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Comparative genomics of bacterial sequence elements associated with chromosome structure
バクテリアゲノムにおける染色体構造関連配列の比較解析
河野, 暢明(Kono, Nobuaki)
冨田, 勝(Tomita, Masaru)
慶應義塾大学湘南藤沢学会
2012-04
博士論文
The structural characteristics and properties that have been observed in various circular bacterial
chromosomes have become increasingly clear based on the decipherment of the complete
genome sequences of approximately three thousand bacteria. A consideration of chromosome
structure includes various symmetries at many scales, ranging from base composition to the genestrand bias, which are based on the replication origin and a terminal axis, and the supercoiling
involved in the nucleoid structure. In this study, I focused on the chromosome structure andattempted to shed light on the mechanisms related to the formation and preservation of thechromosomes. First, I performed a comprehensive prediction of replication termination sites basedon sequence, and I determined that the sequence is widely conserved in almost all bacteria. Basedon this result, I proposed a model of replication termination from the standpoint of bioinformatics.
The use of this newly defined symmetric structure made it possible to investigate how strongly
symmetric structures have been maintained, and I analyzed the relationship between symmetricstructures and insertion events. Furthermore, I found that the association of nucleoid binding sites
with protein was controlled by cod on usage in the host bacterial genomes. Finally, I designed andimplemented a versatile browser to facilitate the visualization of symmetric structures that cannot
be fully captured by purely numeric information, allowing an intuitive grasp of these structures.
Throughout this dissertation, I performed comprehensive analyses of the genomic functionality,
characteristics, and structural evolution of bacterial chromosome, which serves as a medium ofliving systems. Based on these analyses, I discuss the means by which chromosome compositionand structure have adapted to biological mechanisms in the course of evolution.
先端生命科学プロジェクト 冨田勝研究会2011年度
Thesis or Dissertation
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ISBN

978‑4‑87762‑257‑2‑SFC‑DT2012‑001

oCmparative
Sequence

Genomics
Elements

with

Chromosome

2011年

度

of

Bacterial

Associated
Structure

河野 暢明 政策・
メディア研究科 博士課程

先 端 生命 科 学 プロジェクト

慶應義 塾 大 学 湘 南 藤 沢 学 会

推薦の ことば

ゲ ノ ムDNAは生 命 の 設 計 図 で あ る と 同 時 に 、 遺 伝 情 報 を次 世 代 へ 伝 え る媒 体 と して の 大

き な 役割 を担 っ て い る 。 しか し な が ら進 化 の過 程 に お い て この 媒 体 を維 持 す る た め の 物 理
構 造 的 な 制 約 に つ いて は 未 だ よ く解 明 され て い な い 。 本 論 文 は 、 生 体 内 機 構 に よ っ て 構 造
的 な 制 約 を 受 け て 来 た 遺 伝 情 報 の 媒 体 で あ るゲ ノ ム構 造 を 、 計 算 機 的 な ア プ ロー チ に よ って 解 析 した 、 極 め て 独 創 的 で 優 秀 な 博 士 論 文 で あ る。

慶應義塾大学
環境情報学部教授
冨田勝Comparative

Genomicsof Bacterial Sequence

Associated with Chromosome

Structure

Dissertation by
Nobuaki
Kono

Submitted

in partial fulfillment of the requirementsforthe degreeofDoctorof Philosophy
in the Graduate Schoolof Media and Governance

Keio University
Fujisawa, J apan
February2012

Elements

Keio

UniversitynI

y S G 1858
IADIO

04

Copyright © 2012
Nobuaki Kono
All rights reserved

To my family.

Abstract

The structural characteristics and properties that have been observed in various circular bacterial chromosomes
have become increasingly clear based on the decipherment of the complete genome sequences of approximately
three thousand bacteria. A consideration of chromosomestructure includes various symmetries at many scales,
ranging from base composition to the gene strand bias, which are based on the replication origin and a terminal
axis, and the supercoiling involved in the nucleoid structure. In this study, I focused on the chromosome
structure and attempted to shed light on the mechanisms related to the formation and preservation of thechromosomes. First, I performed a comprehensiveprediction of replication termination sites based on sequence,and I determined that the sequence is widely conserved in almost all bacteria. Based on this result, I proposed amodel of replication termination from the standpoint of bioinformatics. The use of this newlydefined symmetricstructure made it possibleto investigate howstrongly symmetric structures have been maintained, and I analyzed
the relationship between symmetric structures and insertion events. Furthermore, I found that the associationof nucleoid binding sites with protein was controlled by cod onusage in the host bacterial genomes. Finally, I
designed and implemented a versatile browser to facilitate the visualization of symmetric structures that cannot
be fully captured by purely numeric information, allowingan intuitive grasp of these structures. Throughout thisdissertation, I performed comprehensive analyses of the genomic functionality, characteristics, and structural
evolution of bacterial chromosome, which serves as a medium of living systems. Based on these analyses, I
discuss the means by which chromosomecomposition and structure have adapted to biological mechanisms in
the course of evolution.
Keywords: Bacterial circular chromosome, Genome replication, Genomic/chromosomal structure, Nucleoid,
Bioinformatics, Comparative genomics

論文題目

「
バ クテ リア ゲ ノム にお け る染色体 構 造 関連配 列 の比較 解析 」

論 文要 旨ハ イス ルー プ ッ トな ゲ ノム解 読 技術 の発展 に よ り約3,000に も及 ぶバ クテ リアの コ ンプ リー トゲ ノムが解 読 され てきた近 年,多 種 多様 なバ クテ リァの環 状 染色体 に見 られ る構 造 の特 徴 とその性 質 が 明 らか にな って きた.染 色 体
上 に あ る構 造 に は,複 製 開始 ・終結 点 を軸 とす る塩 基組 成 か ら遺伝 子座 に至 るスケ ー ルで観 察 され る対称構 造 や,核様 体 に関 わ る超 螺 旋形 成 な どが 知 られ て い る.そ こで 本研 究 で はバ クテ リァの持 つ環 状 染色 体 に お いて,こ れ
ら染色 体構 造 に着 目 し,そ の誕 生 か ら維持 に関 す るメ カニ ズ ムの解 明 を行 うこ とを 目的 とした.ま ず 対 称構 造 の
起 点 とな る複 製終 結 関連 因 子 の網 羅 的 な予 測 を行 い,ほ ぼ すべ て のバ クテ リア で共 通 して保 存 され て い る こ とを
解 明 した.そ して この結 果 を元 に,計 算機 的 な ァ プロー チか ら複 製 終結 機構 モ デル の提 唱 を行 い,対 称構 造 の中
心 とな る複 製 開始 ・終 結 点 の定 義 を改 めて行 った.こ の様 に定 義 され た ゲ ノム の対 称構 造 を用 い る こ とで,対 称
構 造 と水平 伝播 との 関係 性 の解 析 が可 能 に なっ たた め,バ クテ リァが如 何 に これ まで染 色体 の構 造 を維 持 して き
たか の観 察 を行 った.さ らに,核 様 体 関連 タ ンパ ク質 の結 合 部位 が ホス トバ クテ リアに よっ て コ ン トロー ル され
てい る事 を解 明 した.ま た,こ れ まで 議論 されて きた配 列要 素や 対 称構 造 を視 覚 的 な理 解 に支 え られ た 直感 的な
着 想で 捉 え る事 を可 能 にす るた め,汎 用 的 な ブラ ウザ の設計 と実装 を行 った.本 学 位 論文 にお いて は,上 に挙 げた多次 元 的 な アプ ロー チか ら得 られた 解析 結 果 を も とに,生 命 活動 が遺 伝 情報 の媒体 で あ る染 色体 へ 与 え る影 響を議 論 す る.
キ ー ワ ー ド:バ ク テ リァ 環 状 染 色 体,ゲ較 ゲ ノ ム解 析ノ ム複 製,ゲノ ム ・染 色 体 構 造,核様 体,バイ オ イ ン フ ォ マ テ ィ ク ス,比CONTENTS

1

2

Introduction

1

1.1

Definition of life ..........................................

2

1.2

Exponential growth of biological knowledge ...........................

3

1.3

Structures of bacterial chromosomes and genomes ........................

5

1.4

Organization ............................................

7

Comprehensive

predictionof chromosome

dimer resolution

sites in bacterial

9

genomes

2.1

Background .............................................

•

2.2

Materials and Methods .......................................

•

2.2.1

Software and sequences ..................................

•

2.2.2

Iterated hidden Markov modeling ............................

2.2.3

Calculation of the conservation quantity of dif sequences ................

2.3

Results ................................................
2.3.1 Overview of dif sequence prediction ............................
2.3.2

Prediction results of each phylum .............................

2.3.3

Correlation of the dif sequence position and the GC skew shift-points ........

2.4 Discussion .............................................

10
11
11
12
14

•

15

•

15

•

•

15
19

•

20

2.5

3

Conclusion ................................................27

Validation
tions

replication

termination

3.1

Introduction ................................................

3.2

Materials

3.3

Softwareand sequences .....................................

3.2.2

Selectionof bacteria

3.2.3

Simulationof GC skew formation ................................

3.2.4

Replication

termination

andsimulationof genomic

muta-

genomic

islands

plasmids ...............................

models .................................

Results ...................................................
GC skew formation

simulation .................................

3.3.2

Constructionof threereplication

3.3.3

Evaluationof the replication

termination

models .......................

3.3.4

Simulationsin species

fork-trap

machinery ......................

lacking

Discussion .................................................

The

relationship4.1

Introduction ................................................

4.2

Materials

4.4

using

and Methods ..........................................

3.4

4.3

models

3.2.1

3.3.1

4

of bacterial

betweenthetermination

symmetryof bacterial

Genomesequences

4.2.2

Dataset .................

4.2.3

The

4.2.4

Randomcalculationand software. ..

genomes

and

50
50

of asymmetries

50

. ..

deletions ...........

50
51

Results ......................
4.3.1

Assessmentof the effects

4.3.2

GEIs

base composition .....

4.3.3

Improvement

affectcircular50

and Methods .............

4.2.1

models .....................

rates

of GEIs

from GEIs

..

51
51

deletion

51
55

Discussion ....................

il5

Cod on usage is a selection

pressure

for the H-NS binding

56

sites

5.1

Introduction .......................................

57

5.2

Materials and Methods .................................

57

5.2.1

Software and genome sequences ........................

5.2.2

Discovering motifs and prediction of binding sites from ChIP data

5.2.3

The cod on usages among horizontally-acquired genes and core genes . . .

5.2.4

The binding ratio in each region .......................

5.2.5

Phylogenetic analysis ..............................

. . ..

59
59
59

•

5.3.2

The motif conservation in related bacteria ..................

5.3.3

Index to recognize the binding target .....................

59

•

59
•

Discussion ........................................

Pathway
6.1

58

•

•

5.3.1 Discovering H-NS targeting motifs ......................

6

58

•

•

5.3 Results ..........................................

5.4

57

•

Projector:

Web-based

Zoomable•

Browser73
73

6.1.1 Individual pathway map ............................

6.2

76

Integrated pathway map ............................

Materials and Methods .................................

76

6.2.1

76

Requirement analysis .............................

6.3 Implementation .....................................

6.4

61

72

using Google Maps API

Introduction .......................................

6.1.2

60

•

77

6.3.1 Pathway map ..................................

77

6.3.2

Search and data retrieval ...........................

78

6.3.3

Mapping and editing ..............................

78

Results and Discussion .................................
6.4.1

Requirement analysis .............................

6.4.2

System overview ................................

6.4.3

Search and retrieval ..............................

•

•

•

•

78
78
80
83

6.4.4

7

Pathway

mappingof experimental

data

. . .

83
87

6.5

Chromosome

map ...................

6.6

Limitations .......................

89

6.7

Conclusion .......................

89

90

Conclusions
7.1

The role of scientific

7.2

Chromosome

7.3

Biologicalvalidations ..................

92

7.4

Concluding

remarks ..................

94

structuralvisualization ...........
aspectsof bacterial

91
evolution

Bibliography

92

95

iv

LIST

role

OF

1.1

The

1.2

Symmetric/asymmetric2.1

Prediction

2.2

The phylogenetic

2.3

An example

2.4

Distributionof the genomic distances
dif sites. ................................................

FIGURESof visualization.......................................

4

structure.................................

6

strategy. .........................................
distanceof XerCD

13

in each organism. ..................

for the plot shown in Figure

between

2.2. ........................

17of xerC, xerD and ftsK gene from predicted
20

2.5

The relationship2.6

The difference

2.7

Phylogenetic tree based on rRNA for the comparison
genomes. ................................................

between15

dif sites and GC skew. ......................
dif and GC skew shift-point

positions...............

21
22

of XerCD- and XerH-containingof dif sequences.................................

25

2.8

The conservation

2.9

Variance

3.1

Scheme of GC skew reconstruction

simulation. .......................

32

3.2

Examplesimulation...................

36

3.3

Probabilistic

3.4

Examplesof GC content

distribution...............................

of GC skew reconstruction

error rates.......................................
of simulated

GC skew. ................................
v

26
28

37
39

3.5

Comparisonof RMSE

3.6

Heat

3.7

Boxplotof RMSEof all3.8

Boxplotof RMSEof simulated

3.9

Validation

mapscoresof RMSE

in fourscoresfor probabilistic

simulatedof simulations

models. ..........................40model. .............41

models. ...........................42modelsusing

combination

onlyin Firmicutes. ....................42
the

third

with

genomecod onpositionsandnon-coding

se-quences. .................................................44

4.1

Bacterialasymmetries.........................................49

4.2

The

4.3

The correlationof between
the asymmetries
origins
and termini...........................................53

4.4

Improvement

5.1

Sequence

5.2

Synonymous

5.3

Average

5.4

The

averageof synonymous

5.5

The

numberof H-NS

5.6

The

frequency

5.7

Binding

5.8

Phylogenetic

5.9

Alignment

6.1

Output6.2

Reference

6.3

User

interface. .............................................84

6.4

Data

mapping. .............................................85

6.5

Manual6.6

Chromosome

extentof change

in asymmetry

rates

logo

in threeof H-NScod ontribasic

bind

usage

motif

of binding

rates

in several

re-predictedof H-NS

examplemotif

cod on. .......................64

usages................................65
usage

in 113bacteria...................66

sites. ................................67

genes. ...............................69

editingin dissimilargroup..........................71

of MEGU......................................75

pathway

andmapmap. .......................................81

annotation

replication

63

tree............................................70
resultfixedsites..................................

sites. ...................................68
type

and

54

cod onbindingof thecases..................................

of tribasic

cod onsize. ....................52of pathway

viewer.......................................88vi

maps........................86

●

●

●

●

●

●

●

O

●

●

●

●

●

●

●

●

●

■

●

●

●

●

●

■

●

●

vii

O

●

O

●

●

map..

●

dif sites in a chromosome

●

7.1

91

LIST

2.1

Prediction

result5.1

List

dissimilar

6.1

Comparison
requirementof theOF TABLES

overview. .....................................16

group......................................60of existing
pathway-related
analysis..........................................82

software

viii

and

databases

according

to

the

LIST

AIMS
AJAX
Ala
B. subtilis
CAI
CDR
ChIP
E. collF. alni
GCSI
GEIs
H-NS
HGT
HMM
KOPS
Lew
NAP
Pro
RMSE
SOAP
SVG
Ser
Val
ZUI
bp
ssDNA
tAI

OF ABBREVIATIO\SArchitecture-imparting sequences
Asynchronous JavaScript + XML
AlanineBacillus subtilis
Cod on Adaptation Index
Chromosome dimer resolution
Chromatin immunoprecipitation
Escherichia coll
Franlcia alni
GC skew index
Genomic islands
Histone-like nucleoid structuring protein
Horizontal gene transfer
Hidden Markov model
FtsK-orienting polar sequences
LeucineNucleoid-associated protein
pralineRoot mean square error
Simple Object Access Protocol
Scalable Vector Graphics
SelineValineZoomable user interfacebase pairsingle strand DNA
tRNA Adaptation IndexlxAcknowledgments
First, I sincerely wish to express my great appreciation to Professor Masaru Tomita and Assistant Professor
Kazuharu Arakawa. Professor Masaru Tomita, who provided an excellent entree to the beginning of life as ascientist and have always given me a great deal of freedom. Therefore, I was always able to conduct studies
freely, and I came to think of science as the ultimate game. Assistant Professor Kazuharu Arakawa has not
only been an adviser who gives valuable advice to me throughout my studies but has also become my rolemodel as a scientist. My existence has centered around him, and I have adopted his scientific philosophy as
my own. I owe a very important debt to the members of my dissertation committee, Professor Mitsuhiro
Itaya, Associate Professor Yasuhiro Naito, Professor Akio Kanai, and Professor Tomoyoshi Saga, for theirextremely helpful and scintillating comments regarding this dissertation. I also wish to express my gratitude toa number of respected people: Assistant Professor Rintaro Saito (University of California, USA), Dr. Yoshiaki
Ohashi (Human Metabolome TechnologiesInc., Japan), Project Research Associate Katsuyuki Yugi, AssistantProfessor Hitomi Sano-Itoh, Dr. Ayako Yachie-Kinoshita,Dr. NozomuYachie,Dr. YoshihiroToya, Dr. Kosuke
Fujishima, Dr. Koshi Imami, Vincent Piras, Yoshiteru Negishi, Mikiko Hattori, Dr. Yuka Iwasaki-Watanabe,
Dr. YukinoOgawa, Hikaru Taniguchi, Kousaku Shinoda, Dr. Junichi Sugahara, Motomu Matsui, Taiko Nishino,
Chikako Arakawa-Oki,Saeka Tani, and Shinya Murata. I will never forget the wonderful time I spent with these
individuals.
I appreciate the cheerful days spent with several members of the Institute for Advanced Biosciences, Keio
University during the seven years of my studies. Kahori Takane, Atsuko Shinhara, Kaoru Sugahara-Kikuta,
Kentaro Hayashi, Haruna Imamura, Miho Tanaka, Keisuke Marita, Daiki Yamada, Seiya Fujimoto, Kazuki Oshita, Shinnosuke Murakami, Fujitaka Baba, Keiko lino, Takayuki Ebi, Mana Ogawa-Nakagawa, Yuriko Hasebe,Hitoshi Iuchi, Keisuke Wada, Mizuki Sata, Hanae Shimo, Yuka Hirose, Chikako Okubo, Shiori Komine, Seiko
Nakatsuka, Tamami Toki, Jun Yamakubo, Gembu Maryu, Karyn Kawamoto, Kyoko Ishino, Taiyo Miyahara,
Hidetoshi Itaya, and Shiori Hashimoto have served as tremendous emotional supports to me, and I am deeply
grateful to each one of them. In particular, Kiyofumi Hamashima, Toshinori Tsuboi, Keita Ikegami, Tadasu
Nozaki, Yuki Shindo and Norikazu Saiki have provided a stimulus-rich environment and witty conversations,friendships and meaningful discussions with them have been essential for my personal growth during my college
life.
My deepest appreciation goes to my peers, Ryu Ogawa, Satoshi Tamaki, Hiroyuki Nakamura, Ayaka Hiroe,
Tsuyoshi Akuzawa, Hiroaki Suzumura, Maria Iga-Takeuchi, Mia Iwasaki, Akihito Kawasaki, Daiki Nakatsu,
and Ryoji Yanashima, who entered the Graduate School of Media and Governance, Keio University, at thesame time that I did, and who provided very precious experiences. We have talked about the future, scienceand life and passed the days happily. Mere words could never express my gratitude, yet I will try to explain
how grateful I am for everything that they have done.
I would like to express special thanks to several dear and irreplaceable
people. Marika Nakai has contributedtomy progress with a dedicatedand thoughtful
heart and has always emotionally
supportedand encouragedmethroughout
my doctoral
program.
SatoruNishidaand Shingo Iwata have been very understanding
and closefriends throughout
all my college years, and they have supported
me with their love and good humor.
Finally, but most importantly,
Kimie Kono. They have madeI would like to offer warm thanks
to my dearest
parents,
Tetsuaki
innumerable
contributions
to my well being throughout
my life, andgratitude

Theirfor their

genuinelove.

thoughtfulness

and kindnessxwill always

be remembered.

Kono andI feel deep

CHAPTER

1Introduction"Omniscellulae cellula."-Rudolf

Virchow

1.1.

1.1

Definition

DEFINITION

OF LIFEof lifeanswerbut also
thefollowing
answers
for theproposition`definition'thatand `recognition'
life.aIs
a symbiont
Whatislife?
Thisquestion
isabiologicalclassical
andpracticalrequiresnotofonly
philosophicalalive? Is a virus alive? Moreover,are cells or biomoleculesalive? The definition of life is an essential challenge
in life science and serves as a fundamental pillar for the recognition of life. However,there is still no broadly
accepted definition of life (Chyba and McDonald 1995). In 1944, the Nobel laureate and physicist Erwin
Schrodinger wrote a book with this title, `What is Life?', and attempted to define life through physics andchemistry. In the famous chapter entitled `Order, disorder and entropy', he made the following statement
about living organisms: "What an organismfeeds upon is negative entropy. Or, to put it less paradoxically, theessential thing in metabolism is that the organism succeedsin freeing itself from all the entropy it cannot help
producing while alive.". He suggested that energy exchange, known as metabolism, in the living organism isimportant for avoiding decay (Schrodinger 1944). Another famous definition of life, which has been acceptedwithin the origins-of-lifecommunity, is the `chemicalDarwinian' definition. The Darwinian definition statesthat "Life is a self-sustained chemicalsystem capable of undergoingDarwinian evolution" (Joyce 1994). Living
things should be able to reproduce and evolve through natural selection. In addition to these two definitions,as discussed above, many other definitions of life have been proposed. Although Palyi et al. (Palyi et al. 2002)
and papa (papa 2004) listed approximately 100 different definitions that have been proposed over many years,
some of these lists have been refined to small categories using two approaches (Malaterre 2010). One approach
lists the elements of a sufficient or necessary system for a living organism. Examples include dynamic low
entropy systems (von Bertalanffy 1968), a hierarchical organization of open systems (Prigogine 1980;Prigogine
and Stengers 1984), and an unformalizable or non-computational system; this approach is referred to `the listbased definition'. According to this approach, de Duve and Koshland propounded the followingseven pillars
as the principle items for the definition of life: (1) program (DNA: deoxyribonucleic acid), (2) improvisation(response to environment), (3) compartmentalization, (4) energy, (5) regeneration, (6) adaptability, and (7)seclusion (chemicalcontrol and selectivity); these are abbreviated as PICERAS (de Duve 1991;Koshland 2002).
The second approach defines life based on a model that describes the functions of living systems (Maturana
and Varela 1980; Ganti 2003). Ruiz-Mirazo and coworkersascribed an autonomous system with open-ended
evolutionary capacities to a living model and propounded that this system must have four properties: a semipermeable boundary (a membrane), an energy transduction or conversionapparatus (a set of energy currencies),
and two types of interdependent macromolecular components, some of which perform and directly coordinate
self-construction processes (catalysts), while others store and transmit information (records) (Ruiz-Mirazo et
al. 2004). Given these definitionsof life from various perspectives, it would appear difficult to discover common
traits to re definelife clearly. However,when I observe such definitionsat the molecularlevel, the genome actually
underlies life, and the chromosome, which is the medium for the genome, meets an important requirement oflife. For example, enzymes or proteins that are involved in energy transduction or compartmentalization, which
are related to the self-construction process, are encoded by the genome as genes. Evolution, adaptation, or
response to stimuli begins with mutations that occur within the protein coding regions or structures of the
genome. Furthermore, all of these events occur on the chromosome, which is the medium for the genome.
Heredity, growth, regeneration, and recording are performed by the replication system of the chromosome.
This suggestion that the genome underlies life can also be observed in the context of the central dogma. The
central dogma is a concept of molecular biological systems in living organism that was proposed in 1958 byNobel laureate and molecular biologist Francis Crick (Crick 1958; Crick 1970), one of the co-discoverersof thestructure of DNA in 1958 (Watson and Crick 1953). This dogma asserts that genetic information is transferredfrom DNA to RNA to proteins through several processes. These processesare `DNAreplication' (DNA to DNA),`transcription' (DNA to RNA)
, and `translation' (protein production). DNA replication produces a copy of theoriginal genome to transmit the information in the chromosome. According to this flow, the process of DNA
replication, which is associated with the chromosome, plays a primary role for passing the genetic information
on to the subsequent generations. Consequently,the chromosomehas a major responsibility as the medium for
2

1.2.

EXPONENTIALGROWTHthe effective transmissionof hereditary
information
to future generationsof life. In other words, an understandingof the chromosome
is necessary
and this recognition
leads to anotheraspect of `What is life?'.

1.2

Exponential

growthof biological

OF BIOLOGICALKNOWLEDGE

and is dense with the fundamentals
for the primordialrecognitionof life,knowledge

Because the chromosomehas a major responsibility as the medium of the genome, it is thought that evolutionary
footprints are still evident in chromosomal structure. To observe the traces of evolution, genome sequencing
is an essential step in molecular biology. More than a decade has passed since the first complete bacterial
genome sequences, Haemophilus inflnenzae and Mycoplasma genitalium, were reported (Fleischmann et al.
1995; Fraser et al. 1995), and with the completion of the human genome in 2001 (Venter et al. 2001), genome
sequencing technologies achieved a remarkable development. Parallel sequencing technology, which is referred
to as `next-generation sequencing', has contributed to the generation of unprecedented levels of data fromcomplete genome sequences by rapidly decreasing sequencing costs and raising efficiency.Such sequencers have
many kinds of platforms, such as the Roche GS-FLX 454 Genome Sequencer, the Illumina HiSeq 2000, theABI SOLiD analyzer, the Ion Torrent Personal Genome Machine (PGM), and the Helices HeliScope. Eachplatform uses various schemes: pyrosequencing,sequencing by DNA synthesis, oligonucleotideligation, among
other exemplary approaches. The read length or aptitude for de novoor re-sequencingdepends on the platform(see review: Metzker 2010). According to the information released by the Genomes Online Database (GOLD;
Pagani et al. 2011), as of January 2012, there were 12,260 genome projects, including 3,046 complete genome
projects, and the number of projects has nearly doubled over the past two years (Liolios et al. 2010). In
particular, the number of bacterial genome sequence projects has reached approximately 9,000.
The rapid growth of materials has been expected to accelerate the biosciencefield, and a bioinformatics approachis required to process these data. When researchers extract valuable knowledgeor examine a large quantity of
information, the computational approach is an essential process. Currently, various bioinformatics applications
and databases have been provided to manipulate biologicaldata (Wieser et al. 2011; Galperin and FernandezSuarez 2012). Additionally, the field of scientificvisualization has been an integral part of the biosciencesand has
also been an indispensable approach to heuristically comprehend and instinctively understand large-scale data
in molecular biology (Figure 1.1). A large number of tools have been developed for information visualizationand have contributed to the recognition of biologicalinformation, including biomolecular structures, expression
profiles, genome annotation and sequence alignments, molecular pathways, ontology,taxonomy and phylogeny(Tao et al. 2004).

3

1.2.

EXPONENTIALGROWTH

OF BIOLOGICALKNOWLEDGE'come sequence

°

data

eldoI,Ph

1

Organism
Po

Figure.1.1: The role of visualization.
The genome sequences are obtained from organisms, and the numeric position data of sequence elements, e.g. dif sites, Ter sites,or protein binding sites, are detected from genome sequences. The visualization of such positional information by mapping it onto
the illustrated chromosome map enables an understanding of the positional relationships.

4

1.3.

1.3

Structures

STR UCT URES

of bacterial

OF BACTERIAL

chromosomes

and

CHROMOSOMESANDGENOMESgenomesIn the investigation or research of the basic principles of the chromosome, the eubacteria are recognized assuperior model organism because almost all eubacteria have only a single circular chromosome, one copy ofeach chromosome, and a small genome. Furthermore, as indicated above, there are over 2,728 publicly available
bacterial genome sequences because complete genome sequencing, assembly,and annotation can be performedin less than 24 hours (Flicek and Birney 2009;Reeves et al. 2009). According to the accumulation of knowledgeof bacterial genome sequences, the structural features in chromosomeas the media of genetic information havecome to be known. The nucleotide composition of genome made up of the nucleotides ATGC conforms to theChargaff's first parity rule (A : T and G C, Chargaff 1951). Interestingly enough, Chargaff also stated that
this parity can apply within a single strand DNA, known as the Chargaff's second parity rule (Rudner et al.
1968). Sueoka further theorized and reported that the infra-strand parity rule is expected to hold under nostrand bias conditions. If the mutation rates are similar between the two strands, these mutations counteract
the effect of the selections (Sueoka 1995). However,violations of the parity rule are universally observed in local
regionof genomicsequences. The most striking sets of compositional asymmetries exist throughout the genome,
generated by biologicalmechanisms associated with DNA replication. In particular, it is wellknown that various
symmetric or asymmetric structures for bacterial circular chromosomesare maintained (Figure 1.2). For typicalchromosomestructures, there are three types of bias: gene strand bias, oligonucleotidebias, and compositional
strand bias (Rocha 2004; Rocha 2008). Gene strand bias is an example of an asymmetric structure. The
distributions of bacterial genes are different between the leading and lagging strands. Approximately 78% ofgenes are in the leading strand. in Firmicutes bacteria or Mycoplasma(Fraser et al. 1995; Kunst et al. 1997;
Rocha 2002), and 85% of ribosomal protein genes or high-expressiongenes are coded in the leading strand in
Bacillus subtilis and Escherichia coll (McLean et al. 1998). A typical example of a symmetric structure isoligonucleotidebias. There are many recombination- or replication-associated sequences in the chromosome,e.g. architecture-imparting sequences (AIMS; Hendrickson and Lawrence 2006) or Ter sequences. In E. coll,Tus protein binds to the Ter sites to forms a barrier called a fork-trap (Horiuchi et al. 1995;Labib and Hodgson2007), and acts as an antihelicase and allowsforks to enter but not exit the terminus region (Hill et al. 1987;
Hill 1992). As a result, this complex makes the replication fork stall at the Ter site (Kamada et al. 1996;Wakeand King 1997). In order to terminate the replication efficiently,most Ter sites are located in the terminus halfof the genome (Neylon et al. 2005; Mulcair et al. 2006). The recombination-related Chi sequence (Schultz et al.
1981;Kuzminov 1995), which is a recombinational hot spot, is the third most abundant oligomerin the leading
strand (Blattner et al. 1997). During replication, when a recombination event occurs an odd number of times,
the replicated chromosomeis not properly segregated into two daughter chromosomesbut instead produces aconcatenated dimer (Sherratt 2003; Lesterlin et al. 2004). Therefore, many bacteria harbor highly conserved
chromosomedimer resolution (CDR) machinery to separate the dimer chromosomeinto two monomer daughter
chromosomes. In E. coll, chromosomedimers are resolved by two tyrosine recombinases, XerC and XerD, by
the addition of a crossoverat a specific 28 bp sequence called the dif site, which is located in the replication
termination region of the chromosome (Clerget 1991; Blakely et al. 1993). The reaction is coordinated to thelast stages of cell division by an essential cell division protein, FtsK, which functions as a septum-located DNA
translocase (Sterner et al. 1999; Bar re et al. 2000; Bigot et al. 2004; Kennedy et al. 2008; Dubarry andBar re2010). FtsK movesalong the chromosomeunidirectionally towards the dif sequence, thanks to polar andoriented sequences, the KOPS (FtsK-orienting polar sequences;Saleh et al. 2004; Bigot et al. 2005; Bigot et al.
2006). The density of KOPS is higher in the leading strand, with their distribution biased toward the replication
terminus (Bigot et al. 2005; Levy et al. 2005; Bigot et al. 2006), and KOPS are symmetrically conserved in
the two replichores with the same frequencies (Mrazek and Katlin 1998; Salzberg et al. 1998). The mostnotable feature of the asymmetric structure, which is provided by the replication symmetry, is the nucleotide
compositional strand bias, an excess of G over C in the leading strand, known as the GC skew (Lobry lgg6a;
Rocha et al. 2006). Although the localization of GC content can also be observed in eukaryotic organisms as
an isochore (Bernardi 1989; Bernardi 1995), it is not as biased as the bacterial GC skew. Although the details
5

1.3.

STRUCTURES

OF BACTERIAL

CHROMOSOMESANDGENOMESof the GC skew are described briefly in later chapter, when DNA is replicated according to the direction of thepolymerase, the leading strand is synthesized continuously,but the lagging strand is synthesized discontinuously.
As a result, the leading strand has a longer single strand time than the lagging strand, and this difference inreplication mechanism provides a bias toward G and C bases (Coulondre et al. 1978; Lobry lgg6a Re yes etal. 1998;Lobry and Sueoka 2002; Mackiewiczet al. 2003). These symmetric or asymmetric structures dependon the balance of replication arms because a pair of a replication origin and terminus defines the leading andlagging strands.
on

Oligonucleotide

bias)

Gene

Figure 1.2: Symmetric/asymmetricstrandbias

structure.

The blue line represents a leading strand, and the yellow line represents a lagging strand. In almost all bacteria, the circularchromosome is symmetrical about the replication origin and terminus (gray box) and oligonucleotide bias (black arrow icons) andis asymmetrical about the compositional strand bias (green lines) and gene strand bias (blue and yellow arrows).

The bacterial replication mechanism consists of 3 steps: initiation, elongation, and termination. Althoughsimilar to eukaryotes, the mechanisms differ at several points (Rudolph et al. 2010). In eukaryotic cells, theinitiation of replication occurs at multiple replication origins per chromosome, and the frequency is tightly
regulated to exactly once per cell cycle (Diffiey 2004). The cell cycle can be divided in GO (a quiescent stageof cell), GI (DNA synthesis), S (DNA replication), G2 (cell growth), and M phases (mitosis), and almost
all eukaryotic cells progress through these phases. The replication complex is assembled before entry into S
phase (Diffley2004; DePamphilis et al. 2006). In S phase, the replication forks move bl-directionally from theorigins until they encounter other forks originating from neighbor origins (Edenberg and Huberman 1975). The
replication termination sites appear randomly within a 4 kb zonebetween the replication origins (Greenfeder and
Newton 1992;Zhu et al. 1992). By contrast, the initiation of bacterial replication occurs at a single replication
origin. The replication of the chromosome proceeds bl-directionally from the origin to the terminus (Prescott
and Kuempel 1972; Hirose et al. 1983; Schaper and Messer 1995; Schaeffer et al. 2005). While bacterial
6

1.4.

ORGANIZATION

replication is also regulated once per cell cycle (Nielsen and Lobner-Olesen2008), there is a high frequency of
replication, which permits bacterial cells to grow more rapidly than eukaryotic cells and is also responsible forthe difference in cell sizes. For instance, E. coll cells can over-replicate prior to the completion of the previous
replication, resulting in a generation time (20 min.) that is half of the normal replication time (Simmons et al.
2004; Haeusser and Levin 2008).
Furthermore, there is not only structure at the sequence level but also topological structure in the bacterial
chromosome. Although bacteria do not have nuclei and the size of the chromosomeis smaller than the eukaryotic
chromosome, bacterial chromosomesare packaged into a nucleoid structure. The nucleoid is organized by theactions of supercoilingRNA and NAPs, which regulate DNA topology (Dame 2005). In gram-negative bacteria,
at least 12 distinct types of nucleoid-associatedproteins have been reported, and each type has its own DNAbinding preferences (Azam and Ishihama 1999).

1.4

Organization

As described thus far, a myriad of mutational and selectional pressures affects the structures of bacterial genomes
according to their requirements to be effective vehicles of genetic information. In this dissertation, therefore,
I show how such structures in bacterial circular chromosomes have evolved, and how the acquired symmetries
in genomes are maintained, through a series of comprehensive computational analyses. Firstly, in order toclearly define the regions of symmetry, I have comprehensively predicted the chromosome dimer resolution site
dif, using a novel phylogenetic pattern finding approach. Based on this information, I have then conductedcomputer simulations to test the quantitative contribution rates of different evolutionary pressures that haveshaped the characteristic genome structure. Secondly, I have tested intrinsic and extrinsic causes that may
disrupt the evolutionary structured symmetry in the genomes, and how the genome maintains its order despite
the existence of disorders. Specifically, I discuss the evolutionary traces of the maintenance of genomic symmetry
in light of numerous horizontal gene transfer events, and the intrinsic mechanism of the genome to protect
itself using topological chromosome structure with the control of nucleoids. Lastly, I present a sophisticatedsoftware system for the visualization of these numerous evolutionary pressures and structural elements within
the genomes, in order to aid the intuitive understanding of such systematic and complex phenomenon.

The bacterial chromosomehas a wide variety of structures. Although these structural phenomena have beenthoroughly observed, it has not been confirmed how these structures are formed or how bacteria maintain anddevelop these structures. Therefore, I used computational genomics approaches to address these questions byfocusingon the sequenceelements in bacterial chromosomes.This dissertation comprises7 chapters, including an
introduction section (here, Chapter 1). I developed a comprehensiveprediction method for a chromosomedimer
resolution site, known as dif, in silica using a phylogeneticprediction approach based on iterated hidden Markov
modeling (Chapter 2). During the replication process in bacteria with circular chromosomes, an odd numberof homologous recombination events results in concatenated dimer chromosomesthat cannot be partitioned
into daughter cells. However,the dif sequence has only been identified in a few bacteria. Additionally, the dif
sequence is suggested to occur at a site other than the dif site. Accordingly,I aimed to obtain the sequencescomprehensively and predict the dif sequences. Using this phylogenetic approach, dif sites were identified
in 641 organisms among 16 phyla, with a 97.64% identification rate for single-chromosomestrains. The dif
sequence positions were shown to be strongly correlated with the GC skew shift-point that is induced byreplication-associated mutation/selection pressure, but the difference in the positions of the predicted dif sites
and the GC skew shift-points did not correlate with the degree of replication-associated mutation/selection
pressure. This comprehensive identification of unique dif candidates can provide materials to elucidate theappropriate machinery for replication termination (Kono et al. 2011). Using these predicted dif sequences, inthe next chapter, I validated the bacterial replication termination models using a computational simulation of
7

1.4.

ORGANIZATION

genomic mutations in terms of the compositional strand bias and suggested an appropriate model (Chapter3). In bacterial circular chromosomes,replication is known to be terminated when any of the followingoccurs:
the forks progressing in opposite directions meet at the distal end of the chromosome, the replication forks
become trapped by Tus proteins bound to Ter sites, or the termination occurs at a single definite site, dif.
To understand this difference between the known replication machinery and the genomic compositional bias,
I undertook a simulation study of genomic mutations and reported here how different replication termination
models contribute to the generation of replication-related genomic compositional asymmetry. This study could
confirmhow chromosomestructures are formed and how the determination of the replication termination point
enables the definition of a symmetric or asymmetric median line in the circular chromosome. From these
insights, the symmetry of the circular chromosomewas defined. Then, to investigate how the defined symmetricchromosomestructure has been maintained, I considered computationally whether the symmetry of the bacterial
circular chromosomeis disrupted by genomicislands (Chapter 4). The term symmetric chromosomehere means
the compositional strand bias, known as the GC skew. The GC skew is widely used as an in silica method forthe prediction of the replication origin and terminus (Frank and Lobry 2000; warning et al. 2006). However,the strength of the GC skew is extremely variable. Some bacteria have only weak biases (Zhang et al. 2003;
warning et al. 2006; Arakawa et al. 2ooga), and there are many bacteria that have an imbalance in GC skew.
Therefore, I hypothesized that the balance of the GC skew may be affected by mutations in the genome. Among
mutations, genomicislands are considered to have the possibility to change the base composition on a large scale.
Genomic islands are large foreign regions of approximately 10 Kbp to 1 Mbp in the bacterial genome (Rocha
2008), which were most likely acquired by horizontal gene transfer (HGT; Gogarten and Townsend 2005; Juhas
et al. 2009). In Chapter 4, I compared the symmetric structures of natural genomes and artificial genomes inwhich the genomicislands were deleted computationally and investigated the disruption effects and the strengthof the GC skew. In Chapter 5, I extended the analysis to the macroscopic level and observed the side effects of
the nucleoid, which physicallycontrols the bacterial chromosomeupon an acquisition of new function (Chapter5) . H-NS, known as the nucleoid-associatedprotein, changes the topology of DNA and acts as a transcriptional
regulator for the silencingof horizontally acquired genes. Although this transcriptional regulation is well studied,no definitive reason has been provided for the evolution of this function. In this chapter, I investigated codons
in the motifs of H-NS binding regions and found that these codons were used more often in horizontally acquired
genes than in other regions. Furthermore, they were infrequently used in highly expressed and essential genes.
Accordingly,it is likely that the immune-likerole of H-NS developed as a by product,to prevent the disruptionof the bacterial life cycle. Finally I developed a web-based browser for molecular biology with a zoomable userinterface (Chapter 6). An understanding of the positional relationships between gene directions, oligonucleotide
sequence sites, base compositional biases, foreign regions, protein binding sites and replication origins andtermini are important for understanding bacterial chromosomestructures. Although the localization of genes orpositions of some sequence elements are known numerically for one organism, such numeric does not necessarily
lead to instinctive recognitions. Therefore, I implemented a viewerwith a design such that each genomicelement
is mapped on a circular chromosome map to gain an instinctive understanding and inspiration for subsequent
investigations. This system was developed as an additional function of MEGU (Kono et al. 2006) and Pathway
Projector (Kono et al. 2009). The Pathway Projector is a biochemicalpathway browser and was implemented
based on the requirements for a versatile browser: (1) comprehensivesearch features and data access; (2) data
mapping and the ability to edit and annotate maps; (3) an intuitive user experience without the requirement for
installation and regular maintenance. I took advantage of the versatility of the Pathway Projector to add thecircular chromosomemaps. The circular map enabled the visual observation of living organisms from multiple
viewpoints. In Chapter 7, I provided a brief summary of the analyses and perspectives derived from the studies
described in this dissertation in the concluding chapter.
Throughout
this dissertation,
I performed
comprehensive
analyses of the genomic functionality,and evolutionof bacterial
chromosome
structure
which serves as a mediumof living systems,
to discuss how the chromosome
compositionand structures
adaptedto biologicalmechanism
evolution.

8

characteristics,
and would likein the course of

CHAPTER

Comrehensivepchromosome

dimerbacterial"We can only see a shortdistance2

predictionofresolution

sites

in

genomesahead, but we can see plenty

therethat needs

to bedone."-Alan

Turing`Computing

Machineryand Intelligence'

2.1.

2.1

BACKGROUND

Background

InWhenbacteria,
such areplication
recombinationfork
arrestevent occurs
ismainly
an oddrepairednumberbyhomologous
of times in one
recombination
DNA replication(Michelevent
etofal.circular2004).
chromosomes,the replicated chromosomeis not properly segregated into two daughter chromosomesbut instead
produces a concatenated dimer (Sherratt 2003;Lesterlin et al. 2004). Therefore, many bacteria harbor highly
conservedchromosomedimer resolution (CDR) machinery to separate the dimer chromosomeinto two monomer
daughter chromosomes.
In Escherichia coll, chromosome dimers are resolved by two tyrosine recombinases, XerC and XerD, by theaddition of a crossover at a specific 28 bp sequence called the dif site, which is located in the replication
termination region of the chromosome (Clerget 1991; Blakely et al. 1993). The dif sequence contains a pairof palindromic sequence motifs that correspond to the binding domains of XerC and XerD. The reaction iscoordinated to the last stages of cell division by an essential cell division protein, FtsK, which functions as a
septum-located DNA translocase (Sterner et al. 1999;Bar re et al. 2000; Bigot et al. 2004; Kennedy et al. 2008;
Dubarry and Bar re2010). FtsK moves along the chromosomeunidirectionally towards the dif sequence, thanks
to polar and oriented sequences, the KOPS (Saleh et al. 2004; Bigot et al. 2005; Bigot et al. 2006). CDR
is initiated when FtsK reaches dif and its extreme C-terminal domain directly interacts with the C-terminal
domain of XerD (Aussel et al. 2002;Yates et al. 2003;Massey et al. 2004; Yates et al. 2006;Borne et al. 2009).
The dif/XerCD chromosome dimer resolution system seems widely conserved. In vive experimental evidence
for its conservation has been obtained in Xanthomonas campestris, Caulobacter crescentus and Vibrio cholerae(Yen et al. 2002; Val et al. 2008; Wang et al. 2006). In vitro characterization of Xer recombinases and difsites has also been carried in Haemophilus influenzae and Bacillus subtilis (Neils onet al. 1999; Sciochetti etal. 2001). However, the importance of dif/XerCD for the fitness of bacteria has only been demonstrated in
E. coll and V. cholerae (Cornet et al. 1996; Val et al. 2008). In some other bacteria, like Lactococci andStreptococci,chromosomedimer resolution is resolved by single tyrosine recombinases that act at specific difsite (Lc Bourgeois et al. 2007; Nolivos et al. 2010). In this case, dimer resolution still depends on FtsK and
dif is still located opposite the origin of replication between oriented polar sequences (Campo et al. 2004).
Several filamentous phages are known to hijack this site-specific recombination machinery of dif/XerCD fortheir integration into the host chromosome, containing pseudo-dif sequences within these phage genomes (Linet al. 2001; Huber and Waldor 2002; Campes et al. 2003; Val et al. 2005; Derbise et al. 2007; Campes et al.
2010; Das et al. 2010). However,the dif sequence remains intact during such recombination process to ensure
the integrity of chromosomedimer resolution machinery (Blakely 2004; McLeod and Waldor 2004). The dif-likesequencesin phages often contain more variable central region that is longer than the canonical 6 bp (Val et al.
2005; Campes et al. 2010; Das et al. 2010), and the XerD binding arm is considerably degenerate (Lin et al.
2001).
Becausethere is only one origin of replication on bacterial circular chromosomes,replication generally terminatesin a specificregion of the chromosome. This can be followedby the existence of a GC skew on the two replichorearms of the chromosomes with a shift-point opposite the origin of replication (Lobry lgg6a). Based on theobservation that dif sites are generally located at or near the GC skew shift-point, Hendrickson and Lawrence
proposed that replication might generallyterminate at dif, which coordinate replication and chromosomedimer
resolution (Hendricksonand Lawrence 2007). In E. coll, the replication process usually terminates at a narrow
region that includes approximately 5% of the genome length and is located directly opposite the replication
origin (Louarn et al. 1977; de Massy et al. 1987; Hill et al. 1987). This is partly due to the existence of the
Ter/Tus replication fork-trap (Hill et al. 1987). dif is located within the replication fork-trap but termination
occurs precisely at the Tus site, not at dif (Duggin and Bell 2009) and dif is active when displaced outsideof the replication termination region if it is still within the zone where KOPS converges (Cornet et al. 1996).
Nevertheless, the lack of universal conservation of the Tus protein may suggest that replication terminated at

10

2.2.

MATERIALSAND

METHODS

dif sites until the relatively recent takeover by the Ter-Tus system (Duggin et al. 2008). We reasoned therefore
that the comprehensive identification of dif sites and of their location with respect to the GC skew shift-pointin hundreds of complete genomes might provide clues to the evolution of the CDR machinery and its possible
link with the replication termination mechanism in bacterial species.
Prediction of the dif sequences has been reported by several groups with different approaches. Hendrickson and
Lawrence showedthat sequence skew can be used to predict the locations of dif sites, and they identified putative
dif sequences in 25 bacteria based on sequence similarity (Hendricksonand Lawrence 2007). Lc Bourgeois andcolleaguesreported a new type of tyrosine recombinase, named XerS, which is responsiblefor CDR in Streptococciand Lactococciand this recombinase targets a 31 bp sequence element named difsL (Lc Bourgeois et al. 2007).
For comparison, they predicted dif sequences in 22 Firmicutes based on their similarity to that of B. subtilis
with Megablast (Zhang et al. 2000) and on the fact that the dif sequence occurs only once per genome. Val
and colleagues identified that V. choleraechromosomeII, whose many features are plasmid-like,has an original
dif sequence independently, and therefore it has FtsK-dependent CDR (Val et al. 2008). For this purpose, they
predicted dif sequences in five ca-Proteobacteria and ten /s-Proteobacteria that harbor multiple chromosomes,
and discussed a conserved FtsK-dependent CDR on multiple chromosomesbased on the close relative distanceof the positions of dif sequences and the GC skew shift-points. Their prediction method is based on a HMMER(Eddy 1998) score (< 10-5) with a profile built from 27 aligned dif sequences in the largest chromosomes of-y-Proteobacteria species, with manual checkingfor 6 bp spacing between two XerC and XerD binding motifs.
Carney and Roten reported the most comprehensive predictions to date, identifying putative dif sequences in
204 chromosomesin 137 Proteobacteria strains, discussingthe high conservation of dif/XerCD systems and thepossibleloss of dif sequencesin endosymbionts, with suggestions for other CDR mechanisms (Carney and Roten2009). Here, the prediction was based on BLAST searches and YASSalignment (Noe and Kucherov 2005) withthe dif sequences of E. coll and B. subtilis, and candidates were selected based on their proximity to the GC
skew shift-points and a single occurrence per chromosome. Previous predictions were therefore limited to threebacterial phyla: Proteobacteria, Firmicutes and Actinobacteria.
To this end, we describe comprehensive predictions for dif sequences based on a machine learning approach,tracing the phylogenetic conservation patterns of XerCD recombinases and using an iterative hidden Markov
modeling method. Furthermore, we observed the relationship between predicted dif sequence positions and GC
skew shift-points, and investigated whether replication termination occurs at the dif site.

2.2

Materials

2.2.1

Softwareandand

Methods

sequences

All analyses in this study were conducted using programs written in Perl with the G-language Genome Analysis
Environment, version 1.8.10 (Arakawa et al. 2003; Arakawa and Tomita 2006; Arakawa et al. 2008). Hidden
Markov modeling and searching was conducted with HMMER, version 2.3.2 (Eddy 1998). The dif sequenceis the binding site of the XerCD recombinase; therefore, we first selected 734 circular bacterial chromosomes
among 658 species/strains according to their conservation of XerCD using the KEGG (Kyoto Encyclopediaof Genes and Genomes) Orthology database (KO; Kanehisa et al. 2010). We obtained these sequences fromthe NCBI FTP Repository (2009, http://www.ncbi.nlm.nth.gov/Ftp). The followingexperimentally confirmed(E. coll and B. subtilis) or computationally predicted (F. alni) dif sequences were used as seed sequences for
subsequent searches and machine learning:

11

2.2.

E. coll s'-GGTGCGCATAATGTATATTATGTTAAAT-s'

MATERIALSAND

METHODS(Blakely and Sherratt 1994)

B. subtiliss'-ACTTCCTAGAATATATATTATGTAAACT-s'(Sciochetti et al. 2001)
F. alni s'-CACGCCGATAATGCACATTATGTCAAGT-s'(Hendricksonand Lawrence 2007)

2.2.2

Iterated

hiddenMarkovmodeling

XerCD conservation does not immediately imply dif sequence conservation (Val et al. 2008). Therefore, todetermine the phylogenetic conservation patterns of XerCD, we first aligned all XerCD amino acid sequences inthe 734 genomes analyzed in this work with those in organisms with the above-mentioned dif sequences using
ClustalW (Thompson et al. 1994). The average distances of XerC and XerD sequences that were calculated
from this alignment were used to infer phylogenetic conservation patterns among phyla.
Based on the phylogenetic conservation patterns of XerCD, we iteratively created the hidden Markov models(HMM) for the accurate prediction of dif sequences, seeded with the previously described dif sequences (Figure 2.IA). Iterated HMM is shown to be able to build a more diverse and potentially more sensitive models
than regular HMM, by incorporating distant homologoussequences while avoiding the contamination of nonhomologous sequences into the model (Johnson et al. 2010), and thus iterative HMM has been frequently
utilized in bioinformatics and computational biology (Altschul et al. 1997; Karplus et al. 1998; Schaffer et al.
2001; Scheeff and Bourne 2006). In this work, the first profile hidden Markov model was created from the dif
sequences identified in genomes belonging in the same genus as the genome harboring the seed sequence. For
example, in Proteobacteria, the seed sequences came from E. coll;therefore, the dif sequences were searched in
28 genomes belonging to the genus Escherichia by means of fuzzy matching with the seed sequences of E. collK12 using Perl module String::Approx 3.26 (http://search.cpan.org/Njhi/String-Approx-s.26/Approx.pin). For
fuzzy matching, the maximum numbers of insertions, deletions, and substitutions were previously determined
to be 0 bp, 0 bp, and 8 bp, respectively (Blakely and Sherratt 1994). Likewise,initial profiles were created for
Firmicutes based on 24 genomes in the genus Bacillus and for Actinobacteria based on two genomesin the genus
Frankia. Based on these initial profile hidden Markov models, dif sequences were predicted in the genomes of
the closest genus to the seed genus according to the amino acid sequences of XerCD proteins.

12

2.2.

MATERIALSAND

METHODS

A[Threshold]• Score>_ 0• E.vaXw< 10• No other candidates

HMM

HMM

HMM

ProfileProfilePrediction

Prediction

Integration

ProfilehiddenMakovmodelo(&d.rkho
GGTGCG(ATAATGTATAlTTATGTTAAATA4TUG(ATAATGTATATTATGTTAAATGGTGCGCATAAT(ATMTAR,TTAAAG(Esdleridlia 28 strains)

ProfileIntegration
New profile Ndden Nerkov mood

New proae hidden Nerkw model
GGTGC.GC
ATAATGTATATTATGTTAAATGGTGCGCATAATGTATATTATGTTAAATAGTGCGCATAATGTATATTATGTTAAATAGTGCGCATAATGTATATTATGTTAAATGGTGCGCATAATGTATATTATGTTAAAGGGTGCGCATAATGTATATTATGTTAAAGGGTGCGCATAATGTATATTATGTTMATGGTGCGCATAATGTATATTATGTTMATGGTGCGCATAATGTATAl TATGTTAAATGGTGCGCATMTGTATATTATGTTMAT

Shlyella

GGTGCGCATMTGTATATTATGTTMAT

Salmonella

GGTGCCCATAATGTATATTATGTTAAATGGTGCGCATAATGTATATTATGTTAMT

Predicteddi(tequencef
GGTGCGCATAATGTATATTATGTTAMT

Predicted4/recommits
GGTGCGCATMTATATATTATGTTAAATGGTGCGCATAATGTATATTATGTTAMTGGAGCGCATMTGTATATTATGTTAAATGGTGCGCATMTATATATTATGTTAMTGGTGCGCATAATGTATATTATGTTAMTGGAGCGCATMTGTATATTATGTTAMTGGTGCGCATAATGTATATTATGTTAMT

PhylumFigure 2.1: Prediction
strategy.
A: Example of the iterated HMM in Proteobacteria. The first seed profile hidden Markov model is created from the seed dif
sequence of Escherichia coll, by searching for dif sequences in 28 genomes belonging to the genus Escherichia by means of fuzzy
matching. Based on this initial profile hidden Markov model, dif sequences were predicted in the genomes of the closest genus to
the Escherichia genus (in this case, Shigella) according to XerCD amino acid sequences. Subsequently, a new profile is createdusing the previous profile and the newly predicted dif sequences, and this new profile is used to predict in the second closest genus(in this case, Salmonella). In this way, profile creation and dif sequence prediction were repeated recursively in decreasing order ofsimilarity of XerCD from the Escherichia sequence. The iterated HMM is conducted for each phylum. B: Flow chart of the overall
strategy.

13

2.2.

MATERIALSAND

METHODSIn the case of Proteobacteria, an initial profile was created using genomes belonging to the genus Escherichia,
and this profile was used to predict dif sequences in the genus Shigella. Subsequently,a new profile was createdusing the previous profile and the newly predicted dif sequences, and this new profile was used to predict thesecond nearest genus (in the case of Proteobacteria, Salmonella). In this way, profile creation and dif sequence
prediction were iterated in decreasing order of similarity of XerCD from the seed sequences; thus, iterated
HMM was conducted for each phylum. Because no dif seed sequences were available for phyla other than thethree described above, the three profile hidden Markov models obtained by iterated HMM in Proteobacteria,
Firmicutes, and Actinobacteria were used as the initial profiles. At each iterated HMM, predicted candidates
were validated according to the followingcriteria: 1) HMMER score > 10 and e-value < 10-4, 2) leave-one-out
cross-validationusing the new profiles, and 3) conservation of the palindromic structure. For cross-validation,
each time a new profile was created in the iterated HMM, we tested the validity of the training set by leavingout one of the dif sequences from the accumulated set of dif sequences and checking that the prediction of
the left-out sequence by training with all of the other dif sequences is always above the threshold for all dif
sequences collected up to that iteration. For the palindromic structure, positions 7-12 bp and 17-22 bp of
dif sequences, corresponding to the binding sites of XerC and XerD, were checked for complementarities. For
example, the palindromic structure of E. coll dif sequences in bracket notation is "--(--- (((((( -()- )))))) ---)--",and the conservation threshold is set to more than four pairs of complementarities within the 7-12 bp and 17-22
bp positions of the predicted dif sequences.
Although iterated HMM is based on phyla, this taxonomic unit is sometimes too diverse to accurately follow
phylogeny with recursive means. Therefore, prediction was separately conducted in classes instead of phyla for
60 strains, harboring 130 chromosomesfor classes a-, 0- and 'y-Proteobacteria. Similarly,sometimes, a species
is highly phylogenetically distant from the seed organism, making it the case that utilization of profile hidden
Markov models from other phyla is more suitable than own phyla's profile. When iterated HMM fails in suchcases, an alternative seed profile is created using the dif sequences from the top three genomes with the closest
XerCD sequences, as determined by alignment using ClustalW (Figure 2.IB).
GC skew's shift-point, calculated as (C — G)/(C + G), was computed using the "find_ori_ter"function of
the G-language GAE (Arakawa et al. 2003), based on the cumulative GC skew (Grigoriev 1998) at 1 bp
resolution. Although GC skew is widely observed in bacterial species, a number of genomes do not exhibit
notable compositional bias (Arakawa and Tomita 2ooia; Arakawa et al. 2007). To determine the presence ofgenomic nucleotide compositional bias, the GC skew index (GCSI) was calculated for all genomes, and GCSI
> 0.05 was used as the threshold (Arakawa and Tomita 2ooia; Arakawa et al. 2ooga). GCSI quantifies thedegree of GC skew using the compositional distance between the leading and lagging strands and the spectral
amplitude of 1 Hz signal of GC skew graph using Fast Fourier Transform. In this study, the replication origin
is defined based on the cumulative GC skew at 1 bp resolution using the G-language GAE.

2.2.3

Calculationof the conservation

quantityof dif sequences

Conservation quantity was calculated based on the nucleotide variance in each position of dif sequences. Firstly,
we calculated the position-specific base composition of all dif sequences in a group (phylum or class). Subsequently, variance of the most frequent base in that position is calculated from the base composition. For
example,when a group with 100 dif sequenceshas nth base composition of (A, T, G, C = 100, 0, 0, 0) or (A, T,
G, C = 25, 25, 25, 25), the variance is 2,500or 0, respectively. Hence, if the position-specificbase composition isbiased toward any one base, its high variance indicates high degree of conservation. These values are normalized
to percentages for comparisonwith other groups. In the case of multiple chromosomes,since these conservation
quantities were calculated in each strain, the average value was used for normalization.

14

2.3.

2.3

Results

2.3.1

Overviewof dif sequence

RESULTSprediction

We first analyzed the phylogenetic conservation patterns of XerC and XerD in bacterial species by calculating
the distances of their amino acid sequences from those in the seed organisms with known dif sequences (experimentally confirmed: E. coll and B. subtilisand computationally predicted: Frankia alni). As depicted in Figure
2.2 and Figure 2.3, sequence similarity distributions were clearly distinguished by phylum. Sequences belonging to different phyla always showed ClustalW distances of > 0.3, and based on this phylogenetic distribution
pattern, we separately trained and predicted the dif sequences in each phylum using iterated HMM. By this
phylogeneticprediction approach, we predicted dif sequences in 578 genomes out of 592 that harbor the XerCD
recombinase. The same prediction method was applied for 66 organisms with multiple chromosomes,totaling
142 chromosomes,where we could predict dif sequences in 63 organisms with 137 chromosomes(Table 2.1).
All of these predictions resulted in unique hits above the threshold, and their validity was further confirmed
through leave-one-outcross-validation. On the other hand, predictions below the threshold (score < 10 and evalue > 10-4) often resulted in multiple candidates with insufficientscores. When the initial prediction using thestrict threshold failed,we manually checkedthe predicted sequences for the conservationof palindromic structurein the 7-12 bp and 17-22 bp positions, and candidates that were located close to the origin of replication were
removed because the displacement of a dif sequencenear the origin significantlyreduces the growth rate (Cornet
et al. 1996).
0.50 ,B

A

0.50

0.40

0.40
h-

7

0.40

zu
0.30

0.30

z
0.30
C

0
o 0.20
0
0.10

0.20

0.la

0

0 0.20
0
0.10

0

0

0

0.10

0.20

0.30

0.40

0.50

C

0.50

sri

0

0.10

Fsc.herichia coll K-l2

0.20
Escherichia

0.30

0.40

coll K-l2

0.50

0

0

.10

0.20

0.30

Bacillus subtilis

0.40

0.50

168

Figure 2.2: The phylogenetic
distance of XerCD in each organism.
The phylogenetic distances of bacterial genomes to three seed organisms, Escherichia coll (Proteobacteria), Bacillus subtilis (Firmicutes) and Frankia alni (Actinobacteria), were calculated as the average of phylogenetic distances of XerC and XerD. Detailed
example is given in Figure 2.3. A to C are scatter plots of the distances of these genomes to the seed organisms. Axes represent
average distances as calculated by ClustalW. A: Distances from Escherichia coll K-l2 and Bacillus subtilis 168; B: distance from
Escherichia coll K-l2 and Frankia alni ACNl4a; and C: distance from Bacillus subtilis 168 and Frankia alni ACNl4a. Blue represent the genomes of Proteobacteria, green represent Firmicutes, yellow represent Actinobacteria, and the gray marks represent
other phyla. All phyla show strong preferences for seeds from the same phylum.

2.3.2

Prediction

resultsof eachphylumIn Proteobacteria, fuzzy matching in 28 Escherichia strains based on the dif sequence of E. coll K12 forthe creation of an initial seed profile hidden Markov model yielded a unique dif sequence in each of the 28
15

2.3.

Table

chi

2.1:

Prediction

resultoverview.

: chromosomes
SinaleChromosome

Predicted

Oraanism

Proteobacteria
Firmicutes

362

357

100

97

97.00

Actinobacteria
Bacteroidetes

66

66

100.001919100.00Chlamydiae
Chlorobi
Acidobacteria

1414100.0011

11100.003

3

100.003

3

100.003

3

100.001

1

100.001

1

100.001

1

100.001

1

100.00Verrucomicrobia
Chloroflexi
Gemmatimonadetes
Nitrospirae
Elusimicrobia
Tenericutes
Spirochaetes
Cyanobacteria
Planctomvcetes
Total

98.61

1

1

100.00

5

0

0.00

1

0

0.00

592

578

97.64

(chi

cYo(chiSpirochaetes

60 (130)
6 (12)

57 (125)
6 (12)

95.00 (96.15)
100.00 (100.00)

Total

66 (14263 (137Multi

te Chromosomes

Proteobacteria

Oraanism(chi

16

Predicted

94.45

96.48

RESULTS

2.3.

A

Distance

Organ
A- X..0
Eadier.la col (ProuoWCUrv) - XerC
Omar. A. XerO
Eulrrdla ml )PreneE•anrv) - XeroRESULTS

Average

MTEFSASLAPQVEAFLRYLSIERQLSPLTVTSYRRQLSALAEIGEQMGLAMNQTLDAAQV_.
0.140
0.308
MIDLNT----DVERYLRYLSVERQLSPITLLNYQRQLEAIINFASENGLQSWQQCDVoeA._..
.
...
. •• •• •,_•0.112
MKQQDNPLIEQFLDALWLERNLAENTLASYRLDLHALVGWLTHHNSOLLRASPQOLQSFL.
0.004
40
MK-QDLARIEQFLDALWLEKNLAENTLNAYRRDLg44oEWLHHRGLTLATAQSDOLQALL_.

Orga ,. A- XerC
BotlWe. bS. Oirmkwr) - XerCMrEFSASLAPQVEAFLRYLSIERQLSPLTVISYRRQLSALMEIGEQMGLAMWQTLDAAQV._
0000----MENVKNFVKLFVEYLQIEKNYSQYTIVNYVDSIEEFETFLRVQGINGFEEAAYQDT._

Orraram A • Xero
Badlm rW6 Oirmwrtr) - XerOMKQQDNPLIEQFLDALWLERNLAENTLASYRLDLHALVGWLTH--HNSDLLRASPQOLQS...
0028
MKOQ-- -IKDFIHYVANERGLSQNTIVSYERDLKSYSLYLTETLHVIDWNHVTRIHIIQ_.

0.30R

0.1 12
E:dierirhia coll K-I2

0.5

ap
.o

0.5

0.5

0.4

0.4

0.4

0.3

0.3

0.3

0.2

0.2

0.1

0.1

4

z 0.2
"v

m 0.1
0

0
0

0.1

0.2

Escherichia

0.3

0.4

coil K 12

0.5

0
0

0.1
0.2 0.3
0.4
Yersiniapestis Angola(distance to E. coll:0.1)

0.5

0

0.1 0.2 0.3 0.4 0.5
ShewanellabalticaOS 195(distance to E.coll:0.2)

Figure 2.3: An example for the plot shown in Figure 2.2.
This phylogenetic distance was based on XerC and XerD amino acid sequence alignment in each other strain, and we calculated
this distance using the average distance matrix that is generated from the pairwise scores. A: In this case of organism A in
Proteobacteria, the average phylogenetic distances between organism A and Escherichia coll is 0.112, and that between organism
A and Bacillus subtilis is 0.308. According to this scores, the organism A was plotted as the diagram. Since organism A belongsin Proteobacteria (represented as blue in Figure 2.2), it has shorter distance to E. coll than to B. subtilis. B: These graphs areshown by other spectrums. The left figure is Proteobacteria (blue) plot in Figure 2.2A, and colored in each distance (distance 0-0,1,
0.1-0.2, 0.2-0.3 and other are represented by purple, red, orange and gray respectively). The middle and right figures are used
Yersinia pestis Angola (distance = 0.1) and Shewanella baltica oSlgs (distance = 0.2) as x-axis respectively. The observation bysuch spectrum changes shows that the XerCD amino acid sequences are distinguished in each strain.

17

2.3.

RESULTSstrains. Iterated HMM using this seed profile resulted in unique predictions over the validation threshold in
306 genomes. An additional 137 chromosomesin 69 genomes were predicted with iterated HMM separated byclasses, and 10 distant genomeswere predicted using an alternative seed profile created with the 3 most similar
genomes. The predicted dif sequencestotaled 482 in 414 organisms, with a prediction rate of 98.61% for singlechromosomestrains and 95.00%for multiple-chromosomestrains. Predictions failed in eight organisms and ten
chromosomes,namely, Agrobacterium tumefaciens str. C58, Paracoccus denitrificans PDl222 chromosomeI, II(a-Proteobacteria), Burkholderia phytofirmans PsJN chromosome I, Burkholderia sp. 383 chromosome I, III,
Nitrosospira multiformis ATCC 25196 (o-Proteobacteria), Desulfotalea psychrophilaLSvs4 (ö-Proteobacteria),
Sulfurimonas denitrificans DSM 1251and Nitratiruptor sp. SBlss-2 (€-Proteobacteria).
For Firmicutes, fuzzy matching in 17 Bacillus strains (based on the dif sequence of B. subtilis str. 168 forthe creation of the initial seed profile hidden Markov model) yielded a unique dif sequence in each of the 17
strains. Iterated HMM using this seed profile resulted in unique prediction over the validation threshold for 79
chromosomesin 79 genomes. The dif sequences are predicted in a total of 97 organisms, with a prediction rateof 97.00%. Prediction failed in three genomes, namely, Clostridium perfringens str. 13, C. beijerinckiiNCIMB
8052 (Clostridia), and LactobacillushelveticusDPC 4571 (Lactobacillales).
Although no experimentally confirmed dif sequence is available for Actinobacteria, that of F. alni is suggested
to be s'-CACGCCGATAATGCACATTATGTCAAGT-s'(Hendrickson and Lawrence 2007). Therefore, weused this sequence for fuzzy matching in two genomes,Nocardia farcinica IFM 10152 and Mycobacteriumavium
subsp. paratuberculosis K-lo, whoseXerCD amino acid sequenceswere most similar to those of F. alni. Iterated
HMM using this seed profile resulted in successfulpredictions above the validation threshold in all 66 genomes.
In Chlorobi, an initial seed profile was created with predicted dif sequences in Chlorobaculum parvum NCIB
8327 and Prosthecochloris aestuarii DSM 271 that scored above the validation thresholds using the Firmicutes
profile, which resulted in the highest scores compared to those of Proteobacteria and Actinobacteria. Likewise,
the profile of Firmicutes yielded the highest scores in Chlamydiae, where the initial seed profile was createdfrom predicted dif sequences in Chlamydophila pneumoniae CWLo2g and Protochlamydia amoebophila UWE2s,
which were below the validation thresholds, but contained palindromic structure and were located within 0.011.48 degrees from the shift-points of GC skew. Using these seed profiles, iterated HMM successfully predicted
dif sequences in all 11 genomes in Chlorobi and 14 genomes in Chlamydiae.
Because the number of genomes is very small in all of the other phyla, we utilized the profiles of Proteobacteria,
Firmicutes, Actinobacteria, Chlorobi, and Chlamydiae that were created thus far instead of applying iterated
HMM based on specific seed profiles, and all of the following candidates were confirmed based on scores,
palindromic structure, and position. In Elusimicrobia and Tenericutes, all profiles showed high HMMER scores,
and predictions using the profiles of Firmicutes and Chlamydiae predicted identical dif sequences. Similarly,
the profiles of Firmicutes, Chlamydiae, and Proteobacteria predicted identical dif sequences in Nitrospirae, andpredictions based on the profiles of Proteobacteria and Chlorobi were identical in Gemmatimonadetes.
In Spirochaetes, predictions using the profiles of Firmicutes, Chlamydiae and Proteobacteria profiles resulted
in unique dif sequences in species with single chromosomes, and the profiles of Firmicutes were used for thepredictions of 12 chromosomes in 6 species with multiple chromosomes, all with HMMER scores above thevalidation thresholds. The most suitable profiles varied among species in other phyla. In Acidobacteria, the dif
sequence of Acidobacterium capsulatum ATCC 51196 was predicted by the profiles of Firmicutes, Chlamydiae,
and Chlorobi dif sequences, and other species were predicted using the profile of Firmicutes only. In Verrucomicrobia, profiles based on Proteobacteria, Firmicutes and Chlorobi predicted Methylacidiphilum infernorum
V4, and that of Proteobacteria and Firmicutes predicted Opitutus terrae PBgo-l and Akkermansia muciniphila
ATCC BAA-8ss. In Chloroflexi, the Chlorobi profile was suitable for Dehalococcoides sp. BAVl and Dehalococcoides sp. CBDBl, and that of Actinobacteria was used in D. ethenogenes 195 dif sequences. dif sequences were

18

2.3.

RESULTSpredicted in 14 Bacteroidetes strains using the profile of Proteobacteria, and those in five strains were predictedusing alternative profiles created with the three most similar genomes. In this way, we successfully predicted
dif sequences in most phyla, although the prediction failed in the phyla Cyanobacteria and Planctomycetes.

2.3.3

Correlationof thedif sequence

position

and

the

GC

skew

shift-points

Using the predicted dif sequences, we compared their positions within the genome to the shift-points of the GC
skew. Firstly, we analyzed the distributions of relative genomic distances of xerC, xerD and ftsK genes fromthe predicted dif sites. As a result, xerC genes were mostly located near the dif sites, xerD genes were near
the replication origin, and ftsK genes were located mostly in between xerC and xerD genes (Figure 2.4). The
comparison of positions between predicted dif sites and the shift-points of the GC skew showed that the dif
sequencespredicted in the phyla Proteobacteria and Firmicutes correlated significantlywith the GC skew shiftpoints that are highly likely to be located within the terminus region (Spearman's rank-correlation coefficients:
p = 0.844 and 0.715, respectively;Figure 2.5A). The differencesamong these positions fell to within 0.00-1.39%of the genome for ± 10-,and outliers did not exceed 3% in distance relative to the genome size (Figure 2.6).
The above results confirm that chromosome replication and CDR are related, and that show the accuracy ofthe predictions described in this work.

19

Eo

2.4.

N

xerC

xerD

DISCUSSION

fisx

Ca
L0
0
L
a)
E
7C
a)
s
H

Relative genomic distances from dif sites (%)

Figure 2.4: Distributionof the genomic distances of xerC, xerD and ftsK gene from predicted
dif sites.
These histograms represent the distributions of relative genomic distances of xerC, xerD and ftsK genes from dif sites. X-axis
represents the normalized genomic distance (%), and y-axis represents the number of organisms in each group. The boxplots
represent the variance.

To further investigate whether replication terminates at the dif site, by observing the overall contribution of
the genomic selection/mutation pressures of the replication machinery to the collinearity of the dif sequence
positions and GC skew shift-points, we plotted the distances between them against the GC skew index (GCSI)
of genomesto quantify the degree of replicational mutation/selection pressures. GCSI is an index that quantifies
the degree of GC skew of a given genome, which can be used as a comparative measure of the accumulated
replicational mutation/selection pressures (Arakawa and Tomita 2ooia). Since the strength of the GC skew
is speculated to partly correlate with the growth rate of bacteria (warning et at. 2006), high replication
mutation/selection rate indicated by GCSI implies a greater number of replication events in these organisms.
Therefore, if the replication terminates at or around the dif site, even allowing for statistical fluctuations, we
can assume that the increasing number of replication events should shape GC skew shift-points closer to the difsite by the central limit theorem and by the law of large numbers. Hence, genomes with higher GCSI shouldhave closer relative distance between the GC skew shift-points and dif sites, if replication terminates at the difsite. However,as depicted in Figure 2.5B, we observed no correlation between these two variables (Spearman's
rank-correlation coefficientsin Proteobacteria and Firmicutes: p = —0.046and 0.112, respectively).

2.4

Discussion

In this study, we first demonstrated that the conservation of XerCD genes followsphylogenetic conservation
patterns that are specificto each bacterial phylum (Figure 2.2). Based on this principle, we comprehensively
predicted the dif sequences in hundreds of completely sequenced genomes using a recursive strategy that iteratively models and predicts these sequences using profile hidden Markov models. As a result, we obtained
unique candidate dif sequences in 715 chromosomesin 641 strains that were validated through multiple means,
resulting in the largest collection of predicted dif sequences assembledto date. In comparison to previous workby Carney and Roten, which predicted dif sequences in 228 genomes,our predictions coincidedwith their resultsin 208 genomes and we added 507 genomes, including Aromatoleum aromaticum str. EbNl, which Carney andRoten reported to lack the dif/XerCD system. Excluding strains or chromosomeswe could not predict, namely,
A. tumefaciens str. C58, Burkholderia sp. 383 chromosomeI, II, D. psychrophilaLSvs4, N. multiformis ATCC
25196, P. denitrificans PDl222 chromosomeI, II and S. denitrificans DSM 1251,the predicted dif sequences inthis study differedin 12 chromosomesin comparison to the results of Carney and Roten: C. crescentus CBls,
Granulibacter bethesdensisCGDNIHl,l
Pseudoalteromonas haloplanktisTACl2s chromosomeII, Ralstonia eu-2o

2.4.

A

Firmicutes

Proteobacteria

100
p=0.844vi C
O.y^ 75O
0.
•50

DISCUSSION

All bacteria

100

100

p = 0.736

P = 0.715

75

75

50

50

•

ea

•
•

N

•I

25

a.
••

25

25

•

tnvi

•

1•

0

•

•

75

•
41.
100

0.45

0.6

•

0
0

25

50

75

100

0

25

50

75

100

0

25

50

dif positions (%)
B.0

Firmicutes

ProteobacteriaC•o 00
Cl •VIC C
N
Cr G7 •y
vi Cl
4E' I
G ,O
°'
a
•~_' o.1

100

10

10

'M •'of••••••• • S. •of
•.y
;

0.1

N

Clo.01L~
0.0010All bacteria

100

0.1

0.01

0.01

0.3

0.45

0.6

p=0.112

• •.

0.001
0.15

0.15

0.001
0.3

0.45

0.6

0.15

0.3

GCSI

Figure 2.5: The relationshipbetween dif sites and GC skew.
A: Correlation of the GC skew shift-point (corresponding to the replication terminus region, y-axis) and the locations of dif
sequences (x-axis) for genomes with predicted dif sequences. Genomes with no visible GC skew, as indicated by GC skew index(GCSI) < 0.05, are omitted. Both axes are shown as the relative distance in percentage of half of the genome size (replichore size),from the position directly opposite of the replication origin. For example, 0% means that the position is directly opposite of thereplication origin identified by the GC skew shift-point, and 100% means that it is at the replication origin. In other words, thehigher the percentage, the closer the distance to the replication origin. Here the positions of GC skew shift-points and dif sites arestrongly correlated in all three phyla. B: Lack of correlation between the difference in the positions of GC skew shift-points and difsites (y-axis) and the GCSI (x-axis). GCSI is a quantitative measure of the degree of GC skew, where GCSI = 0 is no observableskew, and GCSI = 1 is extremely pronounced skew. Typically GC skew is visible at GCSI > 0.1, and it is pronounced when GCSI
> 0.3. Since we see no correlation in these plots, stronger replication-related mutation bias (i.e. larger GCSI) does not necessarily
result in closer positions of the GC skew shift-point and the dif site. These results suggest that the replication termination occurs
near the dif site, but not at the dif site. The number of dif sites is 517 in all bacteria, 438 in Proteobacteria and 97 in Firmicutes.
The p in this figure is Spearman's rank-correlation coefficient.

21

z

2.4.

DISCUSSION

al 1000E
9)
0 E 100
OL
S
V
O
L
aJ

10

E
m

I
0

10

20

30

40

50

60

70

80

90

100

Difference between dif sequence andGC skew shift-point positions

Figure 2.6: The difference between dif and GC skew shift-point
positions.
The frequency distribution is in a logarithmic scale of the number of chromosomes grouped by the difference between the positionsof the dif sequence and the GC skew shift-point.

22

2.4.

DISCUSSION

tropha H16 chromosomeII, Rhodobacter sphaeroides 2.4.1 chromosomeI, R. sphaeroides2.4.1 chromosome II,
Rickettsia bellii OSU 85-389, R. conorii, R. fells URRWXCal2, R. prowazekii, R. typhi Wilmington, and Shewanella sp. ANA-s. For R. eutropha H16 chromosomeII and P. haloplanktis TACl2s chromosome II, bothstudies predicted positions that were symmetric from the origin of replication, and although experimental confirmation is required to confirm which candidates function in vive, the palindromic structures of the XerCD
binding sites are more conserved in the candidates predicted by our method. Therefore, overall, our results
were identical with those of Carney and Roten for 92% of the genomeanalyzed (208/228), and 11/12 mismatch
resulted in candidates with more conserved XerCD binding sites, with the addition of 507 genomes among
numerous phyla. Carney and Roten noted that some Vibrio species contain two dif sites both located at thevicinity of the GC skew shift-points. Therefore, we further tested whether the predicted dif sites in multiple
chromosomesare all located near the GC skew shift-points. Using 5% genomicdistance as a threshold, 45 outof 54 strains with two chromosomes,including Vibrio species, and 6 out of 9 strains with three chromosomes
showed such agreement of the positions.
There are four factors that may explain the advantages of our results. First, the selection of bacterial strains inthe study by Carney and Roten was limited to genomes harboring XerCD that were identified by their similarity
to those of E. coll, whereas we used all genomes with XerCD orthologs as identified by the KEGG Orthology
database. While there is a little time-delay until the sequences are annotated and incorporated into the KEGG
Orthology database, use of this database provides a more generic and comprehensive starting point. Second,similarity searches using software tools such as BLAST are not suitable for short sequence motifs that undergo
mutation, and the difficultyin identifying only those dif sequences with sequence similarity has been shown forC. crescentus (Jensen 2006) and several classes of Proteobacteria (Val et al. 2008). Third, dif sequences require
two binding motifs of XerC and XerD to be functional (Hayes and Sherratt 1997);therefore, the conservation of
palindromic structure at the 7-12 bp and 17-22 bp positions should be confirmed for each predicted candidate.
Finally, the use of iterated HMM allowed dif sequence prediction using the profiles of closelyrelated species foreach iteration, followingthe phylogenetic conservation pattern of XerCD.
The high predictability shown in this study suggests that the dif/XerCD system of chromosomedimer resolution
is highly conserved among bacterial species and that dif sequences are almost always conserved when XerCD ispresent within the genome. In fact, according to the KEGG Orthology database, XerC and XerD are conserved
in approximately 60-70%of bacterial species, which is a higher percentage than is found for the replication
termination protein Tus (Kobayashi et al. 1989) and for universal genes such as the SOS response repressor
LexA (winterring et al. 1997). In light of the remarkable conservation of the dif/XerCD system, although it isbeyond the scope of this study, explorations of alternative CDR machinery in species that lack the dif/XerCD
machinery would be an interesting area of future research. Chromosomedimer resolution pathways are suggested
to be present in species that lack the dif/XerCD system, and several alternative pathways have been reported
and suggested. Lc Bourgeois et al. reported an unconventional CDR pathway involvingonly one recombinase(XerS) in Streptococciand Lactococci,along with a 31 bp dif sequence (Lc Bourgeois et al. 2007). Similarly,
through computational analysis, Carney and Roten suggested the existence of another pathway, termed XerH,in e-Proteobacteria in place of XerCD and XerS and discussed the likelihood of the existence of dif analogues
in these species (Carney and Roten 2009; Nolivos et al. 2010). The basic strategy of iterated HMM should
be applicable in predicting dif analogues in these specieswhen defined seed sequences and detailed positions ofrecombinase binding sites are elucidated.
Although we limited our analysis to strains containing XerCD orthologs, our predictions failed in several species.
In Proteobacteria, wecould not identify dif sequencesin fiveorganisms and sevenchromosomes,including species
with single chromosomes (Nitratiruptor sp. SB155-2 and S. denitrificansDSM 1251) that are e-Proteobacteria,
where an alternative CDR mechanism involving XerH is suggested (Carney and Roten 2009), and species
with multiple chromosomes (P. denitrificans PDl222 chromosome I, P. denitrificans PDl222 chromosome II,
B. phytofirmans PsJN chromosome I, and Burkholderia sp. 383 chromosomeI and III). Among these, B.
23

2.4.

DISCUSSION

phytofirmans PsJN and Burkholderiasp. 383 contained dif sequences in other chromosomes,indicating that thedif/XerCD system is conserved in these strains. Similarly,in Firmicutes, we could not determine dif sequencein L. helveticus DPC 4571, C. perfringens str. 13 or C. beijerinckii NCIMB 8052. Among these strains, L.
helveticusDPC 4571 has an alternative CDR recombinase XerS in its genome, indicating that the dif/XerCD
system may not be functional. This is an intriguing example of possible evolutionary intermediate with theco-existenceof two systems, presumably resulting from a horizontal gene transfer event. While we are unable
to find a dif sequence corresponding to the XerS machinery, xerS gene in this species is located close to the GC
skew shift-point (xerC: 1,031,814bp, xerD: 1,055,574bp, xerS: 1,228,715bp, and GC skew shift-point: 1,225,733bp), which is indicative of its functionality as shown in previous works (Lc Bourgeois et al. 2007; Carney andRoten 2009; Nolivos et al. 2010). C. perfringens str. 13 and C. beijerinckii exhibit highly biased GC contents(28.57%and 29.86%, respectively), and hidden Markov profiting of AT-rich dif sequences may have failed dueto the background AT-richnessof the genome. Comparative studies of dif/XerCD systems using close relativesof these genomes may provide evolutionary clues regarding the acquisition and loss of CDR machinery. For
example, mapping the types of CDR machinery to the phylogenetictree of e-Proteobacteria obtained using 16SrRNA sequences with the dnaml program in the PHYLIP package (Felsenstein 1989) shows that a XerH typeof CDR machinery may have diverged at an early stage within this phylum. The XerCD type of CDR seems tobe absent in the Campylobacterand Helicobactergenera, except for Helicobacter hepaticus, which suggests theexistence of the XerH type of CDR in the common ancestor of these species (Figure 2.7). The dif candidate in
H. hepaticus was predicted with iterated HMM only marginally above the threshold, with a score of 10.2 and an
e-value of 5.5 x 10-5. Further analysis is required to identify whether this species actually contains dif/XerCDor XerH-type

machinery.

Predictions failed in all species belonging to the phylum Cyanobacteria. Although XerCD is present in these
species, the sequencesimilarity distance of XerCD in Cyanobacteria to those of other phyla was high (averageof
0.358± 0.0159, N = 540), with a minimum distance of 0.322 to Actinosynnema mirum (Actinobacteria), which
exceededthe 0.3 threshold that was shown in Figure 2.1. Therefore, this divergence of XerCD in Cyanobacteria
from those of other phyla implieslow applicabilityof the iterated HMM approach, which utilizesthe phylogenetic
conservation pattern of XerCD. One possible explanation for the prediction failure in this phylum is that thedif sequences and XerCD are highly divergent in Cyanobacteria, preventing their identification with sequence
profiles. The replication origin in Cyanobacteria is yet to be identified, and GC skew is weak in these species,
implying low degree of replicational mutation/selection pressures, which could also be a reason for the failureof prediction in these species.
Predicted dif sequences largely existed in non-coding regions (93.92%). More than half of these coding regionsthat contained dif sequences were hypothetical, with no functional annotation. Furthermore, we found two dif
sequences included in phage ORF in Vibrioand Xanthomonas. While these sequences may be integrated withthe phages by their hijacking of the host recombination machinery, these sequences are speculated to be thefunctional dif sites, due to 1: their unique occurrence within the genomeopposite of the replication origin, and2: their similarity as identified by our phylogeneticmodeling approach. As previously shown in Proteobacteria(Carney and Roten 2009), the XerC binding site is more variable and the XerD binding site is more conservedin all phyla (Figure 2.8), both for genomeswith single chromosomesand for those with multiple chromosomes,
presumably due to the interaction between XerD and FtsK for the initiation of first strand exchange (Aussel etal. 2002). The dif sequences in a-Proteobacteria with single chromosomesshowed higher variation comparedto these of other classes and phyla, but this variation was correlated with variations in genomic GC content(Figure 2.9). These differencesbetween variations partly explain the failure of our prediction in extremely
AT-rich genomes, such as those found in C. perfringens and C. beijerinckii.
Although dif sequences are expected to be located near the shift-point of the GC skew, we did not use this feature to predict and validate dif sequences with iterated HMM; therefore, using the comprehensively predicted dif
sequences across numerous phyla, we were able to directly compare the positions of predicted dif sequences with

24

2.4.

DISCUSSION

XerH typejXerCD
type

XerH type

Compylobacterconcisus

Campylobacter curvus

Figure2.7:

Phylogenetic

tree

based

on

rRNA

for

the

comparisonof XerCD-

and

XerH-containing

genomes.
This phylogenetic tree is constructed using the maximum-likelihood method and is based on 16S rRNAs of 14 organisms
Proteobacteria, whose dif sequences are predicted in this study. The out group is Escherichia coll K12.

25

in e-

DISCUSSION

2.4.

Proteobacterlasl4ea-Proteobacteria

chromosome

54,1•Nrornosorn.

XarC domain

= S7

Xv0

domain

24

26

1E0

71

II
/

6

5 _ 6

7

0

9

10

11112

12

14

15

16

17

19

p-Proteobacterla
xerC

20

21

ll

21

24

/5

26

27

il
12

26

X

15

IS

16

17

IS

19

20

11

0 domain

XerC

J'_1
27

111

sRM
chromosome~.. =1e7y-Proteobacterla

Shechromosome
79

domain11

domainXerD

domain

24

24

73

4.
NJ
M
C0
NC
O~

]5

_3_ 3

4

5

6

7

0

9

10 /1 1/2 /3 14 /5 /6 /7 •10 19 20 21 22 23 21 23 16 27 NI

6-Proteobacterla

Single chromosome

XerC domain

Xero

1

1_

4

4_}

7

6

9

10

II

I/

11

14

IS

16

17;18

19

HI 11

Firmicutes

31

domain21

75515.4 chrornosorne

27

21P

=

97

^^^^

XerC domain

is
WN
pE `
o
Z

/2

^

m
.

Nm

0
..

''`'`'''~^'~^~~~~~~~~~^~^^^^^

..^

2

.

9~.
~^~^^

Actinobacteria
xe,c

..

snie

din

chromosome%err)^~"`^~"^^^"

Bacteroidetes= mdomainXerC

Single chromoam•

domain

19

^^^^

^

I

I

.

111 1 1 1 1
| |

~

~

|^||

NN
'

1

4

'

6

'

'

'

~

ll'~

^

14

15

16

^~

lg~~

^^^

..

....

2

1

4.

6

7..~.

||

||

In
.....

~~^^^^^^~^^

Nucleotide position withindif sequence

Figure 2.8: The conservationof dif sequences.
This figure shows the conservation quantities at each position of dif sequence in each phylum or class (Proteobacteria, Firmicutes,
Actinobacteria, Bacteroidetes, a-Proteobacteria, /s-Proteobacteria, ry-Proteobacteria, and o--Proteobacteria). The black bars represent the degree of conservation in single-chromosomegenomes, and the gray bars represent that of organisms harboring multiple
chromosomes. The labels "XerC domain" and "XerD domain" in these graphs represent the binding sites of these proteins. The
x-axis represents the nucleotide positions in the dif sequence, and the y-axis represents the nucleotide conservation quantity. Y-axis
values were normalized to percentages.

26

2.5.

CONCLUSION

those of the GC skew shift-points to analyze their relationships. As expected, these two positions are highly correlated in terms of genomic loci, confirminga previous work (Hendricksonand Lawrence 2007). In this respect,because GC skew is the cumulative result of replicational selection/mutations, the degree of conservation of the
CDR machinery is presumably in concordance with the degree of replication selection/mutation pressures (i.e.
GC skew), which is partly characterized by the difference in the replication machinery and partly characterized
by the growth rate (Rocha 2002). On the other hand, as shownin Figure 2.5B, the differencesin the positions of
the GC skew shift-point and the strength of the GC skew, as quantified by GCSI, were not correlated. If replication termination occurs at the dif site, as proposed by Hendricksonand Lawrence (Hendricksonand Lawrence2007), a stronger GC skew that is generated by a larger number of replication events and/or a higher mutation
rate should statistically bring the GC skew shift-point closer to the dif site by the central limit theorem andlaw of large numbers. In fact, the overall correlation of these loci leads to the proposal that the dif site is thereplication termination point. However, because a stronger degree of replication mutation/selection pressures
does not bring these two loci closer to each other, they are not in a causal relationship. Therefore, although
the dif sequence is located near the replication termination site for efficient CDR, the replication terminationsite is suggested to be at a site other than the dif site, as was recently shown in vive (Duggin and Bell 2009).
On the other hand, the dif sequences in Firmicutes are more conserved in various phyla because the profile
of Firmicutes was the best suited as the initial profile of iterated HMM in Chlorobi, Acidobacteria, Gemmatimonadetes, Nitrospirae, Elusimicrobia, Tenericutes, and Spirochaetes, where initial seed sequences were notavailable, and those in Proteobacteria were more variable, as shown by the requirement to predict by iterated
HMM in classes instead of phyla. Tus proteins, which are shown to terminate replication in vive, are moreconserved in Proteobacteria and are not widely conserved in other, partly supporting the possible change inreplication termination mechanism by a relatively recent takeover by the Ter-Tus system (Duggin et al. 2008).
On the other hand, to the best of our knowledge, Tus analogues have not been comprehensively searched inother phyla, and therefore further analysis is required in order to fully support this hypothesis.

2.5

Conclusion

By taking the phylogeneticiterated HMM approach and validating predicted candidates through a combinationof HMMER score thresholds, conservation of palindromic structure, and cross-validation, we achieved a comprehensive identification of unique dif candidates in hundreds of genomes. As the result, we obtained uniquecandidate dif sequences in 715 chromosomesin 641 strains that were validated through multiple means, resulting
in the largest collection of predicted dif sequences assembledto date. All of the predicted dif sequences describedin this study, as well as visualizations of dif locations on circular genome maps, are freely available in an on line
database at http://www.g-language.org/data/repter/. The locations of dif sequences can be useful for studiesof the regions surrounding
the replication
chromosome
dimer resolution
mechanisms,

terminus,for phylogenetic
studies of the replication
terminationand can serve as supporting
evidence for GC skew analyses.

andFurthermore, we compared the positions of predicted dif sequences with those of the GC skew shift-points tounderstand the relationship between dif sequence and replication terminus using GCSI. As the result, althoughthese two positions were highly correlated in terms of genomic loci, the differences in the positions of the GC
skew shift-point and the GCSI were not correlated. Therefore, despite the dif sequence is located near the
replication termination site for efficient CDR, the replication termination site is suggested to be at a site otherthan the dif site.

27

2.5 .

CONCLUSION

0.030

0.025

N 0,0200U
17 0,01500
L 0,010
0.005

0Proteobacteria
N=357

ct-Proteobacteria
Js-Proteobacteria
N=97N=39N=

y-Proteobacteria
l8iN=31s-Proteobacteria

Firmicutes
N=97

Actinobacteria
N=66

Bacteroidetes
N = 19Figure 2.9: Variance of GC content distribution.
This graph shows the GC content variance of organisms in each phylum or class (Proteobacteria, Firmicutes, Actinobacteria,
Bacteroidetes, a-Proteobacteria, ls-Proteobacteria, -y-Proteobacteria, and v-Proteobacteria). The x-axis represents the names ofthe phyla or classes and the number of included organisms. The y-axis represents the variance of GC content.

28

CHAPTER

Validation
termination

3of bacterial
modelsgenomic

replication

using

simulationofmutations"Mind fully to command more than a small specialized portion of it."

-Erwin Schrodinger 'What is Life?'

3.1.

3.1

INTRODUCTION

Introduction

Acircularsome proceeds
bacterial
bl-directionallychromosome
hasfromboththe aoriginreplication
to the origin
terminus
and(Prescottaterminus,
andandKuempelreplication
1972;
ofHirosethechromeet al.
1983;Schaper and Messer 1995;Schaeffer et al. 2005). Although the replication termination mechanism is not
as well studied as replication initiation (see Scholefieldet al. 2011 for review), extensive studies have yielded
insight into replication termination in organisms such as Escherichia coll and Bacillus subtilis. The collisionof
two opposing replication forks at a region approximately opposite the origin was initially suggested to be thepredominant mechanism of termination in these organisms (Edlund et al. 1976);however,the finding that moving the replication origin does not change the replication terminus in E. coll (Kuempel et al. 1977;Louarn et al.
1977) led to the identification of a fork-trapping mechanisminvolvingthe 36 kDa Tus protein in E. coll (Mulcair
et al. 2006), and the 14.5 kDa RTP protein in B. subtilis, bound to Ter elements (Sahoo et al. 1995; Wilce etal. 2001). Tus or RTP protein binds to the Ter sites (in E. coll, at the sequence s'-AGNATGTTGTAAYKAA-s': Coskun-Ari and Hill 1997;in B. subtilis, at s'-KMACTAANWNNWCTATGTACYAAATNTTC-3': Wake1997) and forms a barrier called a fork-trap (Horiuchi et al. 1995; Labib and Hodgson 2007). This fork-trap
acts as an antihelicase and allows forks to enter but not exit the terminus region (Hill et al. 1987; Hill 1992).
As a result, this complex makes the replication fork stall at the Ter site (Kamada et al. 1996;Wake and King
1997). In E. coll, most Ter sites are located in the terminus half of the genome (Neylon et al. 2005; Mulcair etal. 2006).
The B. subtilis RTP protein differs from the E. coll Tus protein in both sequence and structure, and thesesystems are not broadly conserved except in species closely related to E. coll or B. subtilis. These observations
suggest a relatively recent introduction of the fork-trap termination mechanism (Duggin et al. 2008). Wang andcoauthors recently constructed a stain of E. coll harboring two origins such that one termination occurred at a
Ter site, whereas another terminated speculatively through fork-collision(Wang et al. 2011). Similarly, thetareplicating plasmidswithout fork-trap machinery may terminate by fork-collision;hence, the fork-collisionmodel
remains a plausible mechanism for replication termination, especially for species without Tus/RTP analogues.
The bl-directional replication machinery of circular bacterial chromosomes subdivides the genome into two
replicating arms, or replichores, with the leading and lagging strands on opposite strands of the DNA duplex.
These two replichores experience asymmetric replication-related mutation pressures due to continuous anddiscontinuous strand synthesis in the leading and lagging strands that results in an excess of G over C in
the leading strand (Lobry lgg6a; Lobry and Sueoka 2002). Such strand compositional asymmetry is typicallyvisualized using a GC skew plot with moving windowsalong the genomic sequence. GC skew is calculated as(C —G)/(C + G), and therefore, its polarity shifts near the replication origin and near the terminus, where
the leading and lagging strands switch roles (Lobry lgg6a; Lobry lgg6b; Grigoriev 1998). The cause for thismutational shift from C to G in the leading strand is likely to be multifactorial, and it is still debated (Rocha
et al. 2006) with several hypotheses having been proposed to date (see details: Francine and Ochman 1997;
Re yeset al. 1998; Frank and Lobry 1999;Lobry and Sueoka 2002;Rocha et al. 2006; Rocha 2008).
The most widely accepted hypothesis is that cytosine deamination occurs in the single stranded DNA (ssDNA),
resulting in a decrease in C in ssDNA (Re yeset al. 1998;Frank and Lobry 1999) becausethe leading strand exists
as ssDNA for a longer time during the replication of the Okazaki fragments in order to serve as lagging strand
template (Marians 1992). Another mutation mechanism that has been proposed is asymmetric transcriptioncoupled repair (Francine et al. 1996), which is based on the strand-specific positioning of transcriptionallyactive genes (Hanawalt 1991) and their asymmetric distributions (McLean et al. 1998). Nevertheless, strand
compositional asymmetry, a type of "footprint" of replication-related mutations, is commonly utilized for insilica predictions of the replication origin and terminus (Frank and Lobry 2000;warning et al. 2006; Touchon
and Rocha 2008). Whereas the GC skew shift-point accurately correlates with the origin of replication in

30

3.1.

INTRODUCTION

most bacterial genomes (Arakawa et al. 2007; Gae and Zhang 2007), the terminus shift-points are often closer
to the chromosome dimer resolution (CDR) site dif than to the Ter sites (Hendrickson and Lawrence 2007;
Higgins 2007). The 28 bp dif sequences are widely conserved in bacteria (Val et al. 2008; Carney and Roten
2009; Kono et al. 2011) and play a central role in CDR as the binding sites of two tyrosine recombinases,
XerC and XerD. In the circular bacterial chromosome, when a recombination event occurs an odd number oftimes in one DNA replication process, the replicated chromosomeforms a concatenated dimer that cannot be
segregated into two daughter chromosomes (Sherratt 2003; Lesterlin et al. 2004). Therefore, many bacteria
have the CDR machinery to separate the dimer chromosomevia homologousrecombination by XerCD into two
monomer daughter chromosomes. The dif sites are located near the terminus region (Clerget 1991; Blakely etal. 1993), but this greater correlation of the dif sites with the GC skew remains enigmatic. With their detailed
computational study of the skewedoligonucleotides,Hendrickson and Lawrence further confirmed that the skew
switch point is closer to the dif site than the Ter site. Based on these observations of their "bioinformatically
optimized" skew shift-point, they speculated that replication termination is most likelyto occur (or had occurredin the course of evolution prior to the introduction of the Ter/Tus system) near the dif sites in 'y-Proteobacteria,
Firmicutes and Actinobacteria, to avoid failure of the CDR system (Hendrickson and Lawrence 2007). In E.
coll,previous studies clearly show that the replication forks travel through the dif site to reach Ter sites in vive
(Breier et al. 2005;Duggin et al. 2008; Duggin and Bell 2009); however,the existence of an unknown replication
termination mechanism near the dif site remains a possibility in species where the fork-trap associated proteins(Tus or RTP) are not conserved.
We conducted a simulation study to elucidate the relationships between replication termination mechanisms
and the genomic compositional bias formed by the replication process. By computationally modeling theabove-mentioned replication termination models, namely the fork-trap, fork-collision, and dif-stop models, in
65 proteobacterial strains (which have circular genomes,Ter/Tus complexes,and dif sites) and in 30 Firmicutes
strains (which do not have Ter/Tus complexes), we tested the ability of each model to reconstruct the GC skew
graph of existing bacterial genomes. In this chapter, we refer to the GC skew calculated from the publishedgenome sequences as "natural GC skew" to distinguish them from artificially constructed GC skew.

31

3.1.

INTRODUCTIONA
GC skew
Bacterial

genomesequence..,.0
Primary sequence ( (0)COC
GC skew

y

oooo'

WM.

cllcgO
V c-.o mutation
V c-i•c mutation, ReplicationIenninus

modelfork-collisionmodel

fork-trap

Off-stop model

model

t,

( V

t2
t3

so cooccc

Shuffling with conserved base compositionr

C PO C

formationcc•cc<(~-----------------------------------------------------------------ti
Te.)

(
,TerC

(

7

c7

(

t4

TerAI
V 7

is

„TerA ( I
V

is

S)
(VV

)

r\/~

-----------)V®171)

n4=m=7)B

CVVVVVV‘VVVVVVV)

C

it

1
e

All

Gomm pol&fl (%)

m----------------a~b o~ua~(KW)®m

Figure 3.1: Scheme of GC skew reconstruction
simulation.
A: A schematic representation of the GC skew reconstruction simulation. The primary sequence was generated based on the shuffledbacterial genome sequence, which had the same base composition as the original sequence. The green and yellow triangles represent
the locations of C
G mutations in the leading strand (or G —>C in the lagging strand). Graphs on the right show the typicalGC skew shape at each simulated time point (ti). The blue bars represent the replication termini. B: Frequency distributionof replication termini in the fork-collision model. Here, replication terminates near a locus directly opposite the origin, and theposition probabilistically fluctuates according to a Gaussian distribution. The distribution was empirically derived from plasmid
sequences that are likely to be terminated by fork-collision mechanisms. C: Frequency distribution of replication termini in thefork-trap model in Escherichia coll str. K-l2 substr. MGl6ss, Escherichia coll IAIl, Proteus mirabilis HI4s2o. Here, replication
termination occurs at Ter sites, but different Ter sites have different rates of fork arrest. D: Frequency distribution of replication
terminus in the dif-stop model in Escherichia coll str. K-l2 substr. MGl6ss. Here, all replication terminates at a single finite
locus dif.

32~

oo4o4lioC

3.2.

3.2

Materials

3.2.1

Software

andand

MATERIALSAND

METHODS

Methods

sequences

All analyses in this study were conducted using programs written in Perl with the G-language Genome Analysis Environment, version 1.8.13 (Arakawa et al. 2003; Arakawa and Tomita 2006; Arakawa et al. 2008).
Statistical analysis and visualizations were performed using the R statistics package, version 2.10.0 (www.Rproject.org). This study targeted 65 Proteobacteria strains that have circular genomes, Ter sequences, Tus
proteins and dif sites, as well as 30 Firmicutes strains that have no Ter/RTP homologues. The existence ofTer sequence was confirmedwith the "oligomer.search" function of the G-language GAE, and RTP homologues
were determined using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Orthology database (KO;
Kanehisa et al. 2010). The genomic and plasmid sequences were obtained from the NCBI FTP Repository(ftp: //www.ncbi.nlm.nth.gov/Ftp) .
3.2.2

Selectionof bacteria

andplasmids

For the purposes of comparingthe three models, target organisms were selected under the appropriate conditions
for circular chromosomes,dif sites, Ter/Tus complexesand genomic compositional asymmetry of the GC skew
indexes (GCSIs) > 0.1 (except for several E. coll strains that scored slightly below 0.1). The GCSI quantifies
the degree of GC skew from the compositional distance between the leading and lagging strands and the extent
to which the GC skew graph shape conforms to a discrete sine curve obtained using the Fast Fourier Transform.
A threshold of 0.1 is relatively strict for ascertaining the existence of compositional bias (Arakawa and Tomita
2ooia; Arakawa and Tomita 2ooib; Arakawa et al. 2ooga).
The Ter and dif sites were identified by a homology search using a Ter consensus sequence and by a recursive
hidden Markov modeling method, respectively (Kono et al. 2011). In bacteria harboring a Tus protein anda replication terminus protein (RTP), s'-AGNATGTTGTAAYKAA-s' (allows mutations at 1, 4 and 16 bases;
Coskun-Ari and Hill 1997) and s'-KMACTAANWNNWCTATGTACYAAATNTTC-s'(Wake 1997) were used as
the Ter consensus sequence. For the set of plasmids used to derive distribution parameters for the fork-collisionmodel, plasmids must have been replicated bl-directionally. Therefore the plasmids with theta replication
machinery were selected according to the followingcriteria: 1) they must be larger than 10 Kbp with sufficient
GCSI (window size: 64, spectral amplitude > 1000; Arakawa et al. 2ooga), 2) they must contain neither Ter
nor dif sites, 3) they must lack the repC gene, which is essential for rolling circle replication (Khan 2000), and
4) no iteron sequences (Haines et al. 2006) are located near 5% region from putative replication origin predicted
by GC skew shift-point.

3.2.3

Simulation

of GC

skew

formation

The simulation of GC skew formation involvesthe followingsteps: 1) shufflingthe genome sequence to create
an unbiased initial sequence for simulation, while maintaining the same nucleotide composition, 2-a) definitionof the leading and lagging strands based on a replication termination model and the position of the replication
origin, 2-b) mutation of one random C to a G in the leading strand, 2-c) repeating from 2-a until the maximum
simulation cycle is reached, and 3) validation of the simulated GC skew by comparison with the original genome
sequence. The shuffled initial sequence was generated with the "shuffleseq"function of the G-language GAE,
which is based on the Fisher-Yates algorithm (Fisher and Yates 1948). All simulations used the same randomized

33

3.2.

MATERIALSAND

METHODS

sequences in each organism to avoid errors associated with shuffling. The maximum simulation cycle number
was determined by the absolute difference in GC content between the whole genome and the leading strand.
The replication origin was defined using the "find_ori_ter"function of the G-language GAE, which is basedon the cumulative GC skew (Grigoriev 1998) at 1 bp resolution. The similarities between the simulated andnatural GC skews were calculated using the root mean square error (RMSE).

3.2.4

Replication

termination

modelsFour replication termination models were constructed: fork-collision, fork-trap, dif-stop, and a control model
terminating at the GC skew shift-point, as described in Eqs. 3.1-4. In these equations, Xi represents thereplication terminus in bacteria i. In the fork-collisionmodel, the positions of fork-collisionwere empiricallydetermined to follow a Gaussian distribution based on observations of the GC skew shift-points in plasmids
that lack fork-trap machinery and dif sites. The mean of this distribution (A) was a locus directly opposite
the replication origin, and the variance was 02. Both of these values were normalized by the genome size
(Eq. 3.1). The termini in the fork-trap model were defined by the locations of Ter sites in each bacterium,{ti, t2, t3,...4,1, each weighted with certain probabilities (Eq. 3.2). The termini in the dif-stop and control
models were represented by the constant positions of dif sites (Cd) or GC skew shift-points (Cs) (Eq. 3.3, 3.4).
fork-collisionmodel:
1

Xz =

exp

2~a
fork-trap—(t—142(3.1)

2a2

model:XZ E {ti, t2i t3, ...)tn}(3.2)

dif-stop model:

control

X g = Cd

(3.3)

Xi=C3

(3.4)

model:

Assuming a simple model where all replication terminates with the fork-trap mechanismand where all replication
forks progress continuouslywithout stalling, replication should always terminate at a furthermost Ter site fromthe origin. In E. coll, this is TerC located at 1,607,184bp, where position directly opposite from origin is at
1,603,784bp and the dif site is at 1,588,773bp. Second farthest Ter in the other replichore, namely TerA in theright replichoreof E. coll, is only encountered if replication fork stalls a sufficienttime (hereafter referred to as 6)
in the right replichore for the replisome in the left replichore to over-travel to reach TerA. Since TerA is located264,013bp apart from a site directly opposite from the origin, and since the average speed of replisome is around
1,000bp/s (Hirose et al. 1983), 6 in E. coll is calculated to be around 5 min. This is in accord with in vive andin vitro findings, that stalling by supercoiling tension, protein blocking, and replisome assembly requires around
4-6 min to restart (Possoz et al. 2006; McGlynn and Guy 2008; Mirkin and Mirkin 2007). Such long stallingis known to occur in vive in around 20% of replication events (Maisnier-Patin et al. 2001). Stalling event
should randomly and thus evenly occur in each replichore, and therefore, in E. coll, furthermost TerC is firstencountered in 80% (without long replisome stalling) +10% (long replisome stalling in the same replichore), and
TerA is first encounteredin the remaining 10%of replication events. Furthermore, we considered the "leakiness"
34

3.3.

RESULTSrate of each Ter site, which is approximately 80% as observed in vive (Sharma and Hill 1995). As a result, in
E. coll, given the farthest inverted Ter sites from replication origin are TerC and TerA, followedby TerB, TerD,
TerE and etc, pausing rate at each Ter site is TerC = 72%, TerA = 10.5%, TerB = 16%, TerD = 1.152%, TerE
= 0.230%. The probability of having long enough stalling time S so that the second furthest Ter site is utilized(20%in E. coll), is different in other species, due to the different distances of second farthest Ter sites from theregion directly opposite of the origin. Assuming normal distribution of fork stall durations, this probability is
calculated using 8 of each species.
To validate these pausing rates, we further determined the optimized pausing ratio that best reconstructs thenatural GC skew, by means of parameter tuning. For this parameter tuning, the patterns of the fork arrest
ratios in each bacterium were tested in 5% increments, but since the comprehensive parameter searching in abacterium harboring ten Ter sites requires the calculation of 10,015,005patterns and is not computationallyrealistic, the calculated combinations were limited to those having a sum total of fork arrest rates over 80%,
with four Ter sites located farthest from the origin, based on in vive observations (Duggin and Bell 2009). As
a result, the pausing rates calculated based on the stalling rates and Ter leakiness were very similar with the
optimized pausing rates (p = 0.725, Spearman's rank-correlation coefficient).

3.3

Results

3.3.1

GC

skew

formation

simulation

Because GC skew represents the evolutionary footprint of a replication-related mutational bias, we attempted
to elucidate the contributions of different replication termination models by computationally reconstructing
the GC skew pattern using simulations of strand-biased mutations. Although the specific substitution types
and mechanisms are likely to be multifactorial, compositional replication strand bias, with only few exceptions,is strongest for G > C in the leading strand of prokaryotes (Rocha and Danchin 2001; Rocha et al. 2006).
Hence, we took the simplest approach to simulating the evolutionary formation of GC skew. We started with
shuffled sequence that had no replication strand bias, and we iteratively introduced C —*G mutations in theleading strand until the GC compositional bias between the leading and lagging strands was equal to thatof existing genomes (Figure 3.IA). The relative amounts of complementary bases should theoretically reachequilibrium when there is no strand bias (Lobry 1995; Sueoka 1995); therefore, replication strand bias should
be reconstructed using only the replication-related mutation bias. Our simulation involves three principal sets
of variables: 1) the initial sequence with no strand bias, 2) the number of simulated mutations (simulation
cycles),and 3) the locations of the replication origins and termini. Although many prokaryotic genomesexhibit
significant replication strand bias, the relative amounts of complementary bases are close to equilibrium acrossthe entire genomic sequence. We generated an artificial genomic sequence with no replication strand bias by
shufflingthe observed sequencewhile maintaining its overall composition. The number of simulated mutations,or the number of simulation cycles, was determined as the absolute difference between the number of G and C
basesin the leading/lagging strands across the whole genome. For example, given an imaginary genome sequenceof 1 Mbp with equal amounts of all four bases, the genomic G or C content would be 250,000bp each. Because
the leading strand of this genomewould be biased toward G, the quantities of G and C bases would be 260,000
bp and 240,000bp, respectively. Here, the absolute difference in G or C content, 10,000,is the number of C —*G mutations required to reconstruct the GC skew, and this number also represents the number of simulation
cycles. The last of the three sets of variables, the location of the replication terminus, is the most central
part of our simulation study. The replication strand bias predominantly causes enrichment of G in the leading
strand, but the definitions of the leading and lagging strands change under different replication termination
models. This is because the locations of replication termination vary according to the models. For example, the
35

3.3.

RESULTSfork-collisionmodel results in probabilistic termination within the region approximately 180 degrees opposite
the origin, whereas the fork-trap model involvingthe Ter/Tus system terminates at multiple but defined finite
locations. Likewise, if replication termination occurs near the dif site or near the GC skew shift-point, thereplication terminus becomes a single finite location. In this simulation study, we assess the reproducibility of
the GC skew graph using varying replication termini inferred by different replication termination models.
We first tested the applicability of such simulations using the E. collK-l2 genome. In E. coll, the numbers of G
and C bases in the whole genome were 1,176,923bp and 1,179,554bp, respectively, and the numbers of G and
C in the leading strand were 1,216,043bp and 1,140,434bp, respectively. Therefore, the number of simulation
cycles was determined to be 39,120 based on the difference between the two compositions. Shuffled initial
sequence with no replication strand bias was generated while maintaining the overall genomicbase composition(A: 24.62%, T: 24.59%, G: 25.37%and C: 25.42%). In this first validation, the replication terminus was definedat a finite location at the GC skew shift-point (1,550,412 bp). This was performed to observe whether thissimplistic simulation could reconstruct the GC skew graph. The similarities between the artificial GC skew andthe natural GC skew graphs were evaluated by root mean square error (RMSE) as well as by the GC skew index(GCSI), which quantifies the degree of GC skew. GC skew is generally visible when GCSI > 0.05 (Arakawa andTomita 2ooia). Although the GC skew shape in the initial sequence (t = 0) was almost completely flat and had
a high RMSE value (GCSI = 0.007 and RMSE = 6.982), the GC skew-likeshape was gradually formed as thesimulation cycles progressed. When the simulation reached 39,120 cycles (the maximum number of iterations),
the artificial GC skew shape showed least differencefrom the natural GC skew as calculated by RMSE (artificial
and natural GCSIs were 0.098 and 0.097, respectively,and the RMSE between them was 0.025). The GC skew
shapes found after different numbers of simulated cycles (t = 0, 10,000, 15,000,20,000, 25,000 and 39,120) aredescribed in Figure 3.2. Probabilistic errors (or standard deviations) associated with the Monte Carlo simulation
procedure used for sequence shufflingand simulating mutations were negligible because the standard deviation
was less than 0.0256 (Figure 3.3).
t = 10,000
GCSI = 0.029. RMSE = 3.878

t= 0
GCSI

= 0.007,

RMSE= 6.982

t= 15,000
GCSI = 0.041, RMSE = 2.693^--••

Simulated

GC ske't = 20,000
GCSI = 0.053, RMSE = 1.692

/A
U
C7

~`''

—

--

---

---

,^e^o^^~»^`»oE. coll
GCSI = 0.097••

Genome position (bp)

Figure 3.2: Example of GC skew reconstruction
simulation.
These figures are simulated GC skews when the simulated cycles (t) were 0, 10,000, 15,000, 20,000, 25,000and 39,120 (the maximum
simulated cycle in E. coll). The GCSIs and RMSEs were described in the upper left of each graph. When the simulated cycle
reaches 39,120 (the bottom-right corner), red line (simulated GC skew) and green line (natural E. coll GC skew) almost completely
overlap.

36

3.3.

RESULTS3Yca0a

Genome position (bp)

These figures show the probabilisticwith negligible average < 0.0256.

3.3.2

Construction

Figure3.3:
Probabilistic
error
simulation
error rates in 1,000 iterations.

of threereplication

termination

rates.
Each error

bar

represents

the standard

deviation,models

Our simulation study involves three replication termination models: fork-collision, fork-trap, and dif-stop. As
described above, these models define the positions of the leading and lagging strands, and they are mathematically modeled based on the existing knowledge of replication termination, with parameters empirically derived
from genomic data.

In the fork-collisionmodel, replication terminates when the two opposite replication forks meet by chance at
the far end of the circular chromosome. Because the collisionoccurs randomly, the termination positions should
followa probabilistic distribution. We derived the distribution by observing the positions of the GC skew shiftpoints in replicons that are highly likely to be terminating solely by fork-collision:namely, plasmids that havebeen replicated bl-directionally with theta replication machinery and lack the Ter/Tus complex (for fork-trap
model) and the dif/XerCD system (for dif-stop model). Using 98 plasmids, the distribution was fit to a Gaussian
distribution (p = 0.295by Kolmogorov-Smirnovtest) centered close to the part of the genomeopposite from theorigin (Figure 3.IB). The distribution was thus derived and normalized to the genome size (see Materials andMethods for detailed parameters), which was used to define the termination position in each simulated cycle ofthe fork-collisionmodel.
In the fork-trap model, replication terminates specificallyat Ter sites (the sites where Tus proteins bind), buteach Ter site individually allowsa certain fraction of the incoming replication forks to pass with different rates.
We therefore needed to obtain the probabilistic ratio of fork-trapping at each Ter site. Based on the time andprobability of accidental stalling of replication forks at sites other than Ter, on the positional relationship among
different Ter sites, and on the leakiness of each Ter site (see Materials and Methods), we could calculate thefrequency distribution and computationally determined the fork-trap rates at each Ter site (Figure 3.IC).
37

3.3.

RESULTS

Unlike the two models described above, the dif-stop model involves predictable termination at a single finite
position without any probabilistic fluctuations. We sought to determine the exact positions of the dif sites inbacterial genomesusing computational predictions (Figure 3.ID). We have previously reported an accurate andcomprehensive prediction of dif sites in 641 bacterial genomes using a recursive hidden Markov model method(Kono et al. 2011), and all positions of dif sites used in this work were obtained from the database accompanyingthat previous study (http://www.g-language.org/data/repter/). Similarly,as a control, weimplemented a modelthat terminates at the GC skew shift-point instead of at the dif site.

3.3.3

Evaluationof thereplication

termination

modelsWe tested the validity of the aforementionedmodels with 65 proteobacterial genomes,including those of E. collstrains and others that have circular chromosomes,Ter/Tus systems, difsites and XerCD homologuesas wellas acompositionalbias of GCSIs > 0.1. Typical examples of the simulated GC skew graphs are provided in Figure 3.4(all simulation results in target organisms are shownin http://web.sic.keio.ac.jprciconia/AppendixFigurel.zip).
Whereas there was no significant differencebetween the dif-stop and fork-collisionmodels (p = 0.069, Wilcoxon
test), the fork-trap model showed significant differencesfrom other models (dif-stop model and fork-collisionmodel, p = 0.011 and p = 0.007, respectively, Wilcoxon test; Figure 3.5). Interestingly, even the control modelscored significantly lower than the fork-trap model (p = 0.022, Wilcoxon test; Figure 3.5), and the control
model, by naive expectation, should best reproduce the GC skew graph because it terminates replication at theGC skew shift-point. Of the three models tested, the fork-trap model seems to best explain the existing GC
skew shapes.
According to above result, it was confirmed that the fork-trap model is appropriate to form the GC skew.
However, this result was observed under conditions that the termination machinery exists solely. Although one
type of termination machinery may be dominant in the existing genomes, other machineries could co-exist at amuch lower prevalence. In order to examine the contribution ratio of each model to construct the GC skew, we
conducted further evaluations of the replication termination models in a hypothetical combination: probabilistic
combination, where the termination models is assumed to coexist under certain probabilistic preferences (Figure
3.6A).

To determine the time of appearance and duration of each type of machinery,we tested all possible combinations
using the three models. For computational efficiency,durations of the models were incremented by unitsof 10% of the total number of simulated cycles, and consequently, 36 patterns were assessed. In this case,none of the different combinations significantly affected the reproducibility of the GC skew (Figure 3.6B).
Nevertheless, combination model often resulted in lower RMSE compared to simulations using only one of thethree termination models independently.
The best probabilistic combination differed among bacterial species. We extracted patterns that performed wellacross all of the 65 genomesused in this work, among the 36 probabilistic combinations tested. The best patternof the probabilistic combinations was 10%-70%-20%,in the order of fork-collision,fork-trap and dif-stop models.
The probabilistic combination model showed less RMSE values than dif-stop and fork-trap models (p < 0.001,
Wilcoxon test; Figure 3.7 and http://web.sic.keio.ac.jp/Tciconia/AppendixFigure2.zip).

38

RESULTS

3.3.

Escherichia

coll str. K-l2

substr.

MGl6ssAround the shift-point (GC skew whindow = 100)

2

~

'

~^

^^^^^

^^`^^

•L•

--~

-2

3

~

^

/

_,-

5

.

-,,"

6
^^

25046
enterica

30.06 ase4o6
serovar

4e,06

Choleraesuis

4se46

se.06

^ S.(•.7.6

str. SC-B6i• 11.111

Around the shift-point (GC skew whindow = 100)Ii

I ..11.

1.01

I
050000016.06

1.se•06

2e..06

2.se4ll6 33406 ase4o6

48406 4.se4o6 50409"1.00

I v.alAround the shift-point (GC skew whindow = 100)I

.l4it2,77I
0500000 te.06

1.5.062646

25e.06

3e..06 a5e.06

4e,06

4se-.06

se.06

Figure 3.4: Examples of simulated
GC skew.
Examples of the overall shapes around the GC skew shift-points (see http://web.sic.keio.ac.jp/"ciconia/AppendixFigurel.zip
forcomprehensive results from all organisms used in this work). The left figures show the overall GC skew graph, and close-ups of theregions around the shift-point are shown to the right. In the right set of graphs, red, green, blue and purple lines show the natural
GC skew, fork-trap model, fork-collision model and dif-stop model, respectively.

39

/

3.3.

3.3.4

Simulationsinspecieslacking

RESULTSmachineryfork-trap

Lastly, we conducted the same analysis for speciesin other phyla to confirmthe observedmodel preferences. For
these analyses, we used 30 Firmicutes speciesthat lack Tus and RTP homologues(and therefore are presumed tolack fork-trap machinery). As a result, significant differenceswere found between the dif-stop and probabilistic
combination models (p < 0.001, Wilcoxon test; Figure 3.8).

GC skew

reproducibilities

0
0N

O U,

O
4)
07
Q NO
0
C
t6
m
E
00
it
0

0

dif-stopmodel

Fork-collision model
Replication

Boxplot
modelsFork-trap model

termination

Shift-stop

model

modelsFigure3.5: Comparisonof RMSEscoresin four models.
of the RMSE scores for four models, representing the similarities between simulated and naturalin 65 bacteria). The p-values were calculated by a Wilcoxon test, * p < 0.05, ** p < 0.01.

40

GC

skews

in the

four

3.3.

A

Probabilistic10%

dif-atop

RESULTS

combinationr

Model:,

10%

30%

/0%
80%

Fork-collision

model

30%

d/l-clop model
Frok-collielon model
lllllllFork-trap

-1

0

12

model

3

r.

a~

CD
0)

Organism

Figure 3.6: Heat map of RMSE scores for probabilistic
combination
model.
A: The conceptual scheme for probabilistic combination of replication termination models. B: The heat map of RMSE scores.
The x-axis represents the 65 organisms, and the y-axis represents the combination patterns. Each color represents one of thethree models (blue = dif-stop model, yellow = fork-collision model and red = fork-trap model), and the width of colored regions
represents their probability. The scales are logarithmic.

41

3.3.

RESULTS

CuII

0w

S

0

C

dif-stop

Fork-collisionFork-trap

Control

Probabilistic

Figure 3.7: Boxplot of RMSE of all simulated models.
The x-axis represents the models (dif-stop, fork-collision, fork-trap, and shift-stop (control) models as well as probabilistic combination) and the y-axis represents the RMSE values. * p < 0.01, Wilcoxon test.

A

Probabilistic

combination
401,1

,.x

a/f-itaj.x

raleaouialon

W

H

cc

ueaai

arc-swa

ForkaoIIIon

Probabilistic
160%-40%)

Figure 3.8: Boxplot of RMSE of simulated
models in Firmicutes.
A: The conceptual schemes for probabilistic combination of replication termination models. B: The x-axis represents the models(dif-stop, fork-collision, and probabilistic combination) and the y-axis represents the RMSE values. * p < 0.05, ** p < 0.001,
Wilcoxon test.

42

3.4.

3.4

DISCUSSION

Discussion

In circular bacterial chromosomes, in vive studies clearly show that replication is terminated by fork-trap
mechanisms involving the Ter/Tus system, which impedes fork progression at specific sites. However, thegenomic compositional bias shaped by replication-related mutation bias, which is an evolutionary footprint of
the replication machinery, has a shift-point of compositional polarity at a site closer to dif than Ter. In thisstudy, we took a theoretical approach to elucidate this paradoxical relationship between the replication-related
genomiccompositional bias and the replication termination mechanismin bacteria. To that end, we conducted asimulation study employing multiple replication termination models. Three main models, namely fork-collision,fork-trap, and dif-stop, as well as one control model that assumes replication termination at the GC skew shiftpoint were tested by computationally reconstructing the GC skew shape in 65 bacteria. Different combinationsof these models were also analyzed. Based on the results, the reproducibility of simulated GC skew was highest
in the fork-trap and fork-collisionmodels (in comparison to that of original genome sequence). Surprisingly, it
was much lower for the dif-stop model and the control model. Our result therefore supports previous in vive
studies (Duggin and Bell 2009) that favor the fork-trap model as the working replication termination model.
Although not intuitively obvious at first sight, the probabilistic usage distributions of the Ter sites better explain
the current GC skew shape than the location of the dif site.
The simulation method for GC skew reconstruction used in this work was based on the most simplistic approach.
The procedure mutates a C to a G in the leading strand for each simulation cycle. We have two justifications forthis approach. First, although the specific types and causes of mutations introduced by the replication process
are likely to be multifactorial and complex, the resultant compositional bias is predominantly in the directionof C —>G in most bacteria (Rocha et al. 2006), as observed in existing genomes. Second, previous discussions
regarding the positioning of Ter, dif, and the GC skew shift-point were based on the GC skew graph, which
does not contain any information about AT composition. Therefore, we have limited our discussions to thereconstruction of the GC skew graph, which only requires the consideration of C G mutations. However,one
other factor that should be considered is the positions of the coding regions. Coding strand bias is as high as
approximately 78% in the leading strand in Firmicutes or Mycoplasma(Fraser et al. 1995;Kunst et al. 1997;
Rocha 2002), and the GC skew is mostly pronouncedonly in the third cod onpositions (McLean et al. 1998). On
the other hand, the 65 Proteobacteria used in this work have relatively little coding strand bias (averaging 58%
in the leading strand), and mutations do not avoid the coding region; they occur all over the genome in these
species (Rocha and Danchin 2001). In this work, we have simulated the GC skew formation using the wholegenome sequence, without excluding any sequences. This is because, in theory, strand bias effects of mutations
induced by other mechanisms than replication should cancel out, unless the mechanism itself is related toreplication (Sueoka 1995). In E. coll and 'y-Proteobacteria utilized in this work, gene orientation bias is almost
even (54.43% in the leading strand in E. coll K12), and therefore transcription/translation-related mutation
bias should have minimal effect on the GC skew in these species. On the other hand, local regions of genomes
and especially the coding sequences are nonetheless subject to other types of mutations than replication, andtherefore we have conducted additional validations to confirm such effects. For this purpose, we have repeated
all three simulations (dif-stop, fork-collision,and fork-trap model) using only the third positions of the codons
and intergenic regions (hereafter referred to as GC skew (GC3/non-coding)), in addition to the GC skew using
whole genome sequences: GC skew (all). As expected, in both simulations, whether using the whole genomeor
only GC3/non-coding regions, the overall results did not change. The RMSE values showed similar tendencies,
where the RMSE medians were 34.980, 37.516, and 1.493, for dif-stop, fork-collision, and fork-trap model,
respectively in GC skew (GC3/non-coding), whereas those of GC skew (all) were 19.243, 27.772, and 16.439,
respectively. Figure 3.9 shows the GC3/non-coding version of Figure 3.5. Overall, both simulations show thatthe fork-trap model can better explain the existing GC skew shape, rather than the dif-stop model.

43

2

3.4.

GC skew

DISCUSSION

reproducibilities

0
0N

L

10
S)
T-

^L
^U)) W~L.t `V
C. Cl)
0
C '-C
4)
E

46 O
O
cc

0

0

dif-stop

Fork-collisionReplication

termination

Fork-trapmodelsFigure 3.9: Validation of simulations
using only the third cod on positions and non-coding
sequences.
This figure shows the boxplot of the RMSE scores for the three replication termination models, representing the similarities between
simulated and natural GC skews (in 65 bacteria). In comparison to Figure 3.5, here the GC skews were calculated and simulated
only in the third cod on positions and non-coding regions. The overall tendencies are identical to Figure 3.5. ** p < 0.01, Wilcoxon
test.

44

3.4.

DISCUSSION

For the fork-collisionmodel, we determined the positions where the forks collide by observing the fluctuationsof the GC skew shift-point in plasmids. Plasmids were used rather than chromosomesfor several reasons. First,
the chromosomal sequences are not suitable for determining these parameters because replication terminationin these replicons involves mechanisms other than fork-collision. Moreover, long chromosomalsequences also
undergo large-scale restructuring, typically by horizontal gene transfer or inversion (Rocha 2008). Inversions
disrupt gene order and the orientations of oligonucleotides(Hilland Gray 1988;Lin et al. 2006), and the genomic
islands acquired through horizontal gene transfer likewise change the genomic structure; they can be as large
as 10,000 bp up to 1 Mbp (Gogarten and Townsend 2005; Juhas et al. 2009). We selected bacterial plasmids
that depend on the host replication machinery based on the absence of the repC gene, which is required forrolling circle replication (van passer et al. 2006) and based on the lack of Ter or dif sites. In these plasmids, theputative locations of frequent fork-collisionsobey a clear Gaussian distribution centered at a position directly
opposite that of the putative origin, as described in Figure 3.IB, suggesting that replication termination occurs
probabilistically through collision and not by the action of specific terminating proteins. The speed of fork
progression in both replichoresseems to be similar, and the replichoresshow almost identical base compositions(R = 0.994).
The probabilistic distributions of the rates of fork-trapping at each Ter site in each bacterium were calculated
from three biochemical evidences: the time and probability of accidental stalling of replication forks at sites
other than Ter, the positional relationship among different Ter sites, and the leakiness of each Ter site. Basedon these evidences, we could calculate the pausing ratio at each Ter site. Furthermore, in order to validate suchpausing rates, we compared these biochemical parameters with a computationally determined pausing ratioby means of parameter search that best reconstructs the natural GC skew using all possible patterns of fork
pausing at various Ter sites (see Materials and Methods). As a result, the calculated pausing rates based on
experimental data were very similar with the optimized pausing rates (p = 0.725, Spearman's rank-correlation
coefficient). Fork-trap model scored best among other replication termination models using either of theseparameters.

The locations of dif sites strongly correlate with those of the GC skew shift-points (p = 0.736) (Kono et al.
2011), and these distances are closer than the nearest Ter sites and the loci directly opposite the replication
origin (the average distance from the GC skew shift-point to a dif site is 0.39%, to the nearest Ter site = 0.68%,
to the side opposite the origin = 2.61% in 65 targeted bacteria). Therefore, by naive expectation, replication
should terminate near the dif sites to produce the GC skew graph seen in existing genomes. However, oursimulation study shows that replication termination at a single finite locus cannot accurately reconstruct theGC skew shape. In fact, a single finite termination model results in a highly acute shift-point, but the actual
shift-point is less acute and more rounded. Such a shape can only be reproduced with probabilistic models (thefork-trap and fork-collisionmodels) (Figure 3.4). Therefore, the probabilistic balance of replication termination
results in the current shift-point position, and the dif sites seem to be co-evolvingand taking advantage of the
genomic compositional bias to be near this probabilistic center of replication termination loci (which allows
for efficient CDR). In fact, FtsK translocase locates the dif site and recruits XerCD recombinase to the sitethrough the guidance of a highly skewed G-rich oligomer, known as the KOPS (Saleh et al. 2004; Bigot etal. 2005; Bigot et al. 2006), taking advantage of the genomic compositional skews and the distribution of theskewed oligomers (Salzberg et al. 1998; Hendrickson and Lawrence 2006). Therefore, our simulation study
suggests that dif sites are not shaping the GC skew by terminating replication at this specificlocus, but rather,
the GC skew shift-point shaped by the replication termination machinery is affecting the location of dif sites.
This is in agreement with in vive studies (Duggin et al. 2008; Duggin and Bell 2009) and with our previous insilica study, showingthat the distance between the dif site and GC skew shift-point is not correlated with GC
skew strength (Kono et al. 2011). Finally, we confirmed the contribution ratio of each model to construct theGC skew using probabilistic combination model. The most optimal combination validated by RMSE was the
10-70-20%(fork-collision,fork-trap, and dif-stop model, respectively) in probabilistic combination. In previous
studies, it has been indicated that the replication fork arrest occurs in 18 to 50% of replication cycles with
45

3.4.

DISCUSSION

several factors, including transcription-replication collisions,fork-trap with Ter/Tus complex, or by in activation
proteins (Kogoma 1997; McGlynn and Lloyd 2002; Xu and Marians 2003; Michel et al. 2007). In addition
to these studies, Maisnier-Patin et al. reported an estimate of at least 20% of all replication forks are stalled
and require replisome re assemblyduring the replication process (Maisnier-Patin et al. 2001). Furthermore,
Hendrickson and Lawrence speculate that the cleavageof dif might occasionally block the progression of forks(Hendrickson and Lawrence 2007). Therefore, our probabilistic combination simulation yielding 10-70-20%
ratios for fork-collision,fork-trap, and dif-stop model seems to fit reasonably well to explain the contributionsof different fork-termination mechanisms.

46

CHAPTER

The

relationshipof bacterial

4

betweencirculargenomic

the symmetry
genomesandislands"We are like a little child entering a huge library . The walls are covered to the ceilings
with books in many different tongues. The child knows that someone must have writtenthese books. It does not know who or how. It does not understand the languages inwhich they are written. But the child notes a definite plan in the arrangement of thebooks—a mysterious order which it does not comprehend, but only dimly suspects."-Albert Einstein

4.1.

4.1

INTRODUCTION

Introduction

Various2008). symmetrical/asymmetrical
A typical example of a symmetricalstructuresarestructure
maintainedis the inorientation
thebacterialof ancircularoligomerchromosome
nucleotide.
(RochaThe
sequences that are associated with recombination or replication—e.g. the Chi sequence (Kowalczykowskietal. 1994), replication-related KOPS (Bigot et al. 2005; Bigot et al. 2006), and AIMS (Hendrickson and
Lawrence 2006)—aresymmetrically conserved in two replichores with the same frequency (Mrazek and Katlin
1998;Salzberg et al. 1998). An example of an asymmetrical structure is gene strand bias. The distributions ofbacterial genes differ between the leading and lagging strands. Approximately 78% of genes are in the leading
strand in Firmicutes bacteria or Mycoplasma(Fraser et al. 1995; Kunst et al. 1997; Rocha 2002) and 85% ofribosomal proteins are encoded in the leading strand in Bacillus subtilis and Escherichia coll (McLean et al.
1998). High-expressiongenes in particular tend to cluster in the leading strand (McLean et al. 1998). Thesesymmetrical/asymmetrical structures are associated with the balance of the replication origin and terminus.
For example, the replication origin and terminus pair is symmetrical in E. coll, and replication proceeds bl-directionallyfrom the origin to a terminus (Prescott and Kuempel 1972; Hirose et al. 1983; Schaper andMesser 1995; Schaeffer et al. 2005). The most notable feature of an asymmetrical structure that is provided
by replication symmetry is a GC skew (Lobry lgg6a; Rocha et al. 2006). The GC skew can be calculated
as (C —G)/(C -I-G) and is visualized as the compositional bias towards G and C bases. The shift-pointsof a GC skew are known to correlate with the replication origin and terminus positions, as the GC skew isbelieved to be a consequenceof the replication process. Although DNA is replicated according to the directionof the polymerase, a leading strand is synthesized continuously, whereas the lagging strand is synthesized
discontinuously. Consequently, the single strand duration of the leading strand is longer than for the lagging
strand, and this difference in the replication mechanism results in a GC skew (Coulondre et al. 1978; Re yes
et al. 1998; Mackiewiczet al. 2003). Based on this finding, analyzing the GC skew has become a common insilica method for predicting the origin and terminus (Frank and Lobry 2000; warning et al. 2006). However,the degree of GC skew is extremely variable, and certain bacteria have only a weak skew (Zhang et al. 2003;
warning et al. 2006; Arakawa et al. 2ooga) (Figure 4.1). Therefore, to select the bacteria for which a predictionof the replication origin and terminus can be made, a quantitative index to measure the strength of the GC
skew has been developed (Arakawa et al. 2ooga). Based on the increased ability of ori/ter prediction using
GC skew, many bacteria with imbalanced ori/ter structures have been identified. It is highly unlikely thatthese detected imbalanced ori/ter structures were maintained in the actual bacterial genomes. Therefore, thedrastic effects in the base composition may perturb the accuracy of ori/ter prediction and incorrectly indicate
imbalances. Among these mutations, GEIs (genomic islands) are considered to have the potential to invoke
large-scale changes in base composition. GEIs are large foreignregions of approximately 10 Kbp - 1 Mbp in thebacterial genome (Rocha 2008) and were most likely acquired via horizontal gene transfer (HGT) (Gogarten
and Townsend 2005; Juhas et al. 2009). If a large GEI is inserted into a bacterial genome, and if the basecompositions of these inserts differ substantially from the host, the insertion will likely reduce the accuracy of
predicting the GC skew. Here, we compared the symmetrical structures between natural genomes and artificial
genomes whose GEIs were computationally deleted and investigated the disruptive effects that were caused by
GEIs.

48

4.1.

INTRODUCTION

OO

OO
Co

OO
Co

>% °O
UC
N
=
CT0 co CD
L
LL 0
0 N

0
0
r

0

0

20

40

60

80

100

Asymmetry (%)

Figure4.1:
Bacterialasymmetries.
This histogramindicates
the asymmetry
in 1,164 bacteria
harboring
circular chromosomes.
of asymmetry
as a percentage.
When asymmetry
is 0%, the bacterial
replication
terminus
replication
origin.

49

Thex-axisis locatedrepresents

preciselytheopposite

extent
to

the

4.2.

4.2

Materials

andMethods

4.2.1

Genomesequences

and

MATERIALSAND

METHODSsoftware

All of the analyses in this study were conducted using programs that were written in Perl with the G-language
Genome Analysis Environment, version 1.8.13 (Arakawa et al. 2003; Arakawa and Tomita 2006; Arakawa et
al. 2008). Statistical analyses and graphic presentations were performed using the R project, version 2.10.0(www.R-project.org). Bacteria that contain a circular chromosome, have a genome larger than 5 Kbp, andare included in the GEIs dataset were selected as the target bacteria (486 strains) . These bacterial genome
sequences were obtained from the NCBI FTP Repository (ftp://www.ncbi.nlm.nth.gov/Ftp).

4.2.2

Dataset

For this study, a set of 486 GEIs was obtained from the IslandPath-DIMOB dataset (Hsiao et al. 2005), which
was curated using IslandViewer (Langille and Brinkman 2009). The IslandPath-DIMOB dataset was screenedby measuring the dinucleotide bias and the presence of mobility genes.

4.2.3

The

calculationof asymmetries

The replication terminus and origin were predicted using the "find_ori_ter"function of the G-language GAE,
which is based on the cumulative GC skew (Grigoriev 1998) at a resolution of 1 bp. The asymmetries between
the replication origin and terminus are represented as percentages. This asymmetry is 0% when the replication
terminus is preciselyopposite of the origin, and the percentage increases as the terminus approaches the origin.
In this analysis, if the replication terminus is found to be located on the left replichore, the asymmetry score will
be negative. The extent of change in asymmetry between the wild-type genome and the GEI-deleted genome
was calculated as dA = AP7,—APd (delta value) and IR =—IAPdI(improvement rate), where APw and
APd are the asymmetries of the wild-type and GEI-deleted genomes,respectively. If the origin and terminus arecloserto the symmetrical position, the IR value will be higher. The asymmetries of the GEI-deleted genomewere
determined using the replication origin and terminus that were re-predicted based on the GC skew. However,inthe calculation of the fixed asymmetry, the replication origin and terminus in the GEI-deleted genome were not
re-predicted. We initially marked the positions of the replication origin and terminus in the wild-type genome
and calculated the asymmetries for each position of the genomes whose GEIs were computationally deleted.

4.2.4

Randomdeletions

A randomization
test was applied
to each bacterium
includingonly the positions
of GEIs, and the number,were maintained.
For each bacterium,
the randomization

to measure
the GEI-specificeffects.
The parameters
length,and replichore
in which the GEIs were locatedtest was run using 100 iterations.

50

4.3.

4.3

Results

4.3.1

Assessmentof theeffects

RESULTSof GEIsTo assess the effects of GEIs on the symmetrical structures of bacterial genomes, we performed an in silica
experiment for 486 bacteria that were predicted to have GEIs according to IslandPath-DIMOB. Specifically,wecomputationally deleted the GEI sequences from the bacterial genomes and re-predicted the replication origins
and termini in the GEI-deleted genomes. The asymmetries were determined by a comparison between the newly
predicted ori/ter and the original ori/ter. Although no significant correlation was observed between GEI lengthand the asymmetries (R = 0.199, Pearson correlation coefficient),the presence of GEIs had a substantial effecton the genome structure regardless of island size (Figure 4.2A). Interestingly, a strongly correlated group (thegray symbolsin Figure 4.2A, R = 0.990, Pearson correlation coefficient)was found using an uncorrelated scatterplot. The genome sizes of the bacteria belongingto the gray group were smaller than those of the other bacteria.
The average genome sizes were 3,682,798bp and 2,547,252bp for the black and gray groups, respectively. A
significant differencewas measured between the black and gray groups (p < 0.0001, t-test, Figure 4.2B).

4.3.2

GEIs

affectbase

composition

In bacteria that have a small genome, GEIs had an effect on the genomic structure. However,it is difficult todetermine whether this effect was caused by changes in base composition. Because the replication origins andtermini in both the original and GEI-deleted genomes were predicted using the GC skew, the validity of theprediction method cannot be tested. Therefore, we used an additional method to detect the replication origins
and termini. This alternate detection method is based on the fixed positions of the original replication origin
and terminus. First, we recorded the ori/ter positions in the original genome, and then we used the positionsin the GEI-deleted genomes without re-prediction. Therefore, if the deletion of the GEIs does not affect thebase composition, there will be no significantdifference between the re-predicted ori/ter positions and the fixed
positions. Using this approach, although a strong correlation between the fixed and re-predicted asymmetry
was measured (R = 0.86039, Pearson correlation coefficient),a few outlier bacteria were detected (Figure 4.3).

4.3.3

Improvement

rates

from

GEIs

deletion

According to Figure 4.3, because differenceswere observed between the re-predicted and fixed ori/ter positionsin the outliers (the red symbols in Figure 4.3), deleting the GEI may have affected their base compositions.
Therefore, we calculated the improvement rates for these outliers. Because the previous asymmetries included
only the extent of change between the ori/ter positions in the original genomes relative to the respective
GET-deletedgenomes, this approach was unable to confirm the negative or positive effects of the GEIs. The
improvement rates can validate the extent to which the symmetries in the GEI-deleted genomes were improved
relative to the original genomes. The results show that a greater extent of asymmetry in the original genomes
corresponded to higher improvement rates in the GEI-deleted genomes. Figure 4.4 shows the improvement
rates that were calculated using the re-predicted ori/ter positions, fixed ori/ter positions, and randomly deleted
artificial

GEIs.

Moreover,this result also shows that the improvementin the original genome than for the random
artificial

rates were significantly
GEIs deleted genomes.

51

higherbased

on theasymmetries

1i

4.3.

RESULTS

B

A
0

g

CO

B8
a
0
to

NN

CN
C
OU
_ e
W
CD

N

40

o
CCN
CD
N

0

0

10
The extent

2030

of change

40

50

i
m

60

Black group

Gray group

Randomin the asymmetry

Figure 4.2: The extent of change in asymmetry
with genome size.
A: The x-axis indicates the extent of change in the asymmetry, and the y-axis shows the GEI content (%) of each genome. The
gray symbols indicate bacteria with a genome size that is comparatively smaller than the others. B: The mean values of the genome
sizes in poorly correlated (represented by the black symbols in A) and strongly correlated (the gray symbols in A) bacteria. The"Random" bar is the mean of a random sample of 100 genomes. The error bars show the standard deviation.

52

4.3.

RESULTS

".W 0
0
a)
a)
O
a

EE
U)
ca
CD0 X
a)
I-o
0

0

The

Figure4.3:

The

correlation

re-predictedof between

andtermini.
The x-axis indicatesthe re-predicted
coefficient. The red symbols indicate

40

20

asymmetries,
the bacteria

asymmetry

60

properties

80%)

replication

origins

and the y-axis shows the fixed asymmetries.
R = 0.860, Pearson
that differed between their re-predicted
and fixed on/ter positions.

correlation

theasymmetriesof the53

re-predicted

and

fixed

E

4.3.

RESULTS

O N

0

W

N

1
L t
c) C
a)

1•

2

L

O
1

8
0

20

40

60

Asymmetry (%)

Figure 4.4: Improvement
rates in three cases.
This plot shows the improvement rates for each bacterium. The y-axis indicates the improvement rate, and the x-axis is theasymmetry of the original genomes. Each color represents the improvement rate that was calculated using the re-predicted on/ter
positions (black), the fixed on/ter positions (red), or the randomly deleted artificial GEIs (blue). Each line is a linear regression
fit of the data. The solid, dashed and dotted lines are p < 0.01, 0.05, and 0.1, respectively; F-test.

54

4.4.

4.4

DISCUSSION

Discussion

The objective of this research was to validate the effects of GEIs on the symmetry of the structure of bacterial genomes. Using computational deletions of GEIs, we observed a specific relationship between GEIs andgenomic structures. Regardless of GEI length, a subset of the GEI-deleted genomes exhibited changes in theirasymmetries. In particular, bacteria with small genomes were affected by the GEIs. It is logical to expect thatchanges in the symmetrical structure due to the positional distance between the replication origin and terminus
will be shorter or longer based on the deletions of specific regions from the genome. Indeed, there were many
shifts in the positions of the replication origins and/or termini in the GEI-deleted genomes. However,in certain
bacteria, when we re-predicted the positions of the replication origin and terminus after deletion of the GEIs,
the distances of movement were different from the fixed positions, regardless of GEI length of GEIs. This result
suggests that the on/ter prediction results were altered by changes in the GC skew that were caused by thedeletion of the GEIs. Furthermore, when the original genome had an imbalanced symmetric structure, large
improvement rates were observed in these bacteria.
However,there are a few particular aspects that merit further discussion. The first point is the possibility that
the GEIs, including their base composition, might adapt to their new genomic environment. Foreign genomic
sequences have been altered progressivelyby mutations during the long course of their evolution (LawrenceandOchman 1997). For example, bacteria have acquired numerous GEIs by horizontal events, and although thedetection algorithm can identify relatively recently acquired segments, this method may be not able to detectolder imported segments due to their adaptations (Mira et al. 2001; Cropp et al. 2002); in other words, manyof the GEIs that can be detected in various bacterial genomes may not affect the symmetrical structure.
The second point is that any imbalances in the symmetrical structure may have other causes such as inversions,replication mechanism including the differencesin leading strand and lagging strand, or a programmed system
into bacteria for something other purpose. In the Introduction, we presumed that GEIs have the potential tochange the base composition on a large scale and that the disruption of symmetrical structures may be caused
by GEIs. In particular, the asymmetrical replication origin and terminus may be an incorporated system inthe original genome. Because the base composition bias is caused by a mutation that is associated with thereplication process (Frank and Lobry 2000;Rocha 2004), the skew is inseparably related to the replication origin
and terminus. Therefore, if the replication origin and terminus in its pure state (i.e. without GEIs) were notsymmetrical and were instead biased towards one side or the other, this asymmetrical structure might represent
a deflection,and order can be maintained.

55

CHAPTER

usaeis5

a selectionCodon

the H-NS binding"Ce que je vols la n'est qu'une ecorce. Lc plus important

fo

sites

est invisible."-Antoine de Saint-Exupery'Lc Petit Prince'

5.1.

5.1

INTRODUCTION

Introduction

acterial chromosomedoes not form a chromatin structure using histones as eukaryotic chromosome. Howl) ever, the bacterial chromosomeis packaged into a nucleoidstructure in order to compact the genome. The
nucleoid is organized by the act of supercoiling, RNA and nucleoid-associatedproteins (NAPs), which regulate
DNA topology (Dame 2005). Similar DNA packaging machinery is also known for mitochondria (Friddle etal. 2004) and chloroplasts (Sekine et al. 2002). The formation of nucleoid is mediated by the interactions of
DNA and various proteins known as NAPs, and wraps the DNA in less than half of the intracellular volume(Cunha et al. 2001; Wiggins et al. 2010). The DNA-NAPs interactions have several styles as follows,bending,wrapping or bridging (Dolman and Kane 2009; Dillon and Dolman 2010). In gram-negative bacteria, at least
12 types of NAPs have been reported, and the most well-researchedNAP is H-NS. This protein was discoveredby isolating bacterial homologsof eukaryotic histones (Varshaysky et al. 1977), and plays an important role inmodifying the DNA topology to form the nucleoid (Falconi et al. 1988). The over expressionof H-NS leads todimers in solution, and the H-NS proteins can organizeDNA-H-NS-DNAbridges. Recently,it was reported thatthe H-NS protein not only contributes to the genome packaging, but also controls the bacterial transcriptions
along with DNA sequences and range of transacting factors (Browning and Busby 2004). Furthermore, H-NS iscalled as `genome sentinel' because the regulation by H-NS has a key role in selectivelysilencing horizontallyacquired genes (Dolman 2007). The function of gene regulation was highlighted by the recent genome wide
analyses of H-NS binding sites with a DNA microarray or a chromatin immunoprecipitation (ChIP) methodin Escherichia coll, Salmonella and Yersinia (Lucchini et al. 2006; Navarre et al. 2006; Cathelyn et al. 2007;
Banes et al. 2008; Grainger et al. 2006; Oshima et al. 2006; Gordon et al. 2011). In E. coll, Grainger et al.
and Oshima et al. observed the binding sites for H-NS genome widely by using ChIP-on-chip approach, andrevealed that the H-NS binding regions were more AT-rich than the resident genome. Additionally, they foundthat there were somewhat correlations between RNA polymerases and H-NS binding sites in both at promoter
and coding regions (Oshima et al. 2006;Grainger et al. 2006). These results suggest that the trapping of RNApolymerase by H-NS causes the gene silencing. In Salmonella, Lucchini et al. and Navarre et al. found thatthe binding sites of H-NS were essentially restricted to AT-rich region in common with E. coll by ChIP-on-chip
approach (Lucchini et al. 2006; Navarre et al. 2006). Moreover,Lucchini and colleaguesshowed that the H-NS
of Salmonella enterica inhibits the interactions of RNA polymerase and DNA, and functions as a gene silencer
for horizontally-acquiredgenes (Lucchini et al. 2006). These characteristics were very similar to H-NS of E.
coll. Additionally, with increase of the genome wide analyses of binding sites for H-NS, Lang et al. discoveredthat an abundant AT-rich motif were conserved at the high affinity binding regions from the genome wide
ChIP-on-chip experiments. Accordingto their study, this motif was significantlypresent in pathogeny gene andsuggested that the H-NS guards the host genome from pathogeny new genetic materials (Lang et al. 2007).
Thus, the features of binding sites for H-NS were ascertained in genome wide. Despite that H-NS homologous
genes are conserved among gram-negative bacteria, the evolutionary trend of the gene silencingmachinery has
been observed in several other species (Dolman 2004). Here we analyzed how the silencing machinery evolved.
In order to understand the acquisition of gene regulator function, we compared the features of the H-NS target
sites.

5.2

Materials

5.2.1

Softwareandand

Methods

genomesequences

All analyses in this study were conducted in use of programs written in Perl with the G-language Genome
Analysis Environment, version 1.8.13(Arakawa et al. 2003). For all statistical analyses, weused the R statistical
57

5.2.

MATERIALSAND

METHODS

package 2.10.0 (www.R-project.org). This study used 113 Proteobacteria strains which have H-NS homologous
gene of E. coll K-l2 substr. MGl6ss and Salmonella enterica serovar Typhimurium str. LT2 based on sequencesimilarity matches (blastp; e-value < 10-20). We obtained these genomesequences from NCBI FTP Repository(ftp://www.ncbi.nlm.nth.gov/Ftp). Horizontally-acquiredregions were predicted by Alien_Hunter 1.7 which is
implemented based on the interpolated variable order motifs (Vernikosand Parkhill 2006). The essential genedata sets were identified based on DEG 6.5 (Zhang and Lin 2009).

5.2.2

Discovering

motifsandpredictionof binding

sites

from

ChIP

data

The H-NS binding locations were acquired from two previous reports, Kahramanoglou et al. (Kahramanoglou
et al. 2011) and Lucchini et al. (Lucchini et al. 2006). The centers of binding signal were identified as thebinding site in accordance with the methods from each paper. In order to discover the binding site motifs for
H-NS, the 201 bp regions from the upstream 100 bp to downstream 100 bp including the signal site were definedas the binding regions. The motifs in these determined binding regions were searched by using MEME 4.6.1(Bailey and Elkan 1994). The parameters are as follows: the type of sequence model to use was zero or one
occurrence per sequence; motif width ranges were from 8; background distribution file contained the mono- anddi-nucleotide frequencies of the E. coll and S. enterica chromosomes. Using the searched motifs, the bindingsites in other bacteria were predicted by MAST 4.6.1 (Bailey and Gribskov 1998). The threshold was set to
p < 0.001, e-value < 10-10
5.2.3

The

cod onusagesamong

horizontally-acquired

genes

and

core

genes

The tribasic motifs, which are observed in the H-NS binding site motifs with approximately 2-bits (Figure 5.1),
code mainly three amino acids (Leucine (Lew),Seline (Ser), and Valine (Val)), and the synonymous cod onusages
are compared among horizontally-acquiredgenes and core genes. In particular, we calculated the occurrence
rates of the tribasic codons in horizontally-acquired genes and core genes. At this time, the tribasic motif
codons were GTA in Val, AGT in Ser, and CTA in Lew. Alanine (Ala) and praline (Pro), whose codons aremost and least used in E. coll and S. enterica and are four-folddegenerate, were used as the negative controls.
This calculation ignored the start and the stop codons to avoid the extreme bias.

58

5.3.

5.2.4

The

binding

ratio

in

each

RESULTSregionThe frequenciesof H-NS binding sites were calculated by using the number of bound sites divided by the total
length of each gene. This method was applied to high expression gene or essential gene. The screening ofhigh expression genes was done based on Cod onAdaptation Index (CAI) value (Sharp and Li 1987) and tRNA
Adaptation Index (tAI) value (dos Rets et al. 2004). Similarly,the screening of genes whose cod on usages are
similar to essential gene was also done based on the CAI value. At this time, the w-value was calculated fromthe essential genes.

5.2.5

Phylogenetic

analysis

A phylogenetic tree was constructed using a neighbor-joining method with 1,000 bootstrap replicates andalignment of 16S rRNA in 'y-Proteobacteria. These sequences were aligned by MAFFT 6.859 (Katoh and Ton
2008). The cladogram tree was drawn by using FigTree 1.3.1 (http://tree.bio.ed.ac.wk/software/figtree/).

5.3

Results

5.3.1

Discovering

H-NStargetingmotifsWe searched the H-NS binding motifs in E. coll and S. enterica to verify the features of binding sites whichwere conserved between different species. In two previous studies, the binding sites for H-NS were identified bygenome wide ChIP approaches in E. coll and S. enterica (Lucchini et al. 2006; Kahramanoglou et al. 2011).
Therefore we used these data to compare the motifs. The discovery of the binding motifs was implemented
by MEME software (see detail in Materials and Methods about the parameters). As a result, the detectedmotifs were very AT-rich and similar to each other in E. coll and S. enterica (Figure 5.1). Additionally, thesemotifs had characteristic tribasic motifs A, T and A at 4 bp, 6 bp and 7 bp. Since these target motifs arevery short, here we considered that the tribasic motifs may represent some codons. Therefore, we investigatedthe distribution of such cod onusages in horizontally-acquiredgenes and core genes. As a result, every tribasic
motif codons were significantly more frequently used at the horizontally-acquiredgenes than core genes in E.
coll (Figure 5.2A, p < 0.0001, Wilcoxon rank sum test) and S. enterica (Figure 5.2B, p < 0.00001, Wilcoxon
test), whereas the negative controls Ala and Pro could not show the same tendency. Altogether, H-NS seems
to distinguish the horizontal and core genes by using this tribasic motif.

5.3.2

The

motif

conservationin related

bacteria

Subsequently, we applied the same analyses to the related 113 ry-Proteobacteria. In almost all bacteria, thetribasic motif codons were significantly more frequently used in horizontally-acquired genes than core genes(Figure 5.3 and Figure 5.4). According to the plots, it confirmed that the tribasic motif codons usages weresignificantly biased toward horizontally-acquired regions in almost all bacteria (p < 0.0001, Welch t-test).
However,the biases were not observed in all bacteria. 12 bacteria (hereafter regarded as the "dissimilar group";
Table 5.1) did not show the significantdifferences(gray plots in Figure 5.3A, B, and C) . In the dissimilar group,in contrast, the usage of tribasic motif codons in core gene is significantly higher than horizontally-acquired
gene instead of other tendency. As an example, we showed the bar chart for Buchnera aphidicola str. APS

59

5.3.

similarto Figure

5.2(Figure 5.3D).

Table

5.1:

List

of thedissimilar

Bacteria

Escherichia coll str. K-l2 substr. MGl6ss
Salmonella enterica subsp. enterica serovar Typhimurium str. LT2
Baumannia
cicadellinicola str. He
Buchnera aphidicola str. 5A
Buchnera aphidicola str. APS
Buchnera aphidicola str. Tuci
Candidatus
Blochmannia pennsylvanicus
str. BPEN
Candidatus
Hamiltonella defensa 5AT
Photorhabdus
luminescens subsp. laumondii TTOl
Proteus mirabilis HI4s2o
Vibrio fischeri ESll4
Vibrio fischeri MJll
Vibrio harveyi ATCC BAA-lll6
Wigglesworthia glossinidia endosymbiontof Glossina brevipalpis
a This percentage

5.3.3

Index

to

RESULTSis the ratio

betweenrecognize

the number

theof predictedbindingbindinggroup.

Genome size (bp)4,639,675
4,857,432 . 686,194
642,122640,681641,895
791,654
2,110,331
5,688,987
4,063,606
2,897,536
2,905,029
3,765,351
697,724sites and genome

AT (%) Binding rate (%).
49210
0.074
47.778.:._0.080
66.761
0.375
73.7140.681
73.6880.678
73.7080.680
70.4350.580
59.6720.210
57.1750.180
61.1000.247
61.0470.165
61.1160.164
54.4530.052
77.521
0.938

size.

targetIn this section, we attempted to explain why the H-NS selects the motifs including tribasic motifs as the target
sites. If H-NS binds to any sites, it has an undeniable effect on the bacterial growth because the expressions ofbound genes are suppressed. In fact, since H-NS binding sites are twice more frequent in the coding region thanin the intergenic region (covering 0.056% and 0.061% of coding regions, and 0.028% and 0.032% of intergenic
regions) in E. coll and S. enterica , an H-NS binding site therefore needs to be selected based on the expression
level as well as the coding requirements. In order to assess the gene expression level, we used CAI values withribosomal genes as the reference set to assess the gene expression in related bacteria. In E. coll, actually, the
H-NS did not bind to the high CAI genes in comparison to the low CAI genes (Figure 5.5). As a result, there wassignificant relationship between the CAI values and the frequenciesof H-NS binding frequenciesin all bacteria
but dissimilar group (Figure 5.6A). The higher the CAI values, the lower the binding frequency in genes. In
the dissimilar group, however, the relationship was not observed (Figure 5.6A). Similarly,the tAl values also
showed the significant relationship (Figure 5.6B). The higher tAI values, the lower the binding frequency ingenes. In the dissimilar group, however, the relationship was not observed (Figure 5.6B). Hence, the H-NS
may avoid the genes that are translationally optimized. Furthermore, the relationship among the cod onusage
or base composition of essential genes and H-NS binding rates in E. coll were analyzed. The result revealed
that the H-NS did not bind to genes having similar cod onusage as essential genes (CAI > 0.8). On the other
hand, this binding frequency was only observed at the cod on usage level, and not at the level of similar basecomposition (AT% ± 0.01%) (Figure 5.7). Finally, we observed the phylogenetic relationship among the target
bacteria (Figure 5.8). The phylogenetictree resulted that the dissimilar group bacteria diverged, and they werelocated far from E. coll and S. enterica.

60

5.4.

5.4

DISCUSSION

Discussion

In this research,
we aimed to understandhow H-NS acquired
the genome
featuresof H-NS binding
motifs.
As a result,it was suggested
that thecaused as a by productof avoiding the silencingof important
genes.

sentinelgenomefunctionby analyzingthesentinelfunctionmight beWe first confirmed that the H-NS binding site motifs were common to E. coll and S. enterica (Figure 5.1), andwere very AT-rich. The appropriateness of these confirmed motifs was also supported by previous studies, forexample, Navarre et al. and Lang et al. reported the H-NS target site is 78% AT composition (Navarre et al.
2006; Lang et al. 2007), and Gordon et al. showed that the H-NS binds to AT-rich minor groove (Gordon etal. 2011). In this study, two independently obtained ChIP data were used to discover binding motifs, namely,that in E. coll using ChIP-seq by Kahramanoglou and coworkers(Kahramanoglou et al. 2011), and that in S.
enterica using ChIP-on-chip by Lucchini and colleagues (Lucchini et al. 2006). Although they did not use thesame target bacteria and methods, the same motifs could be detected from their binding data sets. Previously,
in -y-Proteobacteria, it is known that there is a relationship between H-NS structures and genome similarities(Tendeng and Bert in 2003). We could confirm that using experimental data. This fact attributes that thebinding site motifs for their H-NS are tightly conserved because of these strong sequence similarities.
However,since such detected target motifs were common and short segments, this carries a risk of having thebindings and inhibitions everywhere on the chromosome. Thereby, we focused on the tribasic motifs whichwere dominantly conserved with nucleotides at 4 bp, 6 bp, and 7 bp positions in target motif. These tribasic
motifs showed distributions throughout the whole genome. Especially, the tribasic motif codons were present
in horizontally-acquiredgenes than the core genes. Hence, these codons may be the important key to recognize
the foreign genes. In order to understand the selection reason of these codons, we considered that the cod on
usage in each gene is a selection pressure of H-NS binding motifs. In E. coll, actually, H-NS less frequently
binds to genes having similar cod onusage to essential genes, because the essential genes should be avoided by
the gene repression (Figure 5.7). Furthermore, in related bacteria, the H-NS binding motifs were not found ingenes which have similar cod on usage to ribosomal genes (Figure 5.6). Hence, H-NS avoided to bind to theessential or ribosomal genes.
This fact was found in almost all related bacteria, but some species did not show the same tendency. According
to the bacterial phylogenetic tree (Figure 5.8), it seemed that some bacteria were branched early (we called suchbacteria as dissimilar group). These differentresults in dissimilar group are suspected to be due to a property ofeach H-NS protein structure. In previous study, it was suggested that the "QGR" motif may be used by H-NS
family proteins to bind DNA (Gordon et al. 2011). Therefore we observed the amino acid sequences in target
bacteria, and the alignment result showed that there were variability of sequences between dissimilar group andothers (Figure 5.9). Although the "QGR" motifs were conserved in almost all H-NS proteins, the dissimilargroup sequences showed different trends around 100th residue. If the same H-NS binding site is maintained indissimilar group bacteria, the H-NS will not function as the genome sentinel, because the tribasic motif cod on
usages of foreign genes were different from core genes. The genomes in dissimilar group evolved independently,
and might have changed the H-NS target site. On the other hand, in B. aphidicola, other hypothesis about theevolutionary change of H-NS is also considerable. Buchnera is an insect endosymbiont and has nearly minimal
gene set and small genome size, approximately 0.6 Mbp, because of interdependence with host species (Brinza
et al. 2009). The B. aphidicolagenome sequences used in this study were three strains, 5A (NC_011833),APS(NC_002528)and Tusi (NC_011834),and these strains have hns genes. However, the hns homologous genes
were not found in other genome sequenced strains, B. aphidicolaBp (NC_004545),Sg (NC_008513),and Cc(NC_004061),using blastn (e-value < 10-5). Therefore, in such endosymbiont genome, H-NS functions may be
lost.

61

5.4.

DISCUSSION

Thus, H-NS targeted the tribasic motif codons which were more frequently used in horizontally-acquired genes
than core genes. H-NS regulates DNA topology and organizes the nucleoid structure to compact bacterial
chromosomes like an eukaryotic chromatin structure. In order to package the chromosome, H-NS should bindto many positions in bacterial chromosome. However, because gene densities in bacterial genomes are high(average gene density in targeted bacteria: 84.580%), H-NS might avoid the risk of having a critical effect onbacteria to select target genes, for example essential or ribosomal genes. Accordingly, the tribasic motif codons
were chosen in consequence of the avoidance, and the genome sentinel function is acquired as a by product ofgenome packaging mechanism.

62

5.4.

Escherichia

Salmonella

coll K-l2

2

Typhimurium

2

N

=1 .a

~1

TA
AT

UJ1.,tTenterica

DISCUSSION

T

wein(01,-C

A
T

0

MEME(noSSC) 09.08.1103:10

T
A
1

A
M

e!

T
~n

A
cc

n

T
co

MEME (no SSC) 09.08.11 03:20

Figure 5.1: Sequence logo of H-NS bind sites.
The sequence logos of the H-NS binding site motifs in E. coll and S. enterica. The regions marked by dashed line are the prominent
nucleotides (tribasic motifs).

63

T
N

DISCUSSION

5.4.

Escherichia
0 0
0U
O
E $

coll K-l2

Val

Ser

Lew

Ala

**
(GTm

**
(AGT)

**
(CTA)

ti
(GCG)

CoreG

HG CorG

Salmonella
Pro

enterica

Typhimurium

Val

Ser

Lew

Ala

Pro

**

**

**

ti

n. s.

HG CoreG

HG CneG

HG CoieG

HG CoreG

HG CareG

(CCC'

U.0
O
CD cr
co co
C
0
o
U0N
N
0E
CC
OC
O

HG CoreG

HG

HG CoreG

HG CoreG

Figure 5.2: Synonymous
cod on usage of tribasic motif cod on.
The synonymous cod on usages in each horizontally-acquired gene (HG: light colors) and core gene (CoreG: dark colors). In E. colland S. enterica, colored three bars represent the cod on usage in each amino acid (Val: blue, Ser: green, and Lew: yellow) in HG
and CoreG. The light and dark gray bars are the negative controls (Ala and Pro). For example, in blue bar (Val), the tribasic
motif codons (GTA) were over 19% of total Val codons in HG (light blue), but in CoreG (dark blue bar), the usage was about
16%. The error bars indicate each standard deviation. The usage of tribasic motif cod on in HG is significantly higher than CoreG(* p < 0.01, ** p < 0.0001). On the other hand, the usage of tribasic motif cod on in CoreG is significantly higher than HG (tip < 0.0001). n.s. (no significance, Wilcoxon rank sum test).

64

5.4.

70

70

A
(Val)
O

B
(Ser)

Buchnera aphidicola str. APS

60

DISCUSSION

60

Buchneraaphidicolastr. APS
•

50

Q
a)
OU
C
In
c)
en

50

~

40

30

30

20

20

P=2.o42xio.10

P= 7.545 x io-'4

10

y

10

10

CO a

20

30

40

50
F-{j}-.

60

70

20

to

40

30

00

50

60

70

F--in

V
70

O
U
0)
as
.0

C
(Lew)

0

Buchnera

aphidicola

str. APS

n.s.

n.s.

n.s.

n.s.

Val

So,

Lou

Intergenic

o ea

50

8

a)

Cs

•

m
a) m

40 o

a)
•30

0

~20

U
P=1.gsixio.10
10

10

20
F -=-

30
i

40
mom00 0 0®

50

6070
o

Average tribasic motif cod on usages in HG (%)

Figure 5.3: Average tribasic motif cod on usages.
These scatter plots A - C represent the average usages of tribasic motif codons in horizontally-acquired genes (HG) and core genes(CoreG). D: This figure is the bar chart about Buchnera aphidicola str. APS similar to Figure 5.2. The x-axes are the averagecontent percentages of tribasic motif codons in HG, and the y-axes are the percentages in CoreG in each bacterium. Colored plots
represent each bacterium, and the Val (A), Ser (B), and Lew (C) are drawn as blue, green and yellow respectively. The gray plots
are the bacteria which do not have significant differences. These tribasic motif codons were significantly more frequently used at
the HG than CoreG in almost all bacteria, but there were 12 exception bacteria including Buchnera. n.s. (no significance, Wilcoxon
rank sum test).

65

O

5.4.

DISCUSSION

0
N.

0

i-co
N'
O)

as

C 0
"

Lo

o

U`r
o
>,M
C
O
C

V)
•N

O
T

HG (Val)CoreG (Val)HG (Ser)

CoreG (Ser)HG (Lew) CoreG (Lew) HG (Ala)

CoreG (Ala)

Negative control

Figure 5.4: The average of synonymous
cod on usage in 113 bacteria.
The x-axis is the synonymous cod on usages in each bacterium. The usage of tribasic motif cod on in horizontally acquired gene
(HG) is significantly higher than core gene (CoreG) (** p < 0.0001). On the other hand, the usage of tribasic motif cod on in CoreG
is significantly higher than HG (ft p < 0.0001).

66

5.4.

All bacteria

co 0
0
0

but dissimilar

group

Dissimilar

N
O
O

to a)
.N
a)
C
'O

DISCUSSION

group

O
O
O

C :bO
O
co O

y)
z

O
0

-o
a) t
'O 0
O
a) O
0.
a) L

cD
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w-O

O
0

NO
>+ U
C O
a) O
F a)
iw
a)
L
1— O O

N0
OO

O
O

OO
OO
-2sZ<-1

Z<-2-1 sZ<0

I
**

OsZ<1

I

**

1sZ<2

i

**

I

**

2sZ

Z<-2

I

L

*

-2sZ<-1In.s.

-1sZ<0

_
n.s.

OsZ<1

I

1sZ<2

I
n.s.

_
n.s.

2sZ

I

—J
n.s.

CAIvalue (Z-score)

CAIvalue (Z-score)

Figure 5.5: The number of H-NS binding sites.
The distribution of the H-NS binding sites by using E. coll ChIP-seq experimental data (Kahramanoglou et al. 2011). The x-axes
represent the CAI values. The y-axes are the number of H-NS binding sites per one gene. The number of H-NS binding sites per
one genes showed lower as CAI values were higher. * p < 0.01, ** p < 0.0001, n.s. (no significance), Welch t-test.

67

5.4.

A

All bacteria

but dissimilar

group

Dissimilar

DISCUSSION

group

CO

U,
z
2
a)
U
a)
a) a)
O
VC
a)

a)
C
a) C)

CO

a)0,
C
O
La
)
a
co
+v.+N
Cl)
C
_

0C
_
a) L .Q
w
a)
I—
O

< 0.3
L

< 0.6

< 1.0

<0.3

**

**

All bacteria

but dissimilar

Dissimilar

group

group

CO

en
z

a)

n.s.

CAI value

CAI value

B

< 1.0

<0.6

m,c
a) a)

a~ c
0
a) 0
n.
0a
)
O
U)
UC mc
a) L
0.
a)

a)
3
0
Q a) Cu)
L w :n °
a) t
H

< 0.3

< 0.6

**

<1

< 0.3
,.0

< 0.6

< 1.0

n.s.

tAl valuetAl value

Figure 5.6: The frequency of binding sites.
The x-axes represent the CAI values (A) and tAI values (B), and the y-axes are the frequencies of the predicted H-NS bindingsites. These gray bars are all bacteria but dissimilar group, and black bars are the dissimilar group. ** (p < 0.0001), n.s. (nosignificance), Welch t-test.

68

5.4.

DISCUSSION

L
0

Similarbase combosition

genes

All genes

w
a) c
0
a)
a
0
4*

U)
a) c
a)
a)

co

.

c
0
z

±wO
4*
U)
a)
is
a)
c
La
c
00

N
O

Similar CAI genes
Essential

genes

0

O
0

Figure 5.7: Binding rates in several type genes.
The y-axis is the predicted H-NS binding rates. The bars mean each gene group, essential gene group (red), all gene group (blue),similar base composition to essential genes group (green), similar cod on usage to essential genes group (orange). The essential genedata sets were identified based on the database of essential genes (DEG 6.5; Zhang and Lin 2009).

69

5.4.

DISCUSSION

Buchnera aphtdkcola str. APS
Buchnera aphklicola str. 5A
Buchnera aphtdlcola str. Tuci
Candidatus Bkrchmannia pennsylvaaicos str. BPEN B
aumannia cicadellinicola str. He
Wigglesworfhla glossinidla endosymbiont of Glossina brevipalpis

Candidatus Hamtltonella deans
enorhabdus bovienii SS-2oo4
Photorhabdus

SAT X

luminescens subsp. laurnondii TTOl

Rahnella sp. 79602
Sodalls glossinidius str.
Somalia
Pectobacterium
Pectobacterlum
Dickeyacarotovomm subsp. carotovorum PC1
atrosepticum SCRIio4s

Edwardsiella ictalurt 93.146 C
ronobactersakazakii
ATCC BAA-8g4
Cronobacter turicensis zsos2
PantoeaEnterobacter sp. 638
Klebsiella

Pantoea sp. At-gb
Enterobacter cloacae subsp. cloacae ATCC 13047

Cibobacter koseri ATCC BAA-8gs Y
ersinia entemcolitica subsp. palearctica 105.58(r)Eschertchia
Shigella

Outgroup(Herbaspirillum

seropedicae

SmRl ; p-proteobacteria)

Figure 5.8: Phylogenetic
tree.
This phylogenetic tree was built based on 16S rRNA sequences of 113 Proteobacteria including E. coll K-l2 and S. enterica LT2
using a neighbor-joining method with 1,000 bootstrap replicates. The dissimilar group bacteria are, shown by red. Herbaspirillum
seropedicae SmRl (o-Proteobacteria) was used as an out group.

70

5.4.

DISCUSSION

81.......90........100.......110.......120......

Figure 5.9: Alignment
result of H-NS in dissimilar group.
This figure shows the alignment result of H-NS amino acid sequences in a part of target bacteria with ClustalX (Lark in et al. 2007).
The organisms surrounded by a red border are dissimilar group. The dissimilar group sequences showed different trends aroundthe 100th residue.

71

CHAPTER

6

Pathway Projector: Web-based
Zoomable

Browserusing

GoogleMaps

API"Imagination

is more important

than knowledge ."-AlbertEinstein

6.1.

6.1

INTRODUCTION

Introduction

With along
tradition
ofbeing
adescriptive
discovery
science,thefieldofscientificvisualization
hasbeenan integral part of biosciencesand has also been an indispensable approach for understanding complex,
large-scale data in molecular biology. Numerous approaches for information visualization have been successfully
utilized and have contributed to the understanding of genomic information, including those for the protein 3D
structure, sequence alignment, and phylogenetic trees (Tao et al. 2004). Genome browsers, such as Gbrowse(Stein et al. 2002), UCSC Genome Browser (Kuhn et al. 2009), and Ensembl (Hubbard et at 2009), havebeen a particular success because they provide a visual context (Mangan et al. 2008). Genome browsers show
gene structures and their locations within the genome, and they can also be used to map novel knowledge
and experimental data to display them in a genomic context. Systems biology approaches (Kitano 2002; Zhu
et al. 2009) attempt to understand cellular processes as a system of molecular interactions. In post-genomic
research, these approaches demand another context for biochemicalpathways in order to understand biologicalinformation. A biochemicalpathway is a series of reactions that consists of enzymes, proteins, and molecularcompounds (Papin et al. 2003), and is a useful context for understanding how gene disruptions or alterations ofconditions associate with a phenotype (Ekins et al. 2007). For example, in microarray or proteomic experiments,
researchers can map their experimental data through pathway mapping systems, such as ArrayXPath II (Chunget al. 2005), GenMAPP (Salomonis et al. 2007), and Pathway Explorer (Mlecnik et al. 2005), to gain acomprehensive understanding of cellular regulation and to explore the existence of alternative pathways aftergene deletions or change in conditions. Therefore, visualization approaches allow for an intuitive understandingof a large quantity of data that is inherently difficult to comprehend, while biochemical pathways provide asuitable context for observing the systematic cellular behavior that is analyzed through "-omics" experiments(Adriaens et al. 2008). Pathway browserswill thus enhance systems biology research.

6.1.1

Individual

pathwaymap

We first have developed a web application that visualizes complexomics data of multiple layers simultaneously
for individual pathway map, including transcriptome, proteome, and metabolome, onto an integrated pathway
diagram derived by connecting the individual KEGG pathway maps; the mapped images are generated in
Scalable Vector Graphics (SVG) for easy editing by hand or with computer programs or drawing software. The
web application is available at http://megu.lab.keio.ac.jp/.
We present a new generic pathway visualization tool primarily to aid the heuristics of researchers during theobservation of system-levelregulation of interacellular interactions. For this purpose, here we define the requirements as follows:1) ability to map data from multiple layers of omics, 2) visualization based on familiar pathway
map but with a cell-wideview, 3) flexibilityin editing with manual methods and with computer-friendly format
and semantics, and 4) interactivity to connect with external data.
The software system was developed using the generic bioinformatics workbench, G-language Genome Analysis
Environment (Arakawa et al. 2003; Arakawa and Tomita 2006; Arakawa et al. 2008), and was implemented asa web-service. SVG is supported in most browsers either natively with Firefox and Opera, or with free plug-in
availablefrom Adobe as in Internet Explorer or Safari. Individual pathway diagrams are drawn in SVG using thecoordinates in KEGG Markup Language (KGML), and the integrated pathway diagram is drawn manually byconnecting the individual pathway diagrams. For example, the integrated map for all carbohydrate metabolism
encompasses 17 pathways including glycolysis, the TCA cycle, the pentose phosphate cycle, and many othermetabolic pathways, in order to capture the cell-wide activity at a glance. Cellular compartmentalization is
also based on the KEGG pathway representation.

73

6.1.

INTRODUCTION

The SVG format has several advantages over other graphic formats, including object-based vector representationfor easy editing, resolution-free zooming suitable for the observation of large integrated pathways, interaction
and animation features for richer interfaces, and data in regular text format as a subset of an eXtensible Markup
Language (XML) that can be easily handled by computer programs or text editors. SVG files can also be loaded
and edited as objects with common graphic software such as GIMP and Adobe Illustrator, so that the users
can add new graphics and components (that are not in KEGG, for example) onto the mapped pathway image,and customize the colors and values manually as desired.
To mapspecific

data

onto thepathway

diagrams,

users are required

only to selectthe target

pathway

from thepull-downmenu and input the data in the text-area
at our web site. The input data are presented
as a list ofcomma-delimited"Name,Value"
pairs, where each line represents
one molecular
entity.
"Name"
is the nameof an entry to be mapped
onto the pathway,
which can be either a canonicalor standard
name for genes andmRNAs,
the EC number
for proteins,
and the KEGG compound
ID for compounds.
"Value" is the intensityof the color used for mapping
represented
by an integer between
1 and 100. Values for genes and proteinsarerepresented
by the range of colors between
red and green, and those for metabolites
betweenblue and green.
Sample data and demonstrations
are available
at the software
web page as working examples.

Since this system is based on the KEGG pathway representation, labels sometimes overlap. In order to avoid
this, information windowscan be toggled by pressing the key 1 to 4 to show/hide the labels. Pressing the `1'
key shows compound data (`2' key hides these data), `3' key shows gene data (`4' key hinds these data) andtoggles the Information Window, which contains information on the color levels, names, and EC numbers of
the nodes displayed directly beside the pathway nodes. Furthermore, most of the objects are also. draggable.
All nodes are linked to external resources in KEGG; the hyperlinks are activated by clicking the nodes.
The primary objective of this system is in the pathway mapping using familiar pathway diagrams of the KEGG
database, for omics experimental data. Therefore, our primary focus is in the simultaneous mapping of thequantitative information about biomolecules including the genes, mRNAs, proteins, and metabolites on a single
pathway map. Currently MEGU focuses on the visualization of the high-throughput
experimental data such as
microarray and metabolome, and has limitation in the visualization of the molecular interaction networks.

Using MEGU, we generated pathway graphics of integrated carbohydrate metabolism mapped (Figure 6.1) withthe microarray data of 38 knockout mutants of the two-component regulatory system in Escherichia coll K-l2
Wslio (Oshima et al. 2002).

74

6.1.

A

•
••a

,:

•

INTRODUCTION

B

+a
4

:•4;•
o et".•r•••.4Ja••~,•r•I•0• ,r4.

riIN

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lit •a;•4106•'iO3.•,...a%•ty"
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•

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3:yr.'••p~a~is.'it ,•t•i~ss•• $w•.. ~....at••#m •+~a•wr*41/6op~•r it•4a•~1~•,are,ria...ass
^

y_ v.

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bar...ez..1•

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4/3

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•

at

Figure 6.1: Output example of MEGU.
A: Integrated diagram of carbohydrate metabolism pathways mapped and generated with MEGU. The rectangular nodes correspond
to mRNAs or proteins and circles correspond to metabolites. The visualized pathway diagram based on the coordinates of KEGG
database is generated in SVG format from the web-service, so that any part of the pathway can be easily enlarged without affecting
the resolution of image, as shown in (B) Glycolysis Pathway. The map can be further toggled to show information windows (C) andbar graphs of the molecular amount (D) with interactive and animated manipulations, and the objects are hyperlinked to external
resources. Corresponding entries of KEGG can be accessed by activating the hyperlink on each of the nodes.

75

r

v

6.2.

6.1.2

Integrated

pathway

MATERIALSAND

METHODS

map

Most existing pathway maps have been provided as parts of major public pathway databases at their websites.
These maps are subdivided into individual pathways, in part due to technical limitations in manipulatinglarge images on the World Wide Web. Given that pathways are essentially connected in vive and that highly
comprehensive experimental data that encompass a wide variety of pathways is readily available, arbitrary
partitioning of pathways is often not useful for the mapping and observation of comprehensive experimental
data. For instance, the glycolysis/gluconeogenesispathway (mapoooio) in KEGG (Kanehisa et al. 2008) links
to five pathways: the citrate cycle (mapooo2o), the pentose phosphate pathway (mapoooso), starch and sucrose
metabolism (mapoosoo), carbon fixation in photosynthetic organisms (mapooiio) , and propanoate metabolism (mapoo64o). Users have to constantly switch back and forth between the maps to observe reactions thatencompass multiple pathways. Therefore, with the advancement in web development technologies (Zhang et al.
2009), several pathway databases have started to release integrated pathway maps that allow comprehensive
viewing. For example, the KEGG Atlas (Okuda et al. 2008), iPath (Letunic et al. 2008) and the new beta version
of Reactome (Matthews et al. 2009) display comprehensiveintegrated pathway maps without page transitions,which have been implemented as zoomable and scalable maps. The Omics Viewer (Paley and Karp 2006) in
BioCyc (Karp et al. 2005) implements this feature with pop-ups upon mouse-over action. These interface
technologiesthat enable the continuous display of a large image at different scales without page transitions arecollectively known as the zoomable user interface (ZUI). ZUI is successfullyutilized for the representation ofgeographicalinformation as typified by Google Maps (http://maps.google.com/), as well as for the visualizationof gene networks and the implementation of genome browsers (Uchiyama et al. 2006; Arakawa et al. 2oogb;
Itch and Watanabe 2009; Obayashi et al. 2009).
Despite the recent availability of several integrated pathway maps, the abstraction level of represented entities
in these maps is often not sufficient to map experimental data, which is primarily due to the objectives ofeach pathway database. For example, the KEGG Atlas and Omics Viewer do not show genes and enzymes
as nodes, but instead represent them as reaction edges. The Reactome only shows enzymes as nodes, which
limits its applicability for the mapping of microarray data, since many enzymes exist as heteromers that arecomprised of several proteins. Similarly, when only reactions are represented in the map, data from metabolomic
experiments cannot be mapped. To the best of our knowledge, there is currently no pathway browser that can
map experimental data for genes, enzymes, and metabolites simultaneously using a comprehensive integrated
pathway map.
In this chapter, we present Pathway Projector,a pathway browser that allows the mapping of multi-omics
information on an integrated pathway map through an intuitive user interface and ZUI. The integrated pathway
map of Pathway Projector is based on the widely used layout of the KEGG Atlas with the addition of nodes
for genes and enzymes for the mapping of experimental data from transcriptomic,
proteomic, and metabolomic
experiments. We also identify and discuss the requirements for an ideal pathway browser.

6.2

Materials

6.2.1

Requirement

andMethods

analysis

As a result of a close collaboration
between our bioinformatics
group and experimental
biologists,
we have first
identified
the requirements
for a pathwaybrowser.
We have analyzed
the requirement
especiallyin consideration of the current
situations
facing the biologists
workingwith large-scale
omics data with systems
biologyapproaches,
where they require a comprehensive
understandingof cellular workings.
Therefore,
the pathway

76

6.3.

IMPLEMENTATION

browser should be continuous and global, covering the omics layers of genome, transcriptome, proteome, andmetabolome, while being intuitive and not requiring too many user interactions to allow rapid navigation forthe day-to-day heuristic usage. The requirements are categorized in five groups: (RI) pathway representation,(R2) data access, (R3) mapping and editing, (R4) data export and exchange, and (R5) availability.

6.3

Implementation

6.3.1

Pathwaymap

Several pathway maps are already commonly used by biologists. We therefore chose to utilize the layout ofan existing familiar pathway map as the basis of our pathway browser rather than creating a new one. The
KEGG Atlas was selected for several reasons: (1) a global integrated map is provided; (2) it is a part of oneof the most popular pathway databases (Arakawa et al. 2005; Kono et al. 2006; Werner 2008); and (3) thereferencepathway layout can be utilized for the representation of pathways in a wide variety of organisms. The
KEGG database provides various tools and a wealth of pathway-related data that are curated with controlled
identifiers and external references. These identifiers and references are useful for the implementation of many
functions of the pathway browser and for the interoperability of the tool. Since the KEGG Atlas only represents
metabolites as nodes per se, all related gene and enzyme nodes have been automatically added on the midpointof reaction edges of the KEGG Atlas pathway map and a SVG file has been generated in Perl. To calculate the
midpoints of edges, all quadratic bezier curves used for the representation of reaction edges were converted intopolylinesfor computational efficiency.Followingthe automatic positioning of enzyme nodes, several nodes weremanually curated using Adobe Illustrator CS3 13.0.2 for layout optimization. Enzyme nodes were partitioned
into multiple compartments when several genes comprisedthe heteromeric enzyme, with a horizontal layout forgenes < 6 or with two rows of up to six columns for genes < 12. A list of gene names only are shown for genes> 12. As a result, the reference pathway map contains 1,572 metabolite nodes and 1,813 enzyme nodes. As
examples of the organism specific pathways, there are 1,365gene nodes in E. coll and 2,883 in human.
In order to provide the pathway browser as cross platform software without the need for user maintenanceand installation, Pathway Projector is implemented as a web application using HTML and JavaScript with the
AJAX (Asynchronous JavaScript + XML) web development paradigm. The Ext IS 2.0 (http://extjs.com/)library was utilized to implement the overall user interface framework. ZUI for the large pathway map is
implemented by using the Google Maps API for intuitive and familiar user interface and to take advantage
of many on line tools that are associated with the API. Since large images need to be split up into image
tiles that are 256 x 256 pixels for use with the Google Maps API, the original large pathway map of 8,192 x
8,192pixels was split using the "generateGMap" function available in the G-language GAE v.1.8.8 (Arakawa
et al. 2003; Arakawa and Tomita 2006; Arakawa et al. 2008) to produce five zoom levels. The numberof tiles increases by the power of 4 depending on the zoom level: 1 in level 0, 4 in level 1, 16 in level 2,
64 in level 3, 256 in level 4, and 1,024 in the maximum zoom level 5. The pathway map has been madeclickableby sending the coordinate information and asynchronouslyretrieving related information upon the user
mouseclick event, which is displayed through the InfoWindowfunction of Google Maps API as an information
window. The information window contains the retrieved annotation of each component, including the common
name, identifier, structural formula or chemical equation, and links to external databases such as KEGG,
PubChem (Sayers et al. 2009), ChEBI (Degtyarenko et al. 2008), and MSDchem (Dimitropoulos et al. 2006)
for metabolites and, ExPASy (Kiefer et al. 2009), MetaCyc (Caspi et al. 2008), Brenda (Chang et al. 2009),
IntEnz (Fleischmann et al. 2004), PUMA2 (Maltsev et al. 2006), and IUBMB (McDonald et al. 2009) for
enzymes (see http://web.sic.keio.ac.jp/Tciconia/AppendixTablel.cps for a complete listing for organism-specificdatabase references). Although the default referencepathway only contains external links to enzymes, when an
77

6.4.

RESULTSANDDISCUSSION

organism specific pathway map is opened from the "Organism Selection" tab, the information window on thenodes also shows gene-centric links to suitable databases for the organism (e.g. EcoGene: Rudd 2000; MGI:
Collins et al. 2ooia; Collins et al. 2ooib). Nodes within the pathway map show their corresponding labels(commonnames for genes and metabolites, EC number for enzymes) at zoom levels higher than 4, utilizing thesemantic zooming capabilities of Google Maps API.
Organism-specificpathwaymaps

generatedusing

the reference

6.3.2

Searchandfor 843 species,

pathway

data

map

based

includingbothon KEGG

eukaryotes

and prokaryotes,

were

subsequently

Orthology.

retrieval

Pathway Projector has four types of search functionalities: (1) by keyword and identifiers; (2) by molecular
mass; (3) by computation of possible routes between two metabolites; and (4) by sequence similarity. Searches
by keyword, identifier, and molecular mass were established by searching through a server-side database reconstructed from KEGG flat filedistributions. The results are listed in a search result panel and are also visuallyhighlighted by red markers placed onto the respective components on the pathway map. Additional gray polylines are drawn on corresponding reaction edges when the search result points to enzymes or genes. The redmarkers can be clickedto invokean information windowto display more detailed annotations. The route searchand similarity search capabilities are implemented as wrappers of PathComp (http://www.genome.ad.jp/keggbin/mk_pathcomp_html) and KEGG BLAST Search (http://blast.genome.jp/) to take advantage of existing
KEGG services. Since PathComp calculates every possible route between two given metabolites from all combinations in the KEGG database, only the paths that are availablewithin the KEGG Atlas layout are displayedin the results. For the BLAST search, sequence type (nucleotide or protein) and the type of BLAST program(blastn, blastp, blastx, tblastx), are automatically interpreted by the system, in which sequences comprising
80% A, T, G, C, or N are' considered to be nucleotide sequences. The Pathway prediction tool reconstructs
the pathway from given multi-FASTAsequence files using BLAT and SwissProt (Bairoch et al. 2009) using the
GEM System (Arakawa et al. 2006).

6.3.3

Mapping

andediting

The Pathway mapping tool modifies the SVG map based on user input, by changing the visibility, size, color,and labels of edges and nodes, and subsequently creates an overlay. Users can also place predefined icons or any
image availablein the World Wide Web by URLs and show the directionality of reactions by adding arrowheads
to reaction edges. When values for time-series or multiple conditions are specified for nodes, graphs or charts
generated by the Google Chart API (http://code.google.com/intl/en/apts/chart/) are displayed on the nodes.
Quikmaps (http://quikmaps.com/) was utilized to implement manual annotation and editing capabilities.

6.4

Results

6.4.1

Requirement

andDiscussion
analysis

The requirements are categorized in five groups: (RI) pathway representation, (R2) data access, (R3) mappingand editing, (R4) data export and exchange, and (R5) availability (see Table 6.4.2 for a summary).

78

6.4.

RESULTSANDDISCUSSION(RI) Pathway representation
A biochemical

pathway

is often

subdivided

into smallermaps

harboring

specific

biologicalprocesses,

such

as

glycolysis,
the TCA cycle, and the pentose
phosphate
pathway.
Capturing
the large systematic
pictureofcellular dynamics
that spans several of these specific pathways,
however,
is difficult,especiallyin light of theavailabilityof large-scale,comprehensive
experimental
data from high-throughput"-omics" measurements
thatencompassvarious pathways.
An integrated
pathway
map that connects
all subdivided
pathwaysinto a single
large pathway is more suitable
and intuitive
as a context for information
mappingrather than rotating
throughhundredsof specific maps.

While several pathway components are conserved among different organisms, each species also has its own
specific genes, metabolites, and enzymes, and therefore has some unique set of pathways. While a reference
pathway map with all known pathway components regardless of the organism, such as the one that is available
in the KEGG database, may be a useful gateway, a researcher focusing, for example, on E. coll is primarily
interested in the pathways of that organism, which makes all the other components dispensable. The availabilityof organism-specificpathway maps is therefore essential for this purpose, as well as for comparative studies
among different organisms. Furthermore, it is desirable for a user to be able to reconstruct a pathway map
from his/her own genomicsequence to keep up with the rapid availability of new genomes,both for comparative
study and for the functional annotation of the genome.
Althoughthe majorityof existing pathway

maps belong to the metabolic

pathways,

numerous

aspects of cellular

processes are formulated as pathways, including signal transduction,
genetic information processing and maintenance, gene regulation, and the cell cycle. The availability of a drug resistance pathway map, for example,
will facilitate drug discovery.

(R2) Data access
A pathway is a gateway to a wide variety of biologicalinformation, including genomic sequences, the functionalannotations of genes, the biochemistry of enzymatic reactions, and the chemistry of metabolites. A pathwaybrowser should, therefore, provide links to associated data in external public databases such as PathCase(Elliott et al. 2008). Various search capabilities are also essential for such highly complex and large-scale
information in addition to simple query types, such as keywords and identifiers. The primary outcome fromgenomic, transcriptomic, and proteomic experiments is the sequence information, which demands sequencesimilarity searches that incorporate nucleotide or amino acid sequences. Likewise, metabolomic experiments
produce spectrograms with molecular mass and retention times, from which corresponding compounds need tobe identified.
When observing
experimental
data in the context
of pathways,
a biologist is often interested
in a subset pathor route within
a complex
pathwaymap.
For example,
in gene knockout
experiments,
biologists
are often
interested
in the change in flux or gene expression
within a given route as well as in the activationof alternative
paths. Hence, computationand searches for possible routes between two given components
are desirable
for theobservationof alternative
paths and for the predictionof unidentified
intermediate
molecules
that are difficult
to measure
by standard
means.

(R3) Mapping and editing
An essential
feature of a pathwaybrowser is the ability to map and editchanging
the colors, sizes, and shapesof pathwaygraphics
accordinggraphs of data onto the locations
pathway.
Since high-throughput
transcriptomics,
proteomics,
andof respective
measurement
metabolomics

experimental
data,
to the experimental

which
data

can involveor placing

entities,
to visualize the data in the context
of the biochemical
technologies
are available,
large-scale
experimental
data fromshould be supported.
For this reason, molecularcomponents,

79

6.4.

RESULTSANDDISCUSSION

including genes, proteins, enzymes, and metabolites, should be represented and be available for mapping within
the pathway map. While genes, proteins, and enzymes can usually be represented as single nodes or sometimes
simply as edges in most existing pathway maps, heteromeric enzymes, which are formed from several proteins
and, therefore, from several genes, require dedicated nodes to correctly map data from microarray experiments.
Furthermore, experimental measurement is often performed on multiple conditions or in a time series, which
requires the simultaneous mapping of multiple data to observe the changes and differences. These can bevisualized, for example, by using animations that show pathway changes according to time as in the Omics
Viewer (Paley and Karp 2006) or by displaying graphs on each object as in VANTED (Junker et al. 2006).

Naive mapping that is based upon a predefined pathway map does not allow novel pathways or entities to bemapped, which limits the applicability of a pathway browser to the evolving knowledge in molecular biology.
For example, noncoding RNAs, as typified by micro RNAs (miRNA) or small nucleolar RNAs (snoRNA), or
phosphorylated isoforms of proteins are emerging areas of researchesthat are actively being explored and could
benefit from interpretation within a biochemical context. Therefore, the pathway map should be able to be
freely edited and annotated by adding nodes, edges or data, as in WikiPathways (pica et al. 2008).
(R4) Data export and exchange
For interoperability and data management, pathway data and mapping results should be downloadable in astandard XML imageformat, such as SBML (Systems BiologyMarkup Language: Hucka et al. 2004) or BioPAX(http://www.biopax.org).
(R5) Availability
In orderbrowseravailable

to be platform-independentand interoperable
without
should be available
as a web-based
application
thatfor academic
users.

6.4.2

Systemmaintenanceand installation
efforts, a pathway
requiresno registration
fees and that is freely

overview

The Pathway Projector was implemented according to the aforementioned requirements, including the availability of a large-scale comprehensive pathway map, pathways from a wide variety of organisms, and searching and mapping capabilities. The software is freely available for academic users without any registration
at http://www.g-language.org/PathwayProjector/. Since it has been implemented as a web application, thissoftware is cross-platform and requires no installation or maintenance. Moreover, use of the AJAX web development paradigm provides an intuitive user experience similar to that of desktop applications. The main
pathway map of Pathway Projector was reconstructed from the popular KEGG Atlas layout by adding nodes
for enzymes and genes. As an example, the map for E. coll K12 contains 1,365 genes, 1,813 enzymes, and1,572metabolites (Figure 6.2). Circular and rectangular nodes represent metabolites and genes/enzymes, respectively, and the names of genes, enzymes, and metabolites are displayed within the map at high zoom levels
by means of semantic zooming. Reaction edges are color coded according to the followingpathway categories:
aqua represents glycan biosynthesis and metabolism, blue represents carbohydrate metabolism, green represents
lipid metabolism, red represents nucleotide metabolism, purple represents energy metabolism, yellowrepresents
amino acid metabolism, pink represents metabolism of cofactors and vitamins, dark red represents biosynthesisof secondary metabolites, orange represents metabolism of other amino acids, and magenta represents biodegradation and metabolism of xenobiotics. Pathway Projector utilizes the Google Maps API for the implementationof ZUI and enables smooth navigation through panning and zooming without page transitions using a mouse
scroll wheel or double clicks. Every component in the pathway map shows more detailed information by clicking
on the nodes, which opens up an information window containing annotations, such as chemical and structural
80

6.4.

RESULTSANDDISCUSSION

formulas and links to external public databases (Figure 6.3). Detailed information about the nodes can bealternatively accessed through "Mouse Over Mode" that can be toggled from the right-most search result panel,with which users can simply move the mouse over to the nodes to show information in the sub-window locatedin the bottom panel. Users can therefore use Pathway Projector as a generic browser and as a gateway forvarious pathway-related resources.
The default pathway map of Pathway Projector is the reference pathway map, but organism-specificpathway
maps can be selected from a list of 870 organisms available from the Organism Selection tab located in theupper left section of the user interface (Figure 6.3A). This list of organisms can be searched incrementally or
can be sorted by species names, domains, kingdoms, and subphyla by clicking on the column headers. An
organism-specificmap is opened as a closabletab next to the reference pathway map tab upon double clicking
on the desired row in the list. These maps show gene names within enzyme nodes, and the information window
also contains organism specific database links, such as EcoCyc for E. coll (Keseler et al. 2009) and SGD for
yeast (Hong et al. 2008). Since tab-browsing has been adopted as an effectiveapproach in navigating the World
Wide Web, as seen in numerous web browsers with this function, tab-browsing was utilized for the navigationof different organism-specificmaps, which allows the user to quickly switch between species for comparative
study.

Figure6.2: Reference
pathwaymap.
Pathway Projector provides an integrated pathway map that is based upon the KEGG Atlas, with the addition of nodes for genes
and enzymes. Circles represent metabolites, and rectangles represent enzymes that are further subdivided into several compartments
indicating the composite genes for heteromeric enzymes. Nodes are labeled with names or EC numbers at high zoom levels.

81

_

6.4.

Table

6.1:

Comparisonof existing

pathway-related

softwareanddatabases

RESULTSANDDISCUSSION

accordingto therequirement

analysis.
RI

R2

R3

R5

R4

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PathwayPro(ector
(this
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ti r

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f=1'i

ElloCyc
BloCycPathway Tools Software (Paley and Kare 20061
i,v;IthwaK4olsaSoftware(TyanGenMAPPSalomontsetal2ooi
GenMAPP (Salomonts et al 20071

:a

CI

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in

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IngenuityPathwayAnalysts*2ISi.L•
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of al 2008)
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UKonoetone al2oo6)M...~MEGUlseC——:r i ..y,

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Pathway Explorer (Mlecn(k of al 2005)

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wit•Pathways (pica of al 2008)
This table summarizes the functions of existing pathway tools according to the requirements identified in this work (Rt) pathway representation, (R2) data
access, (R3) mapping and editing, (R4) data export and exchange, and (R5) availability Closed circles Indicate satisfactory implementations, and open
circlespartialcorn/
implementations't httprepresent//www biocarta*2 http //www ingenuity corn/

82

6.4.

6.4.3

Searchand

RESULTSANDDISCUSSION

retrievalUsers can search through the pathway components from the search box located in the upper right corner (Figure
6.3D) using keywordsand identifiers for genes, enzymes, pathways, and metabolites, or with the molecular massof metabolic compounds (default range within ± 10 mass number). Search results are directly shown within thepathway map, marked with red pins on the nodes as well as gray lines highlighting the corresponding edges forreactions. A list of search results is also available in the right-most panel (Figure 6.3E), where the search range
for molecular mass can be adjusted. Clicking on a marker or an entry in the result list invokes an information
window.
In order to identify the paths or the existence of alternative pathways, possible routes between two metabolites
can be searched in Pathway Projector.
Starting and ending metabolites for the route search can be selectedfrom within the information window. The search results are displayed as lists in the search result panel, similar
to keyword searches, and routes are highlighted after clicking on the route number. This feature is especially
useful when observing the change in the flux distribution upon gene knockout or over-expression experiments
to identify the existence of alternative pathways or for the prediction of the concentrationof immeasurable
metabolites from the changes in neighboring compounds.

A sequence similarity search using nucleotide or amino acid sequenceshas been implemented based upon KEGG
BLAST and is displayed in a pop-up window that is opened by clickingon the "Tools"button located next to thesearch box (Figure 6.3C). The system automatically interprets the type of sequence (nucleotide or protein) andsubsequently chooses the appropriate program (blastn, blastp, blastx, tblastx); therefore, the user only needs
to paste in a sequence of interest to the text area and choose the type of database to run the BLAST search.
The search results are marked with highlighted edges on the referencepathway and are also listed in the search
result panel with KEGG Orthology identifiers, species names, e-values, and links to organism-specificpathway
maps with the BLAST result. The "Pathway Prediction" tab, which is another tool that can be found in the"Tools" window, reconstructs the pathway from given multi-FASTA amino acid files and draws the resulting
pathway map accompanied by corresponding e-values. Users can use this feature to analyze novel organismsthat are not included in Pathway Projector or to analyze pathways in any given gene set, such as those included
in an environmental metagenome database.

6.4.4

Pathway

mappingof experimental

data

The mapping feature of Pathway Projector is available from the "Tools" window, which allows full customization of pathway diagrams by changing the color, size or width, labels, and node image (specified by preset
icons or URLs of images). In addition, directionality can be indicated by arrow heads, and graphs of multiple
conditions or time series can be displayed (Figure 6.4). Users can map data from transcriptomic, proteomic,
and metabolomic experiments, and multiple "-omics" data can be simultaneously represented on a single map.
Because the node representing an enzyme is subdivided into multiple compartments when the enzyme is heteromeric and, therefore, comprised of several genes, transcriptome data can be correctly mapped onto individual genes. Entities are specifiedusing the KEGG identifier (e.g. Coooio), EC number (e.g. 1.1.1.1), andgene names, while the basal pathway map is specified by the KEGG Organism identifier (e.g. "eco" for E.
coll K12). The graph of time-series or multi-condition data can also be visualized on top of the corresponding
node by specifyingthe values as comma-separated vectors, and the graphs can be viewed at higher resolution
by invoking the information window. Users can also alternatively upload a tab-delimited data file, where thefirst rows are component identifiers, and the columns are experimental data. By placing these graphs on themetabolic pathway, researchers can easily interpret complex multiple experiments in the context of biochemical
pathways and subsequently identify the systematic response to perturbations. Map generation for Google Maps83

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Figure6.3: User interface.
A: Organism selection tab lists all available organism-specific pathways, which are opened as new tabs upon selection. B: The
information window is opened by clicking on the entities represented in the map or on the markers that are shown as search
results. This window shows detailed information about the selected entity, including names, images of structures, and molecularweight, and provides links to external databases. Furthermore, the selection of two metabolites as starting and ending compounds
through this window results in the computation of possible paths between the two selected compounds. The result of path search
is displayed in the right-most result panel and as highlighted lines on the map. C: Data mapping, sequence similarity searches, andpathway reconstructions based on sequence data, are available in a pop-up window that can be invoked from the "Tools" button.
D: The search box located in the top-right corner automatically interprets the given query type and searches accordingly based on
keywords, molecular mass, or identifiers. E: This panel displays the search results as a list. Users can locate the entities by opening
an information window, which automatically moves the map to show the selected object in the center. Links to downloadable
pathway images and editing and annotation palettes are located above the search results.

84

9

6.4.

RESULTSANDDISCUSSION

generally requires several minutes of computation time since thousands of tiled images must be prepared on
the server; however, through multi-thread and multi-core optimization, Pathway Projector is able to generate
the mapped image as Google Maps ZUI in less than 20 seconds on a dual quad-core CPU node. The mapped
image is available as a downloadable image or as a transparent overlay, which can be toggled to display bypressing on the "Customized" button located in the upper right corner of the map. Because the mapping
feature can specify the basal organism map and because it is implemented as an overlay of existing pathway
maps, users can take advantage of this mapping feature for cross-speciescomparative study. For example, tocompare the pathway maps of human and mouse, creating an overlay of mouse maps, which can be simply accomplishedby creating a mapping layer with "Organism:mmu",on top of the human map allowsrapid switching
between the two pathways. Simple Object Access Protocol (SOAP) web service is also available for data mapping for interoperability with other tools. Users can access this service through programming languages such
as Perl, Python and Ruby, or through intuitive SOAP clients with graphical user interfaces such as Taverna
workbench (Hull et al. 2006). Web Service Description Language (WSDL) file is available at http://www.glanguage.org/PathwayProjector/PathwayProjector.wsdl and detailed documentation as well as sample scripts
for several programming languages are available in the on linedocumentation, section annotation, subsection
SOAP service (http://www.g-language.org/PathwayProjector/annotation.html#soap).

V
23.3.14rrr

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[ID],[color],[size],[arrow (+/-)],[label]

Figure 6.4: Data mapping.
An example result and required data formats for mapping are shown. For graph mapping, the target compound ID and chart typeshould be defined with a colon "ID:line". The graph data is written on the next line and "//" is placed on the final line. For nodeor edge mapping, user-generated data can be input line-by-line following such order by comma, ID, color, size, arrow, and label.
Furthermore, details of the graph picture are shown when the graph is clicked.

Pathway Projector is also equipped with editing capabilities, such as drawing lines, placing icons and text labels,
and adding annotations, to add novel findings that are not included in the reference pathway (Figure 6.5). The
85

6.4.

RESULTSANDDISCUSSION

editing tool is opened by clicking on the "Open Map Editor" link in the right-most panel. The line tool andscribble tool are available for drawing lines and curves, respectively, and the brush size and color for these tools
can be con figured. Text labels and icons can be dragged and dropped onto the map, and these objects can beclicked to show an editable information window in which the users can add custom annotations. The edited
and annotated pathway map data are downloadable as XML files that can be saved and exchanged. This XML
file is based on the Quikmaps format, and contains the editing log including the texts, coordinates, color andsize of lines or icons. Hence, users can recapture the manually edited map by pasting this XML log and thenclicking on the "import" button in a new session. This XML file can be shared among researchers to share themanual annotations.
Imp onDrawin/1.neBrrts
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Figure6.5: Manual
editingandannotationof pathwaymaps.
The pathway editing and annotation
palette can be invoked from the link located at the top of the search results panel. 21 pie-set
icons are available to be dragged and dropped anywhere in the map, functioning
as original markers.
Users can freely move thesemarkers around the map and can also add annotations
and comments
by clicking on the markers.
Several other drawing
featuresare available,
including the line tool for drawing lines, scribble tool for free drawing, and text tool for placing labels. Brush colorand size can be customized.
Users can export the edited map as an XML file from the "Get XML" button, and this file can beshared and imported
by other users to recapture
the edited map.

86

lliliil

COHioo6

6.5.

6.5

Chromosome

CHROMOSOME

MAP

map

We developed a chromosome map browser as an extra feature of the Pathway Projector. Recognition of thepositional relationship between gene directions, oligonucleotide sequence sites, the base compositional bias,
and replication origin and terminus is important for understanding bacterial chromosomestructures. However,since the localizations of genes or the positions of some sequence elements are identified numerically, only such
numbers can not lead the instinctive recognition. Therefore, we implemented a viewer designed to map eachgenomicelement on a circular chromosomemap in order to develop an instinctive understanding and inspiration
for subsequent research (Figure 6.6). This system is available at http://ws.g-language.org/g4/Repter.
(R' 1) Chromosome map representation

Chromosomemaps need to be large in size for the observation of individual genes because bacterial genomes areat least approximately 160,000bp in size. However,the important point is that the minimum unit needed here
is a gene, not a nucleotide. This is because the study of chromosomestructure does not focus on promoters,
start/stop codons, or other short segments that consist of just a few nucleotides. Therefore, the map requires
a resolution of 1 base per 1 pixel.
Thus, we generated
a large chromosome
map adapted
to the average number of genes to meet this requirement.
For example, the number of genes in E. coll is approximately
4,000. Hence, the radius of the circular chromosome
becomes 3,000 pixels, calculated
as 4 pixels per gene. However, although
researchers
may be able to get a quick
overview of suchthese chromosome
panninglarge images,
maps wereand zooming

without

they cannot circumstantiallyalso represented
using Google
page transitions

using

a mouserecognize each element in these maps.
Therefore,
Maps API, which enables smoothnavigationvia
scrollwheel

or double

clicks.

(R'2) Data annotation

In Chapter 2, the chromosomestructure was discussed. The term "chromosomestructure" includes the genestrand bias, the base composition bias, and the positional relationship between several sequence elements (difor Ter sites). Therefore, these annotations should be represented on the chromosome maps using many color
variations.

In order to clarify the gene strand bias, blue bars representing the genes on the leading strand and orange bars
representing the genes on the laggingstrand were arranged in each independent concentric circle. The GC skew
was represented by a red wavy line and located near the center of the circle. Additionally, several sequence
elements were mapped onto the chromosomemap using a red marker (dif site) or a yellowor purple marker (Tersites) in each genome position. Finally, the replication origin and terminus were connected with a black line. As
a result, this circular chromosomemap enables the distinction of replichores, and users can intuitively observe
the symmetric/asymmetric structure constructed by the gene strand bias, base composition bias, replication
origin and terminus, and sequence elements.
(R'3) Map editing

Since the chromosomemaps are generated based on GenBank data, the maps do not allow novel annotations
or sequence elements to be mapped. For example, other sequence elements (e.g. KOPS, Chi sequence, AIMS,or other important sequences), some protein binding sites, horizontally acquired regions, and inverted regions
represent information emerging from my research. Therefore, the chromosomemap should be freely editable byadding icons, text, or drawings, as in Pathway Projector (R3).
This

chromosome

map

browser

is alsoequippedwithediting87

capabilities

to fulfill

requestssuch

as drawing

•

6.5.

Pathway P,,e,CHROMOSOME

MAP

O.

-4
U
Uw

.,., 0,10 VI ill11
intNit;w~i>ti_z"

alt

Escherichia coilstr: K-l2l2substr.Wslio
60~

14,646,332op

_

IOL
/ ter

..

4Si isaii®0

6 $k,+quikmaps

Figure 6.6: Chromosome
map viewer.
This figure shows an overview of a circular chromosome map. The red pin indicates a dif site, the yellow pins indicate direct Ter
sequences, and the purple pins indicate complement Ter sequences. The blue and orange lines represent genes in the leading andlagging strands, respectively. The green line represents the frequency of the tribasic motif, and the pink boxes represent horizontal
gene transfer (HGT) regions. The dif sequence is located near the replication terminus (ter), the boundary line of the gene-strand
bias, and the shift-point of the GC skew (red lines), as well as between the inverted Ter sequences (indicated by the yellow andpurple pins).

88

W

6.6.

LIMITATIONS

lines, placing icons, and inserting
text labels, as well as adding annotations,to add novel findings that are notincludedin the original browsed map. The detailed
operative
procedure
for this editing function
can be foundin the subsection"Pathway
mappingof experimental
data."

6.6

Limitations

Pathway Projector is currently limited to the metabolic pathway, due to the availability of the global map
in KEGG Atlas. Therefore, pathways such as signaling networks, gene regulation, and cell cycle are notsupported by our software. Since our software tool is semi-automated to import information from KEGG,
large-scale maps for these particular pathways, if they become available, will be relatively easy to integrate into
Pathway Projector. Secondly,the KEGG pathway maps are generic implementations, for which the reference
pathway serves as the net sum of all known reactions from a multitude of organisms. There are cases forwhich a specific pathway map dedicated to just a single species of interest is instead more desirable, such as
the PMN (http://www.plantcyc.org/) for plants, EcoCyc for E. coll, and MGD (Butt et al. 2008) for mouse.
Although links are provided to these databases for organism-specificinformation, we may need to consider usingorganism-specificmaps for well-curated pathways in addition to those of KEGG, when integrated pathways
become available in these websites. Nevertheless, the basic user interface frameworks based on the AJAX
technology for searching and mapping, as well as the ZUI based on Google Maps API, are applicable forother pathway maps as long as the coordinate information is provided along with the map image. Finally,
Pathway Projector does not provide pathway data in an exchangeable format such as SBML and BioPAX, as
noted in the requirement analysis section. However, since our system is based on the KEGG maps, KGML
(KEGG Markup Language) can be readily converted into the BioPAX format, and these BioPAX file can be
down loadedfrom ftp://ftp.genome.jp:/pub/ob/community/biopax. The map editing features provided in thissoftware are intended
for quick note taking for informationexchange along researchers.
While this feature allows
free editing and annotations
on the pathway
maps, these editings are overlays of additional
information,andare not true modifications
to the existing pathway map. For the generationof static pathway map images, users
should use the mapping
tool to generatea custom pathway
map, and download
this image to make permanentmodifications.

6.7

Conclusion

In light of the advent of high-throughput measurement technologies among several layers of "-omics," understanding the systematic workings of the intracellular activities is critical, and biochemical pathways provide anindispensable context for this purpose. Since pathways are essentially connected in vive, the use of a global
map is desirable, especially for the mapping of comprehensive experimental data. Pathway Projector is an
intuitive browser that has been designed for this purpose by providing a large-scale metabolic pathway map
based on the popular KEGG Atlas layout, with the addition of gene and enzyme nodes. Pathway Projector has
been implemented with user-friendly interfaces while also providing its software as web-based application forcross-platform availability. Integrated search capabilities, as well as mapping and editing capabilities, facilitate
exploratory and heuristic data analysis. Therefore, Pathway Projector can serve as a useful gateway for pathway
information analysis.

89

CHAPTER

7

Conclusions"Scienceis what you kn

ow, philosophy

is what you do not know."-Bertrand

Russell

7.1.

7.1

The

role

of scientific

THE

ROLE

OF SCIENTIFIC

VISUALIZATION

visualization

Weorhumans
scanningcan
force
capturemicroscopy
topological(SFM:
structures,
van Noortsuch
et al.
as2004;nucleoids,
Damewith2005).
atomicHowever,force
microscopy
we cannot capture(AFM)
biochemicalpathways or genomic structures if they are not illustrated computationally as a map. The visualization of genomic information has enormous potential: it will contribute to the debate regarding the structuresof chromosomes,particularly the symmetric structures. The symmetric structures, in this dissertation, are aphenomenon that uses the replication origin and terminus as a line-symmetric axis, and various types of elements are scattered along the rule of line-asymmetry. By marking the position of each element on a circularchromosomemap, I was able to capture these relative positional relationships immediately. Thus, I implemented
a specialized browser for my research that incorporated various genomic information (Chapter 6), and I wasable to confirm several known or unknown findings. For example, the symmetric structures are not entirelyconserved in all of the bacterial chromosomes.In particular, there is a rich diversity in the intensity of the GC
skew and the gene-strand bias. Although this knowledge has been reported by previous researchers (Arakawa
et al. 2ooga), the illustrated chromosomemap enabled us to view the degree of imbalance of the GC skew
shift-points and gene localizations holistically. Additionally, when the predicted dif sites are mapped onto thechromosomemap, I observed that almost all of the dif sites are located at or near the replication terminus, theboundary line of gene-strand bias, the shift-point of GC skew, or between the inverted Ter sequences (Figure7.1). However, these positional relationships are not observed in all bacteria, and several dif sequences arelocated far from these positions. Based on this visual information, I questioned whether bacterial replication
has terminated at close dif sites, or where it has occurred, at least during the process of forming GC skews.
These feedbacks and interactions are among the results and ideas that may be generated by the extraction of
information on an intuitive level from increasing amounts of data.

W10
MIII I

ligtase

II

30%

ter

\~

nw-4

Figure 7.1: dif sites in a chromosome
map.
This figure is a section of a circular chromosome map of E. coll. The red pin indicates a dif site, the yellow pins indicate direct Ter
sequences, and the purple pins indicate complement Ter sequences. The blue and orange lines represent genes in the leading andlagging strands, respectively. The dif sequence is located near the replication terminus (ter), the boundary line of gene-strand bias,
and the shift-point of GC skew (red lines), as well as between the inverted Ter sequences (indicated by the yellow and purple pins).

91

7.2.

7.2

Chromosome

CHROMOSOME

structural

STRUCTURAL

aspects

ASPECTSof bacterial

OF BACTERIALEVOLUTION

evolution

The high predictability obtained using a recursive hidden Markov model suggested that the dif/XerCD systemof chromosomedimer resolution was highly conserved among bacterial species and that the dif sequences were
almost always conservedwhen XerCD was present within the genome. There may be many factors contributing
to chromosomedimer resolution, but I presume that the circular structure of the chromosome is one of themost important considerations. When a recombination event occurs an odd number of times in a single DNA
replication event, the replicated chromosome is not properly segregated into two daughter chromosomes but
instead produces a concatenated dimer (Sherratt 2003; Lesterlin et al. 2004). However, if the bacteria havelinear chromosomes, this problem does not occur. The bacteria with linear genomes do not need to have achromosomedimer resolution mechanism because there are topological differencesbetween circular and linear
chromosomes. Therefore, because the bacterial chromosomeforms a closed circular structure, a chromosome
dimer resolution mechanism may become necessary.
Given this disadvantage, why do bacteria have circular chromosomes? A few bacteria have linear chromosomes(e.g. Streptomycesspecies; Volffand Altenbuchner 2000,and Borrelia burgdorferi;Ferdowsand Barbour 1989),
but almost all bacterial chromosomesare circular structures. In this study, I hypothesized that this structural
evolution might be associated with the accuracy of copying the genetic information. The primal mechanism forthe duplication of genetic information is a simple replication reaction that synthesizes nucleotides using otheroligonucleotidesas templates. The reaction between the nucleotidesmay be catalyzed by the substrate itself, andsuch enzymes were reported as yieldingself-sustainedreplication (Lincolnand Joyce 2009). However,this primal
duplication reaction is certainly not semiconservative replication based on self-replication. Semiconservative
replication requires nucleotides that are complementary to the template segment when the hybridization of
nucleotides occurs, but the hybridized nucleotides in the primal duplication reaction do not necessarily havethe same length as the template oligonucleotide. Indeed, pyranosyl-RNA could be replicated by a shorter
oligonucleotide than the template base sequence (Belli et al. 1997). If the template oligonucleotides havecertain important gene regions, such as polymerase or ligase (of course, in the primary genome, the `gene'might not be established as a protein-coding region and might be nothing more than an architectural feature,similar to an active site of a ribozyme), then the primal organisms are exposed to the risk that part or all ofthese regions will not be duplicated. Hence, this system is insufficientfor the accurate inheritance of genetic
information, and the circular structure may be employed to resolve such length-independent problems. As aresult of adopting a circular structure, because the oligonucleotidesare always duplicated with the same length,
the accuracy of the inheritance is ensured.

7.3

Biologicalvalidations

The dif sequences were conserved in almost all bacteria. This universal conservation indicated that bacterial
replication may terminate at the dif site. It is desirable to terminate at the dif site for an effective procedure
for resolving dimeric chromosomes. Moreover, this hypothesis is supported by many computational aspects
based on the directions of oligonucleotide sequences and the correlations between GC skew shift-points anddif sites (Hendricksonand Lawrence 2007; Higgins 2007). Based on these previous studies, the dif-stop model
was suggested as one of several replication termination models. Indeed, our comprehensiveprediction method
confirmedthat the comparison of positions between the predicted dif sites and the shift-points of the GC skew
showedthat the dif sequencespredicted in the phyla Proteobacteria and Firmicutes correlated significantlywith
the GC skew shift-points, which are highly likely to be located within the terminal region. However,althoughthese two positions were highly correlated in terms of genomic loci, the differencesin the positions of the GC
skew shift-point and the GCSI were not correlated. Therefore, although the dif sequence is located near the
92

7.3.

BIOLOGICALVALIDATIONS

replication termination site for efficient CDR (chromosomedimmer resolution), the replication termination site
was suggested to be at a site other than the dif site (Kono et al. 2011). Thus, the appropriate replication
termination model needed to be validated.
The most fundamental problem in the understanding of the replication termination mechanism was that thepositional information of the dif sequences had been unknown. In the three replication termination models
described in Chapter 3, the fork-trap, fork-collision, and dif-stop models, it was not difficult to detect thetermination positions predicted by the fork-trap and fork-collisionmodels. The replication termini in thefork-trap model are Ter sites (the sites where Tus proteins bind). The Ter sites could be identified by theirconsensus sequence. Similarly, in the fork-collisionmodel, replication terminates when the two oppositely
directed replication forks meet by chance at the far end of the circular chromosome. Because the collisionoccurs
randomly, the termination positions should followa probabilistic distribution centered on the site opposite fromthe origin of replication. However,because the dif sites cannot be detected by a simple homologysearch, previous
predictions have been limited to a few bacterial phyla (Val et al. 2008; Carney and Roten 2009). Therefore,
the comprehensive prediction of dif sites in bacterial genomes enabled the validation of the bacterial replication
termination models using simulationsof genomic mutations. Accordingly,from the viewpoint of the replicationrelated mutational bias, I suggest that the fork-trap model is the most appropriate model for the replication
termination system, not the dif-stop model. The validations of the replication termination models were also
performed in the case in which the three models function together. When the probabilistic combination models
were performed, the best probabilistic combination differedamong bacterial species. Therefore, it was suggested
that replication termination is multifactorial and associates with the genome size, gene density, and/or growth
speed of each bacterium. This suggestion was also supported by the validation in Firmicutes, the members ofwhich do not contain the Ter/Tus complex.
These investigations were limited to Proteobacteria or Firmicutes, but the results may also apply to otherbacteria harboring clear GC skews. The most typical bacterium in this study is E. coll, which does not havea high GCSI score. Although the GCSI is not very high, the dif-stop model was not observed to be the mostappropriate. A GCSI of 0.9 is a borderline score for accepting the existence of a clear GC skew. However,among the completely sequenced genomes, approximately 70% of the bacteria show GCSI scores greater than
0.9. Therefore, the only bottleneck of this restricted result is the Ter sequence, and our results which, thedif-stop model is not appropriate, may apply to almost all bacteria.

I had conducted rigorous examinations of the calculation methods for determining each termination rate, cautious constructions of the three termination models, and statistical comparisons. However,the construction of
the dif-stop model should consider another role of the dif site, namely, its involvement in the integration andexcision of exogenousDNA. At the dif site, two tyrosine recombinases, XerCD, resolvethe dimeric chromosome(Clerget 1991; Blakely et al. 1993). At this time, XerCD can also function in the integration and excision offoreign DNA (Huber and Waldor 2002). The integration acts on bacteriophages, such as CTX(I) (Val et al.
2005) or VGJl) (Das et al. 2011), and is promoted by XerCD. As a result, the integrations have the potential
to result in the disruptions of chromosomeproportion, regardless of the replication mechanism. Therefore, theintegrated regions will be considered when researchers investigate the areas around dif sites. To resolve thisproblem, it is necessary to exclude the integration-reported bacteria from the target bacteria, and my research
did not use such bacteria (e.g. Vibriogenus).
Not only these dif-related phage integrations but also HGTs are detected in several genomic positions. These
regions are called GEIs and are consideredto havethe potential to invokelarge-scalechanges in base composition
because they are large foreign regions of approximately 10 Kbp - 1 Mbp in the bacterial genome (Rocha 2008).
Given a quantitative index to measure the strength of the genomic symmetries based on the GC skew, many
bacteria with imbalanced ori/ter structures have been identified. It is highly unlikely that these detected
imbalanced ori/ter structures were maintained in the actual bacterial genomes. Therefore, we investigated the

93

7.4.

drastic effectsof the bacteria

CONCLUDINGREMARKSon the base composition.
However, regardlessof the lengths or positionof the HGTs,
very few
showed the symmetry
disruptions
caused by HGT. This result suggested
the robustnessof thegenomic

symmetries

7.4

Concluding

in bacterial

genomes.

remarks

I have researched the symmetric structure defined by DNA replication and the topological structure defined bygene expression throughout this dissertation. What I began to see is that the living organism has conductedan `adaptation to biological mechanism' as a part of the ways in which it changes throughout its evolution.
The most important point is that this `adaptation to biological mechanism' is different from the response to
the environment and from the adaptability in PICERAS, an acronym that is known as the definition of life
(Koshland 2002). The trigger is an internal environment rather than an external one. Replication and gene
expressionare key mechanismsof fundamental biologicalsystems. Replication can only progress unidirectionallybecauseit requires a free s'-OH (hydroxyl)to attach the s'-phosphate of the incoming nucleotidein both strands.
Accordingly,the DNA synthesis mechanism introduces a difference between the leading and lagging strands,
and there was a bias of the accumulative rates of mutation between strands. This bias results in GC skew as
a part of the formation of the base compositional bias. Similarly, in the replication mechanism, the bacterial
genomes have adapted to increase the efficiencyof FtsK translocation on the DNA. FtsK progresses toward
the replication termination region prior to the completion of cell division and functions as a key player inchromosomedimer resolution (Lesterlin et al. 2004). In exchange for the circular chromosome structure,
which enhances the accurate inheritance of the genetic information, bacteria accept the risk of the formationof dimeric chromosomes by recombination. Because of that risk, almost all bacterial circular chromosomes
would be expected to gain the dif site and XerCD in their genomes. Although the adaptational evolution ofchromosomestructure in biologicalmechanism does not yet occur at this point, such adaptation might occur tocontrol FtsK translocation. Currently, this FtsK translocation has been oriented by KOPS (FtsK-orienting polar
sequences). The KOPS are highly skewedin the genome (Bigot et al. 2005) and constitute a typical example ofoligonucleotidebias. There are also contributions from other biologicalmechanisms, including octamer skews(Salzberg et al. 1998). The Chi sequenceis known as a recombinational hot spot, and it interacts with RecBCD.
When properly oriented, the Chi sequence controls recombinational repair (Taylor et al. 1985; Kuzminov 1995)
in conjunction with the conformational changes that switch RecBCDfunction from exonucleaseto recombinase(Dixon and Kowalczykowski1995; Singleton et al. 2004; Handa et al. 2005). Therefore, approximately 75%
of Chi sequences are oriented toward the direction of replication (Blattner et al. 1997). In gene expression, I
found that the nucleoid-associatedproteins avoid the frequently used codons in essential or highly expressed
genes as their target binding sites. Although the repressive function of H-NS toward the expression of particulargenes has been described as a genome sentinel (Dolman 2007), I considered the possibility that the genome
sentinel function might be caused as a by product of avoiding the silencing of important genes. Of course, theinfluence of the avoidance alters the binding site of NAPs and affects the topological chromosome structure.
These adaptations to biological mechanismare as slowas the adaptability and response to environment seen in
PICERAS but will certainly accumulate gradually. The observation of the adaptation to biologicalmechanism
will support the understanding of the importance of each mechanism and lead to the `recognition' of life.

94

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