Consider SARS , for example. Maybe they are better prognosticators than we are when it comes to the flu and other respiratory diseases. They certainly have had much more direct experience. The consequences of SARS on global travel were enormous. The usually bustling Hong Kong airport was deserted. At least the few who did arrive there did not have to worry about waiting for their luggage. The spread of SARS was a remarkable event, when you think about it. One infected individual—a physician, in fact—from south China treated a patient who had an unusual pneumonia.
Clinicians usually assume community-acquired pneumonia is not very transmissible—a major mistake here, as this turned out to be, unfortunately, an exception.
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He then went to Hong Kong, where he stayed at the Hotel Metropole, a popular business hotel, and became sick. He believed he had the same disease that had killed the patient he had treated earlier. He went to the hospital, told his healthcare providers about his odd patient, and warned them to be careful. Apparently they did not pay much attention. There were 99 healthcare workers infected in Hong Kong alone. At the same time, another dozen people were infected in the Hotel Metropole by this index patient.
This is what was responsible for the dissemination of SARS essentially worldwide. Of course, everyone likes to say that it was an interesting coincidence that he stayed in Room There no longer is a Room at the Hotel Metropole, by the way. We had a few near-misses with SARS. One doctor from Singapore did go to New York, but did not get sick until he was on his way home and was put into isolation in Germany. Just to put in a small plug for one of my favorite causes of course, this is completely biased : ProMED -mail, the listserv for reporting and discussion of emerging infections. There was a little item that appeared there in February , just questioning whether something odd was going on in China, with reports of deaths.
There were several different cases in perhaps five cities in southern China, but they were not reported or recognized at the time. He did a survey and found that animal slaughterers and wild animal handlers had a much greater chance of becoming seropositive. Because the ultimate link to humans was another cute little animal, Paguma larvata , the palm civet, which is actually a prized food animal in south China, particularly during the winter. It is very expensive. The civets became infected, it would appear, in the live animal markets, probably from contact with bats according to work by Peter Daszak and colleagues.
Wild-caught and farmed civets—yes, they do farm them—that were tested were all negative for the SARS coronavirus. Those of you who know Don Low, as many do, know that he was right at the front line there; I remember that when I saw him at one of our Forum meetings just after the crisis was over, he was exhausted. By the end of all this, there were about 8, cases, most of them in the original area, but a few in other widely scattered places, with over deaths, or about a 10 percent case fatality rate.
Not a trivial disease. This also was the first time the World Health Organization WHO had really acted aggressively, which got the Canadians very annoyed, since WHO issued a travel advisory recommending that travelers avoid Toronto. But WHO acted very effectively and was able, I think, to solve some of the scientific and disease-control problems rather quickly.
There is probably a parallel story with HIV origins. We do not know how it entered the human population. It may very well have been through a similar mechanism as SARS.
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Hospitals also provide opportunities for emerging infections. Transmission of infections by contaminated injection equipment is well known. Most of the Ebola cases arose this way. In summary, there are some recognizable factors responsible for precipitating or enabling emergence, such as ecological factors or globalized travel and trade. So there are even more of them now, but I think they are recognizable.
We know what is responsible for emerging infections and should be able to prevent them. What are we going to do about this? One thing we can do is improve disease surveillance. I will put in another plug for ProMED here. The reality is that we need better early-warning systems and more effective disease control, implemented without delay. I am sure the other contributors to this chapter will have additional suggestions and insights into the problem and about how we might begin to make the world safer. We must get serious about this. Our future as a species may well depend on it someday.
A systematic literature survey suggests that there are species of human pathogen. Of these, 87 were first reported in humans in the years since The new species are disproportionately viruses, have a global distribution, and are mostly associated with animal reservoirs. Their emergence is often driven by ecological changes, especially with how human populations interact with animal reservoirs. In this review, we will be particularly concerned with species of pathogen that have recently been reported to be associated with an infectious disease in humans for the first time. However, our focus does fairly reflect one of the major public health concerns of the early 21st century, the possible emergence of new pathogens species and novel variants OSI At first glance, a pre-occupation with yet-to-emerge disease problems may seem extravagant, given the massive and all too immediate health burdens imposed by malaria, tuberculosis, measles, and other familiar examples.
As we shall discuss, the great majority of novel pathogens have not caused public problems on anything like this scale. However, AIDS reinforced by knowledge of other plagues occurring throughout human history—see Diamond reminds us that the possibility that they could do so is real. In the early stages of the emergence of a new disease, it is a possibility that all too often cannot easily be dismissed as current concerns about H5N1 influenza A virus attest. A second reason for concern is that outbreaks of new diseases, and the public reaction to them, can cause economic and political shocks far greater than might be anticipated.
The SARS epidemic, for example, resulted in fewer than deaths but cost the global economy many billions of dollars King et al.
Variant CJD, which has caused just over deaths mostly confined to the UK, has had a global economic impact of a similar magnitude. Moreover a better understanding of the natural history of the emergence of new infectious diseases should inform our ability to combat them and, as the SARS epidemic illustrated, rapid, coordinated intervention can be highly effective. Although the existence of pathogens has been recognized for centuries, the first comprehensive list of human pathogen species was not published until Taylor, Latham, and Woolhouse This list was generated from a comprehensive review of the secondary literature available at the time see Taylor, Latham, and Woolhouse for full details.
Each entry was a distinct species known to be infectious to and capable of causing disease in humans under natural transmission conditions. Species only known to cause infection through deliberate laboratory exposure were excluded. Species only known to cause disease in immuno-compromised patients and species only associated with a single human case of infection e.
Ectoparasites such as ticks and leeches were not included. The list included species names that appeared in either 1 a text book published within the previous 10 years, or 2 standard web-based taxonomy browsers see below , or 3 an ISI Web of Science citation index search covering the preceding 10 years. In subsequent work e. This methodology has the advantage that it is or, at least, aspires to be systematic, transparent and reproducible by other researchers.
However, it does have its limitations and two of these in particular are worth highlighting. More fundamentally, using the species as the unit of analysis ignores a wealth of important and interesting variation that occurs within species in traits such as virulence factors, antigenicity, host specificity or antibiotic resistance.
With these caveats noted however, a survey of recognized species represents a natural starting point for investigations of the diversity of human pathogens. A subset of human pathogen species of special interest here is those that have only recently been discovered. In practice, however, most post pathogens probably fall into categories 2 to 5. For example, phylogenetic evidence has demonstrated clearly that the evolutionary origins of the human immunodeficiency viruses pre-date their discoveries in the s by at least several decades van Heuverswyn et al. To provide a more complete picture of new pathogens the list of species described above was supplemented in early by searching the WHO , CDC , and ProMed web sites and the primary literature.
Based on the above methodologies an updated version of the previously reported surveys generates a list of species of human pathogen. The most diverse group is the bacteria over species with fungi, helminths and viruses making up most of the remainder Table Of these species of human pathogen, 87 have been discovered from onwards Table The composition of the subset of new species is very different from the full list. New species are dominated by viruses, and there are relatively few bacteria, fungi or helminths Table Within these broad categories certain taxa stand out: human retroviruses were not reported until ; most of the new fungi are microsporidia; and almost half the new bacteria are rickettsia.
Single-stranded RNA viruses make up the largest subset of new species 45 species but are only marginally over-represented. Similarly, bunyaviruses are the largest single family but are also only marginally over-represented in the list of new viruses. In summary, since new human pathogen species have been discovered at an average rate of over 3 per year. For those pathogen species discovered in the post period, the geographic location of the first reported human case s can often be determined from the primary literature, at least to within specific countries and often to specific regions or municipalities.
However, this is not possible for all new pathogen species. For example, although the early history of HIV -1 has been exhaustively investigated the exact origin of the first reported human case remains unclear Barre-Sinoussi et al. Similarly, the only reported human case of European bat lyssavirus 2 in a human could have resulted from exposure in Finland, Switzerland or Malaysia Lumio et al. Moreover, some new human pathogens were already endemic or ubiquitous in the human population when they were first discovered; examples include human metapneumovirus and human bocavirus.
For those pathogens which were discovered previously, but were only recently associated with human disease such as commensals which have become pathogenic in patients immunosuppressed due to infection with HIV the geographic origin is taken as the location in which the patient became sick if the patient was not reported as having recent travel history. Figure shows a map of the points of origin of the first human cases of disease caused by 51 of the 87 pathogen species discovered since Data of this kind must be interpreted cautiously, not least because of likely ascertainment bias variable likelihood of detection and identification of novel pathogens in different parts of the world.
Nonetheless, Figure does make the important point that the emergence of new pathogens shows a truly global pattern, with multiple incidents being reported from every continent except Antarctica with other gaps apparent in, for example, the Middle East and central Asia. There is no striking tendency for new pathogens to be more likely to be reported from tropical rather than temperate regions, or from less developed regions, or from more densely populated regions.
World map indicating points of origin of the first reported human cases of disease caused by 51 novel pathogen species since Locations are identified to municipality or region occasionally country , jiggled as necessary to avoid overlap. Relatively few human pathogens are known solely as human pathogens. The remainder also occur in other contexts: as commensals; or free-living in the wider environment; or as infections of hosts other than humans. Overall, probably no more than 50 to species are specialist human pathogens. These range from major killers such as Plasmodium falciparum , mumps virus, Treponema pallidum , smallpox and HIV -1 to those causing more minor problems such as the human adenoviruses and rhinoviruses.
They are normally benign but are sometimes pathogenic, for example if introduced into the blood system via a wound or in association with AIDS or other immunosuppressive conditions. Examples include the streptococci and Candida spp. Here, we do not take sapronotic to include pathogens which are transmitted via the fecal-oral route or via a free-living stage of a complex parasite life cycle. Most sapronoses are bacteria or fungi, plus some protozoa, and cause sporadic infections of humans. Few are highly transmissible directly or indirectly between humans, an important exception being Vibrio cholerae.
Some human pathogens e. Many more pathogens—over species—are capable of infecting animal hosts other than humans. These range from species where humans are largely incidental hosts—such as rabies or Bartonella henselae —to species in which the main reservoir sensu Haydon et al. Note that the WHO definition does not include human pathogen species which recently evolved from animal pathogens, such as HIV Nor does it include pathogens with complex life cycles where vertebrate animals are involved only as intermediate hosts with humans as the sole definitive host.
It does, however, include reverse zoonoses. Few of the 87 new human pathogen species in Table are commensals or sapronoses. Even some of the nonzoonotic pathogens, notably HIV -1 and HIV-2, are recently evolved from pathogens of nonhuman vertebrates Keele et al. The reservoirs of the new, zoonotic human pathogens are mainly mammals, although a small number are associated with birds Figure However, the reservoirs include a wide range of mammal groups with ungulates, carnivores, and rodents most frequently involved, but also bats, primates, marsupials and occasionally other taxa Figure These observations must be interpreted with some caution because our knowledge of the host range of many pathogens is still incomplete.
Nevertheless, the data available give the impression that taxonomic relatedness is less important than ecological opportunity as a determinant of the reservoirs of novel human pathogens. Homo sapiens as a species is classified within primates and, beyond that, the most closely related major groups are the rodents and lagomorphs. Ungulates, carnivores, and bats are more distant relatives. One related observation is that emerging human pathogens are especially likely to have a broad host range which includes more than one of these groups Woolhouse and Gowtage-Sequeira Counts of recently discovered human pathogens species see Table associated with various categories of non-human animal reservoirs.
Some pathogens species are associated with more than one category of reservoir. These data should be regarded as no more As discussed earlier, not all the pathogens in the list of new species should be regarded as truly emerging; some have only recently been identified as the causative agents of established infectious diseases. However, for 30 or more of new species the literature suggests various drivers deemed to be associated with their emergence at the present time.
These drivers can be considered within a framework originally suggested by the Institute of Medicine IOM , noting that this framework was devised with reference to all emerging and re-emerging infectious diseases, not just newly discovered pathogen species. The most commonly cited drivers fall within the following IOM categories: economic development and land use; human demographics and behavior; international travel and commerce; changing ecosystems; human susceptibility; and hospitals. Economic development and land use, and especially changes in economic development and land use, are associated with the emergence of pathogens such as Nipah virus and Borrelia burgdorferi through activities such as intensification of farming and forest encroachment respectively.
Human demographics and behavior, and especially changes in human demographics and behavior, are associated with the emergence of pathogens such as HIV -1 and Hepatitis C virus through activities such as sexual activity and intravenous drug use. International travel and trade are increasing as part of the process of globalization and are associated with the emergence of pathogens such as SARS coronavirus. Changing ecosystems covers unintended consequences of human activities such as desertification, pollution, and climate change and is associated with the emergence of pathogens such as the hantaviruses.
The other most commonly cited drivers are to do with human population health. Human susceptibility is particularly important in the context of coinfections associated with AIDS e. The hospitals category covers iatrogenic transmission e. The 87 new species of human pathogen are associated with public health problems of hugely variable magnitudes. At the other extreme, Menangle virus is known to have infected only 2 farm workers in which it may have caused a mild febrile illness Chant et al. Menangle virus does not appear to be highly infectious to or transmissible between humans and has not so far been associated with severe disease.
In the following section we consider the kinds of epidemiological and biological differences that underlie the vast difference in public health impacts between pathogens such as HIV-1 and pathogens such as Menangle virus. A useful aid to conceptualizing the process of pathogen emergence is the pathogen pyramid. The concept of the pathogen pyramid was first put forward by Wolfe et al.
A very similar framework but with a more formal mathematical underpinning was adopted by Woolhouse, Haydon, and Antia The pyramid we use here has four levels corresponding to exposure, infection, transmission, and epidemic spread Figure Wolfe, Dunavan, and Diamond subdivided epidemic spread into in their terminology : Stages 4a, b, and c, infectious diseases that exist in animals but with different balances of animal-to-human and human-to-human spread where Stage 4c corresponds to reverse zoonoses as defined above ; and Stage 5, pathogens exclusive to humans corresponding to specialist human pathogens as defined above.
The pathogen pyramid adapted from Wolfe, Dunavan, and Diamond Each level represents a different degree of interaction between pathogens and humans, ranging from exposure through to epidemic spread. Some pathogens are able to progress from one more Level 1: Exposure The first stage of the emergence of a new pathogen is the exposure of humans to that pathogen.
The only barrier to exposure is insufficient overlap between habitats occupied by humans and habitats occupied by the pathogen. Changes in human ecology, particularly patterns of land use and interactions with animal reservoirs, are likely to change our exposure to potential new pathogens, as are changes in the ecology of the pathogens, their reservoirs or their vectors, e.
We do not know how many potential human pathogen species there are which we have not yet been exposed to, but we do know that human pathogens make up only a fraction of the known biodiversity of viruses, bacteria, fungi, protozoa and helminths, which in turn probably makes up only a fraction of the biodiversity which exists Dykhuizen Level 2: Infection The second stage of pathogen emergence is reached if the pathogen proves capable of infecting humans, possibly causing disease.
As reviewed above, we know of species that have reached this stage. Others may have done so but have yet to be identified. Others may do so in the future but, to date, we have had no or insufficient exposure to them. Clearly, there will often be significant biological barriers—referred to as species barriers—preventing organisms infecting other kinds of host from infecting humans. We do not, for example, share any pathogens with plants, very few with invertebrates, and only a small number with cold-blooded vertebrates e. In contrast, we share many more of our pathogens with birds, and we share more than half with other species of mammal.
Indeed, the species barrier at least between humans and other mammals may not be as profound as is sometimes implied. According to Cleaveland et al. These data imply that, given the opportunities for exposure to pathogens that proximity to domestic animals must surely provide, many pathogens, perhaps even a majority, are capable of crossing the species barrier and infecting humans.
As suggested by the IOM report, an important contributor to the ability of a new pathogen to infect humans is variation in human susceptibility. In some cases this variation might have a genetic basis; for example, apparently pre-existing genetic variation in human susceptibility to HIV Arien, Vanham, and Arts More commonly, phenotypic variation in the human population will be important, particularly factors which compromise the human immune system.
The most striking examples come from the wide range of opportunistic infections associated with the immunosuppressive effects of HIV infection; these include several pathogen species, such as the microsporidia Brachiola algerae and Enterocytozoon bieneusi which were first recognized in AIDS patients. Level 3: Transmission The third stage of pathogen emergence is reached if a pathogen that can infect humans also proves capable of transmission from one human to another.
Transmission in this context need not be direct e. The requirement is simply that an infection of one human leads ultimately to an infection of another. In most cases the barriers preventing transmission will be biological, often reflecting tissue tropisms within the human host since pathogens normally need to access the gut, upper respiratory tract, urogenital tract or especially for vector-borne infections blood in order to be able to exit the body. However, sometimes such barriers can be overcome by changes in human behavior.
The two best examples concern prion diseases. Kuru is only transmitted through cannibalism, which is extremely rare in most human societies. Again, these barriers to human-to-human transmission are far from insuperable. Although information is lacking for many pathogen species Taylor, Latham, and Woolhouse , the literature suggests that a substantial minority—at least species, over one third of the total, and possibly many more—are transmissible between humans.
This represents a quantitative rather than qualitative distinction and it can be made more formally precise by reference to the concept of the basic reproduction number, R 0. R 0 can be defined as the average number of secondary cases of infection produced when a primary case is introduced into a large population of previously unexposed hosts adapted from Anderson and May The distinction between Level 3 and Level 4 pathogens can be expressed in terms of R 0. If R 0 is less then one then, on average, a single primary case will fail to replace itself and although there may be chains of transmission these will be self-limiting—this corresponds to Level 3.
On the other hand, if R 0 is greater than one then, on average, a single primary case will produce more than one secondary case and, at least initially, there will be an exponential increase in the number of cases and ultimately a major epidemic is possible—this corresponds to Level 4. The barriers between Level 3 and Level 4 are both biological and epidemiological. The biological barriers are to do with pathogen infectivity, host susceptibility, the infectiousness of the infected host and for how long the host is infectious whether this is terminated by recovery or death. The epidemiological barriers are to do with the rate and pattern of contacts between infectious and susceptible hosts.
The rate and pattern of contacts can increase, and hence R 0 can increase, independently of the pathogen, as a result of shifts in host demography or behavior. In the context of human hosts such shifts could constitute changes in factors such as population density e. These might be augmented by changes in host susceptibility due to the kinds of factors listed earlier. Clearly, for the same pathogen R 0 can vary considerably from one human population to another. Similarly, different strains of the same pathogen species may have very different R 0 values in humans, e.
In principle, this barrier might seem quite fragile; the kinds of changes in host demography and behavior alluded to above are certainly occurring. In practice, it is not clear how many species of human pathogen have reached Level 4 since we have estimates of R 0 values within human populations for only a handful of them. Based on earlier studies Taylor, Latham and Woolhouse ; Woolhouse and Gowtage-Sequeira a plausible estimate is that to pathogen species are capable of causing major outbreaks within human populations, with half to two-thirds of these being specialist human pathogens and the remainder also occurring in animal reservoirs or the wider environment.
This implies considerable attrition between levels 3 and 4 of the pathogen pyramid. We can now consider where the 87 new human pathogen species fit within the pathogen pyramid. It is immediately clear that the majority of them are at Level 2; they can infect humans but are rarely if at all transmitted between humans.
At the other extreme, although there are a number that appear to be at Level 4, most of these are pathogens which are probably long established in human populations but have only recently been recognized, such as human metapneumovirus or hepatitis C virus. In between, at Level 3, there is a significant minority of new pathogens that are somewhat transmissible between humans but which have so far been restricted to relatively minor outbreaks.
These include Andes virus, human torovirus and some Encephalitozoon spp. For these species the value of the basic reproduction number R 0 is of particular interest, especially if it lies close to one, the threshold for potential epidemic spread. R 0 can be estimated from data on the distribution of outbreak sizes as follows. The quantitative analysis of outbreak data used to estimate R 0 is based upon a methodology developed by Jansen et al.
Here, we apply the technique see also Matthews and Woolhouse to data on human outbreaks of Andes virus see Figure for details. Andes virus is an emerging South American hantavirus and there are concerns that, unusually for hantaviruses, it can be transmitted directly between humans Wells et al.
Most reports of Andes virus represent sporadic cases i. This pattern—many small outbreaks and a few larger ones—is typical of a wide range of infectious diseases Woolhouse, Taylor, and Haydon The best estimate of R 0 based on these data lies in the range 0. This is well below one and in reality is likely to be an over-estimate since at least some of the clusters of cases may reflect exposure to a common source rather than, as is assumed in the analysis, person-to-person spread. However, the analysis does suggest that occasional larger outbreaks will occur the R 0 estimates are consistent with up to 1 in outbreaks being of size 10 or more without necessarily implying that there has been a major change in Andes virus epidemiology.
Jansen et al. Analysis of Andes virus outbreaks. Frequencies of outbreaks of different sizes grey bars are compared with the fit of a statistical model to the data open bars. Outbreak data are taken from Wells et al. The model is more So far we have examined the emergence of new species of human pathogens over time scales of a few decades. However, the origins of many human pathogens are considerably more ancient, extending back over time scales of thousands to millions of years.
This process has been reviewed by, among others, Weiss , Diamond , and Wolfe, Dunavan, and Diamond Of particular interest here are examples of pathogens which have emerged in human populations as a result of successfully crossing the species barrier from an animal reservoir and reaching Level 4 status. Any analysis must be prefaced by the observation that we have good evidence for the origins of only a small minority of pathogens, plausible hypotheses usually based on the epidemiologies of related species for some of the remainder, and no information at all for the majority.
That said, 16 examples of putative species jumps are listed in Table Inspection of this list suggests two tentative observations. First, although a variety of different kinds of pathogen are listed including several species of bacteria and protozoa, the majority are viruses. Second, a variety of different animal reservoirs are involved: primates, ungulates, rodents and birds. Wolfe, Dunavan, and Diamond point out that primates are much better represented in this list than might be expected given their much more modest role as reservoirs of modern zoonoses.
This may reflect both the much greater ecological overlap between humans and other primates in the distant past and the notion that pathogens of our closest relatives are more likely to be epidemiologically successful in humans. The latter idea is supported by the observation that two of the most recent examples of successful species jumps— HIV -1 and HIV-2—have primate origins Keele et al.
Similarly, several human pathogens with much deeper evolutionary origins, perhaps even pre-dating Homo sapiens as a distinct species, are also most closely related to modern primate pathogens. Examples include the hepatitis B and G viruses Simmonds It is worth noting that species jumps can occur in both directions. For example, it is thought that Mycobacterium bovis —predominantly a cattle pathogen—evolved from the human pathogen M.
Both are sufficiently divergent from their closest relatives— SIV cpz and SIV smg respectively—in terms of both their genome sequences and their biologies to be regarded as distinct species. This has probably occurred within the last years. In a nonhuman context, over even shorter time scales we have seen the evolution of another new species of pathogen, canine parvovirus CPV , associated with a cat virus, feline panleukopenia virus FPV , jumping into dogs Parrish and Kawaoka CPV has spread to dog populations around the world in only a few years.
All of these examples concern RNA viruses, and RNA viruses differ from pathogens with DNA genomes in having far higher nucleotide substitution rates and so the potential for rapid adaptation to new host species Holmes and Rambaut The importance of this kind of genetic lability has been explored by Antia et al. These authors suggested that the potential for successful adaptation which they defined as becoming sufficiently transmissible that R 0 in humans became greater than one is sensitive both to the size of initial outbreaks determined mainly by the initial R 0 value and, especially, to the rate of genetic change and the genetic distance to be traveled.
As discussed earlier, the initial R 0 value is a function not only of pathogen biology but also of features of human demography and behavior which promote transmission and thus the kinds of changes in these mentioned above have the potential to increase the likelihood of the evolution of new human pathogens. The successful adaptation of a nonhuman pathogen to humans is itself a highly stochastic process. This is illustrated by the early evolution of the human immunodeficiency viruses see Van Heuverswyn et al. There is phylogenetic evidence for numerous introductions of SIVs into human populations; most of these failed to become established Arien et al.
This pattern raises the question of where, in practice, the relevant genetic changes that allow a pathogen to successfully invade a human population occur. Antia et al. However, it may be that genetic change within the original reservoir whether animal or environmental is also critical for producing variants which are capable of infecting humans in the first place. With a handful of exceptions, such as the simian immunodeficiency viruses, we typically have very little information on the genetic and functional diversity of human pathogens or their immediate ancestors in nonhuman reservoirs.
This is a potentially important topic for future research but a reasonable working hypothesis, supported by our knowledge of the origins of HIV , is that genetic variation in nonhuman pathogen populations does occasionally and incidentally produce human infective variants, and this explains why so many novel human pathogens are RNA viruses Woolhouse, Taylor, and Haydon This idea is further supported by the observation that RNA viruses tend to have broader host ranges than DNA viruses Cleaveland, Laurenson, and Taylor ; Woolhouse, Taylor, and Haydon , implying that they can more easily adapt to new host species.
The implication of the preceding discussion is that pathogen evolution is not only an important driver of progression up the pathogen pyramid over long time scales but that, especially for RNA viruses, this process may be relevant over much shorter time scales as well. In addition, we note that evolution is clearly a key driver of the emergence of new variants of existing human pathogen species, with potentially significant epidemiological consequences. This is evident in the generation of antibiotic resistant bacteria and chloroquine resistant malaria, as well as variants expressing novel virulence factors e.
Finally, we note that an important feature of new pathogens is that they have not been previously subject to evolutionary constraints on their virulence i. In such cases evolutionary constraints on pathogen virulence may be weakened or absent Woolhouse, Taylor and Haydon Putting these observations together it is unsurprising that many new human pathogens e. It seems likely that the kinds of ecological changes that have been associated with pathogen emergence in the recent past see IOM will continue to occur in the immediate future, e.
In that case, we can reasonably anticipate the reporting of yet more new species of human pathogen currently happening at a rate of over 3 per year— Table in the immediate future as well. The survey of new pathogen species reported since suggests the kinds of pathogens that are most likely to emerge in the future. Four characteristics are expected to be particularly important:.
The above criteria are certainly not intended as absolute predictors of pathogen emergence; a good historical counterexample is syphilis new to the Old World in the late 15th century, its origins remain disputed but it is a bacterium not associated with nonhuman reservoirs— Weiss, Even so, it is helpful to have some indication of what kinds of new pathogen we are most likely to encounter.
The first line of defense against any emerging pathogen is its rapid detection and identification. Recent practical experience with BSE and SARS demonstrates that rapid detection and identification leading to the rapid introduction of preventive measures can prove highly effective in combating outbreaks of novel diseases Wilesmith ; Stohr Moreover, computer simulation studies motivated by concerns about the possible emergence of pandemic influenza suggest that only if a new strain is detected in the very earliest stages and interventions are put in place extremely promptly is their any realistic prospect of curtailing an epidemic Ferguson et al.
Surveillance for novel pathogens, however, does present some particular challenges. Initially, this is likely to depend on clinical observation, such as the reporting of clusters of cases of disease with unusual symptoms. Internet surveillance for reports of unusual disease outbreaks is also possible and, in the longer term, generic diagnostic tools—for example, lab-on-a-chip tests for all known human viruses—should become available OSI The map of reports of new pathogen species Figure argues strongly that surveillance needs to be global, especially considering the unprecedented rates of international travel and trade that can allow new infectious diseases, such as SARS , to spread around the world over time scales of days or weeks.
Pathogen emergence is an international problem. Another key lesson from surveying novel pathogens is the importance of animal reservoirs in the emergence of new infectious diseases. One implication of this is that surveillance in reservoir populations likely to be an effective tool for monitoring risks to humans Cleaveland, Meslin, and Breiman On top of this, it may often be the case that most scientific knowledge of the basic biology of an unusual human pathogen lies, at least initially, with the veterinary community rather than the medical community.
Palmarini lists a number of examples of this: infectious cancers, retroviruses, lentiviruses, transmissible spongiform encephalopathies, rotaviruses, and papilloma viruses. To this list could be added coronaviruses and ehrlichiosis. More generally, it is now widely recognized that humans share the majority of their pathogens with other animals Taylor, Latham, and Woolhouse However, understanding the process of emergence requires much more than an understanding of the basic biology of the host-pathogen interaction, important though this undoubtedly is. A theme of this review has been the importance of ecological factors for the emergence of new pathogens.
These examples emphasize that disease emergence is a multi-disciplinary problem and needs to be understood at a number of scientific levels. Collaborations need to be developed not just between the human and animal health branches of the biomedical research community but also with researchers covering a much wider range of disciplines. The pathogen pyramid provides a useful conceptual framework for thinking about the process of the emergence of a new species of human pathogen. However, it is immediately clear that at each level of the pyramid there are some important gaps in our knowledge.
First, we still have very little idea of the diversity of pathogens to which humans are being or could be exposed. Systematic surveys across a range of possible sources of new pathogens notably other mammal species using techniques such as shotgun sequencing are possible in principle, and would provide this information.
Establishing a priori which pathogens are capable of infecting humans is even more challenging. A first step would be to identify the cell receptors used by the recognized species of human virus. At present, we have this information for only around half of the virus species. Estimating the transmission potential of a new pathogen within the human population can only be achieved by closely monitoring initial outbreaks. Analysis of such data can provide some early warning of crucial epidemiological changes as illustrated by the analysis of measles data mentioned above.
Real time analysis of epidemic data can also provide timely estimates of the transmission potential see Lipsitch et al. On the other hand, for many of the rarer human pathogens we do not currently know whether or not they are transmissible between humans Woolhouse It is extremely likely that we will encounter new species of human pathogen in the near future. We urgently need the scientific and logistic capacity to rapidly detect and evaluate the threat that new pathogens present and to intervene quickly and effectively wherever necessary.
Experience of SARS provides some encouragement that, given adequate resources, efforts to combat emerging pathogens can be successful, but further challenges lie ahead. One of the unifying goals of this workshop, as well as of the Forum on Microbial Threats, has been to promote the study of microbes, not only to enhance our understanding of their present roles in the world but also, we hope, to predict their future changes e. This was, of course, one of the life missions of Joshua Lederberg, who helped create the Forum and who this workshop is honoring. Evolutionary studies help us understand the past and interpret the present, and from a combination of those two we have some possibility of being able to predict the future.
Since Lederberg was also keen on evolutionary studies Lederberg, , , it is appropriate for a workshop in his honor to focus on Microbial Evolution and Co-Adaptation. I would like to note that I feel a personal connection to Joshua Lederberg, as I received much of my microbiology training from Ann Ganesan who had been a Ph. However, anyone, with or without a specific connection to Lederberg, can learn a great deal about him and his work through a wonderful website made available by the National Library of Medicine. PubMed Central is a centralized archive of freely available, full-text versions of scientific publications.
In this paper, I am focusing on one key aspect of evolution: the origin of novelty , or how new forms, functions, processes, and properties originate. In addition, I consider some of the factors that influence the likelihood that novelty will originate—something generally referred to as evolvability. I note that I focus here on work from my lab and am not attempting to review the entire field. I have been interested in the origin and novelty and evolvability, particularly as they occur in microbes, since I was introduced to microbes as an undergraduate through studies of hydrothermal vent ecosystems.
Actually, I had written a paper on this back in high school, but it was not until college that I truly focused on the topic. A bit later, in , my career—and that of most other microbiologists and evolutionary biologists—was changed forever with the publication of the first complete genome of any free-living organism Fleischmann et al. It was then that I shifted my research to the integration of evolutionary analyses with studies of genome sequences.
For better or for worse, I coined the term for this field: phylogenomics Eisen et al. Note that the way I use this term is a bit different than some others in the community. Many people use the term phylogenomics to refer to the use of genome-scale data e. This will also demonstrate the usefulness of this approach for understanding the past, interpreting the present, and—maybe—predicting the future. Throughout this workshop, we have seen many examples of genome sequencing leading to wonderful insights about the microbial world. Indeed, it can be said that genome sequence data have sparked a renaissance in microbiology.
It is important to realize, however, that much of this renaissance rests on one particular step in the analysis—the prediction of gene function based on gene sequence. This step is critical because typically one generates the genome sequence of a particular organism, most of whose genes will not have been studied experimentally. Prediction of gene function adds value to the genome sequence data because such predictions can guide further computational and experimental studies of the organism. My first phylogenomic tale illustrates how, in the course of a genome-sequencing project, the evolutionary analysis of a particular gene can enable us to make more accurate predictions about the function of that gene in a particular organism and, in some cases, can also provide insight into the evolutionary processes in that organism, as well.
This is the story of one such organism, Helicobacter pylori , a bacterium that dwells in the stomachs of humans and some other mammals. For many years, these stomach dwellers were generally ignored. However, thanks in a large part to the work of people like Barry Marshall, it is now known that H.
Due to its medical importance as well as its novel ability to tolerate very high acidity, this species was one of the first targeted for genome sequencing. In , the genome of one strain was published Tomb et al. At that time, as a Ph. I had become convinced of this myself through analysis of the trickle of genome sequence data for humans, yeast, and other organisms that had already begun to flow before the first complete genome was published. Back in , I had even published a paper showing the benefits of evolutionary reconstructions in studies of one family of proteins, the SNF2 family Eisen et al.
Although the benefits were clear to me, others were not so sure. Fortunately, at the time I was teaching a class with Rick Myers, a professor in the genetics department and the head of the Stanford Human Genome Center. Also, since he was one of the people I had been badgering, he suggested I try to come up with an example of where the inclusion of evolutionary analysis could have benefited their work. Luckily for me, there was a claim in the H. The authors reported Tomb et al. The ability of H. However, orthologues of MutH and MutL were not identified.
This was right up my alley because I was working on the evolution of DNA mismatch repair at the time. A DNA mismatch can be created when the wrong base is put into a newly synthesized strand by the enzyme carrying out DNA replication i. Thus, a mismatch indicates a replication error. Mismatch repair is a process whereby, immediately following DNA replication, repair enzymes scan for mismatches between the template and newly synthesized DNA strands. When the mismatch repair machinery finds one, it removes a section of DNA containing the mismatch from the newly synthesized strand.
That section is then resynthesized using the original and presumably accurate template strand as a guide. Mismatch repair is vital. It greatly reduces the mutation rate by correcting many of the replication errors made by DNA polymerase. It was because of my knowledge of the evolution of mismatch repair that the report in the H. I knew that every time a mismatch repair system had been found in an organism, regardless of whether that organism was from the bacteria, mammals, plants, yeast, or a variety of other groups, and regardless of whether it was found by genetics, by biochemistry, or even by targeted cloning, the pattern was the same.
The system always required at least one member of the MutS family of proteins and one member of the MutL family. Yet, according to the paper, H. So I decided to look at this in more detail. My first step was to recheck the genome sequence analysis. First, I took all known MutL-like proteins and searched them against the H. Given that they had determined the complete genome of this strain, the absence of BLAST match suggested there was indeed no MutL encoded in the genome.
So I took this protein and then used it to search against all known sequences from other organisms, to see to what it was most similar. This in essence was mimicking the searches done in the analysis of the genome, and the result seemed quite convincing Table All of the proteins that were most similar to the H. This description of the related proteins, also known as their annotation, was clearly what led the authors to conclude that this protein was involved in mismatch repair.
This left me with a conundrum. There was no MutL protein encoded in the genome, yet there was, apparently, a MutS protein. Many possible explanations came to mind, all of which were interesting. Or it might have recently lost its MutL, as had been seen in many strains of E. Alternatively, perhaps the MutS-like protein was not a normal MutS involved in mismatch repair, but rather was used for a different function in this organism.
Although these, along with yet other explanations, seemed plausible, one observation suggested to me that the latter explanation—that the MutS-like protein was doing something else—might be the correct one. In addition, I knew from my prior work Eisen et al. So my next step was to investigate the evolutionary history of the MutS proteins, including the new one from H. I did this by generating a multiple sequence alignment of all available MutS sequences and inferring an evolutionary tree from that alignment.
Given this finding along with the observation that H. I followed this up with a more comprehensive evolutionary study Eisen, b that came to the same conclusion. I would like to point out that this was not simply an esoteric exercise. Mismatch repair has great significance due to its role in modifying the mutation rate.
This has important implications for the evolution of virulence, pathogenicity, and drug resistance. Many papers published since this initial analysis have confirmed that H. In fact, the entire group of epsilon proteobacteria of which H. Thus, the question arises: Do all of these organisms have high mutation rates? Or have they evolved some compensatory process that reduces mutation rate even without mismatch repair? At least from current data, it seems that many members of this group do have somewhat elevated mutation rates.
For example, when the Sanger Center was sequencing the genome of a close relative of H. This suggests that the mutation rate for this strain is quite high. Awareness of this dynamic is vitally important when designing therapeutics to target organisms that lack mismatch repair. This example illustrates how evolutionary analysis of a gene found in a genome can not only tell us something about the biology of that organism, but can also help us to predict its evolvability. This H. In this regard, I must point out that I am far from unique in holding this view.
For example, while I was working on the use of phylogenetic trees, multiple groups were showing how classifying proteins into families and subfamilies was critical for predicting function Sonnhammer et al. My approach to this functional prediction was somewhat different from these subfamily- or ortholog-focused approaches in that I have argued that one needs to actively use the tree itself by using an approach known as character state reconstruction Figure Character state reconstruction is a commonly used method in phylogenetics whereby one can infer for particular traits also known as characters the history of change between different forms of those traits also known as states.
Normally, character state reconstruction is used to infer information about ancestral nodes in a tree e. It is relatively straightforward to use these methods to infer information about protein function by treating each protein much as you would treat different organisms. We like to talk about life on earth evolving out of the primordial soup. We could just as easily say that the most interesting digital life on our computer screens today evolved out of the slime mold.
You can think of Segel and Keller's breakthrough as one of the first few stones to start tumbling at the outset of a landslide. Other stones were moving along with theirs -- some of whose trajectories we'll follow in the coming pages -- but that initial movement was nothing compared to the avalanche that followed over the next two decades. At the end of its course, that landslide had somehow conjured up a handful of fully credited scientific disciplines, a global network of research labs and think tanks, and an entire patois of buzzwords.
Thirty years after Keller challenged the pacemaker hypothesis, students now take courses in "self-organization studies," and bottom-up software helps organize the Web's most lively virtual communities. But Keller's challenge did more than help trigger a series of intellectual trends. It also unearthed a secret history of decentralized thinking, a history that had been submerged for many years beneath the weight of the pacemaker hypothesis and the traditional boundaries of scientific research.
People had been thinking about emergent behavior in all its diverse guises for centuries, if not millennia, but all that thinking had consistently been ignored as a unified body of work -- because there was nothing unified about its body. There were isolated cells pursuing the mysteries of emergence, but no aggregation. Indeed, some of the great minds of the last few centuries -- Adam Smith, Friedrich Engels, Charles Darwin, Alan Turing -- contributed to the unknown science of self-organization, but because the science didn't exist yet as a recognized field, their work ended up being filed on more familiar shelves.
From a certain angle, those taxonomies made sense, because the leading figures of this new discipline didn't even themselves realize that they were struggling to understand the laws of emergence. They were wrestling with local issues, in clearly defined fields: how ant colonies learn to forage and built nests; why industrial neighborhoods form along class lines; how our minds learn to recognize faces.
You can answer all of these questions without resorting to the sciences of complexity and self-organization, but those answers all share a common pattern, as clear as the whorls of a fingerprint. But to see it as a pattern you needed to encounter it in several contexts. Only when the pattern was detected did people begin to think about studying self-organizing systems on their own merits.
Keller and Segel saw it in the slime mold assemblages; Jane Jacobs saw it in the formation of city neighborhoods; Marvin Minsky in the distributed networks of the human brain. What features do all these systems share? In the simplest terms, they solve problems by drawing on masses of relatively stupid elements, rather than a single, intelligent "executive branch. They get their smarts from below. In a more technical language, they are complex adaptive systems that display emergent behavior. In these systems, agents residing on one scale start producing behavior that lies one scale above them: ants create colonies; urbanites create neighborhoods; simple pattern-recognition software learns how to recommend new books.
The movement from low-level rules to higher-level sophistication is what we call emergence. Imagine a billiard table populated by semi-intelligent, motorized billiard balls that have been programmed to explore the space of the table and alter their movement patterns based on specific interactions with other balls. For the most part, the table is in permanent motion, with balls colliding constantly, switching directions and speed every second.
Because they are motorized, they never slow down unless their rules instruct them to, and their programming enables them to take unexpected turns when they encounter other balls. Such a system would define the most elemental form of complex behavior: a system with multiple agents dynamically interacting in multiple ways, following local rules and oblivious to any higher-level instructions. But it wouldn't truly be considered emergent until those local interactions resulted in some kind of discernible macrobehavior.
Say the local rules of behavior followed by the balls ended up dividing the table into two clusters of even-numbered and odd-numbered balls. That would mark the beginnings of emergence, a higher-level pattern arising out of parallel complex interactions between local agents. The balls aren't programmed explicitly to cluster in two groups; they're programmed to follow much more random rules: swerve left when they collide with a solid-colored; accelerate after contact with the three ball; stop dead in their tracks when they hit the eight ball; and so on.
Yet out of those low-level routines, a coherent shape emerges. Does that make our mechanized billiard table adaptive? Not really, because a table divided between two clusters of balls is not terribly useful, either to the billiard balls themselves or to anyone else in the pool hall. But, like the proverbial Hamlet -writing monkeys, if we had an infinite number of tables in our pool hall, each following a different set of rules, one of those tables might randomly hit upon a rule set that would arrange all the balls in a perfect triangle, leaving the cue ball across the table ready for the break.
That would be adaptive behavior in the larger ecosystem of the pool hall, assuming that it was in the interest of our billiards system to attract players. The system would use local rules between interacting agents to create higher-level behavior well suited to its environment. Emergent complexity without adaptation is like the intricate crystals formed by a snowflake: it's a beautiful pattern, but it has no function. The forms of emergent behavior that we'll examine in this book show the distinctive quality of growing smarter over time, and of responding to the specific and changing needs of their environment.
In that sense, most of the systems we'll look at are more dynamic than our adaptive billiards table: they rarely settle in on a single, frozen shape; they form patterns in time as well as space. A better example might be a table that self-organizes into a billiards-based timing device: with the cue ball bouncing off the eight ball sixty times a minute, and the remaining balls shifting from one side of the table to another every hour on the hour. That might sound like an unlikely system to emerge out of local interactions between individual balls, but your body contains numerous organic clocks built out of simple cells that function in remarkably similar ways.
An infinite number of cellular or billiard-ball configurations will not produce a working clock, and only a tiny number will. So the question becomes, how do you push your emergent system toward clocklike behavior, if that's your goal? How do you make a self-organizing system more adaptive?
That question has become particularly crucial, because the history of emergence has entered a new phase in the past few years, one that should prove to be more revolutionary than the two phases before it. In the first phase, inquiring minds struggled to understand the forces of self-organization without realizing what they were up against. In the second, certain sectors of the scientific community began to see self-organization as a problem that transcended local disciplines and set out to solve that problem, partially by comparing behavior in one area to behavior in another.
By watching the slime mold cells next to the ant colonies, you could see the shared behavior in ways that would have been unimaginable watching either on its own. Self-organization became an object of study in its own right, leading to the creation of celebrated research centers such as the Santa Fe Institute, which devoted itself to the study of complexity in all its diverse forms.
The Code by Margaret O'Mara | magoxuluti.tk: Books
But in the third phase -- the one that began sometime in the past decade, the one that lies at the very heart of this book -- we stopped analyzing emergence and started creating it. We began building self-organizing systems into our software applications, our video games, our art, our music. We built emergent systems to recommend new books, recognize our voices, or find mates. For as long as complex organisms have been alive, they have lived under the laws of self-organization, but in recent years our day-to-day life has become overrun with artificial emergence: systems built with a conscious understanding of what emergence is, systems designed to exploit those laws the same way our nuclear reactors exploit the laws of atomic physics.
Up to now, the philosophers of emergence have struggled to interpret the world. But they are now starting to change it. What follows is a tour of fields that aren't usually gathered between the same book jacket covers. We'll look at computer games that simulate living ecologies; the guild system of twelfth-century Florence; the initial cell divisions that mark the very beginning of life; and software that lets you see the patterns of your own brain.
What unites these different phenomena is a recurring pattern and shape: a network of self-organization, of disparate agents that unwittingly create a higher-level order. At each scale, you can see the imprint of those slime mold cells converging; at each scale, the laws of emergence hold true. This book roughly follows the chronology of the three historical phases. The first section introduces one of the emergent world's crowning achievements -- the colony behavior of social insects such as ants and termites -- and then goes back to trace part of the history of the decentralized mind-set, from Engels on the streets of Manchester to the new forms of emergent software being developed today.
The second section is an overview of emergence as we currently understand it; each of the four chapters in the section explores one of the field's core principles: neighbor interaction, pattern recognition, feedback, and indirect control. The final section looks to the future of artificial emergence and speculates on what will happen when our media experiences and political movements are largely shaped by bottom-up forces, and not top-down ones. Certain shapes and patterns hover over different moments in time, haunting and inspiring the individuals living through those periods.
The epic clash and subsequent resolution of the dialectic animated the first half of the nineteenth century; the Darwinian and social reform movements scattered web imagery through the second half of the century. The first few decades of the twentieth century found their ultimate expression in the exuberant anarchy of the explosion, while later decades lost themselves in the faceless regimen of the grid.
You can see the last ten years or so as a return to those Victorian webs, though I suspect the image that has been burned into our retinas over the past decade is more prosaic: windows piled atop one another on a screen, or perhaps a mouse clicking on an icon. These shapes are shorthand for a moment in time, a way of evoking an era and its peculiar obsessions. For individuals living within these periods, the shapes are cognitive building blocks, tools for thought: Charles Darwin and George Eliot used the web as a way of understanding biological evolution and social struggles; a half century later, the futurists embraced the explosions of machine-gun fire, while Picasso used them to re-create the horrors of war in Guernica.
The shapes are a way of interpreting the world, and while no shape completely represents its epoch, they are an undeniable component of the history of thinking. When I imagine the shape that will hover above the first half of the twenty-first century, what comes to mind is not the coiled embrace of the genome, or the etched latticework of the silicon chip. It is instead the pulsing red and green pixels of Mitch Resnick's slime mold simulation, moving erratically across the screen at first, then slowly coalescing into larger forms.
The shape of those clusters -- with their lifelike irregularity, and their absent pacemakers -- is the shape that will define the coming decades. I see them on the screen, growing and dividing, and I think: That way lies the future. About The Author. Photo Credit: Alexa Robinson. Steven Johnson. Product Details. Raves and Reviews. Resources and Downloads. Get a FREE e-book by joining our mailing list today!
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