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On the reconstruction of the ancestral bacterial genomes in genus Mycobacterium and Brucella

  • 1Email author,
  • 1, 3,
  • 2,
  • 1 and
  • 1
BMC Systems Biology201812 (Suppl 5) :100

https://doi.org/10.1186/s12918-018-0618-2

  • Published:

Abstract

Background

To reconstruct the evolution history of DNA sequences, novel models of increasing complexity regarding the number of free parameters taken into account in the sequence evolution, as well as faster and more accurate algorithms, and statistical and computational methods, are needed. More particularly, as the principal forces that have led to major structural changes are genome rearrangements (such as translocations, fusions, and so on), understanding their underlying mechanisms, among other things via the ancestral genome reconstruction, are essential. In this problem, since finding the ancestral genomes that minimize the number of rearrangements in a phylogenetic tree is known to be NP-hard for three or more genomes, heuristics are commonly chosen to obtain approximations of the exact solution. The aim of this work is to show that another path is possible.

Results

Various algorithms and software already deal with the difficult nature of the problem of reconstruction of the ancestral genome, but they do not function with precision, in particular when indels or single nucleotide polymorphisms fall into repeated sequences. In this article, and despite the theoretical NP-hardness of the ancestral reconstruction problem, we show that an exact solution can be found in practice in various cases, encompassing organelles and some bacteria. A practical example proves that an accurate reconstruction, which also allows to highlight homoplasic events, can be obtained. This is illustrated by the reconstruction of ancestral genomes of two bacterial pathogens, belonging in Mycobacterium and Brucella genera.

Conclusions

By putting together automatically reconstructed ancestral regions with handmade ones for problematic cases, we show that an accurate reconstruction of ancestors of the Brucella genus and of the Mycobacterium tuberculosis complex is possible. By doing so, we are able to investigate the evolutionary history of each pathogen by computing their common ancestors. They can be investigated extensively, by studying the gene content evolution over time, the resistance acquisition, and the impacts of mobile elements on genome plasticity.

Keywords

  • Mycobacterium tuberculosis
  • Genome rearrangements
  • Ancestral reconstruction
  • Bacterial lineages
  • Pathogens
  • Evolution

Background

Mycobacterium tuberculosis (MTB) is the etiologic agent of human tuberculosis (TB), that is one of the oldest recorded human afflictions which is still among the main worldwide death causes. In 2015, more than 10 million people became ill with TB and approximately 2 millions died from the disease, almost exclusively in low and middle income countries. Moreover, it induces a major global health problem, since about one-third of the world’s population has latent TB. Hence this is the first infectious disease declared by the World Health Organization (WHO) as a global emergency. More precisely, tuberculosis is caused by pathogens belonging to the Mycobacterium tuberculosis complex (MTBC) which consists of different species that are typical human pathogens (Micobacterium canettii, africanum, and tuberculosis), rodent ones (M. microti), or even Mycobacteria with a large host spectrum like bovis [1, 2]. Even if these organisms are genetically similar, they exhibit large differences with regard to epidemiology, pathogenicity, and host spectrum. Mycobacterium tuberculosis spreads throughout the human population since thousands of years, as the TB form that attacks bone and causes skeletal deformities can be still identified on individuals who died from it several thousands years ago, like ancient Egyptian mummies with apparent tubercular deformities.

The MTBC species are classified in 6 phylogenetic lineages which can be further divided into sublineages showing phenotypic differences reflecting for example their virulence (pathogenicity). The species members of the Mycobacterium tuberculosis complex have a clonal structure with large genome similarity (more than 99.9 percent of DNA sequences in common [3]). Compared to more ancient species, this complex has more virulent chromosomes [4]. As they have the same ancestor [5], the fact that we can find rodent and human pathogens, and other with a larger spectrum, is indeed surprising. To study M. tuberculosis DNA sequence, its virulent laboratory strain M. tuberculosis H37Rv is commonly used. This strain consists of a single circular chromosome composed by 4,411,532 nucleotides and 3906 protein genes. DNA homology studies and comparison of 16S rRNA coding regions have permitted to establish how they are linked, showing a 95−100% DNA relatedness. For example, there is only one difference between the 16S rRNA gene sequence of M. tuberculosis and the one of M. bovis.

The long-term coevolution of Mycobacterium tuberculosis with humans [6] has led to a more or less large geographic spread of the different phylogenetic lineages of MTBC. Moreover, some of the lineages appear to have a large geographic distribution, while others seem to be restricted to a smaller group of human host populations. Over time, MTBC genomes have evolved through genomic repetition or replacement (insertion sequences, etc.) and genomic modification at different scales of complexity. In this latter case, modifications range from small-scale ones resulting from mutation or indels to larger ones occurring on DNA strands (inversion, duplication, or deletion).

Obviously, understanding the past and future evolution of the MTBC would be of great interest, leading to the ability to study the ancestors and to understand the evolution history of species, and finally to an improved knowledge of the mechanisms of resistance and virulence acquisition in human tuberculosis. Fortunately, the relatively short time-frame during which the MTBC emerged (this bacteria is quite recent [7]), the relatively low genomes lengths and the recombination scarcity, together with an easier access to ancient and current DNA sequences, are favourable factors to address this question. Therefore, it should be possible to design a model of evolution for this set of genomes, in order to recover their evolution history and to predict their future evolution.

Another interesting group of pathogenic bacteria to be investigated is the genus Brucella which causes Brucellosis, a disease that primarily affects animals, especially domesticated livestock, producing abortion and other reproductive disorders. Human can also be infected, mainly through animal-to-person spread, in which case long-lasting flu-like symptoms are observed. Like tuberculosis, brucellosis is a global problem, since it is the most common bacterial infection spread from animals to humans worldwide. After the recent identification of the species B. vulpis, a total of eleven species have been identified within the genus Brucella according to their pathogenicity and preferential animal host [8, 9], among which the six classically recognized species are: B. melitensis, B. abortus, B. suis, B. ovis, B. canis, and B. neotomae. B. abortus and B. melitensis are the most important species regarding prevalence and morbidity in humans and domestic animals.

Clearly, a detailed knowledge of the Brucella phylogeny would also be of great interest. First, the phylogenetic reconstruction can lead to an enhanced understanding of the ecology, evolutionary history, and host relationships of this genus. Second, it can be used to discover suitable genotyping methods for rapid detection and diagnostic measures, used for example in epidemiological studies to facilitate human disease research. Moreover, as the Brucella genus is highly conserved and has low genetic variation, the phylogenetic reconstruction is still a challenge, even if the Brucella genus is probably easier to tackle than the MTBC.

This requires the development of new algorithms for the detection and evolution of genomic changes. Researchers studying this question focus mainly on the nucleotidic mutations prediction, and take specific forms for the matrix of mutations that seem not in accordance with recent experimental evaluations, see [10]. These evolutionary models must be constructed in a different manner, to better reflect what really occurred. Moreover, the important effects of other genome changes (such as nucleotide insertions and deletions, large-scale recombination, or repeated sequence changes) have to be considered more deeply, and an effective ancestral reconstruction of ancient bacteria should be carried out.

This research work is an extension of an article presented to the 5th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2017, [11]). Its main objective is to show that, if we focus on strongly related bacterial chromosomes, the reconstruction of their most recent common ancestors is possible in practice. In order to do so, we propose a pragmatic approach that mix already published reconstruction algorithms with new original scripts and a human cross-validation. As an illustrative example, we provide the ancestral reconstruction of 65 genomes of the Mycobacterium tuberculosis complex, and of the 47 Brucella genomes that are available on the NCBI database.

The dynamics of the evolution process in DNA sequences results from local evolutionary events that consist in SNPs or indels. Genomic rearrangements, which are larger alterations of the genetic organization, can take the form of inversions and transpositions, or occur by chromosome fusion and fission. Obviously, over time such large-scale mutations have affected gene order and content, therefore they have a prominent role in speciation [12]. A key problem when studying evolutionary change at the level of a DNA sequence, which is investigated by the research work presented in this article, is the problem of ancestral sequence reconstruction. This one is as follows: given an evolutionary tree relating organisms and the DNA genomic sequences of the leaf species, predict the DNA sequence of all ancestral species in the tree. Many biological studies have addressed this problem and thus various methods have been proposed for inferring ancestral sequences. Apart from ancestral genome reconstruction problem, biomolecular evolution is usually devised through the evolution of core and pan-genome. Below is a brief overview on ancestral genome reconstruction.

Similarities in sequences or in the gene order (genome composition) are usually considered in up-to-date ancestral reconstruction methods. The first case, based on sequence similarity, can be considered as resolved now, at least when indels are not considered [1321]. Indeed, considering a phylogenetic tree and its associated DNA alignment, Bayesian inference or maximum likelihood approaches can be applied to estimate ancestral states of nucleotides [22, 23]. The main problem is the insertion-deletion case, which is usually disregarded [24]. The small number of models that consider indels focus on the parcimony approach, or consider the evolutionary model called Thorne-Kishino-Felsenstein [25]. Combinatorics investigations are applied in the case of larger modifications, by modeling these recombinations as permutations of homologous sequences. This reformulation leads to the well-known genome rearrangement problem [26], in which the shortest edit operations that can map one chromosome to another are searched. Note that this NP-hard problem [12, 27] is directly related to the sequence length and the number of mutations, while genomes considered in this article are quite small and have faced only a low amount of recombination: the difficulty can be circumvented for such genomes.

The remainder of this article is organized as follows. The methodology proposed for ancestral reconstruction is detailed in the next section. Results of the application of this approach on the Mycobacterium tuberculosis complex (specifically on two of its species, namely M. tuberculosis and M. canettii) and on the Brucella genus case (focusing specifically on the B. abortus and B. melitensis species) are investigated in the third section. Finally, this research article ends with a discussion and a conclusion with future work.

Methods

Let us now detail our concrete ancestral reconstruction for bacterial genomes, illustrated through a first set of strains detailed hereafter.

Data acquisition and processing

A python script has firstly been written to automatically download all the complete genomes of Mycobacterium genus available on the NCBI database, encompassing 2 africanum, 15 bovis, 5 canettii, 1 microti, and 42 tuberculosis. Note that canettii and tuberculosis are well represented in this dataset, which is helpful to study how virulence has appeared in the first species, and if the second one is at the origin of the MTBC complex 40,000 years ago. Details about these 65 genomes are provided in Table 1.
Table 1

The considered Mycobacterium strains

Accession (GenBank)

Organism name

Sequence length (bp)

Nickname

CP010335.1

Mycobacterium tuberculosis strain 2242

4,419,839

tuberculosis1

CP010336.1

Mycobacterium tuberculosis strain 2279

4,405,033

tuberculosis2

NC_000962.3

Mycobacterium tuberculosis H37Rv

4,411,532

tuberculosis3

NC_002755.2

Mycobacterium tuberculosis CDC1551

4,403,837

tuberculosis4

NC_009525.1

Mycobacterium tuberculosis H37Ra

4,419,977

tuberculosis5

NC_009565.1

Mycobacterium tuberculosis F11

4,424,435

tuberculosis6

NC_012943.1

Mycobacterium tuberculosis KZN 1435

4,398,250

tuberculosis7

NC_016768.1

Mycobacterium tuberculosis KZN 4207

4,394,985

tuberculosis8

NC_016934.1

Mycobacterium tuberculosis UT205

4,418,088

tuberculosis9

NC_017522.1

Mycobacterium tuberculosis CCDC5180

4,405,981

tuberculosis10

NC_017524.1

Mycobacterium tuberculosis CTRI-2

4,398,525

tuberculosis11

NC_018078.1

Mycobacterium tuberculosis KZN 605

4,399,120

tuberculosis12

NC_018143.2

Mycobacterium tuberculosis H37Rv

4,411,709

tuberculosis13

NC_020089.1

Mycobacterium tuberculosis 7199-99

4,421,197

tuberculosis14

NC_020559.1

Mycobacterium tuberculosis str. Erdman = ATCC 35801 DNA

4,392,353

tuberculosis15

NC_021054.1

Mycobacterium tuberculosis str. Beijing/NITR203

4,411,128

tuberculosis16

NC_021194.1

Mycobacterium tuberculosis EAI5/NITR206

4,390,306

tuberculosis17

NC_021251.1

Mycobacterium tuberculosis CCDC5079

4,414,325

tuberculosis18

NC_021740.1

Mycobacterium tuberculosis EAI5

4,391,174

tuberculosis19

NC_022350.1

Mycobacterium tuberculosis str

4,408,224

tuberculosis20

NZ_AP014573.1

Mycobacterium tuberculosis str. Kurono DNA

4,415,078

tuberculosis21

NZ_CP002871.1

Mycobacterium tuberculosis HKBS1

4,407,929

tuberculosis22

NZ_CP002882.1

Mycobacterium tuberculosis BT2

4,401,899

tuberculosis23

NZ_CP002883.1

Mycobacterium tuberculosis BT1

4,399,405

tuberculosis24

NZ_CP002885.1

Mycobacterium tuberculosis CCDC5180

4,414,346

tuberculosis25

NZ_CP007027.1

Mycobacterium tuberculosis H37RvSiena

4,410,911

tuberculosis26

NZ_CP007803.1

Mycobacterium tuberculosis K

4,385,518

tuberculosis27

NZ_CP007809.1

Mycobacterium tuberculosis strain KIT87190

4,410,788

tuberculosis28

NZ_CP009100.1

Mycobacterium tuberculosis strain ZMC13-264

4,411,507

tuberculosis29

NZ_CP009101.1

Mycobacterium tuberculosis strain ZMC13-88

4,411,515

tuberculosis30

NZ_CP009426.1

Mycobacterium tuberculosis strain 96075

4,379,376

tuberculosis31

NZ_CP009427.1

Mycobacterium tuberculosis strain 96121

4,410,945

tuberculosis32

NZ_CP009480.1

Mycobacterium tuberculosis H37Rv

4,396,119

tuberculosis33

NZ_CP010330.1

Mycobacterium tuberculosis strain F28

4,421,903

tuberculosis34

NZ_CP010337.1

Mycobacterium tuberculosis strain 22115

4,401,829

tuberculosis35

NZ_CP010338.1

Mycobacterium tuberculosis strain 37004

4,417,090

tuberculosis36

NZ_CP010339.1

Mycobacterium tuberculosis strain 22103

4,399,422

tuberculosis37

CP010340.1

Mycobacterium tuberculosis strain 26105

4,426,489

tuberculosis38

NZ_CP012090.1

Mycobacterium tuberculosis W-148

4,418,548

tuberculosis39

NZ_CP012506.1

Mycobacterium tuberculosis strain SCAID 187.0

4,379,515

tuberculosis40

NZ_HG813240.1

Mycobacterium tuberculosis 49-02

4,412,379

tuberculosis41

CP010329.1

Mycobacterium tuberculosis strain F1

4,428,621

tuberculosis42

NC_015758.1

Mycobacterium africanum GM041182

4,389,314

africanum1

CP010334.1

Mycobacterium africanum strain 25

4,386,422

africanum0

CP010333.1

Mycobacterium microti strain 12

4,370,115

microti

NC_015848.1

Mycobacterium canettii CIPT 140010059

4,482,059

canettii0

NC_019951.1

Mycobacterium canettii CIPT 140070010

4,525,948

canettii1

NC_019950.1

Mycobacterium canettii CIPT 140060008

4,432,426

canettii2

NC_019952.1

Mycobacterium canettii CIPT 140070017

4,524,466

canettii3

NC_019965.1

Mycobacterium canettii CIPT 140070008

4,420,197

canettii4

NC_002945.3

Mycobacterium bovis AF2122/97

4,345,492

bovis0

NC_008769.1

Mycobacterium bovis BCG Pasteur 1173P2

4,374,522

bovis1

NC_012207.1

Mycobacterium bovis BCG str. Tokyo 172 DNA

4,371,711

bovis2

NZ_CP003494.1

Mycobacterium bovis BCG str. ATCC 35743

4,334,064

bovis3

NC_016804.1

Mycobacterium bovis BCG str. Mexico

4,350,386

bovis4

NC_020245.2

Mycobacterium bovis BCG str. Korea 1168P

4,376,711

bovis5

NZ_CP009449.1

Mycobacterium bovis strain ATCC BAA-935

4,358,088

bovis6

NZ_AM412059.1

Mycobacterium bovis BCG str. Moreau RDJ

4,340,116

bovis7

NZ_CP008744.1

Mycobacterium bovis BCG strain 3281

4,410,431

bovis8

NZ_CP012095.1

Mycobacterium bovis strain 1595

4,351,712

bovis9

NZ_CP009243.1

Mycobacterium bovis BCG strain Russia 368

4,370,138

bovis10

NZ_CP013741.1

Mycobacterium bovis strain BCG-1 (Russia)

4,370,705

bovis11

CP010331.1

Mycobacterium bovis BCG strain 26

4,351,313

bovis12

CP010332.1

Mycobacterium bovis strain 30

4,336,227

bovis13

NZ_CP014566.1

Mycobacterium bovis BCG str. Tokyo 172 substrain TRCS

4,371,707

bovis14

After the data acquisition stage, the next step is to align the downloaded sequences [28, 29]. Prior to the Multiple Sequence Alignment (MSA), genomes must be operated such that each sequence starts to the same location and is read in the same direction: we deal with circular genomes. This is why a sequence of reference (200 bp from M. tuberculosis H37Rv) and its reverse complement have been blasted locally. Then, a circular shift and/or a reverse complement of the whole sequence have been applied when required.

Most of the well-known alignment tools have failed to align these genomes, due to their size, while we do not want to split the sequences, to reduce the complexity of the alignment, as this multiplies the intermediate steps, increasing by doing so the risks of errors. It was not the case of AlignSeqs, available in the R module called decipher [30]. This latter achieved to perform the MSA in an accurate and rapid way. With this tool, multiple sequence alignments are done by aligning 2 genomes first, and then adds a third genome, etc., until all the sequences are aligned [31].

Phylogeny

The alignment of multiple genomes of Mycobacterium leads to the visualization of synteny blocks, emphasizing the location of large inversions.

A manual reverse of these inversions were possible, leading to an improvement of the alignment of the 65 genomes. This is beneficial for the next stage of the pipeline, namely the phylogenetic investigation. This stage has been performed using RAxML, in which the phylogenetic tree is reconstructed according to a maximum likelihood approach [32]. Note that, thanks to the manual reverse of inversions, the obtained tree has been computed using almost all the complete genomes (only columns with indels are ignored), while without this manual operation, all columns inside the inversion are disregarded. Being based on almost all the genomes, and being strongly supported according to bootstrap values, the obtained tree is trustworthy, and we can reasonably consider it as a backbone to reconstruct ancestral states of MTBC nucleotides.

The proposed ancestral reconstruction is in two parts: 1-length modifications (SNPs and indels) are first considered, before investigating larger modifications (insertion, deletion, or duplication of large scale subsequences). These two case are detailed below.

Ancestral reconstruction: the mononucleotidic variants case

The treatment is divided in two sub-parts: insertion-deletions on the one hand, and single nucleotide polymorphisms on the other hand. The second case is simple, and its difficulty is only in the separation between real SNPs and polymorphism induced by an indel recombination. The first case is more complicated, as indels may be related to mobile elements or tandem repeats. These two cases are detailed below.

Ancestral reconstruction of SNPs is realized as follows. We first compute the marginal probability distributions in each nucleotide of internal vertices in the phylogeny obtained previously. Assuming a site independence, we have applied the sum-product message passing method [33] to calculate these distributions. This method has been applied by using PHAST [34], which is able to reconstructs ancestral indels too (parsimony approach).

Ancestral reconstruction: the case of larger variants

In the case of mid-size modifications over time, a string algorithm has been first designed to detect sequence inversions (even in the case of small and noisy ones). However, and due to the fact that MTBC complex is reputed to evolve in a clonal manner, only artifacts have been detected by applying this algorithm on supercomputer facilities. This will not be the case if this pipeline is applied to more recombinating bacteria like the Pseudomonas or Yersinia genus. Note that, up to now, duplications have not yet been regarded, as the synteny block analysis performed previously has shown that large scale duplications have not occurred in the MTBC case.

Conversely, midsize indels and SNPs have been investigated in details by using PHAST. This investigation has allowed us to notice that: (1) In most of the cases, the situation is obvious, leading either to a deletion or an insertion at a well specific location inside the phylogenetic tree, like in Figs. 1 and 2. (2) These larger variants events are rare in various lineages (e.g., tuberculosis), as illustrated in Table 2. (3) In the case of indels of size ≥ 2, the parsimony approach of PHAST produces frequently a wrong ancestral state deduction, which must be modified by hand. Note that its competitors have been tested too, and they all presented worse reconstructions on our specific dataset. (4) The inserted sequence has, in general, not faced additional mutations over times.
Fig. 1
Fig. 1

Indels on internal nodes of the tree of some M. canettii species

Fig. 2
Fig. 2

Ancestral reconstruction of one problematic indel in the alignment

Table 2

Single nucleotide polymorphism between species (100.X is the name of an ancestral species, cf. the phylogeny)

 

M. canettii SNPs

M. tuberculosis SNPs

Father

Children

No. of SNPs

Children

No.of SNPs

100

canettii0

1

tuberculosis19

5

 

canettii1

9

tuberculosis17

14

100.2

canettii2

1041

tuberculosis24

1

 

canettii3

12398

tuberculosis10

0

100.3

100

28

tuberculosis27

0

 

100.2

735

tuberculosis28

0

98

-

-

100.2

1

 

-

-

100.3

0

100.4

-

-

98

0

 

-

-

tuberculosis16

1

100.X

100.3

111

100

5

 

canettii4

438

100.4

1

This semi-automatic pipeline for ancestral genomes has finally succeeded to reconstruct the genomes at each internal node of the tree, which can be done because the number of recombination of more than one nucleotide is low. These recombinations have mainly been deduced manually, while state-of-the-art tools have not been able to reach an acceptable level of accuracy.

Figure 3 summarizes all the ancestral reconstruction process, in which the gray boxes are operated manually, while the other stages are automatic. Indeed, obtained results on mononucleotidic variants have been carefully checked by naked eye, as the number of such variants is lower than one hundred, while ad hoc algorithms were designed to deal with variants of larger size, see Fig. 4.
Fig. 3
Fig. 3

Flowchart of the proposed approach

Fig. 4
Fig. 4

Ancestral reconstruction of a M. canettii SNP

CRISPR investigation

Another particular DNA pattern that can evolve through Evolution is the so-called CRISPR one. CRISPR refers to repeated DNA sequences that help to preserve organisms from noticeable threats like viruses. These sequences are a fundamental component of some immune systems, which helps to protect their organism’s health. Such repeated DNA sequences are found in archaeal and bacterial genomes. These sequences range in size from 23 to 47 base pairs.

The name of CRISPR refers to an acronym which stands for Clustered Regularly Interspaced Short Palindromic Repeat [35, 36]. The CRISPR system was initially found as part of an immune system of sorts in some bacteria, used for cutting apart foreign DNA. It consists of two parts of the protein itself, which is the workhorse of the CRISPR system: a bacterial enzyme named Cas9, and a small RNA, called the guide RNA, that matches the DNA sequence to be nicked [37].

Results

The Case of Mycobacterium Tuberculosis Complex

All the 65 Mycobacterium genomes have been aligned thanks to the AlignSeqs function described previously. We thus obtained a first representation of synteny of all of them, see Fig. 5. As can be seen, genomes are very similar in the MTBC case, and only a low number of recombinations have occurred within these genomes.
Fig. 5
Fig. 5

Synteny blocks of Mycobacterium strains available online

As an illustrative example of the phylogenetic study depicted in Sec. 3, the phylogeny of M. canettii is represented in Fig. 6 (outgroup: M. tuberculosis). We selected the GTR Gamma model of nucleotide substitution as recommended by JModelTest 2.0, and the tree has been computed by RAxML. Note that the obtained tree is well-supported, as well as in the M. tuberculosis cased, whose supports are larger than 98% (cf. Fig. 7). Indeed, with these bacteria, we have not to find the most supported tree based on the largest subset of core genes, as aligning the whole complete genomes leads to a well supported tree: it is not possible to improve the results, which is nice as the core genome is many times greater than in the chloroplast case (and so, it is not sure that the heuristic approach presented in our previous articles [32, 38, 39] can succeed to find the optima).
Fig. 6
Fig. 6

M. canettii phylogeny (outgroup: M. tuberculosis)

Fig. 7
Fig. 7

M. tuberculosis phylogeny (GTR Gamma model and outgroup:M. africanum)

The obtained results on mononucleotidic variants have been humanly verified, which has been possible due to a low number of variants (cf., for instance, to Tables 3 and 4).
Table 3

Number of columns of the MSA with SPNs or indels for M. canettii (large deletions are counted character by character)

 

canettii0

canettii1

canettii2

canettii3

canettii4

tuberculosis1

tuberculosis1

3354

1150

27437

61346

7510

0

canettii4

4833

7971

27468

60987

0

7510

canettii3

60957

61233

62717

0

60987

61346

canettii2

27256

27260

0

62717

27468

27437

canettii1

3524

0

27260

61233

7971

1150

canettii0

0

3524

27256

60957

4833

3354

Species entries are in boldface

Table 4

Variations in the alignment of the M. tuberculosis clade under consideration

 

tuberculosis4

tuberculosis19

tuberculosis17

tuberculosis16

tuberculosis27

tuberculosis28

tuberculosis24

tuberculosis10

tuberculosis4

0

199770

214401

219205

216387

217235

216919

217186

tuberculosis19

199770

0

212403

219039

216908

216672

216726

216953

tuberculosis17

214401

212403

0

216808

216534

217011

216786

216882

tuberculosis16

219205

219039

216808

0

216669

216916

216251

216678

tuberculosis27

216387

216908

216534

216669

0

142974

189148

199505

tuberculosis28

217235

216672

217011

216916

142974

0

189460

199412

tuberculosis24

216919

216726

216786

216251

189148

189460

0

194315

tuberculosis10

217186

216953

216882

216678

199505

199412

194315

0

Species entries are in boldface

166 indels and 2956 SNPs have finally been detected, when considering the 5 M. canettii (see Fig. 8). Figure 9, for its part, collects the positions of the 25 indels and 394 SNPs that have been detected in the clade of the 8 M. tuberculosis.
Fig. 8
Fig. 8

SNPs location of mononucleotidic variants of M. canettii

Fig. 9
Fig. 9

SNPs location of mononucleotidic variants of M. turberculosis

In the considered Mycobacterium strains, only a few important inversions have been detected, such as the inversion present in the last ancestor of 140070010, CIPT 140010059, 140070017, 140060008, and 140070008, as shown in Fig. 10. 99% of DNA sequence identity has been obtained when considering all the blocks of synteny of tuberculosis. We can conclude that these genomes are highly conserved: highly similar regions without any rearrangement, with only small indels and a large inversion.
Fig. 10
Fig. 10

Synteny blocks in M. canettii. Each genome is colored according to the position of the corresponding region in the first genome (gray if a region is unshared)

We can conclude from this study that ancestral genome reconstruction is possible when considering close or clonal bacteria, and all the material needed in such a pipeline has been designed. But, for the sake of comparison, it may be interesting to deep investigate the results of this semi-automatic reconstruction method on a quite more stable genus, namely the Brucella, on which human validation of algorithm results is easier (see Tables 5, 6 and 8 for an illustration of their alignment and SNP differences). Such new investigations are conducted in the next section.
Table 5

Differences in the alignment on chromosome 1 of abortus

 

abortus0

abortus1

abortus2

abortus3

abortus4

abortus5

abortus6

abortus7

abortus8

abortus9

abortus10

abortus11

abortus12

melitensis1

abortus0

0

2320

1030

4304

7194

7481

5308

4891

4850

7837

839

12693

4695

18486

abortus1

2320

0

1772

5150

6658

8371

4911

5071

5030

8022

1762

12841

5621

16724

abortus2

1030

1772

0

3996

6866

7116

5033

4603

4576

7470

537

12958

4385

18049

abortus3

4304

5150

3996

0

10010

5955

2649

853

2462

6271

3800

11488

4738

16568

abortus4

7194

6658

6866

10010

0

13161

9784

9884

9892

12820

6601

17617

10413

22727

abortus5

7481

8371

7116

5955

13161

0

6834

6408

6441

425

6911

15180

7869

16608

abortus6

5308

4911

5033

2649

9784

6834

0

2103

505

6494

4807

11411

5745

16113

abortus7

4891

5071

4603

853

9884

6408

2103

0

1907

6055

4393

11534

5321

16337

abortus8

4850

5030

4576

2462

9892

6441

505

1907

0

6102

4350

11524

5342

16581

abortus9

7837

8022

7470

6271

12820

425

6494

6055

6102

0

7253

14833

8210

16283

abortus10

839

1762

537

3800

6601

6911

4807

4393

4350

7253

0

12818

4157

17940

abortus11

12693

12841

12958

11488

17617

15180

11411

11534

11524

14833

12818

0

14057

24464

abortus12

4695

5621

4385

4738

10413

7869

5745

5321

5342

8210

4157

14057

0

18905

melitensis1

18486

16724

18049

16568

22727

16608

16113

16337

16581

16283

17940

24464

18905

0

Species entries are in boldface

Table 6

Single nucleotide polymorphism in Brucella melitensis

Chromosome 1 SNPs

  

Fathers

Children

No. of SNPs

100.4

100.3

64

 

melitensis1

74

100.2

melitensis3

106

 

melitensis2

8

100.X

100.5

4458

 

melitensis0

104

100

melitensis6

840

 

melitensis5

997

100.5

100

372

 

100.4

689

100.3

100.2

23

 

melitensis7

26

The Case of Brucella genus

The pipeline presented in the previous section is now applied on another genus, namely the Brucella one, for the sake of comparison and to broader the discussion. Complete sequences of the 47 available genomes have been downloaded from NCBI, namely by species: B. abortus (14 genomes), melitensis (8), sui (16), ovis (1), canis (3), ceti (2), pinnipedialis (2), neotomae (0), microti, inopinata, and vulpis, as described in Table 7.
Table 7

Brucella genus: genome information

Accession (GenBank)

Organism name

Sequence length(bp)

Nickname

NC_006932.1

Brucella abortus biovar 1 str. 9-941 chromosome 1

2,124,241

abortus0

NC_006933.1

Brucella abortus biovar 1 str. 9-941 chromosome 2

1,162,04

 

NC_010742.1

Brucella abortus S19 chromosome 1

2,122,487

abortus1

NC_010740.1

Brucella abortus S19 chromosome 2

1,161,449

 

NC_016795.1

Brucella abortus A13334 chromosome 1

2,123,773

abortus2

NC_016777.1

Brucella abortus A13334 chromosome 2

1,162,259

 

NZ_CP007663.1

Brucella abortus strain 63 75 chromosome 1

2,124,677

abortus3

NZ_CP007662.1

Brucella abortus strain 63 75 chromosome 2

1,155,633

 

NZ_CP007681.1

Brucella abortus strain BDW chromosome 1

2,128,683

abortus4

NZ_CP007680.1

Brucella abortus strain BDW chromosome 2

1,160,817

 

NZ_CP007682.1

Brucella abortus strain BER chromosome 1

2,125,180

abortus5

NZ_CP007683.1

Brucella abortus strain BER chromosome 2

1,163,338

 

NZ_CP007700.1

Brucella abortus strain NCTC 10505 chromosome 1

2,123,620

abortus6

NZ_CP007701.1

Brucella abortus strain NCTC 10505 chromosome 2

1,161,669

 

NZ_CP007705.1

Brucella abortus bv. 9 str. C68 chromosome 1

2,124,100

abortus7

NZ_CP007706.1

Brucella abortus bv. 9 str. C68 chromosome 2

1,155,846

 

NZ_CP007709.1

Brucella abortus bv. 6 str. 870 chromosome 1

2,124,096

abortus8

NZ_CP007710.1

Brucella abortus bv. 6 str. 870 chromosome 2

1,157,058

 

NZ_CP007738.1

Brucella abortus strain BFY chromosome 1

2,124,832

abortus9

NZ_CP007737.1

Brucella abortus strain BFY chromosome 2

1,1633,26

 

NZ_CP007765.1

Brucella abortus bv. 2 str. 86/8/59 chromosome 1

2,123,991

abortus10

NZ_CP007764.1

Brucella abortus bv. 2 str. 86/8/59 chromosome 2

1,162,137

 

NZ_CP008774.1

Brucella abortus strain BAB8416 chromosome 1

2,116990

abortus11

NZ_CP008775.1

Brucella abortus strain BAB8416 chromosome 2

1,156,120

 

NZ_CP009626.1

Brucella abortus 104M chromosome 2

1,162,580

abortus12

NZ_CP009625.1

Brucella abortus 104M chromosome 1

2,122,847

 

NZ_LN997863.1

Brucella sp. F60 genome assembly BVF60 chromosome 1

2,177,010

sp

NZ_LN997864.1

Brucella sp. F60 genome assembly BVF60 chromosome 2

1,061,127

 

NZ_CP007759.1

Brucella canis strain RM6/66 chromosome 2

1,206,801

canis3

NZ_CP007758.1

Brucella canis strain RM6/66 chromosome 1

2,105,950

 

NC_010103.1

Brucella canis ATCC 23365 chromosome 1

2,105,69

canis0

NC_010104.1

Brucella canis ATCC 23365 chromosome 2

1,206,800

 

NC_016778.1

Brucella canis HSK A52141 chromosome 1

2,107,023

canis1

NC_016796.1

Brucella canis HSK A52141 chromosome 2

1,170,489

 

NZ_CP007629.1

Brucella canis strain SVA13 chromosome 1

2,106,955

canis2

NZ_CP007630.1

Brucella canis strain SVA13 chromosome 2

1,203,360

 

NC_022905.1

Brucella ceti TE10759-12 chromosome 1

2,117,718

ceti

NC_022906.1

Brucella ceti TE10759-12 chromosome 2

1,160,316

 

NC_007618.1

Brucella melitensis biovar Abortus 2308 chromosome 1

2,121,359

melitensis0

NC_007624.1

Brucella melitensis biovar Abortus 2308 chromosome 2

1,156,948

 

NZ_CP008751.1

Brucella melitensis strain 20236 chromosome 2

1,185,741

melitensis7

NZ_CP008750.1

Brucella melitensis strain 20236 chromosome 1

2,126,134

 

NZ_CP007762.1

Brucella melitensis bv. 1 str. 16M chromosome 2

1,177,791

melitensis6

NZ_CP007763.1

Brucella melitensis bv. 1 str. 16M chromosome 1

2,116,984

 

NZ_CP007761.1

Brucella melitensis bv. 3 str. Ether chromosome 2

1,187,961

melitensis5

NZ_CP007760.1

Brucella melitensis bv. 3 str. Ether chromosome 1

2,122,766

 

NC_017283.1

Brucella melitensis NI chromosome 2

1,176,758

melitensis4

NC_017248.1

Brucella melitensis NI chromosome 1

2,117,717

 

NC_017247.1

Brucella melitensis M5-90 chromosome 2

1,185,778

melitensis3

NC_017246.1

Brucella melitensis M5-90 chromosome 1

2,126,451

 

NC_017245.1

Brucella melitensis M28 chromosome 2

1,185 615

melitensis2

NC_017244.1

Brucella melitensis M28 chromosome 1

2,126,133

 

NC_012442.1

Brucella melitensis ATCC 23457 chromosome 2

1,185,518

melitensis1

NC_012441.1

Brucella melitensis ATCC 23457 chromosome 1

2,125,701

 

NC_013119.1

Brucella microti CCM 4915 chromosome 1

2,117,050

microti

NC_013118.1

Brucella microti CCM 4915 chromosome 2

1,220,319

 

NC_009505.1

Brucella ovis ATCC 25840 chromosome 1

2,111,370

ovis

NC_009504.1

Brucella ovis ATCC 25840 chromosome 2

1,164,220

 

NC_015857.1

Brucella pinnipedialis B2/94 chromosome 1

2,138,342

pinnipedialis0

NC_015858.1

Brucella pinnipedialis B2/94 chromosome 2

1,260,926

 

NZ_CP007743.1

Brucella pinnipedialis strain 6/566 chromosome 1

2,139,033

pinnipedialis1

NZ_CP007742.1

Brucella pinnipedialis strain 6/566 chromosome 2

1,191,996

 

NZ_CP010851.1

Brucella suis strain Human/AR/US/1981 chromosome 2

1,207,241

suis0

NZ_CP010850.1

Brucella suis strain Human/AR/US/1981 chromosome 1

2,107,845

 

CP009095.1

Brucella suis strain ZW043 chromosome 2

1,215,956

suis1

CP009094.1

Brucella suis strain ZW043 chromosome 1

2,224,908

 

CP009097.1

Brucella suis strain ZW046 chromosome 2

1,311,857

suis2

CP009096.1

Brucella suis strain ZW046 chromosome 1

2,181,422

 

NZ_CP008756.1

Brucella suis strain BSP chromosome 2

1,410,995

suis3

NZ_CP008757.1

Brucella suis strain BSP chromosome 1

1,902,870

 

NZ_CP007718.1

Brucella suis bv. 3 str. 686 chromosome 2

1,190,208

suis4

NZ_CP007719.1

Brucella suis bv. 3 str. 686 chromosome 1

2,107,052

 

NZ_CP007716.1

Brucella suis strain 513UK chromosome 2

1,187,980

suis5

NZ_CP007717.1

Brucella suis strain 513UK chromosome 1

2,131,717

 

NZ_CP007696.1

Brucella suis bv. 2 strain Bs143CITA chromosome 2

1,398,244

suis6

NZ_CP007695.1

Brucella suis bv. 2 strain Bs143CITA chromosome 1

1,926,295

 

NZ_CP007721.1

Brucella suis bv. 2 strain Bs396CITA chromosome 2

1,401,375

suis7

NZ_CP007720.1

Brucella suis bv. 2 strain Bs396CITA chromosome 1

1,927,083

 

NZ_CP007698.1

Brucella suis bv. 2 strain Bs364CITA chromosome 2

1,401,378

suis8

NZ_CP007697.1

Brucella suis bv. 2 strain Bs364CITA chromosome 1

1,927,594

 

NC_004310.3

Brucella suis 1330 chromosome 1

2,107,794

suis9

NC_004311.2

Brucella suis 1330 chromosome 2

1,207,381

 

NC_010169.1

Brucella suis ATCC 23445 chromosome 1

1,923,763

suis10

NC_010167.1

Brucella suis ATCC 23445 chromosome 2

1,400,844

 

NC_017251.1

Brucella suis 1330 chromosome 1

2,107,783

suis11

NC_017250.1

Brucella suis 1330 chromosome 2

1,207,380

 

NC_016797.1

Brucella suis VBI22 chromosome 1

2,108,637

suis12

NC_016775.1

Brucella suis VBI22 chromosome 2

1,207,451

 

NZ_CP006961.1

Brucella suis bv. 1 str. S2 chromosome 1

2,107,842

suis13

NZ_CP006962.1

Brucella suis bv. 1 str. S2 chromosome 2

1,207,433

 

NZ_CP007691.1

Brucella suis bv. 2 strain PT09143 chromosome 1

1,926,480

suis14

NZ_CP007692.1

Brucella suis bv. 2 strain PT09143 chromosome 2

1,398,285

 

NZ_CP007693.1

Brucella suis bv. 2 strain PT09172 chromosome 1

1,926,716

suis15

NZ_CP007694.1

Brucella suis bv. 2 strain PT09172 chromosome 2

1,398,326

 
Table 8

Single nucleotide polymorphism in Brucella abortus

 

Chromosome 1 SNPs

Chromosome 2 SNPs

Fathers

Children

No. of SNPs

Children

No. of SNPs

100.2

abortus10

55

abortus10

41

 

abortus0

72

abortus0

38

100

abortus2

37

abortus2

25

 

abortus1

55

abortus1

15

100.3

100

37

100

17

 

100.2

5

100.2

0

100.X

100.3

24

100.3

15

 

abortus4

84

abortus4

51

Note that the genome of Brucella abortus has two circular chromosomes. The first one is 2,124,241 bp long in the Brucella abortus biovar 1 str. 9-941 reference genome, while the second chromosome is of 1,162,204 bp. Other species in the Brucella genus are comparable in genome size. For instance, the Brucella melitensis strain 16M is constituted of 3,294,931 bp disseminated in two circular chromosomes: chr. I has 2,117,144 bp, while chromosome II has 1,177,787 bp. On both of these chromosomes, approximately 3100 ORFs were predicted. In the latter, genes encoding for DNA replication, protein synthesis, core metabolism, and cell-wall biosynthesis can be found on both chromosomes [40, 41].

We operated the sequences so that they share the same orientation (which may need a transconjugate operation) and the same sequence of 200 nucleotides as starting point (which may require a circular shift), if we except local SNPs. This has been achieved using a local blast, with the beginning of Brucella abortus 2308 as an arbitrary reference. After such operations, a syntheny representation of Brucella genomes can be obtained, as shown in Fig. 11. The particular case of B. abortus is depicted in Fig. 12.
Fig. 11
Fig. 11

Brucella, chromosome 1: a high sequence similarity with little recombination events

Fig. 12
Fig. 12

Synteny map of Brucella abortus (a) chromosome 1 and (b) chromosome 2. Genomes investigation tends to show a high sequence similarity with little recombination events. Each genome is colored according to the position of the corresponding region in the first genome, or gray if a region is unshared

A few inversions have appeared in this representation. For instance, in the B.abortus case, we found a significant inversion at the last common ancestor of strains“biovar_1_str._9-941”, S19, A13334, “strain_BDW”, “bv._2_str._86/8/59”, and 104M. We have manually reversed these inversions, so that an accurate alignment of the whole genomes can be performed. Using this alignment, a very well supported phylogenetic tree has been obtained. For the sake of illustration, a subtree corresponding to the phylogeny of the Brucella abortus species is depicted in Fig. 13, and in Fig. 14 for B. melitensis. It has been obtained using the entire genome sequences with RaxML, GTR Gamma model, and Brucella melitensis as outgroup. As can be shown, all branches exhibit a 100% bootstrap support value.
Fig. 13
Fig. 13

Well-supported phylogeny of Brucella abortus species calculated on the entire chromosome 1. The outgroup is melitensis, while RaxML has been launched with the GTR Gamma model

Fig. 14
Fig. 14

Well supported phylogeny of Brucella melitensis species

At this stage, all the material required to attack the ancestral reconstruction of Brucella genomes are on hand. We first have focused on the abortus and metilensis reference species, to investigate the potential origin and the history of the global spread of these Brucellas. We have considered the global alignment of both chromosomes 1 and 2 of the available complete strains, using decipher R package [42], and the tree depicted in Figs. 13 and 14. We firstly achieved a comparative whole-genome single nucleotide polymorphism analysis of these strains collected and downloaded from the NCBI. 32 indels and 373 SNPs have been detected in the clade containing these 6 variants of chromosome 2, and 609 SNPs and 325 indels in chromosomes 1, as shown in Fig. 15. The same has been computed for B. melitensis, leading to 6178 variants and 335 indels, see Fig. 16. This has been achieved using homemade python scripts on aligned sequences.
Fig. 15
Fig. 15

SNPs location in Brucella abortus species. (a) Chromosome 1, (b) chromosome 2

Fig. 16
Fig. 16

Single nucleotide polymorphism in Brucella melitensis species

At mononucleotidic variant level, the treatments of SNPs and of indels have been separated. Examples of mononucleotidic ancestral reconstructions are provided in Fig. 17. Differences between ancestors and their children are, for their part, provided in Tables 6 (abortus) and 8 (melitensis).
Fig. 17
Fig. 17

Nucleotides in the ancestral nodes and their children on Brucella abortus species. a Chromosome 1 b chromosome 2

Figure 12 shows homologous regions among many Brucella abortus genomes, as identified by FindSynteny (R). On the one hand, the similarity and preservation of synteny blocks on Brucella abortus are especially pronounced in chromosome 1, with highly similar regions and without rearrangement of homologous backbone sequences as shown in Fig. 12a. Chromosome 2, on the other hand, is more diverse. There is above all a significant reversal in the Brucella abortus genomes of the clade consisting of abortus 0, 1, 2, 4, 10, and 12 as shown in Fig. 12b. The same information is provided for B. melitensis (chromosome 1) in Fig. 18. These differences most likely represent distinct evolutionary origins, for instance related to the nature of functional genes in the two chromosomes.
Fig. 18
Fig. 18

Dotplot of Brucella melitensis species, chromosome 1

We finally analyzed the CRISPR locus sequences of 14 Brucella abortus strains by using CRISPRs web service (http://crispr.i2bc.paris-saclay.fr). The orthologous sequence shared between Brucella abortus genomes and the CRISPR spacer have shown a significant similarity of the spacer sequences. Figure 19, for its part, shows the CRISPR space sequence lengths and their positions inside abortus genomes. For the B. melitensis case, information are provided in Fig. 20.
Fig. 19
Fig. 19

Brucella abortus phylogenetic tree: estimation of the CRISPRs length and locations by using the CRISPRFinder web server [36]

Fig. 20
Fig. 20

CRISPR investigation in B. melitensis

Discussion

Various algorithms and methods can be found in the literature to resolve, at least partially, the ancestral genome reconstruction problem. We have shown that these existing methods are not accurate and mature enough to be applied on a real case scenario. This is particularly evident when indels or single nucleotide polymorphisms are mixed with repeated sequences. The main drawback of these methods is that they intend to solve all the cases, while some situations are up-to-now too difficult to be resolved automatically. However, in mid-size genomes that have faced a low number of recombinations over time, as for Brucella and Mycobacterium, these problematic situations can be signaled, and a human cross-validation can reinforce the accuracy of the ancestral reconstruction algorithm.

As a proof of concept, all ancestral genomes of all M. canettii available on the NCBI database have been reconstructed, as well as all the ancestors of the available M. tuberculosis complete genomes. At each time, the single nucleotide polymorphism level has first been investigated, before considering the cases of indels and large scale recombination.

Obtained results show that a concrete and accurate reconstruction can be achieved by coupling human decisions on problematic situations with automatic inference of ancestral states in easy to resolve ones, at least for some non recombinant bacteria. With such a reconstruction, it may be possible to deeply investigate the evolution of genomes over time, and possibly to predict their future modifications.

Conclusion

In this article, we presented a semi-automatic pipeline that achieves to completely and accurately reconstruct the ancestral genomes of some clonal bacteria. In this pipeline, the case of SNPs and indels of 1 nucleotide has been resolved using the sum-product message passing algorithm, while larger modifications have been studied by a parsimony approach coupled with a manual deduction.

The obtained ancestors have not yet been investigated in this study, as it was not the objective of this proof of concept. They will be studied with ad hoc algorithms to design, to investigate the evolution of gene content on the one hand, and of mobile elements on the other hand [43, 44]. The rate at which such loss or gain occurs will be examined carefully, and we will study if some particular functionality are more affected by these mutations. To say this differently, we will investigate if modifications have a real impact during the evolution of genomes.

Abbreviations

Indels: 

Insertion or deletion of bases in the genome of an organism

MTB: 

Mycobacterium tuberculosis

MTBC: 

Mycobacterium tuberculosis complex

SNPs: 

Single nucleotide polymorphisms

TB: 

Tuberculosis

WHO: 

World health organization

Declarations

Acknowledgements

All computations have been performed on the Mésocentre de calculs supercomputer facilities of the University of Bourgogne Franche-Comté.

Funding

The publication costs of this article was funded by the University of Bourgogne Franche-Comté.

Availability of data and materials

The datasets supporting the conclusions of this article have been downloaded from the NCBI website https://www.ncbi.nlm.nih.gov. Scripts to download them automatically are available on demand.

About this supplement

This article has been published as part of BMC Systems Biology Volume 12 Supplement 5, 2018: Selected articles from the 5th International Work-Conference on Bioinformatics and Biomedical Engineering: systems biology. The full contents of the supplement are available online at https://bmcsystbiol.biomedcentral.com/articles/supplements/volume-12-supplement-5.

Authors’ contributions

All authors have conceived and commented on the initial drafts of the manuscript and approved its final version. CG, BN, BA, JFC, and MS designed and performed experiments, analysed data and wrote the paper. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

No human, animal or plant experiments were performed in this study, and ethics committee approval was therefore not required.

Consent for publication

Informed consent has been obtained from all participants included in the analyzed studies, and the studies are being conducted in accordance with the declaration of Helsinki.

Competing interests

The authors declare no competing financial interests.

Publisher’s Note

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Authors’ Affiliations

(1)
FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department, Univ. Bourgogne Franche-Comté (UBFC), 16 Route de Gray, Besançon, 25000, France
(2)
Department of Computer Science, Al-Mustansiriyah University, Baghdad, 10052, Iraq
(3)
Department of Computer Science, Diyala University, Diyala, 32001, Iraq

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