How Can We Use Natural Variation in Disease Resistance to Understand Host Pathogen Interactions and Devise New Therapies?
WORKING GROUP DESCRIPTION
The genetic variation in the genomes of pathogenic microbes and the organisms they infect provides a DNA sequence record of the evolutionary “arms race” between host and pathogen. Specific pathogens are increasingly recognized as powerful selective forces in the evolution of all organisms and similarly the need for the pathogen to adapt to the host is a major force driving pathogen evolution. Dramatic recent progress in genetics and genomics provides numerous exciting insights into this process, which may identify previously unrecognized host defense pathways, as well as new opportunities for therapeutic intervention.
Polymorphic susceptibility and/or resistance alleles at multiple genetic loci have been identified in human populations, as illustrated by the classic example of the sickle cell hemoglobin mutation, which confers resistance to malaria. The spectacular resources now available with the completion of the genome sequences for numerous mammalian hosts, as well as their specific pathogens, provide unprecedented opportunities to dissect these complex pathways of interaction and to identify new targets for therapeutic intervention.
Consider the numerous examples of genetic variation that contribute to the host response to infectious pathogens in terms of resistance, increased susceptibility, or varying response. Are there any general themes that can be derived from the growing number of examples of such genetic variants and are there specific approaches that can be taken at the genome level to identify large numbers of clinically important variants?
As such specific resistance and susceptibility alleles are identified, often with widely different prevalences among human populations, what specific social or policy issues does this raise when approaching these populations?
What approaches should be taken to increase the interaction between infectious disease experts and geneticists in harvesting this enormous dataset? Are new database structures and novel bioinformatic approaches necessary to effectively analyze this sequence variation information, including the interaction between the separate but ultimately related genomes of host and pathogen?
Dean, M., M. Carrington, and S. J. O’Brien. 2002. Balanced polymorphism selected by genetic versus infectious human disease. Annual Review of Genomics and Human Genetics 3:263-292.
Fortin, A., M. M. Stevenson, and P. Gros. 2002. Susceptibility to malaria as a complex trait: big pressure from a tiny creature. Human Molecular Genetics 11(20):2469-2478.
O’Brien, S. J., and G. W. Nelson. 2004. Human genes that limit AIDS. Nature Genetics 36(6):565-574.
Segal, S., and A. V. Hill. 2003. Genetic susceptibility to infectious disease. Trends in Microbiology 11(9):445-448.
WORKING GROUP SUMMARY – GROUP 1
Summary written by:
Aria Pearson, Graduate Student, Science Writing, University of California, Santa Cruz
Working group members:
Philip Awadalla, Assistant Professor, Genetics, North Carolina State University
James Brody, Assistant Professor, Biomedical Engineering, University of California, Irvine
Elliott Crouser, Assistant Professor, Division of Pulmonary and Critical Care Medicine, The Ohio State University Medical Center
Alison Farrell, Senior Editor, Nature Medicine
Aria Pearson, Graduate Student, Science Writing, University of California, Santa Cruz
Christopher Plowe, Professor, Medicine, University of Maryland School of Medicine
Bernhard Rupp, Structural Genomics Group Leader, Biosciences, University of California – Lawrence Livermore National Lab
Katherine Spindler, Professor, Microbiology and Immunology, University of Michigan Medical School
Sarah Tishkoff, Associate Professor, Biology, University of Maryland
Elizabeth Winzeler, Associate Professor, Cell Biology, The Scripps Research Institute
Hsiang-Yu Yuan, Graduate Student, Institute of Biomedical Sciences, Academia Sinica
Hongyu Zhao, Ira V. Hiscock Associate Professor, Public Health and Genetics, Yale University
Michael Zwick, Assistant Professor, Human Genetics, Emory University School Of Medicine
Two people catch the same common cold. One suffers from a raging sore throat, weeks of sniffling and sneezing, and a nasty cough. The other breezes through it, barely noticing a sniffle or two.
Why? Is sufferer number 2 blessed with genetic differences that confer natural resistance? How can we use this natural variation to illuminate host-pathogen interactions and pave the way for new therapies?
Asking these questions may seem silly when talking about the common cold, but when dealing with diseases such as malaria, TB, or HIV, it could mean life or death.
At the end of four intense discussion sessions spread over three days, the working group—which included some of the leading scientists in genetics, medicine, immunology, cell biology, and engineering—had outlined an ambitious strategy for tackling the problem. But first they had to redefine it.
“We can’t leave the pathogen out,” one group member said. The group decided their assigned question, “How can we use natural variation in disease resistance to understand host-pathogen interactions and devise new therapies?” implied restricting the discussion to genomic variation in the host. The group felt that in elucidating host-pathogen interactions the pathogen could not be ignored, so the group decided to broaden the topic to include natural variation in the pathogen as well. Then, going one step further, the group decided to throw environmental variation into the mix, because environmental factors are sure to play a significant role in disease resistance.
Examples of natural resistance abound. There’s the famous relationship between the allele for sickle cell anemia and malaria resistance. Another example was provided by a group member who described a village in Africa where two tribes live together. Members of one tribe are completely resistant to monkey pox while members of the other tribe die from it. Another group member mentioned hepatitis B. Eight percent of people in China are infected with the virus, but while some die, others are fine. Finding the underlying cause of this variability in disease resistance and using that knowledge to combat disease is becoming increasingly possible, given the advances in genomic science.
The group decided to focus on humans, a marked contrast to the other focus group dealing with this question, which decided to start with mouse models. Every member of the group agreed that sequencing the genomes of all humans would be ideal, however, that being unrealistic, the group thought it sensible to begin with large-scale association studies, most likely conducted in Africa, where the most variation exists and the burden of disease is profound.
The interdisciplinary studies would involve sequencing all host and pathogen genomes to identify the mechanisms of interaction and pinpoint therapeutic or vaccine targets. The ethical and legal issues would be staggering, but the group felt the potential benefits to society of controlling or eradicating deadly diseases would make the endeavor worthwhile.
Of course, the cost of sequencing a human genome (currently around $20 million, though that price tag will soon drop to around $100,000, according to scientists at the conference) will have to come down significantly before such studies are feasible. But technologies are constantly improving and the $1,000 genome is not far off, according to another group at the conference.
Large cohort studies: It’s all about the phenotype
One of the key problems that arise when carrying out large-scale association studies is the issue of phenotypic characterization. Diagnosing diseases accurately is difficult in developing countries like Africa, where many people are infected with more than one pathogen, as well as multiple strains of the same pathogen, and environmental conditions are extremely variable.
The best approach is an integrative analysis, in which the host genome and the pathogen genome are evaluated together, in an environmental context, to determine the effects on the phenotype. This would require people who view things from the perspective of the host to work closely with people who analyze the behavior of pathogens, an important development resulting from these types of studies.
“In most medical schools, for example, there’s a department of infectious disease and there are people who work in human genetics and they rarely talk to each other,” the group spokesperson said at the final presentation. This has slowed progress in genomic research in regard to infectious disease, according to the group. The studies the group proposed would get the two disciplines talking, which is one of the goals of the National Academies Keck Futures Initiative Conference.
The studies would also require tools for detecting and analyzing multiple infections, and multiple strains of the same infectious agent—quickly and cheaply. An ideal device would be handheld, would run on batteries, and be able to separate and sequence the host genome and the genomes of all the microbes in a single drop of blood.
Assuming that sequencing becomes affordable, and appropriate technologies become available, another challenge associated with these studies is finding large, diverse study populations. “To really pull this thing off you may need study samples on the size of 10,000 humans,” the spokesperson said.
With projects of this scale, the ELSI—or ethical, legal, and social issues—would require serious time and attention. For instance, there would be an obligation to follow up with medical treatment for all the participants. “If you’re going to screen for something, you’re obligated to tell them about it and treat them,” a group member pointed out. Cross-cultural communication issues would also arise. The group felt that to alleviate some of these problems, partnerships should be cultivated with local scientists and
public health officials. This would help researchers get a feel for the local ethical and political attitudes.
These partnerships would be part of a broader infrastructure needed to appropriately integrate local data collection with large-scale genomic science. To carry out what the group called “big science,” consortia would be needed involving partnerships between public and private institutions, to help address multiple diseases and to advance funding opportunities, and between scientists and legal professionals, to aid in obtaining informed consent and addressing privacy issues.
Finding pathways and selecting targets: Quite an obstacle course
Inferring pathways of host-pathogen interaction from the data on genomic variation would be a daunting task. The group decided protein-protein interactions would be the place to start and called for a systems biology approach, in which many complex interactions are integrated in order to produce a model of the whole system. They also acknowledged the need for computational tools to make inferences about interactions. Statistically speaking, dealing with three sources of variation—in the host, the pathogen, and the environment—would be a challenge.
Once the pathways are found, possible therapeutic or vaccine targets would have to be identified. This is a challenge in drug or vaccine development because target selection has been notoriously poor in the past, leading to failed product development. “Knowing the variation of the host and pathogen allows us to select targets that are less likely to fail,” one member assured the group. Once the candidate gene is identified through genome sequencing, the key would be to select multiple targets, looking at other genes found near the candidate gene that may influence its function. With the targets in hand, the next step would be to carry out standard methods of drug development, including target validation in animal models, functional assay development, and high-throughput screening to identify promising leads. The group decided the main obstacle for this phase of the project would be developing appropriate assays, which are expensive and time consuming. One solution would be to bring in additional funding from private companies at this stage. The group said these funding sources usually come in at a later stage when there is less risk, but involving them earlier would be crucial for this project.
Societal benefits: Where health goes, money follows
“If you improve health in general in a population, you get what’s called a demographic transition,” a group member said. After an initial boom in population, people start choosing to have smaller families, which helps lead to economic development. In addition to the obvious benefits of improving health and economics, these studies would increase research and development capacity in developing countries by building facilities and forming partnerships between local scientists and large institutions.
From a scientific standpoint, the group felt the project would increase basic understanding of how humans and pathogens interact, and provide a model for interdisciplinary science.
There was some discussion of the possible advances in personalized medicine that could come from these studies. The group agreed that a “one size fits all” approach is the norm right now in drug development and that this approach has serious limitations. One group member described the process of prescribing medicine as a game of trial and error. The doctor says, “Try this, it usually works.”
The studies that the group proposed could be geared toward improving the “one size fits all” method by looking for a “magic bullet” that would really fit all. But the group members agreed that finding magic bullet cures for diseases is very unlikely and decided the studies should be designed to encourage development of more precise personalized medicine, where the right therapy is selected for the right person based on the person’s genotype.
By the end of the four sessions most of the group members were satisfied with the plan they had proposed. They called for science at its grandest: large cohort studies built on complex infrastructures and partnerships with the goal of finding therapies for the world’s deadliest diseases, using natural genomic variation as a guide. Some thought the strategy should involve more population biology, and others weren’t sure whether the question was answered as fully as it could have been, but most were content.
“It was kind of nerve wracking at first, but it came together,” one group member mused. As the group got up to leave at the end of the last session, another member said, “So we’re done. It’s kind of sad. It’s like the end of summer camp.”
WORKING GROUP SUMMARY – GROUP 2
Summary written by:
Chandra Shekhar, Graduate Student, Science Writing, University of California, Santa Cruz
Working group members:
Agnes Awomoyi, Microbiology and Immunology, University of Maryland, Baltimore
Phillip Berman, Scientific Director, Global Solutions for Infectious Diseases
Bruce Beutler, Professor, Department of Immunology, The Scripps Research Institute
Karen T. Cuenco, Research Assistant Professor, School of Medicine, Boston University
Dennis Drayna, Chief, Section on Systems Biology of Communication Disorders, National Institute on Deafness and Other Communication Disorders, National Institutes of Health
Michael Fasullo, Senior Research Scientist, Cancer Research, Ordway Research Institute
Jonathan Kahn, Assistant Professor, School of Law, Hamline University
Rob Knight, Assistant Professor, Chemistry and Biochemistry, University of Colorado, Boulder
Erin McClelland, Research Fellow, Medicine, Albert Einstein College of Medicine
Bob Roehr, Freelance Science Writer
Michael Rose, Director, Intercampus Research Program on Experimental Evolution, University of California Systemwide
Chandra Shekhar, Graduate Student, Science Writing, University of California, Santa Cruz
Hongmin Sun, Life Sciences Institute, University of Michigan
Shan Wang, Associate Professor, Materials Science and Engineering, Stanford University
“TB is a very attractive disease.”
Taken out of context the above remark may seem bizarre, but spoken during a focus group discussion on natural variation in disease resistance it made perfect sense. Group members wanted to start by picking a well-known disease that clearly affected some people while leaving others in the same community unaffected. Based on the genomic principles that underlie this variation, the group would propose research projects to develop new therapies for infectious disease.
The first step was agreeing on a model disease. One member proposed TB. Another preferred sepsis. Others suggested flu, HIV, malaria, and smallpox. “Looks like everyone is trying to push their favorite disease,” one member commented.
An hour of spirited debate followed. The TB camp battled the HIV camp. The bacterium faction crossed swords with the virus faction, as the plasmodium fans watched with amusement. The sepsis camp tried to enter the fray but was quickly routed by the chronic disease contingent. In the midst of this intellectual sparring, some members felt we should not focus on one disease but on common disease pathways.
The group finally hammered out a consensus: we would examine the effect of genomic variations on disease outcomes—genotype-phenotype relationships—for high-impact infectious diseases, including poorly understood ones. Some diseases, like malaria, have well-known polymorphisms, with different genes leading to different outcomes. Do similar polymorphisms exist for other diseases?
A visitor who joined the group’s second session pointed out the benefits of using mouse models to identify mechanisms underlying such polymorphisms. It is relatively easy to manipulate a mouse’s genome by knocking out or modifying genes. It then becomes possible to work backward from the phenotype: selecting disease-resistant specimens from randomly mutated mice and identifying the relevant gene mutations. These mutant mice can be quickly bred in vast numbers to study genetic aspects of disease resistance.
Human genetic studies, in contrast, are much more limited in scope: because humans can’t be genetically manipulated, one has to work with a relatively smaller pool of naturally existing variations.
Although mouse models can yield useful results, as one member pointed out, “mice are not humans.” Humans and mice share 98 percent of
their genes and have similar disease pathways. But mice have a few specialized genes with no human counterparts. So one has to be cautious in applying insights from mouse models—or from any other animal models—to human diseases. Nonetheless, studying the effects of mutated mouse genes is a powerful technique for linking genetic variations to disease resistance. It is fast, versatile, and relatively safe.
The group had concluded the previous session with the idea of finding high-impact diseases with significant polymorphisms. As we continued the discussion, we decided to distinguish between initial infection and disease outcome. Some hosts are more resistant to infection but once infected, succumb rapidly. Others catch infections easily but tolerate them better. Yet others resist both infection and spread of disease or are vulnerable to both. Different polymorphisms expressed at the onset and during the progress of disease may be responsible for these variations.
One member mentioned a genomic chip that researchers are developing to track a host’s responses to disease. Such a chip could shed light on such genotype-phenotype relationships; for instance, it could measure the level of cytokines, or proteins, the body uses to fight viral infection.
The group’s third session began with a discussion of the wide gap separating the science and the clinical applications of genomics. While we came up with ideas for exploration in both science and applications, we saw our task as bridging the gap between them.
Genomics is undoubtedly a powerful tool, but pure genomic information is only a beginning. A bicycle is much more than an inventory of part diagrams. Likewise, an organism is more than an inventory of gene sequences. Existing genomic databases tend to be sparsely annotated—information about what the genes do is often absent, or described elsewhere in the literature. To make genomics useful, it is necessary to relate genes to their functions and describe the physiological interactions between them. In other words, genomics must make full use of old-fashioned genetics, gene by gene.
The human genome doesn’t exist in isolation. The genome’s medical environment includes the health and nutritional status of the patient, the drugs administered by physicians, even the complex of microbes that live on the skin, in the digestive or reproductive tracts, or elsewhere in the body. Because this environment influences how each human gene is expressed, it should be considered when describing the gene’s function. Mouse models can help in establishing such linkages between gene expression and environmental variations.
The group went on to discuss the state of the art in clinical genomics. Tools now exist to model variation in patient and pathogen genomes. It is possible to model the patient’s immune responses, both innate and acquired, at any given instant, although relating them to gene expression could still be a challenge. Existing tools can also provide a genomic snapshot of co-infecting pathogens. The group coined the term “static characterization” to describe current capabilities, because they do not yet permit us to determine the dynamics of infection and immune response.
The group went on to discuss the time-varying and evolving aspects of infectious diseases.
Some diseases, such as HIV and TB, can be either acute or chronic. What genetic variation could trigger a switch between them? In TB the transition is signaled by the activation of macrophages—cells produced by the immune system. Is it possible to find similar pathways for other diseases? Would this help in devising therapies?
How do pathogens evolve inside a host? Is it different for acute and chronic disease? Is it affected by the host response? How does the environment influence this? When several pathogens are present, how do they interact? And what is the role played by commensals—microbes normally present in the body?
As the ideas flowed, a pattern emerged. Dynamic characterization of infectious disease—tracking the pathogen genome as well as the host immune response in real time—seemed key to understanding the genomics of disease resistance and devising new therapies. It would put several new and powerful tools in the hands of physicians. It would allow them to monitor the patient’s immune responses, both innate and acquired, at any given instant, much as they now dynamically characterize blood pressure or serum oxygen levels in hospitals. It would permit them to perform population genetic assays of the pathogens infecting the patient. It would also eventually enable them to track the patient’s physiological genomics, as different genes are expressed and the immune system adjusts its response to fight the evolving infection.
At the final session the group worked on refining and tightening its research ideas into two main proposals; each would be a bridge between genomic science and clinical genomics.
The first bridge addresses an evolutionary question: can we predict pathogen evolution as a function of host genome and environmental variations? Attempts to predict the evolution of influenza virus have already been published in the literature; our group was interested in seeing whether
this was a practical goal for biomedical genomics generally. Each host is essentially a new evolutionary experiment for the pathogen. The progress of an infectious disease depends on how well the pathogen adapts to its environment. Our group proposed to track the ensemble of pathogen genomes as they adapt to the host under a range of environmental variations, including presence or absence of therapeutic intervention. We proposed to test this idea using model systems, such as bacteriophages in different types of bacterial host. With a bacterial model system, this should be a feasible short-term research project; the work could later be extended to mouse models.
The second bridge deals with an ecological question: can we understand how co-infecting pathogens affect one another during the disease process? Certain co-infections, such as HIV and TB, are synergistic, whereas others, such as hepatitis B and C, are antagonistic. Why this happens is unclear, but genomic factors, both in the host and pathogen, almost certainly play a role. Solving this puzzle, we believe, could yield fundamental insights into host-pathogen interaction in successful resistance to infection. Again, the group proposed testing the importance of this phenomenon using a model systems approach: multiple phage infections of bacteria.
These two genomic bridges will show whether the evolutionary and ecological dynamics of infection are tractable and causally important (as we believe them to be). If this is indeed the case, it will establish the value of dynamic characterizations of disease genomics, leading to new therapies dynamically tailored to disease course. This will be an important advance over current medical treatment of infectious disease.