̽��ֱ�� of Cambridge - Nik Cunniffe

Virus attracts bumblebees to infected plants by changing scent

fpjl2 — Thu, 11 Aug 2016 18:05:25 +0000

Plant scientists at the ̽��ֱ�� of Cambridge have found that the cucumber mosaic virus (CMV) alters gene expression in the tomato plants it infects, causing changes to air-borne chemicals – the scent – emitted by the plants. Bees can smell these subtle changes, and glasshouse experiments have shown that bumblebees prefer infected plants over healthy ones.

Scientists say that by indirectly manipulating bee behaviour to improve pollination of infected plants by changing their scent, the virus is effectively paying its host back. This may also benefit the virus: helping to spread the pollen of plants susceptible to infection and, in doing so, inhibiting the chance of virus-resistant plant strains emerging.

̽��ֱ��authors of the new study, published today in the journal PLOS Pathogens, say that understanding the smells that attract bees, and reproducing these artificially by using similar chemical blends, may enable growers to protect or even enhance yields of bee-pollinated crops.

“Bees provide a vital pollination service in the production of three-quarters of the world’s food crops. With their numbers in rapid decline, scientists have been searching for ways to harness pollinator power to boost agricultural yields,” said study principal investigator Dr John Carr, Head of Cambridge’s Virology and Molecular Plant Pathology group.

“Better understanding the natural chemicals that attract bees could provide ways of enhancing pollination, and attracting bees to good sources of pollen and nectar – which they need for survival,” Carr said.

He conducted the study with Professor Beverley Glover, Director of Cambridge ̽��ֱ�� Botanic Garden, where many of the experiments took place, and collaborators at Rothamsted Research.

CMV is transmitted by aphids – bees don’t carry the virus. It’s one of the most prevalent pathogens affecting tomato plants, resulting in small plants with poor-tasting fruits that can cause serious losses to cultivated crops.

Not only is CMV one of the most damaging viruses for horticultural crops, but it also persists in wild plant populations, and Carr says the new findings may explain why:

“We were surprised that bees liked the smell of the plants infected with the virus – it made no sense. You’d think the pollinators would prefer a healthy plant. However, modelling suggested that if pollinators were biased towards diseased plants in the wild, this could short-circuit natural selection for disease resistance,” he said.

“ ̽��ֱ��virus is rewarding disease-susceptible plants, and at the same time producing new hosts it can infect to prevent itself from going extinct. An example, perhaps, of what’s known as symbiotic mutualism.”

̽��ֱ��increased pollination from bees may also compensate for a decreased yield of seeds in the smaller fruits of virus-infected plants, say the scientists.

̽��ֱ��findings also reveal a new level of complexity in the evolutionary ‘arms race’ between plants and viruses, in which it is classically believed that plants continually evolve new forms of disease-resistance while viruses evolve new ways to evade it.

“We would expect the plants susceptible to disease to suffer, but in making them more attractive to pollinators the virus gives these plants an advantage. Our results suggest that the picture of a plant-pathogen arms race is more complex than previously thought, and in some cases we should think of viruses in a more positive way,” said Carr.

Plants emit ‘volatiles’, air-borne organic chemical compounds involved in scent, to attract pollinators and repulse plant-eating animals and microbes. Humans have used them for thousands of years as perfumes and spices.

̽��ֱ��researchers grew plants in individual containers, and collected air with emissions from CMV-infected plants, as well as ‘mock-infected’ control plants.

Through mass spectrometry, researchers could see the change in emissions induced by the virus. They also found that bumblebees could smell the changes. Released one by one in a small ‘flight arena’ in the Botanic Gardens, and timed with a stopwatch by researchers, the bees consistently headed to the infected plants first, and spent longer at those plants.

“Bees are far more sensitive to the blends of volatiles emitted by plants and can detect very subtle differences in the mix of chemicals. In fact, they can even be trained to detect traces of chemicals emitted by synthetic substances, including explosives and drugs,” said Carr.

Analysis revealed that the virus produces a factor called 2b, which reprograms genetic expression in the tomato plants and causes the change in scent.

Mathematical modelling by plant disease epidemiologist Dr Nik Cunniffe, also in the Department of Plant Sciences at Cambridge, explored how the experimental findings apply outside the glasshouse. ̽��ֱ��model showed how pollinator bias for infected plants can cause genes for disease-susceptibility to persist in plant populations over extremely large numbers of generations.

̽��ֱ��latest study is the culmination of work spanning almost eight years (and multiple bee stings). ̽��ֱ��findings will form the basis of a new collaboration with the Royal Horticultural Society, in which they aim to increase pollinator services for cultivated crops.

With the global population estimated to reach nine billion people by 2050, producing enough food will be one of this century’s greatest challenges. Carr, Glover and Cunniffe are all members of the Cambridge Global Food Security Initiative at Cambridge, which is involved in addressing the issues surrounding food security at local, national and international scales.

̽��ֱ��use of state-of-the-art experimental glasshouses at Cambridge Botanic Garden, and equipment at Cambridge and Rothamsted, was funded by the Leverhulme Trust.

Study of bee-manipulating plant virus reveals a “short-circuiting” of natural selection. Researchers suggest that replicating the scent caused by infection could encourage declining bee populations to pollinate crops – helping both bee and human food supplies.

Modelling suggested that if pollinators were biased towards diseased plants in the wild, this could short-circuit natural selection for disease resistance

John Carr

John Carr/Alex Murphy

Researcher Sanjie Jiang inside the 'flight arena' in the glasshouse of the Cambridge ̽��ֱ�� Botanic Garden.

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

California’s sudden oak death epidemic now ‘unstoppable’ and new epidemics must be managed earlier

jeh98 — Mon, 02 May 2016 19:02:00 +0000

Sudden oak death – caused by Phytophthora ramorum, a fungus-like pathogen related to potato blight – has killed millions of trees over hundreds of square kilometres of forest in California. First detected near San Francisco in 1995, it spread north through coastal California, devastating the region’s iconic oak and tanoak forests. In 2002 a strain of the pathogen appeared in the south west of England, affecting shrubs but not oaks, since English species of oak are not susceptible. In 2009 the UK strain started killing larch – an important tree crop – and has since spread widely across the UK.

In a study published today in PNAS, researchers from the ̽��ֱ�� of Cambridge have used mathematical modelling to show that stopping or even slowing the spread of P. ramorum in California is now not possible, and indeed has been impossible for a number of years.

Treating trees with chemicals is not practical or cost-effective on the scales that would be necessary for an established forest epidemic. Currently the only option for controlling the disease is to cut down infected trees, together with neighbouring trees that are likely to be infected but may not yet show symptoms. “By comparing the performance of a large number of potential strategies, modelling can tell us where and how to start chopping down trees to manage the disease over very large areas,” explains Dr Nik Cunniffe, lead author from Cambridge’s Department of Plant Sciences.

̽��ֱ��authors say that preventing the disease from spreading to large parts of California could have been possible if management had been started in 2002. Before 2002 not enough was known about the pathogen to begin managing the disease. Their modelling also offers new strategies for more effectively controlling inevitable future epidemics.

In close liaison with colleagues from DEFRA and the Forestry Commission, models developed in Cambridge are already an integral part of the management programme for the P. ramorum epidemic in the UK. ̽��ֱ��models are used to predict where the disease is likely to spread, how it can be effectively detected and how control strategies can be optimised.

Sudden oak death is known to affect over one hundred species of tree and shrub, presenting a significant risk to the biodiversity of many ecosystems. ̽��ֱ��death of large numbers of trees also exacerbates the fire risk in California when fallen trees are left to dry out. There is now concern that the disease may spread to the Appalachian Mountains, putting an even larger area of trees at risk.

“Our study is the first major retrospective analysis of how the sudden oak death epidemic in California could have been managed, and also the first to show how to deal with a forest epidemic of this magnitude,” explains Cunniffe.

“Even if huge amounts of money were to be invested to stop the epidemic starting today, the results of our model show this cannot lead to successful control for any plausible management budget. We therefore wanted to know whether it could have been contained if a carefully-optimised strategy had been introduced sooner. Our model showed that, with a very high level of investment starting in 2002, the disease could not have been eradicated, but its spread could have been slowed and the area affected greatly reduced.”

̽��ֱ��model also indicates how policymakers might better plan and deploy control when future epidemics emerge.

“It is a tool by which we can make a better job next time, because it is inevitable that there will be a next time,” says Professor Chris Gilligan, senior author and also from the Department of Plant Sciences. “With this sort of epidemic there will always be more sites to treat than can be afforded. Our model shows when and where control is most effective at different stages throughout a developing epidemic so that resources can be better targeted.”

“It can be tempting for authorities to start cutting down trees at the core of the infected area, but for this epidemic our research shows that this could be the worst thing to do, because susceptible vegetation will simply grow back and become infected again,” explains Cunniffe.

Cunniffe, Gilligan and colleagues found that instead treating the ‘wave-front’ – on and ahead of the epidemic in the direction that disease is spreading – is a more effective method of control. They also found that ‘front-loading’ the budget to treat very heavily and earlier on in the epidemic would greatly improve the likelihood of success.

“Unlike other epidemic models, ours takes account of the uncertainty in how ecological systems will respond and how the available budget may change, allowing us to investigate the likelihood of success and risks of failure of different strategies at different points after an epidemic emerges,” says Gilligan.

“Whenever a new epidemic emerges, controlling it becomes a question of how long it takes for us to have enough information to recognise that there is a problem and then to make decisions about how to deal with it. In the past we have been starting from scratch with each new pathogen, but the insight generated by this modelling puts us in a better position for dealing with future epidemics,” he adds.

̽��ֱ��researchers say that the next step in dealing with well-established epidemics such as sudden oak death is to investigate how to protect particularly valuable areas within an epidemic that – as they have demonstrated – is already too big to be stopped.

̽��ֱ��methodology is already being applied to create related models for diseases that threaten food security in Africa, such as pathogens that attack wheat and cassava.

This research was enabled by funding from the BBSRC, DEFRA, NSF, USDA and the Gordon and Betty Moore Foundation.

Inset image: Extensive control starting in 2002 could have greatly slowed epidemic spread (map shows risk of infection in 2030 under no control on left; control on and ahead of wave-front on right) (Nik Cunniffe).

New research shows the sudden oak death epidemic in California cannot now be stopped, but that its tremendous ecological and economic impacts could have been greatly reduced if control had been started earlier. ̽��ֱ��research also identifies new strategies to enhance control of future epidemics, including identifying where and how to fell trees, as “there will be a next time”.

It is a tool by which we can make a better job next time, because it is inevitable that there will be a next time

Professor Chris Gilligan

David Rizzo

Large-scale tree mortality in northern Sonoma County, California

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Uninfected or asymptomatic? Diagnostic tests key to forecasting major epidemics

cjb250 — Tue, 05 Apr 2016 18:00:00 +0000

Emerging epidemics pose a significant threat to human health worldwide. A principal challenge in infectious disease epidemiology is accurately forecasting the threats posed by diseases early in emerging outbreaks. Accurate real-time forecasts of whether or not initial reports of cases of disease will be followed by a major outbreak – an epidemic in which large numbers of people become infected – are necessary to determine which control measures should be deployed.

For all infectious diseases, there is a delay between infection and the appearance of symptoms, known as the ‘incubation period’, during which infected individuals are classed as ‘presymptomatic’. ̽��ֱ��incubation period, say researchers from the Department of Plant Sciences at Cambridge, can drive significant uncertainty in forecasting during the earliest stages of epidemics.

In research funded by the Biotechnology and Biological Sciences Research Council, the team used mathematical modelling to evaluate the effect of presymptomatic infection on predictions of major epidemics, choosing the Ebola virus as a case study. Their results, published today in the online journal PLOS Computational Biology, show for the first time that precise estimates of the current number of infected individuals – and consequently the chance of a major outbreak in the future – cannot be inferred from data based on symptomatic cases alone. This is the case even if factors such as the average infection rate and the death or recovery rates of individuals in the population can be estimated accurately.

“If we are able to use diagnostic tests to determine whether individuals who do not show symptoms are susceptible or are instead infected but not showing symptoms, we’ll be in a better position to estimate the chance of a major outbreak,” says Dr Nik Cunniffe, who led the study. “Since the reliability of diagnostic tests affects the extent to which forecasting is possible, it’s important not just to develop new diagnostic tests, but also to ensure those we have are continually refined and promptly deployed.”

Although the researchers chose Ebola as a representative case study of a disease for which reports of initial cases are not always followed by a large epidemic, they say their results are applicable to other outbreaks, including not just those that affect humans.

“These findings – that accurate forecasting relies on informing models with data on presymptomatic infections – hold true for anything from the current Zika outbreak through to animal diseases such as bluetongue and even plant pathogens such as Xylella fastidiosa, that is currently causing such devastation to olive groves in southern Italy,” adds first author Robin Thompson, a former PhD student at the Department of Plant Sciences, and now a postdoctoral researcher at the ̽��ֱ�� of Oxford.

̽��ֱ��researchers acknowledge that their models are based on an idealised setting, in which symptomatic cases and deaths were recorded perfectly and in which the values of disease transmission parameters were known exactly. However, they say that additional uncertainty will only make forecasting even more challenging. Presymptomatic infection alone makes prediction imprecise, reinforcing the need to better estimate levels of hidden infection in populations using diagnostic testing.

Reference
Thompson, RN, Gilligan, CA, Cunniffe, NJ. Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks. PLOS Computational Biology; 5 April 2016; DOI: 10.1371/journal.pcbi.1004836

Major epidemics such as the recent Ebola outbreak or the emerging Zika epidemic may be difficult to forecast because of our inability to determine whether individuals are uninfected or infected but not showing symptoms, according to a new study from the ̽��ֱ�� of Cambridge. ̽��ֱ��finding emphasises the need to develop and deploy reliable diagnostic tests to detect infected individuals whether or not they are showing symptoms, say the researchers.

If we are able to use diagnostic tests to determine whether individuals who do not show symptoms are susceptible or are instead infected but not showing symptoms, we’ll be in a better position to estimate the chance of a major outbreak

Nik Cunniffe

DFID - UK Department for International Development

Ultra-violet screening for potentially Ebola-carrying liquids (cropped)

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

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