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.     

Viral OTU Deubiquitinases: A Structural and Functional Comparison

Recent studies have revealed that proteases encoded by three very diverse RNA virus groups share structural similarity with enzymes of the Ovarian Tumor (OTU) superfamily of deubiquitinases (DUBs). The publication of the latest of these reports in quick succession prevented proper recognition and discussion of the shared features of these viral enzymes. Here we provide a brief structural and functional comparison of these virus-encoded OTU DUBs. Interestingly, although their shared structural features and substrate specificity tentatively place them within the same protease superfamily, they also show interesting differences that trigger speculation as to their origins.The covalent attachment of ubiquitin (Ub) to protein substrates, i.e., ubiquitination, plays a pivotal regulatory role in numerous cellular processes [1]–[5]. Ubiquitination can be reversed by deubiquitinases (DUBs) [6] and, not surprisingly, various virus groups encode such DUBs to influence ubiquitin-mediated host cell processes [7]–[21]. Some of these viral DUBs resemble proteases belonging to the Ovarian Tumor (OTU) superfamily [22]–[28]. Makarova et al. previously identified OTU proteases as a novel superfamily of cysteine proteases from different organisms [29], and their bioinformatics-based analysis included several of the viral enzymes discussed here. Recently reported structures of these viral DUBs include the OTU domains of the nairoviruses Crimean-Congo hemorrhagic fever virus (CCHFV) [22]–[24] and Dugbe virus (DUGV) [25], the papain-like protease (PLP2) domain of the arterivirus equine arteritis virus (EAV) [26], and the protease (PRO) domain of the tymovirus turnip yellow mosaic virus (TYMV) (Figure 1A–1D) [27], [28]. These viruses are strikingly diverse, considering that nairoviruses are mammalian negative-strand RNA viruses, while the mammalian arteriviruses and plant tymoviruses belong to separate orders of positive-strand RNA viruses.Open in a separate windowFigure 1Viral and eukaryotic OTU domain structures and viral protein context.Crystal structures of (A) CCHFV OTU (3PT2) [23], (B) DUGV OTU (4HXD) [25], (C) EAV PLP2 (4IUM) [26], (D) TYMV PRO (4A5U) [27], [28], (E) yeast OTU1 (3BY4) [57], and (F) human OTUD3 (4BOU) [46]. The β-hairpin motifs of CCHFV OTU and DUGV OTU are indicated in boxes in panels A and B, respectively, and the zinc-finger motif of EAV PLP2 is boxed in panel C. Active sites are indicated with arrows. The CCHFV OTU, DUGV OTU, EAV PLP2, and yeast OTU1 domains were crystallized in complex with Ub, which has been removed for clarity. Structure images were generated using PyMol [60]. (G) Schematic representation of the CCHFV large (L) protein [61], [62]. A similar organization is found in the DUGV L protein, but is not depicted. The OTU domain resides in the N-terminal region of this protein and is not involved in autoproteolytic cleavage events [48]. (H) Schematic representation of the EAV polyprotein 1ab [63]. PLP2 resides in nonstructural protein 2 (nsp2) and is responsible for the cleavage between nsp2 and nsp3 [51]. (I) Schematic representation of the TYMV ORF1 polyprotein [50]. PRO resides in the N-terminal product of this polyprotein and is responsible for two internal cleavages [49], [50]. Key replicative enzymes are indicated in G, H, and I. Colored arrowheads denote cleavage sites for the indicated protease domains. HEL, helicase; PLP, papain-like protease; RdRp, RNA-dependent RNA polymerase; SP, serine protease.Ubiquitination often involves the formation of polyubiquitin chains [1], which can target the ubiquitinated substrate to the proteasome for degradation [2] or modulate its protein–protein interactions, as in the activation of innate immune signaling pathways [3], [4]. Interestingly, several cellular OTU DUBs were found to negatively regulate innate immunity [30]–[33]. Likewise, both nairovirus OTU and arterivirus PLP2 were recently shown to inhibit innate immune responses by targeting ubiquitinated signaling factors [7]–[9], [26], [34], [35]. In contrast to eukaryotic OTU DUBs, both of these viral proteases were found to also deconjugate the Ub-like protein interferon-stimulated gene 15 (ISG15) [7], [36], which inhibits viral replication via a mechanism that is currently poorly understood [37]. Interestingly, coronaviruses (which, together with the arteriviruses, belong to the nidovirus order) also encode papain-like proteases targeting both Ub and ISG15 that were shown to inhibit innate immunity [11]–[13], [38]–[42] but belong to the ubiquitin-specific protease (USP) class of DUBs [6], [43], [44]. The presence of functionally similar, yet structurally different proteases in distantly related virus families highlights the potential benefits to the virus of harboring such enzymes.The proteasomal degradation pathway is an important cellular route to dispose of viral proteins, as exemplified by the turnover of the TYMV polymerase [45]. Moreover, the degradation of this protein is specifically counteracted by the deubiquitinase activity of TYMV PRO, which thus promotes virus replication [10]. The functional characterization of viral OTU DUBs remains incomplete and future studies will likely reveal additional roles in replication and virus–host interplay.Polyubiquitin chains can adopt a number of different configurations, depending on the type of covalent linkage present within the polymer [1]. A distal Ub molecule can be linked via its C-terminus to one of seven internal lysine residues present in a proximal Ub molecule via an isopeptide bond. Alternatively, in the case of linear chains, the C-terminus of the distal Ub is covalently linked to the N-terminal methionine residue of the proximal Ub via a peptide bond. While human OTU proteases often show a distinct preference for one or two isopeptide linkage types [46], nairovirus OTUs and TYMV PRO appear to be more promiscuous in their substrate preference [22], [25]. However, like most human OTU proteases, they seem unable to cleave linear polyubiquitin chains in vitro [22], [25], [46]. Arterivirus PLP2 has not been extensively studied in this respect.It is important to note that many positive-strand RNA viruses, including arteriviruses and tymoviruses, encode polyproteins that are post-translationally cleaved by internal protease domains [47]. Thus, while CCHFV OTU is not involved in viral protein cleavage and its activity seems dispensable for replication (Figure 1G) [48], both arterivirus PLP2 and tymovirus PRO are critically required for viral replication due to their primary role in polyprotein maturation (Figure 1H, 1I) [49]–[53]. Interestingly, while both EAV PLP2 and TYMV PRO can process peptide bonds in cis and in trans [50], [51], PRO does not cleave peptide bonds in linear polyubiquitin chains in vitro [25]. To date, activity of EAV PLP2 towards linear polyubiquitin chains has not been reported.Based on mutagenesis of putative catalytic residues, arterivirus PLP2 and tymovirus PRO were initially generally classified as papain-like cysteine proteases [51], [54], [55]. Now that crystal structures of these proteases are available, it is possible to search the DALI server [56] in order to identify structurally similar domains. Using the 3-dimensional coordinates of TYMV PRO, the most recently solved structure of a viral OTU protease, such a query identifies structural similarity with eukaryotic OTU DUBs as well as the nairovirus OTU domains and EAV PLP2 ([57] further highlights their similarities (Figure 2A–2C), and these comparisons together clearly position them within the OTU DUB superfamily. Sequence comparisons alone were insufficient to demonstrate this conclusively, as the similarity of viral OTU domains to each other and to eukaryotic OTU proteases is very limited and mostly restricted to the areas surrounding the active site residues [29].Open in a separate windowFigure 2Superpositions of the viral OTU proteases with yeast OTU1 and one another.Superpositions of yeast OTU1 (3BY4) [57] with (A) CCHFV OTU (3PT2) [23], RMSD: 1.8 Å over 112 residues, (B) EAV PLP2 (4IUM) [26], RMSD: 2.8 Å over 69 residues, and (C) TYMV PRO (4A5U) [27], [28], RMSD: 1.4 Å over 76 residues. Superpositions of the yeast OTU1-Ub complex with (D) the CCHFV OTU-Ub complex and (E) the EAV PLP2-Ub complex, highlighting the difference in the orientation of Ub between the two viral OTU domains versus the eukaryotic yeast OTU1 domain. The Ub that is complexed with yeast OTU1 is depicted in yellow, while the Ub complexed with CCHFV OTU or EAV PLP2 is depicted in orange. (F) Superposition of EAV PLP2 and TYMV PRO, RMSD: 2.5 Å over 53 residues. (G) Close-up of the active site region (boxed) of the superposition depicted in F. Side chains of the catalytic cysteine (Cys270 and Cys783 for EAV PLP2 and TYMV PRO, respectively) and histidine (His332 and His869 for EAV PLP2 and TYMV PRO, respectively) residues are shown as sticks, as well as the active site Asn263 for EAV PLP2. The backbone amide group of Asp267 likely contributes to the formation of the oxyanion hole in the active site of EAV PLP2, yet a functionally equivalent residue is absent in TYMV PRO. The Gly266 and Gly268 residues flanking Asp267 in EAV PLP2 are depicted as sticks as well, for clarity. Note the alternative orientation of the active site cysteine residue of TYMV PRO which, unlike EAV PLP2, was not determined in covalent complex with an Ub suicide substrate. All alignments were generated using the PDBeFOLD server [64], and thus the reported RMSD values differ from those reported in [60]. RMSD, root-mean-square deviation.

Table 1

Three-dimensional structural alignment of viral OTU domains against selected structures in the Protein Data Bank using the DALI server [56].

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.     

A Neutralizing Anti-gH/gL Monoclonal Antibody Is Protective in the Guinea Pig Model of Congenital CMV Infection

Human cytomegalovirus (HCMV) is the most common cause of congenital virus infection. Congenital HCMV infection occurs in 0.2–1% of all births, and causes birth defects and developmental abnormalities, including sensorineural hearing loss and developmental delay. Several key studies have established the guinea pig as a tractable model for the study of congenital HCMV infection and have shown that polyclonal antibodies can be protective [1]–[3]. In this study, we demonstrate that an anti-guinea pig CMV (GPCMV) glycoprotein H/glycoprotein L neutralizing monoclonal antibody protects against fetal infection and loss in the guinea pig. Furthermore, we have delineated the kinetics of GPCMV congenital infection, from maternal infection (salivary glands, seroconversion, placenta) to fetal infection (fetus and amniotic fluid). Our studies support the hypothesis that a neutralizing monoclonal antibody targeting an envelope GPCMV glycoprotein can protect the fetus from infection and may shed light on the therapeutic intervention of HCMV congenital infection in humans.     

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.     

Helicobacter pylori's Unconventional Role in Health and Disease

The discovery of a bacterium, Helicobacter pylori, that is resident in the human stomach and causes chronic disease (peptic ulcer and gastric cancer) was radical on many levels. Whereas the mouth and the colon were both known to host a large number of microorganisms, collectively referred to as the microbiome, the stomach was thought to be a virtual Sahara desert for microbes because of its high acidity. We now know that H. pylori is one of many species of bacteria that live in the stomach, although H. pylori seems to dominate this community. H. pylori does not behave as a classical bacterial pathogen: disease is not solely mediated by production of toxins, although certain H. pylori genes, including those that encode exotoxins, increase the risk of disease development. Instead, disease seems to result from a complex interaction between the bacterium, the host, and the environment. Furthermore, H. pylori was the first bacterium observed to behave as a carcinogen. The innate and adaptive immune defenses of the host, combined with factors in the environment of the stomach, apparently drive a continuously high rate of genomic variation in H. pylori. Studies of this genetic diversity in strains isolated from various locations across the globe show that H. pylori has coevolved with humans throughout our history. This long association has given rise not only to disease, but also to possible protective effects, particularly with respect to diseases of the esophagus. Given this complex relationship with human health, eradication of H. pylori in nonsymptomatic individuals may not be the best course of action. The story of H. pylori teaches us to look more deeply at our resident microbiome and the complexity of its interactions, both in this complex population and within our own tissues, to gain a better understanding of health and disease.Common wisdom circa 1980 suggested that the stomach, with its low pH, was a sterile environment. Then, endoscopy of the stomach became common and, in 1984, pathologist Robin Warren and gastroenterologist Barry Marshall saw an extracellular, curved bacillus, often in dense sheets, lining the stomach epithelium of patients with gastritis (inflammation of the stomach) and ulcer disease [1]. Soon, the medical community understood that the gram-negative bacterium Helicobacter pylori, not stress, is the major cause of stomach inflammation, which, in some infected individuals, precedes peptic ulcer disease (10%–20%), distal gastric adenocarcinoma (1%–2%), and gastric mucosal-associated lymphoid tissue (MALT) lymphoma (<1%) [2]–[5]. Thus, H. pylori gained distinction as the only known bacterial carcinogen [6]. It is believed that half of the world''s population is infected with H. pylori; however, the burden of disease falls disproportionately on less-developed countries. The incidence of infection in developed countries has fallen dramatically, for unknown reasons, with a corresponding decrease in gastric cancer [7]. This public health success is tempered by the recent demonstration of an inverse relationship between H. pylori infection and esophageal adenocarcinoma, Barrett''s esophagus, and reflux esophagitis [8]. H. pylori has been with humans since our earliest days, thus it is not surprising that its relationship is that of both a commensal bacterium and a pathogen, causing some diseases and possibly protecting against others. In addition, it is genetically diverse, likely as a result of constant exposure to both environmental and immunological selection, suggesting that genetic diversification is a strategy for long-term colonization.     

Archaeological Support for the Three-Stage Expansion of Modern Humans across Northeastern Eurasia and into the Americas

Background

Understanding the dynamics of the human range expansion across northeastern Eurasia during the late Pleistocene is central to establishing empirical temporal constraints on the colonization of the Americas [1]. Opinions vary widely on how and when the Americas were colonized, with advocates supporting either a pre-[2] or post-[1], [3], [4], [5], [6] last glacial maximum (LGM) colonization, via either a land bridge across Beringia [3], [4], [5], a sea-faring Pacific Rim coastal route [1], [3], a trans-Arctic route [4], or a trans-Atlantic oceanic route [5]. Here we analyze a large sample of radiocarbon dates from the northeast Eurasian Upper Paleolithic to identify the origin of this expansion, and estimate the velocity of colonization wave as it moved across northern Eurasia and into the Americas.

Methodology/Principal Findings

We use diffusion models [6], [7] to quantify these dynamics. Our results show the expansion originated in the Altai region of southern Siberia ∼46kBP , and from there expanded across northern Eurasia at an average velocity of 0.16 km per year. However, the movement of the colonizing wave was not continuous but underwent three distinct phases: 1) an initial expansion from 47-32k calBP; 2) a hiatus from ∼32-16k calBP, and 3) a second expansion after the LGM ∼16k calBP. These results provide archaeological support for the recently proposed three-stage model of the colonization of the Americas [8], [9]. Our results falsify the hypothesis of a pre-LGM terrestrial colonization of the Americas and we discuss the importance of these empirical results in the light of alternative models.

Conclusions/Significance

Our results demonstrate that the radiocarbon record of Upper Paleolithic northeastern Eurasia supports a post-LGM terrestrial colonization of the Americas falsifying the proposed pre-LGM terrestrial colonization of the Americas. We show that this expansion was not a simple process, but proceeded in three phases, consistent with genetic data, largely in response to the variable climatic conditions of late Pleistocene northeast Eurasia. Further, the constraints imposed by the spatiotemporal gradient in the empirical radiocarbon record across this entire region suggests that North America cannot have been colonized much before the existing Clovis radiocarbon record suggests.     

Culture-Independent Microbiological Analysis of Foley Urinary Catheter Biofilms

Background

Prevention of catheter-associated urinary tract infection (CAUTI), a leading cause of nosocomial disease, is complicated by the propensity of bacteria to form biofilms on indwelling medical devices [1], [2], [3], [4], [5].

Methodology/Principal Findings

To better understand the microbial diversity of these communities, we report the results of a culture-independent bacterial survey of Foley urinary catheters obtained from patients following total prostatectomy. Two patient subsets were analyzed, based on treatment or no treatment with systemic fluoroquinolone antibiotics during convalescence. Results indicate the presence of diverse polymicrobial assemblages that were most commonly observed in patients who did not receive systemic antibiotics. The communities typically contained both Gram-positive and Gram-negative microorganisms that included multiple potential pathogens.

Conclusion/Significance

Prevention and treatment of CAUTI must take into consideration the possible polymicrobial nature of any particular infection.     

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.

Status and the Brain

Social hierarchy is a fact of life for many animals. Navigating social hierarchy requires understanding one''s own status relative to others and behaving accordingly, while achieving higher status may call upon cunning and strategic thinking. The neural mechanisms mediating social status have become increasingly well understood in invertebrates and model organisms like fish and mice but until recently have remained more opaque in humans and other primates. In a new study in this issue, Noonan and colleagues explore the neural correlates of social rank in macaques. Using both structural and functional brain imaging, they found neural changes associated with individual monkeys'' social status, including alterations in the amygdala, hypothalamus, and brainstem—areas previously implicated in dominance-related behavior in other vertebrates. A separate but related network in the temporal and prefrontal cortex appears to mediate more cognitive aspects of strategic social behavior. These findings begin to delineate the neural circuits that enable us to navigate our own social worlds. A major remaining challenge is identifying how these networks contribute functionally to our social lives, which may open new avenues for developing innovative treatments for social disorders.

“Observing the habitual and almost sacred ‘pecking order’ which prevails among the hens in his poultry yard—hen A pecking hen B, but not being pecked by it, hen B pecking hen C and so forth—the politician will meditate on the Catholic hierarchy and Fascism.” —Aldous Huxley, Point Counter Point (1929)

From the schoolyard to the boardroom, we are all, sometimes painfully, familiar with the pecking order. First documented by the Norwegian zoologist Thorleif Schjelderup-Ebbe in his PhD thesis on social status in chickens in the 1920s, a pecking order is a hierarchical social system in which each individual is ranked in order of dominance [1]. In chickens, the top hen can peck all lower birds, the second-ranking bird can peck all birds ranked below her, and so on. Since it was first coined, the term has become widely applied to any such hierarchical system, from business, to government, to the playground, to the military.Social hierarchy is a fact of life not only for humans and chickens but also for most highly social, group-living animals. Navigating social hierarchies and achieving dominance often appear to require cunning, intelligence, and strategic social planning. Indeed, the Renaissance Italian politician and writer Niccolo Machiavelli argued in his best-known book “The Prince” that the traits most useful for attaining and holding on to power include manipulation and deception [2]. Since then, the term “Machiavellian” has come to signify a person who deceives and manipulates others for personal advantage and power. 350 years later, Frans de Waal applied the term Machiavellian to social maneuvering by chimpanzees in his book Chimpanzee Politics [3]. De Waal argued that chimpanzees, like Renaissance Italian politicians, apply guile, manipulation, strategic alliance formation, and deception to enhance their social status—in this case, not to win fortune and influence but to increase their reproductive success (which is presumably the evolutionary origin of status-seeking in Renaissance Italian politicians as well).The observation that navigating large, complex social groups in chimpanzees and many other primates seems to require sophisticated cognitive abilities spurred the development of the social brain hypothesis, originally proposed to explain why primates have larger brains for their body size than do other animals [4],[5]. Since its first proposal, the social brain hypothesis has accrued ample evidence endorsing the connections between increased social network complexity, enhanced social cognition, and larger brains. For example, among primates, neorcortex size, adjusted for the size of the brain or body, varies with group size [6],[7], frequency of social play [8], and social learning [9].Of course, all neuroscientists know that when it comes to brains, size isn''t everything [10]. Presumably social cognitive functions required for strategic social behavior are mediated by specific neural circuits. Here, we summarize and discuss several recent discoveries, focusing on an article by Noonan and colleagues in the current issue, which together begin to delineate the specific neural circuits that mediate our ability to navigate our social worlds.Using structural magnetic resonance imaging (MRI), Bickart and colleagues showed that the size of the amygdala—a brain nucleus important for emotion, vigilance, and rapid behavioral responses—is correlated with social network size in humans [11]. Subsequent studies showed similar relationships for other brain regions implicated in social function, including the orbitofrontal cortex [12] and ventromedial prefrontal cortex [13]. Indeed, one study even found an association between grey matter density in the superior temporal sulcus (STS) and temporal gyrus and an individual''s number of Facebook friends [14]. Collectively, these studies suggest that the number and possibly the complexity of relationships one maintains varies with the structural organization of a specific network of brain regions, which are recruited when people perform tests of social cognition such as recognizing faces or inferring others'' mental states [15],[16]. These studies, however, do not reveal whether social complexity actively changes these brain areas through plasticity or whether individual differences in the structure of these networks ultimately determines social abilities.To address this question, Sallet and colleagues experimentally assigned rhesus macaques to social groups of different sizes and then scanned their brains with MRI [17]. The authors found significant positive associations between social network size and morphology in mid-STS, rostral STS, inferior temporal (IT) gyrus, rostral prefrontal cortex (rPFC), temporal pole, and amygdala. The authors also found a different region in rPFC that scaled positively with social rank; as grey matter in this region increased, so did the monkey''s rank in the hierarchy. As in the human studies described previously, many of these regions are implicated in various aspects of social cognition and perception [18]. These findings endorse the idea that neural plasticity is engaged in specifically social brain areas in response to the demands of the social environment, changing these areas structurally according to an individual''s experiences with others.Sallet and colleagues also examined spontaneous coactivation among these regions using functional MRI (fMRI). Measures of coactivation are thought to reflect coupling between regions [19],[20]; these measures are observable in many species [21],[22] and vary according to behavior [23],[24], genetics [25], and sex [26], suggesting that coactivation may underlie basic neural function and interaction between brain regions. The authors found that coactivation between the STS and rPFC increased with social network size and that coactivation between IT and rPFC increased with social rank. These findings show that not only do structural changes occur in these regions to meet the demands of the social environment but these structural changes mediate changes in function as well.One important question raised by the study by Sallet and colleagues is whether changes in the structure and function of social brain areas are specific outcomes of social network size or of dealing with social hierarchy. After all, larger groups offer more opportunity for a larger, more despotic pecking order. In the current volume, Noonan and colleagues address this question directly by examining the structural and functional correlates of social status in macaques independently of social group size [27]. The authors collected MRI scans from rhesus macaques and measured changes in grey matter associated with social dominance. By scanning monkeys of different ranks living in groups of different sizes, the authors were able to cleave the effects of social rank from those of social network size (Figure 1).Open in a separate windowFigure 1Brain regions in rhesus macaques related to social environment.Primary colors indicate brain regions in which morphometry tracks social network size. Pastel colors indicate brain regions in which morphometry tracks social status in the hierarchy. Regions of interest adapted from [48], overlaid on Montreal Neurological Institute (MNI) macaque template [49].The authors found a network of regions in which grey matter measures varied with social rank; these regions included the bilateral central amygdala, bilateral brainstem (between the medulla and midbrain, including parts of the raphe nuclei), and hypothalamus, which varied positively with dominance, and regions in the basal ganglia, which varied negatively with social rank. These regions have been implicated in social rank functions across a number of species [28]–[32]. Importantly, these relationships were unique to social status. There was no relationship between grey matter in these subcortical areas and social network size, endorsing a specific role in social dominance-related behavior. Nevertheless, grey matter in bilateral mid-STS and rPFC varied with both social rank and social network size, as reported previously. These findings demonstrate that specific brain areas uniquely mediate functions related to social hierarchy, whereas others may subserve more general social cognitive processes.Noonan and colleagues next probed spontaneous coactivation using fMRI to examine whether functional coupling between any of these regions varied with social status. They found that the more subordinate an animal, the stronger the functional coupling between multiple regions related to dominance. These results suggest that individual differences in social status are functionally observable in the brain even while the animal is at rest and not engaged in social behavior. These findings suggest that structural changes associated with individual differences in social status alter baseline brain function, consistent with the idea that the default mode of the brain is social [33] and that the sense of self and perhaps even awareness emerge from inwardly directed social reasoning [34].These findings resonate with previous work on the neural basis of social dominance in other vertebrates. In humans, for example, activity in the amygdala tracks knowledge of social hierarchy [28],[35] and, further, shows activity patterns that uniquely encode social rank and predict relevant behaviors [28]. Moreover, recent research has identified a specific region in the mouse hypothalamus, aptly named the “hypothalamic attack area” [36],[37]. Stimulating neurons in this area immediately triggers attacks on other mice and even an inflated rubber glove, while inactivating these neurons suppresses aggression [38]. In the African cichlid fish Haplochromis burtoni, a change in the social status of an individual male induces a reversible change in the abundance of specialized neurons in the hypothalamus that communicate hormonally with the pituitary and gonads [39]. Injections of this hormone in male birds after an aggressive territorial encounter amplifies the normal subsequent rise in testosterone [40]. Serotonin neurons in the raphe area of the brainstem also contribute to dominance-related behaviors in fish [29],[31] and aggression in monkeys [41].Despite these advances, there are still gaps in our understanding of how these circuits mediate status-related behaviors. Though regions in the amygdala, brainstem, and hypothalamus vary structurally and functionally with social rank, it remains unknown precisely how they contribute to or respond to social status. For example, though amygdala function and structure correlates with social status in both humans and nonhuman primates [27],[28],[35],[42], it remains unknown which aspects of dominance this region contributes to or underlies. One model suggests that the amygdala contributes to learning or representing one''s own status within a social hierarchy [28],[35]. Alternatively, the amygdala could contribute to behaviors that support social hierarchy, including gaze following [43] and theory of mind [44]. Lastly, the amygdala could contribute to social rank via interpersonal behaviors or personality traits, such as aggression [45], grooming [45], or fear responses [46],[47]. Future work will be critical to determine how signals in these regions relate to social status; direct manipulation of these regions, possibly via microstimulation, larger-scale brain stimulation (e.g., transcranial magnetic stimulation and transcranial direct current stimulation), or temporary lesions, will be critical to better understand these relationships.The work by Noonan and colleagues suggests new avenues for exploring how the brain both responds to and makes possible social hierarchy in nonhuman primates and humans. The fact that the neural circuits mediating dominance and social networking behavior can be identified and measured from structural and functional brain scans even at rest suggests the possibility that similar measures can be made in humans. Although social status is much more complex in people than it is in monkeys or fish, it is just as critical for us and most likely depends on shared neural circuits. Understanding how these circuits work, how they develop, and how they respond to the local social environment may help us to understand and ultimately treat disorders, like autism, social anxiety, or psychopathy, that are characterized by impaired social behavior and cognition.     

A Vertebrate-Specific Chp-PAK-PIX Pathway Maintains E-Cadherin at Adherens Junctions during Zebrafish Epiboly

Background

In early vertebrate development, embryonic tissues modulate cell adhesiveness and acto-myosin contractility to correctly orchestrate the complex processes of gastrulation. E-cadherin (E-cadh) is the earliest expressed cadherin and is needed in the mesendodermal progenitors for efficient migration [1], [2]. Regulatory mechanisms involving directed E-cadh trafficking have been invoked downstream of Wnt11/5 signaling [3]. This non-canonical Wnt pathway regulates RhoA-ROK/DAAM1 to control the acto-myosin network. However, in this context nothing is known of the intracellular signals that participate in the correct localization of E-cadh, other than a need for Rab5c signaling [3].

Methodology/Principal Findings

By studying loss of Chp induced by morpholino-oligonucleotide injection in zebrafish, we find that the vertebrate atypical Rho-GTPase Chp is essential for the proper disposition of cells in the early embryo. The underlying defect is not leading edge F-actin assembly (prominent in the cells of the envelope layer), but rather the failure to localize E-cadh and β-catenin at the adherens junctions. Loss of Chp results in delayed epiboly that can be rescued by mRNA co-injection, and phenocopies zebrafish E-cadh mutants [4], [5]. This new signaling pathway involves activation of an effector kinase PAK, and involvement of the adaptor PAK-interacting exchange factor PIX. Loss of signaling by any of the three components results in similar underlying defects, which is most prominent in the epithelial-like envelope layer.

Conclusions/Significance

Our current study uncovers a developmental pathway involving Chp/PAK/PIX signaling, which helps co-ordinate E-cadh disposition to promote proper cell adhesiveness, and coordinate movements of the three major cell layers in epiboly. Our data shows that without Chp signaling, E-cadh shifts to intracellular vesicles rather than the adhesive contacts needed for directed cell movement. These events may mirror the requirement for PAK2 signaling essential for the proper formation of the blood-brain barrier [6], [7].     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Preinfection Human Immunodeficiency Virus (HIV)-Specific Cytotoxic T Lymphocytes Failed To Prevent HIV Type 1 Infection from Strains Genetically Unrelated to Viruses in Long-Term Exposed Partners

Understanding the mechanisms underlying potential altered susceptibility to human immunodeficiency virus type 1 (HIV-1) infection in highly exposed seronegative (ES) individuals and the later clinical consequences of breakthrough infection can provide insight into strategies to control HIV-1 with an effective vaccine. From our Seattle ES cohort, we identified one individual (LSC63) who seroconverted after over 2 years of repeated unprotected sexual contact with his HIV-1-infected partner (P63) and other sexual partners of unknown HIV-1 serostatus. The HIV-1 variants infecting LSC63 were genetically unrelated to those sequenced from P63. This may not be surprising, since viral load measurements in P63 were repeatedly below 50 copies/ml, making him an unlikely transmitter. However, broad HIV-1-specific cytotoxic T-lymphocyte (CTL) responses were detected in LSC63 before seroconversion. Compared to those detected after seroconversion, these responses were of lower magnitude and half of them targeted different regions of the viral proteome. Strong HLA-B27-restricted CTLs, which have been associated with disease control, were detected in LSC63 after but not before seroconversion. Furthermore, for the majority of the protein-coding regions of the HIV-1 variants in LSC63 (except gp41, nef, and the 3′ half of pol), the genetic distances between the infecting viruses and the viruses to which he was exposed through P63 (termed the exposed virus) were comparable to the distances between random subtype B HIV-1 sequences and the exposed viruses. These results suggest that broad preinfection immune responses were not able to prevent the acquisition of HIV-1 infection in LSC63, even though the infecting viruses were not particularly distant from the viruses that may have elicited these responses.Understanding the mechanisms of altered susceptibility or control of human immunodeficiency virus type 1 (HIV-1) infection in highly exposed seronegative (ES) persons may provide invaluable information aiding the design of HIV-1 vaccines and therapy (9, 14, 15, 33, 45, 57, 58). In a cohort of female commercial sex workers in Nairobi, Kenya, a small proportion of individuals remained seronegative for over 3 years despite the continued practice of unprotected sex (12, 28, 55, 56). Similarly, resistance to HIV-1 infection has been reported in homosexual men who frequently practiced unprotected sex with infected partners (1, 15, 17, 21, 61). Multiple factors have been associated with the resistance to HIV-1 infection in ES individuals (32), including host genetic factors (8, 16, 20, 37-39, 44, 46, 47, 49, 59, 63), such as certain HLA class I and II alleles (41), as well as cellular (1, 15, 26, 55, 56), humoral (25, 29), and innate immune responses (22, 35).Seroconversion in previously HIV-resistant Nairobi female commercial sex workers, despite preexisting HIV-specific cytotoxic T-lymphocyte (CTL) responses, has been reported (27). Similarly, 13 of 125 ES enrollees in our Seattle ES cohort (1, 15, 17) have become late seroconverters (H. Zhu, T. Andrus, Y. Liu, and T. Zhu, unpublished observations). Here, we analyze the virology, genetics, and immune responses of HIV-1 infection in one of the later seroconverting subjects, LSC63, who had developed broad CTL responses before seroconversion.