Britt Koskella Uncategorized

Tree diseases (old posts)

I have decided to get this section of my lab page going again after a very busy year of moving my lab to UC Berkeley and becoming a parent. As a first pass, I’ve decided to collapse my tree diseases tab and Blog tab into one. So here are two old posts that need a new home (I was unable to move the comments, so I apologize for losing those):

Tree disease update: Ash dieback        (published Dec 26, 2012)

Photo: Courtesy of The Danish Nature Agency/HEATHCLIFF O'MALLEY
Photo: Courtesy of The Danish Nature Agency/HEATHCLIFF O’MALLEY

If you’re living in the UK or elsewhere in Europe, then you’ve already heard about the scary new disease of ash trees, known here as Ash dieback or in France as “Chalarose.” Like many tree diseases that have devastated forests, this one is caused by a fungus, Hymenoscyphus pseudoalbidus (anamorph: Chalara fraxinea [1]). According to the Forestry Commission, the fungus was introduced in February 2012 by import of infected trees from a nursery in the Netherlands to one in Buckinghamshire. It has already caused serious problems in Poland (where 3,000 infected trees fell on one windy night), Denmark (killing 95% of their trees), Lithuania (resulting in mortality of 60% of all ash stands), Austria, Belgium, the Czech Republic, Finland, France, Germany, Hungary, Italy, the Netherlands, Norway, Slovenia and Sweden, and is now wreaking havoc across the UK (Map of confirmed infections). This is very bad news indeed for our woodlands, as ash trees make up over 10% of the trees we have.

As a scientist, when I first hear about an emerging disease the questions I want answers to are: 1) Where did it come from? 2) How is it transmitted? 3) Why is it becoming a problem now? 4) What are the symptoms? And 5) Is there any natural variation in susceptibility? So, after surfing around on the web and reading some recent papers, here is what we know so far:

1) Where did it come from?  As with many emerging diseases, the answer to this is not clear. The pathogen is closely related to another non-pathogenic fungus, Hymenoscyphus albidus, which has been widespread in Europe since the mid 19th century. (Interestingly, it seems as though the spread of the pathogenic species has lead to local extinctions of the native fungus in parts of Denmark [2].) The fungus was first described in 2006, but seems to have been around since the early 90s, when it devastated forests in Poland. Whether the species is a mutant of the closely related, yet harmless, H. albidus remains to be seen. As we are now in the genome era, the most informative way of determining how this new species came to be is to take a comparative genomic approach. For many pathogens, comparing their whole genome to those of closely related, but non-pathogenic species has allowed researchers both to infer the phylogeny of the pathogen and to identify regions of the genome that are likely to be involved in making it harmful to its host. This approach has been successfully used to identify candidate genes associated with virulence in a number of Pseudomonas syringae bacterialpathogens [3] and to demonstrate the movement of genes from pathogens to free-living bacteria[4].

Comparative genomics can also help elucidate whether the pathogen is truly newly emerged (which you could conclude if there was very little genetic variation among the isolates collected from different parts of the species range) or whether it has been around for a while. Surprisingly, there is evidence from molecular characterization of strains from Finland, Estonia and Latvia that there is quite a bit of variation already, suggesting that this fungal species has either been around for quite some time already or has evolved multiple times from a closely related species [5]. For now, we will have to wait and see what story the H. pseudoalbidus genome has to tell us.

2) How is it transmitted? The latest headlines from BBC and the Guardian state that the number of sightings has doubled in the last month, with about 300 confirmed cases in the UK. (Of course, what they don’t emphasize is that the awareness of the disease has increased exponentially since the news broke that the first case had been found back in early November.) So how is it moving around? And how is it infecting new trees once it arrives? It seems that the sexual stage is wind dispersed, with a suggested dispersal distance of 20 to 30 km per year.

The best way to determine how a disease is transmitted is by having lots of data over multiple years and then building a series of models that predict how the spread would look under varying assumptions – e.g. if it is limited by the dispersal rate of a vector, such as an insect, or the wind – and then testing which model fit the actual spread the best. Given the speed at which this pathogen has spread across Europe, it seems that wind dispersal is the most likely explanation for spread within forests and that human transport of saplings from country to country is the most likely explanation for new epidemics. It’s unclear exactly how the pathogen infects new trees once it gets to a new place, but there is some evidence it is through the leaves (this is true of many plant pathogens).

3) Why is it becoming a problem now? Again, the short answer is that we don’t know. The longer answer is that it may be related to our colder, damper summers over the past few years or it may be related to tree stress.

4) What are the symptoms? A little video from the Forestry commission on how to identify the symptoms of ash dieback:

5) Is there any natural variation in susceptibility? The good news is that there is some burgeoning evidence for genetic resistance to the disease, as not all trees die of infection. A study of trees in Denmark found that individuals with early leaf senescence (i.e., those that lost their leaves early in the season) were less susceptible than late-senescing individuals [6]. Interestingly, early leaf loss is also a symptom of the disease. Unfortunately, it looks like only a very small proportion of trees are resistant [7]; most likely too few to prevent a severe population crash. However, the presence of resistance at all suggests that evolution can work its magic. That being said, natural selection can be a painfully slow process, especially for such a long-lived organism. Therefore, a little bit of artificial selection (where individuals that are known to be resistant are crossed and their offspring are planted) could be a good solution to slowing the spread of the disease and eventual eradication.

Finally, let’s not forget that it’s not just the ash trees that are under threat! These trees are host to about 30% of all UK lichens, many of which are currently considered endangered species, and any number of bacterial species [8]. Also, there are at least two moth species that specialize on ash: the Dusky thorn (Ennomos fuscantaria) the centre-barred sallow (Atethmia centrago). This disease therefore has the potential to be a real threat of local ecosystems if left unchecked.

For more information:

1) If, like me, you have no idea what an anamorph is, it turns out the species was described twice in two different stages of its life cycle: the sexual stage – the teleomorph, and the asexual stage – the anamorph. The former is usually a fruiting body while the latter is more mold-like, which explains why they might have been considered different species.

2) McKinney, L., Thomsen, I., Kjær, E., Bengtsson, S., & Nielsen, L. (2012). Rapid invasion by an aggressive pathogenic fungus (Hymenoscyphus pseudoalbidus) replaces a native decomposer (Hymenoscyphus albidus): a case of local cryptic extinction? Fungal Ecology, 5 (6), 663-669 DOI: 10.1016/j.funeco.2012.05.004.

3) Baltrus, David A., et al. “Dynamic evolution of pathogenicity revealed by sequencing and comparative genomics of 19 Pseudomonas syringae isolates.” PLoS pathogens 7.7 (2011): e1002132.

4) Ma, Li-Jun, et al. “Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium.” Nature 464.7287 (2010): 367-373.

5) Rytkonen, A., Lilja, A., Drenkan, R., Gaitner, T., and Hantula, J. 2010. First record of Chalara fraxinea in Finland and genetic variation among isolates sampled from Åland, mainland Finland, Estonia, and Latvia. For. Path.

6) McKinney, Lea Vig, et al. “Genetic resistance to Hymenoscyphus pseudoalbidus limits fungal growth and symptom occurrence in Fraxinus excelsior.” Forest Pathology 42.1 (2011): 69-74.

7) McKinney, Lea Vig, et al. “Presence of natural genetic resistance in Fraxinus excelsior (Oleraceae) to Chalara fraxinea (Ascomycota): an emerging infectious disease.” Heredity 106.5 (2010): 788-797.

8) Ellis, Christopher J., Brian J. Coppins, and Peter M. Hollingsworth. “Tree fungus: Lichens under threat from ash dieback.” Nature 491.7426 (2012): 672-672.

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Why I study tree diseases:                               (published Nov 26, 2012)

About 5 years ago this month, my research took a very interesting twist. When I arrived at Oxford as an NSF international research fellow with a plan to work on bacteria and phages in tomato plants, I discovered (thanks to conversations with the amazing Gail Preston) that the same bacterial species causing the disease I was interested in, Pseudomonas syringae, was also killing all of the horse chestnut trees in Britain. Continue reading “Tree diseases (old posts)” →

Britt Koskella Uncategorized

Two new Evolutionary Applications research highlights

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.”

Literature cited

Barrangou, R. 2015. The roles of CRISPR–Cas systems in adaptive immunity and beyond. Current Opinion in Immunology 32:36–41.

Belhaj, K., A. Chaparro-Garcia, S. Kamoun, N. J. Patron, and V. Nekrasov 2015. Editing plant genomes with CRISPR/Cas9. Current Opinion in Biotechnology 32:76–84.

Chen, S., N. E. Sanjana, K. Zheng, O. Shalem, K. Lee, X. Shi, D. A. Scott, J. Song, J. Q. Pan, R. Weissleder, H. Lee, F. Zhang, and P. A. Sharp 2015. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:1246–1260.

Kaiser, J., and D. Normile 2015. Embryo engineering study splits scientific community. Science 348:486–487.

Kanchiswamy, C. N., M. Malnoy, R. Velasco, J. S. Kim, and R. Viola 2015. Non-GMO genetically edited crop plants. Trends in Biotechnology. doi:10.1016/j.tibtech.2015.04.002 [In press].

Kennedy, E. M., and B. R. Cullen 2015. Bacterial CRISPR/Cas DNA endonucleases: a revolutionary technology that could dramatically impact viral research and treatment. Virology 479:213–220.

Liao, H. K., Y. Gu, A. Diaz, J. Marlett, Y. Takahashi, M. Li, K. Suzuki, R. Xu, T. Hishida, C.-J. Chang, C. Rodriguez Esteban, J. Young, and J. C. I. Belmonte. 2015. Use of the CRISPR/Cas9 system as an intracellular defense against HIV-1 infection in human cells. Nature Communications 6:6413.

Paez-Espino, D., I. Sharon, W. Morovic, B. Stahl, B. C. Thomas, R. Barrangou, and J. F. Banfield. 2015. CRISPR immunity drives rapid phage genome evolution in Streptococcus thermophilus. mBio 6:e00262-15.

Pellagatti, A., H. Dolatshad, S. Valletta, and J. Boultwood. 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].

Sanguino, L., L. Franqueville, T. M. Vogel, and C. Larose. 2015. Linking environmental prokaryotic viruses and their host through CRISPRs. FEMS Microbiology Ecology 91:fiv046.

Schoonbeek, H. J., H. H. Wang, F. L. Stefanato, M. Craze, S. Bowden, E. Wallington, C. Zipfel, and C. J. Ridout 2015. Arabidopsis EF-Tu receptor enhances bacterial disease resistance in transgenic wheat. New Phytologist 206:606–613.

Tabashnik, B. E., Y. Carrière, M. Soberón, A. Gao, and A. Bravo. 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.

Tripathi, L., A. Babirye, H. Roderick, J. N. Tripathi, C. Changa, P. E. Urwin, W. K. Tushemereirwe, D. Coyne, and H. J. Atkinson. 2015. Field resistance of transgenic plantain to nematodes has potential for future African food security. Scientific Reports 5:8127.

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.”

Literature cited

Craft, M. E. 2015. Infectious disease transmission and contact networks in wildlife and livestock. Philosophical Transactions of the Royal Society of London B: Biological Sciences 370:1669.

González, E., A. Salvo, and G. Valladares. 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.

Manley, R., M. Boots, and L. Wilfert. 2015. Emerging viral disease risk to pollinating insects: ecological, evolutionary and anthropogenic factors. Journal of Applied Ecology 52:331–340.

McMahon, D. P., M. A. Fürst, J. Caspar, P. Theodorou, M. J. F. Brown, and R. J. Paxton. 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.

Viana, M., S. Cleaveland, J. Matthiopoulos, J. Halliday, C. Packer, M. E. Craft, K. Hampson et al. 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.

Britt Koskella Uncategorized

Applied evolution in fisheries science

For this month’s research highlights in Evolutionary Applications, I cover a few new papers that demonstrate the importance of thinking about evolution and ecology in fisheries science.

“The pressure on both natural and managed fish stocks to keep pace with worldwide consumption presents a number of critical challenges, including the prevention of population collapse, management of disease, and understanding of the impact that fishing practices may have on life history traits. Addressing such challenges requires the integration of data from long term population monitoring, empirical work, theoretical analysis, and implementation of policy change. Fortunately, many fish populations have been monitored either actively or passively over long periods of time, generating some of the best datasets with which to characterize the impact of human-mediated selection on population-level change.

The intensity of selection acting on fished populations has long been predicted to significantly impact upon life history traits. In a recent theoretical exploration of the consequences of commercial fishing, Lise Marty and coauthors highlight how exploitation of fish populations can lead to slower growth, early maturation, and higher investment in reproduction within stocks (Marty et al. 2015). The authors use an individual-based eco-genetic model to examine harvest-induced genetic change and show not only that fishing can influence life history trait evolution, but also that it can reduce effective population size and erode additive genetic variation. Together, they argue, these effects are likely to hinder recovery even after intense fishing has ceased.

Another recent theoretical analysis examining the consequences of fisheries on stock populations suggests that common fishing policies can result in disruptive selection for maturation strategies (Landi et al. 2015). Using an eco-evolutionary model, Pietro Landi and colleagues demonstrate how the interplay between adaptation of fish stocks and adaptation of fisheries policy can lead to dimorphism within populations, with some individuals reaching maturation early and others late, investing instead in growth and fecundity. This work highlights the potentially complex outcomes of size-selective harvesting and the need for adaptive policies that take into account evolutionary change of fish populations.

Harvest-mediated shifts in life history have thus far been demonstrated under a variety of scenarios. Recent empirical work examining size and weight distributions of exploited sea cucumber populations in Turkey finds evidence for the loss of larger size classes, as predicted from intensive size-dependent harvesting (González-Wangüemert et al. 2015). By comparing fishery and nonfishery populations, Mercedes González-Wangüemert and collaborators show that individuals from protected populations tend to be larger and heavier, with higher genetic diversity than those from exploited populations. Given that sea cucumber over-exploitation is a relatively recent and growing phenomenon, this work offers an important new data point in a rapidly expanding body of evidence for rapid fisheries-mediated evolutionary change in fish stocks.

Finally, just like natural populations, managed fish stocks face a constant onslaught of pests and pathogens. This is further exacerbated by high population densities, increased movement of disease agents among populations, and potentially by selection for desirable traits that are negatively correlated with resistance. A recent review by Kevin Lafferty and coauthors examines the ongoing challenges associated with controlling the emergence and spread of disease within fisheries and aquaculture, highlighting a number of significant infectious agents with severe economic impacts. The authors further explore how the novel evolutionary environment of fish farms might influence pathogen evolution, for example leading to higher virulence, and whether host resistance is likely to evolve under current fishing practices (Lafferty et al. 2015).

For bacterial pathogens within fish farms, there has been increasing interest in the use of bacteriophages as control agents. Although there is still uncertainty in regard to best practice for the application of phages within these complex environments, work from the laboratory suggests this as a promising avenue, especially in combination with other control measures. Recent work by Daniel Castillo and collaborators undertook a study on the common fish pathogen, Flavobacterium psychrophilum, to examine both the genetic changes underlying the evolution of bacterial resistance to phage and the physiological changes associated with such resistance (Castillo et al. 2015). They found numerous mutational changes underlying resistance, suggesting that resistance can be attained relatively easily and via a number of mechanisms, but also that these resistance mutations are often associated with a loss of virulence when measured in vitro.

Overall, the application of evolutionary and ecological theory to fisheries management over the last few decades has proven invaluable, but there remains a great need for further empirical and observational datasets testing the predictions put forward. Furthermore, translating such knowledge into policy change continues to present a formidable challenge for the field.”

Literature cited

  • Castillo, D., R. H. Christiansen, I. Dalsgaard, L. Madsen, and M. Middelboe 2015. Bacteriophage resistance mechanisms in the fish pathogen Flavobacterium psychrophilum: Linking genomic mutations to changes in bacterial virulence factors. Applied and environmental microbiology 81:1157–1167.
  • González-Wangüemert, M., S. Valente, and M. Aydin 2015. Effects of fishery protection on biometry and genetic structure of two target sea cucumber species from the Mediterranean Sea. Hydrobiologia 743:65–74.
  • Lafferty, K. D., C. D. Harvell, J. M. Conrad, C. S. Friedman, M. L. Kent, A. M. Kuris, E. N. Powell et al. 2015. Infectious diseases affect marine fisheries and aquaculture economics. Annual review of marine science 7:471–496.
  • Landi, P., C. Hui, and U. Dieckmann 2015. Fisheries-induced disruptive selection. Journal of theoretical biology 365:204–216.
  • Marty, L., U. Dieckmann, and B. Ernande 2015. Fisheries-induced neutral and adaptive evolution in exploited fish populations and consequences for their adaptive potential. Evolutionary Applications 8:47–63.
Britt Koskella Uncategorized

Previous two research highlights for Evolutionary Applications

For the past two research highlights at Evolutionary Applications, I first covered a great paper summarizing the many way evolutionary theory can be applied to current issues by Scot Carroll and colleagues:

“As we highlight each month in this section, the application of evolutionary theory to issues affecting the health and well-being of human, agricultural, and natural populations is gaining increasing momentum. In a recent review article written for Science, Scott Carroll et al. take on the now monumental task of synthesizing the many ways that evolutionary biology can be used to address global challenges (Carroll et al. 2014). They comprehensively explore the main problems being tackled with an evolutionary approach, ranging from populations evolving too quickly (such as emerging pathogens or pests evolving resistance to treatment) to populations not evolving quickly enough (for example those being negatively affected by human-mediated change).

The authors begin by identifying what they see as the two key paradigms of applied evolutionary biology: (i) managing contemporary evolution (i.e., manipulating the rapid evolutionary response of short-lived organisms with large population sizes, such as bacterial pathogens) and (ii) altering the phenotype–environment mismatch (i.e., responding to populations of long-lived organisms such as trees that are no longer well adapted to their local environment due to shifts in climatic conditions or changes in biotic interactions). As a great example of such a mismatch, the authors highlight the increasing rates of obesity, diabetes, and heart disease in the human populations as a result of a more sedentary lifestyle with diets rich in sugars and fat. They then identify a number of promising research avenues that either have addressed or have the potential to address current global challenges, covering a wide range of approaches including the use of genetic engineering to more appropriately match genomes to their environment, the use of ‘refuges’ in agriculture and combination treatments against pests and pathogens to hinder the evolution of resistance, and introducing nonlocal genotypes which are predicted to perform better under given environmental conditions into natural populations to increase local adaptation.

The article nicely separates these conceptual approaches into strategies for slowing unwanted evolution or directly influencing fitness of pests and pathogens, strategies for reducing the mismatch between phenotype and the local environment, and strategies for increasing group performance by selecting on group-level traits. For example, the authors discuss the success of artificially selecting for group yield in agricultural plots rather than individual fitness as a means for decreasing competition among plants. Critically, the piece also emphasizes the need to take a unified approach in meeting international objectives for sustainable development and suggests a need for stricter enforcement of guidelines in order to ensure best practice is achieved despite temptation to put profit or immediate success ahead of sustainable solutions.

Overall, the review acts as a unique and remarkable resource both for researchers and students who are new to the field of applied evolution and those who actively contribute to the field.

Carroll, S. P., P. S. Jørgensen, M. T. Kinnison, C. T. Bergstrom, R. F. Denison, P. Gluckman, T. B. Smith et al. 2014. Applying evolutionary biology to address global challenges. Science 346:1245993.”

And then discussed recent applications in molecular evolution, including two new papers using comparative genomics of mosquitos to better understand the evolution of these important disease vectors:

“The study of changing sequence composition of DNA, RNA and proteins over time has offered some of the most fundamental insights into the evolutionary process to date. From understanding how populations and ultimately species diverge to the study of how particular selection pressures affect changes in genotype and phenotype, our knowledge of evolution would be a fraction of what it is now without the major advances made in the field of molecular evolution. Recent technological and bioinformatical improvements have continued to expand these insights, and have also offered key applications such as the ability to model and predict pathogen evolution, monitor the effective population size of threatened species, and help understand what constitutes a healthy microbiome.

Two recent studies, both led by Nora Besansky and published in Science, emphasize the power and challenges of comparative genomics when working to understand the evolution of disease vectors. First, Daniel Neafsey and colleagues report the sequencing, assembly, and comparison of genomes from 16 Anopheles mosquito species (Neafsey et al. 2014). As 11 of these species are considered major disease vectors, comparison among the genomes allowed the researchers to examine underlying genes that may be associated with vectoring capacity. The results suggest that, relative to the Drosophila genus, the Anopholes’ genomes are remarkably flexible, with rapid rates of gene loss/gain, increased loss of introns, and shuffling of genes on the X chromosome. The data suggest a mechanism for the observed functional diversity across the species, especially in those traits such as chemosensory ability that are associated with adaptation to host feeding and therefore disease vectoring. However, comparison among genomes was hampered by what are most likely high levels of interspecific gene flow, or introgression, as described in a separate paper by Michael Fontaine and coauthors (Fontaine et al. 2014). Depending on which genomic segment the authors used to build phylogenetic trees, a remarkably different pattern emerged; trees based on autosomal sequences tended to group the three major vectors of malaria together, while those built using the X chromosome suggest early radiation of these three species and persistent introgression on the autosomes. Together, these studies offer tantalizing hypotheses for the adaptive significance of among-species gene flow and genomic plasticity in allowing the Anopholes genus to act as vectors for a wide array of pathogens.

In addition to the increasing power of genomics and phylogenomics, the use of transcriptional profiling has also proven invaluable to the field. A recent review of novel insights gained through transcriptomic analyses of natural populations by Mariano Alvarez and collaborators highlights the utility of this approach in testing how genotype translates to phenotype, and how this translation is influenced by environment-specific gene expression (Alvarez et al. 2014). Such variation can have dramatic implications for the process of adaptation as well as our ability to predict the response of populations to rapid environmental changes such as those resulting from pathogens, pollutants, or climate change. More recent advancement in transcriptomics includes the ability to profile gene expression of single cells, as discussed by Nicola Crosetto and coauthors in a new paper reviewing recent progress in spatiotemporal transcriptomics (Crosetto et al. 2015). Among the many applications of this powerful approach to unravelling among-cell expression differences is the ability to examine heterogeneity of tumour cells to predict drug sensitivity of various cancers.

The use of sequence data to infer evolutionary processes is not limited to single species. Indeed, the use of metagenomics to infer the composition of species from environmental samples has greatly enhanced our understanding of microbial diversity. In its simplest form, metagenomic analysis allows for a culture-independent characterization of microbial community composition. This type of analysis has gained much recent attention for its application in understanding the microbiomes of eukaryotic species. For example, recent work by Julia Goodrich and colleagues examined how human genetics shapes the relative abundances of various gut bacteria by comparing microbiotas across 416 pairs of twins (Goodrich et al. 2014). The authors first discovered a clear heritability for a subset of bacterial taxa, most notably those from the family Christensenellaceae, which were also correlated with low host body-mass index (BMI). The authors then went a step further by adding a particular species of Christensenellaceae into an obese-associated microbiome and inoculating sterile mice with either the unaltered or altered microbial community. In this way, they were able to demonstrate not only correlation with host metabolism in humans but also to infer causation, as mice supplemented with this species showed reduced weight gain relative to those not receiving the supplement.

The simultaneous analysis of multiple genomes within a single environmental sample also allows for assessment of selection acting on genes shared by members of the community. A terrific example of this comes from recent work by Molly Gibson and collaborators who examined the so-called ‘resistome’ of microbial communities from soil and the human gut, in this case focusing on the genes conferring resistance against 18 antibiotics typically used in clinical settings (Gibson et al. 2015). The authors used a new database of protein families to assign antibiotic resistance functions to each metagenomic segment, and were able to demonstrate that the antibiotic resistance genes found in environmental versus human-associated microbiota were functionally different, perhaps suggesting less gene flow among these communities than previously thought.

Overall, the recent advancements in both omics and bioinformatics have been game-changing for the field of molecular evolution, and the application of such new approaches and technologies have only begun to surface. The potential for advancement in clinical and agricultural settings is already being realized, and application to the management of natural populations, including the spread of disease, is already following.

Alvarez, M., A. W. Schrey, and C. L. Richards. 2014. Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution? Molecular Ecology, doi: 10.1111/mec.13055

Crosetto, N., M. Bienko, and A. van Oudenaarden. 2015. Spatially resolved transcriptomics and beyond. Nature Reviews Genetics16:57–66

Fontaine, M. C., J. B. Pease, A. Steele, R. M. Waterhouse, D. E. Neafsey, I. V. Sharakhov, X. Jiang et al. 2014. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 125852:4

Gibson, M. K., K. J. Forsberg, and G. Dantas. 2015. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. The ISME journal 9:207–216

Goodrich, J. K., J. L. Waters, A. C. Poole, J. L. Sutter, O. Koren, R. Blekhman, M. Beaumont et al. 2014. Human genetics shape the gut microbiome. Cell 159:789–799

Neafsey, D. E., R. M. Waterhouse, M. R. Abai, S. S. Aganezov, M. A. Alekseyev, J. E. Allen, J. Amon et al. 2014. Highly evolvable malaria vectors: the genomes of 16 Anopheles mosquitoes. Science 347:1258522.”

Britt Koskella Uncategorized

The ever-evolving field of agriculture

For this month’s Evolutionary Applications research highlight, I discuss recent uses of evolutionary theory in driving agricultural practice:

“The earliest application of evolutionary theory, although unknowingly at the time, was artificial selection of crops and animals for food production. Ever increasing technical advances in breeding, genetic engineering and comparative genomics have since led to a rapid acceleration in the rate of such selection, although many of the basic principles underlying the process have remained the same over time. For example, whereas we used to inter-breed among genotypes and even species to generate standing genetic variation upon which to select, we can now introduce specific genes of interest directly into the preferred genetic background.

Much of crop domestication historically has involved increased yield and size (for example of fruit or seed), and this has resulted in parallel and often convergent selection upon traits and even genes of interest. Recent work by Dorian Fuller and colleagues used archaeological plant remains from around the world to examine the parallel acquisition of so-called “domestication syndrome traits” across both plant species and regions (Fuller et al. 2014). The authors found differences in the rate of evolution among domestication traits, but also saw remarkably similar rates across regions over periods spanning several centuries to millennia. This work was part of a special feature in PNAS on “the modern view of domestication,” in which 25 researchers from across five fields came together to both discuss progress being made in research on domestication and to identify key challenges for the future (Larson et al. 2014). The feature highlights the role of past domestication in shaping the variation in agricultural species we observe today and suggests future studies should address the role that the contemporary environmental and ecological context may have played in influencing selection on traits in the past.

Improved understanding of the evolutionary process as well as major technological advances means the pace of artificial selection has intensified and our ability to respond to changes in both the abiotic and biotic environment has improved greatly. Our ability to translate understanding of plant genetics and genomics into meaningful applications in crop science is discussed in a new piece by Pamela Ronald (Ronald 2014). The work emphasizes not only the great potential that marker assisted selection, genetic engineering, and genome editing hold in translational research, but also the great need to ensure such technologies benefit farmers in less well-developed countries. Of course the success of newly introduced agricultural varieties will depend on both the local environment and the subsequent evolution of other interacting species. As such, two new papers have focused on the importance of taking into account the evolutionary response of disease agents when guiding disease management practice in agriculture (Burdon et al. 2014; Zhan et al. 2014). For example, Jeremy Burdon and coauthors review the success of strategies such as stacking resistance genes, introducing partial or adult-only resistance, or using mixtures of host types to hinder pathogen evolution (Burdon et al. 2014). Similarly, Jiasui Zhan and collaborators discuss the importance of mimicking the spatial and temporal dynamics of natural host-pathogen coevolution when designing disease management strategies, and emphasize that resistance strategies with immediate short-term benefits are often the least durable in the long term (Zhan et al. 2014).

Given the rapid potential for adaptation, many predictions regarding pest or pathogen evolution can be directly tested in the laboratory in order to inform better disease management. Recent work by Julia Hillung and colleagues examined the adaptation of a plant RNA virus to various ecotypes of Arabidopsis thaliana in order to determine the specificity and consequences of evolution on one host to infectivity on another. They use experimental evolution to show rapid increases in infectivity and virulence on the host background in which the virus has been adapted, but also demonstrate that some host types select for viral populations that are more generally infective to other types (Hillung et al. 2014). These results are particularly intriguing in that they suggest manipulation of host types in an agricultural setting could predictably alter the outcome of pathogen evolution. Such rapid evolution is not restricted to the laboratory; evidence from the Western corn rootworm on maize crops indicates that the pest is evolving resistance to the toxins produced by genetically engineered plants that were introduced into production only in 2003 (Gassmann et al. 2014).

Importantly, the utility of evolutionary theory for agricultural practice is not limited to pest and pathogen interactions. The increasingly clear role of the microbiomes across the rhizosphere and phyllosphere suggest great potential for application of both community ecological and evolutionary thinking. Suzanne Donn and coauthors examined the changing soil microbiome of intensive wheat crops across years and found that, relative to soil in the absence of plants, rhizosphere communities changed substantially over time in the presence of plant roots and these temporal dynamics could be explained well based on the stage of plant development (Donn et al. 2014). Such knowledge about tightly coevolved plant-microbe interactions could help inform better management of soils and guide efforts to develop plant probiotics. Another attractive application of evolution to agriculture that has received recent attention is the incorporation of inclusive fitness theory. Toby Kiers and Ford Denison discuss ways in which artificial selection can be focused on improving cooperation among crop plants and the microbial symbionts with which they interact (Kiers and Denison 2014). For example, the authors suggest that the use of those crop types capable of imposing strong sanctions against “cheating” rhizobial bacteria strains (i.e. those that do not fix nitrogen as effectively) could increase the dominance of more mutualistic strains in the soil.

Overall, although artificial selection has been central to agricultural practice since its dawn, we are still constantly improving our ability to speed up the selective process, incorporate adaptation across heterogeneous environments, and allow for a more responsive management program in the face of coevolving enemies and mutualists. As such, there remains great promise in our ability to increase crop yield and decrease the use of pesticides and fertilizers through the application of evolutionary thinking.”

Literature cited

Burdon, JJ, LG Barrett, G Rebetzke, and PH Thrall 2014. Guiding deployment of resistance in cereals using evolutionary principles. Evolutionary Applications 7:609–624.

Donn, S, JA Kirkegaard, G Perera, AE Richardson, and M Watt. 2014. Evolution of bacterial communities in the wheat crop rhizosphere. Environmental Microbiology, doi: 10.1111/1462-2920.12452.

Fuller, DQ, T Denham, M Arroyo-Kalin, L Lucas, CJ Stevens, L Qin, RG Allaby et al. 2014. Convergent evolution and parallelism in plant domestication revealed by an expanding archaeological record. Proceedings of the National Academy of Sciences 111:6147–6152.

Gassmann, AJ, JL Petzold-Maxwell, EH Clifton, MW Dunbar, AM Hoffmann, DA Ingber, and RS Keweshan 2014. Field-evolved resistance by western corn rootworm to multiple Bacillus thuringiensis toxins in transgenic maize. Proceedings of the National Academy of Sciences 111:5141–5146.

Hillung, J, JM Cuevas, S Valverde, and SF Elena 2014. Experimental evolution of an emerging plant virus in host genotypes that differ in their susceptibility to infection. Evolution 68:2467–2480.

Kiers, ET, and RF Denison 2014. Inclusive fitness in agriculture. Philosophical Transactions of the Royal Society B: Biological Sciences369:20130367.

Larson, G, DR Piperno, RG Allaby, MD Purugganan, L Andersson, M Arroyo-Kalin, L Barton et al. 2014. Current perspectives and the future of domestication studies. Proceedings of the National Academy of Sciences 111:6139–6146.

Ronald, PC 2014. Lab to farm: applying research on plant genetics and genomics to crop improvement. PLoS Biology 12:e1001878.

Zhan, J, PH Thrall, and JJ Burdon 2014. Achieving sustainable plant disease management through evolutionary principles. Trends in Plant Sciences 19:570–575.

Britt Koskella Uncategorized

As I see it: the value of double blind peer review

When Michelle Tseng (founding editor of Evolutionary Applications) asked me many years back how I felt about double blind peer review, I was fairly agnostic. Wouldn’t most reviewers be able to guess anyway? Surely the system isn’t biased enough to warrant such an obstacle? How will reviewers know what sort of overlap the study has with other work the authors have published? And so forth. I am now changing my stance. And yes, this change is based on only an N of 1, so I would love to hear the thoughts and experiences of others who’ve gone the process lately (translation: comments welcome!! And no need to agree, of course).

A year and a half or so ago, I was contacted by Derek Lin, a previous undergraduate in John Thompson’s lab at UC Santa Cruz with whom I had collaborated on an experiment examining bacterial resistance against multiple phages. He was contemplating his next career move, and was considering both graduate school and a medical degree, but was currently enjoying his job as a teacher in the Bay Area. Derek also said he was interested in collaborating again and working on another paper, so we set up a time to Skype and compared ideas. After some brainstorming and looking around, Derek came up with the idea for a review article focusing on the human-associated pathogen, Helicobacter pylori, as he had been intrigued by a recent paper suggesting that the range of beneficial to pathogenic symptoms correlating with H. Pylori infection might be due to mismatched strains and hosts (see Kodamam et Al. 2014). I agreed that this would be an interesting topic to explore, and thus began the collaboration.

After over a year of research and back and forth of who knows how many drafts, Derek and I were ready to submit (but were both a bit nervous, as neither of us had ever worked on this particular pathogen before. Would the reviewers wonder why we thought we were in a position to write such a piece?). At the same time, I had an email from Craig Primmer, the Evolutionary Applications Reviews Editor, reminding me that the journal now had a special reviews and synthesis section. I thought to myself: everything’s coming up Milhouse! It was the perfect fit, as we had worked to take an evolutionary angle in reviewing the literature and to put forward some ways in which evolutionary theory could be applied to this topic. We submitted, and a little over a month later had our reviews back.

Okay… Here begins my conversion. Like many of you, I imagine, I am used to fairly patronising reviews that seem to always use the working assumption that I have not thought about alternative interpretations and hypotheses, do not have the expertise needed to write a paper, or am generally a numpty. These are always hard to read because, of course, I do have imposters syndrome and find putting my ideas and research out there into the public domain to be judged hard enough already. It takes me quite a while and a lot of work before I feel confident enough to submit a paper… So having negative reviews, especially when I find them unconstructive and occasionally just plain wrong, but worded strongly, is hard to swallow. I thought this was just how the review process worked, and that I needed tougher skin to stay in this field. This may still be the hard and fast truth, but I have just had the first seed of doubt planted.

When I got the recent reviews back from Evol Apps, I read through them and smiled. Not because they were overwhelmingly positive; they had some really useful criticisms and pointed out key gaps we needed to fill. Rather, my smile was due to the fact that they seemed to have been written with the underlying assumption that we knew what we were talking about (even referred to the piece as an “extensive and up-to-date review” – Rev 1, which “offers a welcome, balanced perspective” – Rev 2). How refreshing! I don’t know who the referees were, but I do know that I suggested 5 names in the field whom I’ve never met but seemed to be leading the way in H. Pylori research. After all, the point of this peer review process was to ensure we had not misrepresented or misunderstood the current state of the field. Later that day I was sharing this story at lunch where it was pointed out to me that the reviewers may have thought the authors were big shots in the field, or at least that they couldn’t rule this possibility out. Indeed, the real benefit of double blind peer review, to my mind, became obvious: the referees had to review the work on the science behind it, not the authors. I won’t speculate here as to whether the difference in the tone of these reviews from so many of my others comes down to a gender issue, or is simply due to my early(ish) career status, or is just a general phenomenon (although there are some empirical reasons to believe the first option may be true in some cases, e.g. here and here). All I can say is that this experience has made me much more likely to consider a journal with double blind peer review in the future. In part because I am a scientist, and don’t believe any study (or blog post) that is not based on properly replicated data.

More resources of interest:

Kodaman, Nuri, et al. “Human and Helicobacter pylori coevolution shapes the risk of gastric disease.” Proceedings of the National Academy of Sciences 111.4 (2014): 1455-1460.

http://blog.sciencewomen.com/2008/01/peer-review-and-gender-bias.html

http://www.europeanwomeninmaths.org/women-in-math/report/gender-bias-in-peer-review-studies-and-reports

http://bioscience.oxfordjournals.org/content/56/9/712.short

http://www.nature.com/news/journals-weigh-up-double-blind-peer-review-1.15564

Britt Koskella Uncategorized

Disease evolution and ecology across space

For this month’s Evolutionary Application Research Highlight, I explored:

Disease evolution and ecology across space

“How infectious disease spreads both from individual to individual and across a landscape will depend upon many inter-related factors, including the genetic composition of host and pathogen populations, the pathogen transmission rate, host density, population connectivity, and the evolutionary response of both host and pathogen over time. As such, the study of infectious disease straddles a number of fields and approaches. A key advance in the field has come from incorporation of spatial structure into both theoretical and empirical studies of the epidemiology, evolution and ecology of disease. Researchers taking this approach were recently brought together for a workshop entitled “Spatial evolutionary epidemiology” in Montpellier, France organized by Sébastien Lion and Sylvain Gandon from the Centre d’Écologie Fonctionnelle et Évolutive (CEFE). The workshop spanned topics from infection genetics in Daphnia, to cooperation and conflict in microbial populations, to the utility of spatial epidemiological models for designing better crop planting strategies, and emphasized the broader need to think about the importance of spatial heterogeneity in order to predict the spread and evolution of pathogens.

The recent literature includes a number of studies at the cutting edge of this field. For example, a model based on interactions between bacteria and their bacteriophage parasites by Ben Ashby and collaborators has demonstrated the importance of spatial structure in shaping the breadth of host resistance. They find that hosts and parasites from spatially structured populations should be less constrained by costs associated with ‘generalism’ than those from well-mixed populations, and therefore that spatial structure is likely to increases the breadth of host resistance/parasite infectivity, especially when this increased breadth carries a significant fitness cost (Ashby et al. 2014). Another potential effect of spatial structure on host-parasite interactions is through changing rates of infection, as opposed to infection range. A recent empirical study of bacteria and phages by Pavitra Roychoudhury and coauthors experimentally evolved phages on spatially structured agar plates for 550 phage generations and found increased phage fitness was associated with a mutation conferring slower phage adsorption rate to bacterial host cells. This is in line with theoretical work suggesting spatial structure may lead to reduced parasite infectivity (e.g. Boots and Sasaki 1999) and builds upon previous empirical work from non-microbial systems in support of this theory. Furthermore, the authors develop a system-specific, spatially explicit model to explain how low attachment probability might lead to increased phage fitness through higher plaque density as a result of trade-offs between phage diffusion and adsorption (Roychoudhury et al. 2014).

Studying spatial structure in natural populations is of course a more challenging goal, as one loses the ability to control for the type of heterogeneity across which a study is performed. Despite these potential limitations, recent work by Jussi Jousimo, Ayco Tack, and collaborators examined the impact of connectivity among over 4000 populations of the plant, Plantago lanceolata, on resistance to a co-occurring fungal pathogen, Podosphaera plantaginis, over a 12-year period. Using this large spatial dataset, the authors were able to explain the unexpected observation that highly connected populations typically showed lower pathogen prevalence and higher pathogen extinction than more isolated populations. They demonstrate that hosts from highly connected populations are in fact more resistant to the pathogen than those from more isolated populations, most likely as a result of stronger parasite-mediated selection over time (Jousimo et al. 2014). This work highlights the importance of incorporating host and pathogen evolution into models predicting the spread of disease across space, and emphasizes the potential for differential parasite-mediated selection across a host metapopulation.

Finally, work by David Rasmussen and colleagues has demonstrated the strength of incorporating spatial structure into phylodynamic (coalescent) approaches to inferring past disease dynamics from genealogies (Rasmussen et al. 2014). Motivated to explain the common discrepancy between phylodynamic inferences and those made from hospital records, the authors apply their new method to a case study of mosquito-borne dengue virus in southern Vietnam. They demonstrate that a spatially structured susceptible-infected-recovered (SIR) model resulted in similar patterns to those seen from the hospitalization data, including large seasonal fluctuations in disease, and that the incorporation of ecological complexity into coalescent models increases the accuracy of inferences to disease demography.

Overall, these recent studies demonstrate the added value of incorporating spatial structure into epidemiological, phylodynamic, and evolutionary/ecological models of infectious disease. Although the current body of theory on the topic offers clear predictions for how spatial structure might influence disease evolution and spread, there remains a paucity of empirical and observational studies testing these key ideas. Moving forward, the more we understand about these eco-evolutionary feedbacks, the better we will be able to manage emerging disease in natural, agricultural and human populations.”

References cited:

Ashby, B., Gupta, S. and A. Buckling. 2014. Spatial Structure Mitigates Fitness Costs in Host-Parasite Coevolution. The American Naturalist 183:E64-E74.

Boots, M. and A. Sasaki. 1999. ‘Small worlds’ and the evolution of virulence: infection occurs locally and at a distance. Proceedings of the Royal Society of London. Series B: Biological Sciences 266:1933-1938.

Jousimo, J., Tack, A. J., Ovaskainen, O., Mononen, T., Susi, H., Tollenaere, C. and A. L. Laine. 2014. Ecological and evolutionary effects of fragmentation on infectious disease dynamics. Science 344:1289-1293.

Rasmussen D. A., Boni M. F. and K. Koelle. Reconciling phylodynamics with epidemiology: the case of dengue virus in southern Vietnam. Molecular biology and evolution 31:258-271.

Roychoudhury, P., Shrestha, N., Wiss, V. R. and S. M. Krone. 2014. Fitness benefits of low infectivity in a spatially structured population of bacteriophages. Proceedings of the Royal Society B: Biological Sciences 281:20132563.

Britt Koskella Uncategorized

The role of the microbiome in shaping evolution

For this month’s Evolutionary Applications research highlights I look at the role of the microbiome in shaping evolution:

“Over the past century, the study of genetics has revolutionized our understanding of life on earth. Our knowledge of trait heritability from parent to offspring has been central to predict the trajectory of evolution, studying disease, and successful breeding of crops and animals. The field of genetics continues to grow in leaps and bounds due to next-generation sequencing, metagenomic approaches, genetic engineering, a better understanding of epigenetics, and, most recently, the creation of synthetic chromosomes (Annaluru et al. 2014). Despite these advances, however, there is still an active debate regarding how much variation in phenotype is explained by nature (the genome) versus nurture (the environment; recently reviewed in Lynch and Kemp 2014). In addition, it is increasingly apparent that a significant proportion of the so-called missing heritability may be explained by host-associated microbial communities, the microbiome.

The microbiome of eukaryotes has been associated with traits ranging from disease susceptibility to digestion to behavior and even holds the potential to drive speciation (Brucker and Bordenstein 2013). This rapidly growing field already has its own journal (‘Microbiome,’ established in 2013) and has been the focus of a recent special issue of Microbial Ecology on ‘Nature’s microbiome’ (Russell et al. 2014), in which 28 research groups present new ideas and data on the composition and function of the microbiome and on how microbe–microbe and host–microbe interactions might shape evolution. Among the recent headlines, we have seen a role for soil-associated microbes in creating the taste of particular wine vintages (Bokulich et al. 2014) and good evidence that immune defense is modulated both directly and indirectly by our microbiota (recently reviewed in Abt and Pamer 2014). In addition, work by Maggie Wagner and colleagues on a wild relative of Arabidopsis has uncovered the key role of soil microbiota both in shaping flowering time and in influencing the intensity of selection on flowering time (Wagner et al. 2014).

Given the complexity of studying the human microbiome, much of our current understanding comes from work carried out on germ-free mice. A recent study by Jeremiah Faith and coauthors, which introduced 94 bacterial consortia of diverse sizes chosen at random from human fecal samples, was able to uncover key roles of the microbiota in inflammatory responses, obesity and variation in metabolites in mouse hosts (Faith et al. 2014). Determining how human microbiomes are shaped and how they may have coevolved with the population requires very large datasets to account for the great variation in diet, geography, race, and lifestyle among individuals. However, recent insight into how microbiota may have changed due to urbanization has come from Stephanie Schnorr and colleagues, who sequenced the microbiome of individuals from the Hadza hunter–gatherer community in Tanzania (Schnorr et al. 2014). They find evidence suggesting that, relative to individuals from urban communities in Italy, the Hadza microbiota is typically more species rich and lacks the typically common Bifidobacterium. Of course, as recently discussed by Eva Boon and collaborators, the importance of variation in microbiota composition among individuals and populations is less about what species are there than it is about what genes are there (Boon et al. 2014). This is both because of redundancy in function among microbial species and also due to the ability of bacteria to horizontally transmit genetic material among genomes, such that one population can readily evolve a new function simply by acquiring the necessary genes.

Given the current open questions regarding the function and composition of human microbiota, we are not yet at the stage of developing artificial communities as treatment for disease. However, in extreme cases of Clostridium difficile infection, doctors have been turning to fecal transplants as a way of resetting the microbiome of their patients with remarkably high success rates. Susana Fuentes and colleagues recently tracked changes in the microbiome of patients before, during and after such transplants and found a marked and long-lasting increase in microbial diversity after the transplant (Fuentes et al. 2014). It remains to be determined whether success rate is affected by interactions between the host genotype and the transplant microbiota, but we can look to data from other organisms for such clues. For example, Marie-Lara Bouffaud and coauthors report a significant relationship between rhizobacterial communities and genetic distance of their plant hosts, and this relationship held when looking only at single bacterial species (Bouffaud et al. 2014). On the other hand, work by Julie Reveillaud and colleagues found no clear signature of host relatedness in explaining the microbiota associated with coral hosts, although their data do suggest species-specific microbiota communities even across geographically distant deep sea populations (Reveillaud et al. 2014).

Another potential application of microbiome research is the use of ‘prebiotics,’ particular dietary fibers, to manipulate the composition of the microbiota. Amandine Everard and coauthors tested the impact of prebiotic treatment on mice that were fed high-fat diets and found that the differing composition in microbiota of treated mice acted to counteract inflammation and metabolic disorders induced by the high-fat diet (Everard et al. 2014). However, to fully translate the burgeoning microbiome data into practical applications, such as the use of pre- or probiotics to prevent/treat disease or to alter organismal phenotype in a predictable way, we need to untangle the complexity of social interactions among microbes more generally. This idea has been highlighted by Helen Leggett and coauthors, who review the wide range of ways in which microbes interact within their eukaryotic hosts (Leggett et al. 2014). The review emphasizes that better insight into microbe–microbe social evolution, both within and between species, will be central to better predicting the evolution of virulence, drug resistance, and the spread of infectious disease. The idea of harnessing information about social interactions, including those between microbes, to design novel treatments has been coined ‘Hamiltonian medicine’ and recently conceptualized by Bernard Crespi and colleagues (Crespi et al. 2014).

Together, the wealth of new data emphasizes that microbiota play central roles in shaping the health, ecology, and evolution of their hosts. The application of microbiota research is currently hindered by the complexity of the interactions (both among microbes and between the microbiota and the host), but the potential for application of this knowledge seems limitless.”

References cited:

Abt, M. C., and E. G. Pamer. 2014. Commensal bacteria mediated defenses against pathogens. Current Opinion in Immunology 29:16–22.

Annaluru, N., H. Muller, L. A. Mitchell, S. Ramalingam, G. Stracquadanio, S. M. Richardson, and M. E. Linder. 2014. Total synthesis of a functional designer eukaryotic chromosome. Science 344:55–58.

Bokulich, N. A., J. H. Thorngate, P. M. Richardson, and D. A. Mills 2014. Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate. Proceedings of the National Academy of Sciences 111:E139–E148.

Boon, E., C. J. Meehan, C. Whidden, D. H. J. Wong, M. G. Langille, and R. G. Beiko 2014. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiology Reviews 38:90–118.

Bouffaud, M. L., M. A. Poirier, D. Muller, and Y. Moënne-Loccoz 2014. Root microbiome relates to plant host evolution in maize and other Poaceae. Environmental Microbiology, DOI: 10.1111/1462-2920.12442.

Brucker, R. M., and S. R. Bordenstein 2013. The hologenomic basis of speciation: gut bacteria cause hybrid lethality in the genus Nasonia. Science 341:667–669.

Crespi, B., K. Foster, and F. Úbeda 2014. First principles of Hamiltonian medicine. Philosophical Transactions of the Royal Society B: Biological Sciences 369:20130366.

Everard, A., V. Lazarevic, N. Gaïa, M. Johansson, M. Ståhlman, F. Backhed, and P. D. Cani 2014. Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity. The ISME Journal, DOI:10.1038/ismej.2014.45.

Faith, J. J., P. P. Ahern, V. K. Ridaura, J. Cheng, and J. I. Gordon 2014. Identifying gut microbe–host phenotype relationships using combinatorial communities in gnotobiotic mice. Science Translational Medicine 6:220ra11.

Fuentes, S., E. van Nood, S. Tims, I. Heikamp-de Jong, C. J. ter Braak, J. J. Keller, and W. M. de Vos 2014. Reset of a critically disturbed microbial ecosystem: faecal transplant in recurrent Clostridium difficile infection. The ISME Journal, DOI:10.1038/ismej.2014.13.

Leggett, H. C., S. P. Brown, and S. E. Reece 2014. War and peace: social interactions in infections. Philosophical Transactions of the Royal Society B: Biological Sciences 369:20130365.

Lynch, K. E., and D. J. Kemp 2014. Nature-via-nurture and unravelling causality in evolutionary genetics. Trends in Ecology & Evolution 29:2–4.

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