"Searching for Novel Forms of Life" talk by Jonathan Eisen 12/16/15

Sea

Searching for Novel Forms of Life

Jonathan A. Eisen UC Davis

@phylogenomics

Diversity of Life Workshop Pacifica, CA

December 16, 2015

Once You Find Something Alive …

You find a CLE


You find a CLE

Separate Origin from Known Life?

Common Origin with Known Life?


You find a CLE



Homologies w/ Known Life?


You find a CLE




No


You find a CLE




Yes

How Novel Is It?


You find a CLE




Yes

How Novel Is It?

• Novel form • Novel function • Novel phylogeny

How Novel Is It?

• Novel form • Novel function • Novel phylogeny

How Novel Is It?

Phylogeny

Archaea

Worse Classification of Cultured Taxa by rRNA

rRNA rRNArRNA

ACUGC ACCUAU CGUUCG

ACUCC AGCUAU CGAUCG

ACCCC AGCUCU CGCUCG

Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG

EukaryotesBacteria

CarlWoese

Woese 3 Domain Tree

rRNA Phylotyping: One Taxon

DNA

ACTGC ACCTAT CGTTCG

ACTGC ACCTAT CGTTCG

ACTGC ACCTAT CGTTCG

Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACTGCACCTATCGTTCG

EukaryotesBacteria Archaea

Many sequences from one sample all point to the same branch on the tree

NormPace

Expanded Tree (Pace 1997)

Archaea

Eukaryotes

Bacteria

Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007.

Based on tree from Pace 1997 Science 276:734-740

Is There Anything Like This?

Archaea

Eukaryotes

Bacteria



??????

Metagenomics

metagenomics

ACUGC ACCUAU CGUUCG

ACUCC AGCUAU CGAUCG

ACCCC AGCUCU CGCUCG

Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG

Taxa Characters S ACUGCACCUAUCGUUCG

E ACUCCAGCUAUCGAUCG

C ACCCCAGCUCUCGCUCG

EukaryotesBacteria Archaea

rRNA Tree of Life



Eukaryotes

??????

Archaea

Bacteria

Scanned through GOS data for rRNAs that fit this pattern

rRNA Tree of Life



Eukaryotes

??????

Archaea

Bacteria

??????????

RecA vs. rRNA

Eisen 1995 Journal of Molecular Evolution 41: 1105-1123..

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=8587109&query_hl=2

Venter et al., Science 304: 66. 2004

RecA Phylotyping - Sargasso Metagenome

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15001713&query_hl=2

RecA Tree of Life?

Archaea

Eukaryotes

Bacteria

???????????



GOS 1

GOS 2

GOS 3

GOS 4

GOS 5

Novel RecA Sequences in GOS Data

Wu et al PLoS One 2011

Novel RpoBs too

Wu et al PLoS One 2011

GOS 1

GOS 2

GOS 3

GOS 4

GOS 5Wu et al PLoS One 2011

I am happy to wellcome you as a new member of the 4th domain club. If by chance you are passing through Europe I will be delighted to invite you to give a seminar in Marseille and show you our strange bugs.

Kind regards Didier

Phylogenetic ID of Novel Lineages

Virus Origins

2007-2014: GEBA

Figure from Barton, Eisen et al. “Evolution”, CSHL Press based on Baldauf et al Tree

Synapomorphies Exist

Missing Microbes?

Challenge: Poor Sampling

From Wu et al. 2009 Nature 462, 1056-1060

http://www.nature.com/nature/journal/v462/n7276/full/nature08656.html

JGI Dark Matter Project

environmental samples (n=9)

isolation of singlecells (n=9,600)

whole genomeamplification (n=3,300)

SSU rRNA gene based identification

(n=2,000)

genome sequencing, assembly and QC (n=201)

draft genomes(n=201)

SAK

HSM ETLTG

HOT

GOM

GBS

EPR

TAETL T

PR

EBS

AK E

SM G TATTG

OM

OT

seawater brackish/freshwater hydrothermal sediment bioreactor

GN04WS3 (Latescibacteria)GN01

!"#$%&'$LD1

WS1PoribacteriaBRC1

LentisphaeraeVerrucomicrobia

OP3 (Omnitrophica)ChlamydiaePlanctomycetes

NKB19 (Hydrogenedentes)WYOArmatimonadetesWS4

ActinobacteriaGemmatimonadetesNC10SC4WS2

Cyanobacteria()*&2

Deltaproteobacteria

EM19 (Calescamantes)+,-*./'&'012345678#89/,-568/:

GAL35Aquificae

EM3Thermotogae

Dictyoglomi

SPAMGAL15

CD12 (Aerophobetes)OP8 (Aminicenantes)AC1SBR1093

ThermodesulfobacteriaDeferribacteres

Synergistetes

OP9 (Atribacteria)()*&2

CaldisericaAD3

Chloroflexi

AcidobacteriaElusimicrobiaNitrospirae49S1 2B

CaldithrixGOUTA4

*;<%0123=/68>8?8,6@98/:Chlorobi

486?8,A-5BTenericutes4AB@9/,-568/Chrysiogenetes

Proteobacteria

4896@9/,-565BTG3SpirochaetesWWE1 (Cloacamonetes)

C=1ZB3

=D)&'EF58>@,@,,AB&CG56?ABOP1 (Acetothermia)Bacteriodetes

TM7GN02 (Gracilibacteria)

SR1BH1

OD1 (Parcubacteria)

(*1OP11 (Microgenomates)

Euryarchaeota

Micrarchaea

DSEG (Aenigmarchaea)Nanohaloarchaea

Nanoarchaea

Cren MCGThaumarchaeota

Cren C2Aigarchaeota

Cren pISA7

Cren ThermoproteiKorarchaeota

pMC2A384 (Diapherotrites)

BACTERIA ARCHAEA

archaeal toxins (Nanoarchaea)

lytic murein transglycosylase

stringent response (Diapherotrites, Nanoarchaea)

ppGpp

limitingamino acids

SpotT RelA

(GTP or GDP)+ PPi

GTP or GDP+ATP

limitingphosphate,fatty acids,carbon, iron

DksA

Expression of components for stress response

sigma factor (Diapherotrites, Nanoarchaea)

!4

"#$#"%

!2!3 !1

-35 -10

&'()

&*()

+',#-./0123452

oxidoretucase

+ +e- donor e- acceptor

H

'Ribo

ADP

+

'62

O

Reduction

OxidationH

'Ribo

ADP

'6

O

2H

',)##$#6##$#72#####################',)6+ + -

HGT from Eukaryotes (Nanoarchaea)

Eukaryota

O68*62

OH

'6

*8*63

OO

68*62

'6

*8*63

O

tetra-peptide

O68*62

OH

'6

*8*63

OO

68*62

'6

*8*63

O

tetra-peptide

murein (peptido-glycan)

archaeal type purine synthesis (Microgenomates)

PurFPurD9:3'PurL/QPurMPurKPurE9:3*PurB

PurP

?

Archaea

adenine guanine

O

6##'2

+'

'62

'

'

H

H

'

'

'

H

HH' '

H

PRPP ;,<*,+

IMP

,<*,+

A*

GUA *G U

GU

A

*

GU

A UA * U

A * U

Growing AA chain

=+',>?/0@#recognizes

UGA1+',

UGA recoded for Gly (Gracilibacteria)

ribosome

Woyke et al. Nature 2013.

Tanja Woyke

http://www.nature.com/nature/journal/v462/n7276/full/nature08656.html

Microbial Dark Matter Part 2

• Ramunas Stepanauskas

• Tanja Woyke • Jonathan Eisen • Duane Moser • Tullis Onstott

• More accurate phylogeny • Rooting • Incorporating New and Fragmented Data • Lateral gene transfer • More biology about the “novel” lineages

Challenge: Reference Information

Three Domains of Life

Bacteria Archaea Eukaryotes


Archaea and Bacteria as Sister

Bacteria ArchaeaEukaryotes

Eukaryotes and Bacteria as


Archaea and Eukaryotes as


Other Patterns

Archaea


Outgroup for the Tree of Life?


Lateral Gene Transfer

Archaea


Lateral Gene Transfer

Archaea

Automated Genome Tree

Lang JM, Darling AE, Eisen JA (2013) Phylogeny of Bacterial and Archaeal Genomes Using Conserved Genes: Supertrees and Supermatrices. PLoS ONE 8(4): e62510. doi:10.1371/journal.pone.0062510

Jenna Lang

Better Reference Data (e.g., PhyEco Markers)

Phylogenetic group Genome Number Gene Number Maker Candidates

Archaea 62 145415 106

Actinobacteria 63 267783 136

Alphaproteobacteria 94 347287 121

Betaproteobacteria 56 266362 311

Gammaproteobacteria 126 483632 118

Deltaproteobacteria 25 102115 206

Epislonproteobacteria 18 33416 455

Bacteriodes 25 71531 286

Chlamydae 13 13823 560

Chloroflexi 10 33577 323

Cyanobacteria 36 124080 590

Firmicutes 106 312309 87

Spirochaetes 18 38832 176

Thermi 5 14160 974

Thermotogae 9 17037 684

Wu D, Jospin G, Eisen JA (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. PLoS ONE 8(10): e77033. doi:10.1371/journal.pone.0077033

Better Binning (e.g., HIC)

Beitel CW, Froenicke L, Lang JM, Korf IF, Michelmore RW, Eisen JA, Darling AE. (2014) Strain- and plasmid-level deconvolution of a synthetic metagenome by sequencing proximity ligation products. PeerJ 2:e415 http://dx.doi.org/10.7717/peerj.415

Table 1 Species alignment fractions. The number of reads aligning to each replicon present in thesynthetic microbial community are shown before and after filtering, along with the percent of totalconstituted by each species. The GC content (“GC”) and restriction site counts (“#R.S.”) of each replicon,species, and strain are shown. Bur1: B. thailandensis chromosome 1. Bur2: B. thailandensis chromosome2. Lac0: L. brevis chromosome, Lac1: L. brevis plasmid 1, Lac2: L. brevis plasmid 2, Ped: P. pentosaceus,K12: E. coli K12 DH10B, BL21: E. coli BL21. An expanded version of this table can be found in Table S2.

Sequence Alignment % of Total Filtered % of aligned Length GC #R.S.

Lac0 10,603,204 26.17% 10,269,562 96.85% 2,291,220 0.462 629

Lac1 145,718 0.36% 145,478 99.84% 13,413 0.386 3

Lac2 691,723 1.71% 665,825 96.26% 35,595 0.385 16

Lac 11,440,645 28.23% 11,080,865 96.86% 2,340,228 0.46 648

Ped 2,084,595 5.14% 2,022,870 97.04% 1,832,387 0.373 863

BL21 12,882,177 31.79% 2,676,458 20.78% 4,558,953 0.508 508

K12 9,693,726 23.92% 1,218,281 12.57% 4,686,137 0.507 568

E. coli 22,575,903 55.71% 3,894,739 17.25% 9,245,090 0.51 1076

Bur1 1,886,054 4.65% 1,797,745 95.32% 2,914,771 0.68 144

Bur2 2,536,569 6.26% 2,464,534 97.16% 3,809,201 0.672 225

Bur 4,422,623 10.91% 4,262,279 96.37% 6,723,972 0.68 369

Figure 1 Hi-C insert distribution. The distribution of genomic distances between Hi-C read pairs isshown for read pairs mapping to each chromosome. For each read pair the minimum path length onthe circular chromosome was calculated and read pairs separated by less than 1000 bp were discarded.The 2.5 Mb range was divided into 100 bins of equal size and the number of read pairs in each binwas recorded for each chromosome. Bin values for each chromosome were normalized to sum to 1 andplotted.

E. coli K12 genome were distributed in a similar manner as previously reported (Fig. 1;(Lieberman-Aiden et al., 2009)). We observed a minor depletion of alignments spanningthe linearization point of the E. coli K12 assembly (e.g., near coordinates 0 and 4686137)due to edge eVects induced by BWA treating the sequence as a linear chromosome ratherthan circular.

Beitel et al. (2014), PeerJ, DOI 10.7717/peerj.415 9/19

Figure 2 Metagenomic Hi-C associations. The log-scaled, normalized number of Hi-C read pairsassociating each genomic replicon in the synthetic community is shown as a heat map (see color scale,blue to yellow: low to high normalized, log scaled association rates). Bur1: B. thailandensis chromosome1. Bur2: B. thailandensis chromosome 2. Lac0: L. brevis chromosome, Lac1: L. brevis plasmid 1, Lac2:L. brevis plasmid 2, Ped: P. pentosaceus, K12: E. coli K12 DH10B, BL21: E. coli BL21.

reference assemblies of the members of our synthetic microbial community with the samealignment parameters as were used in the top ranked clustering (described above). We firstcounted the number of Hi-C reads associating each reference assembly replicon (Fig. 2;Table S3), observing that Hi-C data associated replicons within the same species (cell)orders of magnitude more frequently than it associated replicons from diVerent species.The rate of within-species association was 98.8% when ignoring read pairs mapping lessthan 1,000 bp apart. Including read pairs <1,000 bp inflated this figure to 99.97%. Fig. 3illustrates this by visualizing the graph of contigs and their associations. Similarly, forthe two E. coli strains (K12, BL21) we observed the rate of within-strain association tobe 96.36%. When evaluated on genes unique to each strain (where read mapping to eachstrain would be unambiguous), the self-association rate was observed to be >99%.

We observed that the rate of association of L. brevis plasmids 1 and 2 with each other andwith the L. brevis chromosome was at least 100-fold higher than with the other constituentsof the synthetic community (Fig. 2). Chromosome and plasmid Hi-C contact maps showthat the plasmids associate with sequences throughout the L. brevis chromosome (Fig. 4;Figs. S3–S5) and exhibit the expected enrichment near restriction sites. This demonstratesthat metagenomic Hi-C can be used to associate plasmids to specific strains in microbialcommunities as well as to determine cell co-localization of plasmids with one another.

Variant graph connectednessAlgorithms that reconstruct single-molecule genotypes from samples containing two ormore closely-related strains or chromosomal haplotypes depend on reads or read pairsthat indicate whether pairs of variants coexist in the same DNA molecule. Such algorithms


Figure 3 Contigs associated by Hi-C reads. A graph is drawn with nodes depicting contigs and edgesdepicting associations between contigs as indicated by aligned Hi-C read pairs, with the count thereofdepicted by the weight of edges. Nodes are colored to reflect the species to which they belong (see legend)with node size reflecting contig size. Contigs below 5 kb and edges with weights less than 5 were excluded.Contig associations were normalized for variation in contig size.

typically represent the reads and variant sites as a variant graph wherein variant sites arerepresented as nodes, and sequence reads define edges between variant sites observed inthe same read (or read pair). We reasoned that variant graphs constructed from Hi-Cdata would have much greater connectivity (where connectivity is defined as the meanpath length between randomly sampled variant positions) than graphs constructed frommate-pair sequencing data, simply because Hi-C inserts span megabase distances. Suchconnectivity should, in theory, enable more accurate reconstruction of single-moleculegenotypes from smaller amounts of data. Furthermore, by linking distant sites with fewerintermediate nodes in the graph, estimates of linkage disequilibrium at distant sites (from amixed population) are likely to have greater precision.

To evaluate whether Hi-C produces more connected variant graphs we compared theconnectivity of variant graphs constructed from Hi-C data to those constructed fromsimulated mate-pair data (with average inserts of 5 kb, 10 kb, 20 kb, and 40 kb). To excludepaired-end products from the analysis, Hi-C reads with inserts under 1 kb were excludedfrom the analysis. For each variant graph constructed from these inputs, 10,000 variantposition pairs were sampled at random, with 94.75% and 100% of these pairs belonging tothe same connected graph component of the Hi-C and 40 kb variant graphs, respectively.


Chris Beitel@datscimed

Aaron Darling @koadman

http://dx.doi.org/10.7717/peerj.415

Phylosift - Automated Bayesian Phylogenomics

Input Sequences rRNA workflow

protein workflow

profile HMMs used to align candidates to reference alignment

Taxonomic Summaries

parallel option

hmmalign multiple alignment

LAST fast candidate search

pplacer phylogenetic placement



search input against references



Infernal multiple alignment


<600 bp

>600 bp

Sample Analysis & Comparison

Krona plots, Number of reads placed

for each marker gene

Edge PCA, Tree visualization, Bayes factor tests

each

inpu

t seq

uenc

e sc

anne

d ag

ains

t bot

h w

orkf

low

s

Aaron Darling @koadman

Erik Matsen @ematsen

Holly Bik @hollybik

Guillaume Jospin @guillaumejospin

Darling AE, Jospin G, Lowe E, Matsen FA IV, Bik HM, Eisen JA. (2014) PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ 2:e243 http://dx.doi.org/10.7717/peerj.243

Erik Lowe

http://dx.doi.org/10.7717/peerj.243

Normalizing Across Genes Tree OTU

Wu, D., Doroud, L, Eisen, JA 2013. arXiv. TreeOTU: Operational Taxonomic Unit Classification Based on Phylogenetic

Dongying Wu

Challenge: Engaging Public

The Rise of Citizen Microbiology

Darlene Cavalier

Eisen Lab Citizen MicrobiologyKitty Microbiome

Georgia Barguil

Jack Gilbert

Project MERCCURI

Phone and

Shoes

Kitty Microbiome Project

tinyurl/kittybiomeHolly Ganz

David Coil

Acknowledgements

DOE JGI Sloan GBMF NSF

DHS DARPA

Aaron Darling Lizzy Wilbanks

Jenna Lang Russell Neches

Rob KnightJack Gilbert Tanja Woyke Rob Dunn

Katie PollardJessica Green

Darlene Cavalier

Eddy RubinWendy Brown

Dongying Wu

Phil Hugenholtz

DSMZ

Sundar

Srijak Bhatnagar David Coil

Alex Alexiev

Hannah Holland-MoritzHolly Bik

John Zhang

Holly Menninger

Guillaume Jospin

David Lang

Cassie Ettinger

Tim HarkinsJennifer Gardy

Holly Ganz