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Recent Work & Current Methods
In 16S rRNA Gene Sequencing
09-09-2014
Qi Zhu, PhD
Senior Scientist
Wonsik Kim, PhD
Senior Scientist
Agenda
Next-Gen Sequencing
16S rRNA Gene Sequencing Intro
Sample Preservation / DNA Isolation (Norgen)
Sequencing / Data Analysis
Case Studies
Next-Gen Sequencing
Massively Parallel Sequencing
High Throughput - Large Scalability - Fast Speed
3
Next-Gen Sequencing
Instrument: Illumina® MiSeq Desktop Sequencer
Length of Reads: 1x36 bps or 2 x 300 bps
Number of Reads: ~25 Million
Data Output: 0.3-15 Gb
Run Time: 4-65 Hours
Illumina® MiSeq
Next-Gen Sequencing
from - An Introduction to Next-Generation
Sequencing Technology - Illumina
Next-Gen Sequencing
from - An Introduction to Next-Generation
Sequencing Technology - Illumina
16S rRNA Gene Sequencing Intro
Environmental Samples
Gut Microbiome
Clinical Samples
Food Assurance
Images copied from www.clientsfirst-us.com, etc
16S rRNA Gene Sequencing Applications
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Environmental Samples - (water - Kakizaki et al. 2012 [abstract], soil
- Rampelotto et al. 2013 [abstract])
Gut Microbiome - (De Angelis et al. 2013 [article])
Clinical Samples (Exterkate et al. 2014 [abstract])
Sterility Monitoring / Contamination Investigation (Oberauner et al.
2013 [article])
Food Quality Assurance - (milk - McInnis et al. 2014 [abstract]
16S rRNA Gene Sequencing in PubMed
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First use of NGS for
16S rRNA Gene
Sequencing
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16S rRNA Gene Sequencing Intro
Cox M J et al. Hum. Mol. Genet. 2013;22:R88-R94 [abstract]
16S rRNA Gene Sequencing Intro
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Identification efficient: compared to traditional identification
methods , 16S rRNA Gene sequencing of bacteria more quickly and
accurately.
Dual-zone detection: a comprehensive upgrade to dual-zone (V3 +
V4) testing, obtaining sequence longer, more accurate analysis of
colonies.
Low cost: less need sequencing data , the low cost of detection.
High sensitivity: can be identified to the low abundance of bacteria.
Approximately 1.5 kb 16S rRNA Gene gene of E.coli showing
the nine variable regions that make it an ideal target as a
phylogenetic marker gene.
Cox MJ, Cookson WO, Moffatt MF. (2013) Sequencing
the human microbiome in health and disease. Hum
Mol Genet 22(R1), R88-94. [abstract]
16S rRNA Gene Sequencing Intro
Kong HH. (2011) Skin microbiome: genomics-based insights into the
diversity and role of skin microbes. Trends Mol Med 17(6):320-8. [article]
16S rRNA Gene Sequencing Intro
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Reads from next-gen sequencing can be BLASTED against curated
databases such as The Ribosomal Database Project (RDP), GreenGenes,
and SILVA for identification and classification.
Related sequences are “clustered” and the number of representatives of
each cluster counted. Clusters of similar sequences are referred to as
“operational taxonomic units” (OTUs).
OTU counts are summarized in a table of relative abundances for each
organism in each sample.
To date, several analysis pipelines have been developed for analysis of
16S rRNA Gene gene sequence data and two commonly used pipelines
are QIIME and Mothur.
QIIME takes users from their raw sequencing output through initial
analyses such as OTU picking, taxonomic assignment, and construction
of phylogenetic trees from representative sequences of OTUs, and
through downstream statistical analysis, visualization, and production of
publication-quality graphics.
Challenging Samples for Metagenomics
www2.fiskars.com
- Humic acids
www.tripadvisor.com
- Salt, Ion
- Low DNA
- Humic acid
- Low DNA (Diluted
DNA)
- Salt, urea
food-zila.com
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Fat
Food preservatives
Polysaccharide
Starch
www.foodmatters.tv
- Fat
- Humic acids
- Processed byproducts
Sample collection / Extraction
Collection/
Preservation
Extraction
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- Room temperature shipping.
- Preserve and stabilize the intact
biological information from the site of
the collection to the facility for DNA
extraction
- Convenient (no ice pack or cooler)
and cost effective (small package for
shipping)
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Challenging with environmental
samples
Requires sequencing inhibitorfree DNA
Sufficient DNA yield
Rapid and easy use
Selected case studies
Lupatini M., Suleiman A, Jacques RS, Antoniolli Z, Ferreira AS, Kuramae E, and
Roesch LF. (2014) Network topology reveals high connectance levels and few key
microbial genera within soils. ENVIRONMENTAL SCIENCE doi: 10.3389/fenvs.
2014.00010 (Soil DNA isolation kit)
Gittel et al. (2014) Distinct microbial communities associated with buried
soils in the Siberian tundra. ISME J 8(4):841-53. (Soil DNA isolation kit)
Strong T, Dowd S, Gutierrez AF, Molnar D, Coffman J (2013) Amplicon
pyrosequencing and ion torrent sequencing of wild duck eubacterial microbiome
from fecal samples reveals numerous species linked to human and animal.
F1000Research 2:224. (Water RNA/DNA purification kit)
McInnis EA, Kalanetra KM, Mills DA, Maga EA . (2014) Analysis of raw goat milk
microbiota: Impact of stage of lactation and lysozyme on microbial diversity. Food
Microbiology 46, 121–131. (Milk genomic DNA isolation kit)
Puthucheary SD, Puah SM, Chua KH (2012) Molecular Characterization of
Clinical Isolates of Aeromonas Species from Malaysia. PLoS ONE 7(2): e30205.
(Bacterial genomic DNA isolation kit)
16S rRNA Gene Sequencing and Data Analysis
Comprehensive Sequencing Experiment
• Sample QC
• Sample preparation (One-Step PCR, < =15 cycles)
• Library preparation and quantification
• High-throughput sequencing
• Advanced bioinformatics analysis
• High level customer support
16S rRNA Gene Sequencing and Data Analysis
One-step PCR Amplicon Region: V3+V4, ~470 bps
Primer Information: Sense – 319F, Antisense – 806R
Control: 10%-70% phix
Starting Sample Material: DNA with RNA free
Sample Requirement: > 2 μg
Sample Concentration: >10 ng/uL
M: 50 bp ladder
1: Rat Stool sample 1
2: Rat Stool Sample 2
N: Negative Control
16S rRNA Gene Sequencing and Data Analysis
Data Analysis Report (1)
Data Analysis Report (2)
Data Analysis Report (3)
Taxa assignments (Phylum) for each sample
Data Analysis Report (4)
Taxa assignments (Class) for each sample
Data Analysis Report (5)
Taxonomy Communities Genus Heatmap (Truncated)
Data Analysis Report (6)
Taxonomy Communities Genus and Abundance Table (Truncated)
16S rRNA Gene Sequencing – Case Studies
Analysis of raw goat milk microbiota
The microbiota of raw goat milk was determined using
next generation sequencing.
Microbiota at early and mid lactation was similar and
distinct from late lactation.
A shift in microbiota occurred at late lactation.
Milk from transgenic goats containing lysozyme had a
similar microbiota over time.
The presence of lysozyme did little to influence the
microbiota of goat milk.
Bacterial community structure at the
family level of the milk from WT (n = 4)
and hLZ transgenic (n = 4) animals at early,
mid and late lactation using NGS (a) and
CLS (b).
McInnis EA, Kalanetra KM, Mills DA, Maga EA . (2014) Analysis of raw goat
milk microbiota: Impact of stage of lactation and lysozyme on microbial
diversity. Food Microbiology 46, 121–131. [abstract]
16S rRNA Gene Sequencing – Case Studies
Distinct microbial communities associated with buried soils in the Siberian
tundra
Researchers surveyed the microbial community structure in
cryoturbated soils from nine soil profiles in the
northeastern Siberian tundra using high-throughput
sequencing and quantification of bacterial, archaeal and
fungal marker genes.
They found that bacterial abundances in buried topsoils
were as high as in unburied topsoils. The abiotic conditions
(low to subzero temperatures, anoxia) and the reduced
abundance of fungi likely provide a niche for bacterial,
facultative anaerobic decomposers of soil organic matter
(SOM).
This study expands the knowledge on the microbial
community structure in soils of Northern latitude
permafrost regions, and attributes the delayed
decomposition of SOM in buried soils to specific microbial
taxa, and particularly to a decrease in abundance and
activity of ECM fungi, and to the extent to which bacterial
decomposers are able to act as their functional substitutes.
Gittel et al. (2014) Distinct microbial communities
associated with buried soils in the Siberian tundra.
ISME J 8(4):841-53. [abstract]
Prokaryotic (a) and fungal (b) community structure shown as relative
abundance on phylum level and based on SSU rRNA Gene gene
Illumina tag sequencing and fungal ITS pyrosequencing, respectively.
16S rRNA Sequencing – Case Studies
Molecular Characterization of Clinical Isolates of Aeromonas Species from
Malaysia
Aeromonas species are common inhabitants of
aquatic environments giving rise to infections in
both fish and humans. Identification of aeromonads
to the species level is problematic and complex due
to their phenotypic and genotypic heterogeneity.
Aeromonas hydrophila or Aeromonas sp were
genetically re-identified using a combination of
previously published methods targeting GCAT, 16S
rDNA and rpoD genes.
This study emphasizes the importance of using more
than one method for the correct identification of
Aeromonas strains. The sequences of the rpoD gene
enabled the unambiguous identification of the 94
Aeromonas isolates in accordance with results of
other recent studies.
Puthucheary SD, Puah SM, Chua KH (2012) Molecular
Characterization of Clinical Isolates of Aeromonas
Species from Malaysia. PLoS ONE 7(2): e30205.[article]
Phylogenetic relationship
of the rpoD sequences
between 94 Aeromonas
isolates and 9 references
strains using neighborjoining method.
16S rRNA Sequencing – Case Studies
Amplicon pyrosequencing and ion torrent sequencing of wild duck eubacterial
microbiome from fecal samples
Investigated the composition of the wild duck
eubacterial microbiome from a fecal sample
revealed that the representative bacterial species
were quite distinct from a pond water sample, we
were able to classify the major operational
taxonomic units representing the majority of the
eubacterial fecal microbiome.
Bacterial species present in the analysis revealed
numerous organisms linked to human and animal
diseases including septicemia, rat bite fever, pig
mastitis, endocarditis, malar masses, genital
infections, skin lesions, peritonitis, wound
infections, septic arthritis, urocystitis, gastroenteritis
and drinking water diseases.
Comparison of Classes of Eubacteria
present in the Duck to the Classes of
Eubacteria present in pond water
using a modified heat map.
Strong T, Dowd S, Gutierrez AF, Molnar D, Coffman J
(2013) Amplicon pyrosequencing and ion torrent
sequencing of wild duck eubacterial microbiome from
fecal samples reveals numerous species linked to human
and animal. F1000Research 2:224. [article]
Company Overview
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•
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Global - Genomics & Proteomics Services Provider
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Headquarters in Houston, Texas - USA
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Offices in USA & China
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Representatives in Japan, Korea, & India
Extensive Experience
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Providing services since 2005
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Processed > 12,000 samples
Primary Technological Advantage
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μParaflo® Microfluidics Technology - customizable
Results
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Diverse customer base (> 1400 institutions, > 40 countries)
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~1000 customer publications
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Excellent reputation in marketplace
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Worldwide sales
30
Global Reach – Distribution Channels
United States
LC Sciences, LLC
Houston, TX
[email protected]
www.lcsciences.com
China
LC Bio
Hangzhou
[email protected]
www.lc-bio.com
India
Genetix Biotech Asia (P)
Ltd
New Delhi
www.genetixbiotech.com
Korea
YeBT
Seoul
[email protected]
www.yebt.net
Japan
ALLIANCE Technology, Inc.
Kyoto City
[email protected]
www.alliance-technology.jp
Thank You!
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