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Metatranscriptomics:
Challenges and Progress
Shaomei He
DOE 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
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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.
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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://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
http://cybernetnews.com/vista-recovery-disc/
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rRNA Removal Methods
Method
rRNA feature used
Input Manipulate
RNA raw RNA
Before cDNA synthesis
Subtractive hybridization
RNase H digestion
Conserved sequence
High
Exonuclease digestion
5’ monophosphate
Gel extraction
Size
Biased poly(A) tailing
2o structure
Low
Sequence feature
Low
No
High abundance
Low
No
Yes
During cDNA synthesis
Not-so-random primers
After cDNA synthesis
Library normalization w/ DSN
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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
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Subtractive Hybridization & Exonuclease Digestion
Subtractive Hybridization
Exonuclease Digestion
MICROBExpress Bacterial mRNA Enrichment
(Ambion)
mRNA-ONLY Prokaryotic mRNA Isolation
(Epicentre)
mRNA
5’ PPP
mRNA
rRNA
5’ P
rRNA
Capture Oligo
Magnetic Bead
Hyb
5’ Monophosphate
Dependent Exonuclease
Exo
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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
Treatments:
Hyb
2 x Hyb
Exo
Hyb + Exo
Exo + Hyb
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Microbial Isolates in the Two Synthetic Communities
Genome
size
(Mbp)
%GC
Phylum
Match 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
Organism
Community 1
Community 2
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Technical Reproducibility
Exo
Exo, rep2
Hyb, rep2
Hyb
Hyb, rep1
Exo, rep1
All treatments exhibited good technical reproducibility.
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rRNA Removal Efficiency
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Read Distribution
Community 1
Community 2
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Observed and Actual rRNA Removal
97%
Before removal
rRNA
mRNA
97
3
- 80
-0
rRNA
85%
After removal
17
3
rRNA
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.
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rRNA Removal (%)
Community rRNA Removal
Community 1:
Hyb + Exo > Hyb > Exo
Community 2:
Hyb + Exo > Exo + Hyb > Exo > 2 x Hyb ≈ Hyb
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rRNA Removal (%)
rRNA Removal and RNA Integrity
Hyb
2 x Hyb
120
Exo
120
r = 0.946
100
120
r = 0.958
110
100
80
60
90
60
40
80
40
20
70
20
5
6
7
8
9
10
11
5
6
7
8
r = 0.874
9
10
11
120
r = 0.945
100
110
100
90
80
80
60
0
60
RIN: RNA integrity number
Exo + Hyb
120
100
80
0
Hyb + Exo
70
60
40
5
6
7
8
9
10
11
5
6
7
8
9
10
11
5
6
RNA Integrity Number (RIN)
More intact RNA  Higher rRNA removal efficiency
7
8
9
10
11
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Enrichment of mRNA & Increase of Detection Sensitivity
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Fidelity of mRNA Relative Abundance
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Fidelity of mRNA Relative Abundance
Community 1
Hyb > Exo > Hyb+Exo
Community 2
Hyb ≈ 2xHyb > Exo > Hyb+Exo ≈ Exo+Hyb
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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.
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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.
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Duplex-specific nuclease (DSN)
Yi et al, Nucleic Acids Res (2011) doi: 10.1093/nar/gkr617
Total RNA
RNA-seq library construction

Denature ds-DNA at high temp

Re-anneal to ds-DNA at lower
temp.

DSN degrades DNA duplex
which is presumably from
abundant transcripts.
Library normalization using DSN
• Efficient on E. coli (final rRNA% = 26 ± 11%)
• Preserved mRNA relative abundance
• Little reduction of the very abundant mRNA
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Still efficient and “faithful” for microbial communities?
Relative abundance of OTU (%)
Typical species rank abundance
3
2.5
2
1.5
1
0.5
0
1
101
201
301
401
501
601
701
801
901
1001
Rank of OTU
Environmental microbial communities are very diverse, with
a long tail of minor community members.
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Termite Hindgut Metatranscriptomics
- A case study
(Preliminary results)
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Termite samples in this study
Species:
Family:
Habitat:
Diet:
Nasutitermes corniger
Termitidae
Laboratory colony
Dry wood
Amitermes wheeleri
Termitidae
Subtropical desert
Cow dung
Aim: Determine system-specific differences between termite
species with different diets.
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Summary

Metatranscriptomics is being advanced by nextgeneration 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.
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Acknowledgement
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Phil Hugenholtz
Susannah Tringe
Edward Kirton
Kanwar Singh
Erika Lindquist
Feng Chen
Jeff Froula
Falk Warnecke
Natalia Ivanova
Martin Allgaier
Zhong Wang
Tao Zhang
R&D group
Production group
Many others!
• Omri Wurtzel
• Rotem Sorek
• Hans Peter Klenk
• Rudolph Scheffrahn
• Jose Escovar-Kousen