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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 Evolutionary Applications

The utility of model system research for applied evolutionary questions

For this month’s Evolutionary Application’s research highlights, I look at recent work exemplifying the use of model systems in addressing questions of applied interest:

“Our ability to apply evolutionary theory is necessarily limited by our understanding of natural systems. Unfortunately, given the finite amount of researcher time and funding, we are faced with a trade-off between studying many systems shallowly and studying fewer systems in more depth. The handful of ‘model systems’ that have emerged thus far act as workhorses across disciplines, allowing for a more complete understanding of each system in fields from molecular genetics and evolution, to development, to physiology, to whole organism biology and ecology. However, given how few such model systems exist, it remains to determine how generalizable they are to less well-studied species and the natural world as a whole.

Among the model systems offering a wealth of insight across fields are the yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, the fly Drosophila melanogaster, and the plant Arabidopsis thaliana. These systems, among others, became models due in part to their ease of use in the lab, relative ubiquity, short generation time, and, in part, due to chance. The breadth of knowledge gained from the study of these and other model systems has driven progress across fields and has allowed for multidisciplinary research and cross talk among researchers that may not have otherwise come together. More recently, there has been a renewed interest in examining these systems back in the field and expanding the work to closely related species to determine whether our knowledge from the laboratory is broadly applicable in nature.

In terms of real life application, the model yeast Saccharomyces cerevisiae has the clearest relevance given its use in fermentation processes, the evolutionary history of which has been recently reviewed by Dashko et al. (2014). However, the tremendous toolbox of genetic techniques that is available has also made S. cerevisiae a central player in answering more basic questions in biology. Recent work by Serero et al. (2014) used a range of molecular approaches to examine mutation accumulation of both wild type and mutator strains of S. cerevisiae (i.e., strains with deficiencies in so-called ‘genome maintenance’ genes). By comparing the genome-wide mutational landscape across mutator types, they demonstrate strain-specific and complex effects of mutagenesis on chromosomal structure, mutations, and aneuploidy. The study emphasizes the tremendous diversity of the mutational landscape and provides an approach for examining genomic variation during clonal evolution, as occurs for example during tumor development in cancer.

The nematode Caenorhabditis elegans has acted as powerful model system for studying evolution due in part to the ability of researchers to freeze and resurrect individuals. A recent review by Gray and Cutter (2014) highlights the great potential the system still holds in exploring mutational processes, mating system and life-history evolution, and host-pathogen coevolution. For example, a new study by Sikkink et al. (2014) has examined the importance of phenotypic plasticity in adapting to extreme environments using the outcrossing sister species C. remanei. They experimentally selected for worms that were able to better withstand heat shock during development and found both increased tolerance of heat shock and altered phenotypic plasticity when reared in a novel environment, emphasizing both the role evolution can play in shaping plasticity and the importance of plasticity in allowing adaptation to novel and/or extreme environments.

Research on the fruitfly, Drosophila melanogaster, has paved the way for our understanding of genetics but has also been central to addressing questions regarding the impact of symbionts and pathogens on eukaryotic fitness (reviewed in Fauvarque 2014). Recent work by Versace et al. (2014) experimentally evolved populations of D. melanogaster infected with Wolbachia from multiple clades under hot or cold environments for 37 generations to test for changes in symbiont composition across environments. They discovered rapid increase in infection rates across replicate populations and treatments, suggesting a strong fitness advantage to hosts carrying the symbiont, as well as striking shifts in the composition of the Wolbachia community under cold, but not hot, environmental conditions. Studies of Drosophila have also been useful in understanding pest emergence and evolution. For example, a study by Atallah et al. (2014) has compared those Drosophila species that feed primarily on rotting fruit with those species that have switched to live fruit. Through morphological comparisons of the pest D. suzukii with its close relatives, the authors propose an evolutionary model to explain the modification of the ovipositor that allows puncture of susceptible fruits, demonstrating the utility of an evolutionary framework for addressing questions of pest emergence and management.

Finally, research focused on Arabidopsis thaliana plants has offered key insights to the genetics of plant adaptation, plant development, and plant–pathogen interactions. Two recent studies have examined natural populations of Arabidopsis to better understand the plant’s ability to move beyond current range limits. First, Wolfe and Tonsor (2014) took advantage of a natural elevational gradient of temperature and precipitation to examine plant adaptation to increase heat and drought. By exposing 48 lineages from across the natural gradient to increasing temperature and decreasing precipitation, they show that 10 of the 12 traits measured differ across the elevations and that populations from the low elevations are most fit in the face of increased heat and drought. In particular, these plants have faster bolting and earlier fruit ripening than those from high elevations, suggesting adaptation toward avoidance of spring heat and drought. In another study, Griffin and Willi (2014) examined natural populations of another Arabidopsis species, A. lyrata, across North America to test whether self-fertilization is more common at the edge of the species range, where effective population size is likely to be lower. Using population surveys and microsatellite markers, they were able to demonstrate at least seven independent transitions from outcrossing to selfing, all of which occurred at the edge of the species range where diversity was lower. Understanding such evolutionary shifts toward self-fertilization offers important insight to the ability of a species to expand its range and potentially to become invasive.

These recent studies highlight the great potential model systems hold for applying evolutionary theory in large part due to their amenability to laboratory conditions and genetic/genomic tools. Overall, it is clear that the knowledge gained from the study of model systems has been greater than the sum of its parts, but the generalizability of such knowledge to nonmodel systems, especially when it comes to translational research, remains an open question.”

References cited:

Atallah, J., L. Teixeira, R. Salazar, G. Zaragoza, and A. Kopp 2014. The making of a pest: the evolution of a fruit-penetrating ovipositor in Drosophila suzukii and related species. Proceedings of the Royal Society B: Biological Sciences 281:20132840.

Dashko, S., N. Zhou, C. Compagno, and J. Piškur 2014. Why, when and how did yeast evolve alcoholic fermentation? FEMS Yeast Research, doi: 10.1111/1567-1364.12161.

Fauvarque, M. O. 2014. Small flies to tackle big questions: assaying complex bacterial virulence mechanisms using Drosophila melanogaster. Cellular Microbiology 16:824–833.

Gray, J. C., and A. D. Cutter 2014. Mainstreaming Caenorhabditis elegans in experimental evolution. Proceedings of the Royal Society B: Biological Sciences 281:20133055.

Griffin, P. C., and Y. Willi 2014. Evolutionary shifts to self-fertilisation restricted to geographic range margins in North American Arabidopsis lyrata. Ecology Letters 17:484–490.

Serero, A., C. Jubin, S. Loeillet, P. Legoix-Né, and A. G. Nicolas 2014. Mutational landscape of yeast mutator strains. Proceedings of the National Academy of Sciences 111:1897–1902.

Sikkink, K. L., R. M. Reynolds, C. M. Ituarte, W. A. Cresko, and P. C. Phillips 2014. Rapid evolution of phenotypic plasticity and shifting thresholds of genetic assimilation in the nematode Caenorhabditis remanei. G3: Genes – Genomes – Genetics 4:1103–1112.

Versace, E., V. Nolte, R. V. Pandey, R. Tobler, and C. Schlötterer 2014. Experimental evolution reveals habitat-specific fitness dynamics among Wolbachia clades in Drosophila melanogaster. Molecular Ecology 23:802–814.

Wolfe, M. D., and S. J. Tonsor 2014. Adaptation to spring heat and drought in northeastern Spanish Arabidopsis thaliana. New Phytologist 201:323–334.

 

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.

Reveillaud, J., L. Maignien, A. M. Eren, J. A. Huber, A. Apprill, M. L. Sogin, and A. Vanreusel 2014. Host-specificity among abundant and rare taxa in the sponge microbiome. The ISME Journal, DOI:10.1038/ismej.2013.227.

Russell, J. A., N. Dubilier, and J. A. Rudgers 2014. Nature’s microbiome: introduction. Molecular Ecology 23:1225–1237.

Schnorr, S. L., M. Candela, S. Rampelli, M. Centanni, C. Consolandi, G. Basaglia, and A. N. Crittenden 2014. Gut microbiome of the Hadza hunter-gatherers. Nature Communications, DOI:10.1038/ncomms4654.

Wagner, M. R., D. S. Lundberg, D. Coleman-Derr, S. G. Tringe, J. L. Dangl, and T. Mitchell-Olds 2014. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecology Letters 17:717–726.

Britt Koskella Microbial coevolution, Phage therapy

Guest post by Sean Meaden

Do we need to watch what we spray? A summary of our recent review on the potential dangers of phage biopesticides.

Guest post by Sean Meaden, PhD student at University of Exeter working on phage-Pseudomonas syringae-plant host interactions.

It seems barely a week goes by without mention of the dangers of antibiotic resistance in popular news stories. Just last month the WHO called antibiotic resistance a ‘global threat’ in a well-publicized press release [1]. Whilst this might be slightly good news for those of us soon to be looking for post-doc positions in the field of microbiology, it certainly isn’t good for public health. The need for alternatives is pressing and it seems there just isn’t the financial incentive for large pharma companies to develop new drugs. Better stewardship of the compounds we already use is crucial, but so is exploring alternative options and exploiting our knowledge of microbial ecology and evolution.

One alternative strategy, known as ‘phage therapy,’ is the utilization of viruses that infect bacteria to decrease density of specific bacterial populations. In nature, phages have been estimated to kill as much as 20% of global bacterial populations each day [2]. As many reviews have noted, the use of phages is nothing new and has been a successful ongoing industry in Georgia and the former Soviet Union since the 1920s [3]. The aim of phage therapy is to find or create a phage that specifically infects a pathogenic strain of bacteria, culture it in the lab and turn it into a useable end product that can be ingested, applied topically or sprayed onto crops (as a biopesticide).

Phage cocktail production
Typical production of phage biopesticide. Reprinted from Meaden S and Koskella B (2013) Exploring the risks of phage application in the environment. Front. Microbiol. 4:358. doi: 10.3389/fmicb.2013.00358

The potential benefits of phage therapy are huge. However, given the negative consequences of improperly managed antibiotic use, we should be careful not to make the same mistakes again. It strikes me that a common theme in the phage therapy literature is ‘It’s OK, phages are naturally occurring,’ implying (in my mind at least) ‘What can go wrong?’ The same argument could have been made about antibiotics. After all, they too are naturally occurring compounds produced for microbial battles among themselves, and are likely to have been around for billions of years [4]. In our recent review [5] we explored the possible negative consequences of phage therapy to assess whether we are likely to make the same mistakes as we did with our often poor stewardship of antibiotics. Below is my summary of the main arguments we outline in the paper. On the whole I think the need for alternatives to antibiotics is huge, and by being prudent with our use of phages as a replacement (or synergistic treatment), and critically evaluating negative consequences, we will be better placed to use phage therapy successfully.

1) Implications of evolved resistance. This fairly obvious point is probably most comparable to antibiotic usage. We know that phages and bacteria undergo arms races of resistance and counter-resistance [6]. If the pathogens that are causing disease evolve resistance to the phages that we use as therapeutic agents, our cure becomes defunct and we have to go back to the drawing board. Finding new infective phages shouldn’t be too hard, but the process of turning them into a usable product that has passed regulatory hurdles is likely to be lengthy. This is a parallel problem to antibiotic production- there must be novel antibiotic compounds in the soil under our feet, but turning them into a lifesaving drug is the tricky part. A solution proposed by a group working on burn patients in Belgium is to create a reactive, cottage-industry style phage therapy centre that quickly screens for phage infectivity from a ready-made library, rather than a single product for widespread consumption (and most desirable to big pharma) [7]. Whilst this approach is great for pathogens that are readily culturable in the lab it might be more difficult for less tractable organisms.

2) Phage mediated attenuation of bacterial resistance. This argument seems to pop out of the literature as ‘OK, the bacteria might evolve resistance, but that won’t matter because resistance is costly so their virulence will be attenuated’. In a few cases this certainly does seem to be the case, for example Filippov et al. found reduced virulence of Yersinia pestis in mice (in other words mouse plague was less deadly when phage resistance had evolved; 8). In other cases, phage resistance actually had the opposite effect, making Pseudomonas aeruginosa more virulent in vitro, and rightly highlighting the need for caution in selecting phages for treatment [9]. Thus, for this argument to be used informatively we need much more data from a variety of systems and under more natural conditions or full-scale trials.

3) Agricultural cross-over. A consistent criticism of global policy on antibiotic stewardship is the use of antibiotics in agriculture. The addition of antibiotics at sub-therapeutic levels is great for feed efficiency and increasing yields. Given the increasing demand for protein in the global diet this issue isn’t trivial. However, it is likely that such practices increase levels of antibiotic resistance, and the bi-directional exchange of resistance genes from farm to community should be worrying. If phage therapy becomes more commonplace in agricultural settings could we see the same effect? Managed carefully, cross-resistance between agricultural and clinical phage therapeutics shouldn’t be a problem, especially given the typically (but not exclusively) high host specificity of phages. But I do think it’s worth acknowledging the potential for interference in order to prevent repeating mistakes of the past.

4) Horizontal gene flow from phage application. Phages are so good at transferring genes among bacterial cells that we use them in the lab to do just that. It’s certain that this horizontal exchange goes on in the environment, so we must ask: if we pump out unnaturally high volumes of phages (especially those with broader host ranges) into the environment, how likely are these phages to move genes around. This is especially problematic when the genes being swapped encode antibiotic resistance, toxins, or virulence factors. In this case, our attempt at a cure could actually make things much worse. We know that phage-mediated gene-transfer has played a part in cholera epidemics [10] and we should be careful about facilitating the spread of other unwanted bacterial traits.

5) Impacts on natural communities. The importance of a ‘healthy’ microbiome is constantly espoused (so much so that Jonathan Eisen has produced an award for ‘Overselling the Microbiome;’ 11). Although the direct effects of a healthy microbiota are still predominantly correlational, minimizing the disruption to a community whilst removing a pathogenic species must surely be the ultimate goal. Phages could hold great potential in this regard as they tend to be fairly specific in their host range (so could act more like snipers) relative to antibiotics (which act more like indiscriminate hand-grenades). On the other hand, artificially high volumes of phages could have unexpected effects on microbial processes, particularly in an agricultural environment. To my knowledge the effects of adding high titres of phages to microbial communities in the environment remains unexplored.

6)   Unpredictability of infection kinetics. This issue is an exciting one- the pioneer of phage therapy, Felix d’Herelle, is spuriously quoted as stating that immunity is contagious as well as the cure [12]. He might have been wrong about the biology but the premise that the cure is transmissible certainly seems possible with replicating phages. The downside is that it makes it hard to predict the persistence of phages in the environment. Unlike an antibiotic with a known half-life, a phage therapy product could continue to persist and replicate in the environment indefinitely. Even at low densities this raises an ethical question of something being uncontainable.

Recently, the ethical imperative of using phage therapy has been stated [13] and there is clearly a need for alternative strategies to antibiotic drugs. All of the concerns raised in our review are addressable and shouldn’t preclude the use of phage therapy in a clinical or agricultural setting. Moreover, all of the questions we have raised are readily answerable given the advances of microbial genomics over the last decade. We just need more data!!

If this summary piqued your interest, check out the whole article here, and open access: http://journal.frontiersin.org/Journal/10.3389/fmicb.2013.00358/full

References:

  1. http://www.who.int/drugresistance/documents/surveillancereport/en/
  2. Suttle, C. A. 1994 The significance of viruses to mortality in aquatic microbial communities. Microb. Ecol. 28, 237–43.
  3. Kutateladze, M. and Adamia, R. 2008 Phage therapy experience at the Eliava Institute. Med Mal Infect 38, 426–430.
  4. D’Costa, V. M. et al. 2011 Antibiotic resistance is ancient. Nature 477: 457–461.
  5. Meaden, S., & Koskella, B. (2013). Exploring the risks of phage application in the environment Frontiers in Microbiology, 4 DOI: 10.3389/fmicb.2013.00358
  6. Buckling, A. and Rainey, P.B. 2002. Antagonistic coevolution between a bacterium and a bacteriophage. Proc. R. Soc. Lond. Ser. B, 269: 931–936
  7. Pirnay, J.-P. et al. 2011. The phage therapy paradigm: pret-a-porter or sur-mesure? Pharmaceutical Research 28:934–937.
  8. Filippov, A. A. et al. 2011 Bacteriophage-resistant mutants in Yersinia pestis: identification of phage receptors and attenuation for mice. PLoS ONE 6, e25486
  9. Hosseinidoust, Z. et al 2013 Evolution of Pseudomonas aeruginosa virulence as a result of phage predation. Appl Environ Microbiol 79: 6110–6116
  10. Waldor, M. K. and Mekalanos, J. J. 1996 Lysogenic conversion by a filamentous phage encoding cholera toxin. Science 272:1910–1914.
  11. http://phylogenomics.blogspot.co.uk/2014/04/overselling-microbiome-award.html
  12. d’Herelle, F. 1924 Immunity in Natural Infectious Disease, Williams & Wilkins
  13. Verbeken, G. et al. 2014 Taking bacteriophage therapy seriously: a moral argument. BioMed Research Int. 2014: Article ID 621316, 8 pages.
Britt Koskella Evolution, Evolutionary Applications

Predicting the evolutionary response of populations to climate change

For this month’s research highlight in Evolutionary Applications, I chose to focus on recent approaches to studying evolutionary responses to climate change:

“Given the increasingly unpredictable weather patterns associated with global climate change, a key aim of current research is to predict whether and how populations will be able to respond. Major advances are being made through a combination of natural, long-term studies and experimental approaches, for example by experimentally manipulating the environment and measuring phenotypic and/or genetic/genomic change. The first issue of Evolutionary Applications this year (Volume 7, Issue 1), under the lead of guest editors Andrew Hendry and Juha Merilä, brought together perspectives from a number of researchers on the leading edge of climate change research. This included multiple synthetic reviews discussing the challenges of teasing apart evolutionary change from more ‘plastic’ responses to environmental perturbation, as well as reviews of the current empirical evidence for and against key theoretical predictions. Overall, the volume highlights that, although data on the evolutionary potential of populations in response to climate change are accumulating, we are still far from where we hope to be in terms of predictive ability, especially given the complexity of most environments (Merilä and Hendry 2014).

Such biological and environmental complexity can be disentangled through the use of experimentally manipulated microcosms, and these studies provide a powerful tool for differentiating correlation from causation and investigating interactive, synergistic effects among multiple factors. Researchers in this field are moving beyond tests of increasing temperature to examine more complex factors, including environmental fluctuations and multiple abiotic or biotic stressors (Jeffs and Leather 2014; Vasseur et al. 2014). Recent work by Jennifer Lau and collaborators manipulated both the atmospheric CO2 concentration and the competitive environment of experimental Arabidopsis thaliana populations. They found that, while populations showed little direct response to increased CO2, there were strong indirect effects of the abiotic environment on the population level response to competitor-mediated selection (Lau et al. 2014). This type of indirect effect of climate change on populations would be overlooked in studies focused solely on climate-specific adaptations. Similarly, the effect of climate change at the community level can often be more striking than effects on a single population or species (Bailey et al. 2014). Sarah Evans and colleagues examined adaptation of soil bacterial communities after a decade of drying and rewetting stress and found that community composition was significantly altered relative to plots experiencing normal precipitation, especially in favor of those taxa exhibiting increased stress tolerance (Evans and Wallenstein 2014). The outcome of adaptation to changing environmental conditions can also differ in very meaningful ways among populations or genotypes. Romain Gallet and coauthors recently evolved replicate populations of Escherichia coli under extreme shifts in pH over 2,000 generations and found adaptation to the new pH in all replicates but evidence for niche width expansion in only two of the four replicates. This latter finding emphasizes the importance of incorporating the evolution of plasticity into predictions regarding climate-mediated adaptation and also highlights the very different evolutionary trajectories that populations may take in response to the same selection pressure (Gallet et al. 2014).

Although these experimental approaches offer important insight to the processes underlying climate-mediated evolutionary change, an understanding of whether a population can respond does not clearly translate into an understanding of whether it will respond. As such, there is still great need for long-term studies from natural populations. Katie Becklin and colleagues have examined changes in leaf physiology across seven conifer species by comparing samples preserved in middens (debris piles) from five time points since the last glacial maximum. Their data suggest physiological adaptations of leaves to climate change, such as decreased stomatal conductance, were influenced by evolutionary history but were not the primary determinant of shifts in community composition (Becklin et al. 2014). Finally, combining both natural and experimental datasets can offer a unique perspective on our ability to predict the effects of climate change. Sean Menke and collaborators have compared the results of long-term experimental data with those from natural populations to demonstrate that, although ant community composition has shifted along an elevational gradient over time in the natural populations, there has been relatively little change over the course of experimental warming (Menke et al. 2014). This work highlights the potential limitations of microcosm studies in isolation, given the biological complexity of populations and communities over space and time. However, these limitations can be overcome in part with biologically meaningful replication of experimental plots. This is nicely demonstrated by a recent study of wetland seed banks from multiple latitudes across two continents in which species diversity of communities from southern latitudes were found to be less affected by experimental warming than those from more northern latitudes (Baldwin et al. 2014).

Together, these studies demonstrate the need to incorporate both biotic and abiotic complexity into models and empirical studies in order to fully understand the resilience of populations and communities in the face of climate change. The impressive range of recent approaches, from experimental manipulation of multiple factors, to seminatural studies of communities spanning a wide range of abiotic conditions, to tests of species-level adaptations from preserved specimens across millions of years, has allowed great strides in our understanding of both if and how populations might respond to the increasingly unpredictable environment.”

Bailey, J. K., M. A. Genung, I. Ware, C. Gorman, M. E. Van Nuland, H. Long, and J. A. Schweitzer 2014. Indirect genetic effects: an evolutionary mechanism linking feedbacks, genotypic diversity and coadaptation in a climate change context. Functional Ecology 28:87–95.

Baldwin, A. H., K. Jensen, and M. Schönfeldt 2014. Warming increases plant biomass and reduces diversity across continents, latitudes, and species migration scenarios in experimental wetland communities. Global change biology 20:835–850.

Becklin, K. M., J. S. Medeiros, K. R. Sale, and J. K. Ward 2014. Evolutionary history underlies plant physiological responses to global change since the last glacial maximum. Ecology Letters, doi: 10.1111/ele.12271.

Evans, S. E., and M. D. Wallenstein 2014. Climate change alters ecological strategies of soil bacteria. Ecology letters 17:155–164.

Gallet, R., Y. Latour, B. S. Hughes, and T. Lenormand 2014. The dynamics of niche evolution upon abrupt environmental change. Evolution, doi: 10.1111/evo.12359.

Jeffs, C. T., and S. R. Leather 2014. Effects of extreme, fluctuating temperature events on life history traits of the grain aphid, Sitobion avenae. Entomologia Experimentalis et Applicata 150:240–249.

Lau, J. A., R. G. Shaw, P. B. Reich, and P. Tiffin 2014. Indirect effects drive evolutionary responses to global change. New Phytologist 201:335–343.

Menke, S. B., J. Harte, and R. R. Dunn 2014. Changes in ant community composition caused by 20 years of experimental warming vs. 13 years of natural climate shift. Ecosphere, 5:art6.

Merilä, J., and A. P. Hendry 2014. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evolutionary Applications 7:1–14.

Vasseur, D. A., J. P. DeLong, B. Gilbert, H. S. Greig, C. D. Harley, K. S. McCann, V. Savage, T.D. Tunney, and M. I. O’Connor 2014. Increased temperature variation poses a greater risk to species than climate warming. Proceedings of the Royal Society B: Biological Sciences, 281:20132612.

Britt Koskella Evolution, Evolutionary Applications

Predicting evolution

For this month’s research highlights in Evolutionary Applications I thought I’d take a look at some of the recent work addressing the predictability of Evolution.

“Whether evolution is predictable becomes a key question when deciding how to translate and apply evolutionary theory to solve real-world problems. Mutations arise by chance, and therefore, it seems fair to question if we can predict when, where, and how a population will respond to a given selection pressure. One of the many exciting results from Richard Lenski’s long-term experimental evolution of Escherichia coli bacterial populations was the clear demonstration that, given a set of so-called ‘potentiating’ mutations, populations followed the same evolutionary trajectory again and again as the experiment was ‘replayed’ (Blount et al. 2008). In their new paper, Diana Blank and collaborators have taken a similar approach by experimentally evolving E. coli mutants with major gene deletions to test if and how the mutants are able to regain function (Blank et al. 2014). By measuring both the likelihood of recovery and the type of mutations underlying recovered function, the authors uncover a relationship between regulatory versus structural mutations and metabolic function, demonstrating a clear and predictable link between molecular change and functional innovation.

The ability to predict how a population will respond to selection pressure is further aided by a good understanding of the ‘fitness landscape’ (the relationship between genotype and reproductive success). If the effect of every possible mutation, and interactions among mutations, on an organism’s fitness was known, we would have a much higher probability of successfully predicting the evolutionary trajectory in response to a given selection pressure such as drug treatment or insecticide use. Recent work by Ashley Acevedo and colleagues used next-generation sequencing to reveal the mutation landscape of poliovirus and to explore the fitness change associated with thousands of mutations across the landscape (Acevedo et al. 2014). This work sets the stage for generating and testing predictions regarding the evolution of RNA viruses, taking us a step closer to designing ‘evolution-proof’ treatment strategies.

The predictability of evolution at the phenotypic level can also be observed in natural populations, in particular when multiple populations are evolving in response to similar selection pressure. For example, a recent study by Rüdiger Riesch and coauthors examined change across nine species of New World livebearing fish that independently colonized toxic, sulfur spring environments (Riesch et al. 2014). They discovered that, as predicted by evolutionary theory, each of the 22 populations evolved toward larger offspring size and fewer offspring number after colonization, emphasizing that parallel adaptation to a specific environment can lead to repeatable evolution at the phenotypic level across both populations and species.

Finally, a good working knowledge of the mutational landscape and genotype–phenotype map can be extremely useful when employing adaptive laboratory evolution to generate organisms of applied interest. Artificial selection has been around since prehistoric times, but recent advances in, for example, stress-induced and transposon mutagenesis as well as experimental evolution have allowed for the rapid generation of organisms with desired traits. These techniques have recently been employed to select for acid- and temperature-tolerant strains of hydrogen-producing photosynthetic bacteria (Cai and Wang 2014) improved carotenoid production in yeast (Reyes et al. 2014) and reduced ethanol content of fermenting wine (Tilloy et al. 2014). For example, 200 generations of artificial selection for reduced ethanol and enhanced glycerol yields of Saccharomyces cerevisiae were enough to generate new strains showing little to no decrease in fermentation activity (Tilloy et al. 2014). In comparison with the speed at which mutants can be generated by engineering and mutagenesis (e.g., Cai and Wang 2014), this method may seem old-fashioned. However, Valentin Tilloy and collaborators argue that given the feasibility of classic methods and the public hesitation surrounding genetic modification, artificial selection still holds great promise for the generation of commercially relevant organisms (Tilloy et al. 2014). The success of such ‘directed evolution’ will of course depend on our ability to choose the appropriate selective pressures, as emphasized by Luis Reyes and coauthors who recently evolved yeast strains under oxidative stress with the goal of selecting for increased production of carotenoids with known antioxidant properties (Reyes et al. 2014).

These recent studies highlight that our ever-increasing understanding of evolutionary trajectories along the fitness landscape, uncovered by the genotype–phenotype map, is allowing both for an increased ability to predict the outcome of evolution and for the improved application of directed evolution in generating desirable phenotypes. Furthermore, the use of next-generation sequencing to generate single nucleotide fitness landscapes will offer a resolution that was once only dreamed of (Wright 1932).”

Acevedo, A., L. Brodsky, and R. Andino 2014. Mutational and fitness landscapes of an RNA virus revealed through population sequencing. Nature 505:686–690.

Blank, D., L. Wolf, M. Ackermann, and O. K. Silander. 2014. The predictability of molecular evolution during functional innovation. Proceedings of the National Academy of Sciences USA, doi: 10.1073/pnas.1318797111.

Blount, Z. D., C. Z. Borland, and R. E. Lenski 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proceedings of the National Academy of Sciences USA 105:7899–7906.

Cai, J., and G. Wang 2014. Photo-biological hydrogen production by an acid tolerant mutant of Rhodovulum sulfidophilum P5 generated by transposon mutagenesis. Bioresource technology 154:254–259.

Reyes, L. H., J. M. Gomez, and K. C. Kao 2014. Improving carotenoids production in yeast via adaptive laboratory evolution. Metabolic engineering 21:26–33.

Riesch, R., M. Plath, I. Schlupp, M. Tobler, and R. Brian Langerhans 2014. Colonisation of toxic environments drives predictable life-history evolution in livebearing fishes (Poeciliidae). Ecology letters 17:65–71.

Tilloy, V., A. Ortiz-Julien, and S. Dequin. 2014. Reducing ethanol and improving glycerol yield by adaptive evolution of Saccharomyces cerevisiae wine yeast under hyperosmotic conditions. Applied and Environmental Microbiology, doi: 10.1128/AEM.03710-13.

Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the Sixth International Congress on Genetics 1:356–366.

Britt Koskella Evolution, Evolutionary Applications

What’s been keeping me busy?

What a whirlwind of a few months it’s been! Stay tuned, as we have some great new data rolling out and a couple of new review/synthesis papers. My lack of blogging recently (which I hope to remedy) is also the result of too many papers to review, lots of travel to and from (with the added complication of being disconnected from London by a washed out train track) – including a recent trip to Lisbon to discuss grapevine trunk disease, and various other projects. One such project which I have thoroughly enjoyed is starting a new “research highlights” section for Evolutionary Applications.

In my opinion, Evol Apps is a great journal that was founded by my good friend, Michelle Tseng just after she finished her PhD in Curt Lively’s lab at Indiana University. At the time, there was no good venue for application of evolutionary theory and no journal aimed at exemplifying how very applicable it can be to everyday problems! I am very happy to be involved with the journal, especially now that it has moved to open access.

The only downside of my taking the time every month to put together a research highlight is that it will no doubt take away from ability to update this blog as much as I would like to (although I will still try). Therefore, I’ve decided to share my first research highlight below – see digital, PDF version here.

“This past year was full of noteworthy evolutionary applications, including a strong focus on the use of combination therapy to slow or stop the spread of drug resistance. In particular, the application of evolutionary theory to resistance of cancers was a hot issue, with a special issue of Evolutionary Applications (volume 6, issue 1), a Nature Reviews Cancer piece about the importance of life-history trade-offs to our understanding of cancer progression (Aktipis et al. 2013), a Nature review article on genomic instability of cancer (Burrell et al. 2013), a Jacques Monod conference on ‘Ecological and evolutionary perspectives in cancer,’ and the 2nd International Biannual Evolution and Cancer Conference. This blossoming interest is uncovering the roles of evolutionary processes in shaping the development, spread, and virulence of cancers, but is also highlighting the current gap in our ability to translate evolutionary theory into successful treatment. An elegant example of the great potential of this approach is the recent work by Shi and coauthors identifying two core resistance pathways in metastatic melanomas (Shi et al. 2014). The researchers found that drug resistance of these cancers occurred most often via two distinct ‘drug escape pathways,’ explaining 70% and 22% of resistance among disease-progressive tissues. These data act as strong justification for the use of combination treatment to target both BRAF inhibitor resistance pathways simultaneously to block these common routes of drug escape.

Similar approaches are being applied to tackle the current antibiotic crisis, as has been reviewed in a recent Nature Reviews Genetics article (Palmer and Kishony 2013). The authors point out the importance of using optimal combinations of antibiotics, ideally chosen based on negative cross-resistance and antagonistic interactions among resistance mechanisms. However, a recent paper by Pena-Miller and colleagues, combining experimental and genomic techniques with mathematical modeling, highlights that caution must be employed when employing combination treatments, as the use of synergistic antibiotics can in fact increase pathogen load under suboptimal treatment durations or subinhibitory drug concentrations (Pena-Miller et al. 2013). A similar warning has now been raised for combination treatment of malaria due to the discovery of high association among resistance alleles in multiple regions of China (Ding et al. 2013). Furthermore, a clear understanding of epistasis among resistance mutations is key to predicting the spread of multidrug-resistant bacteria, as has been nicely demonstrated by Borrell et al. (2013). They found that combinations of drug resistance mutations in Mycobacterium smegmatis showing a competitive fitness advantage in vitro were also the most frequently observed combinations found in multidrug-resistant clinical isolates of human tuberculosis in South Africa.

The application of evolutionary theory to development of combined treatments is certainly not limited to human disease. Experimental evolution of the green chlorophyte, Chlamydomonas reinhardtii, was used by Lagator and coauthors to demonstrate the utility of combined herbicides for slowing the evolution of resistance in agricultural settings (Lagator et al. 2013). However, their data come with a similar word of warning: using these combinations at low doses can select for both increased rates of resistance and a more general resistance mechanism relative to single herbicide treatment.

This small subsample of recent research published in Evolutionary Applications and other high-profile journals on combination treatment emphasizes the power of applying evolutionary theory to treatment design, both in terms of creating more ‘evolution-proof’ combinations and in predicting potential negative consequences of this approach. We look forward to future research on the topic and continued monitoring of treatments designed in the light of evolutionary theory.”

References cited:

Aktipis, C. A., A. M. Boddy, R. A. Gatenby, J. S. Brown, and C. C. Maley 2013. Life history trade-offs in cancer evolution. Nature Reviews Cancer 13:883–892.

Borrell, S., Y. Teo, F. Giardina, E. M. Streicher, M. Klopper, J. Feldmann, … S. Gagneux 2013. Epistasis between antibiotic resistance mutations drives the evolution of extensively drug-resistant tuberculosis. Evolution, Medicine, and Public Health 2013:65–74.

Burrell, R. A., N. McGranahan, J. Bartek, and C. Swanton 2013. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501:338–345.

Ding, S., R. Ye, D. Zhang, X. Sun, H. Zhou, T. F. McCutchan, and W. Pan. 2013. Anti-folate combination therapies and their effect on the development of drug resistance in Plasmodium vivax. Scientific Reports 3:????–????.

Lagator, M., T. Vogwill, A. Mead, N. Colegrave, and P. Neve 2013. Herbicide mixtures at high doses slow the evolution of resistance in experimentally evolving populations of Chlamydomonas reinhardtii. New Phytologist 198:938–945.

Palmer, A. C., and R. Kishony 2013. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nature Reviews Genetics 14:243–248.

Pena-Miller, R., D. Laehnemann, G. Jansen, A. Fuentes-Hernandez, P. Rosenstiel, H. Schulenburg, and R. Beardmore 2013. When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition. PLoS biology 11:e1001540.

Shi, H., W. Hugo, X. Kong, A. Hong, R. C. Koya, G. Moriceau, … R. S. Lo 2014. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discovery 4:80–93.

Britt Koskella Being a scientist

One-year blogiversary

One year ago I decided that I wanted to have a more flexible research page (in addition to the more static page through my university) and so I joined WordPress. I chose WordPress in part because it was free, had good tutorials, and was well known, and in part because Mick Vos (who had been encouraging me to start a blog for a few months) had set up such a great page for his coastal pathogens institute. Although there are a number of great web hosting sites, I am very happy with my decision and think I’ll stay out for another year!

Sycamore, Alder, Ash and Oak tree leaves stamped onto nutrient agar and incubated for 48 hours.
Sycamore, Alder, Ash and Oak tree leaves stamped onto nutrient agar and incubated for 48 hours.

I knew from the start that my blogging would be sporadic, as term times come with grant deadlines, teaching, big experiments and many manuscripts to write and review; so I decided to simply have a blog tab, rather than allowing it to take center stage. As you can see from the dates of my postings, this was probably a wise decision. That being said, the fondness and joy I feel for my blog tab is not well represented by the frequency of my posting. If I could, I would spend much more time on blog posts, and indeed perhaps as things move forward I will find the time to do so. So today, on my one year anniversary of this site, I thought I would describe how starting this blog has changed my career and life (I know that sounds pretty dramatic, but keep reading).

I don’t know where I fall on the introvert/extrovert scale, but those who know me assume I’m way out towards extrovert and those who know me well are likely to think I’m pretty far out on the introvert end. Either way, I know I love to stand in front of a crowd and discuss my research but I still turn bright red on occasion in staff meetings if I simply speak up to raise a mundane point. I enjoy chatting with colleagues and late nights with friends, but I need space and time to myself (and a lot of it). I love doing research and sharing the results with others, but I am still terrified every time I submit a manuscript that my ideas are silly, my stats are wrong, or I missed a large chunk of the literature despite months of reading, analysing data, and writing. This is something no one told me about when it comes to being a scientist; the fact that you are continually judged, and often harshly, by your peers. For those of us who are plagued by self-doubt anyhow, this really is the hardest part. Being good enough, mainly in our own minds.

That is why I often spend two weeks staring at a blank screen before I finally begin the writing of a manuscript. It is like standing on the edge of a cliff and gaining the courage to jump. Once I start the writing process, things can move very very fast. Sometimes I get so excited that I can barely type fast enough. But it’s the getting started that is the constant bottleneck. That’s true for almost all pieces of writing, e.g. grants, manuscripts, and even important emails, but it’s not true for blog posts. With blog posts, like this one, the mood strikes and I go for it. No pressure, no right or wrong,  just putting my thoughts and ideas down on paper. And the best part is that once I finish a blog post and the creative juices are flowing, I can transition straight into a more serious piece of writing; without the hesitation, without the focus on perfection, I just get on with it. And there you have the first way that this blog has helped me.

The second way has to do with visibility and networking. Once my blog had been created, and my first post written, I knew I needed a better way to engage with other bloggers and readers. My wonderful friend Pip had been encouraging me to get into twitter for years, but despite starting an account a few years back, I had never really seen its worth. At long last, I saw a good use and began expanding my twitter network. Over the last year I have gone from occasional tweeter, to manic live tweeter, to a state of equilibrium where I check twitter in the morning, tweet when I have something fun/useful/interesting to say, and check again before bed. I use twitter to keep up on the recent literature, share new papers and blog posts, and to network. This final point deserves a few more sentences, as this was the unexpected reward. Since joining the list of science tweeps I have gained numerous friends, many of whom I’ve now met and many more of whom I still look forward to meeting. I’ve also made connections with folks at conferences that I would not have met if it weren’t for us both tweeting, and I’ve been able to identify a pool of interesting, caring and very clever followers/people I follow, which make me feel very well-connected and well-supported despite living in the (stunningly beautiful) far reaches of Cornwall.

A colleague recently said to me: I don’t tweet or keep a blog because I don’t imagine that anyone else will be interested in what I have to say. This really caught me off guard; am I full of self-importance? Do I blog/tweet because I think I’m particularly interesting? My response to this comment was flippant: I don’t tweet/blog for others, I do it for myself. I don’t know if that’s entirely true, as I have been known to obsessively check views to my blog, and I love getting comments on posts, but there is certainly a clear element of truth in there. Being highly visible (in theory) in such a non-threatening way has bolstered my confidence immensely and has allowed me to find a voice I didn’t know I had.

This past week I attended the first day of the AURORA workshop in London, a leadership program for women in science. I will post more about that in the next few months, but bring it up now because on the first day we were asked to bring a picture/image/object that represents us. I barely had to give it a thought, and printed out a large copy of my twitter avatar. The more I thought about it, the more I liked the idea. The picture represents my science, it represents the importance of social networking in my life, and of course it reminds me of my first post: I am more microbe than me!

So thanks for reading, and for your support. I have about 13 posts in the works (did I mention that I am very good at starting things?!?!) and will be blogging about science things again soon. For now, though, I would just encourage any of you who’ve been toying with the idea of revamping your website, starting a blog, or joining twitter to do so. You may well be surprised at the impact it has on your life, research and productivity (in a good way!)

Britt Koskella Teaching

Coevolutionary interactions course

And so the term begins, as does version 1.0 of my Coevolutionary Interactions course (bio3401). The idea behind the course is to use species interactions and the coevolutionary process to reinforce key theory/ideas in evolution and ecology. To do that, I am taking a case studies approach where I give an interactive lecture on a new topic each week, including key background information students need to put the papers they will read into context. I then assign two papers from the primary literature on the topic, and ask a group of students to lead discussion about the papers during the following class. I have not taught a course in this format before, so will blog again at the end of the term and let you know how it goes.

Calvin always says it best! Credit: Bill Watterson

For now, after rearranging post-it notes ad nauseam, I have decided on the following topics and associated readings:

(1) Introduction to coevolutionary interactions and lecture on competition and character displacement. Readings: Grant, P. R., & Grant, B. R. (2006). Evolution of character displacement in Darwin’s finches. science, 313(5784), 224-226. and Stuart, Y. E., & Losos, J. B. (2013). Ecological character displacement: glass half full or half empty?. Trends in ecology & evolution.

(2) Lecture on mutualisms and trait loss. Readings: Ellers, J., Toby Kiers, E., Currie, C. R., McDonald, B. R., & Visser, B. (2012). Ecological interactions drive evolutionary loss of traits. Ecology Letters, 15(10), 1071-1082. and Koricheva, J., & Romero, G. Q. (2012). You get what you pay for: reward-specific trade-offs among direct and ant-mediated defences in plants. Biology Letters, 8(4), 628-630.

(3) Lecture on mimicry. Readings: Kapan, D. D. (2001). Three-butterfly system provides a field test of Müllerian mimicry. Nature, 409(6818), 338-340. and Jiggins, C. D., Naisbit, R. E., Coe, R. L., & Mallet, J. (2001). Reproductive isolation caused by colour pattern mimicry. Nature, 411(6835), 302-305.

(4) Lecture on plant-pollinator interactions. Readings: Micheneau, C., Johnson, S. D., & Fay, M. F. (2009). Orchid pollination: from Darwin to the present day. Botanical Journal of the Linnean Society, 161(1),   1-19. and Anderson, B., & Johnson, S. D. (2008). The geographical mosaic of coevolution   in a plant–pollinator mutualism. Evolution, 62(1), 220-225.

(5) Lecture on predator-prey interactions and herbivory. Readings: Brodie, E. D., Ridenhour, B. J., & Brodie, E. D. (2002). The evolutionary response of predators to dangerous prey: hotspots and coldspots in the geographic mosaic of coevolution between garter snakes and newts. Evolution, 56(10), 2067-2082. and Agrawal, A. A., Hastings, A. P., Johnson, M. T., Maron, J. L., & Salminen, J. P. (2012). Insect herbivores drive real-time ecological and evolutionary change in plant populations. Science, 338(6103), 113-116.

(6) Lecture on host-parasite interactions. Readings: Lively, C.M., and Dybdahl, M.F. (2000) Parasite adaptation to locally common host genotypes. Nature 405, 679-681. and Koskella, B., & Lively, C. M. (2009). Evidence for negative frequency-dependent selection during experimental coevolution of a freshwater snail and a sterilizing trematode. Evolution, 63(9), 2213-2221.

(7) Lecture on multi-species interactions. Readings: Currie, C. R., Wong, B., Stuart, A. E., Schultz, T. R., Rehner, S. A., Mueller, U. G., … & Straus, N. A. (2003). Ancient tripartite coevolution in the attine ant-microbe symbiosis. Science, 299(5605), 386-388. and Munkacsi, A. B., Pan, J. J., Villesen, P., Mueller, U. G., Blackwell, M., & McLaughlin, D. J. (2004). Convergent coevolution in the domestication of coral mushrooms by fungus–growing ants. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271(1550), 1777-1782.

(8) Lecture on the geographic mosaic theory of coevolution. Readings: Thompson, J. N., & Cunningham, B. M. (2002). Geographic structure and dynamics of coevolutionary selection. Nature, 417(6890), 735-738. and Benkman, C. W., Holimon, W. C., & Smith, J. W. (2001). The influence of a competitor on the geographic mosaic of coevolution between crossbills and lodgepole pine. Evolution, 55(2), 282-294.

(9) Lecture on coadaptation and cospeciation. Readings: Clark, M.A., et al. (2000) Cospeciation between bacterial endosymbionts (Buchnera) and a recent radiation of aphids (Uroleucon) and pitfalls of testing for phylogenetic congruence. Evolution 54, 517-525. and Hafner, M.S., et al. (1994) Disparate Rates of Molecular Evolution in Cospeciating Hosts and Parasites. Science 265, 1087-1090.

(10) Lecture on time shift experiments and experimental coevolution. Readings: Brockhurst, M. A., Morgan, A. D., Rainey, P. B., & Buckling, A. (2003). Population mixing accelerates coevolution. Ecology Letters, 6(11), 975-979. and Koskella, B. (2013). Phage-mediated selection on microbiota of a long-lived host. Current Biology.

(11) Lecture on coevolution within communities, and ecosystem stability.

Finally, for the assessment I’ve decided to model these on my two favorite courses of all times: a) Janis Antonovics Disease course at UVA, where we all had to make posters about a given disease, and b) Curt Lively’s Evolution course at Indiana University, where the final exam was 5 short answer questions verbatim from a list of 20 he had given us weeks before.

Oh yes, and I am also striving to have at least one bit of maths in every lecture! Luckily, I’ve got a great group of (32) students who are already showing full engagement and enthusiasm for the subject. Looking forward to a busy but fun term!

Britt Koskella Reviews and reviewing

F1000Research’s ecology campaign

There are many new journals coming online each day, and it’s hard to know which ones will make it and which won’t (although if you are wondering whether a new journal is considered ‘predatory’ in the sense that they will take your money without investing much effort in your paper, there’s a good list here). For the time being, I have been making the decision based on: 1) discussions with colleagues, 2) the publisher and/or partner, 3) the track record of other projects from the group, and 4) the editors.

For example, I recently went out on a limb, and published a piece with Sean Meaden in an MDPI journal, Viruses. I decided to accept the invitation to publish in a special issue on bacteriophage research because I know and greatly respect the work of the guest editor, Graham Hatfull . In the end, it was a great experience! The managing editor and all staff I worked with were very helpful and professional, and I love the fact that I can track how many people have looked at the abstract and full text of the paper (and proud to say that the latter number is higher than the former!)

I have also had good experience as a reviewer with the new F1000Research journal. I’ve discussed F1000Research before, due to their transparent and real time review process. This online, open access and open science journal publishes the paper online just after submission, and then openly (and without anonymity) publishes the reviews. I will reserve judgement about how well this model works for now, but I give them great credit for experimenting with a system that is often referred to as ‘broken.’

F1000Research has also just announced a new campaign, with an offer for authors of ecology papers: No article processing charge on the first ecology paper you publish with F1000Research until the end of December 2013 (using code ECOL16). This can be on standard research reports, but also on articles that replicate previous datasets, pure data articles and null/negative results. They are also introducing a new category of article, called an ‘Observation article’, which reports serendipitous observations that have not been studied systematically, but that offer a starting point for further exploration.

I for one will  be watching this journal with great expectation, and would love to hear from anyone who has published there.