For the previous two research highlights at Evolutionary Applications, I first examined disease spillover into and from natural populations, and then examine some of the recent work on the CRISPR/Cas system in bacteria:
The CRISPR/Cas revolution
“The evolution of host defenses against parasites and pathogens has resulted in a wide array of mechanisms conferring resistance and tolerance. Many of these adaptations have been co-opted for use in the treatment of disease, for example the use of live vaccines to prime the host immune system through the memory of B and T cells or the creation of transgenic crop plants to increase resistance to pests and pathogens (e.g., Schoonbeek et al. 2015; Tripathi et al. 2015). Indeed, the acquisition of basic knowledge regarding host–pathogen coevolution has underpinned much of the advancement in applied sciences of healthcare and disease management. Few such examples, however, have generated the widespread excitement and rapid development as the CRISPR/Cas system discovered in bacterial and archaeal genomes.
When bacteria coevolve with their bacteriophage viruses, they typically face strong selection to recognize and resist infection by circulating phage genotypes. Among the many mechanisms that have evolved in response to this pressure is the CRISPR/Cas system, which provides adaptive immunity to its host against specific phages. The system is built from clustered regularly interspaced short palindromic repeats (CRISPRs) within the genome that act together with CRISPR-associated (Cas) proteins to target and destroy foreign nucleic acids, including those from viruses and plasmids (reviewed in Barrangou 2015).
In the laboratory, experimental coevolution between bacteria and phages has been used to uncover the exact mechanisms of resistance and counter-adaptation as well as to determine the potential ecological and evolutionary impacts of such coevolution in shaping microbial populations and communities. Recent work by David Paez-Espino and coauthors has clearly demonstrated that phage populations respond rapidly to CRISPR-mediated immunity both through the accumulation of single nucleotide polymorphisms within the region of the phage genome targeted by CRISPR and via rampant recombination among phage types. Using long-term experimental coevolution of Streptococcus thermophiles and phage 2972, they were able to track specific evolutionary responses of the phage populations through deep sequencing and show that mutation rates were much higher than those of corresponding host populations (Paez-Espino et al. 2015). Such a rapid response by phages suggests bacterial host populations will be under a constant selection pressure to renew resistance, and emphasizes the power of the CRISPR/Cas system to confer such evolutionary flexibility.
In natural populations, bacteria–phage coevolution has also been shown to occur rapidly under CRISPR-mediated selection. Laura Sanguino and collaborators have elegantly demonstrated that CRISPR sequences obtained through metagenomics can be used to build bioinformatics networks that link viruses with their coevolving hosts (Sanguino et al. 2015). Using Arctic glacier ice and soil samples, the authors compared the direct repeats of microbial origin and short sequence spacers of viral origin that make up the CRISPR region to uncover the interaction dynamics of hosts and their, often broad host range, viruses. They found more abundant CRISPRs in ice samples relative to soil, possibly indicating higher viral diversity and infectivity rates (although they note this may also be due to limited depth of coverage in the soil metagenome dataset), and evidence for phage-mediated transduction in the bacterial community.
Now, this mechanism of prokaryotic immunity is being successfully developed as a genome-editing tool, including the engineering of mammalian cells. The CRISPR/Cas system holds the potential to knockout specific regions of the genome, alter multiple loci simultaneously, and selectively manipulate gene expression over time. This newly emerging tool not only promises to revolutionize the field of genetics, but also has direct application to the treatment of disease (reviewed in Pellagatti et al. 2015). For example, the Cas9-based DNA editing system is being exploited to help combat viral diseases through the identification of human genes linked to viral replication and the direct targeting of DNA viruses within the human body (reviewed in Kennedy & Cullen 2015). Work by Hsin-Kai Liao and colleagues recently demonstrated how the CRISPR/Cas9 system can be adapted to human cells in order to mount intracellular defense against HIV-1 infection (Liao et al. 2015). Their work shows that engineered cells expressing HIV-targeted CRISPR/Cas9 can be used both to disrupt viral DNA integrated into the host genome and to prevent new viral infection, emphasizing the great therapeutic potential of the system.
The breadth of utility for the CRISPR/Cas system is only beginning to be uncovered, with potential applications ranging from cancer screening (Chen et al. 2015) to editing of crop plant genomes (Belhaj et al. 2015). Among the many perceived benefits of this new technology is the fact that it bypasses the current GMO legislation (Kanchiswamy et al. 2015) and, unlike transgenic crop production (Tabashnik et al. 2015), allows flexible and adaptive genome editing that can be used to stay ahead of any pest and pathogen counter-adaptation. However, the ethical issues surrounding CRISPR/Cas genome editing, especially in the case of altered human embryos (Kaiser & Normile 2015), has yet to be fully addressed and the scientific community must now come together to balance the amazing potential against possible consequences of this powerful new tool.”
2015. The roles of CRISPR–Cas systems in adaptive immunity and beyond. Current Opinion in Immunology 32:36–41.
2015. Editing plant genomes with CRISPR/Cas9. Current Opinion in Biotechnology 32:76–84., , , , and
Ch2015. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:1246–1260., , , , , , , , , , , , and
2015. Embryo engineering study splits scientific community. Science 348:486–487., and
2015. Non-GMO genetically edited crop plants. Trends in Biotechnology. doi:10.1016/j.tibtech.2015.04.002 [In press]., , , , and
2015. Bacterial CRISPR/Cas DNA endonucleases: a revolutionary technology that could dramatically impact viral research and treatment. Virology 479:213–220., and
2015. Use of the CRISPR/Cas9 system as an intracellular defense against HIV-1 infection in human cells. Nature Communications 6:6413., , , , , , , , , , , , and .
2015. CRISPR immunity drives rapid phage genome evolution in Streptococcus thermophilus. mBio 6:e00262-15., , , , , , and .
2015. Application of CRISPR/Cas9 genome editing to the study and treatment of disease. Archives of Toxicology doi: 10.1007/s00204-015-1504-y [Epub ahead of print]., , , and .
2015. Linking environmental prokaryotic viruses and their host through CRISPRs. FEMS Microbiology Ecology 91:fiv046., , , and .
2015. Arabidopsis EF-Tu receptor enhances bacterial disease resistance in transgenic wheat. New Phytologist 206:606–613., , , , , , , and
2015. Successes and failures of transgenic Bt crops: global patterns of field-evolved resistance. Bt resistance: characterization and strategies for GM crops producing Bacillus thuringiensis toxins: 1–4., , , , and .
2015. Field resistance of transgenic plantain to nematodes has potential for future African food security. Scientific Reports 5:8127., , , , , , , , and .
Disease spillover among natural and managed populations
“The recent epidemic of the Ebola virus is a particularly horrific example of the consequences of disease transmission between species. Spillover infection from populations of one species, in which a pathogen may be endemic, coevolved, and often less harmful, into populations of a novel host species has the potential to lead to epidemics of particularly virulent pests and pathogens. Understanding the probability of such spillover and the evolutionary, as well as coevolutionary, processes that occur after a cross-species transmission event occurs is key to predicting disease emergence and spread. This is especially important in the case of transmission between natural populations and managed ones, where disease emergence may have significant societal impact.
A classic example is the spillover of canine distemper virus from domestic dog populations into wild lion populations in the Serengeti, which has lead to a series of disease outbreaks and subsequent population declines. Using data collected across three decades, Viana et al. (2015) recently compared the disease dynamics of dog and lion populations to determine whether and how the two were linked. Their model suggests that although spillover from dog populations was the likely driver of disease in lion populations initially, the peak infection periods for each of the two species became increasingly asynchronous over time, suggesting a role for other reservoir species and/or evolution of the circulating viral strains. This work is an elegant example of the value of long term data sets, especially with the development and application of new statistical and modeling techniques, for examining the changing disease dynamics over time.
One powerful tool for uncovering patterns of spillover is the use of social and contact networks to study transmission, as recently reviewed by Craft (2015). Understanding transmission likelihood is a key first step in determining the selection acting on pathogen populations and the potential for host shifts and the piece outlines current methods for using information about contact within populations, for example as resulting from movement, sociality, or behavior, to help inform questions of disease transmission across livestock and interacting wildlife. Craft distinguishes the utility of social network analysis, in which contact structure within and/or among populations is described, and network modeling, a tool with which to simulate disease spread across a contact network, for predicting the risk and consequences of disease spread. She also discusses how human intervention of spatial structure and group size can alter the likelihood of transmission, both within and among populations, and therefore how spillover involving managed populations may differ from that among wild populations.
Another topical example of spillover from managed into natural populations is the case of wild pollinator exposure to viruses from commercial pollinators. A recent review by Manley et al. (2015) demonstrates the potential threat for movement of RNA viruses into wild pollinators from managed honeybee populations. As many of these viruses are known to be rapidly evolving, such spillover events can lead to pathogen adaptation to novel hosts and eventual host shifts. By collating evidence for viral spillover events among populations, the authors demonstrate the potential importance of cross-species transmission in shaping disease emergence, especially when there are shared ranges, niches or behaviors between managed and wild species. Work by McMahon et al. (2015) used data from a large-scale survey of co-occurring managed honeybee and wild bumblebee populations to explore correlations in prevalence and viral loads between the two, as might be expected if cross-species transmission was common. Although they found a significant association between prevalence of viruses in honeybees and bumblebees, they also report large species-specific differences in prevalence and load across the viruses examined, suggesting more data is needed to determine the direction of transmission.
Of course, not all spillover will have negative consequences; the introduction of natural enemies of pests from wild populations into managed ones can play a key role in keeping infestation levels down. For example, in the case of crops growing near forests, González et al. (2015) recently demonstrated that the diversity of natural enemies capable of controlling herbivores on soybean is dependent on the surrounding forest. By studying crop lands within the Argentine Chaco Serrano forest, they found that both the amount of forest cover and proximity to the forest were important indicators of the richness and taxonomic composition of natural enemy assemblages, including predators and parasitoid species. This highlights the potential benefits of connectedness between natural and managed populations for hindering enemy escape by emerging pests and emphasizes the difficulties of managing pest and pathogen spread between the two given the complex coevolutionary dynamics of communities.”
2015. Infectious disease transmission and contact networks in wildlife and livestock. Philosophical Transactions of the Royal Society of London B: Biological Sciences 370:1669.
2015. Sharing enemies: evidence of forest contribution to natural enemy communities in crops, at different spatial scales. Insect Conservation and Diversity (online early) DOI: 10.1111/icad.12117., , and .
2015. Emerging viral disease risk to pollinating insects: ecological, evolutionary and anthropogenic factors. Journal of Applied Ecology 52:331–340., , and .
2015. A sting in the spit: widespread cross-infection of multiple RNA viruses across wild and managed bees. Journal of Animal Ecology 84:615–624., , , , , and .
2015. Dynamics of a morbillivirus at the domestic–wildlife interface: canine distemper virus in domestic dogs and lions. Proceedings of the National Academy of Sciences, USA 112:1464–1469., , , , , , et al.
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