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Anomalous DNA in prokaryotic genomes

van Passel, M.W.J.

Publication date2006

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Citation for published version (APA):van Passel, M. W. J. (2006). Anomalous DNA in prokaryotic genomes.

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Table of Contents

Table of Contents Chapter 1 Introduction 1 Chapter 2 An in vitro strategy for the selective isolation of

anomalous DNA from unsequenced prokaryotic genomes 29

Chapter 3 δδδδρρρρ-web, an online tool to assess composition similarity of individual nucleic acid sequences 47

Chapter 4 An acquisition account of genomic islands based on

genome signature comparisons 55 Chapter 5 Identification of anomalous sequences in Neisseria

lactamica expands the neisserial gene pool 75 Chapter 6 Plasmid diversity in neisseriae 95 Chapter 7 Compositional discordance between prokaryotic plasmids

and host chromosomes 115 Chapter 8 Summary and discussion, samenvatting, publicatielijst en

curriculum vitae 129

Chapter 1

1

Chapter 1

Introduction

1.1 Prologue

Infectious diseases remain one of the leading causes of death worldwide.

Since the establishment of the germ theory of disease around 1870 by Louis Pasteur

and Robert Koch, which states that infectious diseases are caused by

microorganisms within the body, biomedical research has rigorously investigated

potential remedies against microbial infections. This resulted in better hygiene, the

production of vaccines (for example against anthrax, tetanus, polio), extensive

classes of antimicrobial agents against infectious bacteria (which cause diseases

such as gonorrhoea, pneumonia and meningitis), but also viral inhibitors. These

combined measures brought down the percentage of deaths due to infectious

diseases from 30% at the beginning of the 20th century to 1.5 % at the beginning of

this century in the Netherlands. Still, according to the World Health Report 1996,

infectious diseases kill over 17 million people every year, of which 9 million young

children.

The field of genetics has contributed considerably to the quest for cures for

infections by elucidating bacterial strategies for causing disease. Historically,

genetics has been studied for about one and a half century. The Austrian monk

Gregor Mendel started investigating the basics of genetics in the 19th century using

peas, but it wasn’t until 1944 that Oswald Avery and co-workers discovered that DNA

was the carrier on which hereditary information is stored. The structure of a DNA

molecule was finally resolved in 1953 by James Watson, Francis Crick and Rosalind

Franklin. In 1958, Matthew Meselson and Frederick Stahl found out that replication of

the two DNA strands, which occurs with every cell division, is semi conservative; this

explained how after every cell division the two daughter cells each contain an exact

copy of the original genetic information of the parent cell. Finally, a central dogma of

Chapter 1

2

molecular biology was proposed concerning the flow of genetic information; DNA is

transcribed into an intermediate, RNA, which in its turn is translated to the main

functional units of metabolism, namely proteins. Some of these proteins mediate

DNA replication, which makes the reproduction of DNA come full circle.

In 1995 the first complete genetic sequence of a free-living organism (the

bacterium Haemophilus influenzae) was published, and accordingly started the age

of genomics. Since then, genome sequencing has resulted in the publication of over

250 complete genome sequences, most of which originate from microbes (as the

genomes of microbes are relatively small and manageable compared to plant and

animal genome sequences). These genomes each contain the entire genetic data of

the organism in question, and so give insight in the organisation and expression of

genes, metabolic potential of a microbe, the formation of different species, and

genome evolution. The final shape of all genomes results from hundreds of millions

of years of evolution, and as such they each represent their own account of the

recorded evolutionary history of life.

Studying genome evolution allows insight in the pathogens side of the arms

race between the pathogen and the host (e.g. humans). Understanding genome

evolution is therefore key in finding new vaccines, antibiotics and other therapeutics.

This chapter aims to introduce the main topics of this thesis, which focuses on

bacterial genome composition, in particular of Neisseria and especially on horizontal

gene transfer (HGT), an important contributor to genome evolution. In order to

explain the implications of horizontal gene transfer, a little background in genomics

and bioinformatics is necessary, and is therefore included in this introduction.

Chapter 1

3

1.2.1 Neisseria meningitidis

Neisseria meningitidis, or meningococcus, is a Gram-negative diplococcal

bacterium belonging to the family of Neisseriae, a subdivision of the β-Proteobacteria.

It is an obligate human pathogen that inhabits the naso- and oropharynx. It has been

estimated that in this niche it can be encountered in approximately 10% of the

population (reviewed by [1]), although this may be a grave underestimation [2]. This

bacterium, was first isolated by Anton Weichselbaum from cerebrospinal fluid of a

meningitis patient and identified as the causal agent of a case of meningitis 1887,

and initially named Diplococcus intracellularis [3]. In contrast with the closely related

gonococcus (Neisseria gonorrhoeae), isolated almost a decade earlier by Albert

Neisser [4], which invariably leads to disease in the infected host, the meningococcus

only causes disease in a fraction of the carriers of this bacterium. Therefore, N.

meningitidis could be regarded as a commensal bacterium [5]. However, sporadically

meningococci crosses the mucosal barrier and enter the bloodstream, leading to

various clinical entities such as sepsis, meningitis or both simultaneously (reviewed

by [6]).

1.2.2 Bacterial typing

Meningococcal identification is based on a few distinctive features such as

shape, Gram-stain, and phenotypic characteristics. Meningococcal isolates are

grouped according to a number of antigenic and genotypic characteristics. This

enables intensive epidemiological surveillance, which is important for public health

decisions and the development of vaccination strategies. Traditionally,

meningococcal phenotypes were classified using polyclonal sera against surface

exposed structures [7], but nowadays serological typing is carried out with various

monoclonal antibodies more specifically aimed at the different neisserial antigenic

structures. The capsular polysaccharide, although occasionally absent, designates

the serogroups [8], of which the groups A, B, C, W-135 and Y are predominant

among clinical isolates. The major outer membrane proteins PorB and PorA define

the serotype and serosubtype, respectively, and finally, immunotypes are assigned

according to the lipopolysaccharide (LPS) structure [9, 10]. This results in the

following classification scheme for N. meningitidis:

Chapter 1

4

[serogroup]:[serotype]:[serosubtype]:[immunotype], e.g. B:4:P1.7,4:L3,8. Meanwhile,

serological sero(sub)typing has been largely replaced by typing schemes based on

sequence data of the two variable regions (VR1 and VR2) of the PorA encoding gene

porA, and the partial nucleotide sequence of the outer-membrane protein encoding

gene fetA.

With respect to genotyping approaches, the emphasis has shifted recently

from multilocus enzyme electrophoresis (MLEE, [11]), pulse field gel electrophoresis

(PFGE, [12]), and random amplified polymorphic DNA (RAPD, [13, 14]) to multilocus

sequence typing (MLST). The latter method allows a much higher resolution and is

by far more reproducible and unambiguously comparable (‘portable’) between

different laboratories [15]. Current MLST is based on the sequences of seven

housekeeping genes that contain sufficient variation to allow high resolution and

congruence, resulting in different sequence types, and is used for typing different

organisms (for details see http://www.mlst.net/). This MLST database is freely

available for global epidemiology analyses and surveillance, and can identify clonal

complexes [16] and even suggest the descent of isolates and/or clonal complexes

[17, 18].

However, Neisseria are naturally transformable [19], and recombination

events between strains of different types can distort tree-like interpretations of

phylogenetic analysis. For example, when using MLST, Neisseria meningitidis is

depicted with a ‘fuzzy’ or unclear species definition, as species “…are not ideal

entities with sharp and unambiguous boundaries” [20]. However, MLST analyses

may support further modeling of how species may emerge.

1.2.3 Epidemiology/Incidence

The overall incidence of meningococcal disease varies considerably

throughout the world. In the Netherlands, the frequency is around 2/100,000

inhabitants per year [21], but during epidemics in sub-Saharan African countries the

incidence of disease has reached numbers up to 500/100,000 [22, 23]. Industrialised

countries have also experienced epidemics of N. meningitidis, for example Norway in

1974-1975 [24] and New Zealand from 1990 onwards [25, 26].

Chapter 1

5

Widespread pandemics can be instigated by specific human migratory

patterns, such as the Hajj, and outbreaks of these clonal complexes be followed by

global surveillance via the MLST database. A specific N. meningitidis clone with

serogroup W-135 emerged after the Hajj pilgrimage of 2000, infecting a total of over

40 Hajj pilgrims and their household contacts in the United Kingdom, France, the

Netherlands, and Oman, with an additional number of meningococcal disease cases

in Saudi Arabia related to this outbreak [27]. This serogroup W-135 clone also

caused local epidemics such as in Burkina Faso in 2002 [28]. Recent vaccination

strategies however in this region halted the epidemic [29].

In the Netherlands, the National Reference Laboratory for Bacterial Meningitis

(NRLBM, hosted by the Academic Medical Center and The National Institute for

Public Health and the Environment (RIVM)) has been collecting isolates of N.

meningitidis since 1959 and currently harbours over 37,000 meningitis and/or sepsis

causing isolates available for epidemiological studies. The annual reports of the

NRLBM show that the seasonal distribution of meningococcal disease finds its peak

in the first quarter of each calendar year. Also, the distribution of patients suffering

from meningococcal disease over the different age groups is not uniform; age-

specific incidences per 100,000 inhabitants in the age groups younger than 5 years

and between 15-19 years old are substantially higher than in other age groups [21].

With the advent of molecular epidemiology new meningococcus variants have

been identified, amongst which the so-called Lineage III cluster, first defined in the

Netherlands. This hypervirulent B:4:P1,4 type cluster greatly increased in numbers in

disease cases, until it finally comprised over half of all Dutch meningococcal disease

isolates (figure 1 [21]). The emergence of this cluster halted in the mid-nineties, after

which a gradual decline of Lineage III meningococci was observed. Reasons for the

emergence and decline of this cluster remain obscure.

Chapter 1

6

Figure 1. Meningococcal disease in the Netherlands over the last 40 years (only the main serogroups

are depicted). With hardly any serogroup A present, it is clear that serogroup B is the main cause of

meningococcal disease in the Netherlands, although recently the incidence of serogroup B is

diminishing. From 2001 onwards a substantial increase of serogroup C is visible, followed by a

decrease (kindly provided by dr. A. van der Ende, the Netherlands Reference Laboratory for Bacterial

Meningitis, Academic Medical Center, Amsterdam).

Also visible in figure 1 is a steep increase in the number of cases of

meningococcal disease in the beginning of 2002 in the Netherlands, primarily caused

by N. meningitidis serogroup C [30]. This lead the Public Health authorities to start a

massive nationwide vaccination campaign by mid 2002; 3 million children aged

between 12 months and 19 years were vaccinated in the first year after the campaign

was started. Since then, a N. meningitidis serogroup C vaccine, based on a

conjugate of the polysaccharide capsule, is included in the Dutch vaccination

scheme. This policy almost immediately led to a sharp decrease of meningococcal

disease caused by the serogroup C variant [31], which was predicted by the study on

meningococcal carriage performed in the UK [32]. However, N. meningitidis

serogroup B still causes many cases of meningococcal infection per year in the

Netherlands. Its capsule, consisting of polysaccharides resembling those on host

cells, is poorly immunogenic [33, 34]. This renders the development of a vaccine very

difficult; hence no commercial vaccine is currently available against this serogroup.

Chapter 1

7

However, alternative epitopes are sought after for broad coverage meningococcal

vaccines, such as outer-membrane proteins (Pizza et al., 2000).

Interestingly, studies by Caugant and co-workers showed that among N.

meningitidis carrier isolates few meningococci of disease-causing genotypes are

found [11, 35], which emphasises the importance of epidemiological studies. The

related non-pathogenic Neisseria lactamica [36] has been suggested to be a human

coloniser causing natural immunity against the meningococcus [37, 38], and

potentially, non-pathogenic N. meningitidis variants may cause natural immunity as

well. Braun and co-workers reported the occurrence of cross-reactive epitopes that

are shared by N. meningitidis and N. lactamica [39], and Gorringe and co-workers

described initial research on a N. lactamica based vaccine [40]. Another longitudinal

study showed high N. lactamica carriage rates among infants, which was interpreted

as support for the aforementioned natural immunity hypothesis [41]. Since vaccines

based on one or a few protein epitopes still lack sufficient coverage due to variation,

alternative strategies for vaccine development, such as using commensal Neisseriae

are of interest.

1.2.4 Pathogenesis of meningococcal disease

Relatively little is known about the actual pathogenesis process of

meningococcal disease, as this process exclusively takes place in humans.

Moreover, this process usually occurs very suddenly and dramatically. However,

many different putative virulence-associated factors have been described, such as

adhesion factors such as pili [42-45], the immunoglobulin protease IgA1 [46], the

putative RTX toxins Frp [47-49], the capsular polysaccharide [50], outer membrane

proteins Opa and Opc [51], and lipopolysaccharide (LPS or endotoxin) [52]. More

recently, the formation of biofilms have been studied [53]. Most in vivo studies

concerning invasive meningococcal disease have been performed with animal

models [54]. These animal models are supposed to simulate the infection process in

humans, but often these models require intraperitoneal injection of the virulent or

attenuated bacteria [55], which is a poor imitation of the actual infection route in

humans. However, intranasal immunisation studies in murine models, simulating a

Chapter 1

8

more comparable route of infection, have been performed as well [56, 57], and the

recent introduction of transgenic mice which express human epitopes indicate

improvements in meningococcal infection models [58].

Next to animal models, some in vitro experiments have been conducted with

human tissue [51], but epidemiological data, sequence comparisons and analogies

with gonorrhoea can also give information about potential pathogenicity factors of the

meningococcus [59-61]. Interestingly, with recently developed population dynamics

models, it was shown that outbreaks of meningococcal disease are caused by

diversity in the pathogenicity of meningococcal strains [62]. This is an incentive for

performing genome comparisons between carrier strains of N. meningitidis and

clinical isolates, in order to identify bacterial genetic factors underlying invasiveness.

Our limited understanding of the factors that play an obligatory role in the

pathogenesis of invasive disease is highlighted by the recent description of a single

case of invasive disease caused by an unencapsulated strain in an presumably

immunocompetent patient [63]. This contrasts the general assumption that the

presence of a capsule is pivotal to the virulence of N. meningitidis. Also, strains

lacking PorA, the major outer membrane porin that is currently being considered and

tested as a potential broad-range vaccine-candidate [64], have been found to be able

to cause invasive disease [65], questioning the practicability of such single protein-

based vaccines.

Although meningococcal disease in humans is hard to study directly, the

consequences of meningococcaemia are easily observed, and different studies

showed the devastating effects. In addition to a case fatality rate of approximately 5%

for meningitis, and up to 40% for meningococcal septicaemia, [66], the sequelae of

the disease in survivors include deafness, loss of limbs and mental retardation [67]

This imposes a high burden for a prolonged time, due to the often relatively young

age of the patient. Koomen and colleagues have recently presented a number of

studies in which young patients that survived bacterial meningitis were tested for

several years for mental sequelae [68-70]. Amongst others it was found that after a

meningitis, children were more likely than ‘controls’ to underachieve at school [69].

Chapter 1

9

Although the precise disease process is still largely unknown, a number of

predisposing factors for the human host have been determined. As pathogenesis is a

complex interplay between host and pathogen, the role of the host should not be

underestimated. Host risk factors include (passive) smoking [71], and reduced

immunocompetence [72]. Recently, a higher attack rate of meningococcal disease

rate has been observed in children with a pregnant mother [73], but the cause of this

is still unknown. Also, genetic polymorphisms among components of the diverse

cascades of the immune system are involved in heightened susceptibility for

meningitis [74]. This clearly shows that factors beyond the intrinsic pathogenic

potential of the microbe are important for disease to occur.

1.2.5 Phase variation and antigenic variation

Meningococci employ various strategies to evade the human immune system.

One of these strategies is the variation in gene expression levels via length

differences in simple sequence repeats, coined phase variation [75]. Differences in

length of these sequence repeats, both homopolymeric tracts and other simple

repeats, which may occur in coding and/or in promoter regions, is presumably the

result of slipped strand mispairing during replication and can thereby influence gene

expression levels. Different studies suggested that various loci are responsible for

phase variation frequencies in various genes, such as transformation associated

genes [76], pilli and iron transport genes [77], but also genome maintenance genes

(such as mutS) may be involved [78].

This phase variation strategy allows the bacterium expression versatility of

genes including, but not restricted to, immunologically important surface structures

such as pili, outer membrane proteins or enzymes involved in capsular

polysaccharide biosynthesis, as well as adhesin encoding genes such as opC [79]. It

may also provide adaptation to different environmental niches, during colonisation

and potentially also during the dissemination process. Whole genome analysis of the

N. meningitidis MC58 genome sequence identified over 65 phase-variable genes

[80], whereas the comparative analysis of two additional N. meningitidis genome

sequences revealed a repertoire of over 100 putative phase variable genes [81].

Chapter 1

10

These studies showed that the meningococcal genome sequence contains the most

extensive repertoire of phase variable genes described to date.

A different strategy employed by the meningococcus is called antigenic

variation. This strategy exploits differences in variants of a single surface component,

such as the outer membrane porin PorA, which displays not only variation in

expression levels via phase variation, but also variation in sequence composition,

resulting in antigenically different PorA proteins. Antigenic variation can result from

transformation-mediated recombination, point mutations or replacement of different

alleles present in the genome sequence itself, as has been observed for pilin

structures [82, 83]. Combined, phase variation and antigenic variation make the

meningococcus a highly versatile bacterium.

1.3 Genomics & Bioinformatics

Since the publication of the first complete genome sequence of a free living

organism in 1995, Haemophilus influenzae [84], over 231 prokaryotic and 33

eukaryotic genome sequences have been annotated, and over 1000 genome

sequencing projects are still ongoing (www.genomesonline.org, [85, 86]). This

enormous amount of data has been amassed in order to mine for better vaccine

candidates [87, 88], scan for virulence evolution [89], to fuel industrial interest in

probiotics [90], or to examine adaptation strategies to extreme conditions [91, 92], to

give only a few examples. The application area of prokaryotic genome sequencing

projects is nonetheless biased towards biomedical research and industrial purposes,

as of the sequenced genomes, 52% and 47%, respectively are selected for their

relevance in these fields (www.genomesonline.org). Together, this results in a poor

representation of biological diversity [93] and this bias in its turn may have

consequences for the interpretation of certain types of analyses (e.g. species

diversity estimates due to sampling bias, protein function predictions).

With genome sequencing being highly automated, large scale projects such

as community sequencing are feasible and have been conducted [94, 95]. These

metagenome projects analyse all (microbial) DNA present in a chosen biotope (an

acid mine drainage pool in [94] and a seawater sample of the Sargasso Sea in [95]),

Chapter 1

11

including the DNA of uncultivable microbes, which are still thought to make up most

of microbial life on earth (reviewed by [96]). This approach has shown to be of great

value in estimating oceanic microbial diversity [95] and intraspecies genetic diversity

and metabolic potential [94]. Also, datasets from these metagenomic projects can

instigate new data mining schemes, such as the investigation for the selenoproteome

in the Sargasso Sea environmental genome project [93]. Recently, metagenomic

analyses by Tringe and co-workers have shown that vast amounts of sequences are

needed to yield a complete genome of the predominant species in biologically

complex populations [97]. These authors did however find environment-specific

genes, which allow for habitat-specific fingerprinting.

Furthermore, the release of a great many microbial genome sequences

allowed a critical look at species definition and taxonomy, which until recently was

based solely on phenotypical, morphological and limited genotypical data ([98],

recently reviewed by [99]). Coenye and co-workers suggest a number of approaches

for assessing taxonomic relationships [99]. Genomes can be compared according to

their gene content and gene order (synteny), as well as their nucleotide composition,

such as GC-content and dinucleotide frequencies or genome signature comparisons.

These approaches were compared in a study comprising the lactic acid bacteria as a

test case, and it was concluded that the different whole genome approaches that

were used yielded very similar classification results [100].

The release of these large amounts of data has fueled the informational

technology field considerably, as new and optimized computational approaches were

necessary to manage these complex and extensive databases [101]. An outstanding

example of the enormity of these databases and the potential of bioinformatics was

the finding, by serendipity, of almost entire genome sequences of new

endosymbionts in the raw sequence traces of various Drosophila genome-

sequencing projects [102].

The discipline of bioinformatics could be regarded as a science or a facilitative

technology platform [103]. On the one hand, numerous applications have been

developed that allow users to scan data for motifs, for instance promoter sequences

or genome signatures (which are species specific oligonucleotide frequencies

Chapter 1

12

observed in whole genome sequences). But also different applications have been

developed, such as phylogenomic tree builders, potential protein interaction partner

search tools, models for operon prediction in whole genomes, and also visualisation

software such as Artemis, Bugview, Plasmapper [104-107], which are mostly aimed

at facilitating research. These applications are described amongst others in

specialised web issues of renowned journals. On the other hand, bioinformaticians

may directly develop hypotheses concerning biological phenomena, such as the turn-

over of gene content in Proteobacteria and Archaea [108], the assessment of

functional modules by the determination of pair wise protein interaction [109] or the

origins of gene repertoires in prokaryotes [110].

Large-scale databases have also found their place in specialised sections of

scientific journals. As a response to the maintenance of these large datasets, the

American Society for Microbiology (ASM) recently published a colloquium report

regarding maintenance and improvement of extensive genome sequencing

databases, as this is often neglected due to lack of funds and scientific merit [111].

1.4 Genome composition and evolution

In the process of genome evolution, three major forces play an important role:

gene genesis (e.g. via horizontal acquisition of DNA), gene loss, and genomic

rearrangements (figure 2) ([112] and reviewed by [113]).

The relation between gene content and genome size has been studied by

Konstantinidis and Tiedje [114]. They suggest that prokaryotic species with large

genome sizes have a more extensive metabolic potential, enabling survival in

(different) environments where resources are scarce. The main contributors to

genome expansion are acquisition of DNA and duplication events. As for gene

acquisition or genesis, Daubin and Ochman suggest that a substantial part of new

(small) genes is acquired via horizontal gene transfer from bacteriophages [115,

116], whereas other acquisition events involve much larger gene clusters such as

Genomic Islands (GIs) and Pathogenicity-Associated Islands (PAIs) ([117]). The

origins of these large gene clusters remain obscure, which may be explained by the

relatively small number of different species that have been sequenced.

Chapter 1

13

However, although genomes do incorporate new DNA, they do not grow ever

larger. Cellular processes that eliminate (excess) DNA form the genome must be

present. The existence of species with small genomes, often endosymbionts, is

suggestive of genome evolution leading to niche-specific organisms by the loss of

many regulatory functions [118, 119]. These small-genome organisms are thought to

be derived from larger genome-sized species [120], and may represent an illustration

of the process of genome reduction.

Figure 2. Depicted are the processes involved in genome size evolution in bacteria. Genome

expansion takes place by duplication and acquisition events, whereas genome reduction is mainly

maintained by deletion, either by direct deletion or slow erosion via gene activation and subsequent

deletion (adapted from [121]).

A fine example of recent massive gene decay is found in the genome

sequence of the leprosy bacillus Mycobacterium leprae [122]. The bacterium is

strictly intracellular, but it has a relatively large genome size (3.3 Mb). It has,

presumably relatively recently, undergone massive reduction in genome size

resulting in many pseudogenes, thought to result from extensive recombination.

Pseudogenes are inactivated genes of which the remnants are still present, and

recent analyses of pseudogene content across diverse prokaryote genomes indicate

that pseudogenes are formed and eliminated rapidly from genome sequences [123].

In M. leprae the total number of predicted functional genes is around 1,600

Processes increasing

genome size

Processes reducing

genome size

Gene

acquisition

Loss of

fragments

plication

Deletional

bias

Gene inactivation

Bacterial

genome

Du-

Chapter 1

14

(compared to almost 4,000 in the related M. tuberculosis with a similar genome size),

which is indicative for a genome in flux.

Genomic rearrangements were first visualized when two different complete

genome sequences of the same species were sequenced and compared [124]. With

the exception of operons, functionally involved genes which out of necessity are in

close proximity to each other, the order of genes is often poorly conserved among

bacteria during evolution [125]. This genomic flexibility may have a function in both

the genesis of new genes (such as genomic region duplication events), in the

removal of unnecessary sequences or alteration of transcription levels. Comparisons

of whole genome sequences are best depicted in whole genome alignment graphs,

as described by Eisen and co-workers [126]. The conservation of gene order is

expressed with the synteny parameter, which is used when quantifying genome

sequence similarity.

1.5 Horizontal gene transfer

Even before whole genome sequences were available, horizontal gene

transfer (HGT) was recognized and acknowledged as a factor contributing to

prokaryotic evolution, although the impact of HGT was not fully appreciated until the

genomic era was well underway. One of the most well-known examples of HGT

events are R-plasmids that encode resistance against certain (types of) antibiotics

[127].

Horizontal gene transfer, sometimes addressed as lateral gene transfer,

constitutes an alternative for the orthodox vertical inheritance of genetic traits. There

are three distinct routes which can lead to horizontal acquisition of DNA: 1) direct

uptake of DNA by the cell (transformation), 2) directed DNA transfer via conjugation

and 3) bacteriophage-mediated DNA transfer (transduction) (see figure 3). These

three different routes of horizontal acquisition of DNA have all been studied

extensively; nowadays they constitute basic tools in molecular biology.

The first genome-scale analysis of HGT was performed by Lawrence and

Ochman [128], who found by that approximately 18% of the Escherichia coli genome

has been acquired via horizontal gene transfer. This study of a single genome

Chapter 1

15

sequence opened the door to further analyses, which seem to increase in accuracy

with the availability of more genome sequences (and more readily available

phylogenetic data) and of more parameters for identification procedures.

Although no actual natural transfer events have been witnessed, the results of

HGT can be recognized in different ways. The most obvious is the phylogenetic

approach, in which incongruence in evolutionary relationships between different gene

clusters hint at transfer events. Molecular phylogenetic analyses were originally

performed using nucleotide sequences present in all organisms: those of ribosomal

RNA [129]. Nowadays, with the availability of many different complete genome

sequences, weighted genome trees can be constructed [130], and discrepancies in

these analyses are often explained most parsimoniously by an horizontal transfer

event rather at one of the branches, rather then by a large number of deletion events

at a great many more branches of the phylogenetic tree.

The second approach for the detection of acquisition events consists of

parametric analyses, and is more enthusiastically embraced by bioinformaticians, as

it is relatively easily implemented in software. Different parameters have been

proposed to detect horizontally acquired sequences, and the most well known

approaches are based on codon-usage biases, GC percentage, and dinucleotide

frequency (also called the genome-signature) deviations [131]. Parametric

identification of horizontally transferred DNA is based on the genome hypothesis,

which proposes that for a given prokaryotic genus genomic DNA is relatively constant

in codon usage and GC content [132, 133]. Therefore, horizontally acquired

sequences may differ in codon usage and/or GC composition from the recipient

genome and can be identified in whole genome sequences. Improvements of these

approaches that permit better resolution have been published recently [134, 135].

Currently, with a great many genome sequences tested for putative horizontally

acquired genes, the emphasis has shifted toward functional analyses of horizontally

transferred sequences. Recent analysis of acquired genes suggest a bias towards

three functional categories: cell-surface, DNA binding and pathogenicity-associated

[136]. The observed bias in functional categories may however be the result of the

aforementioned disproportional availability of genome sequences of biomedical and

industrially relevant strains, as compared to other strains (www.genomesonline.org).

Now, with more genome sequences of environmental strains rapidly becoming

Chapter 1

16

available, an increasing variety of acquired gene clusters providing diverse metabolic

capacities are being discovered, emphasising that horizontal genetic transfer is not

limited to virulence traits [117], but may also constitute novel catabolic pathways

involved in for example xenobiotics degradation [91]. It is of note however, that the

parametric approach to the identification of compositionally dissimilar sequences

might not be sufficient to identify all horizontally acquired sequences. Exchange of

DNA between closely related species can lead to the acquisition of non-anomalous

sequences [137]. On the other hand, autochthonous sequences can sometimes be

very different from the rest of the genome sequence [131], as they are highly

expressed or have distinct features such as a strong bias for certain amino acids, but

also ribosomal RNA sequences. Finally, there are genomic sequences, are

suspected to be acquired via HGT, but no definite history or origin can be assigned.

This is a different drawback of parametric detection of anomalous sequences in

genome sequences; no clear cut-off value for the number of putative horizontally

acquired genes can be given without further phylogenetic support.

Besides whole genome sequencing, several other techniques are available to

selectively isolate putative horizontally acquired sequences. These include

subtractive hybridization [138] and representational difference analysis [138, 139],

both techniques with which the differences of two related strains are cloned and

analysed. These approaches rely on the lack of hybridisation between sequences

unique to one of the two strains. However, no dedicated tool is available to score

individual sequences isolated with these techniques for composition dissimilarities

compared to a genome sequence, although for many of these putative horizontally

transferred sequences a representative genome sequence (i.e. a genomic context) is

available. Currently, applications that can test whether a nucleotide sequence is

atypical within a genomic context, and therefore putatively horizontally acquired,

relies solely on whole genome sequences, and disregards all other sequences

available in the databases [136, 140-143].

Obviously, horizontal gene transfer does not create ever-larger prokaryotic

genome sequences. Constraints on HGT must therefore exist and limit the maximum

of acquired sequences that can be stably introduced to host genomes. For transfer

Chapter 1

17

routes such as conjugation or transduction, a limited host range of the transferring

agents may restrict the horizontal spread of genes. Lawrence and Hendrickson

propose a different potential constraint on HGT [144], based on specific

oligonucleotide motifs, distributed asymmetrically between the two DNA strands,

which may be involved in genome replication processes. The differential distribution

of such specific oligonucleotide motifs between different species of microbes may

limit sequence exchange, as the introduction of the DNA would incur a selective

detriment that could potentially offset any benefits provided by the newly acquired

gene products [144]. How this would relate to large-scale genome rearrangements is

still unknown.

Incompatibility may play a significant role in HGT. As sequences are thought

to function optimally in the genomes where they originally belong, sequence

establishment in a new host may not always result in compatibility of the encoded

proteins with the host (intra- and extracellular) environment. Also, most proteins

interact with and/or are dependent on other proteins in complex systems, and the

absence of similar systems in a new host may restrict functionality of acquired

sequences [145]. As a result, it is thought that sequences are removed quickly from

the genome, when they are not beneficial to the host. [121]

Finally, restriction modification systems may form a constraint posed on HGT

from a very different perspective. The defence hypothesis postulates that these

systems play a role in maintaining species identity [146]. Restriction modification

systems have two main activities: that of a restriction endonuclease and that of a

methylase. Both recognize the same nucleotide motif. The endonuclease activity

cleaves unmethylated DNA, which usually consists of acquired DNA that has not yet

been methylated. Genomic DNA is safe from the harmful activity of the

endonuclease, as the methylase protects all recognition sequences. However, the

mobile nature of these restriction modification systems questions their supposed

protective functionality towards species identity [147].

Chapter 1

18

Figure 3. Three different routes for horizontal gene transfer, Transformation (uptake of naked DNA),

conjugation (plasmid-directed DNA transfer) and transduction (bacteriophage-mediated transfer of

DNA).

Chapter 1

19

1.6 Outline of this thesis

The aim of this introduction was to draw the framework in which this thesis

should be placed, as well as to formulate the research questions of this thesis. The

initial aim of the project was to identify neisserial sequences that are responsible for

the hypervirulent nature of certain N. meningitidis genotypes. However, the aim

gradually shifted towards analyses of horizontal gene fluxes amongst prokaryotes in

general, and acquired gene clusters in Neisseriae in particular. In other words, how

can horizontally transferred sequences be isolated without strain-by-strain

comparisons? Next, how can individual sequences, suspected to be acquired via

HGT, be placed within a genomic context? Furthermore we aimed to analyse

horizontal gene fluxes both intragenomically (with genomic islands) and between

species (with plasmids). The first step would be the development of an in vitro

strategy to isolate horizontally acquired sequences from Neisseriae. Different

techniques exist to identify anomalous sequences in silico in completely sequenced

genomes. However, any sequenced genome is merely a representative of a

particular species and it is therefore difficult to extrapolate to unsequenced isolates of

the same species. Bioinformatical approaches have facilitated horizontal gene

transfer identification procedures in whole genome sequences as well as the

mapping of horizontal gene transfer processes. There is a need to implement in vitro

approaches to detect DNA acquisition events in order to unravel this important

contributor to bacterial evolution and diversification.

Chapter two deals with a novel in vitro strategy to selectively isolate

anomalous sequences from unsequenced prokaryotic genomes. The availability of

multiple genome sequences allows comparative genomics to test new strategies to

isolate putative acquired sequences, without performing hybridizations. In this new

strategy we focus on compositional attributes such as the genome signature, instead

of specific sequences potentially involved in DNA transfer processes such as tRNA

synthetase encoding genes. The availability of representative genome sequences is

a prerequisite for this strategy, and fortunately the large number of ongoing whole

genome sequencing projects safeguards a steady increase of such representative

genome sequences, which could expand our technique to different prokaryotic

genera.

Chapter 1

20

Chapter three describes the development of and gives instructions about a

web application that permits nucleotide composition analyses of individual sequences

in comparison to a suitable genomic context. The bioinformatical background of

chapter two is explained extensively in this chapter.

We hypothesized that different large putative horizontally acquired sequences

(i.e. Genomic Islands) within the same genome sequence may be compared with

each other, as it is reasonable to suggest that a single donor was responsible for

multiple transfer events. A comparison of Genomic Islands is described in chapter

four, and an application that allows users to investigate the acquisition account of

given prokaryotes is described. This approach might facilitate donor identification of

putative horizontally acquired genes.

Chapter five comprises a study focussed on the anomalous DNA content of a

Neisseria lactamica strain. N. lactamica is a human commensal residing in the

nasopharynx. This species however does not cause disease, although it is closely

related to the meningococcus. The strategy developed in chapter two to specifically

isolate putative horizontally acquired sequences is applied to the unsequenced N.

lactamica, and the analysis of the detected anomalous sequences in this species is

described.

A typical and well-known prokaryotic mobile element is the plasmid. Chapter

six reports on a range of plasmids isolated from N. lactamica strains. Selfish genetic

elements such as plasmids form a common vehicle for horizontal gene transfer

processes. Not much is known about plasmid sequence diversity amongst different

neisserial species. The (compositional) analyses of neisserial plasmids may reveal

what sequences are being transferred via these mobile elements.

Based on some of the results from chapter six, we perform a database-

analysis of a large number of prokaryotic plasmids, which is described in chapter

seven. Compositional comparisons of plasmid sequences and their respective host

chromosome sequences may confirm compositional compatibility. If compositional

incompatibility would be detected, alternative selection pressures would be exerted

on genomic and epigenetic sequences.

Chapter 1

21

Finally, chapter eight summarises the findings of this thesis and discusses

these further within the context of current developments in the field of molecular

microbiology.

Chapter 1

22

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Chapter 2

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Chapter 2

An in vitro strategy for the selective isolation of

anomalous DNA from prokaryotic genomes

M. W. J. van Passel1, A. Bart1, R. J. A. Waaijer2, A. C. M. Luyf2, A. H. C. van

Kampen2, A. van der Ende1,*

1Department of Medical Microbiology, 2Bioinformatics Laboratory, Academic Medical

Center, Amsterdam, the Netherlands

Adapted from Nucleic Acids Research (2004), 32(14):e114

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Abstract

In sequenced genomes of prokaryotes, anomalous DNA (aDNA) can be

recognised, among others, by atypical clustering of dinucleotides. We hypothesised

that atypical clustering of hexameric endonuclease recognition sites in aDNA allows

the specific isolation of anomalous sequences in vitro. Clustering of endonuclease

recognition sites in aDNA regions of eight published prokaryotic genome sequences

was demonstrated. In silico digestion of the Neisseria meningitidis MC58 genome,

using four selected endonucleases, revealed that of 27 of the predicted small

fragments (300 bp and

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31

Introduction

Horizontal gene transfer (HGT) was already identified in 1944 by the same

experiment that demonstrated the transformation of non-virulent to virulent

Streptococcus pneumoniae [1]. The extent of HGT as an evolutionary phenomenon

had not been addressed quantitatively on genomic scale until Lawrence and Ochman

calculated that approximately 18% of the genome of Escherichia coli MG1665 was

horizontally transferred since its divergence from the Salmonella lineage 100 million

years ago [2]. This identified HGT as a major factor in prokaryotic genome evolution.

Recently, an extensive database of horizontally transferred genes based on complete

bacterial and archaeal genomes has been made available [3].

The rationale behind the computational identification of horizontally transferred

DNA is the genome hypothesis, which proposes that for a given prokaryotic genus

genomic DNA is relatively constant in codon usage and GC content [4, 5]. In contrast,

horizontally acquired anomalous DNA differs in codon usage and/or GC composition

from the recipient genome and can therefore be identified when substantial sequence

information is available.

An additional parameter in lateral genomics is based on oligonucleotide

compositional extremes: the dinucleotide relative abundance values or genome

signature ρ* [6, 7]. The genome signature is constant among members of a genus,

but deviates substantially between members of different genera [8]. When used for

intragenomic comparisons, ρ* makes an excellent parameter for the identification of

anomalous DNA regions. Aberrant dinucleotide frequencies in aDNA are then

expressed as the genome dissimilarity δ*, being the average dinucleotide relative

abundance difference between the aDNA region and the whole genome [6-8].

Although the genome signature is capable of identifying clusters of alien genes and

acquired pathogenicity associated islands (PAI) with an atypical nucleotide

composition, highly expressed regions such as ribosomal clusters can also display

aberrant dinucleotide frequencies [8, 9].

Still, to our knowledge, no method exists that uses (one of) these parameters

and enables the selective isolation of anomalous DNA sequences from a microbial

genome in vitro. In order to develop such a technique we investigated a special

group of oligonucleotide composition extremes: the local overrepresentation in a

Chapter 2

32

genome of palindromic hexanucleotide sequences, specifically restriction

endonuclease recognition sites, in aDNA regions. Like the genomic dinucleotide and

tetranucleotide frequencies [10, 11], frequencies of restriction sites vary between the

genomes of different microbial species [12]. Avoidance of cognate recognition

sequences is probably the operating mechanism [13, 14]. An HGT event between

different organisms may introduce clusters of certain restriction sites in the recipient’s

genome. Therefore, digestion of the chromosomal DNA with such a restriction

endonuclease can produce a limited number of small restriction fragments,

comprising potential anomalous DNA, which can be selectively amplified by adaptor-

linked PCR (ALP [15]). The resulting amplicons can subsequently be subcloned and

identified by sequence analysis.

Clustering of restriction endonuclease recognition sites in diverse aDNA

regions in prokaryotic genomes was illustrated by the in silico assessment of seven

genomes sequences of five different species. The restriction enzymes of which the

hexameric recognition sites are underrepresented were identified for each genome,

and restriction fragments between clustered sites, being smaller than 5 kbp, were

analysed for nucleotide composition concerning GC percentage and genomic

dissimilarity.

Next, the restriction fragments of N. meningitidis MC58 between 300 bp and 5

kb were analysed in silico for both GC content and genome signature compared to

the genomic values. Also, the restriction fragments obtained with the selected

restriction endonucleases from N. meningitidis MC58 and Z2491 strains were

compared.

Finally, in order to demonstrate the applicability of this technique in vitro,

adaptor-linked PCR was performed on chromosomal DNA from strain MC58 digested

by each of the selected restriction endonucleases. The resulting amplicons were

sequenced to verify the predicted sequence composition.

Chapter 2

33

Material and Methods

Bacterial strain and growth conditions

N. meningitidis MC58 is a serogroup B:15:P1.7,16 strain isolated from a case

of invasive infection in the UK [16]. This wild-type MC58 strain lacks the erythromycin

resistance cassette insertion in the capsule gene locus in contrast to the sequenced

strain MC58 [17]. Neisseriae were grown on heated blood (chocolate) agar plates or

in liquid Tryptic Soy Broth (DIFCO) medium at 37°C in a humidified atmosphere of

5% CO2.

Chromosomal DNA preparation and digestion

Chromosomal DNA was isolated with the Puregene DNA isolation kit (Biozym).

Restriction digests and subsequent heat inactivation were carried out according to

the manufacturer’s instructions (Roche).

Adaptor-linked PCR and DNA sequencing

Adaptor-linked PCR was performed as described in [18]. The adaptor and

linker sets are MP19 (5’- ACG TCG ACT ATC CAT GAA CAG ATC 3’) and MP23 (5’-

GAT CTG TTC ATG-3’) for the ScaI-digested genomic template, MP24 (5’-ACC GAC

GTC GAC TAT CCA TGA ACA-3’) and MP20 (5’- CTA GTG TTC ATG -3’) for both

the NheI- and SpeI-digested chromosomal DNA and MP24 and MP23 for the BglII-

digested genomic template. PCR amplicons were purified by agarose gel extraction

(Qiagen) and subcloned into a pCR2.1 vector (Invitrogen) according to the

manufacturer’s instructions. Escherichia coli DH5α was transformed by standard heat

shock procedure. The constructed plasmids were isolated with the Wizard Kit

(Promega). Inserts were sequenced using standard M13 primers or primer walking

on vector or genomic DNA according to the manufacturer’s instruction (ABI).

Sequences were analyzed using the Staden Package (http://www.mrc-

lmb.cam.ac.uk/pubseq/).

Software

The restriction site frequency tables from the various genomes were obtained

from http://tools.neb.com/~posfai/FINISHED. The in silico digestions of the various

Chapter 2

34

sequenced genomes (for accession numbers see Table 1) were performed using the

Restriction Digest tool from The Institute for Genomic Research (TIGR)

(http://www.tigr.org). In silico retrieval and identification of the restriction fragments

was performed with the Position Search/Segment Retrieval tool from TIGR

(http://www.tigr.org). The different genomes of N. meningitidis were compared using

the Artemis Comparison Tool (ACT) (http://www.sanger.ac.uk).

Data analysis

Fragments were designated anomalous in GC composition if the GC content

of the fragment is below the fifth or above the 95th percentile of the genomic GC

content distribution, calculated with a window and step size identical to the fragment

length (http://www.tigr.org).

The δ* value for each restriction fragments was calculated as described earlier

by Karlin and colleagues [7]. In brief, the dinucleotide r