An Increasing Danger of Zoonotic Orthopoxvirus Infections

On May 8, 1980, the World Health Assembly at its 33^rd session solemnly declared that the world and all its peoples had won freedom from smallpox and recommended ceasing the vaccination of the population against smallpox. Currently, a larger part of the world population has no immunity not only against smallpox but also against other zoonotic orthopoxvirus infections. Recently, recorded outbreaks of orthopoxvirus diseases not only of domestic animals but also of humans have become more frequent. All this indicates a new situation in the ecology and evolution of zoonotic orthopoxviruses. Analysis of state-of-the-art data on the phylogenetic relationships, ecology, and host range of orthopoxviruses—etiological agents of smallpox (variola virus, VARV), monkeypox (MPXV), cowpox (CPXV), vaccinia (VACV), and camelpox (CMLV)—as well as the patterns of their evolution suggests that a VARV-like virus could emerge in the course of natural evolution of modern zoonotic orthopoxviruses. Thus, there is an insistent need for organization of the international control over the outbreaks of zoonotic orthopoxvirus infections in various countries to provide a rapid response and prevent them from developing into epidemics.The genus Orthopoxvirus of the family Poxviridae comprises the species variola (smallpox) virus (VARV), with human as its only sensitive host; zoonotic species monkeypox virus (MPXV), cowpox virus (CPXV), vaccinia virus (VACV), and camelpox virus (CMLV); and several others. These orthopoxviruses are immunologically cross-reactive and cross-protective, so that infection with any member of this genus provides protection against infection with any other member of the genus [1], [2]. Traditionally, the species of the Orthopoxvirus genus have been named primarily according to the host animal from which they were isolated and identified based on a range of biological characteristics [1]. Most frequently, zoonotic orthopoxviruses have been initially isolated from animals immediately close to humans being incidental hosts for the virus, the natural carriers of which are, as a rule, wild animals. Correspondingly, the name of an orthopoxvirus species does not reflect the actual animal that is its natural reservoir.With accumulation of the data on complete genome nucleotide sequences for various strains of orthopoxvirus species, it has been found that an interesting feature of the orthopoxvirus genomes is the presence of genes that are intact in one species but fragmented or deleted in another [3]–[8]. These data confirm the concept of a reductive evolution of orthopoxviruses, according to which the gene loss plays an important role in the evolutionary adaptation of progenitor virus to a particular environmental niche (host) and emergence of new virus species [9]. CPXV has the largest genome of all the modern representatives of the genus Orthopoxvirus, and this genome contains all the genes found in the other species of this genus [2], [4], [10]–[12]. Therefore, Cowpox virus was proposed as the closest of all the modern species to the progenitor virus for the genus Orthopoxvirus, while the remaining species, Variola virus included, had appeared as a result of multistage reductive evolution [4], [9], [13].VARV, the most pathogenic species for humans, has the smallest genome of all the orthopoxviruses [2]–[7]. This suggests a potential possibility for emergence of a VARV-like variant from the currently existing zoonotic orthopoxviruses with longer genomes in the course of natural evolution. It is known that although mutational changes are rather a rare event for the poxvirus DNA [13], characteristic of these viruses is the possibility of intermolecular and intramolecular recombinations, as well as genomic insertions and deletions [14], [15]. It has been recently found that duplication/amplification of genomic segments is typical of poxviruses, and in the case of a certain selective pressure (for example, host antiviral defenses), certain genes are able to relatively rapidly accumulate mutations that would provide the virus adaptation to new conditions, including a new host [16].The conducted analysis of the available archive data on smallpox and the history of ancient civilizations as well as the newest data on the evolutionary relationships of orthopoxviruses has allowed me to suggest the hypothesis that smallpox could have repeatedly emerged in the past via evolutionary changes of a zoonotic progenitor virus [17].Because of the cessation of the vaccination against smallpox after its eradication 35 years ago, a tremendous part of the world human population currently has no immunity not only against smallpox, but also against any other zoonotic orthopoxvirus infections. This new situation allows orthopoxviruses to circulate in the human population and, as a consequence, should alter several established concepts on the ecology and range of sensitive hosts for various orthopoxvirus species.The most intricate case is the origin of VACV. For many decades, VACV has been used for vaccinating humans against smallpox, and it was considered that this virus, variolae vaccinae, originates from zoonotic CPXV, introduced to immunization practice by Jenner as early as 1796 [1]. Only in the 20^th century was it found out that the orthopoxvirus strains used for smallpox vaccination significantly differ in their properties from both the natural CPXV isolates recovered from cows and the other orthopoxvirus species examined by that time [18]. Correspondingly, they were regarded as a separate species, Vaccinia virus [19]. Moreover, it was inferred that the VACV natural reservoir was unknown and numerous hypotheses attempted to explain the origin of this virus while passaging progenitor viruses in animals in the process of vaccine production [1], [2], [20].The issue of VACV origin was somewhat clarified after sequencing the complete genome of horsepox virus (HSPV) [21], which appeared to be closely related to the sequenced VACV strains. Only after this was attention paid to the fact that Jenner specified the origin of his vaccine from an infection of the heels of horses (“grease”) and indicated that the vaccine became more suitable for human use after passage through the cow [20]. This suggests that VACV may originate from a zoonotic HSPV, which naturally persisted concurrently with CPXV. Some facts suggest that the infectious materials not only from cow lesions but also from horse lesions were used for smallpox vaccination in the 19^th century. The vaccine lymph from the horse gave the most satisfactory results in inducing an anti-smallpox immunity as well as less side reactions [1]. By all accounts, they gradually commenced using HSPV isolates for smallpox vaccination, the future generations of which recovered decades later were ascribed to the separate species Vaccinia virus [19], rather than CPXV for smallpox vaccination everywhere.Since the 1960s, VACVs have been repeatedly isolated in Brazil [22]. The first VACV isolates were recovered from wild rodents (sentinel mice and rice rat) [23]. Since 1999, an ever-increasing number of exanthematous outbreaks affecting dairy cows and their handlers have been recorded [24]–[27], supplemented recently with outbreaks among horses [28], [29]. Several VACV strains have been isolated during these outbreaks from cows, horses, humans, and rodents [22], [27], [28], [30], [31]. The questions that arise are when and how VACV entered Brazil and the wild nature of the American continent. The more widespread point of view is that VACV strains could be transmitted from vaccinated humans to domestic animals and further to wild ones with subsequent adaptation to the rural environment [22]. My standpoint implies that HSPV/VACV could have been repeatedly accidentally imported from Europe to South America with the infected horses or rodents to be further introduced into wildlife. Possibly, the latter hypothesis more adequately reflects the actual pathway of VACV transmission to the Brazilian environment, since recent phylogenetic studies have suggested an independent origin for South American VACV isolates, distinct from the vaccine strains used on this continent during the WHO smallpox eradication campaign [22], [32]. Presumably, genome-wide sequencing of the viruses will give a more precise answer to the origin of VACV variants isolated in Brazil.In the past, the outbreaks of buffalopox had occurred frequently in various states of India as well as in Pakistan, Bangladesh, Indonesia, Egypt, and other countries [33]. The causative agent, buffalopox virus (BPXV), is closely related to VACV and affiliated with the species Vaccinia virus, genus Orthopoxvirus [2], [34]. Recently, mass outbreaks of buffalopox in domestic buffaloes along with severe zoonotic infection in milk attendants were recorded at various places in India [35], [36]. In several buffalopox outbreaks, the BPXV-caused infections were recorded in cows in the same herds [37]. An increase in BPXV transmission to different species, including buffaloes, cows, and humans, suggests the reemergence of zoonotic buffalopox infection [35], [38]. The buffalopox outbreaks recorded in different distant regions of India are likely to suggest the presence of an abundant natural BPXV reservoir represented by wild animals, most probably rodents. Correspondingly, it is of the paramount importance to perform a large-scale study of the presence of orthopoxviruses in wild animals of India.Thus, yet incomplete data on the modern ecology of VACV and BPXV allow for speculation that the orthopoxviruses belonging to the species Vaccinia virus have a wide host range, are zoonotic, are currently spread over large areas in Eurasia and South America, and that their natural carriers are several rodents.CPXV has relatively low pathogenicity for humans but has a wide range of sensitive animal hosts [2], [39]. Human cowpox is a rare sporadic disease, which develops when CPXV is transmitted from an infected animal to human [2], [40]. This disease is mainly recorded in Europe. In wildlife, CPXV carriers are asymptomatically infected rodents [41], [42]. During the last two decades, reports on an increasing number of CPXV infections in cats, rats, exotic animals, and humans have been published [43]–[47]. Comparative studies of the properties of CPXV isolates recovered from various hosts at different times and in several geographic zones have shown sufficient intraspecific variations [2], [48], [49]. A recent phylogenetic analysis of the complete genomes of 12 CPXV strains recovered from humans and several animal species suggests that they be split into two major Cowpox virus–like and Vaccinia virus–like clades [50]. This means that the criteria of the separation of orthopoxviruses into these two species should be corrected.MPXV is a zoonotic virus causing a human infection similar to smallpox in its clinical manifestations with a lethality rate of 1–8% [51]. The natural reservoir of MPXV is various species of African rodents [8], [10]. The active surveillance data in the same health zone (Democratic Republic of Congo) from the 1980s to 2006–2007 suggest a 20-fold increase in human monkeypox incidence 30 years after the cessation of the smallpox vaccination campaign [52]. This poses the question of whether MPXV can acquire the possibility of a high human-to-human transmission rate, characteristic of VARV, under conditions of a long-term absence of vaccination and considerably higher incidence of human infection. If this occurs, humankind will face a problem considerably more complex than with the smallpox eradication. First and foremost, this is determined by the fact that MPXV, unlike VARV, has its natural reservoir represented by numerous African rodents [2], [53].In its biological properties and according to the data of phylogenetic analysis of the complete virus genomic sequence, CMLV is closest to VARV, the causative agent of smallpox, as compared with the other orthopoxvirus species [1], [8]. Camelpox is recognized as one of the most important viral diseases in camels. This infection was first described in India in 1909. Subsequently, camelpox outbreaks have been reported in many countries of the Middle East, Asia, and Africa [54], [55]. Until recently, it has been commonly accepted that the host range of CMLV is confined to one animal species, camels [1], [55]. However, the first human cases of camelpox have been recently confirmed in India [56]. This suggests that camelpox could be a zoonotic disease. Since camelpox outbreaks occur irregularly in distant regions of the world and the viruses isolated during these outbreaks display different degrees of virulence [55], it is possible to postulate the presence of a wildlife animal reservoir of CMLV other than camels. Since the camelpox outbreaks are usually associated with the rainy season of the year, when rodents are actively reproducing, it is likely that rodents could be the natural carriers of CMLV.It is known that most of the emerging human pathogens originate from zoonotic pathogens [57]–[59]. Many viruses do not cause the disease in their natural reservoir hosts but can be highly pathogenic when transmitted to a new host species. Emerging and reemerging human pathogens more often are those with broad host ranges. The viruses able to infect many animal species are evolutionarily adapted to utilizing different cell mechanisms for their reproduction and, thus, can extend/change their host range with a higher probability [58].There are no fundamental prohibitions for the possible reemergence of smallpox or a similar human disease in the future as a result of natural evolution of the currently existing zoonotic orthopoxviruses. An ever-increasing sensitivity of the human population to zoonotic orthopoxviruses, resulting from cessation of the mass smallpox vaccination, elevates the probability for new variants of these viruses, potentially dangerous for humans, to emerge. However, the current situation is radically different from the ancient one, since many outbreaks of orthopoxvirus infections among domestic animals and humans are recorded and studied.Recently, the efforts of scientists under WHO control are directed to the development of state-of-the-art methods for VARV rapid identification as well as design of new generation safe smallpox vaccines and drugs against VARV and other orthopoxviruses [60]. The designed promising anti-orthopoxvirus drugs display no pronounced virus species specificity. Therefore, they are applicable in the outbreaks caused by any orthopoxvirus species. International acceptance of the designed highly efficient anti-orthopoxvirus drugs ST-246 and CMX001 [60] is of paramount importance.In the areas of high incidence of zoonotic orthopoxviral infections, it would be purposeful to vaccinate domestic and zoo animals as well as the persons closely associated with them using state-of-the-art safe vaccines based on VACV, which has a wide range of sensitive hosts. This would considerably decrease the likelihood for such infections to spread from wildlife into the human environment.In the African region endemic for monkeypox, which also displays a high rate of HIV infection, the population could be vaccinated with the VACV strain MVA, which has been recently demonstrated to be safe even for HIV-infected persons [61].Taking into account the above mentioned increased incidence of outbreaks of animal and human orthopoxvirus infections and their potential danger, it is important to accelerate organization of the international Smallpox Laboratory Network, discussed by the WHO Advisory Committee on Variola Virus Research [62], [63], and orient this network to express diagnosing not only of VARV but also of other zoonotic orthopoxviruses. This will provide constant monitoring of these infections in all parts of the world and make it possible to prevent the development of small outbreaks into expanded epidemics, thereby decreasing the risk of evolutional changes and emergence of an orthopoxvirus highly pathogenic for humans.The international system for clinical sampling and identification of infectious agents has been worked out and optimized while implementing the global smallpox eradication program under the aegis of the WHO as well as anti-epidemic measures and methods for mass vaccination [1]. The accumulated experience is of paramount importance for the establishment of international control not only over currently existing orthopoxvirus infections but also other emerging and reemerging diseases.     

Immunological Mechanisms Mediating Hantavirus Persistence in Rodent Reservoirs

Hantaviruses, similar to several emerging zoonotic viruses, persistently infect their natural reservoir hosts, without causing overt signs of disease. Spillover to incidental human hosts results in morbidity and mortality mediated by excessive proinflammatory and cellular immune responses. The mechanisms mediating the persistence of hantaviruses and the absence of clinical symptoms in rodent reservoirs are only starting to be uncovered. Recent studies indicate that during hantavirus infection, proinflammatory and antiviral responses are reduced and regulatory responses are elevated at sites of increased virus replication in rodents. The recent discovery of structural and non-structural proteins that suppress type I interferon responses in humans suggests that immune responses in rodent hosts could be mediated directly by the virus. Alternatively, several host factors, including sex steroids, glucocorticoids, and genetic factors, are reported to alter host susceptibility and may contribute to persistence of hantaviruses in rodents. Humans and reservoir hosts differ in infection outcomes and in immune responses to hantavirus infection; thus, understanding the mechanisms mediating viral persistence and the absence of disease in rodents may provide insight into the prevention and treatment of disease in humans. Consideration of the coevolutionary mechanisms mediating hantaviral persistence and rodent host survival is providing insight into the mechanisms by which zoonotic viruses have remained in the environment for millions of years and continue to be transmitted to humans.Hantaviruses are negative sense, enveloped RNA viruses (family: Bunyaviridae) that are comprised of three RNA segments, designated small (S), medium (M), and large (L), which encode the viral nucleocapsid (N), envelope glycoproteins (G_N and G_C), and an RNA polymerase (Pol), respectively. More than 50 hantaviruses have been found worldwide [1]. Each hantavirus appears to have coevolved with a specific rodent or insectivore host as similar phylogenetic trees are produced from virus and host mitochondrial gene sequences [2]. Spillover to humans causes hemorrhagic fever with renal syndrome (HFRS) or hantavirus cardiopulmonary syndrome (HCPS), depending on the virus [3]–[5]. Although symptoms vary, a common feature of both HFRS and HCPS is increased permeability of the vasculature and mononuclear infiltration [4]. Pathogenesis of HRFS and HCPS in humans is hypothesized to be mediated by excessive proinflammatory and CD8+ T cell responses (​).

Table 1

Summary of Immune Responses in Humans during Hantavirus Infection.

The Role of Genomics in Tracking the Evolution of Influenza A Virus

Influenza A virus causes annual epidemics and occasional pandemics of short-term respiratory infections associated with considerable morbidity and mortality. The pandemics occur when new human-transmissible viruses that have the major surface protein of influenza A viruses from other host species are introduced into the human population. Between such rare events, the evolution of influenza is shaped by antigenic drift: the accumulation of mutations that result in changes in exposed regions of the viral surface proteins. Antigenic drift makes the virus less susceptible to immediate neutralization by the immune system in individuals who have had a previous influenza infection or vaccination. A biannual reevaluation of the vaccine composition is essential to maintain its effectiveness due to this immune escape. The study of influenza genomes is key to this endeavor, increasing our understanding of antigenic drift and enhancing the accuracy of vaccine strain selection. Recent large-scale genome sequencing and antigenic typing has considerably improved our understanding of influenza evolution: epidemics around the globe are seeded from a reservoir in East-Southeast Asia with year-round prevalence of influenza viruses; antigenically similar strains predominate in epidemics worldwide for several years before being replaced by a new antigenic cluster of strains. Future in-depth studies of the influenza reservoir, along with large-scale data mining of genomic resources and the integration of epidemiological, genomic, and antigenic data, should enhance our understanding of antigenic drift and improve the detection and control of antigenically novel emerging strains.Influenza is a single-stranded, negative-sense RNA virus that causes acute respiratory illness in humans. In temperate regions, winter influenza epidemics result in 250,000–500,000 deaths per year; in tropical regions, the burden is similar [1],[2]. Influenza viruses of three genera or types (A, B, and C) circulate in the human population. Influenza viruses of the types B and C evolve slowly and circulate at low levels. Type A evolves rapidly and can evade neutralization by antibodies in individuals who have been previously infected with, or vaccinated against, the virus. As a result it regularly causes large epidemics. Furthermore, distinct reservoirs of influenza A exist in other mammals and in birds. Four times in the last hundred years these reservoirs have provided genetic material for novel viruses that have caused global pandemics [3]–[8].The genome of influenza A viruses comprises eight RNA segments of 0.9–2.3 kb that together span approximately 13.5 kb and encode 11 proteins [9]. Segment 4 encodes the major surface glycoprotein called hemagglutinin (H), which is responsible for attaching the virus to sialic acid residues on the host cell surface and fusing the virus membrane envelope with the host cell membrane, thus delivering the viral genome into the cell (Figure 1). Segment 6 encodes another surface glycoprotein called neuraminidase (N), which cleaves terminal sialic acid residues from glycoproteins and glycolipids on the host cell surface, thus releasing budding viral particles from an infected cell [10]. Influenza A viruses are further classified into distinct subtypes based on the genetic and antigenic characteristics of these two surface glycoproteins. Sixteen hemagglutinin (H1–16) and nine neuraminidase subtypes (N1–9) are known to exist, and they occur in various combinations in influenza viruses endemic in aquatic birds [10],[11]. Viruses with the subtype composition H1N1 and H3N2 have been circulating in the human population for several decades. Of these two subtypes, H3N2 evolves more rapidly, and has until recently caused the majority of infections [1],[12],[13]. In the spring of 2009, however, a new H1N1 virus originating from swine influenza A viruses, and only distantly related to the H1N1 already circulating, gained hold in the human population. The emergence of this virus has initiated the first influenza pandemic of the twenty-first century [7],[14],[15].Open in a separate windowFigure 1Schematic representation of an influenza A virion.Three proteins, hemagglutinin (HA, a trimer of three identical subunits), neuraminidase (NA, a tetramer of four identical subunits), and the M2 transmembrane proton channel (a tetramer of four identical subunits), are anchored in the viral membrane, which is composed of a lipid bilayer. The large, external domains of hemagglutinin and neuraminidase are the major targets for neutralizing antibodies of the host immune response. The M1 matrix protein is located below the membrane. The genome of the influenza A virus is composed of eight individual RNA segments (conventionally ordered by decreasing length, bottom row), which each encode one or two proteins. Inside the virion, the eight RNA segments are packaged in a complex with nucleoprotein (NP) and the viral polymerase complex, consisting of the PA, PB1, and PB2 proteins.Hemagglutinin is about five times more abundant than neuraminidase in the viral membrane and is the major target of the host immune response [16]–[18]. Following exposure to the virus, whether by infection or vaccination, the host immune system acquires the capacity to produce neutralizing antibodies against the viral surface glycoproteins. These antibodies participate in clearing an infection and may protect an individual from future infections for many decades [19]. Five exposed regions on the surface of hemagglutinin, called epitope sites, are predominantly recognized by such antibodies [16],[17]. However, the human subtypes of influenza A continuously evolve and acquire genetic mutations that result in amino acid changes in the epitopes. These changes reduce the protective effect of antibodies raised against previously circulating viral variants. This “antigenic drift” necessitates frequent modification and readministration of the influenza vaccine to ensure efficient protection (Box 1).

Box 1. Broadly Protective Vaccines

Current influenza vaccines are based on detergent-inactivated viruses. They elicit antibodies with a narrow range of protection that target predominantly the variable regions of the hemagglutinin protein. Accordingly, the seasonal influenza vaccine includes one strain with segments of the surface proteins for each of the A/H1N1, A/H3N2 and B viruses, and it is updated every 1–3 years to match the predominant variants of influenza. Research into vaccines that offer broader protection across diverse subtypes and antigenic drift variants is ongoing [21], [59]–[61]. This research is particularly important with respect to the emergence of novel viruses with pandemic potential, such as the 2009 H1N1 virus. In such an event, the time period between the detection of the virus and the onset of a pandemic is too short to produce a specific vaccine for immediate vaccination of the population. Work in this area is focused on developing vaccines that elicit antibodies against conserved viral components, such as certain regions of hemagglutinin, neuraminidase, and the M2 proton channel in the viral membrane [60]. Other types of vaccines based on live attenuated viruses or plasmid DNA expression vectors, or supplemented with adjuvants, show promise in inducing a more broadly protective immune response [61].To monitor for novel emerging strains, the World Health Organization (WHO) maintains a global surveillance program. A panel of experts meets twice a year to review antigenic, genetic, and epidemiological data and decides on the vaccine composition for the next winter season in the northern or southern hemisphere [20]. If an emerging antigenic variant is detected and judged likely to become predominant, an update of the vaccine strain is recommended. This “predict and produce” approach mostly results in efficient vaccines that substantially limit the morbidity and mortality of seasonal epidemics [21]. The recommendation has to be made almost a year before the season in which the vaccine is used, however, because of the time required to produce and distribute a new vaccine. Problems arise when an emerging variant is not identified early enough for an update of the vaccine composition [22]–[24]. Thus, gaining a detailed understanding of the evolution and epidemiology of the virus is of the utmost importance, as it may lead to earlier identification of novel emerging variants [20].The development of high-throughput sequencing has recently provided large datasets of high-quality, complete genome sequences for viral isolates collected in a relatively unbiased manner, regardless of virulence or other unusual characteristics [9],[25]. Analyses of the genome sequence data combined with large-scale antigenic typing [26],[27] have given insights into the pattern of global spread, the genetic diversity during seasonal epidemics, and the dynamics of subtype evolution. Influenza data repositories such as the NCBI Influenza Virus Resource (http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html) [28] and the Global Initiative on Sharing All Influenza Data (GISAID; http://platform.gisaid.org/) database [29] make the genomic information publicly available, together with epidemiological data for the sequenced isolates. The GISAID model for data sharing requires users to agree to collaborate with, and appropriately credit, all data contributors. A notable success of this initiative has been the contribution of countries, such as Indonesia and China, which have previously been reticent about placing data in the public domain. The WHO also supports the endeavor of rapid publication of all available sequences for influenza viruses and there is hope that comprehensive submission to public databases will soon become a reality [24],[30]. In the future, mining these resources and establishing a statistical framework based on epidemiological, antigenic, and genetic information could provide further insights into the rules that govern the emergence and establishment of antigenically novel variants and improve the potential for influenza prevention and control.     

Introducing the Outbreak Threshold in Epidemiology

When a pathogen is rare in a host population, there is a chance that it will die out because of stochastic effects instead of causing a major epidemic. Yet no criteria exist to determine when the pathogen increases to a risky level, from which it has a large chance of dying out, to when a major outbreak is almost certain. We introduce such an outbreak threshold (T₀), and find that for large and homogeneous host populations, in which the pathogen has a reproductive ratio R₀, on the order of 1/Log(R₀) infected individuals are needed to prevent stochastic fade-out during the early stages of an epidemic. We also show how this threshold scales with higher heterogeneity and R₀ in the host population. These results have implications for controlling emerging and re-emerging pathogens.With the constant risk of pathogens emerging [1], such as Severe Acute Respiratory Syndrome (SARS) or avian influenza virus in humans, foot-and-mouth disease virus in cattle in the United Kingdom [2], or various plant pathogens [3], it is imperative to understand how novel strains gain their initial foothold at the onset of an epidemic. Despite this importance, it has seldom been addressed how many infected individuals are needed to declare that an outbreak is occurring: that is, when the pathogen can go extinct due to stochastic effects, to when it infects a high enough number of hosts such that the outbreak size increases in a deterministic manner (Figure 1A). Generally, the presence of a single infected individual is not sufficient to be classified as an outbreak, so how many infected individuals need to be present to cause this deterministic increase? Understanding at what point this change arises is key in preventing and controlling nascent outbreaks as they are detected, as well as determining the best course of action for prevention or treatment.Open in a separate windowFigure 1The outbreak threshold in homogeneous and heterogeneous populations.(A) A schematic of pathogen emergence. This graph shows the early stages of several strains of an epidemic, where R₀ = 1.25. The black line denotes the outbreak threshold (T ₀ = 1/Log(R₀) = 4.48). Blue thin lines show cases in which the pathogen goes extinct and does not exceed the threshold; the red thick line shows an epidemic that exceeds the threshold and persists for a long period of time. Simulations were based on the Gillespie algorithm [22]. (B) Outbreak threshold in a homogeneous (black thick line) or in a heterogeneous population, for increasing R₀. The threshold was calculated following the method described by Lloyd-Smith et al. [11] and is shown for different values of k, the dispersion parameter of the offspring distribution, as obtained from data on previous epidemics [11]. If the threshold lies below one, this means that around only one infected individual is needed to give a high outbreak probability.The classic prediction for pathogen outbreak is that the pathogen''s reproductive ratio (R₀), the number of secondary infections caused by an infected host in a susceptible population, has to exceed one [4], [5]. This criterion only strictly holds in deterministic (infinite population) models; in finite populations, there is still a chance that the infection will go extinct by chance rather than sustain itself [4]–[6]. Existing studies usually consider random drift affecting outbreaks in the context of estimating how large a host population needs to be to sustain an epidemic (the “Critical Community Size” [4], [7], [8]), calculating the outbreak probability in general [9]–[12], or ascertaining whether a sustained increase in cases over an area has occurred [13]. Here we discuss the fundamental question of how many infected individuals are needed to almost guarantee that a pathogen will cause an outbreak, as opposed to the population size needed to maintain an epidemic once it has appeared (Critical Community Size; see also Box 1). We find that only a small number of infected individuals are often needed to ensure that an epidemic will spread.

Box 1. Glossary of Key Terms

• The Basic Reproductive Ratio (R₀) is the number of secondary infections caused by a single infected individual, in a susceptible population. It is classically used to measure the rate of pathogen spread. In infinite-population models, a pathogen can emerge if R₀>1. In a finite population, the pathogen can emerge from a single infection with probability 1-1/R₀ if R₀>1, otherwise extinction is certain.
• The Critical Community Size (CCS) is defined as the total population size (of susceptible and infected individuals, or others) needed to sustain an outbreak once it has appeared. This idea was classically applied to determining what towns were most likely to maintain measles epidemics [7], so that there would always be some infected individuals present, unless intervention measures were taken.
• The Outbreak Threshold (T₀) has a similar definition to the CSS, but is instead for use at the onset of an outbreak, rather than once it has appeared. It measures how many infected individuals (not the total population size) are needed to ensure that an outbreak is very unlikely to go extinct by drift. Note that the outbreak can still go extinct in the long term, even if T₀ is exceeded, if there are not enough susceptible individuals present to carry the infection afterwards.

We introduce the concept of the outbreak threshold (denoted T₀), which we define as the number of infected individuals needed for the disease to spread in an approximately deterministic manner. T₀ can be given by simple equations. Indeed, if the host population is homogeneous (that is, where there is no individual variability in reproductive rates) and large enough so that depletion of the pool of susceptible hosts is negligible, then the probability of pathogen extinction if I infected hosts are present is (1/R₀)^I ([6], details in Material S.1 in Text S1). By solving this equation in the limit of extinction probability going to zero, we find that on the order of 1/Log(R₀) infected hosts are needed for an outbreak to be likely (black thick curve in Figure 1B), a result that reflects similar theory from population genetics [14]–[16]. Note that this result only holds in a finite population, as an outbreak in a fully susceptible infinite population is certain if R₀>1 ([4], see also Material S.1 in Text S1).This basic result can be modified to consider more realistic or precise cases, and T₀ can be scaled up if an exact outbreak risk is desired. For example, for the pathogen extinction probability to be less than 1%, there needs to be at least 5/Log(R₀) infected individuals. More generally, the pathogen extinction probability is lower than a given threshold c if there are at least −Log(c)/Log(R₀) infected individuals. Furthermore, if only a proportion p<1 of all infected individuals are detected, then the outbreak threshold order is p/Log(R₀). Also, if there exists a time-lag τ between an infection occurring and its report, then the order of T₀ is e^−τ(β-δ)/Log(R₀), where β is the infection transmission rate and 1/δ the mean duration of the infectious period (Material S.1 in Text S1). Finally, we can estimate how long it would take, on average, for the threshold to be reached and find that, if the depletion in susceptible hosts is negligible, this duration is on the order of 1/(β-δ) (Material S.1 in Text S1).So far we have only considered homogenous outbreaks, where on average each individual has the same pathogen transmission rate. In reality, there will be a large variance among individual transmission rates, especially if “super-spreaders” are present [17]. This population heterogeneity can either be deterministic, due to differences in immune history among hosts or differences in host behavior, or stochastic, due to sudden environmental or social changes. Spatial structure can also act as a form of heterogeneity, if each region or infected individual is subject to different transmission rates, or degree of contact with other individuals [18]. In such heterogeneous host populations, the number of secondary cases an infected individual engenders is jointly captured by R₀ and a dispersion parameter k (see Box 2). This dispersion parameter controls the degree of variation in individual transmission rates, while fixing the average R₀. The consequence of this model is that the majority of infected hosts tend to cause few secondary infections, while the minority behave as super-spreaders, causing many secondary infections. Host population heterogeneity (obtained with lower values of k) increases the probability that an outbreak will go extinct, as the pathogen can only really spread via one of the dwindling super-spreading individuals. In this heterogeneous case, we can find accurate values of T₀ numerically. As shown in Figure 1B, if R₀ is close to 1, host heterogeneity (k) does not really matter (T₀ tends to be high). However, if the pathogen has a high R₀ and thus spreads well, then host heterogeneity strongly affects T₀. Additionally, we find that the heterogeneous threshold simply scales as a function of k and R₀² (see Box 2). As an example, if k = 0.16, as estimated for SARS infections [11], the number of infected individuals needed to guarantee an outbreak increases 4-fold compared to a homogeneous population (Material S.3 in Text S1).

Box 2. Heterogeneous Outbreak Threshold

In a heterogeneous host population (see the main text for the bases of this heterogeneity), it has been shown that the number of secondary infections generated per infected individual can be well described by a negative binomial distribution with mean R₀ and dispersion parameter k [11]. The dispersion parameter determines the level of variation in the number of secondary infections: if k = 1, we have a homogeneous outbreak, but heterogeneity increases as k drops below 1; that is, it enlarges the proportion of infected individuals that are either “super-spreaders” or “dead-ends” (those that do not transmit the pathogen). Lloyd-Smith et al. [11] showed how to estimate R₀ and k from previous epidemics through applying a maximum-likelihood model to individual transmission data.Although in this case it is not possible to find a strict analytical form for the outbreak threshold, progress can be made if we measure the ratio of the heterogeneous and homogeneous thresholds. This function yields values that are independent of a strict cutoff probability (Material S.3 in Text S1). By investigating this ratio, we first found that for a fixed R₀, a function of order 1/k fitted the numerical solutions very well. By measuring these curves for different R₀ values, we further found that a function of order 1/R₀ ² provided a good fit to the coefficients. By fitting a function of order 1/kR₀ ² to the numerical data using least-squares regression in Mathematica 8.0 [19], we obtained the following adjusted form for the outbreak threshold T₀ in a heterogeneous population:(1)As in the homogeneous case, T₀ only provides us with an order of magnitude and it can be multiplied by −Log(c) to find the number of infected hosts required for there to be a probability of outbreak equal to 1-c. A sensitivity analysis shows that Equation 1 tends to be more strongly affected by changes in R₀ than in k (Material S.3 in Text S1).The outbreak threshold T₀ of an epidemic, which we define as the number of infected hosts above which there is very likely to be a major outbreak, can be estimated using simple formulae. Currently, to declare that an outbreak has occurred, studies choose an arbitrary low or high threshold depending, for instance, on whether they are monitoring disease outbreaks or modeling probabilities of emergence. We show that the outbreak threshold can be defined without resorting to an arbitrary cutoff. Of course, the generality of this definition has a cost, which is that the corresponding value of T₀ is only an order of magnitude. Modifications are needed to set a specific cutoff value or to capture host heterogeneity in transmission or incomplete sampling.These results are valid if there are enough susceptible individuals present to maintain an epidemic in the initial stages, as assumed in most studies on emergence [6], [11]–[13], otherwise the pathogen may die out before the outbreak threshold is reached (Box 3 and Material S.2 in Text S1). Yet the key message generally holds that while the number of infections lies below the threshold, there is a strong chance that the pathogen will vanish without causing a major outbreak. From a biological viewpoint, unless R₀ is close to one, these thresholds tend to be small (on the order of 5 to 20 individuals). This contrasts with estimates of the Critical Community Size, which tend to lie in the hundreds of thousands of susceptible individuals [3], [7], [8]. Therefore, while only a small infected population is needed to trigger a full-scale epidemic, a much larger pool of individuals are required to maintain an epidemic, once it appears, and prevent it from fading out. This makes sense, since there tends to be more susceptible hosts early on in the outbreak than late on.

Box 3. Effect of Limiting Host Population Size

The basic result for the homogeneous population, T₀∼1/Log(R₀), assumes that during the time to pathogen outbreak, there are always enough susceptible individuals available to transmit to, so R₀ remains approximately constant during emergence. This assumption can be violated if R₀ is close to 1, or if the population size is small. More precisely, if the maximum outbreak size in a Susceptible-Infected-Recovered (SIR) epidemic, which is given byis less than 1/Log(R₀), then the threshold cannot be reached. Since this maximum is dependent on the population size, outbreaks in smaller populations are less likely to reach the outbreak threshold. For example, if N = 10,000 then R₀ needs to exceed 1.06 for 1/Log(R₀) to be reached; this increases to 1.34 if N decreases to 100. Further details are in Material S.2 in Text S1.Estimates of R₀ and k from previous outbreaks can be used to infer the approximate size of this threshold, to determine whether a handful or hundreds of infected individuals are needed for an outbreak to establish itself. Box 4 outlines two case studies (smallpox in England and SARS in Singapore), estimates of T₀ for these, and how knowledge of the threshold could have aided their control. These examples highlight how the simplicity and rigorousness of the definition of T₀ opens a wide range of applications, as it can be readily applied to specific situations in order to determine the most adequate policies to prevent pathogen outbreaks.

Box 4. Two Case Studies: Smallpox in England and SARS in Singapore

A smallpox outbreak (Variola minor) was initiated in Birmingham, United Kingdom in 1966 due to laboratory release. We calculate a threshold such that the chance of extinction is less than 0.1%, which means that T₀ is equal to 7 times Equation 1. With an estimated R₀ of 1.6 and dispersion parameter k = 0.65 [11], T₀ is approximately equal to 9 infections. The transmission chain for this outbreak is now well-known [20]. Due to prior eradication of smallpox in the United Kingdom, the pathogen was not recognised until around the 45^th case was detected, by which point a full-scale epidemic was underway. A second laboratory outbreak arose in 1978, but the initial case (as well as a single secondary case) was quickly isolated, preventing a larger spread of the pathogen. Given the fairly low T₀ for the previous epidemic, early containment was probably essential in preventing a larger outbreak.The SARS outbreak in Singapore in 2003 is an example of an outbreak with known super-spreaders [21], with an estimated initial R₀ of 1.63 and a low k of 0.16 [11]. T₀ is estimated to be around 27 infections. The first cases were observed in late February, with patients being admitted for pneumonia. Strict control measures were invoked from March 22^nd onwards, including home quarantining of those exposed to SARS patients and closing down of a market where a SARS outbreak was observed. By this date, 57 cases were detected, although it is unclear how many of those cases were still ongoing on the date. This point is important, as it is the infected population size that determines T₀.Overall, very early measures were necessary to successfully prevent a smallpox outbreak due to its rapid spread. In theory, it should have been “easier” to contain the SARS outbreak, as its threshold is three times greater than that for smallpox due to higher host heterogeneity (k). However, the first reported infected individual was a super-spreader, who infected at least 21 others. This reflects that in heterogeneous outbreaks, although the emergence probability is lower, the disease spread is faster (compared to homogeneous infections) once it does appear [11]. Quick containment of the outbreak was difficult to achieve since SARS was not immediately recognised, as well as the fact that the incubation period is around 5 days, by which point it had easily caused more secondary cases. However, in subsequent outbreaks super-spreaders might not be infected early on, allowing more time to contain the spread.For newly-arising outbreaks, T₀ can be applied in several ways. If the epidemic initially spreads slowly, then R₀ and T₀ can be measured directly. Alternatively, estimates of T₀ can be calculated from previous outbreaks, as outlined above. In both cases, knowing what infected population size is needed to guarantee emergence can help to assess how critical a situation is. More generally, due to the difficulty in detecting real-world outbreaks that go extinct very quickly, experimental methods might be useful in determining to what extent different levels of T₀ capture the likelihood of full epidemic emergence.     

Evidence for a New Avian Paramyxovirus Serotype 10 Detected in Rockhopper Penguins from the Falkland Islands

The biological, serological, and genomic characterization of a paramyxovirus recently isolated from rockhopper penguins (Eudyptes chrysocome) suggested that this virus represented a new avian paramyxovirus (APMV) group, APMV10. This penguin virus resembled other APMVs by electron microscopy; however, its viral hemagglutination (HA) activity was not inhibited by antisera against any of the nine defined APMV serotypes. In addition, antiserum generated against this penguin virus did not inhibit the HA of representative viruses of the other APMV serotypes. Sequence data produced using random priming methods revealed a genomic structure typical of APMV. Phylogenetic evaluation of coding regions revealed that amino acid sequences of all six proteins were most closely related to APMV2 and APMV8. The calculation of evolutionary distances among proteins and distances at the nucleotide level confirmed that APMV2, APMV8, and the penguin virus all were sufficiently divergent from each other to be considered different serotypes. We propose that this isolate, named APMV10/penguin/Falkland Islands/324/2007, be the prototype virus for APMV10. Because of the known problems associated with serology, such as antiserum cross-reactivity and one-way immunogenicity, in addition to the reliance on the immune response to a single protein, the hemagglutinin-neuraminidase, as the sole base for viral classification, we suggest the need for new classification guidelines that incorporate genome sequence comparisons.Viruses from the Paramyxoviridae family have caused disease in humans and animals for centuries. Over the last 40 years, many paramyxoviruses isolated from animals and people have been newly described (16, 17, 22, 29, 31, 32, 36, 42, 44, 46, 49, 58, 59, 62-64). Viruses from this family are pleomorphic, enveloped, single-stranded, nonsegmented, negative-sense RNA viruses that demonstrate serological cross-reactivity with other paramyxoviruses related to them (30, 46). The subfamily Paramyxovirinae is divided into five genera: Respirovirus, Morbillivirus, Rubulavirus, Henipavirus, and Avulavirus (30). The Avulavirus genus contains nine distinct avian paramyxovirus (APMV) serotypes (Table ​(Table1),1), and information on the discovery of each has been reported elsewhere (4, 6, 7, 9, 12, 34, 41, 50, 51, 60, 68).

TABLE 1.

Characteristics of prototype viruses APMV1 to APMV9 and the penguin virus

Critical Factors Determining Dimerization of Human Antizyme Inhibitor

Ornithine decarboxylase (ODC) is the first enzyme involved in polyamine biosynthesis, and it catalyzes the decarboxylation of ornithine to putrescine. ODC is a dimeric enzyme, whereas antizyme inhibitor (AZI), a positive regulator of ODC that is homologous to ODC, exists predominantly as a monomer and lacks decarboxylase activity. The goal of this paper was to identify the essential amino acid residues that determine the dimerization of AZI. The nonconserved amino acid residues in the putative dimer interface of AZI (Ser-277, Ser-331, Glu-332, and Asp-389) were substituted with the corresponding residues in the putative dimer interface of ODC (Arg-277, Tyr-331, Asp-332, and Tyr-389, respectively). Analytical ultracentrifugation analysis was used to determine the size distribution of these AZI mutants. The size-distribution analysis data suggest that residue 331 may play a major role in the dimerization of AZI. Mutating Ser-331 to Tyr in AZI (AZI-S331Y) caused a shift from a monomer configuration to a dimer. Furthermore, in comparison with the single mutant AZI-S331Y, the AZI-S331Y/D389Y double mutant displayed a further reduction in the monomer-dimer K_d, suggesting that residue 389 is also crucial for AZI dimerization. Analysis of the triple mutant AZI-S331Y/D389Y/S277R showed that it formed a stable dimer (K_d value = 1.3 μm). Finally, a quadruple mutant, S331Y/D389Y/S277R/E332D, behaved as a dimer with a K_d value of ∼0.1 μm, which is very close to that of the human ODC enzyme. The quadruple mutant, although forming a dimer, could still be disrupted by antizyme (AZ), further forming a heterodimer, and it could rescue the AZ-inhibited ODC activity, suggesting that the AZ-binding ability of the AZI dimer was retained.Polyamines (putrescine, spermidine, and spermine) have been shown to have both structural and regulatory roles in protein and nucleic acid biosynthesis and function (1–3). Ornithine decarboxylase (ODC,³ EC 4.1.1.17) is a central regulator of cellular polyamine synthesis (reviewed in Refs. 1, 4, 5). This enzyme catalyzes the pyridoxal 5-phosphate (PLP)-dependent decarboxylation of ornithine to putrescine, and it is the first and rate-limiting enzyme in polyamine biosynthesis (2, 3, 6, 7). ODC and polyamines play important roles in a number of biological functions, including embryonic development, cell cycle, proliferation, differentiation, and apoptosis (8–15). They also have been associated with human diseases and a variety of cancers (16–26). Because the regulation of ODC and polyamine content is critical to cell proliferation (11), as well as in the origin and progression of neoplastic diseases (23, 24), ODC has been identified as an oncogenic enzyme, and the inhibitors of ODC and the polyamine pathway are important targets for therapeutic intervention in many cancers (6, 11).ODC is ubiquitously found in organisms ranging from bacteria to humans. It contains 461 amino acid residues in each monomer and is a 106-kDa homodimer with molecular 2-fold symmetry (27, 28). Importantly, ODC activity requires the formation of a dimer (29–31). X-ray structures of the ODC enzyme reveal that this dimer contains two active sites, both of which are formed at the interface between the N-terminal domain of one monomer, which provides residues involved in PLP interactions, and the C-terminal domain of the other subunit, which provides the residues that interact with substrate (27, 32–41).ODC undergoes a unique ubiquitin-independent proteasomal degradation via a direct interaction with the regulatory protein antizyme (AZ). Binding of AZ promotes the dissociation of the ODC homodimers and targets ODC for degradation by the 26 S proteasome (42–46). Current models of antizyme function indicate that increased polyamine levels promote the fidelity of the AZ mRNA translational frameshift, leading to increased concentrations of AZ (47). The AZ monomer selectively binds to dimeric ODC, thereby inactivating ODC by forming inactive AZ-ODC heterodimers (44, 48–50). AZ acts as a regulator of polyamine metabolism that inhibits ODC activity and polyamine transport, thus restricting polyamine levels (4, 5, 51, 52). When antizymes are overexpressed, they inhibit ODC and promote ubiquitin-independent proteolytic degradation of ODC. Because elevated ODC activity is associated with most forms of human malignancies (1), it has been suggested that antizymes may function as tumor suppressors.In contrast to the extensive studies on the oncogene ODC, the endogenous antizyme inhibitor (AZI) is less well understood. AZI is homologous to the enzyme ODC. It is a 448-amino acid protein with a molecular mass of 50 kDa. However, despite the homology between these proteins, AZI does not possess any decarboxylase activity. It binds to antizyme more tightly than does ODC and releases ODC from the ODC-antizyme complex (53, 54). Both the AZI and AZ proteins display rapid ubiquitin-dependent turnover within a few minutes to 1 h in vivo (5). However, AZ binding actually stabilizes AZI by inhibiting its ubiquitination (55).AZI, which inactivates all members of the AZ family (53, 56), restores ODC activity (54), and prevents the proteolytic degradation of ODC, may play a role in tumor progression. It has been reported that down-regulation of AZI is associated with the inhibition of cell proliferation and reduced ODC activity, presumably through the modulation of AZ function (57). Moreover, overexpression of AZI has been shown to increase cell proliferation and promote cell transformation (58–60). Furthermore, AZI is capable of direct interaction with cyclin D1, preventing its degradation, and this effect is at least partially independent of AZ function (60, 61). These results demonstrate a role for AZI in the positive regulation of cell proliferation and tumorigenesis.It is now known that ODC exists as a dimer and that AZI may exist as a monomer physiologically (62). Fig. 1 shows the dimeric structures of ODC (Fig. 1A) and AZI (Fig. 1B). Although structural studies indicate that both ODC and AZI crystallize as dimers, the dimeric AZI structure has fewer interactions at the dimer interface, a smaller buried surface area, and a lack of symmetry of the interactions between residues from the two monomers, suggesting that the AZI dimer may be nonphysiological (62). In this study, we identify the critical amino acid residues governing the difference in dimer formation between ODC and AZI. Our preliminary studies using analytical ultracentrifugation indicated that ODC exists as a dimer, whereas AZI exists in a concentration-dependent monomer-dimer equilibrium. Multiple sequence alignments of ODC and AZI from various species have shown that residues 277, 331, 332, and 389 are not conserved between ODC and AZI (Open in a separate windowFIGURE 1.Crystal structure and the amino acid residues at the dimer interface of human ornithine decarboxylase (hODC) and mouse antizyme inhibitor (mAZI). A, homodimeric structure of human ODC with the cofactor PLP analog, LLP (Protein Data Bank code 1D7K). B, putative dimeric structure of mouse AZI (Protein Data Bank code 3BTN). The amino acid residues in the dimer interface are shown as a ball-and-stick model. The putative AZ-binding site is colored in cyan. This figure was generated using PyMOL (DeLano Scientific LLC, San Carlos, CA).

TABLE 1

Amino acid residues at the dimer interface of human ODC and AZI

Immunomodulation by Mesenchymal Stem Cells in Veterinary Species

Mesenchymal stem cells (MSC) are adult-derived multipotent stem cells that have been derived from almost every tissue. They are classically defined as spindle-shaped, plastic-adherent cells capable of adipogenic, chondrogenic, and osteogenic differentiation. This capacity for trilineage differentiation has been the foundation for research into the use of MSC to regenerate damaged tissues. Recent studies have shown that MSC interact with cells of the immune system and modulate their function. Although many of the details underlying the mechanisms by which MSC modulate the immune system have been defined for human and rodent (mouse and rat) MSC, much less is known about MSC from other veterinary species. This knowledge gap is particularly important because the clinical use of MSC in veterinary medicine is increasing and far exceeds the use of MSC in human medicine. It is crucial to determine how MSC modulate the immune system for each animal species as well as for MSC derived from any given tissue source. A comparative approach provides a unique translational opportunity to bring novel cell-based therapies to the veterinary market as well as enhance the utility of animal models for human disorders. The current review covers what is currently known about MSC and their immunomodulatory functions in veterinary species, excluding laboratory rodents.Abbreviations: AT, adipose tissue; BM, Bone marrow; CB, umbilical cord blood; CT, umbilical cord tissue; DC, dendritic cell; IDO, indoleamine 2;3-dioxygenase; MSC, mesenchymal stem cells; PGE₂, prostaglandin E2; VEGF, vascular endothelial growth factorMesenchymal stem cells (MSC, alternatively known as mesenchymal stromal cells) were first reported in the literature in 1968.³⁹ MSC are thought to be of pericyte origin (cells that line the vasculature)^21,22 and typically are isolated from highly vascular tissues. In humans and mice, MSC have been isolated from fat, placental tissues (placenta, Wharton jelly, umbilical cord, umbilical cord blood), hair follicles, tendon, synovial membrane, periodontal ligament, and every major organ (brain, spleen, liver, kidney, lung, bone marrow, muscle, thymus, pancreas, skin).^23,121 For most current clinical applications, MSC are isolated from adipose tissue (AT), bone marrow (BM), umbilical cord blood (CB), and umbilical cord tissue (CT; 11,87,99 Clinical trials in human medicine focus on the use of MSC both for their antiinflammatory properties (graft-versus-host disease, irritable bowel syndrome) and their ability to aid in tissue and bone regeneration in combination with growth factors and bone scaffolds (clinicaltrials.gov).¹³¹ For tissue regeneration, the abilities of MSC to differentiate and to secrete mediators and interact with cells of the immune system likely contribute to tissue healing (Figure 1). The current review will not address the specific use of MSC for orthopedic applications and tissue regeneration, although the topic is covered widely in current literature for both human and veterinary medicine.^57,62,90

Table 1.

Tissues from which MSC have been isolated

Natural Infection of Burkholderia pseudomallei in an Imported Pigtail Macaque (Macaca nemestrina) and Management of the Exposed Colony

Identification of the select agent Burkholderia pseudomallei in macaques imported into the United States is rare. A purpose-bred, 4.5-y-old pigtail macaque (Macaca nemestrina) imported from Southeast Asia was received from a commercial vendor at our facility in March 2012. After the initial acclimation period of 5 to 7 d, physical examination of the macaque revealed a subcutaneous abscess that surrounded the right stifle joint. The wound was treated and resolved over 3 mo. In August 2012, 2 mo after the stifle joint wound resolved, the macaque exhibited neurologic clinical signs. Postmortem microbiologic analysis revealed that the macaque was infected with B. pseudomallei. This case report describes the clinical evaluation of a B. pseudomallei-infected macaque, management and care of the potentially exposed colony of animals, and protocols established for the animal care staff that worked with the infected macaque and potentially exposed colony. This article also provides relevant information on addressing matters related to regulatory issues and risk management of potentially exposed animals and animal care staff.Abbreviations: CDC, Centers for Disease Control and Prevention; IHA, indirect hemagglutination assay; PEP, postexposure prophylacticBurkholderia pseudomallei, formerly known as Pseudomonas pseudomallei, is a gram-negative, aerobic, bipolar, motile, rod-shaped bacterium. B. pseudomallei infections (melioidosis) can be severe and even fatal in both humans and animals. This environmental saprophyte is endemic to Southeast Asia and northern Australia, but it has also been found in other tropical and subtropical areas of the world.^7,22,32,42 The bacterium is usually found in soil and water in endemic areas and is transmitted to humans and animals primarily through percutaneous inoculation, ingestion, or inhalation of a contaminated source.^{8, 22,28,32,42} Human-to-human, animal-to-animal, and animal-to-human spread are rare.^8,32 In December 2012, the National Select Agent Registry designated B. pseudomallei as a Tier 1 overlap select agent.³⁹ Organisms classified as Tier 1 agents present the highest risk of deliberate misuse, with the most significant potential for mass casualties or devastating effects to the economy, critical infrastructure, or public confidence. Select agents with this status have the potential to pose a severe threat to human and animal health or safety or the ability to be used as a biologic weapon.³⁹Melioidosis in humans can be challenging to diagnose and treat because the organism can remain latent for years and is resistant to many antibiotics.^12,37,41 B. pseudomallei can survive in phagocytic cells, a phenomenon that may be associated with latent infections.^19,38 The incubation period in naturally infected animals ranges from 1 d to many years, but symptoms typically appear 2 to 4 wk after exposure.^13,17,35,38 Disease generally presents in 1 of 2 forms: localized infection or septicemia.²² Multiple methods are used to diagnose melioidosis, including immunofluorescence, serology, and PCR analysis, but isolation of the bacteria from blood, urine, sputum, throat swabs, abscesses, skin, or tissue lesions remains the ‘gold standard.’^9,22,40,42 The prognosis varies based on presentation, time to diagnosis, initiation of appropriate antimicrobial treatment, and underlying comorbidities.^7,28,42 Currently, there is no licensed vaccine to prevent melioidosis.There are several published reports of naturally occurring melioidosis in a variety of nonhuman primates (NHP; 2,10,13,17,25,30,31,35 The first reported case of melioidosis in monkeys was recorded in 1932, and the first published case in a macaque species was in 1966.³⁰ In the United States, there have only been 7 documented cases of NHP with B. pseudomallei infection.^2,13,17 All of these cases occurred prior to the classification of B. pseudomallei as a select agent. Clinical signs in NHP range from subclinical or subacute illness to acute septicemia, localized infection, and chronic infection. NHP with melioidosis can be asymptomatic or exhibit clinical signs such as anorexia, wasting, purulent drainage, subcutaneous abscesses, and other soft tissue lesions. Lymphadenitis, lameness, osteomyelitis, paralysis and other CNS signs have also been reported.^{2,7,10,22,28,32} In comparison, human''s clinical signs range from abscesses, skin ulceration, fever, headache, joint pain, and muscle tenderness to abdominal pain, anorexia, respiratory distress, seizures, and septicemia.^7,9,21,22

Table 1.

Summary of reported cases of naturally occurring Burkholderia pseudomalleiinfections in nonhuman primates

Mass Spectrometry Based Glycoproteomics—From a Proteomics Perspective

Glycosylation is one of the most important and common forms of protein post-translational modification that is involved in many physiological functions and biological pathways. Altered glycosylation has been associated with a variety of diseases, including cancer, inflammatory and degenerative diseases. Glycoproteins are becoming important targets for the development of biomarkers for disease diagnosis, prognosis, and therapeutic response to drugs. The emerging technology of glycoproteomics, which focuses on glycoproteome analysis, is increasingly becoming an important tool for biomarker discovery. An in-depth, comprehensive identification of aberrant glycoproteins, and further, quantitative detection of specific glycosylation abnormalities in a complex environment require a concerted approach drawing from a variety of techniques. This report provides an overview of the recent advances in mass spectrometry based glycoproteomic methods and technology, in the context of biomarker discovery and clinical application.With recent advances in proteomics, analytical and computational technologies, glycoproteomics—the global analysis of glycoproteins—is rapidly emerging as a subfield of proteomics with high biological and clinical relevance. Glycoproteomics integrates glycoprotein enrichment and proteomics technologies to support the systematic identification and quantification of glycoproteins in a complex sample. The recent development of these techniques has stimulated great interest in applying the technology in clinical translational studies, in particular, protein biomarker research.While glycomics is the study of glycome (repertoire of glycans), glycoproteomics focuses on studying the profile of glycosylated proteins, i.e. the glycoproteome, in a biological system. Considerable work has been done to characterize the sequences and primary structure of the glycan moieties attached to proteins (1–3), and their structural alterations related to cancer (4–6). Recent reports have provided a comprehensive overview of the concept of glycomics and its prospective in biomarker research (7–10). In contrast, this review is focused on recent developments in glycoproteomic techniques and their unique application and technical challenge to biomarker discovery.

Glycoproteomics in Biomarker Discovery and Clinical Study

Most secretory and membrane-bound proteins produced by mammalian cells contain covalently linked glycans with diverse structures (2). The glycosylation form of a glycoprotein is highly specific at each glycosylation site and generally stable for a given cell type and physiological state. However, the glycosylation form of a protein can be altered significantly because of changes in cellular pathways and processes resulting from diseases, such as cancer, inflammation, and neurodegeneration. Such disease-associated alterations in glycoproteins can happen in one or both of two ways: 1) protein glycosylation sites are either hypo, hyper, or newly glycosylated and/or; 2) the glycosylation form of the attached carbohydrate moiety is altered. In fact, altered glycosylation patterns have long been recognized as hallmarks in cancer progression, in which tumor-specific glycoproteins are actively involved in neoplastic progression and metastasis (5, 6, 11, 12). Sensitive detection of such disease-associated glycosylation changes and abnormalities can provide a unique avenue to develop glycoprotein biomarkers for diagnosis and prognosis. In addition, intervention in the glycosylation and carbohydrate-dependent cellular pathways represent a potential new modality for cancer therapies (6, 11, 13). 14, 15) that are glycosylated proteins or protein complexes.

Table I

Listing of some of the US Food and Drug Administration (FDA) approved cancer biomarkers

Comparative Analysis of Myxococcus Predation on Soil Bacteria

Predator-prey relationships among prokaryotes have received little attention but are likely to be important determinants of the composition, structure, and dynamics of microbial communities. Many species of the soil-dwelling myxobacteria are predators of other microbes, but their predation range is poorly characterized. To better understand the predatory capabilities of myxobacteria in nature, we analyzed the predation performance of numerous Myxococcus isolates across 12 diverse species of bacteria. All predator isolates could utilize most potential prey species to effectively fuel colony expansion, although one species hindered predator swarming relative to a control treatment with no growth substrate. Predator strains varied significantly in their relative performance across prey types, but most variation in predatory performance was determined by prey type, with Gram-negative prey species supporting more Myxococcus growth than Gram-positive species. There was evidence for specialized predator performance in some predator-prey combinations. Such specialization may reduce resource competition among sympatric strains in natural habitats. The broad prey range of the Myxococcus genus coupled with its ubiquity in the soil suggests that myxobacteria are likely to have very important ecological and evolutionary effects on many species of soil prokaryotes.Predation plays a major role in shaping both the ecology and evolution of biological communities. The population and evolutionary dynamics of predators and their prey are often tightly coupled and can greatly influence the dynamics of other organisms as well (1). Predation has been invoked as a major cause of diversity in ecosystems (11, 12). For example, predators may mediate coexistence between superior and inferior competitors (2, 13), and differential trajectories of predator-prey coevolution can lead to divergence between separate populations (70).Predation has been investigated extensively in higher organisms but relatively little among prokaryotes. Predation between prokaryotes is one of the most ancient forms of predation (27), and it has been proposed that this process may have been the origin of eukaryotic cells (16). Prokaryotes are key players in primary biomass production (44) and global nutrient cycling (22), and predation of some prokaryotes by others is likely to significantly affect these processes. Most studies of predatory prokaryotes have focused on Bdellovibrionaceae species (e.g., see references 51, 55, and 67). These small deltaproteobacteria prey on other Gram-negative cells, using flagella to swim rapidly until they collide with a prey cell. After collision, the predator cells then enter the periplasmic space of the prey cell, consume the host cell from within, elongate, and divide into new cells that are released upon host cell lysis (41). Although often described as predatory, the Bdellovibrionaceae may also be considered to be parasitic, as they typically depend (apart from host-independent strains that have been observed [60]) on the infection and death of their host for their reproduction (47).In this study, we examined predation among the myxobacteria, which are also deltaproteobacteria but constitute a monophyletic clade divergent from the Bdellovibrionaceae (17). Myxobacteria are found in most terrestrial soils and in many aquatic environments as well (17, 53, 74). Many myxobacteria, including the model species Myxococcus xanthus, exhibit several complex social traits, including fruiting body formation and spore formation (14, 18, 34, 62, 71), cooperative swarming with two motility systems (64, 87), and group (or “wolf pack”) predation on both bacteria and fungi (4, 5, 8, 9, 15, 50). Using representatives of the genus Myxococcus, we tested for both intra- and interspecific variation in myxobacterial predatory performance across a broad range of prey types. Moreover, we examined whether prey vary substantially in the degree to which they support predatory growth by the myxobacteria and whether patterns of variation in predator performance are constant or variable across prey environments. The latter outcome may reflect adaptive specialization and help to maintain diversity in natural populations (57, 59).Although closely related to the Bdellovibrionaceae (both are deltaproteobacteria), myxobacteria employ a highly divergent mode of predation. Myxobacteria use gliding motility (64) to search the soil matrix for prey and produce a wide range of antibiotics and lytic compounds that kill and decompose prey cells and break down complex polymers, thereby releasing substrates for growth (66). Myxobacterial predation is cooperative both in its “searching” component (6, 31, 82; for details on cooperative swarming, see reference 64) and in its “handling” component (10, 29, 31, 32), in which secreted enzymes turn prey cells into consumable growth substrates (56, 83). There is evidence that M. xanthus employs chemotaxis-like genes in its attack on prey cells (5) and that predation is stimulated by close contact with prey cells (48).Recent studies have revealed great genetic and phenotypic diversity within natural populations of M. xanthus, on both global (79) and local (down to centimeter) scales (78). Phenotypic diversity includes variation in social compatibility (24, 81), the density and nutrient thresholds triggering development (33, 38), developmental timing (38), motility rates and patterns (80), and secondary metabolite production (40). Although natural populations are spatially structured and both genetic diversity and population differentiation decrease with spatial scale (79), substantial genetic diversity is present even among centimeter-scale isolates (78). No study has yet systematically investigated quantitative natural variation in myxobacterial predation phenotypes across a large number of predator genotypes.Given the previous discovery of large variation in all examined phenotypes, even among genetically extremely similar strains, we anticipated extensive predatory variation as well. Using a phylogenetically broad range of prey, we compared and contrasted the predatory performance of 16 natural M. xanthus isolates, sampled from global to local scales, as well as the commonly studied laboratory reference strain DK1622 and representatives of three additional Myxococcus species: M. flavescens (86), M. macrosporus (42), and M. virescens (63) (Table ​(Table1).1). In particular, we measured myxobacterial swarm expansion rates on prey lawns spread on buffered agar (31, 50) and on control plates with no nutrients or with prehydrolyzed growth substrate.

TABLE 1.

List of myxobacteria used, with geographical origin