Cross-species transmission, (CST) or spillover, is the ability for a foreign virus, once introduced into an individual of a new host species, to infect that individual and spread throughout a new host population. ^[1] Steps involved in transfers of viruses to new hosts include contact between the virus and the host, infection of an initial individual leading to amplification and an outbreak, and the generation within the original or new host of viral variants that have the ability to spread efficiently between individuals in populations of the new host ^[2] Often seen in emerging viruses where one species transfers to another which in turn transfers to humans. Examples include HIV-AIDS, SARS, Ebola, Swine flu, rabies, and Bird flu. ^[3] Bacterial pathogens can also be associated with CST. ^[4]

The exact mechanism that facilitates the transfer is unknown, however, it is believed that viruses with a rapid mutation rate are able to overcome host-specific immunological defenses. This can occur between species that have high contact rates. It can also occur between species with low contact rates but usually through an intermediary species. Bats, for example, are mammals and can directly transfer rabies to humans through bite and also through aerosolization of bat salvia and urine which are then absorbed by human mucous membranes in the nose, mouth and eyes. ^[5] A host shifting events, is defined as a strain that was previously zoonotic and now circulates exclusively among humans. ^[6]

Similarity between species, for example, transfer between mammals, is believed to be facilitated by similar immunological defenses. Other factors include geographic area, intraspecies behaviours, and phylogenetic relatedness. Virus emergence relies on two factors: initial infection and sustained transmission. ^[7]

Prevalence

Cross-species transmission is the most significant cause of disease emergence in humans and other species. Wildlife zoonotic diseases of microbial origin are also the most common group of human emerging diseases, and CST between wildlife and livestock has appreciable economic impacts in agriculture by reducing livestock productivity and imposing export restrictions. ^[4] This makes CST of major concern for public health, agriculture, and wildlife management.

A large proportion of viral pathogens that have emerged recently in humans are considered to have originated from various animal species. This is shown by several recent epidemics such as, avian flu, Ebola, monkey pox, and Hanta viruses. ^[8] Studies have also indicate that some disease can potentially be re-introduction to human populations through animal hosts after they have been eradicated in humans. There is a risk of this phenomena occurring with morbilliviruses as they can readily cross species barriers. ^[9] CST can also have a significant effect on produce industries. Genotype VI- Avian paramyxovirus serotype 1 (GVI-PMV1) is a virus that arose through cross-species transmission events from Galliformes (i.e. chicken) to Columbiformes, and has become prevalent in the poultry industry. ^[10] CST of rabies virus variants between many different species populations is a major wildlife management concern. Introduction of these variants into non-reservoir animals increases the risk of human exposures and threatens current advances toward rabies control. ^[11]

Many pathogens are thought to have host specialization, which explains the maintenance of distinct strains in host species. ^[6] Pathogens would have to overcome their host specificity to cross to a new host species. Some studies have argued that host specializations may be exaggerated, and pathogens are more likely to exhibit CST than previous thought. ^[6] Original hosts usually have low death rates when infected with a pathogen, with fatality rates tending to be much higher in new hosts ^[12]

Cross-Species Transmission between Humans and Nonhuman Primates

Due to the close relation of humans and nonhuman primates (NHP), disease transmission between NHP and humans is relatively common and can be of major public health concern. Diseases, such as HIV and human adenoviruses have been associated with NHP interactions. ^{[13] [14]}

In places were contact between humans and NHPs is frequent, precautions are often taken to prevent disease transmission. Simian foamy viruses (SFV) is an enzootic retrovirus that has high rates of cross-species transmission and has been known to effect humans bitten by infected NHPs. ^[15] It has caused health concerns in places like, Indonesia were visitors at monkey temples can contract SFV from temple macaques ( Macaca fascicularis). ^[16] TMAdV, ( titi monkey adenovirus) is a highly divergent, sharing <57% pairwise nucleotide identity with other adenoviruses, NHP virus that had a high fatality rate (83%) in monkeys and is capable of spreading through human hosts. ^[12]

Predicting and Preventing Transmission between Species

Prediction and monitoring is important for the study of CSTs and their effects. However, factors that determine the origin and fate of cross-species transmission events remain unclear for the majority of human pathogens. ^[3] This has resulted in the use of different statistical models and methods for the analyzation of CST. The study of the genomes of pathogens involved in CST events is very useful in determining their origin and fate. This is because a pathogens genetic diversity and mutation rate are key factors in determining if it is able to transmit across multiple hosts. This makes it important for the genomes of transmission species to be partially or completely sequenced. ^[12] A change in genomic structure could cause a pathogen that has narrow host range to become capable of exploiting a wider host range. ^[6] Genetic distance between different species, geographical range, and other interaction barriers will also influence cross-species transmission between species. ^[3]

Risk assessment models are also important for CST analysis. One approach is to develop risk-analysis models that break the ‘‘process’’ of disease transmission into component parts. Processes and interactions that could lead to cross-species disease transmission are explicitly described as a hypothetical infection chain. Data from laboratory and field experiments is used to estimate the probability of each component, expected natural variation, and margins of error. ^[16]

Detecting CST and estimating its rate based on prevalence data is challenging. ^[4] Due to these difficulties computational methods are used to analyse CST events and the pathogens associated with them. The explosive development of molecular techniques has opened new possibilities for using phylogenetic analysis of pathogen genetics to infer epidemiological parameters. ^[4] This provides some insight into the origins of these events and how they could be addressed. Methods of CST prevention are currently using both biological and computation data. An example of this is cellular assays and phylogenetic comparisons supporting a role for TRIM5α, the product of the TRIM5 gene, in suppressing interspecies transmission and emergence of retroviruses in nature. ^[17]

Cross-Species Transmission Analyzation

Phylogeny

The comparison of genomic data is very important for the study of cross-species transmission. Phylogenetic analysis is used to compare genetic variation in both pathogens associated with CST and the host species that they infect. Taken together, it is possible to infer what allowed a pathogen to crossover to a new host (i.e mutation in pathogen, change in host susceptibility) and this can be prevented. If the mechanisms a pathogens uses to initially enter a new species are well characterized and understood a certain level of risk control and prevention can be obtained. In contact, a poor understanding of pathogens, and their associated diseases, makes it harder for preventive measures to be taken ^[18]

Alternative hosts can also potentially have a critical role in the in the evolution and diffusion of a pathogen ^[19] When a pathogen crosses species it is often acquires new characteristics. Different pathogen variants can have very different effects on host species. ^[19] Thus it can be beneficial to CST analysis to compare the same pathogens occurring in different host species. Phylogenetic analysis can be used to track a pathogens history through different species populations. Even if a pathogen is new and highly divergent, phylogenetic comparison can be very insightful ^[12] A useful strategy for investigating the history of epidemics caused by pathogen transmission combines molecular clock analysis, to estimate the timescale of the epidemic, and coalescent theory, to infer the demographic history of the pathogen. ^[14] When contracting phylogenies, computer databases and tools are often used. Programs, such as BLAST, are used to annotate pathogen sequences, while databases like GenBank provide information about functions based on the pathogens genomic structure. Tree are constructed using computational methods such as MPR or Bayesian Inference, and models are created depending on the needs of the study. ^[20] Single rate dated tip (SRDT) models, for example, allows for estimates of timescale under the phylogenetic tree. ^[14] Models for CST prediction will vary depending on what parameters need to be accounting for when constructing the model.

Most Parsimonious Reconstruction (MPR)

Parsimony is the principle in which one chooses the simplest scientific explanation that fits the evidence. In terms of building phylogenetic trees, the best hypothesis is the one that requires the fewest evolutionary changes. Using parsimony to reconstruct ancestral character states on a phylogenetic tree is a method for testing ecological and evolutionary hypotheses. ^[21] This method can be used in CST studies to estimate the number of character changes that exist between pathogens in relation to their host. ^[4] This makes MPR useful for tracking a CST pathogen to its origins. MPR is also can also be used to the compare traits of host species populations. Traits and behaviours within a population could make them more susceptible to CST. For example, species which migrate regionally are important for spreading viruses through population networks. ^[22]

Despite the success of parsimony reconstructions, research suggests they are often sensitive and can sometime be prone to bias in complex models. ^[21] This can cause problems for CST analysis that have to consider many variables. Alternatives methods, such as maximum likelihood, have been developed as an alternative to parsimony reconstruction. ^[21]

Using Genetic Markers

Two methods measuring of genetic variation, variable number tandem repeats (VNTRs) and single nucleotide polymorphisms (SNPs) have been very beneficial to the study of bacterial transmission. ^[4] VNTRs, due to the low cost and high mutation rates, make them particularly useful to detect genetic differences in recent outbreaks and while SNPs have a lower mutation rate per locus than VNTRs, they deliver more stable and reliable genetic relationships between isolates. Both methods are used to construct phylogenies for genetic analysis, however SNPs are more suitable for studies on phylogenies contraction. ^[4] However, it can be difficult for these methods accurately simulate CSTs everts. Estimates of CST based on phylogenys made using VNTR marker can be biased towards detecting CST events across a wide range of the parameters. SNPs tend to be less biased and variable in estimates of CST when estimations of CST rates are low and using a low number of SNPs. In general, CST rate estimates using these methods are most reliable in systems with more mutations, markers, and high genetic differences between introduced strains. ^[4] CST is a very complex and models need to account for a lot of parameters to accurately represent the phenomena. Bias in results could be obtained if model are a too simplistic representations of reality. Multiple parameters such as number of mutations accumulated since introduction, stochasticity, the genetic difference of strains introduced, and the sampling effort can make unbiased estimates of CST difficult even with whole-genome sequences, especially if sampling is limited, mutation rates are low, or for pathogens that were recently introduced. ^[4] More information on the factors that influence CST rates is needed for the contraction of more appropriate models to study these events.

The process of using genetic markers to estimate CST rates should take into account several important factors to reduce bias. One, is that the phytogenic tree constructed in the analysis needs to capture the underlying epidemiological process generating the tree. ^[4] The models need to account for how genetic variability of a pathogen influences a disease in a species, not just general differences in genomic structure. Two, the strength of the analysis will depend on the amount of mutation accumulated since the pathogen was introduced in the system. ^[4] This is due too many models using amount if mutation as an indicator of CST frequency. Therefore, efforts are focused on estimating either time since introduction or the substitution rate of the marker (from laboratory experiments or genomic comparative analysis). This is important not only when using the MPR method but also for Likelihood approaches that require an estimation of the mutation rate. ^[4] Three, CST will also affect disease prevalence in the potential host, so combining both epidemiological time series data with genetic data may be an excellent approach to CST study ^[4]

Bayesian Analysis

Bayesian frameworks are a form of maximum likelihood-based analyses and can be very effective in cross-species transmission studies. Bayesian inference of character evolution methods can account for phylogenetic tree uncertainty and more complex scenarios, with models such as the character diffusion model currently being developed for the study of CST in RNA viruses. ^[4] A Bayesian statistical approach presents advantages over other analyses for tracking CST origins. Computational techniques allow integration over an unknown phylogeny, which cannot be directly observed, and unknown migration process, which is usually poorly understood. ^[23] This is important because the parameters surrounded CST events maybe very complex and poorly known so proper models need to account for this. Bayesian inference accommodations protects against these kinds of bias, however it also reduces the power of inferences that can be made by the analysis. ^[23]

The Bayesian frameworks is also wellsuited to bring together different kinds of information. The BEAST software, which has a strong focus on calibrated phylogenies and genealogies, illustrates this by offering a large number of complementary evolutionary models including substitution models, demographic and relaxed clock models that can be combined into a full probabilistic model. By adding spatial reconstruction, these models create the probability of biogeographical history reconstruction from genetic data. ^[23] This could be useful for determining origins of cross-species transmissions. The high effectiveness of Bayesian statistical methods have made them instrumental in evolutionary studies. ^[24] Using Bayesian ancestral host reconstruction under discrete diffusion models can be used to infer the origin and effects of pathogens associated with CST. One study on Human adenoviruses using Bayesian supported a gorilla and chimpanzee origin for the viral species, aiding prevention efforts. ^[13] Despite presumably rare direct contact between sympatric populations of the two species, CST events csn occur between them. The study also determined that two independent HAdV-B transmission events to humans occurred, and that the HAdV-Bs circulating in humans are of zoonotic origin and have probably affected global health for most of our species lifetime. ^[13]

Phylogenetic diffusion models are frequently used for phylogeographic analyses, with the inference of host jumping becoming of increasing interest. ^[3] The extension of a GLM (Generalized Linear Model) diffusion model has allowed the inference of the discrete state transition history while simultaneously identifying the underlying predictors. The GLM diffusion model avoids post hoc statistical analyses of genetic estimates. The Bayesian inference approach enables model averaging over a number of potential diffusion predictors and estimates the support and contribution of each predictor while marginalizing over phylogenetic history. ^[3] For studying viral CST, the flexibility of the Bayesian statistical framework allows for the reconstruction of virus transmission between different host species while simultaneously testing and quantifying the contribution of multiple ecological and evolutionary influences of both CST spillover and host shifting. ^[3] One study on rabies in bats showed geographical range overlap is a modest predictor for CST, but not for host shifts. ^[3] This highlights how Bayesian inferences in models can be used for CST analysis.

See also

• Mathematical modelling of infectious disease
• Novel virus
• Outbreak
• Epidemic
• Bayesian statistics
• Spillover infection

References

External links

• Theo Kypraios. "A Gentle Tutorial in Bayesian Statistics" (PDF) (PDF). Retrieved 2013-11-03.
• Bayesian modeling book and examples available for downloading.
• Bayesian statistics at Wikiversity

Category:Epidemiology Category:Viruses Category:Zoonoses