metatranscriptomics: challenges and progress

Advancing Science with DNA Sequence

Metatranscriptomics:Challenges and Progress

Shaomei He and Edward KirtonDOE Joint Genome Institute

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Metatranscriptomics

Metatranscriptome

The complete collection of transcribed sequences in a microbial community:

Protein-coding RNA (mRNA) Non-coding RNA (rRNA, tRNA, regulatory RNA, etc)

Metatranscriptomics studies: Community functions Response to different

environments Regulation of gene expression


Evolving of Metatranscriptomics

cDNA clone libraries + Sanger sequencing

Microarrays

RNA-seq enabled by next-generation sequencing technologies.

Sorek & Cossart, NRG (2010) 11, 9-16

RNA-seq is superior to microarrays in many ways in microbial community gene expression analysis.


Challenges in Metatranscriptomics

Wet lab Low RNA yield from environmental samples Instability of RNA (half-lives on the order of

minutes) High rRNA content in total RNA (mRNA

accounts for 1-5% of total RNA)

http://cybernetnews.com/vista-recovery-disc/

http://www.nwfsc.noaa.gov/index.cfm

Bioinformatics General challenges with short reads and large data

size Small overlap between metagenome and

metatranscriptome, or complete lack of metagenome reference


rRNA Removal Methods

Method rRNA feature usedInput RNA

Manipulate raw RNA

Before cDNA synthesis

Subtractive hybridization Conserved sequence

HighYes

RNase H digestion

Exonuclease digestion 5’ monophosphate

Gel extraction Size

Biased poly(A) tailing 2o structure Low

During cDNA synthesis

Not-so-random primers Sequence feature Low No

After cDNA synthesis

Library normalization w/ DSN High abundance Low No


Validation of Two Ribosomal RNA Removal Methods for Microbial

Metatranscriptomics

Shaomei He, Omri Wurtzel, Kanwar Singh, Jeff L. Froula, Suzan Yilmaz, Susannah G. Tringe, Zhong Wang, Feng Chen, Erika A. Lindquist, Rotem Sorek and Philip Hugenholtz


Subtractive Hybridization & Exonuclease Digestion

Hyb Exo

Capture Oligo

Magnetic Bead

rRNA

mRNA

Subtractive Hybridization

MICROBExpress Bacterial mRNA Enrichment(Ambion)

Exonuclease Digestion

mRNA-ONLY Prokaryotic mRNA Isolation(Epicentre)

5’ Monophosphate Dependent Exonuclease

rRNA

mRNA

5’ P

5’ PPP


Objectives

Validate the performance of Hyb and Exo kits on

synthetic five-member microbial communities, using

Illumina sequencing to evaluate:

Efficiency of rRNA removal

Fidelity of mRNA relative transcript abundance

Hyb 2 x Hyb Exo Hyb + Exo Exo + Hyb

Treatments:


OrganismGenome

size (Mbp)

%GC PhylumMatch Hyb target sites

Desulfovibrio vulgaris 3.7 63 Proteobacteria Yes

Streptomyces sp. 8-10 71 Actinobacteria Yes

Lactococcus lactis 2.53 35 Firmicutes Yes

Spirochaeta aurantia 4.3 65 Spirochaeta Yes

Lactobacillus brevis 2.3 46 Firmicutes Yes

Kangiella koreensis 2.9 43 Proteobacteria Yes

Catenulispora acidiphila 10.5 70 Actinobacteria Yes

Halorhabdus utahensis 3.1 63 Euryarchaeota No

Microbial Isolates in the Two Synthetic Communities

Community 1

Community 2


Technical Reproducibility

Exo

All treatments exhibited good technical reproducibility.

Hyb

Hyb, rep1

Hyb

, rep

2

Exo, rep1E

xo, r

ep2


rRNA Removal Efficiency


Read Distribution

Community 1

Community 2


Observed and Actual rRNA Removal

- 80 - 0

17 3After removal

97 3Before removal

rRNA mRNA

Observed rRNA reduction = 97% - 85% = 12%

Actual percent removal = 80/97 = 82.5%

Actual removal is much higher than what appears, due to the very high original rRNA content.

97%

rRNA

85%

rRNA


Community rRNA Removal

Community 1: Hyb + Exo > Hyb > Exo

Community 2: Hyb + Exo > Exo + Hyb > Exo > 2 x Hyb ≈ Hyb

rRN

A R

emov

al (

%)


Hyb 2 x Hyb Exo Hyb + Exo Exo + Hyb

rRN

A R

emo

val

(%)

RIN: RNA integrity number

More intact RNA Higher rRNA removal efficiency

rRNA Removal and RNA Integrity

60

70

80

90

100

110

120

5 6 7 8 9 10 11

0

20

40

60

80

100

120

5 6 7 8 9 10 110

20

40

60

80

100

120

5 6 7 8 9 10 1140

60

80

100

120

5 6 7 8 9 10 11

60

70

80

90

100

110

120

5 6 7 8 9 10 11

r = 0.946 r = 0.958 r = 0.874 r = 0.945

RNA Integrity Number (RIN)


Enrichment of mRNA & Increase of Detection Sensitivity


Fidelity of mRNA Relative Abundance


Fidelity of mRNA Relative Abundance

Hyb > Exo > Hyb+Exo

Community 1

Hyb ≈ 2xHyb > Exo > Hyb+Exo ≈ Exo+Hyb

Community 2


Conclusions

rRNA removal efficiency was community composition and RNA integrity dependent.

Exo degraded some mRNA, introducing larger variation than Hyb.

Combining Hyb and Exo provided higher rRNA removal than used alone, but the fidelity was significantly compromised.


Customized subtractive hybridization

Stewart et al, ISME J (2010) 4, 896–907

Customized probes specific to communities of interest

Probes cover near-full-length rRNA, and should also capture partially degraded (fragmented) rRNA

It has been applied on marine metatranscriptome samples to substantially reduce rRNA.


Duplex-specific nuclease (DSN)

• Efficient on E. coli (final rRNA% = 26 ± 11%)• Preserved mRNA relative abundance• Little reduction of the very abundant mRNA

Total RNA

RNA-seq library construction

Library normalization using DSN

Denature ds-DNA at high temp

Re-anneal to ds-DNA at lower temp.

DSN degrades DNA duplex which is presumably from abundant transcripts.

Yi et al, Nucleic Acids Res (2011) doi: 10.1093/nar/gkr617


Still efficient and “faithful” for microbial communities?

0

0.5

1

1.5

2

2.5

3

1 101 201 301 401 501 601 701 801 901 1001

Rank of OTU

Rel

ativ

e ab

un

dan

ce o

f O

TU

(%

)

Environmental microbial communities are very diverse, with a long tail of minor community members.

Typical species rank abundance


Termite Hindgut Metatranscriptomics

- A case study

(Preliminary results)


Summary

Metatranscriptomics is being advanced by next-generation sequencing technologies.

Currently, high rRNA content is still a major bottleneck of metatranscriptomics projects.

Bioinformatically removing rRNA reads should increase computational speed in de novo assembly, and improve the assembly of low-abundance mRNAs. Need to investigate algorithm that is sensitive and computationally efficient to do this for large datasets.


• Phil Hugenholtz• Susannah Tringe• Edward Kirton• Kanwar Singh• Erika Lindquist• Feng Chen• Falk Warnecke• Natalia Ivanova• Martin Allgaier• Steve Lowry• Jeff Froula• Zhong Wang• R&D group• Production group• Many others!

• Hans Peter Klenk

• Omri Wurtzel• Rotem Sorek

Acknowledgement

• Jose Escovar-Kousen

• Rudolph Scheffrahn

metatranscriptomics: challenges and progress

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