Supporting file S1 for: Origin and ecological selection of core and food-specific bacterial communities
associated with meat and seafood spoilage. (Chaillou et al.)
1 – Objectives: Design 454-sequencing runs and analyze 454 libraries. Determine degree of chloroplast
contamination. Establish quality controls to evaluate the potential for methodological bias in our study
design.
2 – Design of multiplex 454 GS FLX Titanium runs
27F and 534R primers were fused with one of ten unique, sample-specific barcodes and added to
sequences of 454 forward primer A and reverse primer B, respectively. These 10 barcoded primer sets
were used for V1-V3 16S amplification of a batch of 10 samples from a given food product at a given time
of analysis (e.g., 10 samples of fresh ground beef analyzed at T0); each sequence read could therefore be
traced back to its sample of origin. For each batch of 10 pooled samples, two emulsion PCRs (emPCR)
were carried out to perform sequencing in both forward (primer A) and reverse (primer B) directions. The
resulting 20 emPCRs were pooled together in a ¼-plate run. In total, four full-plate GS-FLX Titanium runs
were performed, corresponding to 16 batches of a ¼ run each and thus 160 samples sequenced in both
directions.
3 – Figure S1a: Boxplot showing the number of raw reads obtained from the different barcode amplicon
libraries. Sample codes are as described in Table 1 in the manuscript.
Boxes define the interquartile range (Q1-Q3) of the total number of reads (forward A and reverse
B sides sequenced together) obtained from each food type (10 pooled samples from each). The blue line
depicts the median value of reads per sample for the whole dataset. This value was used to normalize
abundance at a fixed library size of 15,000 reads. Sequencing results revealed unusually high variability
among T0 fresh smoked salmon samples (indicated with an arrow). However, this variability did not
significantly impact the overall estimation of diversity in these samples. Indeed, the Chao1 estimator
1
revealed that mean diversity was similar in samples above (179 ± 66 predicted OTUs) and below (175 ± 44
predicted OTUs) the median number of reads.
3 – Figure S1b: Amount of chloroplast contamination in T0 samples expressed as a percentage of relative
abundance.
(most-likely non
SSU sequences)
Rapid detection of chloroplasts was performed following quality filtering of reads; reads were directly
matched with taxonomic assignments using the SILVA analytical pipeline. The inferred origin of reads
from each food product, together with the percentage of the total reads attributable to that origin, is
represented in Figure S1b by color: bacteria - no color, chloroplast – green, no match – yellow. This last
group presumably includes chimeric reads or reads of non-SSU rRNA genes. Chloroplasts were not
detected in TS spoiled samples but were detected in some T0 samples, particularly in poultry sausage
samples (arrow). Here, chloroplast contamination correlates to the use of spices in this food type. An
average of 58% (min: 38%, max: 75%) of all reads obtained from poultry sausage samples were derived
from chloroplasts. Therefore, it is likely that this very high level of contamination hindered our efforts to
fully capture the complete microbial -diversity of poultry sausage T0 samples in comparison to the other
food products analyzed. Indeed, T0 poultry sausage samples demonstrated the greatest divergence from
mean observed OTU numbers and from Chao1 estimates of predicted OTUs as described in Figure 1A in
the main body of the manuscript.
4 – Analyzing biases in DNA extraction and 16S amplicon PCR
We combined the quality control for these two steps into one set of experiments. We aimed to
demonstrate that for each of the eight food types, DNA extraction yield and subsequent 16S rRNA gene
amplification were not affected by bias introduced by our gradient PCR protocol. We therefore chose two
bacteria, each from a different phylum, and artificially co-inoculated them into fresh products at low
natural contamination levels (ca. 102 cfu.g-1). Lactobacillus sakei strain 23K (Firmicute) (Chaillou et al.,
2005) and Serratia proteamaculans strain CD249 (Proteobacteria) (Jaffres et al., 2011) were inoculated
together in six samples (A to F) of each food type in an inverted concentration gradient ranging from 103
2
cfu.g-1 to 108 cfu.g-1 (see Figure S1c). DNA was extracted, and 10 ng were used in temperature-gradient
PCR amplifications with 27F and 534R primers under the conditions described in the main body of the
manuscript (Materials and Methods section). PCR products were diluted 1000-fold, and 5 µl of this
solution was analyzed using quantitative real-time PCR (qPCR). Primer sequences used for the specific
quantification of the two species were designed for this study and are described in Table S1e. The
protocol for qPCR is described in Chaillou et al., 2013. Results of the quantification, shown in Figure S1d,
demonstrated that for all products the concentration gradient of the two species was conserved
following DNA extraction and gradient PCR, and that little bias was observed between meat and seafood
products. These results confirmed that our protocol did not create significant bias, either in detection or
in the relative quantification of bacterial species affiliated with different phyla.
C
D
Figure S1c: Design of co-inoculation experiment for six samples (A-F) of each food type. Grey circles
represent inoculations of Lactobacillus sakei 23K, and white circles represent inoculations of Serratia
proteamaculans CD249.
Figure S1d: qPCR quantification expressed in threshold cycles (Ct) of Lactobacillus sakei 23K (grey) and
Serratia proteamaculans CD249 (white) following DNA extraction and gradient PCR of the 16S region.
Results are shown for samples obtained from four meat products (circles) and four seafood products
(squares).
Table S1e: Description of primers designed in this study for the 16S V1-V3 region and used for qPCR
quantification
Name
Lactobacillus sakei 23K
QEBP-LSA-01F
QEBP-LSA-01R
Serratia proteamaculans CD249
QEBP-SER-01F
QEBP-SER-01R
Sequence 5' -> 3'
Amplicon size (bp)
Efficiency
AAACCTAACACCGCATGGTGTAG
TCAGGTCGGCTATGCATCACGGT
208
0.92
CTAGCTGGTCTGAGAGGATGAC
CCGTCAATGCAATGTGCTATTAACAC
132
0.88
3
5 – Biases in qualitative taxonomic assignment and relative quantification between 454-Titanium
technical replicates
Sequencing artifacts and emulsion PCR (emPCR) may strongly distort bacterial community structures in
pyrosequencing datasets (Schloss et al., 2011; Pinto and Raskin, 2012; Bakker et al., 2012). Therefore, we
used several strategies to ensure the quality and reproducibility of our pyrosequencing analysis. First of
all, to avoid data distortion among the ten samples of a given food type at a given time of analysis, these
ten samples were sequenced in the same ¼-run lane. Furthermore, we conducted two emPCRs for the
PCR pool obtained for each food type at a given time of sampling, one from the forward primer A side
and the second from the reverse primer B side; these were considered pyrosequencing technical
replicates. To analyze the internal variability between replicates, the data obtained from A- and B-side
sequencing were treated separately in our taxonomic assignment pipeline, as described in the main text
of the manuscript and in Supporting File S2. Figures S1f and S1g compare the taxonomic assignments
obtained from each sequencing replicate (assignments at the genus level using a 98% identity threshold;
data not filtered for chimeras).
F
G
Figure S1f: Quantitative and qualitative comparison of technical replicates from forward A- and reverse Bside sequencing of entire 80-sample T0 dataset. Each point represents a taxon (genus level; assigned by
SILVA) identified in one sample with the corresponding read counts in its sequencing replicate.
Figure S1g: Equivalent to Figure S1f but using the TS dataset.
These results show that for each taxon identified, the strength of the correlation between the
quantitative measurements obtained from A- and B-side sequencing deteriorated when fewer than 50
reads were available (1.5 log10). Nevertheless, the correlation between technical replicates was very
strong (Pearson correlation R > 0.90, P < 0.0001). Furthermore, to ensure increased reliability in the
relative quantification analysis, the sum of A- and B-side sequencing was always used for all samples, and
this strategy helped to reduce quantification bias in less commonly recovered taxa. However, as can be
observed in Figures S1f and S1g, in a few cases the quantification obtained from A-side sequencing did
not correlate with that obtained from B-side sequencing, and vice versa. Without exception, these data
were revealed to be chimeras and were removed from the analysis. An additional technical replication
4
test was carried out between two independent runs. Six samples were randomly chosen, three from T0
(PST0-04, GVT0-02, SFT0-10) and three from TS (PSTA-04, GVTA-02, SFTA-10); they were sequenced from
both sides and in two different runs. Figure S1h shows the resulting comparative SILVA analysis that
demonstrates the strong reproducibility of our sequencing analysis between runs.
F
Figure S1h: Quantitative and qualitative comparison of technical replicates obtained from six samples
sequenced in two independent runs. For each run, counts represent the sum of A- and B- side sequences.
Each point represents a taxon (genus level, assigned by SILVA) identified in one sample with the
corresponding read counts in its sequencing replicate.
6 – Comparison of 454-pyrosequencing data with temporal temperature gel electrophoresis (TTGE)
analysis
To verify our qualitative taxonomic assignments, we carried out TTGE analysis on several sets of samples
and compared these results with those obtained using the 454-pyrosequencing protocol. The TS samples
of smoked salmon (SS), salmon fillet (SF), and cooked shrimp (CS) were chosen for this analysis (10
samples of each food product) because studies have already been published using TTGE for these
products (Mace et al., 2013; Broekaert et al., 2011) and a straightforward TTGE protocol was available.
Primers V3P2 and V3P3-GC-Clamp were used to amplify the 16S rRNA V3 gene region (194 bp) as
described in Jaffres et al., 2009. The size of the PCR products was determined in 1% (w/v) agarose gel
(Invitrogen) using an exACTGene 100 bp PCR DNA ladder (Fisher Scientific, Illkirch, France). The PCR
products obtained from the V3 16S rDNA fragment amplification were subjected to TTGE gel analysis.
Migration was performed at 50 V for 12.5h using a temperature gradient of 65-70°C (rate of 0.4°C.h-1) for
bacteria of low GC-content and at 120 V for 6h at a constant temperature of 70°C for bacteria of high GCcontent. Two TTGE ladders for each migration condition were prepared by pooling the PCR products
amplified from DNA extracted from pure strain cultures. Standardization, analysis, and comparison of
TTGE fingerprints were monitored using BioNumerics software, version 6.0 (Applied Maths NV, SintMartens-Latem, Belgium) as described in Mace et al., 2012. Fingerprint bands were assigned to a given
species by comparing the band migration position to that of the reference strain profiles included in the
database. Results are summarized in Table S1i below.
5
Figure S1i: Sequence-read counts obtained using 454-pyrosequencing from the top 13 most abundant
OTUs in seafood TS samples compared with TTGE bands identified from the same samples (in green).
OTUs in red are not present in the TTGE reference database.
OTUs
Ebp0189 Carnobacterium divergens
Ebp0162 Brochothrix thermosphacta group
Ebp1101 Photobacterium phosphoreum group
Ebp1679 Staphylococcus equorum
Ebp1098 Photobacterium kishitanii
Ebp0191 Carnobacterium maltaromaticum
Ebp0795 Lactococcus lactis
Ebp0786 Lactobacillus curvatus
Ebp0738 Lactobacillus fuchuensis
Ebp0569 Serratia proteamaculans group
Ebp1824 Uncultured Photobacterium
Ebp0794 Lactococcus piscium
Ebp0574 Enterococcus faecalis
01
5975
6
22
3489
5
52
0
2713
11
7
4
3
0
02
2
0
0
7643
0
8
6
2120
5
3
0
0
1
03
57
4
57
45
8
2364
5584
0
0
1217
6
0
1077
Smoked salmon (SS) TS samples
04
05
06
07
08
6856
2
124
5
119
4408
84
345
572
7462
2185 4499 3368
771
2586
0
0
81
24
30
315
4451 3460
206
347
578
99
444
1246
510
1
0
0
0
0
1
0
35
0
0
1
4
4404
35
0
252
1
30
1458
0
232
741
369
62
205
2
0
9
1
1355
0
0
0
0
0
09
4404
6121
0
104
0
477
0
0
0
4
0
0
1
10
12316
655
7
9
0
404
0
1
0
52
0
35
0
OTUs
Ebp0191 Carnobacterium maltaromaticum
Ebp0769 Lactobacillus sakei
Ebp1101 Photobacterium phosphoreum group
Ebp0189 Carnobacterium divergens
Ebp0794 Lactococcus piscium
Ebp0786 Lactobacillus curvatus
Ebp0162 Brochothrix thermosphacta group
Ebp1613 Vagococcus fluvialis
Ebp1824 Uncultured Photobacterium
Ebp1098 Photobacterium kishitanii
Ebp0738 Lactobacillus fuchuensis
Ebp1807 Uncultured Vibrionaceae
Ebp0679 Flavobacterium succinans
01
2954
219
562
2095
2425
104
183
246
68
104
451
42
34
02
2984
318
671
127
12
953
127
411
92
154
549
37
150
03
4910
653
133
79
588
644
685
446
14
23
109
9
17
Salmon fillet (SF) TS samples
04
05
06
07
4592 1709 1719
99
1175 5145
154
4791
908
73
3623
131
2323
89
30
2
568
2213
453
8
37
1419
674
195
374
104
502
15
110
130
424
6
159
14
693
19
131
8
610
22
87
143
113
139
94
7
349
11
10
6
334
19
08
663
407
4054
241
35
104
135
30
541
438
61
334
105
09
3348
210
1436
880
15
36
2
50
199
189
26
119
5
10
6010
246
932
611
1174
92
251
364
156
169
8
88
4
OTUs
Ebp0824 Leuconostoc gasicomitatum
Ebp1603 Streptococcus parauberis
Ebp1630 Vibrio ordalii
Ebp0189 Carnobacterium divergens
Ebp1635 Weissela viridescens
Ebp0191 Carnobacterium maltaromaticum
Ebp0195 Carnobacterium inhibens
Ebp0024 Aerococcus viridans group
Ebp0823 Leuconostoc mesenteroides group
Ebp0186 Carnobacterium funditum
Ebp0194 Carnobacterium mobile
Ebp1656 Trichococcus pasteurii group
Ebp0192 Carnobacterium jeotgali
01
6679
734
21
0
0
0
0
29
1
0
0
0
0
02
586
3196
5523
0
0
0
1
51
0
0
0
0
0
03
1916
29
5028
3
1
1658
1
32
0
1
0
0
0
Cooked shrimp (CS) TS samples
04
05
06
07
4
10
4649
0
39
444
337
7903
0
0
117
1015
6321
680
0
0
0
0
0
0
914
985
118
0
39
146
3
0
0
0
157
0
10
2
5
0
911
2505
0
0
21
50
0
0
150
995
0
0
185
563
0
0
08
0
0
0
50
515
50
1483
14
4009
20
109
0
0
09
0
0
0
9
5900
1018
2
492
0
0
4
0
0
10
0
2
0
13
1
0
2650
3323
1
30
3159
0
2
These results indicated that successful identification with TTGE correlated with the abundance of OTUs
revealed by 16S pyrosequencing. However, some of the most abundant OTUs failed to be detected by
TTGE, either because the species had not been previously known from these types of samples in the
literature and thus were not included among the TTGE reference strains, or because TTGE bands of
closely related species (e.g., P. phosphoreum cluster EBP1101, P. kishitanii EBP1098, uncultured
6
Photobacterium EBP1824) might not have been distinguished using TTGE. Furthermore, the TTGE
protocol that was used was revealed to be inadequate for the detection of certain OTUs such as the
EBP0162 Brochothrix thermosphacta cluster and EBP0769 Lactobacillus sakei. Nevertheless, and despite
the lower number of species identified with TTGE, the two techniques were correlated in their specieslevel taxonomic assignments.
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