  the  Atacama  Desert  
  42. Rubrobacter  xylanophilus:  a  desiccaKon  and  radiaKon   resistant  organism  (or

     xerophile)   Image  source:  hbp://microbewiki.kenyon.edu/index.php/File:Rubobacter.gif   Desicca2on  resistance   Waits  out  dry  condiKons  for  years,  emerges  from   dormancy  long  enough  to  eat  and  reproduce.  
  43. Image  source:  hbp://microbewiki.kenyon.edu/index.php/File:Rubobacter.gif   Desicca2on  resistance   Waits  out  dry

     condiKons  for  years,  emerges  from   dormancy  long  enough  to  eat  and  reproduce.     Radia2on  resistance   •  500-­‐1000  rads  are  lethal  to  humans.   •  100,000-­‐500,000  rads  are  used  to  sterilize  surfaces.   •  Some  Rubrobacter  species  can  tolerate  millions  of   rads!   Rubrobacter  xylanophilus:  a  desiccaKon  and  radiaKon   resistant  organism  (or  xerophile)  
  44. Image  source:  hbp://microbewiki.kenyon.edu/index.php/File:Rubobacter.gif   Desicca2on  resistance   Waits  out  dry

     condiKons  for  years,  emerges  from   dormancy  long  enough  to  eat  and  reproduce.     Radia2on  resistance   •  500-­‐1000  rads  are  lethal  to  humans.   •  100,000-­‐500,000  rads  are  used  to  sterilize  surfaces.   •  Some  Rubrobacter  species  can  tolerate  millions  of   rads!     Biotechnological  poten2al   TheoreKcally  could  funcKon  in  the  presence  of  nuclear   wastes,  and  thus  be  engineered  to  assist  in  cleaning   contaminated  sites.     Rubrobacter  xylanophilus:  a  desiccaKon  and  radiaKon   resistant  organism  (or  xerophile)  
  45. OrdinaKon  of  extreme  environments  

  46. OrdinaKon  of  even  more  extreme   environments  

  47. The  personalized  human  microbiome   Forensic  idenKficaKon  using  skin  bacterial

      communiKes.  Fierer  et  al.  Proceedings  of  the   NaKonal  Academy  of  Sciences  (2010).  
  48. The  personalized  human  microbiome   Forensic  idenKficaKon  using  skin  bacterial

      communiKes.  Fierer  et  al.  Proceedings  of  the   NaKonal  Academy  of  Sciences  (2010).  
  49. Our  personal  microbiomes:  the  microbes  living  in  (in   addiKon

     to  on)  our  bodies  are  personally  idenKfying.    
  50. Our  personal  microbiomes:  the  microbes  living  in  (in   addiKon

     to  on)  our  bodies  are  personally  idenKfying.     Null  model:  randomized   personal  idenKfiers  
  51. Peter J. Turnbaugh et al., Nature 2006 An obesity-associated gut

    microbiome with increased capacity for energy harvest Do differences in our microbiota matter?
  52. A  microbial  clock  provides  an  accurate  es2mate  of   the

     postmortem  interval  in  a  mouse  model  system     Metcalf  et  al.,  23  September  2013,  eLife.  
  53. Escherichia (0.001%) Salmonella (0.006%) Clostridium (0.125%) Campylobacter (0.016%) Microbiology  of

     the  Built  Environment:   Who  are  our  cohabitants?    
  54. B: produce A: skin (palm) C: faucet H2 O …and

     where  do   they  come  from?   Diversity,  distribuKon  and  sources  of  bacteria  in  residenKal  kitchens.   Flores  GE,  Bates  ST,  Caporaso  JG,  Lauber  CL,  Leff  JW,  Knight  R,  Fierer  N.   Environmental  Microbiology  (2012)  
  55. Source  tracking  microbes  in  the  NICU     (and  now

     being  done  on  a  hospital-­‐wide  scale  –  see   www.HospitalMicrobiome.com)   Unlikely  source   Likely  source   Bacterial  Diversity  in  Two  Neonatal  Intensive  Care  Units   (NICUs).    Krissi  M.  Hewib,  KM,  Mannino,  FL,  Caporaso,  JG   Knight,  R  and  Kelley,  ST.    PLoS  One  (2013).  
  56. “I  make  no  apologies  for  puwng  microorganisms  on  a  

    pedestal  above  all  other  living  things.  For  if  the  last   blue  whale  choked  to  death  on  the  last  panda,  it   would  be  disastrous  but  not  the  end  of  the  world.  But   if  we  accidentally  poisoned  the  last  two  species  of   ammonia-­‐oxidizers,  that  would  be  another  maber.”     Tom  CurKs   Nature  Reviews  Microbiology  Vol  4,  Issue  488     July,  2006  
  57. This  work  is  licensed  under  the  CreaKve  Commons  AbribuKon  3.0

     United  States  License.  To  view  a   copy  of  this  license,  visit   hbp://creaKvecommons.org/licenses/by/3.0/us/  or  send  a  leber  to  CreaKve  Commons,  171   Second  Street,  Suite  300,  San  Francisco,  California,  94105,  USA.     Feel  free  to  use  or  modify  these  slides,  but  please  credit  me  by  placing  the  following  abribuKon   informaKon  where  you  feel  that  it  makes  sense:  Greg  Caporaso,  www.caporaso.us.