Challenges for metagenomic data analysis and lessons from viral metagenomes [What would you do if sequencing were free?] Rob Edwards San Diego State University Fellowship for Interpretation of Genomes The Burnham Institute for Medical Research

Outline How and why we sequence environments Viral metagenomics –Marine stories –Human stories Pyrosequencing –Mine story Is there a Future?

Why Metagenomics? What is there? How many are there? What are they doing?

How do you sequence the environment? Extract DNA

1.1 g ml g ml g ml g ml -1 CsCl step gradient

How do you sequence the environment? Extract DNA Create library

Linker-Amplified Shotgun Libraries (LASLs) This method produces high coverage libraries of over 1 million clones from as little as 1 ng DNA Soil Extraction Kit David Mead - Breitbart (2002) PNAS

How do you sequence the environment? Extract DNA Create library Sequence fragments

Outline How and why we sequence environments Viral metagenomics –Marine stories –Human stories Pyrosequencing –Mine story Is there a Future?

Why Phages? Phages are viruses that infect bacteria –10:1 ratio of phages:bacteria –10 31 phages on the planet Specific interactions (probably) –one virus : one host Small genome size –Higher coverage Horizontal gene transfer – bp DNA per year in the oceans

Uncultured Viruses 200 liters water g fresh fecal matter DNA/RNA LASL Sequence Epifluorescent Microscopy Concentrate and purify viruses Extract nucleic acids

Bioinformatics BLASTagainst NR –blastx, tblastn, tblastx BLAST against boutique databases –Complete phage genomes, ACLAME, Other libraries, 16S Parsing to present data in a useful format

BLAST and Parsing Submit BLAST to local and remote databases –Local (as fast as possible) –NCBI (one search every 3 seconds) Many concurrent searches –One search versus 1,000 searches Parse data into tables –Access to taxonomy etc

Known 22% Unknown 78% Most Viral Genes are Unknown Breitbart (2002) PNAS Rohwer (2003) Cell TBLAST (E<0.001) 3,093 sequences

60 billion base pairs 60 million sequences GenBank has more than doubled since 2001 …

GenBank has more than doubled since 2001 … but the fraction of unknowns remains constant Edwards (2005) Nature Rev. Microbiol.

All of the new genes in the databases are coming from environmental sequences

Outline How and why we sequence environments Viral metagenomics –Marine stories –Human stories Pyrosequencing –Mine story Is there a Future?

Human-associated viruses More bacteria than somatic cells by at least an order of magnitude More phages than bacteria by an order of magnitude Sample the bacteria in the intestine by sampling their phage

Most Viral DNA Sequences in Adult Human Feces are Unknown Phages Known 40% Unknown 60% Breitbart (2003) J. Bacteriol. TBLAST (E<0.001) 532 sequences Phages 94% Eukaryotic Viruses 6%

No bacteria or viruses in 1 st fecal sample Abundant bacterial and viral communities by 1 week of age Adults Versus Babies >10 8 VLP/g feces

Baby Feces Viruses Most sequences are unknown (≈70%) Similarities to phages from Lactococcus, Lactobacillus, Listeria, Streptococcus, and other Gram positive hosts From microarray studies, sequences are stable in the baby over a 3 month period Same types of phage as present in adult feces –one identical sequence to an unrelated adult!

DNA viruses in feces are phages. Feces ≠ intestines. RNA viruses?

Most Human RNA Viruses are Known Known 92% Unknown 8% TBLAST (E<0.001) ≈36,000 sequences Pepper Mild Mottle Virus 65% Other Plant Viruses 9% Other 26% Zhang (2006) PLoS Biology

Pepper Mild Mottle Virus (PMMV) ssRNA virus; ≈6 kb genome Related to Tobacco Mosaic Virus Infects members of Capsicum family Widely distributed – spread through seeds Fruits are small, malformed, mottled Rod-shaped virions TOBACCO MOSAIC VIRUS k/ppi/links/pplinks/virusems/ Viral particles in fecal sample

S1S2S3S4S5S6S7S8S9PMMV PMMV is common in Human Feces Fecal samples Extract total RNA RT-PCR for PMMV San Diego : 78% people are positive Singapore : 67% people are positive fold increase in feces compared to food PMMV copies per gram dry weight of feces

Indian curry Pork noodle red chili Chicken rice Chinese food Hong Kong chili sauce Hong Kong green chili Vegetarian chili Which Foods Contain PMMV? Chili powder Chili sauces NOT FOUND IN FRESH PEPPERS

Thesunmachine.net Koch’s Postulates

Human microbial metagenome is more important than human genome

Outline How and why we sequence environments Viral metagenomics –Marine stories –Human stories Pyrosequencing –Mine story Is there a Future?

How do you sequence the environment? Extract DNA Create library Sequence fragments Everything so far from 40,000 sequences This is so 2004

How do you sequence the environment? Extract DNA Pyrosequence

454 Pyrosequencing Emulsion-based PCR Luciferase-based sequencing } SDSU DNA extraction from environment Whole genome amplification } 454 Inc. Margulies (2005) Nature

454 Sequence Data (Only from Rohwer Lab) 21 libraries –10 microbial, 11 phage 597,340,328 bp total –20% of the human genome –50% of all complete and partial microbial genomes 5,769,035 sequences –Average 274,716 per library Average read length bp –Av. read length has not increased in 7 months

Growth of sequence data 6 million reads 600 million bp

Cost of sequencing One reaction = $10,000 One reaction = 250,000 reads 250 reads = $10 1 read = 4¢ 1 read = 100bp 1 bp = 0.04¢ ($400 per 1x 1,000,000 bp) Sanger sequencing ca. $1/rxn, 0.2¢/bp –real cost ca. $5/rxn, 1¢/bp 454 sequencing does cot require cloning, arraying etc.

Bioinformatics 597,340,328 bp total 5,769,035 sequences 7 months Existing tools are not sufficient

Current Pipeline Dereplicate BLAST against –16S –Complete phage –nr (SEED) –subsystems

Sequencing is cheap and easy. Bioinformatics is neither.

Outline How and why we sequence environments Viral metagenomics –Marine stories –Human stories Pyrosequencing –Mine story Is there a Future?

The Soudan Mine, Minnesota Red Stuff Oxidized Black Stuff Reduced

Red and Black Samples Are Different Cloned and 454 sequenced 16S are indistinguishable Black stuff Red Cloned Red

Annotation of metagenomes by subsystems A subsystem is a group of genes that work together –Metabolism –Pathway –Cellular structures –Anything an annotator thinks is interesting

There are different amounts of metabolism in each environment

There are different amounts of substrates in each environment Black Stuff Red Stuff

But are the differences significant? Sample 10,000 proteins from site 1 Count frequency of each subsystem Repeat 20,000 times Repeat for sample 2 Combine both samples Sample 10,000 proteins 20,000 times Build 95% CI Compare medians from sites 1 and 2 with 95% CI Rodriguez-Brito (2006). In Review

Examples of significantly different subsystems Red Stuff Arg, Trp, His Ubiquinone FA oxidation Chemotaxis, Flagella Methylglyoxal metabolism Black Stuff Ile, Leu, Val Siderophores Glycerolipids NiFe hydrogenase Phenylpropionate degradation

Subsystem differences & metabolism Iron acquisition Black Stuff Siderophore enterobactin biosynthesis ferric enterobactin transport ABC transporter ferrichrome ABC transporter heme Black stuff: ferrous iron (Fe 2+, ferroan [(Mg,Fe) 6 (Si,Al) 4 O 10 (OH) 8 ]) Red stuff: ferric iron (goethite [FeO(OH)])

Nitrification differentiates the samples Edwards (2006) In review

Not all biochemistry happens in a single organism Anaerobic methane oxidation Boetius et al. Nature, CH 4 + SO > HCO HS - + H 2 S Archaea CH 4 + H 2 O -> HCO OH + H 2 -> CO 2 + H 2 O Bacteria SO H 2 O -> HS - + OH + 2O 2

The challenge is explaining the differences between samples Red Sample Arg, Trp, His Ubiquinone FA oxidation Chemotaxis, Flagella Methylglyoxal metabolism Black Sample Ile, Leu, Val Siderophores Glycerolipids NiFe hydrogenase Phenylpropionate degradation

We are moving away from one organism one reaction and towards studying the biochemistry of whole environments Bacteria don’t live alone

Summary From 454 sequence: –Identify microbial composition –Identify metabolic function –Identify statistically significant differences in metabolism –Who, what, why of microbial ecology

Marine Near-shore water (~100 samples) Off-shore water (~50 samples) Near- and off-shore sediments Metazoan associated Corals Fish Human blood Human stool Sampling Sites Terrestrial/Soil Amazon rainforest Konza prairie Joshua Tree desert Singapore Air Freshwater Aquifer Glacial lake Extreme Hot springs (84 o C; 78 o C) Soda lake (pH 13) Solar saltern (>35% salt)

SDSU Forest Rohwer Mya Breitbart Beltran Rodriguez-Brito Rohwer Lab: Linda Wegley Florent Angly Matt Haynes Also at SDSU Anca Segall Willow Segall Stanley Maloy Math Peter Salamon Joe Mahaffy James Nulton Ben Felts David Bangor Steve Rayhawk Jennifer Mueller MIT: Ed DeLong NSF - Biotic Surveys and Inventories - Biological Oceanography - Biocomplexity FIG Veronika Vonstein Ross Overbeek Annotators Genome Institute of Singapore: Zhang Tao Charlie Lee Chia Lin Wei Yijun Ruan

Viral Community Structure Contigs assembled from fragments with >= 98% identity over 20 bp are a resampling of a single phage genome Contig specturm is the number of contigs that have one sequence, the number that have two sequences, and so on Use both analytical and Monte-Carlo simulations to predict community structure from contig spectrum The Math Guys (2006) In preparation

Determine the actual contig spectrum of the sample Predict a contig spectrum using a species abundance model Compute the error between the actual and predicted Adjust the parameters in the species abundance model to minimize errors Continue this procedure until we obtain the smallest error Find the smallest error, a global minimum Model parameters Error

Fecal Seawater Marine Sediments Viral Communities are Extremely Diverse Lots of rare viral genotypes

Cropland Earthworms Fossil Corals Forest Amphibians River Bacteria Sediment Viruses Seawater Viruses Fecal Viruses Bacteria on Corals Agriculture Soil Bacteria Soil Nematodes Rainforest Spiders Amazon Fish Rainforest Birds Forest Mammals Temperate Forest Beetles Shannon- Wiener Index