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R E S E A R C H A R T I C L E Open Access
Construction of a dairy microbial genome catalog
opens new perspectives for the metagenomic
analysis of dairy fermented products
Mathieu Almeida
1,2,3,9
, Agnès Hébert
4
, Anne-Laure Abraham
1,2
, Simon Rasmussen
5
, Christophe Monnet
4,6
,
Nicolas Pons
3
, Céline Delbès
7
, Valentin Loux
8
, Jean-Michel Batto
3
, Pierre Leonard
3
, Sean Kennedy
3
,
Stanislas Dusko Ehrlich
3
, Mihai Pop
9
, Marie-Christine Montel
7
, Françoise Irlinger
4,6
and Pierre Renault
1,2*
Abstract
Background: Microbial communities of traditional cheeses are complex and insufficiently characterized. The
origin, safety and functional role in cheese making of these microbial communities are still not well understood.
Metagenomic analysis of these communities by high throughput shotgun sequencing is a promising approach to
characterize their genomic and functional profiles. Such analyses, however, critically depend on the availability of
appropriate reference genome databases against which the sequencing reads can be aligned.
Results: We built a reference genome catalog suitable for short read metagenomic analysis using a low-cost
sequencing strategy. We selected 142 bacteria isolated from dairy products belonging to 137 different species
and 67 genera, and succeeded to reconstruct the draft genome of 117 of them at a standard or high quality level,
including isolates from the genera Kluyvera,Luteococcus and Marinilactibacillus, still missing from public database.
To demonstrate the potential of this catalog, we analysed the microbial composition of the surface of two smear
cheeses and one blue-veined cheese, and showed that a significant part of the microbiota of these traditional
cheeses was composed of microorganisms newly sequenced in our study.
Conclusions: Our study provides data, which combined with publicly available genome references, represents the
most expansive catalog to date of cheese-associated bacteria. Using this extended dairy catalog, we revealed the
presence in traditional cheese of dominant microorganisms not deliberately inoculated, mainly Gram-negative
genera such as Pseudoalteromonas haloplanktis or Psychrobacter immobilis, that may contribute to the characteristics
of cheese produced through traditional methods.
Keywords: Genomic libraries, Genome sequencing, Sequence assembly, Next-generation sequencing, Comparative
genomics, Metagenomics, Food bacteria, Dairy ecosystems
Background
Cheeses harbour a diverse microbial community, composed
of a resident “house flora”, that interacts with strains delib-
erately inoculated as starter or adjunct cultures [1-5]. The
cheese microorganisms mainly consist of Firmicutes (lactic
acid bacteria, staphylococci), Actinobacteria (coryneform
bacteria), Proteobacteria, Bacteroidetes, yeasts and moulds.
Their concentration in the final product sometimes exceeds
10
10
cells per gram and it is generally accepted that most of
them are cultivable in laboratory growth media [6-9]. An
inventory of microorganisms with a history of use in food
fermentations was established recently [10]. It contains 195
bacterial species (30 genera) and 71 yeast and mould
species (35 genera). Among these bacterial species, only 80
(41%) comprise at least one isolate for which a genome
sequence isolated from food is available, with almost half of
them within Lactobacillus species (NCBI database, May
2014). Furthermore, this list cannot be considered as ex-
haustive of the cheese microbial diversity, since occurrences
of species previously undetected in milk and cheese are
* Correspondence: pierre.renault@jouy.inra.fr
1
Institut National de la Recherche Agronomique, UMR 1319 MICALIS, 78352
Jouy-en-Josas, France
2
AgroParisTech, UMR MICALIS, 78352 Jouy-en-Josas, France
Full list of author information is available at the end of the article
© 2014 Almeida et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Almeida et al. BMC Genomics 2014, 15:1101
http://www.biomedcentral.com/1471-2164/15/1101
periodically reported [11-13] and isolates affiliated to novel
taxa characterized [4]. Several species, such as Corynebac-
terium casei,Microbacterium gubbeenense,Arthrobacter
arilaitensis,Arthrobacter bergerei,Agrococcus casei,Myce-
tocola reblochoni and Vibrio casei appear to be endemic in
the cheese habitat and the environment of cheese manu-
facturing [14-17]. Environmental reservoirs of cheese mi-
crobial diversity such as milk, cow teat, human skin, brine
baths, ripening room air, wooden vessels and shelves on
which the cheese rests during ripening, have been identi-
fied, but their microorganism content remains largely
uncharacterised [18,19]. Microbial communities of cheeses
and dairy environments also represent largely unexplored
reservoirs of genetic and metabolic diversity with potential
beneficial use for fermented food production. An increase
in the number of genome sequences of dairy bacteria is also
useful for a better understanding of the genetic determi-
nants involved in the adaptation to the dairy habitat and
the generation of functional properties [20-23].
In recent years, high-throughput sequencing technolo-
gies and information technologies have allowed the devel-
opment of new approaches for studying the genetic
diversity of microbial communities. Among these, metage-
nomics is a powerful tool for assessing the phylogenetic
diversity of complex microbial assemblages present in
samples such as soil, sediment, food products or water
[24] and for exploring the functional properties of their
dominant populations. The characterization of metage-
nomic datasets relies on the use of reference databases that
contain sequences of known origin and phenotype. Many
of these studies are carried out by pyrosequencing of single
target genes, such as 16S rDNA sequences, that provide
information restricted to the phylogenetic composition of
the samples [25-31]. On the other hand, shotgun sequen-
cing of whole community DNA provides additional infor-
mation about the functions performed by the microbial
community [32,33]. The length of the reads generated by
current high throughput sequencing technologies is too
short to allow accurate comparative analyses against dis-
tantly related genomes, thus requiring the availability of
reference genomes closely related to organisms from the
environment being studied. Currently, the international
genome databases are biased towards model organisms
andpathogens,and,accordingtoHusonetal.[34],up
to90%ofthesequencesofametagenomicdatasetmay
remain unidentified due to the lack of adequate refer-
ence sequences. The sequencing of several hundred
genomesisnolongeratechnicalissue.However,this
process still remains costly, mainly due to the cost of
the construction of individual libraries for each genome
being sequenced.
In the present study, we selected and sequenced 142
bacterial strains of dairy origin that belong to 137 differ-
ent species and 67 genera. In order to exemplify the
relevance of these new genomes to the understanding of
food microbiota, we used the newly created catalog to
analyse the microbiota of three cheese surfaces sequenced
through whole metagenomic sequencing.
Results
Creation of a dairy reference genome catalog
After bibliographic investigation for bacterial species oc-
curring in dairy products, we collected 142 dairy bacteria
of various origins. The origin of the isolates and their tax-
onomy are shown in Additional file 1: Table S1. The col-
lection comprised 36% Gram negative bacteria, 35% low
GC Gram positive bacteria and 29% high GC Gram posi-
tive bacteria. Among the 67 corresponding genera, four
are genera for which no genome sequence was available in
NCBI databases (May 2014 release): Kluyvera,Luteococcus,
Marinilactibacillus,andMycetocola. The distribution of
the strains according to the type of dairy product and their
geographic origin is shown in Figure 1.
We designed a low-cost library sequencing strategy, by
pooling the bacterial genomes together in a controlled way
prior to sequencing in order to reduce library construction
costs. Unlike other approaches that rely on barcoding,
we de-convolve the individual genomes by using a co-
abundance approach as described in Nielsen et al. [35] and
Le Chatelier et al. [36] (see Additional file 2 supplementary
document for an example of the clustering procedure).
This strategy involves a two-step procedure, as follows. In
the first step, the DNA of the 142 strains was mixed in five
pools of about 30 strains each. To reduce the risk of chi-
meras each pool contained a mix of genomes from diver-
gent genera (see Additional file 3: Table S2). The pools
were sequenced using Illumina paired-end sequencing,
and then assembled to produce five separate collections of
contigs. In a second step, the genomes were redistributed
into six pools of ~90 strains each, which were sequenced
using SOLiD sequencing, and the resulting reads were
mapped to the contigs generated in the first step in order
to estimate the coverage of each contig within the stage 2
pools. We used SOLiD sequencing due to the availability
of this platform at our institution and the lower cost of se-
quencing. However, other low-cost approaches for estimat-
ing the coverage of stage 1 contigs within the stage 2 pools
could be used, such as, e.g., short runs on Illumina instru-
ments. The resulting contig coverage matrix was then used
to cluster together the contigs with correlated coverage
profiles, each cluster corresponding to one of the original
strains (see co-abundance clustering method section). The
stage 1 pool assemblies contained each less than 20,000
contigs with a mean contig size of 40 kbp (see Additional
file 3: Table S2, Illumina assembly pool sheet). After the
clustering procedure, more than 80% of the Illumina
contigs comprising more than 96% of the total length of
contigs could be attributed to individual strains.
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We assessed the quality of the clustering procedure by
mapping the Illumina contigs to the closest NCBI genome
sequences (using BLASTN [37], identity threshold > =90%).
Furthermore, one organism in our collection - Arthrobacter
arilaitensis Re117 –had already been sequenced and was
added to one sequencing pool in order to validate the clus-
tering procedure. We evaluated the correctness of contig
clusters by computing two measures: the dominant genus -
the percentage of the contigs that could be mapped to
related genomes belonging to the same genus as the organ-
ism represented by the cluster; and the reference coverage -
percentage of the total contig size of the pool that could be
mapped to a genome from the dominant genus. We
performedthisanalysisforthe53draftgenomesforwhich
we could identify at least five genomes belonging to the
same genus in the NCBI database. For these genomes,
the mean dominant genus assignment and reference cover-
age percentage were of 97.7% and 89.5%, respectively (see
Additional file 4: Table S3). The dominant genus assign-
ment indicated that only 2.3% of the total length of the
genomes may have been mis-assigned by the clustering
procedure. The reference coverage indicated that on aver-
age 10.5% of the length of each genome may be missing.
The potentially missing information is likely present in the
4% of the fragments that were not assigned and contain
mostly repeated sequences. We performed an optimized
re-assembly procedure for each draft genome, in order to
increase contig size and recover eventual missing parts of
the genomes (see Methods section). Interestingly, the opti-
mized re-assembly process halved the number of contigs
and increased slightly the total contig size of genomes for
which close references were available, such as genomes of
the genera Leuconostoc and Streptococcus.Inordertofur-
ther assess the quality of the draft genomes (including those
without near-neighbors in public databases), we relied
on the six quality submission criteria established by the
Human Microbiome Project (HMP) [38], plus two add-
itional criteria that identify potential miss-assignment
events during the clustering step: phylogenetic marker re-
dundancy and tetranucleotide homogeneity. For the HMP
draft genome quality criteria, 5 criteria correspond to con-
tig and scaffold assembly length and coverage (see Methods
section). The last HMP quality criteria checks the presence
of 99 bacterial essential genes [39], which gives an indica-
tion of the proportion of the genome that has been assem-
bled (see Method section for the threshold used and
supplementary information for the additional criteria). The
phylogenetic marker redundancy tests the redundancy of
40 protein markers expected to be conserved in all bacteria,
not laterally transferable and not duplicated within a gen-
ome [40]. The tetranucleotide homogeneity tests the homo-
geneity of the tetranucleotide signature among all the
contigs of a draft genome. 101 genomes passed the essential
genes HMP criterion and the two additional criteria for
Figure 1 Origin of the 142 selected dairy bacterial isolates in function of the type of dairy product (A) and the geographic area (B).
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mis-assignment detection, indicating that 101 genomes are
almost complete with no mis-assignment evidence (see
Additional file 4: Table S3, draft quality evaluation sheet).
Among these, 72 passed all the quality criteria and were de-
fined as high quality draft genomes. An example is the gen-
ome of Jeotgalicoccus psychrophilus CRBM D2, which was
assembled in only 70 contigs and 20 scaffolds, and had a
contig N50 size of 103 kb. Sixteen additional assemblies
presented incomplete sets of the HMP essential genes cri-
terion but passed the two chimeric test criteria. For the 25
remaining draft genomes, 17 were too incomplete (<1 Mb
in contig cumulative size) and 8 did not pass one of the
two chimeric criteria. The genome of Arthrobacter arilai-
tensis Re117 [20], which was used in pool 1 as control for
the procedure, passed all the HMP and chimeric criteria,
and a comparison with the previously sequenced genome
(Genbank project PRJNA53509) showed an average iden-
tity of 99.98%, a completion level of 95.63% and the ab-
sence of improperly assigned contigs. The two plasmids
present in this bacterium were also partially present in our
draft. The missing sequences corresponded mainly to
transposase regions which were not assembled possibly
due to the assembly and clustering procedure which often
has difficulties reconstructing repeated variable regions. In
total 117 of the draft genome sequences (101 which
passed all quality controls and an Additional 16 that had
no evidence of chimeric contigs) were considered suitable
for submission to public databases (see Additional file 1:
Table S1). The 25 draft genomes with poor quality or pos-
sible contamination were not submitted to public data-
bases, but were used in our project with caution for
phylogenetic analyses.
From the 195 bacterial species or subspecies listed by
Bourdichon et al. [10] to occur in food products, only 80
had at least one food isolate for which a genome se-
quence was available in the NCBI database (NCBI May
2014 release, see Additional file 5: Table S4). The present
study provides genome sequences for 78 additional dairy
isolates, which effectively doubles the number of avail-
able genome sequences of relevance to the study of
fermented dairy products.
In order to better characterize the diversity of the bacter-
ial strains studied here, we reconstructed their phylogenetic
relationships. For that purpose, the genes corresponding to
the 40 phylogenetic protein markers proposed by Mende
et al. [40] were extracted from the 117 high quality draft
genomes in order to build a phylogenetic tree (Figure 2).
The tree shows that the selected bacterial isolates cover a
large biodiversity. Four other trees were constructed (see
Additional file 6: Figure S1, Additional file 7: Figure S2,
Additional file 8: Figure S3 and Additional file 9: Figure S4)
by inclusion of 328 genomic sequences from food-
related bacteria or closely-related species (Bacteroidetes,
Firmicutes, Actinobacteria and Proteobacteria) extracted
from the NCBI database (see Additional file 10: Table S5,
genome references for phylogeny sheet). The classifica-
tions of the genomes sequenced in the present study are
consistent with the NCBI reference genomes, further
confirming the correctness of our reconstruction. In some
cases, for example for Alkalibacterium kapii,Marinilacti-
bacillus psychrotolerans and Luteococcus japonicus, only
distant NCBI reference genomes are available, highlighting
the contribution of our study.
Genomics of cheese bacteria
The high quality draft sequences can be used to perform
comparative genomic studies aimed at understanding the
genetic underpinnings of the adaptation of bacteria to the
food environment, e.g., through the characterization of
metabolic pathways. Here we compared the genomes of
strains from dairy and non-dairy environments for two
genera. In a first example, we compared the genomic
sequences of the four Arthrobacter strains isolated from
cheese to that of 15 environmental isolates. Most bacteria
of the genus Arthrobacter are isolated from environments
such as soil, where they are considered to be ubiquitous
[41]. Interestingly, the four cheese strains share several
properties that may be linked to adaptation to the cheese
habitat, such as a cluster of five genes involved in the ca-
tabolism of D-galactonate, as already described in Arthro-
bacter arilaitensis Re117 [20]. This gene cluster is absent
from the genomes of the 15 Arthrobacter strains of envir-
onmental origin for which a sequence is available (see
Additional file 11: Table S6). It has been hypothesized that
D-galactonate may be produced by yeasts from lactose
during the ripening of cheeses, and the ability to catabolize
this compound could thus be beneficial for Arthrobacter
strains in cheeses [20].
As a second example, we compared the genomes of two
strains of Streptococcus infantarius isolated from Western
African fermented milks, sequenced in this work (3AG
and 11FA), with those of the type strain isolated from in-
fant feces (ATCC BAA-102), and of strain CJ18, isolated
from Eastern African fermented milk. The four strains
contain each 1900–2000 genes and share 1567 genes. The
strains could be divided into two groups, the two Western
African strains, which displayed 99.7% identity on average
within the shared genes (including 1206 fully identical
genes), and the infant feces and the Eastern African
strains, which displayed 99.3% identity (including 485 fully
identical genes) (see Additional file 12: Table S7). The
strains of the two different groups displayed only 98.8%
identity and fewer than 185 fully identical genes, confirm-
ing a clear separation between the Western African food
strain and the two others. Further study of the gene content
of the two Western African Streptococcus infantarius
strains showed that these strains had acquired the ability to
ferment lactose through the LacZS system, as previously
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described for Eastern African strain CJ18 [42]. However,
these genes are located within different regions of the
chromosome and originate from a different donor. While
lacZS may originate from S. thermophilus in the Eastern
African CJ18 strain [43], it has probably been acquired
from S. salivarius in the two Western Africa strains
(see Additional file 13: Figure S5). These data show that
adaptation of S. infantarius to the dairy fermentation
niches occurred convergently and independently in these
strains isolated respectively in Eastern and Western Africa.
Application of the new genomic catalog to the
metagenomic analysis of cheese microbiota
Metagenomic analyses based on sequence mapping on a
set of reference genomes can be used to identify and
quantify genes and species [36]. To determine whether
Figure 2 Global phylogeny of the 117 dairy bacterial isolates sequenced in the present study. The phylogenetic tree is an ITOL circular
visualization [68] with the branch length and the bootstrap values displayed. The tree is based on a concatenated alignment of 40 universal
marker protein families [40]. Only genome sequences from which a minimum of 10 markers could be extracted and which had no contaminating
sequences evidence were considered. The genome of Methanobrevibacter smithii ATCC35061 was used to root the tree. The colors correspond to
the different phyla.
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the addition of the new genome sequences to the 5873
publicly available genomes (bacteria, archaea, yeasts and
moulds) could improve such analyses, we sequenced the
microbial communities from the rind of three cheeses
with protected designation of origin. These three cheeses
were made from cow’s milk and correspond to two soft
smear-ripened cheeses (E and L), and one blue-veined
cheese (G). Extracted DNA was sequenced by SOLiD
technology and reads were assigned to species by map-
ping them to the reference microbial genomes and to
the genome of Bos taurus (see Methods section).
The sequencing of the three samples provided from 8.4
to 15.5 million good quality reads (see Additional file 14:
Table S8). The percentage of good quality reads mapping
to the microbial reference genomes varied from 46.1%
(cheese L) to 57.1% (cheese E) (Figure 3). Interestingly, the
reads that mapped only to the dairy genomes sequenced
in the present study accounted for a large proportion of
the good quality reads (from 16.7% for cheese L to 23.7%
for cheese G). In cheese G, 11.2% of the reads mapped to
the Bos taurus genome (compared to 0.5% and 0.1% for
cheeses L and E, respectively). A deeper investigation of the
cheese E good quality unmapped reads indicated that they
may correspond to (i) strain specific genes belonging to the
pan-genome (including prophages and mobile elements)
and/or missing regions of the draft genomes, (ii) microor-
ganisms for which genomes are still absent from the data-
bases, (iii) distant genomic regions containing indels or
more than 3 mismatches, which cannot be mapped with
Bowtie. Lastly, ~20% of “technically good reads”on average
are inherently un-mappable due to the characteristics of
the SOLiD technology (this number is estimated from an
analysis of the unmapped good quality read percentage in
five different re-sequencing projects using the same
sequencing and mapping techniques as in our paper, see
Additional file 15: Table S9). The most prevalent micro-
organisms detected in the three cheeses are presented
in Table 1 and a more detailed composition is shown in
Additional file 14: Table S8.
In the smear-ripened cheese E, the Arthrobacter arilai-
tensis GMPA29 reference genome was the most repre-
sented, as it corresponded to 19.8% of the total good
Figure 3 Mapping of the good quality reads from the metagenomic sequencing of DNA from the surfaces of three cheeses. The good
quality reads coming from 3 samples of cheese surface were aligned to 5873 genomes coming from NCBI and 117 genomes coming from our
project. The repartition of the good quality reads that map only on the NCBI genomes (blue), on the genome sequenced in our project (green),
on both NCBI and our genome (light green) and on Bos taurus genome (orange) is presented in pie charts. The unmapped good reads are
presented in dark and light grey, respectively those lacking a reference and those potentially unmappable for technical reason.
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Table 1 Most prevalent microorganisms detected by metagenomic sequencing of three cheese surface samples
Reference genome New
(a)
Commercial
cultures (b)
Number of
reads (c)
Number of
CDS (d)
Cumulated
CDS length
Covered
CDS (%) (e)
Covered
sequence length
(%) (f)
Mean
genome
coverage
Mapped
reads (%) (g)
Sequences covered
by perfect match
reads (%) (h)
Cheese E Arthrobacter arilaitensis GMPA29 11 2919327 3299 2875518 95.4 93.1 34.64 19.80 99.5
Psychrobacter immobilis PG1 10 1259254 2941 2824830 99.4 94.3 15.48 8.54 99.9
Vibrio litoralis B4 10 768119 3353 3158767 95.6 82.2 8.36 5.21 92.3
Pseudoalteromonas haloplanktis
TAC125
00 384444 3454 3327182 94.1 70.4 4.00 2.61 93.1
Geotrichum candidum CLIB 918 01 378769 6925 10213537 98.3 38.6 1.30 2.57 95.5
Halomonas sp. 1M45 10 221008 3526 3281784 95.6 77.1 2.31 1.50 93.5
Lactococcus lactis subsp. lactis
Il1403
01 105179 2088 1869362 95.3 54.9 1.80 0.71 96.5
Debaryomyces hansenii CBS767 01 77071 6295 9107395 91.7 16.6 0.30 1.27 85.6
Cheese L Pseudoalteromonas haloplanktis
TAC125
00 1434355 3454 3327182 93.7 89.5 14.95 17.03 97.6
Halomonas sp. 1M45 10 883652 3526 3281784 100.0 99.3 9.16 10.49 99.97
Psychrobacter celer 91 10 146974 2584 2414775 95.6 76.5 2.08 1.74 91.5
Lactococcus lactis subsp. cremoris
A76
01 143400 2470 1969568 92.5 35.4 0.82 1.70 98.1
Penicillium camemberti FM 013 01 83277 14611 22234696 85.1 10.8 0.13 0.99 97.7
Vibrio litoralis B4 10 65937 3353 3158767 90.0 27.6 0.70 0.78 85.9
Providencia heimbachae GR4 10 53280 3824 3516737 83.6 26.7 0.51 0.63 55.3
Geotrichum candidum CLIB 918 01 45165 6925 10213537 95.0 12.7 0.15 0.54 95
Cheese G Arthrobacter bergerei Ca106 11 2878361 3553 3110335 98.6 95.7 31.77 18.58 99.2
Lactobacillus delbrueckii subsp.
bulgaricus ATCC11842
01 1179878 1508 1340406 98.7 95.4 29.44 7.62 99.8
Penicillium camemberti FM 013 01 633123 14611 22234696 99.4 54.2 1.00 4.09 96.5
Streptococcus thermophilus LMG
18311
01 597916 1827 1462709 98.3 86.1 13.62 3.86 99.4
Penicillium roqueforti FM 164 01 254435 12630 23447373 98.5 26.3 0.38 1.64 98.4
Pseudoalteromonas haloplanktis
TAC125
00 146457 3454 3327182 93.5 57.8 1.53 0.95 92.3
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Table 1 Most prevalent microorganisms detected by metagenomic sequencing of three cheese surface samples (Continued)
Debaryomyces hansenii CBS767 01 80179 6295 9107395 94.2 17.6 0.31 0.52 97.1
Psychrobacter aquimaris ER15 174
BHI7
10 74153 2830 2734881 93.7 41.7 0.87 0.48 81.5
(a) Genomes sequenced in the present study (1) or from the NCBI database (0).
(b) Species known to be components of cheese making commercial cultures.
(c) Number of reads mapped on CDS from the reference genome with three or less mismatches on 35 nucleotides.
(d) Number of CDS in the genome. CDS corresponding to insertion sequences, prophages and potential repeated and transferable elements were removed.
(e) Percentage of CDS covered with at least one read.
(f) Percentage of sequence covered by at least one read (sequence is restrained to the selected CDS).
(g) Number of reads aligned with this genome divided by the number of good quality reads.
(h) Length of sequence covered by perfect match reads (with no mismatch on the 35 nt length alignment) divided by the length of the sequence covered by reads.
For each cheese, the data presented correspond to the eight reference genomes with the highest numbers of mapped reads.
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quality reads, followed by Psychrobacter immobilis PG1
and Vibrio litoralis B4, with 8.5 and 5.2% of the reads,
respectively. Furthermore, between 95.4 and 99.4% of
their coding sequences were detected, with a high level
of coverage (from 8.4X to 34.6X). The genomes of
Arthrobacter arilaitensis GMPA29 and Psychrobacter
immobilis PG1 are almost entirely covered (from 99.5 to
99.9% of the reference size), showing that the detected
strains were closely related to the reference strains. The
reference strain Psychrobacter immobilis PG1 was isolated
from the dairy plant that produces the smear-ripened
cheese E, but two years earlier. The high proportion of
perfect matches with reference strain PG1 may thus be
explained by the presence of an offspring of this strain in
cheese E. Many reads were also assigned to the genomes of
the yeasts Geotrichum candidum CLIB 918 and Debaryo-
myces hansenii CBS767.
In the second smear-ripened cheese (cheese L), Pseudoal-
teromonas haloplanktis TAC125, Halomonas sp. 1 M45
and Psychrobacter celer 91 were the three dominant refer-
ence bacteria, with 17.0%, 10.5% and 1.7% of the good qual-
ity matches, respectively. In this cheese sample, the
sequences of the reads mapped to Halomonas sp. 1 M45
were essentially perfect matches (>99.9% of covered posi-
tions with 99.9% perfect match reads, coverage of 9.2X).
These data suggest that the Halomonas strains present in
the cheese sample are almost identical to the reference
strain.However,eventhoughthereferencestrain1M45
has also been isolated from a smear-ripened cheese of the
same protected designation of origin, it originated from an-
other manufacturing plant. More than fifty thousand reads
were assigned to Providencia heimbachae GR4. However,
only 55.3% of covered positions of this reference strain are
without mismatch, which indicates that the strain present
in the cheese sample is not closely related to the reference
strain, and may even correspond to another species. Sur-
prisingly, more than 80 thousand reads (~1% of the total
good quality reads in cheese L) mapped to the Penicillium
camemberti FM 013 genome, with 97.7% of perfect match
reads, even though this species is not known to occur in
smear-ripened cheeses. One may hypothesize that this
could result from cross-contamination due to the manufac-
turing of mould-ripened cheese in the same plant.
The surface of the blue-veined cheese G was dominated
by a strain close to Arthrobacter bergerei Ca106 (18.6% of
the reads, 99.2% perfect match reads). Like for the two
other cheeses, Psychrobacter species seem to be present in
this cheese. Cheese G was probably manufactured with a
thermophilic lactic starter culture, since Streptococcus ther-
mophilus and Lactobacillus delbrueckii species were the
dominant lactic acid bacteria, in contrast to the two other
cheeses, in which Lactococcus lactis was the dominant lac-
tic acid bacterium. Strains related to other reference strains
sequenced in the present study, such as Psychrobacter
aquimaris,Brachybacterium tyrofermentans,Corynebac-
terium ammoniagenes,Brevibacterium antiquum,Micro-
bacterium gubbeenense,Brochothrix thermosphacta and
Marinilactibacillus psychrotolerans,werealsopresentin
the cheeses (>80% perfect match reads, see Additional
file 14: Table S8).Interestingly, among the eight most
prevalent microorganisms detected in each cheese by
metagenomic analysis, two (cheese G), four (cheese E) or
five (cheese L) corresponded to species or genera of gram-
negative bacteria which are not known components of
cheesemaking commercial cultures (Table 1).
Discussion
In the present work, we produced 137 draft genomes iso-
lated from dairy products, which almost doubled the num-
ber of different species isolated from fermented dairy
products. This genome catalog was realized using a low-
cost library sequencing strategy based on a combinatorial
pooling approach in which a reduced number of DNA
pools were sequenced. Pooling strategies have been previ-
ously been used to reduce costs for BAC sequencing [44].
Here we rely on a co-abundance clustering approach that
we developed for reconstructing genomes directly from
metagenomic samples [35]. In the present pooled ap-
proach, only 11 libraries were required to produce 150
draft genomes, leading to a cost of ~200 USD per genome
as opposed to ~500 USD if each genome were sequenced
separately (using best commercial offers available in 2011).
Today, this price differential may be even higher as library
construction costs have not decreased as much as sequen-
cing costs. This cost savings comes with some limitations.
First, we suggest that only distant genomes (i.e. from
different genera at least) should be mixed and sequenced
together to optimize the assembly and clustering steps.
Second, several genomes were poorly sequenced, however
most of them were high GC% draft genome, known to be
difficult to sequence using Illumina sequencing [45]. Also,
about 4% of the total sequence length could not be
assigned to an individual strain and we estimated that on
average 2% of a genome’s sequence may be mis-assigned
due to limitations of the clustering approach. However, an
examination of unassigned fragments > 2 kb showed that
they correspond mainly to mobile elements (plasmids and
phages) while genome data curation showed that poten-
tially mis-assigned fragments are generally < 1 kb, and pri-
marily impact genomes of lower quality. This fact prompts
us to suggest that the use of these drafts for comparative
genomics should be restricted to high quality draft
genomes and to genes present in long contigs or scaffolds
(i.e. bigger than 1 kb). Lastly, the bioinformatics analysis
pipeline is more complex to set up than simple assembly
procedure in single genome sequencing. Despite these
limitations, 117 of the 142 sequenced genomes resulted in
good or high quality draft genomes suitable for submission
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to public database. Some of the remaining 25 genomes
may still be useful as references for metagenomic analyses.
The microbial composition of the surfaces of three
cheeses was investigated by high throughput metagenomic
profiling. As all the DNA present in the cheese samples is
sequenced, the high throughput sequencing may detect
any type of DNA (bacteria, archaea, eukaryotes and
viruses), provided that adequate references are used.
Eukaryotes, such as Geotrichum candidum,Debaryomyces
hansenii,andPenicillium roqueforti,werefound,which
was not surprising, as these fungi are frequently used in
cheesemaking. Interestingly, many reads from cheese G
mapped on the Bos taurus genome. We hypothesize that
this is due to the presence of cow somatic cells in the milk
used for the manufacturing of cheese G. The impact of
milk somatic cells on the ripening of cheeses has been
shown in several studies as associated to the flock
health [46,47].
Shotgun sequencing allows a relative quantification of
DNA molecules present in a sample, based on counting
the number of reads mapped to each member of the
community. High throughput sequencing allows higher
resolution quantitation and we have shown that we can
recover even fairly minor taxonomi groups, such as the
Leuconostoc genus (see Additional file 14: Table S8),
known to be part of the minority population in cheeses
[48]. However, additional experiments may be needed to
validate the identification and quantitation of low abun-
dance populations.
Presence or absence of complete set of genes or of
specific genes, and their level of sequence homology al-
lows also confirming characteristics of particular strains.
For example, cheeses E and L metagenomic profiles indi-
cated the presence of strains closely related, respectively,
to Psychrobacter immobilis PG1 and Halomonas sp.
1 M45 coming from our catalog. Since these reference
strains were isolated in the same cheese factory several
years earlier for the former and in the same DOP from
another factory for the later, our analysis would reflect
the setting up of strains sharing common origins with
the references in these cheeses. Metagenomic profiling
provides thus new perspectives to study cheese ecology
by tracing genomes or genes, which should allow point-
ing out particular strains (e.g. starters, potential “terroir”
or regional strains, contaminants…), and following their
dissemination and development during cheese processes.
The metagenomic profiling of the surfaces of the
three cheeses confirmed that microorganisms that are
not deliberately inoculated constitute a large part of the
microbiota, appearing among the few dominant species
in cheese rind. For example, they are predominant in
cheese L. Several of the corresponding microorganisms,
such as Pseudoalteromonas,Halomonas, Vibrio, Marini-
lactibacillus and Psychrobacter are Gram-negative bacteria
which had been previously detected in such cheeses
[1,2,9,12,13,28,49-53], and also in a recent large amplicon
sequencing study of the microbial composition of 137 dif-
ferent cheese rinds [33]. They may originate from the en-
vironment of cheese manufacturing (brine, tools, surfaces
of shelves …), and their high abundance suggests that they
may have an impact on the properties of the final product.
As the analysed cheeses were marketed and were of very
good quality, these microorganisms cannot be considered
here as spoilers.
Conclusion
The genomes sequenced in the present study considerably
increased the numbers of mapped reads, although a
significant proportion of the metagenomic reads remained
unassigned (~20% once taken into account unmapped
reads inherent to SOLiD technology). These data indicate
that even if more than 6000 genomes are currently avail-
able in public databases (including the genomes we gener-
ated here), additional microorganisms found in traditional
cheeses are still missing from this reference collection.
Further studies are necessary to complete this reference in
order to provide a complete view of the cheese ecosystem.
To our surprise, collecting the present reference strain set
constituted a laborious work, since strains corresponding
to non-starter species are frequently not conserved once
described. We anticipate that the results of this work will
motivate isolation and conservation of new reference
strains, as well as independent isolates of the same species
to support safety assessment, establish biodiversity re-
source and strain specificity in products. Direct sequencing
and assembly as performed for the human microbiome
could also provide new potential references [36], although
this procedure is significantly more expensive. In summary,
the present study considerably extended the effectiveness
of shotgun metagenomic analysis of cheese microbiota.
Even if such analyses require generating and computing
large amounts of sequencing data, the technologies are
evolving rapidly and one may anticipate that in the future,
they will become routine in the investigation of food
microbiota.
Methods
Bacterial isolates and growth conditions
The bacterial isolates working collection was composed
of 142 isolates originating from milk, fermented milks,
and cheeses, and five food isolates that were not of dairy
origin (see Additional file 1: Table S1).
DNA extraction from liquid cultures of bacterial isolates
After cultivation, bacterial cells were harvested by centri-
fugation for 10 min at 12,000 × gand approximately
100 mg of cell pellet were suspended in 400 μl of buffer
(0.4 M NaCl, 2 mM EDTA, 10 mM Tris–HCl, pH 8).
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For gram-positive bacteria, an enzymatic lysis step was
performed by incubating the cells for 1 hour at 37°C after
addition of 50 μl of lysozyme (20 mg/ml) for Actinobac-
teria strains or of 50 μl of lysostaphin (100 Units) for
Staphylococcus strains. One hundred microliters of SDS
(20%) and 40 μl of proteinase K (15 mg/ml) were then
added and the mixture was subsequently incubated for
1 hour at 55°C. One hundred and fifty mg of 0.1 mm-
diameter zirconium beads (Biospec Products, Bartlesville,
OK, USA), 150 mg of 0.5 mm-diameter beads and 500 μl
of phenol/chloroform/isoamylic alcohol (25/24/1; pH 8)
were added to the tube, which was vigorously shaken in a
bead-beater (FastPrep-24 instrument; MP Biomedicals
Europe, Illkirch, France) for 45 s at a speed of 6.0 m/s.
The sample was centrifuged (45 min at 12,000 × g) and
the upper phase was transferred in a Phase Lock Gel-
heavy tube (Eppendorf, Hamburg, Germany) and mixed
with 500 μl of phenol/chloroform/isoamylic alcohol. After
centrifugation (15 min at 12,000 × g), the upper phase was
mixed with 500 μl of chloroform and centrifuged (15 min
at 12,000 × g). DNA was precipitated overnight at −20°C
after addition of 0.1 volume of 3 M sodium acetate and 2
volumes of cold absolute ethanol to the upper phase. After
centrifugation (30 min at 12,000 × g), the DNA pellet was
washed with 70% ethanol and resuspended in 1X TE buf-
fer. Two microliters of RNase solution (10 mg/ml) were
added and the mixture was subsequently incubated for
30 min at 37°C. The concentration and quality of genomic
DNA was evaluated using a NanoDrop ND-1000 spectro-
photometer (NanoDrop Technology Inc., Wilmington,
DE, USA). Moreover DNA (5 μL) was loaded on a 1%
agarose gel and visualized after migration by ethidium
bromide staining.
Sequencing of rrs and rpoB genes from bacterial isolates
The species corresponding to each genome was con-
firmed by sequencing the 16S rRNA or the rpoB gene
(see Additional file 1: Table S1). The rrs gene (encoding
the 16S rRNA) was amplified with primers pA (5′-
AGAGTTTGATCCTGGCTCAG-3′) and pH (5′-AAG
GAGGTGATCCAGCCGCA-3′), as previously described
[54]. Since the rrs gene is not always sufficient to distin-
guish closely related species, especially Enterobacteria-
ceae, the housekeeping gene rpoB (encoding the beta
chain of the DNA-directed RNA polymerase), that has
been shown to resolve phylogenetic relationships in
various bacterial groups [55], was also used. PCR ampli-
fication of rpoB was performed with primers VIC4 (5′-
GGCGAAATGGCDGARAACCA-3′)andVIC6(5′-
GARTCYTCGAAGTGGTAACC-3′)[56].Bothstrands
of the resulting amplicons were sequenced by GATC Bio-
tech (Konstanz, Germany), using the same primers than
for the PCR amplifications. The sequences were then as-
sembled using the CAP3 program [57] and compared to
the GenBank database using the Basic Local Alignment
Search Tool (BLAST) [37] to determine the closest known
relatives of the rrs or rpoB gene sequences.
Genome sequencing
The 147 bacteria DNA samples were distributed in equiva-
lent amounts (0.3 μg) in five metagenomic pools contain-
ing each about 30 genomes. Only different genera were
mixed together in a single pool to improve the assembly
process by reducing the presence of possible identical re-
gions (see the Additional file 2 supplementary information
document). Each pool was sequenced using the Illumina
HiSeq 2000 system, with around 90 million paired-end
reads of 91 nucleotides in length, an insert size of ~350 bp
for pool 1 and ~310 bp for the four other pools. Low qual-
ity reads (with 3 or more “N”), which constituted less than
8% of the total reads, were discarded. The assembly was
performed for the five metagenomic pools independently,
using SOAPdenovo (v1.04) [58]. Kmer size was selected
separately for each pool by evaluating the best contig size,
N50 and N90, and the best percentage of reads re-mapped
to the assembly (see Additional file 3: Table S2, Illumina
assembly sheet). Only contigs of 100 bp in length or more
were kept for further analysis. The draft genomes are avail-
able under the pending BioProject ID PRJEB230 to
PRJEB363 (see Additional file 1: Table S1).
Co-variance clustering
To assign the contigs to their original genomes, we used a
clustering method based on the co-variance principle, de-
rived from method described by Nielsen et al. [35] and Le
Chatelier et al. [59] (see the Additional file 2, supplemen-
tary information). For this purpose, six new DNA pools
were created by mixing genomic DNA so that each DNA
sample has a different combination in the pools, and
therefore each genome has a unique presence signature
(see Additional file 3: Table S2, composition pool sheet).
The six new DNA pools were sequenced using SOLiD
technology 4, with around 90 million single reads in each
pool of 50 nucleotide length. The SOLiD reads were
aligned to the five Illumina contigs using the Bowtie
aligner [60] (see Additional file 3: Table S2, SOLiD map-
ping data sheet). This resulted in the creation of five inde-
pendent contig coverage matrix, used for the clustering
process. This alignment allows calculating a coverage
vector for each contig, corresponding to the presence and
absence of the contig in the 6 combinatorial pools. The
contig coverage vectors are compared to the strain pres-
ence and absence in the 6 combinatorial pools, using the
Pearson correlation coefficient. The contigs are assigned
to the strain with which they share the highest Pearson
correlation, as long as the correlation value is equal to or
higher than 0.95. The Illumina and SOLiD samples are
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accessible under the project ID PRJEB6314 at the SRA
database.
Genes and contigs taxonomical annotation
All contigs were taxonomically annotated by sequence
similarity using BLASTN to a database containing 1411
reference genomes (extracted from NCBI database, June
2012 version). Sequence similarity > =90% on at least 100
nucleotide was used for genus level annotation. This taxo-
nomical assignment was used to calculate the dominant
genus assignment and the reference coverage of the clus-
tering, by considering the 53 strains with at least 5 different
species present in the NCBI database. This restriction was
made in order to reduce the likelihood of incorrect assign-
mentsbytheBLASTNprocedure(seeAdditionalfile4:
Table S3, cluster BLASTN assignation sheet). The un-
assigned contigs were considered as part of the dominant
genus assignment, to differentiate them from the contigs
assigned to a different genus which represent a potential
mis-assignment from the clustering step. The 11 clusters
with less than 500 kb in total contig size were not con-
sidered for the dominant genus assignment and refer-
ence coverage due to their low quality and high level of
fragmentation.
Optimize re-assembly of draft genomes and quality
evaluation
To increase the size of contigs and scaffolds, we per-
formed an optimized de novo re-assembly procedure after
the clustering procedure. The contig pools were used to
recruit reads from their Illumina HiSeq 2000 sequencing
pool, by alignment to the contigs using BWA [61]. The re-
cruited reads were corrected using Quake [62]. A new de
novo assembly was performed for each cluster using only
the reads that were remapped on them, using Velvet [63].
Scaffold gaps were filled using SOAPdenovo GAPCloser
[58]. When a close species reference genome was available
in the NCBI database, we used it for an assisted assembly
procedure. This procedure used the NCBI contigs in com-
bination with the cluster contigs to recruit the reads. This
may recover the missing regions that were lost during the
first assembly and clustering procedures. Reconstructed
genome drafts were classified from low to high quality by
using the Human Microbiome Project assembly criteria
[64]. The threshold used for each HMP criteria validation
are: (1) contig N90 > = 500 bp; (2) 90% or more of the 99
bacterial essential genes are found; (3) 90% or more of the
bases in the assembly have more than 5 fold sequence
coverage; (4) contig N50 > = 5 kb; (5) scaffold N50 > =
20 kb; (6) average contig length > = 5 kb. Two other add-
itional criteria for chimera detection were computed: the
tetranucleotide homogeneity score and function redun-
dancy (see Additional file 4: Table S3, draft genome quality
sheet; and the supplementary information document). The
tetranucleotide homogeneity was calculated by counting
the number of different tetranucleotides in all contigs, di-
vided by the size of the contigs, to produce a tetranucleo-
tide frequency vector. Each tetranucleotide frequency
vectors were compared using the Spearman rho correl-
ation, and the mean rho correlation value of all pairwise
Spearman correlation comparison was calculated. The
draft were considered as potentially contaminated by an-
other draft when the mean Spearman rho was lower than
0.6. The 40 marker protein redundancy was calculated by
first searching for the marker protein in the draft as de-
scribed in the Genome annotation and phylogeny annota-
tion procedure section. Then each protein detected at
least twice was listed in all drafts. A draft was considered
as chimeric if 3 or more markers were redundant.
Genome annotation and phylogeny annotation procedure
RAST (Rapid Annotation using Subsystem Technology)
was used for annotating the genome drafts in the SEED
environment [65]. Genomic drafts passing the core-ratio
HMP criterion and 328 NCBI reference genomes were
used for phylogenic classification (see Additional file 10:
Table S5, genome references phylogeny sheet). For each
draft, 40 markers commonly used for phylogeny classifi-
cation and corresponding to 40 essential proteins [40],
were detected using BLASTP procedure and a marker
reference database of about 1500 complete genomes.
The best hit was selected with at least 50% identity and
50% coverage. Each marker protein was aligned to refer-
ence markers using MUSCLE [66] and the 40 individual
alignments were concatenated to a single alignment.
The missing markers were replaced by gapped lines, not
used for the distance calculation. Only the drafts with at
least 10 markers out of the 40 described above were
selected for the tree. The tree was constructed using
FastTree [67] with the parameters: −gamma -pseudo -spr
4 -mlacc 3 -slownni. The visualization was done using
ITOL [68], in circular mode view and branch length
displayed mode.
Extraction of DNA from cheese samples
For each of the three types of cheeses, five pieces of rind
(8 cm
2
; mean thickness: ~5 mm) were taken using a circu-
lar punch (3.2 cm in diameter) and a knife, mixed together,
and cut in small pieces. Twenty grams of cheese rind were
then mixed with 20 ml of guanidium thiocyanate (4 M) in
Tris–HCl (pH 7.5, 0.1 M) and dispersed with a mechanical
blender (Ultra-Turrax® model T25; Ika Labortechnik,
Staufen, Germany) for 3 min at 14,000 rpm. After adding
2.4 ml of sodium laurylsarcosinate (100 g/l) and gentle
mixing for 1 min, five 1.9 ml-aliquots were added to five 2-
ml tubes containing 350 mg of zirconium beads (0.1-mm
diameter; Sigma, St-Quentin-Fallavier, France) and the
tubes were centrifuged for 10 min at 20,800 gand 4°C.
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The supernatants, which included the fat layer, were re-
moved, and the tubes were frozen at −20°C. After thawing,
600 μl of Tris-EDTA buffer (pH 8.0; 100 mM Tris,
10 mM EDTA), 40 μL of proteinase K (15 mg/ml;
Sigma, St Quentin Fallavier, France) and 100 μlofso-
dium dodecyl sulfate (200 g/l) were added to the tubes,
which were then vortexed for 2 min, and subsequently
incubated for 2 h at 55°C. After cooling on ice, the
tubes were vigorously shaken for 40 s in a bead beater
(FastPrep®-2 System; MP Biomedicals, Illkirch, France)
at a speed of 4.0 m/s. 700 μl of phenol were then added
and the content of the tubes was gently mixed for
1 min. The tubes were centrifuged for 5 min at 20,800 g
and 20°C and the aqueous phases were transferred to
2-ml tubes containing a gel that improves separation
between the aqueous and organic phases (Phase Lock
Gel™Heavy; Eppendorf, Germany). After adding 700 μl
of phenol-chloroform-isoamyl alcohol (25:24:1; saturated
with 10 mM Tris, pH 8.0, 1 mM EDTA) and gentle mixing
for 1 min, another centrifugation was performed for 5 min
at 20,800 gand 20°C and the aqueous phases were trans-
ferred in new Phase Lock Gel™tubes. Another extraction
with 700 μl of phenol-chloroform-isoamyl alcohol was
then performed, and the aqueous phases were recovered in
a 2-ml centrifugation tube, mixed with 5 μlofRNaseA
(20 mg/ml, Sigma), and incubated for 1 h at 37°C. The
DNA was then precipitated by adding 1 ml of isopropanol
and 50 μl of sodium chloride (5 M) and incubating the
tubes overnight at −20°C. The DNA was recovered by cen-
trifugation for 10 min at 20,800 gand 4°C, and the pellets
were subsequently washed three times with 1 ml of 70%
(vol/vol) ethanol. They were then dried for 30 min at 42°C
and dissolved in 50 μl of water, after which the five samples
corresponding to the same cheese were pooled together.
Metagenomic sample mapping
Three DNA samples from cheeses were sequenced using
SOLiD technology, which yielded between 11 and 19
million single reads of 50 nucleotides length. The identi-
fication of species was done in two steps: a first mapping
was done on a catalog reference of 5990 genomes, which
included the 117 draft sequenced in the present study,
and a second mapping with more detailed analyses on a
selection of genomes (see Additional file 10: Table S5,
genomes metagenomic analyse sheet). The first mapping
was performed using Bowtie aligner [60] (with parame-
ters: first 35 nucleotides mapped; 3 mismatches allowed;
10000 matches by read allowed), on the whole database.
This mapping allowed a first selection of genomes.
Genomes with no annotation were cut into fragments of
1000 bases. Genomes with less than 20% of genes (or
less than 20% of fragments) covered by reads were re-
moved. For species expected to be in food microbiota,
several reference genomes were chosen. Otherwise, one
genome was selected for each species. When possible,
genomes with annotations were selected in priority. The
samples were then mapped against the selected genomes
(59 for cheese E, 86 for cheese L, 67 for cheese G; see
Additional file 10: Table S5, sheet genomes metagenomic
analysis) with Bowtie aligner (same parameters, and the –
best –strata option). A first analysis was done to explain
the unmapped reads. The distribution of the mean quality
of the reads for the cheese sample was evaluated for the
mapped reads (see Additional file 16: Figure S6). The
mean quality of the mapped reads was higher than 20 for
more than 95% of the reads. Considering this, reads with a
mean quality under the value of 20 were considered as
“bad quality reads”and not considered for the metage-
nomic analyses. Unmapped good quality reads were then
mapped on Bos taurus genomes with Bowtie (same pa-
rameters). Finally, an analysis of the unmapped good qual-
ity read percentage was performed using the same SOLiD
technology (SOLiD v4) and the same read mapper (Bow-
tie) than the cheese analysis with a set of five bacterial
genome re-sequencing projects (see Additional file 15:
Table S9). While in this experiment 100% of the good
quality reads should have mapped with their reference
genome, only 80% of the good quality reads did, possibly
due to sequencing errors in the reads.
Finally, genomes present in samples were identified
from the second mapping results. Genomic regions that
were less informative and/or that could have been ac-
quired by gene transfer (intergenic regions, tRNA, rRNA,
genes annotated as “transposase”,“integrase”,“IS”,“phage/
prophage”or “plasmids”) were removed. We then quanti-
fied the percentage of the genome covered by at least one
read, and the percentage of the genes covered by at least
one read. A first filter was done by removing genomes to
which less than 700 reads mapped and genomes with less
than 20% of coding DNA sequences covered by at least
one read. In order to estimate if the reads were evenly
mapped across the genome, we computed the average
number of reads per coding DNA sequences. (number of
reads / number of coding DNA sequences). If the average
number was > =10 and the observed percentage of coding
DNA sequences covered by reads > 80% or the average
number between 2 and 10 and the observed percentage of
coding DNA sequences covered by reads > 70%, we con-
sidered the species to be present in the cheese. Finally, as
reads were allowed to map several genomes, some reads
could map to several reference genomes of the same spe-
cies even when only one strain of this species is present in
the sample. Therefore, to get more stringent results, only
one reference genome was kept for each species or sub-
species (the genome with the highest percentage of coding
DNA sequences covered by reads). The relative abun-
dances of the different genomes were calculated by divid-
ing the number of reads assigned to each genome by the
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number of good quality reads. The raw SOLiD and
Illumina read data for all samples has been deposited in
the European Bioinformatics Institute (EBI) European
Nucleotide Archive (ENA) under the accession number
PRJEB6314.
Additional files
Additional file 1: Table S1. Origin and taxonomy of the bacterial
isolates.
Additional file 2: Supplementary information document. This
supplementary information document details the genome assembly
steps, from sequencing to the quality evaluation.
Additional file 3: Table S2. Establishment of the sequencing pools.
Additional file 4: Table S3. Evaluation of the genome clustering
procedure and the quality of the genome drafts.
Additional file 5: Table S4. Bacterial species or subspecies with
technological beneficial use in food products listed by Bourdichon and
coworkers (2012).
Additional file 6: Figure S1. Global phylogeny of 179 Firmicutes
bacterial isolates, including 42 genomes from our project.
Additional file 7: Figure S2. Global phylogeny of 14 Bacteroidetes
bacterial isolates, including 6 genomes from our project.
Additional file 8: Figure S3. Global phylogeny of 180 Proteobacteria
bacterial isolates, including 50 genomes from our project.
Additional file 9: Figure S4. Global phylogeny of 84 Actinobacteria
bacterial isolates, including 32 genomes from our project.
Additional file 10: Table S5. List of the genomes used for
phylogenetic and metagenomic analyses.
Additional file 11: Table S6. Proteins involved in the catabolism of
D-galactonate in Arthrobacter strains.
Additional file 12: Table S7. Genomic comparison of four
Streptococcus infantarius subsp. infantarius strains.
Additional file 13: Figure S5. Phylogeny of 14 LacZ proteins from
Streptococcus strains.
Additional file 14: Table S8. Mapping of the sequencing reads from
the metagenomic analysis of the cheese samples.
Additional file 15: Table S9. Mapping of the sequencing reads from
genomic analysis of already sequenced genome to assess the average
level of reads that could not be mapped inherent to SOLiD technology.
Additional file 16: Figure S6. Mean quality distribution of the reads
from the metagenomic analysis of the cheese samples.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
PR and MA managed the project and designed the analysis. FI, AH, CD and
M-CM performed microbiogical work. AH and FI processed DNA pools. SK and
SDE managed DNA clustering pools sequencing. J-MB, PL and NP supervised
computing facilities. SR realized optimized draft assembly. VL contributed to
genome annotation and submission. MA, PR, A-LA, SR, NP, AH, FI, CM, CD and
M-CM performed the data analysis. MA, PR, FI, CM, AH, CD and MP wrote the
paper. All authors read and approved the final manuscript.
Acknowledgements
M.A. was supported by a grant from the Ministère de la Recherche et de
l’Education Nationale (France). This work was mainly supported by the ANR
grant “Food Microbiomes”(ANR-08-ALIA-007-02) coordinated by P.R. and
additional funding from the Centre National Interprofessionnel de
l’Economie Laitière (France). Computing was facilitated by the funds
obtained from the OpenGPU FUI collaborative research projects, with
funding from DGCIS. Optimized re-assembly draft has been processed with
CBS computing facility (Denmark). We thank Junjie Qin for the experimental
advices on the pool preparations, Cécile Callon for her help for strain
cultivation and DNA extraction, and Henrik Bjørn Nielsen for discussion on
co-abundance clustering methods. We also thank Serge Casaregola and
Joëlle Dupont for providing an early access to the genomes of Geotrichum
candidum CLIB 918, Penicillium roqueforti FM 164 and Penicillium camemberti
FM 013. We finally thank Antoine Hermet and Jérôme Mounier for the
cheese metagenomic DNA extraction.
Author details
1
Institut National de la Recherche Agronomique, UMR 1319 MICALIS, 78352
Jouy-en-Josas, France.
2
AgroParisTech, UMR MICALIS, 78352 Jouy-en-Josas,
France.
3
Institut National de la Recherche Agronomique, US 1367 MGP,
78352 Jouy-en-Josas, France.
4
AgroParisTech, UMR 782 GMPA, 78850
Thiverval-Grignon, France.
5
Center for Biological Sequence Analysis, Technical
University of Denmark, DK-2800 Kongens Lyngby, Denmark.
6
Institut National
de la Recherche Agronomique, UMR 782 GMPA, 78850 Thiverval-Grignon,
France.
7
Institut National de la Recherche Agronomique, UR 545 URF, 15000
Aurillac, France.
8
Institut National de la Recherche Agronomique, UR 1077
MIG, 78352 Jouy-en-Josas, France.
9
Department of Computer Science, Center
for Bioinformatics and Computational Biology, University of Maryland,
College Park, MD 20742, USA.
Received: 20 June 2014 Accepted: 4 December 2014
Published: 13 December 2014
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doi:10.1186/1471-2164-15-1101
Cite this article as: Almeida et al.:Construction of a dairy microbial
genome catalog opens new perspectives for the metagenomic analysis
of dairy fermented products. BMC Genomics 2014 15:1101.
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