Comparative genomic analysis of Leishmania (Viannia) peruviana and Leishmania (Viannia) braziliensis

Abstract

Background:
The Leishmania (Viannia) braziliensis complex is responsible for most cases of New World tegumentary leishmaniasis. This complex includes two closely related species but with different geographic distribution and disease phenotypes, L. (V.) peruviana and L. (V.) braziliensis. However, the genetic basis of these differences is not well understood and the status of L. (V.) peruviana as distinct species has been questioned by some. Here we sequenced the genomes of two L. (V.) peruviana isolates (LEM1537 and PAB-4377) using Illumina high throughput sequencing and performed comparative analyses against the L. (V.) braziliensis M2904 reference genome. Comparisons were focused on the detection of Single Nucleotide Polymorphisms (SNPs), insertions and deletions (INDELs), aneuploidy and gene copy number variations.

Results:
We found 94,070 variants shared by both L. (V.) peruviana isolates (144,079 in PAB-4377 and 136,946 in LEM1537) against the L. (V.) braziliensis M2904 reference genome while only 26,853 variants separated both L. (V.) peruviana genomes. Analysis in coding sequences detected 26,750 SNPs and 1,513 indels shared by both L. (V.) peruviana isolates against L. (V.) braziliensis M2904 and revealed two L. (V.) braziliensis pseudogenes that are likely to have coding potential in L. (V.) peruviana. Chromosomal read density and allele frequency profiling showed a heterogeneous pattern of aneuploidy with an overall disomic tendency in both L. (V.) peruviana isolates, in contrast with a trisomic pattern in the L. (V.) braziliensis M2904 reference. Read depth analysis allowed us to detect more than 368 gene expansions and 14 expanded gene arrays in L. (V.) peruviana, and the likely absence of expanded amastin gene arrays.

Conclusions:
The greater numbers of interspecific SNP/indel differences between L. (V.) peruviana and L. (V.) braziliensis and the presence of different gene and chromosome copy number variations support the classification of both organisms as closely related but distinct species. The extensive nucleotide polymorphisms and differences in gene and chromosome copy numbers in L. (V.) peruviana suggests the possibility that these may contribute to some of the unique features of its biology, including a lower pathology and lack of mucosal development.

Full text

R E S E A R C H A R T I C L E Open Access
Comparative genomic analysis of
Leishmania (Viannia) peruviana and
Leishmania (Viannia) braziliensis
Hugo O. Valdivia
1,2
, João L. Reis-Cunha
1
, Gabriela F. Rodrigues-Luiz
1
, Rodrigo P. Baptista
1
, G. Christian Baldeviano
2
,
Robert V. Gerbasi
2
, Deborah E. Dobson
4
, Francine Pratlong
5
, Patrick Bastien
5
, Andrés G. Lescano
2,3
,
Stephen M. Beverley
4
and Daniella C. Bartholomeu
1*
Abstract
Background: The Leishmania (Viannia) braziliensis complex is responsible for most cases of New World tegumentary
leishmaniasis. This complex includes two closely related species but with different geographic distribution and disease
phenotypes, L. (V.) peruviana and L. (V.) braziliensis. However, the genetic basis of these differences is not well understood
and the status of L. (V.) peruviana as distinct species has been questioned by some.
Here we sequenced the genomes of two L. (V.) peruviana isolates (LEM1537 and PAB-4377) using Illumina high
throughput sequencing and performed comparative analyses against the L. (V.) braziliensis M2904 reference genome.
Comparisons were focused on the detection of Single Nucleotide Polymorphisms (SNPs), insertions and deletions
(INDELs), aneuploidy and gene copy number variations.
Results: We found 94,070 variants shared by both L. (V.) peruviana isolates (144,079 in PAB-4377 and 136,946 in LEM1537)
against the L. (V.) braziliensis M2904 reference genome while only 26,853 variants separated both L. (V.) peruviana genomes.
Analysis in coding sequences detected 26,750 SNPs and 1,513 indels shared by both L. (V.) peruviana isolates against L. (V.)
braziliensis M2904 and revealed two L. (V.) braziliensis pseudogenes that are likely to have coding potential in L. (V.)
peruviana. Chromosomal read density and allele frequency profiling showed a heterogeneous pattern of aneuploidy
with an overall disomic tendency in both L. (V.) peruviana isolates, in contrast with a trisomic pattern in the L. (V.)
braziliensis M2904 reference.
Read depth analysis allowed us to detect more than 368 gene expansions and 14 expanded gene arrays in L. (V.)
peruviana, and the likely absence of expanded amastin gene arrays.
Conclusions: The greater numbers of interspecific SNP/indel differences between L. (V.) peruviana and L. (V.)
braziliensis and the presence of different gene and chromosome copy number variations support the
classification of both organisms as closely related but distinct species.
The extensive nucleotide polymorphisms and differences in gene and chromosome copy numbers in L. ( V.)
peruviana suggests the possibility that these may contribute to some of the unique features of its biology,
including a lower pathology and lack of mucosal development.
* Correspondence: daniella@icb.ufmg.br
1
Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências
Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Full list of author information is available at the end of the article
© 2015 Valdivia et al. 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.
Valdivia et al. BMC Genomics (2015) 16:715
DOI 10.1186/s12864-015-1928-z
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Background
Leishmaniasis is a neglected tropical disease caused by a
group of digenetic protozoan belonging to the genus
Leishmania.Itistransmittedbythebiteofaninfected
female phlebotomine sand fly belonging to the genus
Lutzomyia in the New World and Phlebotomus in the
Old World [1]. Leishmaniasis is endemic in 98 countries
and causes more than 1.5 million cases per year with more
than 350 million people at risk [2, 3].
Leishmaniasis presents a wide spectrum of clinical
manifestations that ranges from cutaneous leishmaniasis
(CL) that affects tissues near the sand fly bite to mucosal
leishmaniasis (ML) that is characterized by a progressive
ulceration at the nares and nasal septum to the lethal
visceral leishmaniasis (VL) that disseminates to visceral
organs causing hepatomegaly, splenomegaly and even
death [3, 4].
The L. (V.) braziliensis complex is one of the most im-
portant Leishmania group in the New World [5]. It com-
prises two closely related species (L. (V.) peruviana and L.
(V.) braziliensis) [6], although there is some controversy
regarding their status as distinct species [6]. As currently
defined, L.(V.) peruviana is an endemic species in Peru
with a limited distribution range within the Andean and
inter-Andean valleys with some narrow areas of sympatry
with L. (V.) braziliensis [7, 8].
L. (V.) peruviana causes CL and has been isolated from
peridomestic mammals including dogs, mice and opos-
sums, revealing its zoonotic status [9]. L. (V.) braziliensis is
widely distributed in South America, although primarily in
the Amazon Basin, and is referred as an anthropozoonosis
[10]. L. (V.) braziliensis infections have a substantially
higher potential to manifest as ML than any other new
world leishmaniasis species, including L. (V.) peruviana
[3,11].However,theparasitegeneticfactorsthatcon-
tribute to the differences in the pathogenesis of these
two species are not well known.
Next generation sequencing has provided several advan-
tages for characterizing species-specific traits across the ge-
nomes of several organisms. In Leishmania it has allowed
to rapidly and comprehensively analyze a wide range of
mutation types, including gene copy number variations
(CNV) and aneuploidy [12]. Recently, CNV and expansions
in tandem gene arrays have been proposed as a mechanism
to increase gene expression with numerous species-specific
gene amplifications [12]. These studies have suggested that
extensive variation among duplicated tandem gene arrays
plays a role in higher expression of their products and a di-
versification process in amplified genes [13]. Moreover,
analysis of the chromosomal content from different cells
within the same isolate have led to conclude that Leish-
mania presents a mosaic structure that may contribute to
gene expression changes in response to environmental al-
teration modulating parasite phenotypes [12, 14].
In this study, we have conducted a comparative genom-
icsanalysisoftwoL.(V.)peruvianaisolates against the ref-
erence genome M2904 of L. (V.) braziliensis.Comparative
assessments have shown important differences in chromo-
some and gene copy number between both species. These
analyses may serve to improve our understanding of para-
site variation between these two closely related species that
could be linked to their different disease phenotypes and to
provide further insights into their status as distinct species.
Results and Discussion
Genome assembly
We used a combined de novo and reference based assembly
approach (Baptista et al. in preparation) to generate a draft
genome for each strain. L. (V.) peruviana mapped reads
showed an overall 92.51 % mapping rate for PAB-4377 and
95.87 % for LEM1537 against L. (V.) braziliensis.Median
genome coverage estimated from mapped reads into 6,899
single copy genes was of 59.1 and 35.0 for PAB-4377 and
LEM1537, respectively.
The L. (V.) peruviana assemblies resulted in 28.51 and
25.27 megabases that were generated from 11,504 and
29,816 contigs in PAB-4377 and LEM1537, respectively.
The resulting ordered assemblies consisted of 37 pseudo-
chromosomes, due to the split of chromosome 20 in the L.
(V.) braziliensis M2904 reference genome (LbrM.20.1 and
LbrM.20.2) and a pseudo-chromosome containing un-
ordered scaffolds (Chromosome 0).
The overall identity between L. (V.) braziliensis and L.
(V.) peruviana calculated with MUMmer [15] confirmed
the close relationship between L. (V.) braziliensis and L.
(V.) peruviana (identity of 87.58 % for PAB-4377 and
77.1 % for LEM1537), and a closer relationship between
the two L. (V.) peruviana isolates (99 %).
SNP and Indel comparisons
Variants were identified following filtering for quality, read
depth and haplotype score as described in the methods.
Comparisons identified 144,079 and 136,946 variants
between L. (V.) braziliensis and L. (V.) peruviana PAB-4377
(115,851 SNPs and 28,228 Indels) and L. (V.) peruviana
LEM1537 (108,826 SNPs and 28,120 Indels), respectively.
Of these; 94,070 variants were shared between the two L.
(V.) peruviana isolates. In contrast, the two L. (V.) peruvi-
ana isolates showed fewer variants among them (26,853).
This finding is consistent with the high similarity obtained
with MUMmer3 between both L. (V.) peruviana isolates
and the greater difference with L. (V.) braziliensis.
Our results show that there is significant genetic differen-
tiation between L. (V.) braziliensis and L. (V.) peruviana
while intra L. (V.) peruviana variation is substantially lower.
For comparison, a previous comparative study between L.
(L.) infantum and L. (L.) donovani reference genomes found
that 156,274 nucleotide changes differentiate between these
Valdivia et al. BMC Genomics (2015) 16:715 Page 2 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

closely related species [16], comparable to what we describe
here for L. (V.) braziliensis and L. (V.) peruviana.
We then focused on the 94,070 variants from L. (V.)
braziliensis that were shared by the two L. (V.) peruvi-
ana lines. Of these; 26,750 SNPs were located in 6,114
coding DNA sequences (CDS) (Additional file 1: Table
S1). Of these, 14,244 SNPs (53.24 %) were synonymous
mutations and 12,462 (46.59 %) were non-synonymous
mutations. Additionally, eight SNPs mutating the anno-
tated start codon (0.03 %) and 36 mutating the anno-
tated stop codon were found (0.13 %). Most genes with
high counts of SNP are hypothetical proteins, kinases
and trafficking proteins stressing the need to characterize
the function of these variable proteins (Table 1).
Variant calls for indels shared by both isolates detected
1,513 sites distributed in 408 CDS (Additional file 1:
Table S2). Of these, 1,014 (67.0 %) were codon deletions,
146 (9.6 %) were insertions, 351 (23.2 %) frameshifts and
two stop codons (0.1 %) were gained. Genes with most
bases affected by indels include hypothetical proteins,
kinesins and a lysine transport protein (Table 2).
Analysis of potential diagnosis targets that could accur-
ately differentiate L. (V.) peruviana from L. (V.) braziliensis
resulted in 270 genes with high SNP density regions be-
tween both species (Additional file 2, Additional file 1:
Table S3). While most of these genes are hypothetical pro-
teins, they could serve to design better molecular diagnosis
tools to discriminate between these closely related species.
Two L. (V.) braziliensis pseudogenes (LbrM.04.0060,
LbrM.28.2130) appeared to be functional in L. (V.) per-
uviana. LbrM.28.2130 codes for an X-pro, dipeptidyl-
peptidase, serine peptidase and has orthologs in other
Leishmania species from the Old and New World sug-
gesting that it could be functional in L. (V.) peruviana.
Peptidases have an important role in parasite survival,
invasion, metabolism and host-parasite interaction [17],
highlighting the importance of confirming coding function
of this potential gene. LbrM.04.0060 codes for a putative
pteridine transporter and shares 84 % identity with a
folate/biopterin in L. infantum. It has been shown that
Leishmania are pteridine auxotrophs and rely on a net-
work of folate and biopterin transporters. Pteridine levels
have a strong influence on metacyclogenesis in L. (L.)
major [18].
Chromosome copy number variation
Chromosome numbers were estimated by the average
read density to each chromosome, and normalized to an
assumed overall genome ploidy of 2n. Normalized chromo-
some copy number clustered around “disomy”although
with significant departures from non-integral values evident
for some chromosomes (Fig. 1). This finding is particularly
important since the L. (V.) braziliensis M2904strainis
mostly trisomic [12].
The most pronounced departure from disomy occurred in
chromosome 31, which presented a read depth betweentet-
rasomy to hexasomy in PAB 4377 and trisomy in LEM1537
(Fig. 1, Additional files 3 and 4). In both isolates, read depth
was evenly distributed along the entire sequence of Chr31,
arguing against region-specific amplification (Fig. 2).
In both samples, chromosomes 1–5 and 7 appear to
be closer to monosomy. This characteristic has also been
estimated for chromosomes 1 and 3 of L. (L.) mexicana
[12]. Interestingly, the pattern of aneuploidy involving
chromosomes 8, 11, 20 and 22 in LEM1537 and 35 in
PAB-4377 is different from the median ploidy of the rest
of the chromosomes in both samples. These chromo-
somes appear to have intermediate read depth between
disomic and trisomic profiles, suggesting a mosaic ploidy
within the cell population (Fig. 1).
It has been suggested that mosaic aneuploidy could be a
mechanism of rapid parasite adaptation in response to en-
vironmental changes within its host [14] and it has been
Table 1 Top ten high SNP count genes in two L. (V.) peruviana isolates
Gene ID Annotation Number of SNP Gene length CN PAB LEM
LbrM.33.3060 Hypothetical proteins 135 14,943 0.97 0.87
LbrM.30.2340 83 11,340 0.98 0.75
LbrM.34.5330 82 19,875 1.07 1.25
LbrM.16.0180 69 13,302 0.82 0.79
LbrM.35.1580 68 16,767 1.01 0.76
LbrM.14.0770 63 12,570 0.68 0.39
LbrM.35.3160 43 12,582 1.05 0.98
LbrM.30.2160 Endosomal trafficking protein RME-8, putative 40 7335 1.20 1.19
LbrM.02.0130 Phosphatidylinositol kinase related protein, putative 39 14,775 0.51 0.47
LbrM.30.1620 protein kinase, putative 38 5112 1.30 1.23
Top ten genes showing high SNP differences in L. (V.) peruviana compared with L. (V.) braziliensis orthologs. Number of SNP and gene length are presented in
nucleotides. Copy number (CN) estimated for the haploid genome of PAB-4377 (PAB) and LEM-1537 (LEM)
Valdivia et al. BMC Genomics (2015) 16:715 Page 3 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

shown to occur in closely related strains [16]. However, its
origin in Leishmania remains to be investigated [16].
A second approach for assessing chromosome number
is based upon allele frequencies. For disomic chromo-
somes, heterozygous SNPs should cluster around 50 %,
while trisomic chromosomes should show two peaks at
33 and 67 % and tetrasomics at 25, 50 and 75 %, [12].
Allele frequency counts for each predicted heterozygous
SNP further confirmed the overall disomic tendency (Fig. 3)
and the highly heterogeneous structure within the cell pop-
ulations with chromosomes presenting mixtures of allele
profiles (Additional files 5 and 6).
Chromosomes with discordance between read depth
analysis and their allele frequencies included chromo-
some 5, 7, 13, 17 and 19 that presented a tetrasomic or
a mixture of trisomic and disomic patterns in PAB-4377
(Additional file 5).
In LEM1537, chromosomes 6 and 9 did not have a
marked allele frequency pattern and chromosome 11,
14 and 25 presented discordance between read depth
and allele frequencies (Additional file 6). Additionally,
chromosomes 22, 23, 28 and 34 presented mixtures of
disomy and monosomy that corresponded with their
estimated read depth (Additional file 6, Fig. 1).
Discordance between allele frequency and read depth
may be explained by cells presenting a high variation in
their ploidies due to chromosome mosaism as has been
previously suggested [12].
Interestingly, chromosome 31 that has been identified
as supernumerary in both isolates appears to have di-
somic pattern (Additional files 5 and 6). This chromo-
some has been previously described as supernumerary in
all Leishmania species [12]. It may be possible that this
chromosome accumulates mutations in disomic alleles
Table 2 Top ten high INDEL count genes in L. (V.) peruviana
Gene ID Annotation Affected nucleotides Gene length CN PAB LEM
LbrM.17.0390 Hypothetical proteins 57 3480 0.63 0.52
LbrM.21.1080 42 2895 0.76 0.56
LbrM.15.1180 Nucleoside transporter 1, putative 28 1848 1.34 1.33
LbrM.34.2710 Hypothetical protein 24 2133 1.43 1.6
LbrM.14.0785 Kinesin, putative 21 957 0.75 1.02
LbrM.31.1470 Hypothetical proteins 21 4089 0.82 0.66
LbrM.32.3450 21 2469 0.74 0.54
LbrM.33.2950 21 3582 0.91 0.77
LbrM.07.1050 RNA binding protein-like protein 19 1377 1.02 1.21
LbrM.25.1000 Hypothetical proteins 18 19,518 0.56 0.33
Top ten high indel count genes in L. (V.) peruviana compared with L. (V.) braziliensis orthologs. Gene length is presented in nucleotides. Copy number (CN)
estimated for the haploid genome of PAB-4377 (PAB) and LEM-1537 (LEM)
Fig. 1 Chromosome copy number in L. (V). peruviana. Chromosome copy number variation in L. (V.) peruviana.aPAB-4377; bLEM-1537. Boxes
represent the estimated copy number for each chromosome and standard deviation from the three methods. Mean genome ploidy is indicated
by a dotted red line
Valdivia et al. BMC Genomics (2015) 16:715 Page 4 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

as has been reported in other chromosomes with the
same pattern in L. (L.) mexicana [12].
Ontology analysis in the supernumerary chromosome
31 showed that this chromosome is enriched in genes
involved in iron metabolism and other related molecular
functions (Table 3, Additional file 1: Table S4). Iron sul-
fur proteins (Fe-S) are crucial for life since they mediate
oxidation-reduction reactions during mitochondrial elec-
tron transport and are involved in the synthesis of amino
acids, biotin and lipoic [19].
Biosynthesis of Fe-S proteins is highly dependent on
iron regulation in the cell [20]. Interestingly, ferrous iron
transporters located in chromosome 31 have been de-
scribed in Leishmania and they appear to be important
for growth, replication and pathology, further stressing
this connection [21, 22].
A sustained copy number increase in chromosome 31
among all Leishmania species [12] could serve as a mech-
anism to facilitate iron uptake and increase gene dosage of
Fe-S proteins in an oxidative stressed environment.
Gene copy number variation
Expanded tandem gene arrays and dispersed genes appear
to be a major source of inter and intra-species variation in
Leishmania [12]. The tandem duplicated gene arrays ana-
lysis showed a total of 20 and 26 expanded arrays in PAB-
4377 (Fig. 4a) and LEM1537 (Fig. 4b), respectively, relative
to the L. (V.) braziliensis reference genome (Additional file
1: Table S5).
In both samples, 14 tandem arrays were shared show-
ing that gene array expansions may vary across strains
from the same species (Additional file 1: Table S5). The
most expanded gene arrays in both isolates belonged
to a group of TATE DNA transposons (OG5_128620),
NADH-dependent reductases (OG5_128620), heat shock
protein 83 (OG5_126623) and hypothetical proteins among
others (Additional file 1: Table S5).
The same analysis in L. (V.) braziliensis M2904 resulted
in 18 tandem gene arrays from which only three arrays
were shared with L. (V.) peruviana (Additional file 1:
Table S6). Interestingly, amastin surface protein arrays
Fig. 2 Chromosome 31 normalized read depth. Normalized read depth for supernumerary chromosome 31. aPAB-4377; bLEM-1537. Estimated
ploidy indicated by a dotted red line. Blue lines represent normalized read depth for each position at the chromosome
Fig. 3 Normalized allele frequency distribution. Normalized allele frequency counts for L. (V.) peruviana.aPAB-4377; bLEM-1537. Blue dots show
normalized counts at heterozygous positions for all disomic chromosomes. Mean count at each allele frequency is indicated by a red line. Cumulative
percentage between 0.4 and 06 heterozygous frequencies support disomic tendency of most chromosomes
Valdivia et al. BMC Genomics (2015) 16:715 Page 5 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

that are present in L. (V.) braziliensis seems to be not
expanded in L. (V.) peruviana.
Amastins have been shown to be highly expressed in
the amastigote life stage and appear to mediate host-
parasite interactions allowing infection and survival [23].
While the effect of this variation remains to be confirmed,
these differences may be related with different host inter-
actions in both species.
We found 398 and 942 dispersed duplicated genes in
PAB-4377 and LEM1537 with 360 expansions in common
(Fig. 4c, d, Additional file 1: Table S7 and S8). Most
expanded genes include thioredoxins, NADH-dependent
fumarate reductases and several hypothetical proteins.
We did not detect an increase in copy number in GP63
genes in L. (V.) peruviana as has been previously shown in
L. (V.) braziliensis [12] reinforcing a previous finding of
GP63 copy number differences between these species [24].
The zinc-metalloprotease GP63 stands out as a major
virulence factor in Leishmania presenting different roles
in the vector and mammal host that aim to protect para-
sites from host immune responses and promote infec-
tion [25]. Therefore, deletion of some GP63 genes in L.
(V.) peruviana could affect parasite-host interactions and
influence its distribution and clinical manifestation with
lack of mucosal development.
The marked intra-species difference in dispersed dupli-
cated genes shows that extensive variation in gene copy
number can occur between isolates belonging to the same
species and supports the hypothesis that chromosome and
gene CNV act as a mechanism of rapid parasite adaptation
[12, 26].
Conclusions
Extensive chromosomal and gene copy number variations
have been described in Leishmania and were proposed as
a mechanism of rapid parasite adaptation to different envi-
ronments and pressures in the host. Our study shows that
there are major differences regarding gene copy number
variations and aneuploidy even between closely related
Leishmania species.
Although highly similar to L. (V.) braziliensis,L. (V.) per-
uviana presents a different set of expanded gene arrays that
can result in different expression profiles between both spe-
cies. Moreover, high SNP and indel counts as well as exten-
sive variation in chromosome and gene copy numbers
between L. (V.) peruviana and L. (V.) braziliensis sup-
port maintaining the classification of both organisms as
closely related but distinct species.
Further analysis including a greater number of L. (V.)
peruviana and L. (V.) braziliensis isolates and the use of
transcriptomic data are needed to assess if these differ-
ences are conserved across isolates of L. (V.) peruviana
and reveal how tandem gene arrays and CNV affect gen-
ome expression.
Methods
Parasite isolates and sequencing
L. (V.) peruviana isolate PAB-4377 was kindly provided by
the U.S. Naval Medical Research Unit No. 6 (NAMRU-6)
and the LEM1537 (MHOM/PE/84/LC39) isolate was
obtained from the Montepellier reference center.
PAB-4377 was confirmed as L. (V.) peruviana by Multi-
locus Enzyme Electrophoresis (MLEE) and sequencing of
the Manose Phosphate Isomerase and 6-phosphogluconate
dehydrogenase genes. LEM1537 is a L. (V.) peruviana
reference strain (MHOM/PE/84/LC39) and has been
widely characterized by MLEE.
Libraries consisting of 350 bp fragments were obtained
and 100 bp paired end reads were generated at the Gen-
ome Technology Access Center (GTAC) at Washington
University in St. Louis by Illumina HiSeq 2000. The ver-
sion 6 of the L. (V.) braziliensis M2904 genome was ob-
tained from the Tritryp database (http://tritrypdb.org/)
to serve as a reference for comparative analysis.
Genome assembly and annotation
L. (V.) peruviana reads were filtered by quality using Trim-
momatic [27] with a minimum base quality cutoff of 30,
leading and trailing base qualities of 28, five bases sliding
window with minimum per base average quality of 20 and
a minimum read length of 70 bp.
A combined De novo and reference based assembly ap-
proach (Baptista et al., in preparation) was used to gen-
erate a draft assembly for each sample. Briefly, De Novo
assemblies were generated using the Velvet optimizer
perl script under Velvet version 1.2.10 [28]. Draft assem-
blies were extended by iterative mapping using IMAGE
[29] and corrected using iCORN2 [30].
For reference-based assembly, reads from each sample
were mapped against the L. (V.) braziliensis M2904 gen-
ome using Bowtie2 [31]. Redundant reads were removed
and a reference-based sequence was generated using
SAMtools Mpileup and vcfutils [32] using base quality
scores greater or equal than 40, mapping quality scores
Table 3 Ontology analysis for chromosome 31
Go ID Description Corrected p-value
51,537 2 iron, 2 sulfur cluster binding 1.08E-03
9055 electron carrier activity 1.85E-02
4198 calcium-dependent cysteine-type
endopeptidase activity
1.85E-02
51,536 iron-sulfur cluster binding 1.85E-02
51,540 metal cluster binding 1.85E-02
4148 dihydrolipoyl dehydrogenase activity 1.85E-02
4197 cysteine-type endopeptidase activity 3.81E-02
8234 cysteine-type peptidase activity 4.94E-02
Valdivia et al. BMC Genomics (2015) 16:715 Page 6 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

greater or equal than 25, coverage greater or equal than
10 reads and less than twice the median genome coverage.
De Novo and referenced based sequences of each sample
were combined using the ZORRO hybrid assembler as pre-
viously described [33]. The final hybrid assemblies were fur-
thered extended and corrected with IMAGE and iCORN
and contigs were scaffolded with SSPACE [34]. Scaffolds
were aligned and orientated into pseudochromosomes with
ABACAS [35] using the L. (V.) braziliensis M2904 genome
as a reference sequence.
MUMmer3 [15] was used to calculate similarity be-
tween the assembled L. (V.) peruviana genomes and
the reference L. (V.) braziliensis. Briefly, identity scores
and number of bases from best local alignments among
Fig. 4 Gene copy number variations in L. (V.) peruviana. Mapping of expanded genes in both L. (V.) peruviana isolates. a,bTandem duplicated gene
arrays in PAB-4377 and LEM-1537, respectively. Outer circle shows gene arrays as red with numbers indicating the calculated array expansion. Disomic
chromosomes are shown in blue and supernumerary chromosomes in orange with outer numbers describing each chromosome. Colored lines map
the location of duplicated arrays in their respective chromosomes. c,ddispersed duplicated genes in PAB-4377 and LEM-1537, respectively. Histogram
and numbers represents the total number of gene expansions in each chromosome
Valdivia et al. BMC Genomics (2015) 16:715 Page 7 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

assembled and reference genomes were retrieved and
normalized with the total number of bases in the draft
genome in order to compute a global identity score.
Read and assembly files are available through the
European Nucleotide Archive under the project num-
ber PRJEB7263.
SNP and pseudogene analysis
To detect SNPs between L. (V.) peruviana and L. (V.) bra-
ziliensis and determine their potential effects on coding
sequences, L. (V.) peruviana reads were mapped onto the
L. (V.) braziliensis M2904 reference genome using Bowtie2
and analyzed using the recommended parameters of
GATK [36]. Briefly, mapped bam files were filtered for
redundant reads and local realignment was performed
around indels in order to remove potential mapping
artifacts. SNPs were called using the haplotype caller
module and raw variants were filtered using GATK’s vari-
ant quality score recalibration selecting sites with a mini-
mum raw coverage of 10, Root Mean Square mapping
quality lower than 40, quality by depth greater than 2
and haplotype score greater than 13. The same method
was employed to call variants between both L. (V.) per-
uviana isolates.
To analyze the effects of SNPs in coding regions of the
L. (V.) peruviana genome, we filtered variant calls of PAB-
4377 and LEM1537 selecting only SNPs shared by both iso-
lates to limit the potential impact of within-species SNP
variability and minimize incorrect SNP calling. The com-
bined variant called was used as input for SnpEff [37] to an-
notate and predict the effects of variants of genes.
To find potential targets sequences to accurately dis-
criminate L. (V.) peruviana from L. (V.) braziliensis we
employed a custom Perl script to screen the genes with
variant calls. These genes were analyzed using a sliding
window of 1000 nucleotides to report the region with the
highest SNP density and the number of SNP that it pre-
sented. Genes with significant SNP calls were detected
using the ROUT test under Graph Pad Prism V5 [38]
We downloaded L. (V.) braziliensis pseudogenes from
the Tritryp database and compared them against L. (V.)
peruviana to detect potential pseudogenes that remained
functional in L. (V.) peruviana. Briefly, L. (V.) peruviana
amino acid fasta sequences were generated using SAM-
tools Mpileup and translated into amino acids for se-
quence alignment against L. (V.) braziliensis pseudogenes
in ClustalΩ[39].
Allele frequency distribution
Allele frequencies for PAB-4377 and LEM1537 assemblies
were obtained from filtered SAMtools Mpileup results as
described elsewhere [12]. Briefly, the proportion of reads
mapping to each heterozygous site under the total mapped
reads for the site was estimated. Allele frequencies were
categorized from 0.1 to 1.0 and normalized by the sum of
all allele frequencies for the chromosome. Allele frequen-
cies distributions were plotted in R and plots from chro-
mosomes sharing the same pattern were combined.
Chromosome and tandem gene array analysis
To analyze chromosome copy number, we combined three
different approaches based on the assumption that the
overall chromosome organization is similar between L.
(V.) braziliensis and L. (V.) peruviana. First, OrthoMCL
was used to select single copy genes from the proteomes
of L. (V.) braziliensis,L. (L.) mexicana and L. (L.) major,L.
(L.) infantum, L. (L.) donovani and L. (Sauroleishmania)
tarentolae (Additional file 1: Table S9).
This group of single copy genes was used to normalize
read mapping counts for each position along the chromo-
some in order to calculate haploid copy number. Second,
the number of reads mapping to the whole chromosome
was counted and normalized by the median number of
mapped reads to the whole genome. Third, we normalized
FPKM (Fragments Per Kilobase per Million fragments
mapped reads) values for each chromosome by the me-
dian FPKM of the whole genome. We plotted the mean
and standard deviations from the three approaches using
Graph Pad Prism V5.
We normalized haploid copy numbers with the average
chromosome ploidy calculated from the allele frequency
analysis to estimate chromosome ploidy. Plots for each
chromosome were generated in R using a sliding window
of 10 kilo bases.
Gene Ontology codes that were significantly overrepre-
sented in the genes of supernumerary chromosomes were
detected using the hypergeometric distribution analysis in
BiNGO [40] with Benjamini and Hochberg false discovery
rate correction.
We defined tandem gene arrays as groups of genes that
are located contiguously in a chromosome and that share
a homology relationship. Dispersed gene duplications are
defined as genes that are duplicated and do not belong to
any tandem array.
Dispersed and tandem gene duplications were identi-
fied using a combination of Bowtie2 and Cufflinks2 [41].
Briefly, mapped reads against L. (V.) braziliensis M2904
and a coding sequence (CDS) GFF file were used as in-
put for Cufflinks2 to determine FPKM for each CDS and
chromosome. Haploid copy number for each CDS was esti-
mated by a proportion of their respective FPKM and the
median FPKM of all CDS in the respective chromosome.
We employed OrthoMCL [42] to identify homology rela-
tionships in mapped CDS and the mean haploid copy num-
ber was estimated for each array as reported by Rogers
[12]. Gene duplications were defined as those greater than
a cutoff of 1.85 for the haploid number computed by our
analysis [12].
Valdivia et al. BMC Genomics (2015) 16:715 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

We employed this same approach to detect expanded
gene arrays in the L. (V.) braziliensis genome using reads
from the M2904 reference strain.
Additional files
Additional file 1: Supplementary tables. (XLSX 1072 kb)
Additional file 2: Top five high SNP density genes. (TIFF 448 kb)
Additional file 3: Normalized read depth for PAB-4377 chromosomes.
(TIFF 2222 kb)
Additional file 4: Normalized read depth for LEM-1537 chromosomes.
(TIFF 2449 kb)
Additional file 5: Normalized allele frequency distributions for
PAB-4377 chromosomes. (TIFF 443 kb)
Additional file 6: Normalized allele frequency distributions for
LEM-1537 chromosomes. (TIFF 441 kb)
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
HOV carried out most bioinformatics analysis, participated in study conception,
design and drafted the manuscript. JLR participated in gene and chromosome
copy number calculations. GFR participated in gene and chromosome copy
number calculations. RPB contributed in genome assembly and manuscript
drafting. GCB participated in study design, coordination and participated. RG
participated in study coordination and manuscript writing. DED participated
in DNA quality control, sequencing and preliminary bioinformatic analysis. FP
participated in study design and coordination. PB participated in study design
and coordination. AGL participated in study design, coordination and
manuscript writing. SB participated in study conception, design, coordination
and manuscript writing. DCB participated in bioinformatic analysis, study design,
coordination and manuscript writing. All authors read and approved the final
manuscript.
Authors’information
Not applicable.
Availability of data and materials
Read and assembly files are available through the European Nucleotide
Archive under the project number PRJEB7263.
Acknowledgements
We thank Nick Dickens for his help with FPKM copy number calculations and
the Genome Technology Access Center in the department of Genetics at
Washington University School of Medicine for their support with next-generation
sequencing. The Center is partially supported by NCI Cancer Center Support
Grant #P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant
#UL1 TR000448 from the National Center for Research Resources (NCRR).
Daniella C. Bartholomeu research was supported by Fundação de Amparo a
Pesquisa do Estado de Minas Gerais (FAPEMIG), Instituto Nacional de Ciência
e Tecnologia de Vacinas (INCTV)—Conselho Nacional de Desenvolvimento
Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior (CAPES). DCB is a CNPq research fellow. HOV, JLRC, GFRL
received scholarships from CAPES and RPB received a scholarship from CNPq.
Stephen Beverley and Deborah Dobson research was supported by NIH
grants R01-AI29646 and R56-AI099364. Francine Pratlong and Patrick Bastien
research was funded by the Institut de Veille Sanitaire, France.
Author details
1
Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências
Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
2
Department of Parasitology, U.S. Naval Medical Research Unit No. 6, Lima,
Peru.
3
Universidad Peruana Cayetano Heredia, School of Public Health and
Management, Lima, Peru.
4
Department of Molecular Microbiology,
Washington University School of Medicine, St. Louis, Missouri, USA.
5
Centre
Hospitalier Universitaire de Montpellier, Departement de
Parasitologie-Mycologie, Centre National de Reference des Leishmanioses,
Montpellier, France.
Received: 7 May 2015 Accepted: 9 September 2015
References
1. Kato H, Gomez EA, Caceres AG, Uezato H, Mimori T, Hashiguchi Y. Molecular
epidemiology for vector research on leishmaniasis. Int J Environ Res Public
Health. 2010;7(3):814–26.
2. Alvar J, Velez ID, Bern C, Herrero M, Desjeux P, Cano J, et al. Leishmaniasis
worldwide and global estimates of its incidence. PLoS ONE. 2012;7(5):e35671.
3. Murray HW, Berman JD, Davies CR, Saravia NG. Advances in leishmaniasis.
Lancet. 2005;366(9496):1561–77.
4. David CV, Craft N. Cutaneous and mucocutaneous leishmaniasis. Dermatol
Ther. 2009;22(6):491–502.
5. Mimori T, Grimaldi Jr G, Kreutzer RD, Gomez EA, McMahon-Pratt D, Tesh RB,
et al. Identification, using isoenzyme electrophoresis and monoclonal antibodies,
of Leishmania isolated from humans and wild animals of Ecuador. Am J Trop
Med Hyg. 1989;40(2):154–8.
6. Fraga J, Montalvo AM, Van der Auwera G, Maes I, Dujardin JC, Requena JM.
Evolution and species discrimination according to the Leishmania heat-shock
protein 20 gene. Infect Genet Evol. 2013;18:229–37.
7. Lucas CM, Franke ED, Cachay MI, Tejada A, Cruz ME, Kreutzer RD, et al.
Geographic distribution and clinical description of leishmaniasis cases in
Peru. Am J Trop Med Hyg. 1998;59(2):312–7.
8. Nolder D, Roncal N, Davies CR, Llanos-Cuentas A, Miles MA. Multiple hybrid
genotypes of Leishmania (viannia) in a focus of mucocutaneous Leishmaniasis.
Am J Trop Med Hyg. 2007;76(3):573–8.
9. Llanos-Cuentas EA, Roncal N, Villaseca P, Paz L, Ogusuku E, Perez JE, et al.
Natural infections of Leishmania peruviana in animals in the Peruvian
Andes. Trans R Soc Trop Med Hyg. 1999;93(1):15–20.
10. Oddone R, Schweynoch C, Schonian G, de Sousa CS, Cupolillo E, Espinosa D,
et al. Development of a multilocus microsatellite typing approach for
discriminating strains of Leishmania (Viannia) species. J Clin Microbiol.
2009;47(9):2818–25.
11. Odiwuor S, Veland N, Maes I, Arevalo J, Dujardin JC, Van der Auwera G.
Evolution of the Leishmania braziliensis species complex from amplified
fragment length polymorphisms, and clinical implications. Infect Genet Evol.
2012;12(8):1994–2002.
12. Rogers MB, Hilley JD, Dickens NJ, Wilkes J, Bates PA, Depledge DP, et al.
Chromosome and gene copy number variation allow major structural
change between species and strains of Leishmania. Genome Res.
2011;21(12):2129–42.
13. Victoir K, Dujardin JC. How to succeed in parasitic life without sex? Asking
Leishmania. Trends Parasitol. 2002;18(2):81–5.
14. Sterkers Y, Lachaud L, Bourgeois N, Crobu L, Bastien P, Pages M. Novel
insights into genome plasticity in Eukaryotes: mosaic aneuploidy in
Leishmania. Mol Microbiol. 2012;86(1):15–23.
15. Delcher AL, Salzberg SL, Phillippy AM Using MUMmer to identify similar
regions in large sequence sets. Current protocols in bioinformatics/editoral
board, Andreas D Baxevanis [et al.] 2003, Chapter 10:Unit 10 13.
16. Downing T, Imamura H, Decuypere S, Clark TG, Coombs GH, Cotton JA,
et al. Whole genome sequencing of multiple Leishmania donovani clinical
isolates provides insights into population structure and mechanisms of drug
resistance. Genome Res. 2011;21(12):2143–56.
17. Caroselli EE, Assis DM, Barbieri CL, Judice WA, Juliano MA, Gazarini ML, et al.
Leishmania (L.) amazonensis peptidase activities inside the living cells and
in their lysates. Mol Biochem Parasitol. 2012;184(2):82–9.
18. Cunningham ML, Titus RG, Turco SJ, Beverley SM. Regulation of
differentiation to the infective stage of the protozoan parasite Leishmania
major by tetrahydrobiopterin. Science (New York, NY). 2001;292(5515):285–7.
19. Waller JC, Alvarez S, Naponelli V, Lara-Nunez A, Blaby IK, Da Silva V, et al. A
role for tetrahydrofolates in the metabolism of iron-sulfur clusters in all
domains of life. Proc Natl Acad Sci U S A. 2010;107(23):10412–7.
20. Kaplan J, McVey Ward D, Crisp RJ, Philpott CC. Iron-dependent metabolic
remodeling in S. cerevisiae. Biochim Biophys Acta. 2006;1763(7):646–51.
21. HuynhC,SacksDL,AndrewsNW.ALeishmania amazonensis ZIP family
iron transporter is essential for parasite replication within macrophage
phagolysosomes. J Exp Med. 2006;203(10):2363–75.
Valdivia et al. BMC Genomics (2015) 16:715 Page 9 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

22. Huynh C, Andrews NW. Iron acquisition within host cells and the pathogenicity
of Leishmania. Cell Microbiol. 2008;10(2):293–300.
23. Jackson AP. The evolution of amastin surface glycoproteins in trypanosomatid
parasites. Mol Biol Evol. 2010;27(1):33–45.
24. Victoir K, Dujardin JC, de Doncker S, Barker DC, Arevalo J, Hamers R, et al.
Plasticity of gp63 gene organization in Leishmania (Viannia) braziliensis and
Leishmania (Viannia) peruviana. Parasitology. 1995;111(Pt 3):265–73.
25. Olivier M, Atayde VD, Isnard A, Hassani K, Shio MT. Leishmania virulence
factors: focus on the metalloprotease GP63. Microbes and Infection/Institut
Pasteur. 2012;14(15):1377–89.
26. Sterkers Y, Lachaud L, Crobu L, Bastien P, Pages M. FISH analysis reveals
aneuploidy and continual generation of chromosomal mosaicism in
Leishmania major. Cell Microbiol. 2011;13(2):274–83.
27. Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, Stitt M, et al. RobiNA: a
user-friendly, integrated software solution for RNA-Seq-based
transcriptomics. Nucleic Acids Res. 2012;40(Web Server issue):W622–7.
28. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly
using de Bruijn graphs. Genome Res. 2008;18(5):821–9.
29. Tsai IJ, Otto TD, Berriman M. Improving draft assemblies by iterative
mapping and assembly of short reads to eliminate gaps. Genome Biol.
2010;11(4):R41.
30. Otto TD, Sanders M, Berriman M, Newbold C. Iterative Correction of
Reference Nucleotides (iCORN) using second generation sequencing
technology. Bioinformatics. 2010;26(14):1704–7.
31. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat
Methods. 2012;9(4):357–9.
32. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The
Sequence Alignment/Map format and SAMtools. Bioinformatics.
2009;25(16):2078–9.
33. Real F, Vidal RO, Carazzolle MF, Mondego JM, Costa GG, Herai RH, et al.
The genome sequence of Leishmania (Leishmania) amazonensis: functional
annotation and extended analysis of gene models. DNA Res. 2013;20(6):567–81.
34. Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W. Scaffolding pre-
assembled contigs using SSPACE. Bioinformatics. 2011;27(4):578–9.
35. Assefa S, Keane TM, Otto TD, Newbold C, Berriman M. ABACAS: algorithm-
based automatic contiguation of assembled sequences. Bioinformatics.
2009;25(15):1968–9.
36. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al.
The Genome Analysis Toolkit: a MapReduce framework for analyzing next-
generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.
37. Cingolani P, Platts A, le Wang L, Coon M, Nguyen T, Wang L, et al. A
program for annotating and predicting the effects of single nucleotide
polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster
strain w1118; iso-2; iso-3. Fly. 2012;6(2):80–92.
38. Motulsky HJ, Brown RE. Detecting outliers when fitting data with nonlinear
regression - a new method based on robust nonlinear regression and the
false discovery rate. BMC Bioinformatics. 2006;7:123.
39. Sievers F, Higgins DG. Clustal Omega, accurate alignment of very large
numbers of sequences. Methods Mol Biol. 2014;1079:105–16.
40. Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess
overrepresentation of gene ontology categories in biological networks.
Bioinformatics. 2005;21(16):3448–9.
41. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential
gene and transcript expression analysis of RNA-seq experiments with
TopHat and Cufflinks. Nat Protoc. 2012;7(3):562–78.
42. Li L, Stoeckert Jr CJ, Roos DS. OrthoMCL: identification of ortholog groups
for eukaryotic genomes. Genome Res. 2003;13(9):2178–89.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
Valdivia et al. BMC Genomics (2015) 16:715 Page 10 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com