Metagenomics at Second Genome

Tanya Yatsunenkotanya@secondgenome.com

• San Francisco based company leveraging microbiome science to enable the discovery and development of human health products through services, collaborations and internal R&D

• Taking a mechanistic approach to discovery – First-of-kind microbiome drug discovery platform with pharma partner

validation– Not Dx, not nutrition, not fecal transplant, not strains as

drugs

• Curator of Greengenes™ database (Todd DeSantis)

• Qiime developer (Justin Kuczynski from Rob Knight Lab)

• Over 200 microbiome studies completed to date across industry, government, academic researchers, nutrition companies, and pharma

Metagenomic (and RNA-seq) Pipeline at SG

Sample1_Right.fastqSample1_Left.fastq

Remove adapters

prinseq-lite

Remove poor quality bases

and short reads

Remove Host DNA

Bowtie2

Remove rRNA

SortmeRNA

Filtered sequences

Metaphlan

Taxonomic Table Functional

Annotation

RapSearch

BioCyc Database

Samples comparison: PCoA, Hierarchical

Clustering;Discriminatory Organisms

and Pathways

Genes, Genomes, Pathway abundance and

coverage

Open source software

Cloud = Amazon AWS spot instances

fastq-mcf

Functional annotationGenes -> Enzymes -> Pathways and Strains

Genes 1 2

GJXV-1205, GTP cyclohydrolase 1 0

GJXV-2161, Na+-driven multidrug pump 0 10

Enzymes 1 2

ENZRXNJXV-1763 1 0

ENZRXNJXV-1765 0 10

Pathways 1 2

NAGLIPASYN-PWY 1 0

PWY-5687 0 10

Strains 1 2Faecalibacterium prausnitzii M-65

1 100

Acidovorax sp.JS42 0 1

Functional assignments Bacterial strain assignments

1 Query Sequence from Sample1: KDYDTAQRVLGNVLVLNIIIGLAFTVLTLIFLD

Connecting genes/enzymes to bacterial genomes

Challenges• ~1% filtered sequences with a significant hit to

BioCyc database

• Assembly with complex microbiota?

• Paired-end sequences are treated independently (for hi-seq)

• Confidence in identification of strains hits from metagenomic and transcriptomic datasets

• Database: KEGG vs BioCyc vs others

• Some samples forward and reverse reads result in different microbiome profiles

Human gene

Mic

robia

l g

ene

Get correlation coefficient (Rho) and p value23 mln correlations, 400 after bonferroni

correction

Correlating human with microbial transcriptome

+Rho

-Rho

Best correlation: Peptidoglycan glycosyltransferase vs Human gene (inflammasome related)

0 20 40 60 80 100 120 140 1600 2 4 6 8 10 12 14

Human gene expression Microbial enzymeexpression

Sam

ple

ID

Best correlation: microbial enzyme vs 5 human genes

0 2 4 6 8 10 12 140

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

Relative Abundance of MICROBIAL ENZYME RXN-11348

Rel

ativ

e A

bu

nd

ance

of

HU

MA

N g

enes

Peptidoglycan glycosyltransferase.

Summary

• Will be happy to discuss our methods and some of the findings

• Currently working on relating human and microbiome functions in disease states

tanya@secondgenome.com