example, the developmental origin and fate of every cell in the whole organism is known. Drosophila has a long history of use as a model for insect and human genetics, and many characterized mutants are available. In fact, a type of receptor on human neutrophils that is important for responding to bacterial infections (Toll-like receptors) was first discovered in Drosophila (Toll receptors). The zebra fish is a newer infection model, but it also has some of the same attractive features as the nematode and fruit fly models (e.g., small size, easy maintenance, short generation time, and ease of genetic manipulation). Zebra fish have the added advantage that they have somewhat more advanced host defense systems than the nematode and fruit fly and are thus a better model for the human immune system, particularly with regard to aquatic pathogens.
Given the genetic distance between these animals and mammals, a certain degree of care must be used in choosing the experimental questions and interpreting the results. For example, although nematodes, fruit flies, and zebra fish have phagocytic cell defenses that exhibit some similarities to that of humans, the systems are not identical and are evolutionarily distant from mammals. For example, insects and worms lack adaptive immune responses as are found in humans and other mammals, and so cannot be used to study antibody responses and inflammation. They also lack many of the immune signaling components present during infection in mammals. While zebra fish are genetically tractable vertebrate models with complete adaptive immune responses in adults, only a few of the immune components have been functionally studied thus far. Indeed, recent studies have also revealed questions regarding the evolutionary conservation of some of the processing and activation of inflammation in zebra fish. Nonetheless, these simple models can be used to generate hypotheses that can later be tested in laboratory rodents or other animals and, in some cases, humans.
Correlation Studies
Another type of modeling that has been used for a long time in epidemiological studies but is relatively new to pathogenesis studies is the statistical analysis of not just microbial populations, but also human and animal populations. At present, this type of modeling is still rather unsophisticated and based on seeking correlations between traits of the organism and outcomes of disease. In other words, the model can be used to ask whether the production of a particular protein is associated in a statistically significant fashion with various aspects of the disease progression in humans. This approach has the advantage of being easy to do because one merely needs to apply preexisting statistical methods. There are, however, two rather serious problems with this approach.
First, this kind of “modeling” is not modeling in the sense that this term is used in physics or chemistry, in which principles are first expressed mathematically in a way that generates specific predictions about the outcome of an experiment. Instead, correlation studies are usually performed without any clear idea of a theoretical connection between the parameters being tested. As such, finding a correlation does not prove cause and effect. There is an urban legend that illustrates this. A gentleman in California happened to pull down a shade in his apartment just before the onset of a particularly severe earthquake and remained convinced for the rest of his life that pulling down the shade had helped to cause the earthquake.
A second problem is that the items to be checked for correlation are chosen by the researcher, and there may or may not be some theoretical underpinnings to the choice. These problems do not necessarily make the correlation studies inappropriate, but these issues do emphasize the need for scrutiny. If the approach is treated as one for potentially generating hypotheses rather than as a method that provides a proof of cause and effect, then the objections disappear. As more mathematicians, physicists, and bioinformatists are becoming interested in applying their tools to study infectious diseases, more sophisticated modeling approaches are beginning to emerge.
As important as new technologies have been, the most important advance has been a new appreciation for the importance of focusing not just on the properties of a bacterium in a test tube, but also on the myriad ways in which the bacterium interacts with its environment and stimulates responses from the human body. In this book, we will place great emphasis on this bacterium-host interaction. It will become clear very quickly that although considerable progress has been made, there is much to be learned and many opportunities for readers of this book to participate in future research in the area of bacterial pathogenesis.
SELECTED READINGS
Ahmed N, Sechi LA. 2005. Helicobacter pylori and gastroduodenal pathology: new threats of the old friend. Ann Clin Microbiol Antimicrob 4:1–10.[PubMed][CrossRef]
Akselrod H, Mercon M, Kirkeby Risoe P, Schlegelmilch J, McGovern J, Bogucki S. 2012. Creating a process for incorporating epidemiological modelling into outbreak management decisions. J Bus Continuity Emerg Plann 6:68–83.[PubMed]
Boon E, Meehan CJ, Whidden C, Wong DH-J, Langille MGI, Beiko RG. 2014. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiol Rev 38:90–118.[PubMed][CrossRef]
Centers for Disease Control and Prevention. 2011. A CDC framework for preventing infectious diseases. http://www.cdc.gov/oid/docs/ID-Framework.pdf.
Centers for Disease Control and Prevention (CDC). 2013. Surveillance for waterborne disease outbreaks associated with drinking water and other nonrecreational water—United States, 2009–2010. MMWR Morb Mortal Wkly Rep 62:714–720. [Incidentally, the MMWR, despite its rather grim name, is a fascinating up-to-the-minute account of what is happening in the infectious disease world. And, need we add, a subscription would be a marvelous gift for that hard-to-please relative or friend.][PubMed]
Centers for Disease Control and Prevention. 2013. Antibiotic resistance threats in the United States, 2013. http://www.cdc.gov/drugresistance/threat-report-2013/.
Crim SM, Griffin PM, Tauxe R, Marder EP, Gilliss D, Cronquist AB, Cartter M, Tobin-D’Angelo M, Blythe D, Smith K, Lathrop S, Zansky S, Cieslak PR, Dunn J, Holt KG, Wolpert B, Henao OL, Centers for Disease Control and Prevention (CDC). 2015. Preliminary incidence and trends of infection with pathogens transmitted commonly through food—Foodborne Diseases Active Surveillance Network, 10 U.S. sites, 2006–2014. MMWR Morb Mortal Wkly Rep 64:495–499.[PubMed]
Goldenberg RL, Culhane JF, Johnson DC. 2005. Maternal infection and adverse fetal and neonatal outcomes. Clin Perinatol 32:523–559.[PubMed][CrossRef]
Grenfell BT, Pybus OG, Gog JR, Wood JL, Daly JM, Mumford JA, Holmes EC. 2004. Unifying the epidemiological and evolutionary dynamics of pathogens. Science 303:327–332.[PubMed][CrossRef]
Gzyl A, Augustynowicz E, Gniadek G, Rabczenko D, Dulny G, Slusarczyk J. 2004. Sequence variation in pertussis S1 subunit toxin and pertussis genes in Bordetella pertussis strains used for the whole-cell pertussis vaccine produced in Poland since 1960: efficiency of the DTwP vaccine-induced immunity against currently circulating B. pertussis isolates. Vaccine 22:2122–2128.