Small organisms - too small to see without help\n giant microbes\n \n Mostly live as single cells\n \n Many different kinds (more on this in a bit)\n \n VIruses not included by some, but I think they count\n \n (Show flu bug slide)\n \n
Small organisms - too small to see without help\n giant microbes\n \n Mostly live as single cells\n \n Many different kinds (more on this in a bit)\n \n VIruses not included by some, but I think they count\n \n (Show flu bug slide)\n \n
\n
\n
Do lots of nasty things\n Get all the good names by the way b/c mostly known through their diseses\n Yersinia pestis\n Vibrio cholerae\n Bacillus anthracis\n Smallpox - ok not all\n Mycobacterium tuberculosis\n Mycobacterium leprae\n Clostridium tetanus\n Clostridium botulinum\n
Mutualists (though names not so good)\n N2 fixation\n C fixation - Chloroplasts inside plants are actually symbiotic bacteria\n Digestion - ruminants and all cellulose\n\n
Cloud of microbes living in / on organisms\n More microbial cells than human\n
\n
\n
Microbes run the planet\n All photosynthesis\n Number of microbial cells\n
\n
\n
\n
\n
\n
Composite tree of life based on diverse data\nmain point - euks monophyletic\n\nSignif LGT in euks, but mostly bacterial genes\nso bulk of euk genome is vertically transmitted\nand we can use these gene to reconstruct their history\n
Composite tree of life based on diverse data\nmain point - euks monophyletic\n\nSignif LGT in euks, but mostly bacterial genes\nso bulk of euk genome is vertically transmitted\nand we can use these gene to reconstruct their history\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
Many prokaryotes cannot be grown and cannot get them from harsh envts, or replicate the envt (i.e volcanic)\nHow do we approach this problem?\n\nResearcher go to various areas to collect samples from envt\n\nGet samples, put into tube\n
Send it out for sequencing, do an alignment with your gene and blast it (search for other organisms) with a similar sequence\n
\n
Functional prediction using a gene tree is just like predicting the biology of a species using a species tree\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
This is a tree of a rRNA gene that was found on a large DNA fragment isolated from the Monterey Bay. This rRNA gene groups in a tree with genes from members of the gamma Proteobacteria a group that includes E. coli as well as many environmental bacteria. This rRNA phylotype has been found to be a dominant species in many ocean ecosystems.\n
DNA and the hidden world of microbesPresentation Transcript
DNA & MicrobesDNA and the Hidden World of Microbes CLIMB Symposium September 12, 2011 Jonathan A. Eisen University of California, Davis
Robin in London Examples
MICROBES
Microbes are smallSize* They are small, by definition* Were not really known untilmicroscope invention
But there are LOTS of themNumbers* 100 million in gram of soil* More cells on Earth than starsin universe* More biomass than plants,animals* 10x cells on humans thanhuman cells* 50x10^6 viruses/ml sea water
Diversity I: Form
Diversity II: Function
Function 1: The Bad
Function 2: The Good Nitrogen Fixation Animal NutritionCarbon Fixation
Function 3: The Unknown
Function 4: The UnusualH2S, pH 0, 95°C CO, 80°C High salt low pH CO2 4°C105°CCH3
Function 5: Food, Fuel, etc Feed microbes a little carbon and they can make some nice things
Function 6: Running the Planet Carbon cycle Nitrogen cycle
Studying microbial diversity• Two main questions • Who is out there? • What are they doing?
Sequencing and Microbes• Sequencing is useful as a tool in studies of microbial diversity for many reasons• It is complimentary to other means of study• Four major “ERAs” in use of sequencing for microbial diversity studies
Era I: rRNA Tree of Life
Era I: rRNA Tree of Life Bacteria • Appearance of microbes not informative (enough) • rRNA Tree of Life Archaea identified two major groups of organisms w/o nuclei • rRNA powerful for many reasons, though not perfect EukaryotesBarton, Eisen et al. “Evolution”, CSHL Press. 2007.Based on tree from Pace 1997 Science 276:734-740
Diversity III: Phylogenetic• Three main kinds of organisms • Bacteria • Archaea • Eukaryotes• Viruses not alive, but some call them microbes• Many misclassifications occurred before the use of molecular methods
The Tree of Life 2006adapted from Baldauf, et al., in Assembling the Tree of Life, 2004
The Tree of Life 2006adapted from Baldauf, et al., in Assembling the Tree of Life, 2004
Era II: rRNA in environment
Culturing Microbes
Great Plate Count AnomalyCulturing Microscope Count Count
Great Plate Count AnomalyCulturing Microscope Count <<<< Count
Great Plate Count Anomaly DNACulturing Microscope Count <<<< Count
Culturing Microbes
Culturing Microbes
Plant/Animal Field Studies
Plant/Animal Field Studies
Plant/Animal Field Studies
Plant/Animal Field Studies
Plant/Animal Field Studies
Plant/Animal Field Studies
Plant/Animal Field Studies
Microbial Field Studies
Microbial Field Studies
Microbial Field Studies
Microbial Field Studies
Microbial Field Studies
Microbial Field Studies
Microbial Field Studies
Microbial Field Studies
CSI Microbiology Collect from environment
CSI Microbiology Collect from environment
rRNA PCR DNA extraction PCR Makes lots Sequence PCR of copies of rRNA genes the rRNA genes in sample rRNA1 5’...ACACACATAGGTGGAGC TAGCGATCGATCGA... 3’ Phylogenetic tree Sequence alignment = Data matrix rRNA2 rRNA1 rRNA2 rRNA1 A C A C A C 5’..TACAGTATAGGTGGAGCT rRNA4 AGCGACGATCGA... 3’rRNA3 rRNA2 T A C A G T rRNA3 rRNA3 C A C T G T 5’...ACGGCAAAATAGGTGGA E. coli Humans rRNA4 C A C A G T TTCTAGCGATATAGA... 3’ Yeast E. coli A G A C A G rRNA4 5’...ACGGCCCGATAGGTGG Humans T A T A G T ATTCTAGCGCCATAGA... 3’ Yeast T A C A G T
Major phyla of bacteria & archaea No cultures Some cultures
The Hidden Majority Richness estimates Hugenholtz 2002 Bohannan and Hughes 2003
CensoredCensored
Era III: Genome Sequencing
1st Genome Sequence Fleischmann et al. 1995
Genomes Revolutionized Microbiology• Predictions of metabolic processes• Better vaccine and drug design• New insights into mechanisms of evolution• Genomes serve as template for functional studies• New enzymes and materials for engineering and synthetic biology
Metabolic Predictions
Lateral Gene TransferPerna et al. 2003
Network of LifeBacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.
Using the Core
WhWhole genome treebuilt usingAMPHORAby Martin Wu andDongying Wu
Microbial genomes From http://genomesonline.org
Phylogenetic Diversity• Phylogenetic diversity poorly sampled• GEBA project at DOE- JGI correcting this
Era IV: Genomes in Environment
Novel Form of Phototrophy Beja et al. 2000
Era IV: Genomes in Environment shotgun sequenceMetagenomics
Metagenomics Challenge
Binning challenge
Weighted % of Clones 0 0.1250 0.2500 0.3750 0.5000 Al ph a Be pro ta teo G p b am rot ac m eo te ba ria Ep ap ct si ro lo t e np eob ria D el rot ac ta e t pr ob eria ot ac C eo te ya b rEFG no ac iaEFTurRNARecARpoB b teHSP70 Fi act ria rm e Ac ic ria tin ut es ob a C cte hl r or ia ob C i FB C hl o Major Phylogenetic Group Sp rof Sargasso Phylotypes iro lex i Fu cha D304: 66. 2004 Metagenomic Phylotyping ei so et no ba es co ct cc er Euus ia ry -T a hVenter et al., Science C rcherm re na aeous rc t ha a eo ta
Metagenomics & Ecology
Field Diversity
Sequencing Technology
Generation I: Manual Sanger
Generation II: Automation
Generation III: No clones
Generation IV: ????
Acknowledgements• $$$ • DOE • NSF • GBMF • Sloan • DARPA• People, places • DOE JGI: Eddy Rubin, Phil Hugenholtz et al. • UC Davis: Aaron Darling, Dongying Wu • Other: Jessica Green, Katie Pollard, Martin Wu, Tom Slezak, Jack Gilbert
Let LinkedIn power your SlideShare experience
+
Let LinkedIn power your SlideShare experience
Customize SlideShare content based on your interests
We will import your LinkedIn profile and you will be visible on SlideShare.
Keep up to date when your LinkedIn contacts post on SlideShare
1–2 of 2 previous next