Cross-species transmission

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Host jump
)
See also: Spillover infection

Cross-species transmission (CST), also called interspecies transmission, host jump, or spillover, is the

bacterial pathogens or other types of microorganisms.[2]

Steps involved in the transfer of pathogens to new hosts include contact between the pathogen and the host; the successful

swine flu, rabies, and avian influenza.[4]

The exact mechanisms which facilitate cross-species transmission vary by pathogen, and even for common diseases are often poorly understood. It is believed that viruses with high mutation rates are able to rapidly adapt to new hosts and thereby overcome host-specific

immunological defenses, allowing their continued transmission. A host shifting event occurs when a strain that was previously zoonotic begins to circulate exclusively among the new host species.[5]

Pathogen transfer is most likely to occur between species which are frequently in close contact with each other. It can also occur indirectly between species with less frequent contact if facilitated by an intermediary species; for example, a

vector species, which in turn transfers the virus to humans.[6][7] The degree of phylogenetic relatedness between host species also influences the likelihood that a pathogen is transmitted between them, likely because of the similarity of the hosts' immunological defenses; for example, most human zoonotic transmissions come from other species of mammals. Pathogens of more distantly related species, on the other hand, such as plant viruses, may not be capable of infecting humans at all. Other factors influencing transmission rates include geographic proximity and intraspecies behaviors.[3] Due to climate change and habitat loss owing to land use expansion,[8] the risk of viral spillover is predicted to significantly increase.[9]

Prevalence and control

Cross-species transmission is the most significant cause of disease emergence in humans and other species. [

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.[2] This makes CST of major concern for public health, agriculture, and wildlife management.[citation needed
]

The authors of a study on the

fleas. Humans usually become infected through the bite of an infected rodent flea." The sanitary control measure instituted by the public health authority was chemical in nature: "Intra- and peridomestic spraying with permethrin was conducted. Deltamethrin was dusted on the tracks and around the burrows of rodents located in a radius of 10 km around the dwelling of the patients. Uncontrolled killing of rats was prohibited."[10]

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,

poultry industry.[13]

CST of rabies virus variants between many different species populations is a major concern of wildlife management. Introduction of these variants into non-reservoir animals increases the risk of human exposures and threatens current advances toward rabies control.[14]

Many pathogens are thought to have host specialization, which explains the maintenance of distinct

fatality rates tending to be much higher in new hosts[15]

Between non-human primates and humans

Further information: Human-to-primate transmission

Due to the close relation of

adenoviruses have been associated with NHP interactions.[16][17]

In places where contact between humans and NHPs is frequent, precautions are often taken to prevent disease transmission.

Macaca fascicularis).[19] 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.[15]

Predicting and preventing transmission between species

Prediction and monitoring are 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.[4] This has resulted in the use of different statistical models for the analysis of CST. Some of these include risk-analysis models,[20] single rate dated tip (SRDT) models,[17] and phylogenetic diffusion models.[4] The study of the genomes of pathogens involved in CST events is very useful in determining their origin and fate.[4] This is because a pathogens genetic diversity and mutation rate are key factors in determining if it can transmit across multiple hosts. This makes it important for the genomes of transmission species to be partially or completely sequenced.[15] A change in genomic structure could cause a pathogen that has a narrow host range to become capable of exploiting a wider host range.[5] Genetic distance between different species, geographical range, and other interaction barriers will also influence cross-species transmission.[4]

One approach to risk assessment analysis of CST is to develop risk-analysis models that break the ‘‘process’’ of disease transmission into 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 are used to estimate the probability of each component, expected natural variation, and margins of error.[19]

Different types of CST research would require different analysis pathways to meet their needs. A study on identification of viruses in bats that could spread to other mammals used the workflow: sequencing of genomic samples → “cleaning” of raw reads → elimination of host reads and eukaryotic contaminants → de novo assembly of the remaining reads → annotation of viral contigs → molecular detection of specific viruses → phylogenetic analysis → interpretation of data.[21]

Detecting CST and estimating its rate based on prevalence data is challenging.

phylogenetic comparisons to support a role for TRIM5α, the product of the TRIM5 gene, in suppressing interspecies transmission and emergence of retroviruses in nature.[22]

Analysis

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 a pathogen, change in host susceptibility) and how this can be prevented in the future. 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[20]

Alternative hosts can also potentially have a critical role in the evolution and diffusion of a pathogen.

demographic history of the pathogen.[17]
When constructing 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. Trees are constructed using computational methods such as MPR or Bayesian Inference, and models are created depending on the needs of the study.[24] Single rate dated tip (SRDT) models, for example, allows for estimates of timescale under a phylogenetic tree.[17] Models for CST prediction will vary depending on what parameters need to be accounted for when constructing the model.[citation needed]

Most parsimonious reconstruction (MPR)

evolutionary hypotheses.[25] This method can be used in CST studies to estimate the number of character changes that exist between pathogens in relation to their host.[2] This makes MPR useful for tracking a CST pathogen to its origins. MPR 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.[26]

Despite the success of parsimony reconstructions, research suggests they are often sensitive and can sometimes be prone to bias in complex models.[25] This can cause problems for CST models that have to consider many variables. Alternatives methods, such as maximum likelihood, have been developed as an alternative to parsimony reconstruction.[25]

Using genetic markers

Two methods of measuring genetic variation,

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.[2]
However, it can be difficult for these methods to accurately simulate CSTs everts. Estimates of CST based on
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 if pathogens were recently introduced.[2]

The process of using genetic markers to estimate CST rates should take into account several important factors to reduce bias. One is that the phylogenetic tree constructed in the analysis needs to capture the underlying epidemiological process generating the tree.

Likelihood approaches that require an estimation of the mutation rate.[2] 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[2]

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.[2] 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.[27]

The Bayesian frameworks are also well suited 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.[27]
This could be useful for determining the origins of cross-species transmissions. The high effectiveness of Bayesian statistical methods has made them instrumental in evolutionary studies.
chimpanzee origin for the viral species, aiding prevention efforts.[16] Despite presumably rare direct contact between sympatric populations of the two species, CST events can 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.[16]

Phylogenetic diffusion models are frequently used for phylogeographic analyses, with the inference of host jumping becoming of increasing interest.[4] The Bayesian inference approach enables model averaging over several potential diffusion predictors and estimates the support and contribution of each predictor while marginalizing over phylogenetic history.[4] 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.[4] One study on rabies in bats showed geographical range overlap is a modest predictor for CST, but not for host shifts.[4] This highlights how Bayesian inferences in models can be used for CST analysis.[citation needed]

See also

References

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External links
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