Content uploaded by Olaya Rendueles
Author content
All content in this area was uploaded by Olaya Rendueles on Jul 06, 2021
Content may be subject to copyright.
RESEARCH ARTICLE
Interplay between the cell envelope and
mobile genetic elements shapes gene flow in
populations of the nosocomial pathogen
Klebsiella pneumoniae
Matthieu HaudiquetID
1,2
*, Amandine Buffet
1
, Olaya RenduelesID
1☯
, Eduardo P.
C. RochaID
1☯
1Microbial Evolutionary Genomics, Institut Pasteur, CNRS, UMR3525, Paris, France, 2Ecole Doctoral
FIRE–Programme Bettencourt, CRI, Paris, France
☯These authors contributed equally to this work.
*matthieu.haudiquet@pasteur.fr
Abstract
Mobile genetic elements (MGEs) drive genetic transfers between bacteria using mecha-
nisms that require a physical interaction with the cellular envelope. In the high-priority multi-
drug-resistant nosocomial pathogens (ESKAPE), the first point of contact between the cell
and virions or conjugative pili is the capsule. While the capsule can be a barrier to MGEs, it
also evolves rapidly by horizontal gene transfer (HGT). Here, we aim at understanding this
apparent contradiction by studying the covariation between the repertoire of capsule genes
and MGEs in approximately 4,000 genomes of Klebsiella pneumoniae (Kpn). We show that
capsules drive phage-mediated gene flow between closely related serotypes. Such sero-
type-specific phage predation also explains the frequent inactivation of capsule genes,
observed in more than 3% of the genomes. Inactivation is strongly epistatic, recapitulating
the capsule biosynthetic pathway. We show that conjugative plasmids are acquired at
higher rates in natural isolates lacking a functional capsular locus and confirmed experimen-
tally this result in capsule mutants. This suggests that capsule inactivation by phage pres-
sure facilitates its subsequent reacquisition by conjugation. Accordingly, capsule
reacquisition leaves long recombination tracts around the capsular locus. The loss and
regain process rewires gene flow toward other lineages whenever it leads to serotype
swaps. Such changes happen preferentially between chemically related serotypes, hinting
that the fitness of serotype-swapped strains depends on the host genetic background.
These results enlighten the bases of trade-offs between the evolution of virulence and multi-
drug resistance and caution that some alternatives to antibiotics by selecting for capsule
inactivation may facilitate the acquisition of antibiotic resistance genes (ARGs).
PLOS BIOLOGY
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 1 / 28
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Haudiquet M, Buffet A, Rendueles O,
Rocha EPC (2021) Interplay between the cell
envelope and mobile genetic elements shapes gene
flow in populations of the nosocomial pathogen
Klebsiella pneumoniae. PLoS Biol 19(7): e3001276.
https://doi.org/10.1371/journal.pbio.3001276
Academic Editor: J. Arjan G. M. de Visser,
Wageningen University, NETHERLANDS
Received: January 13, 2021
Accepted: May 7, 2021
Published: July 6, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pbio.3001276
Copyright: ©2021 Haudiquet et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All individual
quantitative observations (S1 to S18 Dataset) that
underlie the data summarized in the figures and
results of the manuscript are available at: https://
Introduction
Mobile genetic elements (MGE) drive horizontal gene transfer (HGT) between bacteria, which
may result in the acquisition of virulence factors and antibiotic resistance genes (ARGs) [1,2].
DNA can be exchanged between cells via virions or conjugative systems [3,4]. Virions attach
to specific cell receptors to inject their DNA into the cell, which restricts their host range [5].
When replicating, bacteriophages (henceforth phages) may package bacterial DNA and trans-
fer it across cells (transduction). Additionally, temperate phages may integrate into the bacte-
rial genome as prophages, eventually changing the host phenotype [4]. In contrast, DNA
transfer by conjugation involves mating pair formation (MPF) between a donor and a recipi-
ent cell [6]. Even if phages and conjugative elements use very different mechanisms of DNA
transport, both depend crucially on interactions with the cell envelope of the recipient bacte-
rium. Hence, changes in the bacterial cell envelope may affect their rates of transfer.
Klebsiella pneumoniae (Kpn) is a gut commensal that has become a major threat to public
health [7,8] because it is acquiring MGEs encoding ARGs and virulence factors at a fast pace
[2,9]. This propensity is much higher in epidemic nosocomial multidrug-resistant lineages
than in hypervirulent strains producing infections in the community [10]. Kpn is a particularly
interesting model system to study the interplay between HGT and the cell envelope because it
is covered by a nearly ubiquitous Group I (or Wzx/Wzy dependent) polysaccharide capsular
structure [11,12], which is the first point of contact with incoming MGEs. Similar capsule loci
are present across the bacterial phylogeny [13]. There is one single capsule locus in Kpn [14],
located between galF and ugd, which has increased rate of recombination and HGT compared
to the rest of the genome [11,15,16]. This locus contains conserved genes encoding the pro-
teins necessary for the assembly and export of the capsule, which is a multistep biosynthesis
pathway. These conserved genes flank a highly variable region encoding enzymes that deter-
mine the oligosaccharide combination, linkage, and modification (and thus the serotype) [17].
The biochemical determination of the serotype has not been done for the bacteria correspond-
ing to the most recently sequenced genomes. But the genetic content of the capsule locus has
been shown to be a very good predictor of the capsule serotype. Such predictions are called
capsular locus types (CLTs) to distinguish them from experimentally determined serotypes
[17]. Here, we will use CLT to refer to the genomic predictions and serotype to mention the
capsule type. There are more than 140 genetically distinct CLTs, of which 76 have well-charac-
terized chemical structures and are referred to as serotypes [17]. Kpn capsules can extend well
beyond the outer membrane, up to 420 nm, which is 140 times the average size of the peptido-
glycan layer [18]. They enhance cellular survival to bacteriocins, immune response, and antibi-
otics [19–21], being a major virulence factor of the species. Intriguingly, the multidrug-
resistant lineages of Kpn exhibit higher capsular diversity than the virulent ones, which are
almost exclusively of the serotype K1 and K2 [10].
By its size, the capsule can hide phage receptors and block phage infection [22]. Since most
Kpn are capsulated, many of its virulent phages evolved to overcome the capsule barrier by
encoding serotype-specific depolymerases in their tail proteins [23,24]. For the same reason,
phages have evolved to use the capsule for initial adherence before attaching to the primary
cell receptor. Hence, instead of being hampered by the capsule, many Kpn phages have
become dependent on it [25,26]. This means that the capsule may affect the rates of HGT posi-
tively or negatively depending on how it enables or blocks phage infection. Furthermore,
intense phage predation may select for capsule swap or inactivation, because this renders bac-
teria resistant to serotype-specific phages. Serotype swaps may allow cells to escape phages to
which they were previously sensitive, but they may also expose them to new infectious phages.
In contrast, capsule inactivation can confer pan-resistance to capsule-dependent phages [25].
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 2 / 28
figshare.com/projects/Supplementary_Datasets/
114459 The 3980 genomes assemblies analyzed in
this study are publicly available from the RefSeq
database (accession numbers in S1 Dataset) All the
genomes of strains used for experimental evolution
and conjugation assays are publicly available from
ENA (accession numbers in S1 Table).
Funding: This work was supported by an ANR
JCJC (Agence national de recherche) grant [ANR
18 CE12 0001 01 ENCAPSULATION] awarded to O.
R. The laboratory is funded by a Laboratoire
d’Excellence ‘Integrative Biology of Emerging
Infectious Diseases’ grant [ANR-10-LABX-62-
IBEID], the INCEPTION program[PIA/ANR-16-
CONV-0005], and the FRM [EQU201903007835].
M.H. has received funding from the FIRE Doctoral
School (Centre de Recherche Interdisciplinaire,
programme Bettencourt) to attend conferences.
The funders had no role in the study design, data
collection and interpretation, or the decision to
submit the work for publication.
Competing interests: The authors have declared
that no competing interests exist.
Abbreviations: ARG, antibiotic resistance gene;
BBH, bidirectional best hits; CFU, colony-forming
unit; CLT, capsular locus type; DAP,
diaminopimelic acid; GT, glycosyl transferases;
GTA, gene transfer agent; HGT, horizontal gene
transfer; ICE, integrative conjugative element; Kpn,
Klebsiella pneumoniae; LB, Luria–Bertani; MGE,
mobile genetic element; MPF, mating pair
formation; ST, sequence type; WGA, whole-
genome alignment; wGRR, gene repertoire
relatedness weighted by sequence identity; WT,
wild type.
Regarding the effect of capsules on conjugation, very little is known, except that it is less effi-
cient between a few different serotypes of Haemophilus influenzae [27]. The interplay between
MGEs (phages and conjugative elements) and the capsule has the potential to strongly impact
Kpn evolution in terms of both virulence and antibiotic resistance because of the latter’s asso-
ciation with specific serotypes and MGEs.
HGT in Kpn is thought to take place by conjugation or in virions, since it is not part of the
known naturally transformable bacteria [28]. Hence, the capsule needs MGEs to vary by HGT,
but may block the acquisition of the very same MGEs. Moreover, capsulated species are associ-
ated with higher rates of HGT [29]. There is thus the need to understand the capsule’s precise
impact on gene flow and how the latter affects capsule evolution. Here, we leverage a very large
number of genomes of Kpn to investigate these questions using computational analyses that
are complemented with experimental data. As a result, we propose a model of capsule evolu-
tion involving loss and regain of function. This model explains how the interplay of the cap-
sules with different MGEs can either lower, increase, or rewire gene flow depending on the
way capsules affect their mechanisms of transfer.
Results
Gene flow is higher within than between serotype groups
We reasoned that if MGEs are specifically adapted to serotypes, then genetic exchanges should
be more frequent between bacteria of similar serotypes. We used Kaptive [17] to predict the
CLT in 3,980 genomes of Kpn. Around 92% of the isolates could be classed with good confi-
dence level. They include 108 of the 140 previously described CLTs of Klebsiella spp. The pan-
genome of the species includes 82,730 gene families, which is 16 times the average genome. It
contains 1,431 single copy gene families present in more than 99% of the genomes that were
used to infer a robust rooted phylogenetic tree of the species (average ultra-fast bootstrap of
98%, Fig 1A). Rarefaction curves suggest that we have extensively sampled the genetic diversity
of Kpn genomes, its CLTs, plasmids, and prophages (Fig 1B). We then inferred the gains and
losses of each gene family of the pangenome using PastML and focused on gene gains in the
terminal branches of the species tree predicted to have maintained the same CLT from the
node to the tip (91% of branches). This means that we can associate each of these terminal
branches with one single serotype. We found significantly more genes acquired (co-gained) in
parallel by different isolates having the same CLT than expected by simulations assuming ran-
dom distribution in the phylogeny (1.95×, Z-test p<0.0001, Fig 1C). This suggests that Kpn
exhibits more frequent within-serotype than between-serotype genetic exchanges.
Given the tropism of Kpn phages to specific serotypes, we wished to clarify if phages con-
tribute to the excess of intra-CLTs genetic exchanges. Since transduction events cannot be
identified unambiguously from the genome sequences, we searched for prophage acquisition
events, i.e., for the transfer of temperate phages from one bacterial genome to another. We
found that 97% of the strains were lysogens, with 86% being poly-lysogens, in line with our
previous results in a much smaller dataset [25]. In total, 9,886 prophages were identified in the
genomes. Their genes account for 16,319 families (19.5%) of the species pangenome (Fig 1B).
We then measured the gene repertoire relatedness weighted by sequence identity (wGRR)
between all pairs of prophages. The wGRR is a measure of genetic similarity that amounts to 0
if there are no homologs between two genomes, and one if all genes of the smaller genome
have a homolog with 100% identity in the other genome. This matrix was clustered, resulting in
2,995 prophage families whose history of vertical and horizontal transmissions was inferred
using the species phylogenetic tree (see “Prophage detection”). We found 3,269 independent
infection events and kept one prophage for each of them. We found that pairs of independently
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 3 / 28
infecting prophages are 1.7 times more similar when in bacteria with identical rather than dif-
ferent CLTs (Fig 1D; two-sample Kolmogorov–Smirnov test, p<0.0001). To confirm that
phage-mediated HGT is favored between strains of the same CLT, we repeated the analysis of
gene co-gains after removing the prophages from the pangenome. As expected, the preference
Fig 1. Gene flow is higher between strains of the same serotype. (A) Core genome phylogenetic tree with the 22 Klebsiella
quasipneumoniae subsp. similipneumoniae (misannotated as Kpn in RefSeq) strains as an outgroup (blue branches). The annotation circle
represents the 108 CLTs predicted by Kaptive. The largest clusters of CLTs (>20 isolates) are annotated (full list in https://doi.org/10.6084/
m9.figshare.14673156). (B) Rarefaction curves of the pangenome of prophages, plasmids, capsule genes, and all remaining genes (Genome).
The points represent 50 random samples for each bin (bins increasing by 10 genomes). The inset bar plot represents the percentage of gene
families of the Kpn pangenome including genes of plasmids or prophages (https://doi.org/10.6084/m9.figshare.14673141). (C) Histograms
of the excess of intra-CLT co-gains in relation to those observed inter-CLT (Observed/Expected ratio obtained by 1,000 simulations). The
analysis includes all genes (center), excludes prophages (bottom), or excludes plasmids (top) (https://doi.org/10.6084/m9.figshare.
14673147). (D) Gene repertoire relatedness between independently acquired prophages in bacteria of different (inter-CLT, gray) or
identical CLT (intra-CLT, black). The insert is a zoom of the distribution for the highest values of wGRR (https://doi.org/10.6084/m9.
figshare.14673144). (E) Linear regression of the wGRR between pairs of prophages and the number of capsular residues in common
between their hosts. The points represent the mean for each category, with their size corresponding to the number of pairs per category.
Error bars represent the standard error of the mean. The regression was performed on the original raw data, but only the averages are
represented for clarity (https://doi.org/10.6084/m9.figshare.14673165). CLT, capsular locus type; GLM, generalized linear model; Kpn,
Klebsiella pneumoniae; wGRR, gene repertoire relatedness weighted by sequence identity.
https://doi.org/10.1371/journal.pbio.3001276.g001
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 4 / 28
toward same-CLT exchanges decreased from 1.95×to 1.73×(Fig 1C). This suggests that HGT
tends to occur more frequently between strains of identical serotypes than between strains of
different serotypes, a trend that is amplified by the transfer of temperate phages.
Most of the depolymerases that allow phages to overcome the capsule barrier act on specific
disaccharides or trisaccharides, independently of the remaining monomers [30–32]. This
raises the possibility that phage-mediated gene flow could be higher between strains whose
capsules have common oligosaccharide residues. To test this hypothesis, we compiled the
information on the 76 capsular chemical structures previously described [33]. The genomes
with these CLTs, 59% of the total, show a weak but significant proportionality between pro-
phage similarity, and the number of similar residues in their host capsules (Fig 1E), i.e., pro-
phages, are more similar between bacteria with more biochemically similar capsules.
Recombination swaps biochemically related capsules
To understand the genetic differences between serotypes and how these could facilitate swaps,
we compared the gene repertoires of the different capsular loci (between galF and ugd,S1 Fig).
As expected from previous studies [11,17], this analysis revealed a clear discontinuity between
intra-CLT comparisons that had mostly homologous genes and the other comparisons, where
many genes (average = 10) lacked homologs across serotypes (Wilcoxon test, p<0.0001, Fig
2A). As a result, the capsule pangenome contains 325 gene families that are specific to a CLT
(out of 547, see “Pan- and persistent genomes”). This implies that serotype swaps require the
acquisition of multiple novel genes by horizontal transfer. To quantify and identify these CLT
swaps, we inferred the ancestral CLT in the phylogenetic tree and found a rate of 0.282 swaps
per branch (see “Serotype swaps identification”). We then identified 103 highly confident
swaps, some of which occurred more than once (Fig 2B). We used the chemical characteriza-
tion of the capsules described above to test if swaps were more likely between serotypes with
more similar chemical composition. Indeed, swaps occurred between capsules with an average
of 2.42 common sugars (mean Jaccard similarity 0.54), more than the average value across all
other possible CLT pairs (1.98, mean Jaccard similarity 0.38, Wilcoxon test, p<0.0001, S2A
Fig). Interestingly, the wGRR of the swapped loci is only 3% higher than the rest of pairwise
comparisons (S2B Fig). This suggests that successful swaps are poorly determined by the dif-
ferences in gene repertoires. Instead, they are more frequent between capsules that have more
similar chemical composition.
The existence of a single capsule locus in Kpn genomes suggests that swaps occur by homol-
ogous recombination at flanking conserved sequences [11]. We used Gubbins [34] to detect
recombination events in the 25 strains with terminal branch serotype swaps and closely related
completely assembled genomes (see “Detection of recombination tracts”). We found long
recombination tracts encompassing the capsule locus in 24 of these 25 genomes, with a median
length of 100.3 kb (Fig 2C). At least one border of the recombination tract was less than 3-kb
away from the capsule locus in 11 cases (46%). Using sequence similarity to identify the origin
of the transfer, we found that most recombination events occurred between distant strains and
no specific clade (Fig 2D). We conclude that serotype swaps occur by recombination at the
flanking genes with DNA from genetically distant isolates but chemically related capsules.
Capsule inactivation follows specific paths, might be driven by phage
predation, and spurs HGT
We sought to investigate whether the aforementioned swaps occur by an intermediary step
where cells have inactivated capsule loci. To do so, we first established the frequency of inacti-
vated capsular loci. We used the Kaptive software to detect missing genes, expected to be
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 5 / 28
encoded in capsule loci found on a single contig. We also used the Kaptive database of capsular
proteins to detect pseudogenes using protein–DNA alignments in all genomes. We found 55
missing genes and 447 pseudogenes, among 9% of the loci. The frequency of pseudogenes was
not correlated with the quality of the genome assembly (see “Identification of capsule pseudo-
genes and inactive capsule loci”), and all genomes had at least a part of the capsule locus. We
cannot exclude that some of these mutations fixated during passage prior to sequencing if
these conditions strongly select for capsule loss. However, many isolates harbor several pseu-
dogenes, which suggests that capsule inactivation is not due to very strong selection of one
inactivating mutation during passage. We classed 11 protein families as essential for capsule
production (Table A in S1 Text). At least one of these essential genes was missing in 3.5% of
Fig 2. Homologous recombination events lead to frequent CLT swaps.(A) Histogram of the comparisons of gene repertoire relatedness
(wGRR) between capsular loci of the same (intra-) or different (inter-) CLT (https://doi.org/10.6084/m9.figshare.14673171). (B) Network
of CLT swaps identified by ancestral state reconstruction, with edge thickness corresponding to the number of swaps, and numbers x
within nodes corresponding to the CLT (KLx). (C) Recombination encompassing the capsule locus detected with Gubbins. The positions
of the tracts are represented in the same scale, where the first base of the galF gene was set as 0 (https://doi.org/10.6084/m9.figshare.
14673150). (D) Putative donor–recipient pairs involved in the CLT swaps of panel C indicated in the Kpn tree. CLT, capsular locus type;
Kpn, Klebsiella pneumoniae; wGRR, gene repertoire relatedness weighted by sequence identity.
https://doi.org/10.1371/journal.pbio.3001276.g002
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 6 / 28
the loci, which means these strains are likely non-capsulated (Fig 3A). These variants are scat-
tered in the phylogenetic tree with no particular clade accounting for the majority of these var-
iants (S3 Fig), e.g., there are non-capsulated strains in 61 of the 617 sequence types (STs)
identified by Kleborate. These results suggest that capsule inactivation has little phylogenetic
inertia, i.e., it is a trait that changes very quickly, either because the variants are counterselected
or because capsules are quickly reacquired. Accordingly, we could not detect significant phylo-
genetic inertia with Pagel Lambda test [35] (p>0.05). Hence, capsule inactivation is frequent,
but non-capsulated lineages do not persist for long periods of time.
We further investigated the genetic pathways leading to capsule inactivation. Interestingly,
we found that the pseudogenization frequency follows the order of biosynthesis of the capsule
(linear regression, p= 0.001, R
2
= 0.78), with the first (wbaP or wcaJ) and second steps (glyco-
syl transferases, GT) being the most commonly inactivated when a single essential gene is a
pseudogene (Fig 3B). The overall frequency of gene inactivation drops by 14% per rank in the
biosynthesis chain. To confirm these results, we made several controls. To show that this is not
simply an effect of gene length, we normalized the inactivation frequency by gene length and
found the same relationship (S4A Fig,p= 0.005, R
2
= 0.7). To check that this was not a muta-
tion hotspot induced by simple sequence repeats, which are prone to polymerase-slippage–
induced mutations and sequencing errors, we searched for mono- and di-nucleotide tracts.
We found that their frequency in the commonly inactivated pair wcaJ/wbaP was smaller than
in the rarely mutated group of genes wzc/wzx/wzy (S4B Fig). Finally, we verified that our anal-
ysis was not impacted by a higher allelic diversity in these genes in the reference database. We
found that the most genetically diverse genes were not the most inactivated (S4C Fig).
Together, these results support the idea that selection plays a key role in the fixation of inacti-
vating mutations in the early genes of the capsule biosynthetic pathway.
Fig 3. Loss of function in the capsule locus. (A) Distribution of the numberof inactivated capsule genesper genome, split in2
categories: loci lacking a functional essential capsule gene (blue, non-capsulated strains), and other loci only lacking nonessential
capsule genes (white, not categorized as non-capsulated strains) (https://doi.org/10.6084/m9.figshare.14673153). (B) Linear regression
between the inactivation frequency and the rank of each gene in the biosynthesis pathway (p= 0.001, R
2
= 0.78). The numbers in the
scheme of the capsule assembly correspond to the order in the biosynthesis pathway (https://doi.org/10.6084/m9.figshare.14673153).
(C) Frequency of inactivated capsule genes arising in the non-capsulated clones isolated in 8 different strains after approximately 20
generations in LB growth medium. Genomes containing several missing genes and pseudogenes are not included (https://doi.org/10.
6084/m9.figshare.14673177). GT, glycosyl transferases; LB, Luria–Bertani.
https://doi.org/10.1371/journal.pbio.3001276.g003
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 7 / 28
To test if similar results are found when capsules are counterselected in the laboratory, we
analyzed a subset of populations stemming from a short evolution experiment in which popu-
lations of 8 different strains of Klebsiella spp. were diluted daily during 3 days (approximately
20 generations) under agitation in Luria–Bertani (LB), a medium known to select for capsule
inactivation [36]. Each strain belongs to a different phylogenetic group (ST), encompassing 6
different serotypes and isolation sources (Table B in S1 Text). We have previously shown that
under such conditions, phage pressure accelerates capsule loss [25]. After 3 days, non-capsu-
lated clones emerged in 22 out of 24 populations from 8 different ancestral strains. We isolated
one non-capsulated clone for Illumina sequencing from each population and searched for the
inactivating mutations with the same pipeline as for the genomic dataset. We also compared
our method with two popular tools used to detect new mutations in evolved isolates, breseq
[37] and snippy, which rely on read mapping onto a reference genome. All 3 approaches
yielded comparable conclusions, although some mutations were not found by all 3. Overall, 13
out of the 16 inactivated or deleted genes found by our method were also identified by either
breseq,snippy, or both. Read mapping approaches detected other types of mutations, like inter-
genic mutations, and few mutations in the rest of the genome, which are not detectable by our
targeted approach (https://doi.org/10.6084/m9.figshare.14673177). We found that most of
these were localized in wcaJ and wbaP (Fig 3C). In accordance with our comparative genomics
analysis, we found fewer loss-of-function mutations in GTs and wzc and none in the latter
steps of the biosynthetic pathway, except for one large deletion event encompassing almost all
the capsular locus (https://doi.org/10.6084/m9.figshare.14673177). Studies focusing on the
mutations conferring phage resistance in Kpn have also reported an abundance of loss-of-
function mutations in capsule genes leading to a non-capsulated phenotype [25,38], especially
wcaJ [39,40]. These results strongly suggest that mutations leading to the loss of capsule pro-
duction impose a fitness cost determined by the position of the inactivated gene in the biosyn-
thesis pathway.
Once a capsule locus is inactivated, the function can be reacquired by (1) reversion muta-
tions fixing the broken allele; (2) restoration of the inactivated function by acquisition of a
gene from another bacterium, eventually leading to a chimeric locus; and (3) replacement of
the entire locus leading to a CLT swap. Our analyses of pseudogenes provide some clues on
the relevance of the 3 scenarios. We found 111 events involving nonsense point mutations.
These could eventually be reversible (scenario 1) if the reversible mutation arises before other
inactivating changes accumulate. We also observed 269 deletions (100 of more than 2 nucleo-
tides) in the inactivated loci. These changes are usually irreversible in the absence of HGT. We
then searched for chimeric loci (scenario 2), i.e., CLTs containing at least 1 gene from another
CLT. We found 35 such loci, accounting for approximately 0.9% of the dataset (for example, a
wzc_KL1 allele in an otherwise KL2 loci), with only one occurrence of a wcaJ allele belonging
to another CLT, and none for wbaP. Finally, the analysis of recombination tracts detailed
above reveals frequent replacement of the entire locus between galF and ugd by recombination
(S1 Fig, scenario 3).
Since reacquisition of the capsule function might often require HGT, we enquired if capsule
inactivation was associated with higher rates of HGT. Indeed, the number of genes gained by
HGT per branch of the phylogenetic tree is higher in branches where the capsule was inacti-
vated than in the others (2-sample Wilcoxon test, p<0.0001, Fig 4A), even if these branches
have similar sizes (S6 Fig). We compared the number of phages and conjugative systems
acquired in the branches where capsules were inactivated against the other branches. This
revealed significantly more frequent (3.6 times more) acquisition of conjugative systems
(Fisher exact test, p<0.0001, Fig 4B) upon capsule inactivation. This was also the case, to a
lesser extent, for prophages. Intriguingly, we observed even higher relative rates of acquisition
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 8 / 28
of genes and MGEs in branches where the serotype was swapped (Fig 4A and 4B). The latter
branches are 2.7 times longer than the others (S6 Fig), which may be due to the recombination
tracts associated with the swap. The difference in branch lengths between the swapped and
inactivated categories are on the order of magnitude of the difference in the number of genes
gained, suggesting approximately similar rates of gene gain in both categories. In contrast, the
difference in terms of gained genes between branches with capsule swaps and branches where
capsules remained unchanged is much larger than 2.7. Hence, branches where capsules were
swapped, like those where they were inactivated, represent periods of more frequent acquisi-
tion of conjugative elements and phages relative to the periods where the capsule remained
unchanged. The acquisition rate is particularly larger for conjugative systems in branches
where the capsule was inactivated and larger for prophages in branches that swapped (6.5 ver-
sus 4.5 times more). Overall, the excess of HGT in periods of capsule inactivation facilitates the
reacquisition of a capsule and the novel acquisition of other potentially adaptive traits.
Conjugative systems are frequently transferred across serotypes
The large size of the Kpn capsule locus is difficult to accommodate in the phage genome, and
the tendency of phages to be serotype specific makes them unlikely vectors of novel capsular
loci. Also, the recombination tracts observed in Fig 2C are too large to be transduced by most
temperate phages of Kpn, whose prophages average 46 kb [25]. Since Kpn is not naturally
transformable, we hypothesized that conjugation is the major driver of capsule acquisition.
Around 80% of the strains encode a conjugative system, and 94.4% have at least one. Plasmids
alone make 25.5% of the pangenome (Fig 1B). To these numbers, one should add the genes
present in integrative conjugative elements (ICEs). Unfortunately, there is currently no
method to identify ICEs accurately in draft genomes. By subtracting the total number of conju-
gative elements from those identified in plasmids, we estimate that 41% of the conjugative sys-
tems in Kpn are not in plasmids but in ICEs. Since ICEs and conjugative plasmids have
Fig 4. Changes in capsule state impact HGT. (A) Number of genes gained in terminal branches of the phylogenetic
tree where the capsule was inactivated, swapped, and in the others (2-samples Wilcoxon tests). (B) Increase in the
frequency of acquisition of prophages and conjugative systems on branches where the capsule was either inactivated or
swapped, relative to the other branches, as represented by odds ratio (Fisher exact test). :p-value <0.0001 (https://
doi.org/10.6084/m9.figshare.14673159). HGT, horizontal gene transfer.
https://doi.org/10.1371/journal.pbio.3001276.g004
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 9 / 28
approximately similar sizes [41], the joint contribution of ICEs and plasmids in the species
pangenome is very large.
We identified independent events of infection by conjugative systems as we did for pro-
phages (see above). The 5,144 conjugative systems fell into 252 families with 1,547 infection
events. On average, pairs of conjugative systems acquired within the same CLT were only 3%
more similar than those in different CLTs. This suggests that phage- and conjugation-driven
HGT have very different patterns, since the former tend to be serotype specific, whereas the
latter are very frequently transferred across serotypes. This opposition is consistent with the
analysis of co-gains (Fig 1C), which were much more serotype dependent when plasmids were
excluded from the analysis and less serotype dependent when prophages were excluded. To
further test our hypothesis, we calculated the number of CLTs where one could find each fam-
ily of conjugative systems or prophages and then compared these numbers with the expecta-
tion if they were distributed randomly across the species. The results show that phage families
are present in much fewer CLTs than expected, whereas there is no bias for conjugative sys-
tems (Fig 5). We conclude that conjugation spreads plasmids across the species regardless of
Fig 5. Serotype specificity of prophages and conjugative systems. O
CLT
: observed number of serotypes infected per
family of homologous element. E
CLT
: expected number of serotypes infected per family of homologous element
generated by 1,000 simulations (see “CLT specificity”). This measure aims to capture the number of different CLT that
each prophage and conjugative system were able to infect, compared to what was expected by chance given the
phylogeny. When the elements distribute randomly across CLTs, the value is 0.5 (dashed line). Very low values
indicate high serotype specificity. One-sample Wilcoxon test. :p-value <0.0001 (https://doi.org/10.6084/m9.
figshare.14673162). CLT, capsular locus type.
https://doi.org/10.1371/journal.pbio.3001276.g005
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 10 / 28
serotype. Together, these results reinforce the hypothesis that conjugation drives genetic
exchanges between strains of different serotypes, decreasing the overall bias toward same-sero-
type exchanges driven by phages.
Capsule inactivation results in increased conjugation frequency
Together, these elements led us to hypothesize that capsule inactivation results in higher rates
of acquisition of conjugative elements. This is consistent with the observation that terminal
branches associated with inactive capsules have a higher influx of conjugative systems than
prophages (Fig 4B). In the absence of published data on the impact of the capsule on conjuga-
tion frequency, we tested experimentally our hypothesis on a diverse set of Klebsiella isolates
composed of four strains from different STs: three Klebsiella pneumoniae sensu stricto (sero-
types K1 and K2) and one Klebsiella variicola (serotype K30, also found in Kpn). To test the
role of the capsule in plasmid acquisition, we analyzed the conjugation frequency of these
strains and their non-capsulated counterparts, deprived of wcaJ, the most frequent pseudogene
in the locus both in the genome data and in our experimental evolution (see “Conjugation
assay”). For this, we built a plasmid that is mobilized in trans, i.e., once acquired by the new
host strain, it cannot further conjugate, due to their lack of a compatible conjugative system.
This allows to measure precisely the frequency of conjugation between the donor and the
recipient strain. In agreement with the results of the computational analysis, we found that the
frequency of conjugation is systematically and significantly higher in the mutant than in the
associated wild type (WT) for all four strains (paired Wilcoxon test, p-value = 0.002, Fig 6). On
average, non-capsulated strains conjugated 8.06 times more than capsulated strains. In strain
CG43, this difference was 20-fold.
Interestingly, the difference in conjugation rates between the mutants and their WT is
inversely proportional to the conjugation frequency in the WT, possibly because some WT
strains already conjugate at very high rates. Cell densities were normalized to the same optical
density (OD
600
= 0.9) before mating, and the donor culture was the same for all recipient
strains per biological replicate. Cells were allowed to conjugate for one hour. Still, there were
slightly fewer colony-forming unit (CFU; 3.2×less, paired Wilcoxon test, p<0.001) on the
membranes with non-capsulated mutants than on those with the WT. This may lead to a slight
underestimation of the conjugation frequency in non-capsulated mutants. As a consequence,
we may have underestimated the differences in conjugation frequency. Overall, these experi-
ments show that the ability to receive a conjugative element is increased in the absence of a
functional capsule. Hence, non-capsulated variants acquire more genes by conjugation than
the others. Interestingly, if the capsule is transferred by conjugation, this implies that capsule
inactivation favors the very mechanism leading to its subsequent reacquisition.
Discussion
The specificity of many Kpn phages to one or a few chemically-related serotypes is presumably
caused by their reliance on capsules to adsorb to the cell surface and results from the long-
standing coevolution of phages with their Kpn hosts. One might invoke environmental effects
to explain these results, since populations with identical or closely related serotypes might
often co-occur and thus potentiate more frequent cross infections. However, the same ecologi-
cal bias would be expected to increase the rates of conjugation between identical or closely
related serotypes. This could not be detected, suggesting that bias in intra-serotype gene flow is
mediated by phages. It is well known that some phages carry capsule depolymerases acting on
disaccharides or trisaccharides [30–32] that may be similar across serotypes and thus favor
transfer of phages between these cells. This fits our previous studies on the infection networks
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 11 / 28
of Kpn prophages [25] and suggests that a population of cells encoding and expressing a given
serotype has more frequent phage-mediated genetic exchanges with bacteria of identical or
chemically similar serotypes (Fig 7A). In this context, phages carrying multiple capsule depoly-
merases have broader host range and may have a key role in phage-mediated gene flow
between very distinct serotypes. For example, one broad host range virulent phage has been
found to infect 10 distinct serotypes because it encodes an array of at least 9 depolymerases
[24].
The interplay between the capsule and conjugative elements has been much less studied.
Our comparative genomics analyses reveal that conjugation occurs across strains of identical
or different CLTs at similar rates. Furthermore, our experimental data shows that non-capsu-
lated mutants are up to 20 times more receptive to plasmid conjugation than the capsulated
WT bacteria, an effect that seems more important for the WT that are poor recipients. These
results are likely to be relevant not only for non-capsulated strains, but also for those not
expressing the capsule at a given moment. If so, repression of the expression of the capsule
may allow bacteria to escape phages and endure extensive acquisition of conjugative elements.
Fig 6. Capsules negatively impact conjugation. Conjugation frequency of WT and their associated non-capsulated
(ΔwcaJ) mutants. The conjugation efficiency (T/N, see “Conjugation assay”) is represented on a log scale. Each pair of
points represents a biological replicate. Strains and their capsule locus type are listed in the order in which they appear
from the top to the bottom of the plot. The p-value for the paired Wilcoxon test is displayed (https://doi.org/10.6084/
m9.figshare.14673168). Kpn, Klebsiella pneumoniae; Kva, Klebsiella variicola; CG, clonal group; WT, wild type.
https://doi.org/10.1371/journal.pbio.3001276.g006
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 12 / 28
These results may also explain a long-standing conundrum in Kpn. The hypervirulent line-
ages of Kpn, which are almost exclusively of serotypes K1 and K2 [10,42], have reduced pan-
genome, plasmid, and capsule diversity. They also often carry additional factors like rmpA up-
regulating the expression of the capsule [42]. In contrast, they are very rarely multidrug resis-
tant. Our data suggest that the protection provided by thick capsules hampers the acquisition
of conjugative elements, which are the most frequent vectors of antibiotic resistance. Further-
more, the moments of capsule swap, repression of expression, or inactivation are expected to
be particularly deleterious for hypervirulent clones, because the capsule is a virulence factor,
Fig 7. A model for the interplay between serotypes and mobile elements. (A) The capsule impacts Kpn gene flow. A bacterial
population expressing a given serotype (blue or green) preferentially exchanges DNA by phage-mediated processes with bacteria of
identical or chemically similar serotypes. Such flow may be rare toward non-capsulated bacteria because they are often resistant to Kpn
phages. In contrast, conjugation occurs across serotypes and is more frequent to non-capsulated bacteria. (B) Proposed model for
serotype swaps in Kpn. The capsule locus is colored according to its type. Capsule inactivation is occasionally adaptive. The
pseudogenization process usually starts by the inactivation of the genes involved in the early stages of the capsule biosynthesis, as
represented by the size of the red cross on the capsule assembly scheme. Non-capsulated strains are often protected from Kpn phage
infections while acquiring more genes by conjugation. This increases the likelihood of capsule reacquisition. Such reacquisition can
bring a new serotype, often one that is chemically similar to the previous one, and might be driven by conjugation because of its high
frequency in non-capsulated strains. Serotype swaps rewire phage-mediated genetic transfers. IM, inner membrane; Kpn, Klebsiella
pneumoniae; OM, outer membrane.
https://doi.org/10.1371/journal.pbio.3001276.g007
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 13 / 28
thus further hampering their ability to acquire the conjugative MGEs that carry antibiotic
resistance. This may have favored a specialization of the clones into either hypervirulent or
multidrug resistance. Unfortunately, the capsule is not an insurmountable barrier for conjuga-
tion, and recent reports have uncovered the emergence of multidrug-resistant hypervirulent
clones [43,44]. The most worrisome pattern of evolution of such strains is the fusion of the res-
ident virulence plasmids of hypervirulent clones with highly mobile multidrug resistance plas-
mids [45]. Strikingly, the recently isolated MS3802 clone, which belongs to the hypervirulent
lineage ST23 and harbors a chimeric virulence/resistance plasmid, is string test negative and
encodes a strongly degraded KL1 capsule locus [46]. Capsule inactivation may have facilitated
the acquisition of a conjugative resistance plasmid that co-integrated with the resident viru-
lence plasmid.
We observed that branches of isolates lacking a functional capsule have higher rates of
acquisition of conjugative systems than prophages, whereas those where there was a capsule
swap have the inverse pattern (Fig 4B). What could justify these differences in the interplay of
the capsule with phages and conjugative elements? Phages must adsorb on the cell surface,
whereas there are no critical positive determinants for incoming conjugation pilus [47]. As a
result, serotype swaps may affect much more the flow of phages than that of conjugative ele-
ments. Capsule inactivation may have an opposite effect on phages and plasmids: it removes a
point of cell attachment for phages, decreasing their infection rates, and removes a barrier to
the conjugative pilus, increasing their ability to transfer DNA. Hence, when a bacterium has
an inactivated capsule, e.g., because of phage predation, it becomes more permissive for conju-
gation. In contrast, when a bacterium acquires a novel serotype, it may become sensitive to
novel phages resulting in rapid turnover of its prophage repertoire. These results implicate that
conjugation should be much more efficient at spreading traits across the entire Kpn species
than phage-mediated mechanisms, whose role would be important for HGT between strains
of similar or closely related serotypes (Fig 7A).
The existence of serotype swaps has been extensively described in the literature for Kpn
[16] and many other species [48,49]. Whether these swaps imply a direct serotype replacement,
or an intermediate non-capsulated state, is not sufficiently known. Several processes are
known to select for capsule inactivation in some bacteria, including growth in rich medium
[36], phage pressure, and immune response [25,26,39,50] (Fig 7B). Because of the physiological
effects of these inactivations, and their impact on the rates and types of HGT, it is important to
quantify the frequency of inactivated (or silent) capsular loci and the mechanisms favoring it.
Our study of pseudogenization of capsular genes revealed a few percent of putative non-capsu-
lated strains scattered in the species tree, opening the possibility that non-capsulated strains
are a frequent intermediate step of serotype swap. The process of capsule inactivation is shaped
by the capsule biosynthesis pathway, the frequency of pseudogenization decreasing linearly
with the rank of the gene in the capsule biosynthesis pathway. This suggests a major role for
epistasis in the evolutionary pathway leading to non-capsulated strains. Notably, the early
inactivation of later genes in the biosynthesis pathway, while the initial steps are still func-
tional, can lead to the sequestration of key molecules at the cell envelope or the toxic accumula-
tion of capsule intermediates (Fig 7B). Accordingly, Δwza and Δwzy mutants, but not ΔwcaJ,
lead to defects in the cell envelope of the strain Kpn SGH10 [51]. In Acinetobacter baumannii,
high-density transposon mutagenesis also recently revealed that inactivation of genes involved
in the last steps of capsule biosynthesis is much more deleterious than those encoding the early
steps [52].
Capsule reacquisition is more likely driven by conjugation than by phages, which are gener-
ally dependent on the capsule to infect their host. Hence, the increased rate of acquisition of
conjugative elements by non-capsulated strains may favor the process of capsule reacquisition,
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 14 / 28
and, eventually, serotype swap. Cycles of gain and loss of capsular loci have been previously
hypothesized in the naturally transformable species Streptococcus pneumoniae, because vacci-
nation leads to counterselection of capsulated strains, and natural transformation seems to
increase recombination in non-capsulated clades [53]. Accordingly, tracts with a median
length 42.7 kb encompassing the capsule locus were found in serotype-swapped S.pneumoniae
isolates [54]. In Kpn, such tracts were more than twice larger, which is consistent with the role
of conjugation in capsule swap. Interestingly, these swaps are more frequent between CLTs
encoding serotypes with common sugar residues, independently of their overall genetic relat-
edness. Understanding this result will require further work, but it suggests that genomic adap-
tation to the production of specific activated sugars can lead to genetic incompatibilities with
other capsular genes.
Our results are relevant to understand the interplay between the capsule and other mobile
elements in Kpn or other bacteria. We expect to observe more efficient conjugation when the
recipient bacteria lack a capsule in other species. For example, higher conjugation rates of
non-capsulated strains may help explain their higher recombination rates in Streptococcus
[55]. The serotype specificity of phages also opens intriguing possibilities for them and for
virion-derived elements. For instance, some Escherichia coli strains able to thrive in freshwater
reservoirs have capsular loci acquired in a single-block horizontal transfer from Kpn [56]. This
could facilitate interspecies phage infections (and phage-mediated HGT), since Kpn phages
may now be adsorbed by these strains. Gene transfer agents (GTA) are co-options of virions
for intraspecies HGT that are frequent among alpha-proteobacteria [57] (but not yet described
in Kpn). They are likely to have equivalent serotype specificity, since they attach to the cell
envelope using structures derived from phage tails. Indeed, the infection by the Rhodobacter
capsulatus GTA model system depends on the host Wzy capsule [58], and non-capsulated vari-
ants of this species are phage resistant [59] and impaired in GTA-mediated transfer [60]. Our
general prediction is that species where cells tend to be capsulated are going to coevolve with
phages, or phage-derived tail structures, such that the latter will tend to become serotype spe-
cific. We speculate that future developments on the systematic detection of depolymerase
genes will shed light on depolymerase swaps between phages, as a reciprocal phenomenon to
capsule serotype swaps. Here, we could not make such an analysis since we identified very few
depolymerases. Similar difficulties to find depolymerases were recently observed [25,61], sug-
gesting that many such proteins remain to be identified.
These predictions have an impact in the evolution of virulence and antimicrobial therapy.
Some alternatives to antibiotics—phage therapy, depolymerases associated with antibiotics,
pyocins, and capsular polysaccharide vaccines—may select for the inactivation of the capsule
[25,38,39]. Such non-capsulated variants have often been associated with better disease out-
comes [62], lower antibiotic tolerance [21], and reduced virulence [20]. However, they can
also be more successful colonizers of the urinary tract [63]. Our results suggest that these non-
capsulated variants are at higher risk of acquiring resistance and virulence factors through con-
jugation, because ARGs and virulence factors are often found in conjugative elements in Kpn
and in other nosocomial pathogens. Conjugation may also eventually lead to the reacquisition
of functional capsules. At the end of the inactivation–reacquisition process, recapitulated on
Fig 7, the strains may be capsulated, more virulent, and more antibiotic resistant.
Materials and methods
Genomes
We used the PanACoTa tool to generate the dataset of genomes [64]. We downloaded all the
5,805 genome assemblies labeled as Klebsiella pneumoniae sensu stricto (Kpn) from NCBI
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 15 / 28
RefSeq (accessed on October 10, 2018). We removed lower-quality assemblies by excluding
genomes with L90>100. The pairwise genetic distances between all remaining genomes of the
species was calculated by order of assembly quality (L90) using MASH [65]. Strains that were
too divergent (MASH distance >6%) to the reference strain or too similar (<0.0001) to other
strains were removed from further analysis. The latter tend to have identical capsule serotypes,
and their exclusion does not eliminate serotype swap events. This resulted in a dataset of 3,980
strains which were re-annotated with prokka (v1.13.3) [66] to use consistent annotations in all
genomes. Erroneous species annotations in the GenBank files were corrected using Kleborate
(https://github.com/katholt/Kleborate). This step identified 22 Klebsiella quasipneumoniae
subspecies similipneumoniae (Kqs) genomes that were used to root the species tree and
excluded from further analyses. The accession number for each analyzed genome is presented
in https://doi.org/10.6084/m9.figshare.14673156, along with all the annotations identified in
this study.
Pan- and persistent genome
The pangenome is the full repertoire of homologous gene families in a species. We inferred the
pangenome with the connected-component clustering algorithm of MMSeqs2 (release 5) [67]
with pairwise bidirectional coverage >0.8 and sequence identity >0.8. The persistent genome
was built from the pangenome, with a persistence threshold of 99%, meaning that a gene fam-
ily must be present in single copy in at least 3,940 genomes to be considered persistent.
Among the 82,730 gene families of the Kpn pangenome, there were 1,431 gene families present
in 99% of the genomes, including the Kqs. We used mlplasmids to identify the “plasmid” con-
tigs (default parameters, species “Klebsiella pneumoniae” [68]). To identify the pangenome of
capsular loci present in the Kaptive database, we used the same method as above, but we low-
ered the sequence identity threshold to >0.4 to put together more remote homologs.
Phylogenetic tree
To compute the species phylogenetic tree, we aligned each of the 1,431 protein families of the
persistent genome individually with mafft (v7.407) [69] using the option FFT-NS-2, back-
translated the sequences to DNA (i.e., replaced the amino acids by their original codons) and
concatenated the resulting alignments. We then made the phylogenetic inference using
IQ-TREE (v1.6.7.2) [70] using ModelFinder (-m TEST) [71] and assessed the robustness of the
phylogenetic inference by calculating 1,000 ultra-fast bootstraps (-bb 1,000) [72]. There were
220,912 parsimony-informative sites over a total alignment of 1,455,396 bp, and the best-fit
model without gamma correction was a general time-reversible model with empirical base fre-
quencies allowing for invariable sites (GTR+F+I). We did not use the gamma correction
because of branch length scaling issues, which were 10 times longer than with simpler models,
and is related to an optimization problem with big datasets in IQ-TREE. The tree is very well
supported, since the average ultra-fast bootstrap support value was 97.6% and the median was
100%. We placed the Kpn species root according to the outgroup formed by the 22 Kqs strains.
The tree, along with Kleborate annotations, can be visualized and manipulated in https://
microreact.org/project/kk6mmVEDfa1o3pGQSCobdH/9f09a4c3.
Capsule locus typing
We used Kaptive [17] with default options and the “K locus primary reference” to identify the
CLT of strains. The predicted CLT is assigned a confidence level, which relies on the overall
alignment to the reference CLT, the allelic composition of the locus, and its fragmentation
level. We assigned the CLT to “unknown” when the confidence level of Kaptive was indicated
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 16 / 28
as “none” or “low,” as suggested by the authors of the software. This only represented 7.9% of
the genomes. After this filtering, we simply considered that 2 CLT are the same if they are both
annotated with the same KLx name.
Identification of capsule pseudogenes and inactive capsule loci
We first compiled the list of missing expected genes from Kaptive, which is only computed by
Kaptive for capsule loci encoded in a single contig. Then, we used the Kaptive reference data-
base of Kpn capsule loci to retrieve capsule reference genes for all the identified serotypes. We
searched for sequence similarity between the proteins of the reference dataset and the 3,980
genome assemblies using blastp and tblastn (v.2.9.0) [73]. We then searched for the following
indications of pseudogenization: stop codons resulting in protein truncation, frameshift muta-
tions, insertions, and deletions (https://doi.org/10.6084/m9.figshare.14673153). Truncated and
frameshifted coding sequences covering at least 80% of the original protein in the same reading
frame were considered functional. Additionally, a pseudogene did not result in a classification
of inactivated function if we could identify an intact homolog or analog. For example, if
wcaJ_KL1 has a frameshift, but wcaJ_KL2 was found in the genome, the pseudogene was
flagged and not used to define non-capsulated mutants. Complete gene deletions were identi-
fied by Kaptive among capsular loci encoded on a single contig. We built a dictionary of genes
that are essential for capsule production by gathering a list of genes (annotated as the gene
name in the Kaptive database) present across all CLTs and which are essential for capsule pro-
duction according to experimental evidence (Table A in S1 Text). The absence of a functional
copy of these essential genes resulted in the classification “non-capsulated” (except wcaJ and
wbaP, which are mutually exclusive). To correlate the pseudogenization frequency with the
order in the capsular biosynthesis process, we first sought to identify all glycosyl transferases
from the different CLTs and grouped them in one category. To do so, we retrieved the GO
molecular functions listed on UniProtKB of the genes within the Kaptive reference database.
For the genes that could be ordered in the biosynthesis chain (Table A in S1 Text), we com-
puted their frequency of inactivation by dividing the count of inactivated genes by the total
number of times it is present in the dataset.
To test that sequencing errors and contig breaks were not leading to an excess of pseudo-
genes in certain genomes, we correlated the number of pseudogenes (up to 11) and missing
genes with 2 indexes of sequence quality, namely, the sequence length of the shortest contig at
50% of the total genome length (N50) and the smallest number of contigs whose length sum
makes up 90% of genome size (L90). We found no significant correlation in both cases (Spear-
man correlation test, p-values >0.05), suggesting that our results are not strongly affected by
sequencing artifacts and assembly fragmentation.
Genetic similarity
We searched for sequence similarity between all proteins of all prophages or conjugative sys-
tems using MMSeqs2 with the sensitivity parameter set to 7.5. The hits were filtered (e-
value <10
−5
,35% identity, coverage >50% of the proteins) and used to compute the set of
bidirectional best hits (BBH) between each genome pair. BBH were used to compute the gene
repertoire relatedness between pairs of genomes (weighted by sequence identity):
wGRRA;B¼Pi
idðAi;BiÞ
minð#A;#BÞ;
as previously described [74], where A
i
and B
i
are the pair iof homologous proteins present in
A and B (containing respectively #Aand #Bproteins), id(A
i
, B
i
) is their sequence identity, and
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 17 / 28
min(#A, #B) is the number of proteins of the element encoding the fewest proteins (#Aor #B).
wGRR varies between 0 and 1. It amounts to 0 if there are no homologous proteins between
the genomes, and one if all genes of the smaller genome have a homolog in the other genome.
Hence, the wGRR accounts for both frequency of homology and degree of similarity among
homologs.
Inference of genes ancestral states
We inferred the ancestral state of each pangenome family with PastML (v1.9.23) [75] using the
maximum-likelihood algorithm MPPA and the F81 model. We also tried to run Count [76]
with the ML method to infer gene gains and losses from the pangenome, but this took a pro-
hibitive amount of computing time. To check that PastML was producing reliable results, we
split our species tree (cuttree function in R, package stats) in 50 groups and for the groups that
took less than a month of computing time with Count (2,500 genomes), we compared the
results of Count to those of PastML. The 2 methods were highly correlated in term of number
of inferred gains per branch (Spearman correlation test, Rho = 0.88, p-value <0.0001). We
used the results of PastML, since it was much faster and could handle the whole tree in a single
analysis. Since the MPPA algorithm can keep several ancestral states per node if they have sim-
ilar and high probabilities, we only counted gene gains when both ancestral and descendant
nodes had one single distinct state (absent ➔present).
Analysis of conjugative systems
To detect conjugative systems, Type IV secretion systems, relaxases, and infer their MPF types,
we used TXSScan with default options [77]. We then extracted the protein sequence of the
conjugation systems and used these sequences to build clusters of systems by sequence similar-
ity. We computed the wGRR (see “Genetic similarity”) between all pairs of systems and clus-
tered them in wGRR families by transitivity when the wGRR was higher than 0.99. This means
that some members of the same family can have a wGRR <0.99. This threshold was defined
based on the analysis of the shape of the distribution of the wGRR (S5A Fig). We used a recon-
struction of the presence of members of each gene family in the species phylogenetic tree to
infer the history of acquisition of conjugative elements (see “Inference of genes ancestral
state”). To account for the presence of orthologous families, i.e., those coming from the same
acquisition event, we kept only 1 member of a wGRR family per acquisition event. For exam-
ple, if a conjugative system of the same family is present in 4 strains, but there were 2 acquisi-
tion events, we randomly picked 1 representative system for each acquisition event (in this
case, 2 elements, 1 per event). Elements that resulted from the same ancestral acquisition event
are referred as orthologous systems. We combined the predictions of mlplasmids and TXSScan
to separate conjugative plasmids from ICEs. The distribution of conjugative system’s MPF
type in the chromosomes and plasmids is shown in S7 Fig.
Prophage detection
We used PHASTER [78] to identify prophages in the genomes (accessed in December 2018).
The category of the prophage is given by a confidence score that corresponds to “intact,”
“questionable,” or “incomplete.” We kept only the “intact” prophages because other categories
often lack essential phage functions. We further removed prophage sequences containing
more than 3 transposases after annotation with ISFinder [79] because we noticed that some
loci predicted by PHASTER were composed of arrays of insertion sequences. We built clusters
of nearly identical prophages with the same method used for conjugative systems. The wGRR
threshold for clustering was also defined using the shape of the distribution (S5B Fig). The
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 18 / 28
definition of orthologous prophages follows the same principle than that of conjugative sys-
tems, they are elements that are predicted to result from one single past event of infection.
Serotype swaps identification
We inferred the ancestral state of the capsular CLT with PastML using the maximum-likeli-
hood algorithm MPPA, with the recommended F81 model [75]. In the reconstruction proce-
dure, the low confidence CLTs were treated as missing data. This analysis revealed that
serotype swaps happen at a rate of 0.282 swaps per branch, which are, on average, 0.000218
substitutions/site long in our tree. CLT swaps were defined as the branches where the descen-
dant node state was not present in the ancestral node state. In 92% of the swaps identified by
MPPA, there was only 1 state predicted for both ancestor and descendant node, and we could
thus precisely identify the CLT swaps. These swaps were used to generate the network in
Fig 3A.
Detection of recombination tracts
We detected recombination tracts with Gubbins v2.4.0 [34]. Our dataset is too large to build
one meaningful whole-genome alignment (WGA). Gubbins is designed to work with closely
related strains, so we split the dataset into smaller groups defined by a single ST. We then
aligned the genomes of each ST with snippy v4.3.8 (https://github.com/tseemann/snippy), as
recommended by the authors in the documentation. The reference genome was picked ran-
domly among the complete assemblies of each ST. We analyzed the 25 groups in which a CLT
swap happened (see above) and for which a complete genome was available as a reference. We
launched Gubbins independently for each WGA, using default parameters. We focused on the
terminal branches to identify the recombination tracts resulting in CLT swap. We enquired on
the origin of the recombined DNA using a sequence similarity approach. We used blastn [73]
(-task megablast) to find the closest match of each recombination tract by querying the full
tract against our dataset of genome assemblies and mapped the closest match based on the bit
score onto the species tree.
Identification of co-gains
We used the ancestral state reconstruction of the pangenome families to infer gene acquisi-
tions at the terminal branches. We then quantified how many times an acquisition of the same
gene family of the pangenome (i.e., co-gains) occurred independently in genomes of the same
CLT. This number was compared to the expected number given by a null model where the
CLT does not impact the gene flow. The distribution of the expectation of the null model was
made by simulation in R, taking into account the phylogeny and the distribution of CLTs. In
each simulation, we used the species tree to randomly redistribute the CLT trait on the termi-
nal branches (keeping the frequencies of CLTs equal to those of the original data). We ran
1,000 simulations and compared them with the observed values with a 1-sample Z-test [80]:
Acquisition specificity score ¼Pg
Ig ðIg1Þ
Tg ðTg1Þ;
where the numerator is the number of pairs with gains in a CLT, and the denominator is the
number of all possible pairs. With each gene family of the pangenome g, the number of gene
gains in strains of the same CLT I, and the total number of gene gains T. This corresponds to
the sum of total number of co-gains within a CLT, normalized by the total number of co-gains
for each gene. This score captures the amount of gene acquisitions that happened within
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 19 / 28
strains of the same CLT. If the observed score is significantly different than the simulations
assuming random distribution, it means that there was more genetic exchange within CLT
groups than expected by chance.
CLT specificity
We used the ancestral reconstruction of the acquisition of prophages and conjugative systems
to count the number of distinct CLT in which such an acquisition occurred. For example, one
prophage family can be composed of 10 members, coming from 5 distinct infection events in
the tree: 2 in KL1 bacteria and 3 in KL2 bacteria. Therefore, we count 5 acquisitions in 2 CLT
(CLT
obs
= 2). The null model is that of no CLT specificity. The distribution of the expected
number of CLT infected following the null model was generated by simulation (n= 1,000), as
described above (see “Identification of co-gains”), and we plotted the specificity score as fol-
lows:
Specificity score ¼CLTobs
ðCLTobs þCLTexpÞ;
where CLT
obs
is the observed number of CLT infected, and CLTexp is the mean number of CLT
infected in the simulations. Thus, the expected value under nonspecificity is 0.5.
Under our example of 5 acquisitions in 2 CLT (CLT
obs
= 2), a CLTexp of 2 would mean that
there is no difference between the observed and expected distributions across CLTs, and the
specificity score is 0.5. Values lower than 0.5 indicate a bias toward regrouping of elements in
a smaller than expected number of CLTs, whereas values higher than 0.5 indicate over-disper-
sion across CLTs. The statistics computed on Fig 5 are the comparison of all the specificity
scores for all the prophage and conjugative systems families to the null model (score = 0.5)
with a 1-sample Wilcoxon signed rank test.
Handling of draft assemblies
Since more than 90% of our genome dataset is composed of draft assemblies, i.e., genomes
composed of several chromosomal contigs, we detail here the steps undertaken to reduce the
impact of such fragmentation on our analysis. We only included prophages and conjugative
systems that are localized on the same contig (see “Prophage detection” and “Analysis of con-
jugative systems”). Kaptive is able to handle draft assemblies and adjust the confidence score
accordingly when the capsule locus is fragmented, so we relied on the Kaptive confidence
score to annotate the CLT, which was treated as missing data in all the analysis when the score
was below “Good” (see “Capsule locus typing”). For the detection of missing capsular genes,
performed by Kaptive, we verified that only non-fragmented capsular loci are included (see
“Identification of capsule pseudogenes and inactive capsule loci”). For the detection of capsule
pseudogenes, we included all assemblies and flagged pseudogenes that were localized on the
border of a contig (last gene on the contig). Out of the 502 inactivated/missing genes, 47 were
localized at the border of a contig. We repeated the analysis presented on Fig 3B after remov-
ing these pseudogenes and found an even better fit for the linear model at R
2
= 0.77 and
p= 0.004. Of note, such contig breaks are likely due to IS insertions, forming repeated regions
that are hard to assemble, so we kept them in the main analysis.
Analyses of lab-evolved non-capsulated clones
To pinpoint the mechanisms by which a diverse set of strains became non-capsulated, we took
advantage of an experiment performed in our lab and described previously in [36]. Briefly, 3
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 20 / 28
independent overnight cultures of 8 strains (Table B in S1 Text) were diluted 1:100 into 5 mL
of fresh LB and incubated at 37˚C under agitation. Each independent population was diluted
1:100 into fresh LB every 24 hours for 3 days (approximately 20 generations). We then plated
serial dilutions of each population. A single non-capsulated clone per replicate population was
isolated based on their translucent colony morphology, except in 2 replicate populations
where all colonies plated were capsulated. We performed DNA extraction with the guanidium
thiocyanate method [81], with modifications. DNA was extracted from pelleted cells grown
overnight in LB supplemented with 0.7 mM EDTA. Additionally, RNAse A treatment (37˚C,
30 minutes) was performed before DNA precipitation. Each clone (n= 22) was sequenced by
Illumina with 150pb paired-end reads, yielding approximately 1 GB of data per clone. The
reads were assembled with Unicycler v0.4.4 [82], and the assemblies were checked for pseudo-
genes (see “Identification of capsule pseudogenes and inactive capsule loci”). We also used bre-
seq [37] and snippy (https://github.com/tseemann/snippy) to verify that there were no further
undetected mutations in the evolved sequenced clones (https://doi.org/10.6084/m9.figshare.
14673177).
Generation of capsule mutants
Isogenic capsule mutants were constructed by an in-frame deletion of wcaJ by allelic exchange
as reported previously [36]. Deletion mutants were first verified by Sanger, and Illumina
sequencing revealed that there were no off-target mutations.
Conjugation assay
Construction of pMEG-Mob plasmid. A mobilizable plasmid named pMEG-Mob was
built by assembling the region containing the origin of transfer of the pKNG101 plasmid [83]
and the region containing the origin of replication, kanamycin resistance cassette, and IPTG-
inducible cfp from the pZE12:CFP plasmid [84] (see Table C in S1 Text, and plasmid map, S8
Fig). We amplified both fragments of interest by PCR with the Q5 high fidelity DNA polymer-
ase from New England Biolabs (NEB), with primers adapted for Gibson assembly designed
with Snapgene, and used the NEB HiFi Builder mix following the manufacturer’s instructions
to assemble the 2 fragments. The assembly product was electroporated into electro-competent
E.coli DH5αstrain. KmR colonies were isolated, and correct assembly was checked by PCR.
Cloned pMEG-Mob plasmid was extracted using the QIAprep Spin Miniprep Kit, and electro-
poration into the donor strain E.coli MFD λ-pir strain [85]. The primers used to generate
pMEG-Mob are listed in Table D in S1 Text.
Conjugation assay. Recipient strains of Klebsiella spp. were diluted at 1:100 from a LB
overnight into fresh LB in a final volume of 3 mL. Donor strain E.coli MFD λ-pir strain (dia-
minopimelic acid (DAP) auxotroph), which is carrying the pMEG-Mob plasmid, exhibited
slower growth than Klebsiella strains and was diluted at 1:50 from an overnight into fresh
LB + DAP (0.3 mM) + Kanamycin (50 μg/ml). Cells were allowed to grow at 37˚C until late
exponential growth phase (Optical density; OD of 0.9 to 1) and adjusted to an OD of 0.9.
The cultures were then washed twice in LB and mixed at a 1:1 donor–recipient ratio. Donor–
recipient mixes were then centrifuged for 5 minutes at 13,000 rpm, resuspended in 25-μL
LB+DAP, and deposited on a MF-Millipore Membrane Filter (0.45 μm pore size) on nonselec-
tive LB+DAP plates. The mixes were allowed to dry for 5 minutes with the lid open, and then
incubated at 37˚C. After 1 hour, membranes were resuspended in 1-mL phosphate-buffered
saline (PBS) and thoroughly vortexed. Serial dilutions were plated on selective (LB+Km) and
non-selective (LB+DAP) plates to quantify the number of transconjugants (T) and the total
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 21 / 28
number of cells (N). The conjugation efficiency was computed with the following:
Conjugation efficiency ¼T
N
This simple method is relevant in our experimental setup because the plasmid can only be
transferred from the donor strain to the recipient strain, and the duration of the experiment
only allowed for minimal growth [86]. The lack of the conjugative machinery of MPF type I
(the MPF type of RK2) within the plasmid and in the recipient strains prevents the transfer
across recipient strains (see “Analysis of conjugative systems”).
Data analysis
All the data analyses were performed with R version 3.6 and Rstudio version 1.2. We used the
packages ape v5.3 [87], phangorn v2.5.5 [88], and treeio v1.10 [89] for the phylogenetic analy-
ses. Statistical tests were performed with the base package stats. For data frame manipulations
and simulations, we also used dplyr v0.8.3 along with the tidyverse packages [90] and data.
table v1.12.8.
Supporting information
S1 Fig. Comparison of 2 capsular loci. Two CLTs (KL112 and KL24) involved in CLT swap,
with the essential genes for capsule expression colored in blue. Gray tracks correspond to the
sequence identity (computed using blastn) above 90% (see scale) to indicate highly similar
homologs (liable to recombine). CLT, capsular locus type.
(TIFF)
S2 Fig. Similarity between swapped CLT and other CLT. (A) Comparison of sugar composi-
tion similarity (Jaccard similarity) between swapped vs. others CLTs. (B) Comparison of
genetic similarity (wGRR) between swapped vs. others CLTs. The p-value displayed is for the
2-sample Wilcoxon test (https://doi.org/10.6084/m9.figshare.14673180). CLT, capsular locus
type; wGRR, gene repertoire relatedness weighted by sequence identity.
(TIFF)
S3 Fig. Phylogenetic distribution of the inactivated capsular loci. The blue dots represent
the putative inactivated capsules, which have at least 1 essential gene for capsule production
pseudogenized or deleted (https://doi.org/10.6084/m9.figshare.14673156).
(TIFF)
S4 Fig. Controls for the inactivated capsule gene analysis. (A) Linear regression between the
inactivation frequency normalized by average gene length and the rank of each gene in the bio-
synthesis pathway (p= 0.005, R
2
= 0.7) (https://doi.org/10.6084/m9.figshare.14673183). (B)
Number of SSR in the core capsule genes in the Kaptive reference database (https://doi.org/10.
6084/m9.figshare.14673174). (C) Genetic diversity of core capsule genes within the Kaptive
reference database, represented by the percent of identity of all pairwise alignments of the pro-
teins from different reference capsule loci (https://doi.org/10.6084/m9.figshare.14673192).
SSR, simple sequence repeats.
(TIFF)
S5 Fig. Distribution of the similarity measured by wGRR between pairs of conjugative sys-
tems (A) and between pairs of prophages (B) for wGRR >0. The arrows represent the
threshold (wGRR >0.99) set for clustering into families of highly similar elements. Since we
performed transitive clustering to build the families, some elements belonging to the same
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 22 / 28
families have wGRR <0.99. We annotated the distribution of conjugation systems belonging
to the same MPF type, which shows that systems of the same MPF are very similar but are
below the selected threshold for clustering (https://doi.org/10.6084/m9.figshare.14673144 and
https://doi.org/10.6084/m9.figshare.14673186). MPF, mating pair formation; wGRR, gene rep-
ertoire relatedness weighted by sequence identity.
(TIFF)
S6 Fig. Changes in capsule state and branch length. The capsule state changes among
branches of the species tree are represented on the xaxis, and the branch length is represented
on the yaxis in substitution per site. Individual points represent the mean for each group, and
the bars represent the standard error. The p-values for the t test are represented on top of each
comparisons (https://doi.org/10.6084/m9.figshare.14673159). We also performed a 2-sample
Wilcoxon test to compare the medians (“Others” vs. “inactivation”: p<0.0001; “Others” vs.
“Swap”: p<0.0001).
(TIFF)
S7 Fig. Distribution of conjugation system MPF types. Conjugation systems are classified in
2 categories according to their genomic location, which was predicted with the mlplasmids
classifier. The MPF was predicted with the CONJscan module of MacSyfinder. Absolute num-
ber of systems are displayed for each category (https://doi.org/10.6084/m9.figshare.14673189).
MPF, mating pair formation.
(TIFF)
S8 Fig. pMEG-Mob plasmid genetic map. pMEG-Mob was constructed by Gibson assembly
from plasmids pKNG101 and pZE12. It encodes a colE1/pUC origin of replication (high copy
number), a selectable marker (Kanamycin resistance cassette, green), the mobilizable region of
pKNG101 which is composed of the origin of transfer of RK2 and 2 genes involved in conjuga-
tion (traJ and traK), a counter selectable marker (sacB), and an inducible CFP gene (IPTG
induction). pMEG-Mob can only be mobilized in trans and thus can only be transferred from
a strain expressing the RK2 conjugative machinery, which is absent from the panel of strains
we used as recipients.
(TIFF)
S1 Text. Supporting tables with references. Supporting information containing detailed list
of essential capsule genes, strains, plasmids, and primers used in this study.
(PDF)
Acknowledgments
We thank Rafał Mostowy, Jorge Moura de Sousa, Nienke Buddlelmeijer, Olivier Tenaillon,
Marie Touchon, and other lab members for fruitful discussions. We thank Sylvain Brisse for
providing us with Klebsiella strains. We thank Christiane Forestier and Damien Balestrino for
providing the pKNG101 plasmid and Jean-Marc Ghigo and Christophe Beloin for the gift of
pZE12::CFP used to construct pMEG-Mob and E.coli MFD λ-pir.
Author Contributions
Conceptualization: Matthieu Haudiquet, Olaya Rendueles, Eduardo P. C. Rocha.
Data curation: Matthieu Haudiquet, Olaya Rendueles, Eduardo P. C. Rocha.
Formal analysis: Matthieu Haudiquet.
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 23 / 28
Funding acquisition: Olaya Rendueles, Eduardo P. C. Rocha.
Investigation: Matthieu Haudiquet, Olaya Rendueles, Eduardo P. C. Rocha.
Methodology: Matthieu Haudiquet.
Project administration: Olaya Rendueles, Eduardo P. C. Rocha.
Resources: Matthieu Haudiquet, Amandine Buffet, Olaya Rendueles, Eduardo P. C. Rocha.
Software: Matthieu Haudiquet, Eduardo P. C. Rocha.
Supervision: Olaya Rendueles, Eduardo P. C. Rocha.
Validation: Matthieu Haudiquet, Olaya Rendueles, Eduardo P. C. Rocha.
Visualization: Matthieu Haudiquet.
Writing – original draft: Matthieu Haudiquet, Olaya Rendueles, Eduardo P. C. Rocha.
Writing – review & editing: Matthieu Haudiquet, Olaya Rendueles, Eduardo P. C. Rocha.
References
1. Diard M, Hardt W-D. Evolution of bacterial virulence. FEMS Microbiol Rev. 2017; 41:679–697. https://
doi.org/10.1093/femsre/fux023 PMID: 28531298
2. Navon-Venezia S, Kondratyeva K, Carattoli A. Klebsiella pneumoniae: a major worldwide source and
shuttle for antibiotic resistance. FEMS Microbiol Rev. 2017; 41:252–275. https://doi.org/10.1093/
femsre/fux013 PMID: 28521338
3. Cabezo
´n E, Ripoll-Rozada J, Peña A, de la Cruz F, Arechaga I. Towards an integrated model of bacte-
rial conjugation. FEMS Microbiol Rev. 2015; 39:81–95. https://doi.org/10.1111/1574-6976.12085
PMID: 25154632
4. Touchon M, Moura de Sousa JA, Rocha EP. Embracing the enemy: the diversification of microbial gene
repertoires by phage-mediated horizontal gene transfer. Curr Opin Microbiol. 2017; 38:66–73. https://
doi.org/10.1016/j.mib.2017.04.010 PMID: 28527384
5. Bertozzi Silva J, Storms Z, Sauvageau D. Host receptors for bacteriophage adsorption. FEMS Microbiol
Lett. 2016; 363:fnw002. https://doi.org/10.1093/femsle/fnw002 PMID: 26755501
6. de la Cruz F, Frost LS, Meyer RJ, Zechner EL. Conjugative DNA metabolism in Gram-negative bacte-
ria. FEMS Microbiol Rev. 2010; 34:18–40. https://doi.org/10.1111/j.1574-6976.2009.00195.x PMID:
19919603
7. Forster SC, Kumar N, Anonye BO, Almeida A, Viciani E, Stares MD, et al. A human gut bacterial
genome and culture collection for improved metagenomic analyses. Nat Biotechnol. 2019; 37:186–192.
https://doi.org/10.1038/s41587-018-0009-7 PMID: 30718869
8. Rice LB. Federal Funding for the Study of Antimicrobial Resistance in Nosocomial Pathogens: No
ESKAPE. J Infect Dis. 2008; 197:1079–1081. https://doi.org/10.1086/533452 PMID: 18419525
9. Yang X, Wai-Chi Chan E, Zhang R, Chen S. A conjugative plasmid that augments virulence in Klebsiella
pneumoniae. Nat Microbiol 2019; 4:2039–2043. https://doi.org/10.1038/s41564-019-0566-7 PMID:
31570866
10. Wyres KL, Wick RR, Judd LM, Froumine R, Tokolyi A, Gorrie CL, et al. Distinct evolutionary dynamics
of horizontal gene transfer in drug resistant and virulent clones of Klebsiella pneumoniae. PLoS Genet.
2019;15. https://doi.org/10.1371/journal.pgen.1008114 PMID: 30986243
11. Pan Y-J, Lin T-L, Chen C-T, Chen Y-Y, Hsieh P-F, Hsu C-R, et al. Genetic analysis of capsular polysac-
charide synthesis gene clusters in 79 capsular types of Klebsiella spp. Sci Rep. 2015; 5:15573. https://
doi.org/10.1038/srep15573 PMID: 26493302
12. Follador R, Heinz E, Wyres KL, Ellington MJ, Kowarik M, Holt KE, et al. The diversity of Klebsiella pneu-
moniae surface polysaccharides. Microb Genom. 2016; 2:e000073. https://doi.org/10.1099/mgen.0.
000073 PMID: 28348868
13. Rendueles O, Garcia-GarceràM, Ne
´ron B, Touchon M, Rocha EPC. Abundance and co-occurrence of
extracellular capsules increase environmental breadth: Implications for the emergence of pathogens.
PLoS Pathog. 2017; 13:e1006525. https://doi.org/10.1371/journal.ppat.1006525 PMID: 28742161
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 24 / 28
14. Wyres KL, Gorrie C, Edwards DJ, Wertheim HFL, Hsu LY, Van Kinh N, et al. Extensive Capsule Locus
Variation and Large-Scale Genomic Recombination within the Klebsiella pneumoniae Clonal Group
258. Genome Biol Evol. 2015; 7:1267–1279. https://doi.org/10.1093/gbe/evv062 PMID: 25861820
15. Mostowy RJ, Holt KE. Diversity-Generating Machines: Genetics of Bacterial Sugar-Coating. Trends
Microbiol. 2018; 26:1008–1021. https://doi.org/10.1016/j.tim.2018.06.006 PMID: 30037568
16. Holt KE, Lassalle F, Wyres KL, Wick R, Mostowy RJ. Diversity and evolution of surface polysaccharide
synthesis loci in Enterobacteriales. ISME J. 2020; 14:1713–1730. https://doi.org/10.1038/s41396-020-
0628-0 PMID: 32249276
17. Wyres KL, Wick RR, Gorrie C, Jenney A, Follador R, Thomson NR, et al. Identification of Klebsiella cap-
sule synthesis loci from whole genome data. Microb Genomics. 2016; 2:e000102. https://doi.org/10.
1099/mgen.0.000102 PMID: 28348840
18. Wang H, Wilksch JJ, Lithgow T, Strugnell RA, Gee ML. Nanomechanics measurements of live bacteria
reveal a mechanism for bacterial cell protection: the polysaccharide capsule in Klebsiella is a respon-
sive polymer hydrogel that adapts to osmotic stress. Soft Matter. 2013; 9:7560–7567. https://doi.org/10.
1039/C3SM51325D
19. Campos MA, Vargas MA, Regueiro V, Llompart CM, Albertı
´S, Bengoechea JA. Capsule polysaccha-
ride mediates bacterial resistance to antimicrobial peptides. Infect Immun. 2004; 72:7107–7114. https://
doi.org/10.1128/IAI.72.12.7107-7114.2004 PMID: 15557634
20. Corte
´s G, Borrell N, de Astorza B, Go
´mez C, Sauleda J, Albertı
´S. Molecular Analysis of the Contribu-
tion of the Capsular Polysaccharide and the Lipopolysaccharide O Side Chain to the Virulence of Klebsi-
ella pneumoniae in a Murine Model of Pneumonia. Infect Immun. 2002; 70:2583. https://doi.org/10.
1128/IAI.70.5.2583-2590.2002 PMID: 11953399
21. Fernebro J, Andersson I, Sublett J, Morfeldt E, Novak R, Tuomanen E, et al. Capsular Expression in
Streptococcus pneumoniae Negatively Affects Spontaneous and Antibiotic-Induced Lysis and Contrib-
utes to Antibiotic Tolerance. J Infect Dis. 2004; 189:328–338. https://doi.org/10.1086/380564 PMID:
14722899
22. Soundararajan M, von Bu¨nau R, Oelschlaeger TA. K5 Capsule and Lipopolysaccharide Are Important
in Resistance to T4 Phage Attack in Probiotic E. coli Strain Nissle 1917. Front Microbiol. 2019; 10:2783.
https://doi.org/10.3389/fmicb.2019.02783 PMID: 31849915
23. Latka A, Maciejewska B, Majkowska-Skrobek G, Briers Y, Drulis-Kawa Z. Bacteriophage-encoded
virion-associated enzymes to overcome the carbohydrate barriers during the infection process. Appl
Microbiol Biotechnol. 2017; 101:3103–3119. https://doi.org/10.1007/s00253-017-8224-6 PMID:
28337580
24. Pan Y-J, Lin T-L, Chen C-C, Tsai Y-T, Cheng Y-H, Chen Y-Y, et al. Klebsiella Phage ΦK64-1 Encodes
Multiple Depolymerases for Multiple Host Capsular Types. J Virol. 2017; 91:e02457–16. https://doi.org/
10.1128/JVI.02457-16 PMID: 28077636
25. de Sousa JAM, Buffet A, Haudiquet M, Rocha EPC, Rendueles O. Modular prophage interactions
driven by capsule serotype select for capsule loss under phage predation. ISME J. 2020; 14:2980–
2996. https://doi.org/10.1038/s41396-020-0726-z PMID: 32732904
26. Hsieh P-F, Lin H-H, Lin T-L, Chen Y-Y, Wang J-T. Two T7-like Bacteriophages, K5-2 and K5-4, Each
Encodes Two Capsule Depolymerases: Isolation and Functional Characterization. Sci Rep. 2017;
7:4624. https://doi.org/10.1038/s41598-017-04644-2 PMID: 28676686
27. Stuy JH. Plasmid transfer in Haemophilus influenzae. J Bacteriol. 1979; 139:520–529. https://doi.org/
10.1128/jb.139.2.520-529.1979 PMID: 313393
28. Johnston C, Martin B, Fichant G, Polard P, Claverys J-P. Bacterial transformation: distribution, shared
mechanisms and divergent control. Nat Rev Microbiol. 2014; 12:181–196. https://doi.org/10.1038/
nrmicro3199 PMID: 24509783
29. Rendueles O, de Sousa JAM, Bernheim A, Touchon M, Rocha EPC. Genetic exchanges are more fre-
quent in bacteria encoding capsules. PLoS Genet. 2018; 14:e1007862. https://doi.org/10.1371/journal.
pgen.1007862 PMID: 30576310
30. Buerret M, Joseleau J-P. Depolymerization of the capsular polysaccharide from Klebsiella K19 by the
glycanase associated with particles of Klebsiella bacteriophage φ19. Carbohydr Res. 1986; 157:27–51.
https://doi.org/10.1016/0008-6215(86)85058-3 PMID: 3815416
31. Rieger-Hug D, Stirm S. Comparative study of host capsule depolymerases associated with Klebsiella
bacteriophages. Virology. 1981; 113:363–378. https://doi.org/10.1016/0042-6822(81)90162-8 PMID:
7269247
32. Thurow H, Niemann H, Stirm S. Bacteriophage-borne enzymes in carbohydrate chemistry: Part I. On
the glycanase activity associated with particles of Klebsiella bacteriophage No. 11. Carbohydr Res.
1975; 41:257–271. https://doi.org/10.1016/s0008-6215(00)87024-x PMID: 236830
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 25 / 28
33. Patro LPP, Rathinavelan T. Targeting the Sugary Armor of Klebsiella Species. Front Cell Infect Micro-
biol. 2019; 9:367. https://doi.org/10.3389/fcimb.2019.00367 PMID: 31781512
34. Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA, Bentley SD, et al. Rapid phylogenetic analy-
sis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids
Res. 2015; 43:e15. https://doi.org/10.1093/nar/gku1196 PMID: 25414349
35. Pagel M. Inferring the historical patterns of biological evolution. Nature. 1999; 401:877–884. https://doi.
org/10.1038/44766 PMID: 10553904
36. Buffet A, Rocha EPC, Rendueles O. Nutrient conditions are primary drivers of bacterial capsule mainte-
nance in Klebsiella. Proc Biol Sci. 2021; 288:20202876. https://doi.org/10.1098/rspb.2020.2876 PMID:
33653142
37. Deatherage DE, Barrick JE. Identification of mutations in laboratory evolved microbes from next-gener-
ation sequencing data using breseq. Methods Mol Biol. 2014; 1151:165–188. https://doi.org/10.1007/
978-1-4939-0554-6_12 PMID: 24838886
38. Hesse S, Rajaure M, Wall E, Johnson J, Bliskovsky V, Gottesman S, et al. Phage Resistance in Multi-
drug-Resistant Klebsiella pneumoniae ST258 Evolves via Diverse Mutations That Culminate in
Impaired Adsorption. MBio. 2020; 11:e02530–19. https://doi.org/10.1128/mBio.02530-19 PMID:
31992617
39. Tan D, Zhang Y, Qin J, Le S, Gu J, Chen L, et al. A Frameshift Mutation in wcaJ Associated with Phage
Resistance in Klebsiella pneumoniae. Microorganisms. 2020; 8:378. https://doi.org/10.3390/
microorganisms8030378 PMID: 32156053
40. Cai R, Wang G, Le S, Wu M, Cheng M, Guo Z, et al. Three Capsular Polysaccharide Synthesis-Related
Glucosyltransferases, GT-1, GT-2 and WcaJ, Are Associated With Virulence and Phage Sensitivity of
Klebsiella pneumoniae. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.00010 PMID:
30728810
41. Cury J, Oliveira PH, de la Cruz F, Rocha EPC. Host Range and Genetic Plasticity Explain the Coexis-
tence of Integrative and Extrachromosomal Mobile Genetic Elements. Mol Biol Evol. 2018; 35:2230–
2239. https://doi.org/10.1093/molbev/msy123 PMID: 29905872
42. Wyres KL, Lam MMC, Holt KE. Population genomics of Klebsiella pneumoniae. Nat Rev Microbiol.
2020; 18:344–359. https://doi.org/10.1038/s41579-019-0315-1 PMID: 32055025
43. Chen Y, Marimuthu K, Teo J, Venkatachalam I, Cherng BPZ, De Wang L, et al. Acquisition of Plasmid
with Carbapenem-Resistance Gene blaKPC2 in Hypervirulent Klebsiella pneumoniae, Singapore.
Emerg Infect Dis. 2020; 26:549–559. https://doi.org/10.3201/eid2603.191230 PMID: 32091354
44. Lam MMC, Wyres KL, Wick RR, Judd LM, Fostervold A, Holt KE, et al. Convergence of virulence and
MDR in a single plasmid vector in MDR Klebsiella pneumoniae ST15. J Antimicrob Chemother. 2019;
74:1218–1222. https://doi.org/10.1093/jac/dkz028 PMID: 30770708
45. Lan P, Jiang Y, Zhou J, Yu Y. A global perspective on the convergence of hypervirulence and carbape-
nem resistance in Klebsiella pneumoniae. J Glob Antimicrob Resist. 2021; 25:26–34. https://doi.org/10.
1016/j.jgar.2021.02.020 PMID: 33667703
46. Herna
´ndez M, Lo
´pez-Urrutia L, Abad D, De Frutos Serna M, Ocampo-Sosa AA, Eiros JM. First Report
of an Extensively Drug-Resistant ST23 Klebsiella pneumoniae of Capsular Serotype K1 Co-Producing
CTX-M-15, OXA-48 and ArmA in Spain. Antibiotics (Basel). 2021;10. https://doi.org/10.3390/
antibiotics10020157 PMID: 33557209
47. Pe
´rez-Mendoza D, de la Cruz F. Escherichia coli genes affecting recipient ability in plasmid conjugation:
Are there any? BMC Genomics. 2009; 10:71. https://doi.org/10.1186/1471-2164-10-71 PMID:
19203375
48. Chang B, Nariai A, Sekizuka T, Akeda Y, Kuroda M, Oishi K, et al. Capsule Switching and Antimicrobial
Resistance Acquired during Repeated Streptococcus pneumoniae Pneumonia Episodes. J Clin Micro-
biol. 2015; 53:3318–3324. https://doi.org/10.1128/JCM.01222-15 PMID: 26269621
49. Swartley JS, Marfin AA, Edupuganti S, Liu LJ, Cieslak P, Perkins B, et al. Capsule switching of Neis-
seria meningitidis. Proc Natl Acad Sci U S A. 1997; 94:271–276. https://doi.org/10.1073/pnas.94.1.271
PMID: 8990198
50. Verma V, Harjai K, Chhibber S. Restricting ciprofloxacin-induced resistant variant formation in biofilm of
Klebsiella pneumoniae B5055 by complementary bacteriophage treatment. J Antimicrob Chemother.
2009; 64:1212–1218. https://doi.org/10.1093/jac/dkp360 PMID: 19808232
51. Tan YH, Chen Y, Chu WHW, Sham L-T, Gan Y-H. Cell envelope defects of different capsule-null
mutants in K1 hypervirulent Klebsiella pneumoniae can affect bacterial pathogenesis. Mol Microbiol.
2020; 113:889–905. https://doi.org/10.1111/mmi.14447 PMID: 31912541
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 26 / 28
52. Bai J, Dai Y, Farinha A, Tang AY, Syal S, Vargas-Cuebas G, et al. Essential gene analysis in Acineto-
bacter baumannii by high-density transposon mutagenesis and CRISPR interference. J Bacteriol. 2021.
https://doi.org/10.1128/JB.00565-20 PMID: 33782056
53. Andam CP, Hanage WP. Mechanisms of genome evolution of Streptococcus. Infect Genet Evol. 2015;
33:334–342. https://doi.org/10.1016/j.meegid.2014.11.007 PMID: 25461843
54. Croucher NJ, Kagedan L, Thompson CM, Parkhill J, Bentley SD, Finkelstein JA, et al. Selective and
Genetic Constraints on Pneumococcal Serotype Switching. PLoS Genet. 2015;11. https://doi.org/10.
1371/journal.pgen.1005095 PMID: 25826208
55. Chewapreecha C, Harris SR, Croucher NJ, Turner C, Marttinen P, Cheng L, et al. Dense genomic sam-
pling identifies highways of pneumococcal recombination. Nat Genet. 2014; 46:305–309. https://doi.
org/10.1038/ng.2895 PMID: 24509479
56. Nanayakkara BS, O’Brien CL, Gordon DM. Diversity and distribution of Klebsiella capsules in Escheri-
chia coli. Environ Microbiol Rep. 2019; 11:107–117. https://doi.org/10.1111/1758-2229.12710 PMID:
30411512
57. Lang AS, Zhaxybayeva O, Beatty JT. Gene transfer agents: phage-like elements of genetic exchange.
Nat Rev Microbiol. 2012; 10:472–482. https://doi.org/10.1038/nrmicro2802 PMID: 22683880
58. Westbye AB, Kuchinski K, Yip CK, Beatty JT. The Gene Transfer Agent RcGTA Contains Head Spikes
Needed for Binding to the Rhodobacter capsulatus Polysaccharide Cell Capsule. J Mol Biol. 2016;
428:477–491. https://doi.org/10.1016/j.jmb.2015.12.010 PMID: 26711507
59. Flammann HT, Weckesser J. Composition of the cell wall of the phage resistant mutant Rhodopseudo-
monas capsulata St. Louis RC1-. Arch Microbiol. 1984; 139:33–37. https://doi.org/10.1007/
BF00692708
60. Brimacombe CA, Stevens A, Jun D, Mercer R, Lang AS, Beatty JT. Quorum-sensing regulation of a
capsular polysaccharide receptor for the Rhodobacter capsulatus gene transfer agent (RcGTA). Mol
Microbiol. 2013; 87:802–817. https://doi.org/10.1111/mmi.12132 PMID: 23279213
61. Townsend EM, Kelly L, Gannon L, Muscatt G, Dunstan R, Michniewski S, et al. Isolation and Character-
ization of Klebsiella Phages for Phage Therapy. Phage (New Rochelle). 2021; 2:26–42. https://doi.org/
10.1089/phage.2020.0046 PMID: 33796863
62. Kostina E, Ofek I, Crouch E, Friedman R, Sirota L, Klinger G, et al. Noncapsulated Klebsiella pneumo-
niae bearing mannose-containing O antigens is rapidly eradicated from mouse lung and triggers cyto-
kine production by macrophages following opsonization with surfactant protein D. Infect Immun. 2005;
73:8282–8290. https://doi.org/10.1128/IAI.73.12.8282-8290.2005 PMID: 16299325
63. Ernst CM, Braxton JR, Rodriguez-Osorio CA, Zagieboylo AP, Li L, Pironti A, et al. Adaptive evolution of
virulence and persistence in carbapenem-resistant Klebsiella pneumoniae. Nat Med. 2020; 26:705–
711. https://doi.org/10.1038/s41591-020-0825-4 PMID: 32284589
64. Perrin A, Rocha EPC. PanACoTA: A modular tool for massive microbial comparative genomics. bioR-
xiv. 2020 [cited 5 Oct 2020]. https://doi.org/10.1101/2020.09.11.293472
65. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome
and metagenome distance estimation using MinHash. Genome Biol. 2016; 17:132. https://doi.org/10.
1186/s13059-016-0997-x PMID: 27323842
66. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014; 30:2068–2069.
https://doi.org/10.1093/bioinformatics/btu153 PMID: 24642063
67. Steinegger M, So
¨ding J. MMseqs2 enables sensitive protein sequence searching for the analysis of
massive data sets. Nat Biotechnol. 2017; 35:1026–1028. https://doi.org/10.1038/nbt.3988 PMID:
29035372
68. Arredondo-Alonso S, Rogers MRC, Braat JC, Verschuuren TD, Top J, Corander J, et al. mlplasmids: a
user-friendly tool to predict plasmid- and chromosome-derived sequences for single species. Microb
Genomics. 2018; 4:e000224. https://doi.org/10.1099/mgen.0.000224 PMID: 30383524
69. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in per-
formance and usability. Mol Biol Evol. 2013; 30:772–780. https://doi.org/10.1093/molbev/mst010
PMID: 23329690
70. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm
for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015; 32:268–274. https://doi.org/10.
1093/molbev/msu300 PMID: 25371430
71. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selec-
tion for accurate phylogenetic estimates. Nat Methods. 2017; 14:587–589. https://doi.org/10.1038/
nmeth.4285 PMID: 28481363
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 27 / 28
72. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: Improving the Ultrafast Boot-
strap Approximation. Mol Biol Evol. 2018; 35:518–522. https://doi.org/10.1093/molbev/msx281 PMID:
29077904
73. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture
and applications. BMC Bioinformatics. 2009; 10:421. https://doi.org/10.1186/1471-2105-10-421 PMID:
20003500
74. Bobay L-M, Rocha EPC, Touchon M. The Adaptation of Temperate Bacteriophages to Their Host
Genomes. Mol Biol Evol. 2013; 30:737–751. https://doi.org/10.1093/molbev/mss279 PMID: 23243039
75. Ishikawa SA, Zhukova A, Iwasaki W, Gascuel O. A Fast Likelihood Method to Reconstruct and Visual-
ize Ancestral Scenarios. Mol Biol Evol. 2019; 36:2069–2085. https://doi.org/10.1093/molbev/msz131
PMID: 31127303
76. Csűo
¨s M. Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood. Bioinfor-
matics. 2010; 26:1910–1912. https://doi.org/10.1093/bioinformatics/btq315 PMID: 20551134
77. Abby SS, Rocha EPC. Identification of Protein Secretion Systems in Bacterial Genomes Using MacSy-
Finder. Methods Mol Biol. 2017; 1615:1–21. https://doi.org/10.1007/978-1-4939-7033-9_1 PMID:
28667599
78. Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the
PHAST phage search tool. Nucleic Acids Res. 2016; 44:W16–21. https://doi.org/10.1093/nar/gkw387
PMID: 27141966
79. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. ISfinder: the reference centre for bacterial
insertion sequences. Nucleic Acids Res. 2006; 34:D32–36. https://doi.org/10.1093/nar/gkj014 PMID:
16381877
80. Touchon M, Perrin A, de Sousa JAM, Vangchhia B, Burn S, O’Brien CL, et al. Phylogenetic background
and habitat drive the genetic diversification of Escherichia coli. PLoS Genet. 2020; 16:e1008866.
https://doi.org/10.1371/journal.pgen.1008866 PMID: 32530914
81. Pitcher DG, Saunders NA, Owen RJ. Rapid extraction of bacterial genomic DNA with guanidium thiocy-
anate. Lett Appl Microbiol. 1989; 8:151–156. https://doi.org/10.1111/j.1472-765X.1989.tb00262.x
82. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: Resolving bacterial genome assemblies from short
and long sequencing reads. PLoS Comput Biol. 2017; 13:e1005595. https://doi.org/10.1371/journal.
pcbi.1005595 PMID: 28594827
83. Kaniga K, Delor I, Cornelis GR. A wide-host-range suicide vector for improving reverse genetics in
gram-negative bacteria: inactivation of the blaA gene of Yersinia enterocolitica. Gene. 1991; 109:137–
141. https://doi.org/10.1016/0378-1119(91)90599-7 PMID: 1756974
84. Lutz R, Bujard H. Independent and Tight Regulation of Transcriptional Units in Escherichia coli Via the
LacR/O, the TetR/O and AraC/I1-I2 Regulatory Elements. Nucleic Acids Res. 1997; 25:1203–1210.
https://doi.org/10.1093/nar/25.6.1203 PMID: 9092630
85. Ferrières L, He
´mery G, Nham T, Gue
´rout A-M, Mazel D, Beloin C, et al. Silent mischief: bacteriophage
Mu insertions contaminate products of Escherichia coli random mutagenesis performed using suicidal
transposon delivery plasmids mobilized by broad-host-range RP4 conjugative machinery. J Bacteriol.
2010; 192:6418–6427. https://doi.org/10.1128/JB.00621-10 PMID: 20935093
86. Zhong X, Droesch J, Fox R, Top EM, Krone SM. On the meaning and estimation of plasmid transfer
rates for surface-associated and well-mixed bacterial populations. J Theor Biol. 2012; 294:144–152.
https://doi.org/10.1016/j.jtbi.2011.10.034 PMID: 22085738
87. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in
R. Bioinformatics. 2019; 35:526–528. https://doi.org/10.1093/bioinformatics/bty633 PMID: 30016406
88. Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2011; 27:592–593. https://doi.org/10.
1093/bioinformatics/btq706 PMID: 21169378
89. Wang L-G, Lam TT-Y, Xu S, Dai Z, Zhou L, Feng T, et al. Treeio: An R Package for Phylogenetic Tree
Input and Output with Richly Annotated and Associated Data. Mol Biol Evol. 2020; 37:599–603. https://
doi.org/10.1093/molbev/msz240 PMID: 31633786
90. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, Franc¸ois R, et al. Welcome to the Tidyverse.
J Open Source Softw. 2019; 4:1686. https://doi.org/10.21105/joss.01686
PLOS BIOLOGY
Capsule shapes gene flux in Klebsiella pneumoniae
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001276 July 6, 2021 28 / 28
