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Keywords:

  • abiotic stress factors;
  • carbon dioxide;
  • climate change;
  • food security;
  • mycotoxigenic fungi;
  • temperature

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

This paper examines the available information on the potential for climate-change impacts on mycotoxigenic fungi and mycotoxin contamination of food crops pre- and postharvest. It considers the effect of changes in temperature/water availability on mycotoxin contamination, especially incidences where aflatoxin B1 and ochratoxin A production has been influenced. The potential of using preharvest models to predict risk from deoxynivalenol (DON) in wheat, fumonisin B1 in maize and aflatoxins in maize and peanuts in different continents are considered in the context of potential for adaptation to include climate-change scenarios. Available information suggests that slightly elevated CO2 concentrations and interactions with temperature and water availability may stimulate growth of some mycotoxigenic species, especially under water stress. The accumulated knowledge on interacting conditions of water/temperature effects on optimum and boundary conditions for growth and mycotoxin production has been used to predict the effects that +3 and +5°C increases under water stress would have on growth/mycotoxin production by mycotoxigenic species. Various spatial scales, from toxin gene expression to regional approaches using geostatistics, are examined for their use in understanding the impact that climate change may have on food contamination in developing and developed countries. The potential for using an integrated systems approach to link gene expression data, phenotypic toxin production under different interacting abiotic conditions is discussed using Fusarium species and DON as examples. Such approaches may be beneficial for more accurate predictions of risk from mycotoxins on a regional basis and also the potential for new emerging toxin threats.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

Food security has become a very important issue worldwide and the potential effects of climate change on yields and quality of food crops, including mycotoxins, is now receiving scientific attention, especially from a risk analysis perspective. Many staple crops (cereals, nuts, fruits) can be colonized and infected by fungi from the genera Aspergillus, Fusarium and Penicillium which can contaminate the edible parts with toxic secondary metabolites: mycotoxins. These can have a significant effect on human and animal health because they can be carcinogenic (e.g. aflatoxins) and they are very heat-stable and thus difficult to destroy during processing. This has resulted in strict legislative limits in many parts of the world for mycotoxins in a wide range of foodstuffs (European Commission, 2006). However, in African countries where legislation is often applied to export crops only, consumption of mycotoxin contaminated staple foods is a significant risk, with rural populations exposed to aflatoxins throughout their lives, with serious impacts on their health (Wagacha & Muthomi, 2008). This is exemplified by the recent outbreak of acute aflatoxicosis in Kenya (Lewis et al., 2005). Table 1 summarizes the most important mycotoxigenic species, the mycotoxins they produce and the effects they can have. The most extensive legislation on limits for mycotoxin contamination of raw commodities and derived products exists in the EU (Table 2). In developing countries these are not as extensive, especially for internal consumption of staple food products (van Egmond et al., 2007).

Table 1.   Summary of the major species, associated mycotoxins and effects that they cause for which legislation is in place or being considered (European Commission, 2006)
SpeciesMycotoxinEffects
  1. IARC, International Agency For Research on Cancer.

  2. aPreviously Aspergillus ochraceus.

  3. bLegislation imminent.

Aspergillus flavusTotal aflatoxinsPotent human carcinogen, affects the liver, adverse effects in various animals, especially chickens
A. parasiticusAflatoxin B1
A. nomiusAflatoxin M1 (milk)
A. carbonariusOchratoxin ASuspected by IARC as human carcinogen. Carcinogenic in laboratory animals and pigs, affects kidney function
A. westerdijkiaea
Penicillium verrucosum
Fusarium graminearumF. culmorumType B Trichothecenes;DeoxynivalenolEffects on alimentary/circulatory system, causes vomiting, feed refusal
F. culmorumZearalenoneIdentified by IARC as a possible human carcinogen, hyperoestrogenism in cattle
F. poae
F. langsethiaeF. sporotrichioidesType A Trichothecenes;T-2, HT-2 and totalbInhibits RNA, DNA, protein synthesis in eukaryotic cells; Cytotoxic and immunosuppresive
F. verticillioidesFumonisins (B1, total)Cytoxic, toxic to pigs and poultry. Cause of equine eucoencephalomalacia (ELEM), fatal disease of horses
F. proliferatum
Penicillium expansumPatulinNeurotoxic, immunotoxic, immunosuppresive, genotoxic and teratogenic (IARC group 3)
Table 2.   Raw commodities and their derived products for which maximum mycotoxin regulated limits are in place in the EU (European Commission, 2006)
ProductAflatoxins (AFA)Ochratoxin A (OTA)PatulinDeoxy-nivalenol (DON)Zearalenone (ZEA)Fumonisins (FB1)
  1. a+Indicates legislative limits in place for these commodities in the EU.

  2. bDried.

  3. cJuice.

  4. dSolid.

Cereals+a+ ++ 
Maize++ +++
Nuts+     
Fruits+b +cd   
Apples  +cd   
Grapes +bc    
Spices+     
Coffee +    
Wine++    
Milk+     

Climate change is expected to have a profound effect on our landscape worldwide. For some areas, climatic models have projected a marked decrease in summer precipitation and increases in temperature, which would result in concomitant drought stress episodes. The environment in which crops will be grown in the next 10–20 years may change markedly with atmospheric CO2 concentrations expected to rise at the rate of 1·5 μmol year−1. Because of this increase and that of other greenhouse gases, the global temperature is expected to rise at the rate of 0·03°C year−1. The EU green paper on climate change in Europe also suggests that effects will be regional and be either detrimental or advantageous depending on region. Thus, in Southern Europe, changes may equate to an increase of 4–5°C with longer drought periods, resulting in increasing desertification, and a decrease in crop yields. In areas of Western and Atlantic Europe, changes of 2·5–3·5°C with drier and hotter summers are envisaged. In Central Europe, an increase of 3–4°C, higher rainfall and floods are forecast, although longer growing periods may benefit crop yields. Northern Europe would expect a mean temperature increase of 3–4·5°C, with a significant increase in precipitation of 30–40%. This may lead to increases in crop yields and perhaps new crop cultivation patterns (European Commission, 2007; Solomon et al., 2007).

The Mediterranean zones have been identified as a climate-change hot spot where extreme changes in temperature, CO2 and rainfall patterns are predicted. This could increase the risk of migration of pathogens which might occur as a result of shifts in response to warmer, drought-like climatic conditions. Effects on plant physiology, including stomatal patterns on leaf surfaces, will influence transpiration and photosynthetic capacity, and affect invasion by pests and pathogens. There is evidence that increased CO2 (600/700 p.p.m. ≡ μmol mol−1) and temperature (4°C) may modify the phyllosphere mycoflora of cereals during ripening (Magan & Baxter, 1996). This may influence the colonization by mycotoxigenic fungal genera which contaminate food and raw materials.

In developing countries drought stress may be particularly important in terms of food security. For example, marginal land where stress tolerant sorghum was grown has been replaced with maize especially in Africa. Maize as well as peanuts are particularly prone to infection during water stress. This leads to increased preharvest aflatoxin contamination of food affecting nutritional quality to significantly impact on consumption or the ability to export. Xerophilic fungi such as Wallemia sebi, Xeromyces bisporus and Chrysosporium spp. could also become more important colonizers of food commodities, as they can grow under very dry conditions (0·65–0·75 water activity (aw)) where there is less competition from the majority of mesophilic fungi (Magan & Aldred, 2007a). Some of these, including W. sebi can produce metabolites such as walleminol and walleminone which can be toxic to animals and humans (Piecková & Kunová, 2002). Recent studies suggest that secondary metabolites may play a role in competitive interactions between xerophilic fungi in extreme dry conditions (Leong et al., 2010).

Recent reviews have examined some aspects of the impact that climate change may have on plant breeding, plant diseases and mycotoxins in Europe, Australia and Africa (Garrett et al., 2006; Boken et al., 2008; Chauhan et al., 2008, 2010; Miraglia et al., 2009; Paterson & Lima, 2010). These have predominantly examined historical information and tools to assess impacts on crop yield of drought episodes, lack of water or elevated temperatures. For example, Paterson & Lima (2010) considered plant disease models in relation to fungal pathogens extensively, as well as some aspects of the potential for using preharvest models for predicting contamination by mycotoxigenic fungi of temperate and tropical cereals and grapes. They also summarized the optimum ranges for growth and mycotoxin production for key mycotoxigenic species. However, no attempt was made to identify the influence of temperature/water availability on production of mycotoxins in key food crops pre- or postharvest. Boundary models for growth and mycotoxin production by a range of mycotoxigenic fungi developed at Cranfield can be effectively used to predict these effects. There is also the potential for using geostatistics to identify hotspots of mycotoxin risk. The reviews by Chauhan et al. (2008, 2010) have provided valuable information on the use of seasonal soil moisture and temperature conditions in both maize and peanuts to determine risks of aflatoxin exceeding legal limits in Australia. These could have applications in Africa and other regions for predicting climate-change influences on these staple crops.

No consideration was given in these recent reviews to interactions between elevated CO2 levels and temperature/water availability on growth and mycotoxin production of fungal species. The potential impact of climate change on chemical and other control treatments for the fungi or their mycotoxin were also not discussed. Furthermore, the potential for integrating molecular and ecological information to predict impacts of environment/climate change on mycotoxin production have not been considered by any of these studies. This review will address these aspects to summarize the effects of key environmental parameters on mycotoxigenic fungi and their ability to produce mycotoxins. We will consider (i) preharvest effects of climate change on mycotoxin contamination of key crops, (ii) postharvest contamination, (iii) influence of CO2, temperature and water availability interactions on growth and mycotoxin production in vitro and in stored grains, and (iv) the potential for integrating molecular and ecological data for predicting environmental conditions at which risk from mycotoxins may occur. The potential future research areas which need to be addressed are then discussed.

Preharvest mycotoxins and possible influences of climate change

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

The clearest example of climate change influencing mycotoxins in preharvest crops is that of the changes in profile and infection of small grain cereals by fusarium head blight pathogens and DON contamination. In these cases, Fusarium culmorum has been gradually outcompeted by Fusarium graminearum because of changes in crop rotation and the increasing use of cultivars of maize for animal feed, which can be grown in more northern latitudes. This has been reviewed as a case study by Chakraborty & Newton (2011).

In 2003–4 there were very hot and dry episodes in parts of northern Italy where maize is a key animal feed for cattle in the important cheese production regions. Fusarium verticillioides and fumonisin contamination occurs in such maize grain (Giorni et al., 2007). This species and other fusaria from Section Liseola have optima of 25–30°C for growth at which higher fumonisin concentrations are produced (Marín et al., 1995). However, because of the very dry conditions in those years, Aspergillus flavus became a significant problem. Aspergillus flavus has a wide range of temperature tolerance (19–35°C) with about 28°C optimum for growth and 28–30°C for aflatoxin production (Sanchis & Magan, 2004). Some strains of A. flavus can grow even at 0·73 aw (free water in the host tissue) and produce aflatoxins at 0·85 aw, whilst in contrast, F. verticillioides growth is very marginal at 0·90 aw and fumonisins are produced at >0·93 aw (Sanchis & Magan, 2004). Contamination with aflatoxin B1 was significant, resulting in high levels of aflatoxin M1 in the milk of animals fed with this maize. Significant economic losses were incurred in this high value and important industry. Thus A. flavus, a more xerotolerant mycotoxigenic species than F. verticillioides, was able to actively colonize the ripening maize by outcompeting the more common non-xerophilic Fusarium species. Studies of relative nutritional utilization and niche overlap indices showed the relative competitiveness of A. flavus versus F. verticillioides in carbon source utilization experiments relevant to maize under different temperature and water stress conditions (Giorni et al., 2009). The isolated strains were able to colonize ripening maize rapidly and produce both aflatoxins and sterigmatocystin (Giorni et al., 2007).

Climate change may also have a direct effect on contamination of a range of food commodities. Peanuts under drought stress will develop cracking in pods resulting in a significant increase in A. flavus and aflatoxin contamination. Other good examples are pistachios which can develop hull splitting under heat/drought stress; maize kernel integrity can be compromised by increased silk cut (Cotty & Jaime-Garcia, 2007); late harvesting and heavy rain episodes may also influence harvest quality and further mycotoxin contamination if not dried efficiently.

Cottonseed is particularly susceptible to aflatoxin contamination in the drier regions of the USA. Unusually warm and humid conditions have been shown to result in much higher contamination with aflatoxin in Arizona in genetically modified Bt cottonseed. Indeed, delayed harvest, late irrigation, rain and dew during warm periods were all shown to be associated with increased aflatoxin contamination (Bock & Cotty, 1999; Cotty & Jaime-Garcia, 2007).

Geostatistics has been a good tool to provide assistance with examining specific regional hotspots which may represent high risk from mycotoxin contamination, based on meteorological data. Studies using this approach have been made with regard to aflatoxin in cottonseed. This has helped determine the spatial and temporal variation in aflatoxin contamination over very wide regions and where correlations were found with rain episodes (Jaime-Garcia & Cotty, 2003). More recently, studies in Europe have used a similar approach in examining the regions in Europe which may be hot spots for Aspergillus carbonarius contamination of wine grapes and the European regions which were at a higher risk for contamination with ochratoxin A (Battilani et al., 2006). This study applied geostatistics to analyse the weather patterns over a period of 3 years (2001–3). This showed that the highest contamination with black aspergilli was just prior to harvest. At this stage the spatial variability was significantly related to latitude and longitude, showing a positive west–east and north–south gradient. Predictive maps showed that the highest incidence in Europe of A. carbonarius was likely to be in Greece, southern France and southern Italy. Interestingly, the thermo-wetness maps for the 3 years showed a similar trend to that for isolation of the mycotoxigenic fungi and indeed correlated well with ochratoxin A contamination of wines in these regions, especially red wine. However, ecological data on A. carbonarius has suggested that conditions which favour growth, and subsequent high incidence, are different from those which are optimum for ochratoxin A production. Thus, ochratoxin A production occurs in the range 0·92–0·99 aw with maximum accumulation at 0·95–0·99 aw depending on isolates (Mitchell et al., 2004; Belli et al., 2005). Temperature optimum for ochratoxin production is at 20°C, followed by 15°C, and markedly lower at 30–37°C. In contrast, maximum growth occurs at close to 30°C.

The use of a geostatistical approach could be a powerful tool for predicting the changes in spatial structure based on key crop growth stages and integration with meteorological data, especially thermo-wetness values during relevant periods: in the case of grapes, just prior to harvest. This could be useful for facilitating timely fungicide applications and pest control in order to minimize contamination during wine production. Changes in weather conditions on a regional scale would then be used to determine specific times, such as just prior to harvest, when analysis can be used to predict the level of risk from contamination with ochratoxin A. The other area where geostatistics has been applied has been to understand and determine the spatial structure of mycotoxins in stored cereals and to ascertain how many samples may be required to obtain a true representative sample (Rivas Casado et al., 2009a,b, 2010).

In Mali, West Africa, Boken et al. (2008) have examined the use of advanced high resolution radiometer (AVHRR) satellite data to examine the relationship between peanuts yield, total precipitation and maximum reproductive phase of the peanut plants. They used normalized difference vegetation index (NDVI) to enable the analyses to be done. Whilst the correlation between peanut development and yield as an indicator of drought and aflatoxin contamination was only moderate (R2 = 0·56) this approach could be used more widely to understand and monitor possible effects of climate change on mycotoxin contamination on regional basis.

The recent work by Chauhan et al. (2008, 2010) has demonstrated that it is possible to utilize an agricultural production systems simulator to calculate an aflatoxin risk index (ARI) in both maize and peanuts in Australia. For maize they related seasonal temperature and soil moisture during the critical silking period to determine the ARI. They showed that both dry and hot climates made maize prone to a much higher aflatoxin contamination risk. For peanuts, they used the fractional amounts of available soil water during the crucial pod-filling period to determine the ARI. This showed that historically there has been an increase in aflatoxin contamination of peanuts in Australia related to increases in ambient temperature and decreases in rainfall. This has been developed into a web-interface tool for practically real-time use of this model. This approach is very valuable to predict high- and low-risk years in relation to climatic fluxes and may have application in West Africa, where maize is also a important staple crop.

There are a number of existing predictive models which have been developed worldwide for fusarium head blight and the risk of DON contamination in small grain cereals and for maize and fumonisin contamination. Table 3 summarizes some of the models which have been developed for this purpose. For example, the DONCAST model is based on the weather conditions just prior to and during wheat head emergence. It includes the input of local or regional weather data, number of rain days just prior to anthesis, and later, during ripening, and maximum, minimum and mean temperatures. Validation has shown that, provided the information is up to date, risks from DON contamination can be very accurately predicted and this is now being used for timing and sustainable fungicide applications based on the relative level of risk (Hooker et al., 2002). However, this model has required adaptation for regional applications in South America and in Europe where additional factors have had to be incorporated. Other approaches for DON prediction have used a more systems approach based on the life cycle of F. graminearum (Rossi et al., 2003a,b). They have developed a risk index (TOX-risk) which is calculated daily for F. graminearum/F. culmorum over the growing season. The risk is calculated based on the following equation:

  • image

where SPO is the sporulation rate, DIS the dispersal rate, INF the infection rate, GS the host growth stage and INV the invasion rate.

Table 3.   Summary of some of the preharvest predictive models for mycotoxins and related diseases, country where developed, crops and limitations (adapted from Prandini et al., 2009)
Predictive modelsDisease/mycotoxinCropLimitsReferences
  1. FHB, fusarium head blight; DON, deoxynivalenol; ZEA, zearalenone.

ArgentinaFHBWheatSite- and year-specificMoschini & Fortugno (1996)
Fernandez et al. (2005)
BelgiumFHBWinter wheatInstrumental (radar) availabilityDetrixhe et al. (2003)
Dalla Marta et al. (2005)
CanadaDONCereal grainDo not consider: crop rotation, crop variety, tillage, fertilization, etcHooker et al. (2002)
Hooker & Schaafsma (2003)
Schaafsma & Hooker (2007)
ItalyFHB, DON, ZEAWheatLow accuracy for high TOX-riskRossi et al. (2003a,b)
USAFHBSpring and winter wheatLow accuracyDe Wolf et al. (2003)
van Maanen & Xu (2003)
Xu (2003)
Madden et al. (2004)
ItalyF. verticillioidesMaizeAspects of dynamic cycle of fungi are neededRossi et al. (2003a,b, 2006)
EuropeP. verrucosumCereal grainLack of field and storage management effectsPardo et al. (2006)

These parts of the life cycle are all influenced by air temperature, relative humidity, rainfall, relative number of rain days, wetness duration and free water in the host tissue (aw). The specific fungal species and host growth stage are also considered. Rossi et al. (2003a,b) developed two regression equations based on artificial-inoculation experiments for DON and for zearalenone. The model produces two indices, i.e. risk from fusarium head blight of wheat, and for mycotoxin contamination in wheat grain. They have found a good correlation in validation experiments in Italy.

Maize has also received attention with regard to predicting fumonisin contamination caused by Fusarium section Liseola species. For example, de la Campa et al. (2005) and Schaafsma & Hooker (2007) have used a similar approach to that used for DONCAST and used climatic information relevant to the growing period from 10 days prior to 50% silking to 14 days post-silking. This included data such as temperature (max, min) and rain × max temperature prior to silking and whether a maximum of 34°C is reached at 50% silking. Subsequent changes in temperature and amount of rain post-silking were used to predict the risk from fumonisins in maize and genetically modified maize. They found that weather explained about 47% of the statistical variability in fumonisins, insect damage 17%, hybrid type 14% and Bt hybrids 11%.

Battilani et al. (2003) also used a system level model to define the components of the life cycle of F. verticillioides which included germination rates, growth rates, sporulation rates and fumonisin production rates in relation to important climatic conditions of temperature and water availability, especially of host tissue during silking and the maize ear ripening phase.

Recently van der Fels-Klerx et al. (2009) examined the use of key indicators and a Delphi technique to identify 12 key factors during the wheat production chain. They have suggested that, whilst relevant data are not available in all European countries their theoretical model uses key indicators and information sources at a European level to identify emerging mycotoxins. They demonstrated the potential use of the geographical emerging mycotoxin identification system (GEMIS) model as an example, on a European scale, to examine the effects of changes in temperature (+2°C) and rainfall (+3 mm) on heading date and cereal productivity areas. However, this does not consider interaction between factors on specific mycotoxins or the impact that CO2 × temperature × drought impacts might have.

The question arises as to whether these models, which have primarily been developed for determining levels of risk of mycotoxins in cereals, can be modified appropriately, to take into account the impact that climate change might have on the host physiology, and on the life cycle of mycotoxigenic fungi. This would help to predict changes in patterns of mycotoxin contamination. For example, how will climate-change factors interact with fungicide and pesticide applications preharvest? The efficacy may be significantly modified with, in some cases, less effective control of mycotoxigenic pathogens and toxin contamination. This may be particularly important in Africa and South America where climate change may result in a significant increase in mycotoxin contamination of staple food crops.

Changes in temperature and aw will also be keys in determining the effects that different pre- and postharvest fungicide treatment will have. Available data suggest that these factors can modulate the response of different mycotoxin producing species to these control agents. Medina et al. (2007a) analysed the effect of carbendazim (0–450 μg mL−1) on ochratoxin A producing Aspergillus carbonarius in a grape juice-based solid medium (pH 4·5) modified with glycerol to 0·94, 0·96 and 0·98 aw. Data showed that under conditions of stress, at the lowest aw (0·94), growth was stimulated by all carbendazim concentrations. An increase in ochratoxin A production was detected under water and fungicide stress conditions increasing toxin 20-fold at 0·94 aw 28°C and the highest concentration of carbendazim when compared to untreated controls. A reduction in temperature of 3°C at this aw decreased toxin production by 50%.

Medina et al. (2007b) examined the efficacy of natamycin produced by Streptomyces natalensis against strains of A. carbonarius growth and ochratoxin A (OTA) production under different environmental conditions. They concluded that natamycin has a powerful action at low concentrations against A. carbonarius. Both aw and temperature modulated the effect of natamycin. Whilst at higher aw natamycin was able to control growth at 15°C, increasing the temperature to 20°C led to growth stimulation under reduced concentrations of fungicide. At 20°C a slight increase of OTA was observed at the highest aw levels in the presence of intermediate concentrations of natamycin.

Preharvest models have so far excluded CO2 × temperature × water stress effects on mycotoxigenic fungi infecting staple food products. Prandini et al. (2009) pointed out the limits of existing predictive models, especially in the field. Descriptive models appear to be good but have to be modified for each climatic region. More complex models require more detailed inputs for accuracy and also need to have a user friendly interface. It may be that real time updates of key climate data are required for updating risk levels on a macro and regional scale to have applications as a risk management tool.

Climate change and postharvest contamination by mycotoxins

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

Stored food is usually alive and respiring actively postharvest. In stored food ecosystems there are complex interactions between abiotic and biotic factors including pest immigration and emigration and changes in inter-granular atmosphere (Magan & Aldred, 2007b). Poor postharvest storage management can lead to significant dry matter loss and accumulation of mycotoxins postharvest (Magan et al., 2010). Thus, there is a need to consider the potential impacts that climate change may have on this ecosystem. Grain silos for example can harbour pests which could multiply more rapidly under elevated temperature producing more metabolic water. The resultant increase in condensation and wet pockets can initiate spoilage mould activity with the possibility for increased contamination with mycotoxins such as ochratoxin A, aflatoxins and perhaps trichothecenes in damp grain. The potential for increased spontaneous heating of such stored products may well be exacerbated under such conditions. The spatial distributions and types of mycotoxins which may occur postharvest may change significantly, thus making accurate measurements of actual contamination levels more difficult. The potential for new emerging mycotoxins may also increase. For example, infection by F. langsethiae can remain symptomless in oats and, in some cases in barley, making it difficult to detect and determine where mycotoxin contamination may be originating (Edwards, 2009). It has been suggested that because of the late harvestings of oats some T-2 and HT-2 contamination may occur postharvest (S. G. Edwards, Harper Adams University College, Newport, UK, personal communication). Thus, management practices postharvest within a hazard analysis critical control point (HACCP) framework may need to be significantly modified.

Whilst preservatives based on aliphatic acids are commonly used in feed grain, they are fungistatic and the right concentrations need to be applied for effective control. Indeed studies suggest that some of these are not very effective in controlling fumonisins in maize (Marín et al., 1998, 2000). Under modified and elevated temperature and humidity conditions their volatility may increase resulting in less effective coverage and thus less control of mycotoxigenic moulds. This can lead to an increase in mycotoxins entering the feed chain. However, detailed studies on mycotoxins under various storage conditions are limited. In contrast, there is a wealth of information from in vitro studies on the influence of cardinal environmental factors on the growth and mycotoxin production by toxigenic fungi. These can be used to better understand what may be expected under climate change.

CO2 and interaction with other key environmental factors on growth and mycotoxin production

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

Previous studies have shown that fungi are able to withstand quite high concentrations of CO2 (Magan & Aldred, 2007b). Thus tolerance of climate change where perhaps a tripling of the existing CO2 is predicted to occur taking the concentrations to 800–1000 p.p.m. may not be problematic for mycotoxigenic fungi. However, the available information on mycotoxigenic fungi with regards to CO2 × temperature × aw will be considered in this section.

Studies in the 1980s by Magan & Lacey (1984) examined in detail the interactions between reduced O2 concentration (21–0·14%) or elevated CO2 (5–15%) balanced with nitrogen and interactions with temperature (14, 23°C) and water stress (0·98–0·85 aw). Some mycotoxigenic fungi such as Alternaria alternata and F. culmorum were included in these studies on wheat-based media. The latent periods prior to growth were significantly increased by >5% CO2 regardless of aw or temperature. With regard to growth, this study showed that at 23°C A. alternata growth was inhibited by >5% CO2 at 0·98 and 0·95 aw. However, at 0·90 aw growth was stimulated by 5% and 10% CO2. This also occurred at 14°C and 0·95 aw. In contrast, growth of F. culmorum was inhibited significantly by >5% CO2 at 0·98 and 0·95 aw at both temperatures, whilst it appeared to be unaffected at 0·90 aw. Interestingly, sporulation of these and other spoilage fungi was unaffected by increased CO2 regardless of temperature or imposed water stress level. In contrast, 0·14% O2 had significant effects on asexual sporulation of 15 spoilage and mycotoxigenic fungi examined (Magan, 1982; Magan & Lacey, 1984). Unfortunately, no mycotoxin analyses were carried out in these studies.

Other studies have been carried out to examine effects of CO2 × temperature × aw interactions on growth and mycotoxin production. However, in these studies much higher CO2 concentrations were used. For example, the effects of elevated CO2 on growth of Aspergillus ochraceus (=A. westerdijkiae), Penicillium verrucosum and ochratoxin A production have been determined (Paster et al., 1983; Cairns-Fuller et al., 2005). Recently, studies have suggested that up to 50% CO2 had only a slight impact on ochratoxin A production by A. carbonarius over a range of aw conditions, with aw being a more important factor than CO2 (Pateraki et al., 2007). Samapundo et al. (2007b) found that fumonisin B1 production by Fusarium section Liseola species (F. verticillioides, F. proliferatum) was inhibited by 30% CO2 at 0·984 aw. However, only a few experiments have examined A. flavus. Previously, Wilson & Jay (1975) tested a high CO2 treatment (61·7% CO2 balanced with O2 and N2) on moist maize and found that A. flavus growth was visible after 4 weeks at 27°C. Contamination with aflatoxin at elevated CO2 was lower than that in air.

Recent studies by Giorni et al. (2008b) considered both in vitro and in situ effects of CO2 × aw interactions on growth and aflatoxin production on maize-based media and in stored maize grain. Overall, growth was more rapid at 0·95 than 0·92 aw (P < 0·01), whilst interaction with CO2 significantly decreased the ability to grow and colonize maize grain (Table 4). The use of modified atmospheres at 25% and 50% CO2 resulted in about 30–35% inhibition of growth (CFU g−1 grain). Exposure to 75% CO2 resulted in >50% inhibition of growth regardless of aw level. The summary of effects both in vitro and in situ is shown in Figure 1. This demonstrates that very high CO2 concentrations are needed to control growth and associated aflatoxin production by A. flavus.

Table 4.   Effect of modified atmosphere and free water in host tissue (aw) on (a) in vitro growth (colony diameter, 7 days’ incubation) and aflatoxin B1 production at 25°C (14 days’ incubation) and (b) populations of Aspergillus flavus and aflatoxin B1 production at 25°C (0, 7, 14 and 21 days’ incubation). Different letters within columns indicate statistically significant differences (from Giorni et al., 2008b)
 (a) Synthetic medium(b) Maize grain
Growth (mm)AFB1 (ng g−1)CFU g−1 (log 10)AFB1 (ng g−1)
  1. ND, not detected.

% CO2 in air
067 a713 b7 a300 a
2540 b1237 a6 b79 bc
5019 c62 c8 a5 c
757 d9 d6 b128 b
aw
0·9225 b541 a6b40b
0·9541 a470 b8a216 a
Time (days)
0NDND4 b0 c
7NDND6 b242 a
14NDND7 b81 b
21NDND8 a60 b
image

Figure 1.  Relative impact of different CO2 concentrations on aflatoxin B1 production by Aspergillus flavus. Data are shown on a 0–1 scale that represents a rate of toxin production (0: no aflatoxin; 1: maximum aflatoxin production) and include datasets from in vitro and from maize grain after 14 days of incubation at 25°C. Treatments followed by different letters are significantly different (from Giorni et al., 2008b).

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In other studies of modified atmospheres with different CO2 levels balanced with O2 and N2 showed that A. flavus grew on wheat and rye bread with up to 75% CO2 (Suhr & Nielsen, 2005). Exposure to 70% CO2 when at 0·80 aw prevented spoilage of bakery products; however, when aw was 0·85 or 0·90 spoilage was only delayed (Guynot et al., 2003). In storage the potential impact of CO2 will mainly be the result of interactions with other factors such as temperature, water availability and nutritional quality of the food commodity.

Water and temperature stress impacts on growth and mycotoxin production: climate-change implications

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

The two most important factors which affect the life cycle of all microorganisms including mycotoxigenic moulds are water availability and temperature (Magan, 2007). These interacting factors influence germination, growth, sporulation and mycotoxin production (Sanchis & Magan, 2004). Extreme drought episodes, desertification and fluctuations in wet/dry cycles will have an impact on mycotoxigenic fungi. In soil, survival of water stress is predominantly determined by the total water potential and matric and solute components, whilst in food matrices the solute component and hence water activity are more important (Magan, 2007).

Sanchis & Magan (2004) and Magan & Aldred (2007b) have integrated the available ecological data on optimum and marginal interacting conditions for growth and for mycotoxin production. The contour maps produced have been used to predict what changes in (i) growth and (ii) mycotoxin production might occur when temperature is increased by either +3 or +5°C at different water availabilities. Table 5 shows the changes which would occur for A. alternata and alternariol, its monomethyl ether, and altenuene including changes in actual production concentrations. It is worthwhile noting that under water stress conditions, at 0·90 aw, there will be no growth or toxin production if temperature is increased by 5°C.

Table 5.   Changes in growth and toxin production by Alternaria alternata, Fusarium spp., Aspergillus spp. and Penicillium spp. as a result of increase in temperature of 3 or 5°C under different water-stress conditions
 Growth Toxins
awμmax range/tempμ+3μ+5awτmax range/tempτ+3τ+5
  1. μmax, maximum growth rate (mm day−1); μ+3, growth rate with 3°C increase; μ+5, growth rate with 5°C increase; τmax, maximum toxin production (μg g−1); τ+3, predicted toxin production with 3°C increase; τ+5, predicted toxin production with 5°C increase; NG, no growth; NP, no toxin production.

  2. For Aspergillus and Penicillium species: τmax, maximum toxin production (ng g−1); τ+3, predicted toxin production with 3°C increase; τ+5, predicted toxin production with 5°C increase.

  3. aMinimum water availability for toxin production.

Alternaria alternata0·952–1/252–11–0·5Altenuene0·95100–40/2540–2020–5
0·900·1–0·5/250·5–0·1NG0·9020–5/255–NPNP
Alternariol0·95500–100/2540–2020–5
0·9020–5/25NPNP
Alternariol monomethyl ether0·95400–100/25100–10NP
0·90100–10/25NPNP
Fusarium proliferatum0·954–3/283–22–1Fumonisin0·95>1000/201000–100100–50
0·900·5–0·1/28NGNG0·93a50–10/1550–1050–10
Fusarium verticillioides0·954–3/254–34–3Fumonisin0·9510000–1000/2010000–10001000–100
0·900·5–0·1/250·5–0·1NG0·93a10/1550–1050–10
Fusarium culmorum0·953–1/203–13–1DON0·951–0·25/200·25–0·10·1–0·01
0·901–0v1/201–0·11–0·10·93a0·25–0·01/20NPNP
Fusarium graminearum0·95>4/204–22–1DON0·951–0·1/201–0·10·1–0·01
0·901–0·1/201–0·11–0·10·93aNPNPNP
Aspergillus westerdijkiae0·955–4·5/304·45–3·954·25–3·75Ochratoxin A0·951065·7–1014·9/30719·4–685·1488·5–465·3
0·901·85–1·35/301·85–1·351·80–1·300·9054·1–51·6/3051·6–49·249·9–47·6
Aspergillus carbonarius0·95>6/30>66–5Ochratoxin A0·952000–1500/201000–500500–NP
0·904–3/304–34–30·901000–500/20500–NP500–NP
Aspergillus flavus0·956·9/355·65·0Aflatoxin B10·953082–2278/37102–1386·1–NP
0·902·9/371·40·70·90448·5–331·5/371–NPNP
Penicillium verrucosum0·95>4/254–33–2Ochratoxin A0·95>50/20>5050–30
0·902–1/252–11–0·50·9050–30/2050–3050–30
Penicillium expansum0·956·9–5·5a/256·9–5·56·9–5·5Patulin0·95Not available  
0·902·9–1·6a/252·9–1·62·9–1·60·90Not available  

Table 5 show the impact that changes in temperature at different aw levels will have on both growth rates and on concentrations of alternaria toxins, fumonisins, trichothecenes, ochratoxin A, aflatoxin B1 and patulin production. These data provide useful information on the possible impacts of interactions between two of the key environmental factors which have been implicated in climate change. Unfortunately, few if any detailed studies have been carried out to examine twofold and threefold existing CO2 concentrations in relation to changes in temperature and aw. We believe that such studies are now timely to help in developing more accurate risk models for mycotoxins.

Figure 2a gives an example of the contour maps for growth optimum and boundary conditions of aw × temperature based on eight strains of Fusarium langsethiae. Figure 2b shows the effect on mean production of T-2 + HT-2 toxin on an oat-based medium for these eight strains (Medina & Magan, 2010, 2011). This shows that the marginal boundary conditions, especially of temperature are very broad for this species. This information is important as the EU is considering legislative limits for these two toxins in small grain cereals.

image

Figure 2.  Effect of interacting conditions of aw × temperature conditions on (a) growth rate of Fusarium langsethiae and (b) combined T-2 + HT-2 toxin production on an oat-based medium (mean of eight strains from the UK, Norway, Sweden and Finland). Numbers on the isopleths refer to growth rates (mm day−1) and concentrations of T-2 + HT2 toxins (μg g−1 oat medium).

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Predictive models have been produced from in vitro studies to predict the behaviour of a range of mycotoxigenic fungi (Table 6). Recently, predictive modelling was used to identify the boundary layers for growth and for ochratoxin A production by strains of A. carbonarius from Greece (Natskoulis et al., 2009). They were able to validate the data against other sets from other parts of Europe. Mateo et al. (2008) used artificial neural networks (NN) to predict DON production by F. graminearum in wheat cultures taking as inputs the fungal contamination level of the cereal, water activity for fungal growth, temperature and time. The data matrix was used to train and validate various types of NN. Recently, they have used this approach for predicting ochratoxin A production by A. carbonarius (Mateo et al., 2009). This could be a useful approach in identifying the potential risks associated with climate change. Indeed, some isolates from Greece were found to be much more thermo- and xerotolerant than those from other parts of Europe (Tassou et al., 2007). Since parts of Greece are considered to be a hot spot for ochratoxin A in red wine this could have implications for survival, growth and increased contamination with this mycotoxin under modified climatic conditions.

Table 6.   Summary of different model types, mycotoxigenic moulds examined for predicting germination, growth or mycotoxin production (adapted from Garcia et al., 2009)
ModelMycotoxigenic speciesFactorsReferences
BaranyiAspergillus section FlaviawGibson et al. (1994)
Aspergillus carbonariusaw, tempTassou et al. (2007)
Aspergillus flavus, Aspergillus parasiticusaw, temp, CO2Samapundo et al. (2007a,b)
Fusarium verticillioides, Fusarium proliferatumaw, tempSamapundo et al. (2005)
GompertzA. carbonariusaw, temp, pHMarín et al. (2006a,b)
LinearF. verticillioides, F. proliferatumaw, tempMarín et al. (1999, 2008, 2009)
Aspergillus ochraceus, A. flavus  
A. flavus, Alternaria alternataaw, temp, pH, preservativeSautour et al. (2002)
F. verticillioides, F. proliferatumaw, tempVelluti et al. (2004)
A. ochraceus, Penicillium verrucosumaw, tempPardo et al. (2004, 2005, 2006)
Aspergillus section Nigriaw, tempBelli et al. (2005)
P. verrucosumaw, tempCairns-Fuller et al. (2005)
Fusarium culmorum, Fusarium graminearumaw, tempHope et al. (2005)
A. carbonariusaw, temp, preservativeMedina et al. (2007b)

Molecular and genetic analyses of toxin genes to assess impacts of climate change

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

Mycotoxin biosynthetic pathways have been largely elucidated in many of the key mycotoxigenic fungi (e.g. A. flavus and aflatoxin B1; F. graminearum and deoxynivalenol; F. verticillioides and fumonisin B1). The genes involved in mycotoxin production are often clustered together and key marker genes in the pathways have been identified. A microarray has been developed and used to study the impact of interacting ecological conditions on expression of the whole gene cluster for some of these mycotoxigenic fungi (Schmidt-Heydt & Geisen, 2007). Key marker genes include aflD (=NOR1) gene involved in aflatoxin B1 production, TRI5 in F. graminearum and related trichothecene producers, and the FUM1 gene in F. verticillioides for fumonisin production. Schmidt-Heydt et al. (2008, 2009) examined the effect of stress factors and suggested that two peaks of toxin production can be ascertained: one under conducive conditions of temperature and aw and a second peak, when the fungus is under stress, close to marginal conditions for growth, where increased mycotoxin production may result. This was shown using the mycotoxin microarray with sub-arrays for the genes involved in tricothecene production by F. graminearum, and for aflatoxin B1 production by A. flavus, and ochratoxin A production by P. verrucosum. Studies of the FUM1 gene expression in relation to total water stress and matric stress suggested a higher sensitivity to the latter (Jurado et al., 2008; Marín et al., 2010a,b). Interestingly, increasing ionic solute stress resulted in an increase in FUM1 gene expression at 0·93/0·95 aw in cultures of F. verticillioides. In contrast, studies with F. proliferatum showed that this was not the case for this fumonisin producer (Marín et al., 2010b). This may partially be related to environmental stress tolerance but also to the fact that F. verticillioides is a very specific pathogen of maize, whilst F. proliferatum has a very wide host range.

Recent studies by Schmidt-Heydt et al. (2011) have used an integrated systems approach which may well be needed to understand the impact of interacting conditions of environmental factors, relative gene expression and mycotoxin production. This has been demonstrated for F. culmorum and F. graminearum. Models were developed such that gene expression data for some key genes in the biosynthetic pathways for deoxynivalenol could be used to predict the amount of DON which might be produced under different temperature and aw conditions. Figure 3 shows an example of the contour maps which can be produced using this approach in relation to the expression of specific genes (TRI5, TRI6) under various aw and temperatures. The contour maps based on the model show the relative amounts of DON which may be produced as these factors change. This approach would enable changes in CO2, elevated temperature and water stress impacts to be predicted based on gene expression of some key genes. In this case, six genes were used in the predictive model.

image

Figure 3.  Relationship for Fusarium culmorum between (a) TRI5 gene expression × aw and (b) TRI6 gene expression × temperature on relative amounts of deoxynivalenol production based on a model constructed from trichothecene gene expression and phenotypic deoxynivalenol production under different aw × temperature conditions (partially adapted from Schmidt-Heydt et al., 2010). Numbers on the contours refer to deoxynivalenol (μg g−1). Gene expression is relative to the copy number of the β-tubulin gene.

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Conclusions and research needs

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References

This review has focused on the available information which may be relevant to fungal genera which are known to produce mycotoxins on some key commodities which are relevant to both developing and developed countries. Climate-change factors will have a profound effect on both growth and relative mycotoxin production. Paterson & Lima (2010) suggest that a major risk in developed countries will be in temperate climates. This is based on temperatures increasing to >30°C, which would be conducive to aflatoxin production and represent a significant risk. They also have suggested that in colder climates mycotoxins such as patulin and ochratoxin A may become more important as warmer climatic conditions would be conducive to Penicillium expansum and P. verrucosum colonization, respectively. We believe that in regions such as Africa, hotter tropical climates and longer periods of drought stress would have a significant impact on the amounts of food produced, and would also be more prone to contamination with mycotoxins. This will have a direct impact on food security and nutritional quality, which may be compromised. Miraglia et al. (2009) have suggested that in dealing with food security, impact of climate change on mycotoxins be considered on a case-by-case basis because of the different optimum and marginal conditions for growth and production of toxins. A decrease in productivity of crops for both human food and animal feed in certain regions may also increase the risk of contamination. This would be further impacted by recent drivers for the use of arable land for biofuels production.

It may be that under certain environmental stress conditions there is a stimulation of toxin production (Schmidt-Heydt et al., 2008). Whilst water and temperature stress are important the components of water stress may also be important. Mycotoxigenic fungi are more sensitive to matric water stress than solute stress (Ramirez et al., 2004; Giorni et al., 2008a). The question is whether some mycotoxigenic fungi may become more adapted to such changing stress conditions. Also, whether other xerophilic fungi, some producing secondary metabolite toxic to humans and animals, may become dominant in a changing environment. Recent studies suggest that these and others, such as Eurotium species, are more likely to become active and grow effectively at extreme environments, especially under drought stress (Williams & Hallsworth, 2009; Chin et al., 2010). The interaction with elevated CO2 is another important factor. Previous studies with elevated sulphur dioxide (SO2) showed that pests and some pathogens and phyllosphere fungal populations are modified at increased concentrations (McLeod, 1988; Magan & McLeod, 1991; Mansfield et al., 1991). There is a need to combine available and new data on tolerance to changes in CO2 × temperature × aw conditions to test and validate existing models to improve prediction of mycotoxin risk. An integrated systems approach may be necessary to ensure the accuracy of predictions.

Finally, key areas for future research should include: harmonization of surveillance and mycotoxin monitoring methodologies on a regional basis; estimating the impact of elevated CO2 on host–pathogen interaction by integrating molecular, physiological and analytical assessments of mycotoxigenic fungal activity and mycotoxins; safer storage systems in relation to changing abiotic and biotic factors for different commodities; and improved models of growth and mycotoxin boundary conditions in relation to climate-change factors to predict contamination.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Preharvest mycotoxins and possible influences of climate change
  5. Climate change and postharvest contamination by mycotoxins
  6. CO2 and interaction with other key environmental factors on growth and mycotoxin production
  7. Water and temperature stress impacts on growth and mycotoxin production: climate-change implications
  8. Molecular and genetic analyses of toxin genes to assess impacts of climate change
  9. Conclusions and research needs
  10. References
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