Identification of factors impacting reproducibility
and quality of microbiome profile analysis
Evgueni Doukhanine, Anne Bouevitch, Lindsay Pozza, Carlos Merino and Rafal M. Iwasiow
DNA Genotek Inc., Ottawa, Canada

Introduction

Results – Impact of extraction
BB

Unstabilized

The evolved characterization of human-associated microbial communities has given scientists a new perspective on
diseases of the GI tract. Metagenomic profiles associated with diabetes, inflammatory bowel disorders and obesity areshowing diagnostic, prognostic and monitoring potential. These microbial communities are dynamic systems that havebeen shown to respond to dietary, environmental, and even seasonal changes. Considering this intrinsic complexity,successful identification and validation of biomarkers requires the execution of large, longitudinal population studies.
The proper execution of such studies relies on capturing an accurate representation or ‘taking a snapshot’ of the donor’s
biology at the point-of-collection and reliably reproducing it through the analytical pipeline.

T0*
BV
*
BB

14 days, 23°C

Microbiome-Wide Association Studies (MWAS) require the establishment of scalable and reliable methods for the
pre-analytical (collection and stabilization), in vitro (processing and assay), and in silico (computational analysisand interpretation) handling of biological material and resultant data. In the present study, the variability introduced
in metagenomic profiles by pre-analytical and in vitro conditions was quantified and compared under different
collection conditions including unstabilized samples or samples stabilized with OMNIgene®•GUT (OMR-200).
OMNIgene•GUT is a device for fecal self-collection, stabilization and homogenization for microbiome analysis.

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Bray-Curtis distance

BBOMNIgene•GUTT0

While driving variability in a metagenomics workflow to zero may be unachievable, our results illustrate thatpre-analytical conditions—by far—have the largest impact on accuracy and reproducibility of microbiome profiling.
Clinically relevant taxa, (such as Prevotella copri)) when left vulnerable to time and temperature can overgrow,leading to spurious results. Furthermore, collection and proper stabilization with OMNIgene•GUT can greatly
reduce this source of variability.

BVBB14 days, 23°C
BV

If a goal for the scientific community is to produce more informative, reproducible and scalable studies at thepopulation-level, we propose OMNIgene•GUT as a pre-analytical standard for fecal sample collection andstabilization for metagenomic analysis.

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Bray-Curtis distance

Figure 2: Differences in mechanical lysis during sample extraction have a significant impact on the microbiome profile. Proper stabilization/
homogenization technology with OMNIgene•GUT can significantly reduce differences seen between mechanical lysis methods. Significant difference wasevaluated by two-way ANOVA test with no rank matching (*p-value<0.015).

Materials and methods
Sample collection and DNA extraction

Results – Pre-analytical changes during transport

T0FreshBiologicalSite 1Site 2Site 3OMNIgene•GUT∆ Time∆ Time
∆ TempBiologicalSite 1Site 2Site 3Ext. 1

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Rep. 1

Rep. 2

UnstabilizedFresh

OMNIgene•GUTdbilize

shpeed°C-80

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OMDonor 16

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en
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Donor 30en

shipped

ed

ipped

biliz

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e•GUT-80°C

shFreOM
NIged

shipp

ipped

Donor 150Donor 24

Figure 4: Microbiome profile changes observed during shipping are donor specific. Samples either collected and stabilized in OMNIgene•GUT or collectedwithout stabilizing chemistry were shipped and evaluated for % abundance of taxons of interest, Ruminococcus torque (A) and Prevotella copri (B).
Sample transport without proper stabilization resulted in large taxon changes that are not consistent between donors. Samples collected in OMNIgene•GUTand shipped did not significantly differ in their microbiome compared to fresh samples.

B
Unstabilized

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OM
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Results – Sources of variability
A

Ruminococcaceae (family)
Lachnospiraceae (family)
Collinsella
Phascolarctobacterium
Blautia
Clostridium
Eubacterium
Clostridiales (order)
Ruminococcus
Bacteroides

3% Abundance of Prevotella copri

40sh• Two-sample Kolmogorov–Smirnov test was used as a nonparametric evaluation of the equality of continuous, one-dimensional probabilitydistributions.
• Two-way ANOVA test evaluated influence of two different categorical independent variables on one continuous dependent variable.

50°C

Statistical methods utilizedB

shA

60sta

n = 29 donors-80

Study #2

n = 6 donors• Study #1 16S rRNA V4 hypervariable region paired-end amplicon sequencing.
• Study #2 16S rRNA V3/V4 hypervariable region paired-end amplicon sequencing.
• For both studies used Illumina® MiSeq® instrument, sequences were quality filtered using QIIME and custom scripts. Paired-end reads weremerged and searched against the Greengenes reference database, clustered at 97% by UCLUST. After data normalization, sample-to-sample
distance was measured using Bray-Curtis distance on operational taxonomic unit (OTU) abundance data (utilizes taxon abundance differences
across samples, employing a pair-wise normalization by dividing the sum of differences by the sum of all abundances).

PCR/sequencing

Bifidobacterium
Alistipes
Unidentified
Parabacteroides
Eubacterium
Ruminococcus
Clostridium
Faecalibacterium
Clostridiales (order)
Bacteroides

0.50.4

Donor 20

Figure 3: Exposure of fecal samples to environmental conditions has a significant impact on the microbiome profile. (A) Temperatures experienced during
shipping of collected stool samples collected in Ottawa and sent to Vancouver (Canada) by UPS with a return trip. Samples were extracted immediately upon return.
(B) Genus level differences in % abundance (top ten by abundance are shown) between shipped, frozen and fresh samples from representative donors showcase
donor dependent profile changes during unstabilized transport. OTUs that were not resolved to genus level are classified in brackets. (C) Samples collected inOMNIgene•GUT and shipped did not significantly differ in their microbiome profiles compared to -80°C samples. Unstabilized shipped samples significantly differedin their microbiome profile compared to OMNIgene•GUT shipped samples as measured by 2 sample Kolmogorov–Smirnov test (p-value < 0.0001).

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16S rRNA Sequencing

DNA
extraction

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Study #1

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PCR
rep

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Bray-Curtis distance% Abundance of Ruminococcus torque

14d

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Donor 18-80°C

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BV

OM

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Fresh

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OMNIgene•GUT shipped

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Fresh

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hip0%

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Unstabilized shipped

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Unstabilized OMNIgene•GUT30%

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Hours spent in shipping

40%

Fre140NI

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70

Abundance at genus level (%)

1050%

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pe

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6-8°C

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5 hours780%

ize

OMNIgene•GUT

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11-12°C

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Study designs

21-23°C
16°C

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Abundance at genus level (%)

Temperature

B100%ippAsta• Donors each collected one stool specimen and sampled equal amounts into tubes without a chemistry preservative and into
OMR-200 which includes a stabilizer.
• Samples were extracted or held at varying temperatures according to the study designs (below).
• DNA was extracted from samples using the PowerFecal® DNA Isolation Kit (MoBio® Laboratories) using either Bead Vortexer (BV)
(MoBio® Laboratories) or Mini-Beadbeater (BB) (BioSpec®).

Discussion

Biological reps
Extraction reps
PCR reps
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Bray-Curtis distance

63 %Figure 1: Sources of variability during microbiome evaluation and impact of stabilization/homogenization of samples. (A) Diagram describes
the method in attaining replicates at each potential stage of variability during a microbiome study. (B) Bray-Curtis distance comparison within eachof the source of variability shows that the largest contributor is improper stabilization of collected samples. ∆ Time, 14 days at 23°C.

0.40• Gut microbiome studies have inherent sources of variability at the point-of-collection, extraction and sequencing which leads to requirements
of increased replicates to build confidence in data.
• Choice of extraction methodology for gut samples leads to differences in data quality. Sample homogenization and stabilization reduce thevariability introduced by extraction.
• Sample transport leads to unpredictable taxonomy level changes that can make significant changes to data interpretation. Clinically relevant
microbiome results can be misinterpreted due to artificial changes of the microbiome. In our example, Prevotella copri, which has been correlatedwith rheumatoid arthritis1, only shows consistent and accurate results compared to fresh when the OMNIgene•GUT stabilizing chemistry is utilized.

Conclusions• Accurate measurement of a host microbiome requires standardization in methods of sample collection, stabilization, handling and extraction.
• Our data illustrates that pre-analytical and analytical factors contribute to microbiome variability, however, biospecimen stabilization is by far
the most critical.
• OMNIgene•GUT collection, stabilization and homogenization technology effectively mitigates environmental impacts, enabling accurate
representation of the in vivo biology.

Citations:
1 Scher JU, et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife. 2013;2Superior samples
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