Table Of ContentTHE MOLECULAR EPIDEMIOLOGY
OF HUMAN VIRUSES
THE MOLECULAR
EPIDEMIOLOGY OF
HUMAN VIRUSES
Edited by
Thomas Leitner
Swedish Institute for Infectious Disease Control
17182 Solna, Sweden
....
"
Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication Data
The molecular epidemiology ofhuman viruses / edited by Thomas Leitner.
p. cm.
Inc1udes bibliographical references and index.
ISBN 978-1-4613-5420-8 ISBN 978-1-4615-1157-1 (eBook)
DOI 10.1007/978-1-4615-1157-1
1. Virus diseases -Epidemiology. 2. Molecular epidemiology. 3. Viral genetics. 1.
Leitner, Thomas
RA644.V55 M65 2002
615.5'75-dc21 2002073053
Copyright © 2002 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 2002
Softcover reprint ofthe hardcover lst edition 2002
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Table of Contents
Preface VII
Part One: Introduction to Molecular Epidemiology
1. The Use of Molecular Epidemiology
Thomas Leitner
2. DNA Technology for Molecular Analysis of Viruses 11
Deirdre 0' Meara and Joakim Lundeberg
3. Phylogenetic Approaches to Molecular Epidemiology 25
Keith A. Crandall and David Posada
4. The HIV Databases: History, Design and Function 41
Bette Korber and Carla Kuiken
Part Two: Application of Genetic Methods to Investigate Virus Spread
5. The Evolution of Primate Lentiviruses and the Origins of 65
AIDS
Elizabeth Bailes, Roy R. Chaudhuri, Mario L. Santiago,
Frederic Bibollet-Ruche, Beatrice H Hahn and Paul M
Sharp
6. Recombination and Molecular Epidemiology of HIV -1 and 97
Enteroviruses
Mika Salminen
7. Molecular Epidemiology of Human T Cell Leukemia! 121
Lymphoma Viruses Type 1 and Type 2 (HTLV-1/2) and
Related Simian Retroviruses (STLV-1, STLV-2 and
STLV-L/3)
Antoine Gessain, Laurent Meertens and Renaud Mahieux
8. Molecular Epidemiology, Evolution and Dispersal of the 167
Genus Flavivirus
Paolo M de A. Zanotto and Ernie A. Gould
9. Molecular Epidemiology of Hepatitis C Virus 197
Alexandra Cochrane and Peter Simmonds
10. Seeking a Pale Horse: The 1918 Pandemic Influenza Virus 217
Thomas G. Fanning, Ann H. Reid, Thomas A. Janczewski
and Jeffery K. Taubenberger
11. Molecular Epidemiology in Measles Control 237
Claude P. Muller and Mick N. Mulders
12. Public Health Surveillance and the Molecular Epidemiology 273
of Rabies
James E. Childs, John W Krebs and Jean S. Smith
13. Molecular Epidemiology of Rotavirus 313
Christian Mittelholzer and Lennart Svensson
14. Respiratory Syncytial Virus 329
Patricia A. Cane
15. Molecular Epidemiology ofHantavirus Infections 351
Ake Lundkvist and Alexander Plyusnin
16. Molecular Epidemiology of Arenaviruses 385
Remi N. Charrel and Xavier de Lamballerie
17. Molecular Epidemiology of Hepatitis B Virus 405
Jane N. Zuckerman and Arie J. Zuckerman
18. Genomic Diversity of Human Papillomaviruses and its 419
Impact on Molecular Epidemiological Research
Hans-Ulrich Bernard
Index 439
Preface
The field of molecular epidemiology spans many new sciences,
including bioinformatics and molecular biology as well as established
disciplines such as medicine and mathematics. This makes the use of
molecular epidemiological methods both exciting and solid at the same
time. In the last decade the use of molecular investigations in virology
have avalanched, but a comprehensive overview has been lacking even
though almost every journal in medicine, biology, mathematics, evolution,
computer science and epidemiology can display papers on this topic. It is,
therefore, the intension of this book to collect some central information
from this ocean of knowledge and perhaps provide a springboard from
which both those who are new to the field and the experts in one niche can
jump off. This book contains eighteen chapters covering fundamental
knowledge, the high-tech nuts and bolts in the researcher's toolbox, as well
as examples from the majority of human viruses, a review of the
fascinating recent history and a state of the art snap-shot.
Molecular epidemiology means different things to different
people. To some, it is an exiting opportunity to apply modem techniques,
be it biochemical or computational, on almost anything, even viruses. For
others, it means an objective and careful method to extract important
information from a virus system that otherwise would be difficult, if not
impossible, to retrieve. Then there are those who think molecular
epidemiology is just another new fluke in the fast molecular biology and
bioinformatics rush, and that it, at best, is another way of telling what they
already knew. In addition, to define what is and is not molecular
epidemiology can be rather difficult. But surely, with the increased speed
at which DNA sequencing improves, computing powers escalate and new
emerging viruses are discovered, molecular epidemiology is going to
become even more important and interesting in the future.
In this endeavor, I wish to sincerely thank all the authors who have
contributed to this book. The expertise and active research of the
contributing authors has given this book in-depth descriptions and updated
views of a vast and diverse field. Their careful and extensive reviews have
together created an impressive collection of knowledge on the molecular
epidemiology of human viruses. I am also grateful to Laura Leitner for her
indefatigable help in various phases of the manuscript preparations and to
Joanne Tracy and Dianne Wuori at Kluwer for their encouragement and
patience during this project.
Thomas Leitner, May 2002
The Molecular Epidemiology of Human Viruses. 2002.
Thomas Leitner, ed. Kluwer Academic Publishers, Boston.
Chapter 1
The Use of Molecular Epidemiology
Thomas Leitner
Department of Virology
Swedish Institute for Infectious Disease Control
17182 Solna, Sweden
1. INTRODUCTION
The use of molecular methods to assess the spread of infectious
diseases has become an important way to follow epidemiological patterns.
While traditional epidemiology concentrates on the host, the molecular
methods focus on the etiological agent, which in this book is the virus that
causes the disease one wants to monitor. Naturally, many modern
epidemiological studies include data from both hosts and pathogens, and a
study without one of these two legs will suffer in its value. As we will see
throughout this book, there are many advantages with the use of molecular
methods. As an example of the strength of molecular methods, phylogenetic
analyses of human immunodeficiency virus type 1 (HIV -1) have proven to be
able to reconstruct transmission chains on the individual level to solve
criminal investigations. Traditional epidemiology, where one expects all
information given by the subjects to be true, could obviously not solve such
situations. Other examples where molecular epidemiology excels include the
situation when animals are involved and when the subjects are unavailable for
questioning (e.g., Chapters 12 and 15).
The definition of the term molecular epidemiology is somewhat
unclear from the literature. In older literature the term refers to any
epidemiological tracking done with any method that uses molecular analyses.
Thus, this is a very wide definition. It will include the analysis of both
inorganic chemicals and organic molecules such as sugars, proteins, antibodies
and nucleic acids. In more modern literature the term, however, most often
refers to the use of information derived from nucleic acids or proteins of the
pathogen. In its more simple form the analysis can be a molecular typing of
the organism that causes a certain disease. For instance, we may perform a
2 Leitner
polymerase chain reaction (peR) to conclude that a recent outbreak
somewhere was caused by a certain type of bacteria. In more advanced studies
the DNA sequence of a gene fragment from the pathogen is analyzed by
phylogenetic inference methods. For organisms that evolve more rapidly, like
many viruses, this analysis will not only tell us which type is spreading but
also how it is spreading. As mentioned previously, in the case of HIV- 1 we are
able to link individuals to common sources of infection through analysis of
DNA sequences from viral genes with phylogenetic reconstruction methods.
2. RECONSTRUCTION OF A TRUE TRANSMISSION HISTORY
Phylogenetic reconstruction attempts to reconstruct the evolutionary
history of the sequences under study. If the sequence of a certain gene from a
virus is evolving during its spread from one human to another, then the
reconstructed phylogeny should tell us how the virus was spread. There are a
number of assumptions that have to be made to reconstruct a phylogeny as will
be discussed later in this book, but the critical question is whether a
transmission chain can or cannot be reconstructed by phylogenetics at all. To
answer this question one can refer to many studies on the performance of
various phylogenetic methods to reconstruct predefined phylogenetic trees (see
for instance Nei, 1991; Hillis et at., 1994; Swofford et at., 1996). While these
studies give important information on the powers and pitfalls of the methods,
they still deal with hypothetical situations. To study real evolution is difficult
for most biological systems because the evolutionary changes are so slow. In
contrast, for many viruses we have the opportunity to follow evolution at real
time due to high mutation rates. For HIV-l, which evolves at a rate of 6.7 ±
2.1 x 10-3 nucleotide substitutions site-1 year-1 in its hypervariable region V3
(Leitner and Albert, 1999) as compared to most mammal chromosomal genes
that evolve at a rate of approximately 10-9, knowledge from a true transmission
history has been evaluated by phylogenetic reconstruction.
The Swedish transmission chain has shown that phylogenetic
reconstruction of sequences from viral genes indeed can reconstruct
epidemiological patterns in great detail (Leitner et at., 1996). Figure 1 shows
the reconstructed phylogeny using env V3 region sequences of HIV-l
compared to the true history. Encouragingly, the reconstruction succeeded to
infer the true history at 94% accuracy. The only error in branching pattern that
was made was in indicating the direction of the transmission between a mother
and her child. All transmission interactions were, however, completely correct.
The reason why the phylogenetic tree did not agree with the transmission
history in this single case does not necessarily mean that the phylogenetic tree
was wrong. Actually, both the tree and the history may be correct due to the
The Use ofM olecular Epidemiology 3
fact that the variant that infected the child had gone extinct in the mother at the
time point of sampling (Leitner and Fitch, 1999).
A B
f"I-.."..,.,,.,,....--- p10.9939 L.---:~!"I!!""_ p1 0.9939
r-----------
p7.6760
L~rI====~'3i7----p6.6767
L.----l:..:.......:.:.._
p1.719
1980 1982 19841986 19881990 19921994 o 2 3 4 5 6 7 8 9 10
Year Genetic distance ("!o)
Fig. 1. (A) The true transmission tree of the Swedish transmission chain. The index case, a Swedish male
(pI) who became HIV-l infected in Haiti 1980, infected several females (p2, 5, 7, 8, and 11) between 1981
and 1983. In addition, samples from a later male sexual partner (p6) and two children (p3 and 9) of the
females were included in the phylogeny. Blood samples were obtained at different time points between 1986
and 1993. From some individuals more than one sample was available. The information about when the
transmissions had occurred (internal nodes) and when the samples were obtained (tips) was compiled into a
tree that shows the evolutionary history of the transmitted virus populations. At each tip patient and sample
number is indicated. (8) A reconstructed phylogenetic tree based on the HIV -1 env V3 region. The
reconstruction was done using maximum likelihood analysis under a general-time-reversible nucleotide
substitution model.
The discrepancy between branch lengths of the phylogenetic tree and
the transmission history is obvious (Fig. 1). Especially short branches tend to
be estimated too long. Even though we use substitution models that are
capable of giving a linear relationship between genetic distance and time, this
discrepancy remains (Leitner and Albert, 1999; Leitner and Fitch, 1999). Once
again the explanation lies in the fact that there is a large degree of variation in
the virus within one infected person. The discrepancy can be explained by the
available genetic variation at the time point of transmission, i.e. the effective
population size, and the relationship of the transmitted variant in the recipient
to the variants that continue to evolve in the donor. The genetic divergence
that exists between these two virus populations is expressed by their
pretransmission interval, a unit that will explain the discrepancy between
branch lengths of a phylogenetic tree and a transmission history (Fig. 2). Other
types of "time errors" caused by, for instance, uncertainties in sampling times
and ancestral time points will require other measures such as generalized
regression analyses and log likelihood ratio tests (Huelsenbeck and Rannala,
1997; Korber et al., 2000; Rambaut, 2000).
The results from the Swedish transmission chain lend ample support to
the use of phylogenetic reconstruction to infer epidemiological spread at a
4 Leitner
very detailed level. As long as the researcher is careful to apply a fairly
realistic model of evolution and enough sequence information, one can be
pretty confident in the results when investigating any viral system.
Evolutionary Time
T1 T2
)
C
C
BC -LlBC- c
Dc
B
B
ABC
D
abc
LlAB
A
A
a
I I
tl t2
Transmission Time
Fig. 2. The pretransmission interval (8) describes the difference between the time of transmission and the
most recent common ancestor (MRCA) of the transmitted lineage and the donor lineage. The thin line tree is
the transmission history and the bold line tree is the evolutionary history of the transmitted virus. At tl
patient A infects patient B, and at t2 B infects C. The virus that infects B shares its MRCA with A at n, and
the virus that infects C shares its MRCA with B at T2. The pretransmission interval when A infects B is tl -
n = 8AB and t2 - T2 = 8B c when B infects C. Because of the pretransmission interval, the transmitted
lineage and the donor lineage will be separated by a genetic distance at the time of transmission.
3. INFORMATION FLOW
Investigating the spread of a human virus by molecular methods
involves several steps. Naturally, we need epidemiological background
information about the samples. Depending on the questions we want to answer
we may be interested in geographical data relating to the sample, time point of
sampling, disease stage of the patient, gender, age, risk group, anti viral
treatment, etc. The type of material (serum, plasma, type of tissue, etc) and
genetic regions (polymerase gene, envelope gene, etc) to be analyzed will be
important for what type of answer we will get. The number of samples that we
will be able to analyze will often depend on the cost of time involved in the
laboratory method. Therefore, there will be a relationship between the number
of samples and the method of analysis.
