METAGENOMICS: Sequencing the

unknown

Paula González

INTRODUCTION

Metagenomics is the study of

a collection of genetic material

from a mixed community of

organisms, usually referring to

microbial communities.

Functional analysis

Metabolic profiling

Sequencing

Metagenomic dataset

Taxonomic profiling

INTRODUCTION

A typical current computational

meta’omic pipeline to analyze and contrast

microbial communities

SEQUENCING

Extract DNA or RNA from a microbial community in its entirety.

Library construction and short-read sequencing of genomes/transcripts.

Size and depth coverage of genome assemblies

Information on relative species abundance

Illumina platform is preferred for meta’omic sequencing.

TAXONOMIC PROFILING

Two different approaches:

•Intrinsic: From reference genomes, the sequences-free classifier are used to bin new meta’omic reads. Whole-genome searches.

•Extrinsic: Compare metagenomic sequences with reference sequences in order to identify phylogenetic origin. Marker-based

approaches.

Phylogenetic tree of rhodopsinlike

genes in the Sargasso Sea data

FUNCTIONAL ANALYSIS

Linking sequence to function:

•Similarities between discovered genes and those in the databases

•Predicting secondary structure and recognizing protein motifs

Limitations:

•Analysis of proteins need: large quantities, purified and even crystallized

•Completeness of existing data

Solution:

SCREEN THE METAGENOMIC

LIBRARIES DIRECTLY FOR

EXPRESSED FUNCTIONS

(Function-driven metagenomics)

ASSOCIATION NETWORKS

Understand the microbes ecology, environmental response and interorganismal interactions: interspecies and intercellular relationships.

Bioinformatic challenge. Different approaches get only descriptive covariation.

Interactions?

MODELS OF MICROBIOME EVOLUTION

Evolution within microbial communities can occur on two different time scales:

• Over the course of millennia: structure of host-associated communities evolve more slowly in tandem with their hosts’ physiology and immune systems.

• Over the course of days, weeks or years: microbial genome plasticity allows remarkably rapid acquisitions of novel mutations and laterally transferred genes.

Rhizobia infection

in root development

PREDICTIVE BIOINFORMATIC MODELS

Ultimate goals: to develop predictive models of the whole-community response to changing stimuli.

Fist attempts: Artificial neural network Predict ocean bacterial community

Mechanistic, relying on joint metabolic networks

Descriptive systems biology of microbial physiological “rules”

MODELS

SARGASSO SEA

An ocean environment thought to be relatively low in diversity.

Its microbial community is one of the largest metagenomic sequencing project.

Reported 1.214.207 protein-encoding genes, 10 times more protein sequences than were present in databases.

Underscore the significant challenges and opportunities associated with archiving, integrating and analyzing metagenomic sequence databases.

Allowed studies of whole-genome genomic variability, structural

organization and evolution in taxa.

CONCLUSIONS AND OUTLOOK

Whole-genome metagenomic shotgun sequencing validates experimental and bioinformatic procedures to answer typical biological questions.

Statistical methods for biomarker discovery and phenotype prediction can be performed with the data provided.

Next step in microbial community systems biology will be to integrate and meta-analyze multiple metagenomic data sets.

METATRANSCRIPTOMICS

METAPROTEOMICS

METABOLOMICS

METAGENOMICS

INTEGRATION

BIBLIOGRAPHY

http://ghr.nlm.nih.gov/glossary=metagenomics

Handelsman, J., et al. "The new science of metagenomics: Revealing the secrets of our microbial planet." p. 47- 84. Nat Res Council Report 13

(2007).

Segata, Nicola, et al. "Computational meta'omics for microbial community studies." Molecular systems biology 9.1 (2013): 666.

Gill, Steven R., et al. "Metagenomic analysis of the human distal gut microbiome." science 312.5778 (2006): 1355-1359.

Venter, J. Craig, et al. "Environmental genome shotgun sequencing of the Sargasso Sea." science 304.5667 (2004): 66-74.