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Genomic tools to assist breeding for drought tolerance
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ScienceDirect Genomic tools to assist breeding for drought tolerance Peter Langridge1 and Matthew P Reynolds2 Water deficit or drought stress is a major limitation to crop production globally. Plant breeders have used a wide range of technologies to successfully breed varieties that perform well under the growth conditions for their target environments but they are always seeking new opportunities to enhance rates of genetic gain. Under drought, yield is determined by the integration of variable levels of water deficit across the developmental life of the crop. Genomics technologies were seen as a path to understand the genetic and environmental complexity of drought stress. To be relevant to breeding programs, genomic studies must consider the nature of drought stress in the target environment and use plant material and phenotyping techniques that relate to field conditions. Addresses 1 Australian Centre for Plant Functional Genomics, University of Adelaide, Urrbrae SA 5064, Australia 2 International Maize and Wheat Improvement Centre, CIMMYT, El Batan 56130, Texcoco, Mexico Corresponding author: Langridge, Peter ([email protected])
Current Opinion in Biotechnology 2015, 32:130–135 This review comes from a themed issue on Plant biotechnology Edited by Inge Broer and George N Skaracis For a complete overview see the Issue and the Editorial Available online 19th December 2014 http://dx.doi.org/10.1016/j.copbio.2014.11.027 0958-1669/# 2014 Elsevier Ltd. All rights reserved.
The drought environment The term ‘drought stress’ hides great complexity and varies greatly between crops and environments. For example, maize yield may fail due to moderate stress whose timing delays anthesis-silking interval beyond a critical threshold [1]. Wheat on the other hand can show a linear response to water, and this plasticity permits it to be grown under highly unpredictable conditions, including severe moisture stress [2]. While rainfall is probably the least predictable environmental factor in most crop environments, breeding has been very effective at targeting lines to a wide range of water availabilities [3]. Consequently, for most major crops, farmers have access to varieties that perform well under the typical environmental conditions for their region. For wheat, farmers in the UK have varieties that can take advantage of highly favourable production conditions and routinely yield up Current Opinion in Biotechnology 2015, 32:130–135
to 15 t/ha while Australian farmers sow varieties that produce an average of 1.5 t/ha. However, both UK and Australian wheat breeders are seeking improved performance of their varieties under drought. In reality the problem they are trying to tackle is environmental variability rather than drought per se. Below average and unfavourable distribution of rainfall is the core problem. Therefore we need to ask how can we improve the ability of our crops to cope with unusually low rainfall and are the requirements the same for the UK as for the Australian varieties? If genomics tools are to be of value to breeding activities, we need to consider how they can help breeders tackle this question. Importantly, we need to show that these tools can add value over and above what can be achieved using existing breeding and selection techniques.
Genomics applications Broadly speaking genomics tools offer knowledge and information about single genes, pathways or gene networks, and genome structure and behaviour. This knowledge and information can be deployed in several ways. Where individual genes controlling the trait of interest are known, the gene knowledge can be used to identify, discover and tag individual alleles and to develop and deploy molecular markers to track the desired alleles in breeding programs [4]. Armed with gene knowledge, novel alleles can be sought in diverse germplasm pools, including wild relatives, expression variation can be studied and new alleles, both structural and expression, can be created either through genetic engineering or through the new genome editing techniques. At the genome structure level, genomics and whole genome analysis can help breeders design optimal recombination strategies and deploy some new breeding techniques, such as genomic selection [5]. Marker information can also be valuable in genetic dissection to distinguish between traits with an independent genetic basis that could, for example, be combined to achieve cumulative gene action versus trait expression associated with alternate alleles at a given loci that can only be traded-off against each other. The areas where genomics tools are being applied are where the genetic control of the target trait is clearly defined and, consequently simple, or where information on individual genes is not required and genome structural and predictive models can be used [6]. The area where genomics has struggled to have an impact is also the area where genomics was thought to offer the greatest potential, namely in describing and defining complex traits www.sciencedirect.com
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where genotype x environment interactions exert a major effect, such as drought tolerance [7]. The limitation may not lie with the genomics technologies but rather with our understanding of the genetic basis of target traits and the interactions with an environment that is in constant flux.
The nature and components of drought stress First we need to consider the nature of drought stress in more detail. Drought stress is not static; it can occur at any time during the crop growth, it can vary in severity and it frequently occurs in conjunction with other environment stresses, such as heat and salinity [8]. Different tissues or organs of the plant will respond differently to drought stress and the level of stress will fluctuate diurnally, high stress during peak photosynthetic periods and low stress overnight [9]. The nature of a plants’ response will also vary greatly depending on whether the plant is entering stress for the first time or after several exposures and whether they are recovering from stress after a rainfall or irrigation event [10]. Drought tolerance can take on several very different forms (Figure 1) including: the ability of the plant to access water-determined largely by the architecture and health of the root system [11,12,13]; the efficiency of water use by the plant-usually based on a series of compromises between the ability to access CO2 for photosynthesis while limiting water loss [14], the allocation of resources (roots versus shoots, carbon storage versus growth) [15];
protection against damage-especially to reproductive growth-resulting from dehydration and heat [16], including oxidative stress [17].
Defect elimination There are several areas where relatively simple traits, from a genetic perspective, can be directly tackled to enhance the performance of the crop plant under waterlimited condition [18]. Perhaps the most important area where this has been shown is related to root function where there are some very effective examples of significant advances through the applications of genomics. Root health and vigour can dramatically affect the ability of the plant to access water; nematodes, fungi and bacteria can all damage root systems, while nutrient deficiencies (particularly P, Zn and Fe) and toxicities (salt, Al, B) can limit root vigour (for example [19]). In some environments deep roots will help capture moisture [11,12,20] while in others, where deep water is absent or even toxic (e.g., saline or acid), strong surface roots may be the best option [21]. In many cases our best option for improving drought tolerance is to tackle these components or associated stresses since these are often under simple genetic control and can be rapidly and reliably phenotyped. For example, incorporating resistance to nematodes [22] or tolerance to toxic levels of boron [23] or salt [24] may offer significant improvements in drought tolerance. This strategy of defect elimination has been widely deployed and resulted in significant improvement in crop performance.
Phenotyping for drought tolerance Figure 1
YIELD = WU x WUE x HI Photo-Protection Leaf morphology • wax/pubescence • posture/rolling Pigments • chl a:b • carotenoids Antioxidants • various candidates
Transpiration Efficiency WUE of leaf photosynthesis • low 12/13C discrimination • PGR signals (ABA, ethylene, etc) Spike/awn photosynthesis
Water Uptake Partitioning (HI) Partitioning to stem carbohydrates Harvest index • Rht alleles • Avoid grain abortion (PGR signals)
Rapid ground cover • Leaf area • Coleoptile length/seed size Access to water by roots • Ψ leaf (spectrometry) • IR thermometry Dehydration avoidance • osmotic adjustment
Current Opinion in Biotechnology
A conceptual model of drought adaptive traits in wheat (adapted from Reynolds and Tuberosa, 2008); WU, WUE, and HI stand for water uptake, water use efficiency, and harvest index, respectively. www.sciencedirect.com
Following on from the above discussion, the most important drought tolerance phenotype is yield under conditions where water availability is below expectations for the target environment. How can this trait be measured, or more specifically how do we expose plants to a relevant drought stress and measure the impact of the stress on yield or component physiological, developmental, biochemical or genetic traits [25,20,26]? While some believe controlled facilities are the only way to achieve scientific precision, abstraction from reality can produced unacceptable levels of artefacts. These errors can be reduced if knowledge of the environmental and biological systems being simulated is used to design phenotyping platforms. Although we lack comprehensive biophysical information about many of the drought environments worldwide, some general rules of thumb can still be applied. For example, drought screens often fail to differentiate between water use efficiency (WUE) and water uptake (WU) as targets for improvement. While both have benefits in the right agronomic context, they are very different phenomena, and require distinct screening protocols. Another key limitation to controlled facilities is that crop plants normally grow as a community of genetically Current Opinion in Biotechnology 2015, 32:130–135
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identical, or very similar, individuals and the plants are likely to behave very differently when grow alone or in a small group relative to the field or production scenario. Indeed a major outcome of breeding has been improvements in tolerance to competition stress [27,28]. The field soil environment is also very different from pots and potting mixes used in greenhouses. Recently, several groups are moving to large pots or tubs in an effort to create more realistic controlled environment scenarios [29]. Even so, achieving depth and volume of soil may not be sufficient as plant growth is also sensitive to soil temperature and even temperature gradients [30]. Secondly, we can grow the plants under field conditions but closely monitor the field environment. In this approach plants would be grown in diverse environments [31]. The more environments tested, the greater the ability to detect and qualify useful variation. In this approach it is important not to deviate too far from the normal production environment for the lines, populations or varieties being tested [29]. There is little value in assessing lines developed for an environment where yield losses due to drought range are never below 20% or 30% in an environment where losses can exceed 80%, except where responsiveness to favourable seasons is an important factor in determining farmers’ income. Although we have described two options for assessing drought stress, it is also possible to create semi-controlled field conditions though the use of rainout shelters, irrigation, enclosures to simulate heat stress and various related strategies. These systems offer greater flexibility than standard field trials but are usually limited in scale [32]. The drought scenarios most widely applied in genomics experiments are conducted in controlled environment systems. Although the drought scenarios used tend to vary greatly, they usually bear little relationship to field situations [33]. There have been some studies that use field grown material and these tend to have greater credibility although they also generally show greater variation between replicates [34].
Opportunities for genetic gain As noted above, in considering the role of genomics tools in drought improvement, we need to be conscious of the impressive advances that continue to be made in breeding [35]. For crops that have been subject to breeding and selection for some time, many key traits related to adaptation to water stressed environments may already have been optimised. This is particularly the case for the cereals where phenology has usually been well matched to the target environments [36]. However for some crops that have been recently introduced into new production regions, such as many of the grain legumes, there may still be a good opportunity to optimise phenology [37]. Current Opinion in Biotechnology 2015, 32:130–135
Several association mapping studies have attempted to identify regions where particular haplotypes have resulted from selective sweeps linked to adaption to specific environments (for example, [13,38]). With some exceptions (for example, [11,39]) these studies have generally failed to identify novel regions (as opposed to the already know phenology loci) [37]. There are several possible reasons for this failure including the complexity and diversity of the different environmental factors, the likelihood of multiple genetic and biochemical mechanisms that can lead to a particular outcome and the genetic complexity and probable epistasis between the multiple adaptive loci. It is probable that all these factors play a part. However, based on recent modelling studies on detecting selective sweeps using haplotype structure [40,41], we may see resolution of this problem as the size of association mapping panels expand, the accuracy of the phenotyping improves and the density and reliability of genotyping information improves.
Omics for gene discovery and breeding — a resource for supporting other approaches Drought genetics and physiology has been dominated for several decades by the view that we can develop physiological or biochemical models for improved drought response and then design experiments and crossing strategies to test these approaches (Graphical abstract). This strategy has had some successes, including the application of carbon isotope discrimination to select for transpiration efficiency in Australia [14] and canopy temperature to select for deeper roots [36,11,12], an approach that has been adopted by many wheat breeders worldwide and led to large scale adoption of physiological breeding approaches [36,42]. The advent of whole genome, metabolome, proteome and related technologies offered a new and non-hypothesis driven approach, as well as providing a valuable tool to further dissect proven adaptive traits. It was generally hoped that if one generates a series of large ‘omics’ datasets from drought adapted versus unadapted lines under stressed and non-stressed conditions, a series of genes or pathways would emerge that could be associated with enhanced drought response [43]. To date these studies have essentially just confirmed the complexity of drought response and provide few novel insights. However, these datasets do now provide a resource that can be linked to other approaches. For example, if a major QTL related to yield is identified, the underlying genes can be scanned across the gene expression databases to identify those that are up or down regulated under drought stress, altered expression of a gene related to a stress pathway may be particularly relevant or there may be significant allelic variation across a germplasm panel [13,37]. Frequently we are seeing presence or absence polymorphisms for genes related to stress adaption so this can also be a clue to potential candidates [44,45,46]. www.sciencedirect.com
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Table 1 Classes of genes shown to have an effect on drought tolerance responses when overexpressed in transgenic plants (based on information in Hu and Xiong [47]). Gene class Protein kinases Transcription factors Protein degradation Protein modification Hormone metabolism Osmotic adjustment Dehydrins ROS Scavenging Amino acid metabolism
Gene source
Transgenics evaluated
Field trials
Rice, tobacco, Arabidopsis Rice, Arabidopsis, wheat, tomato, potato Rice Rice Rice, Arabidopsis Rice, E. coli, Phaseolus aconitiformis Rice, barley Rice, maize Rice
Rice Rice, wheat, barley, tomato, potato Rice Rice Rice, soybean, cotton Rice, wheat
Rice, wheat, maize Rice, wheat, barley, maize
Rice, maize, wheat Rice, maize, wheat Rice
Rice Rice Rice
Rice
Table 2 Drought tolerance crops approved for cultivation (information from ISAAA, [50]). Crop Sugarcane Maize
Developer PT Perkebunan Nusantara XI Monsanto
Approved for cultivation
Date of approval
Indonesia
2013
Canada, Japan, USA
2010, 2012, 2011
Genetic engineering drought tolerance Genetic engineering represents an important tool for delivery of gene information and knowledge to crop improvement. The most widely used approach for identifying potential useful genes, has been through targeting specific pathways or processes known to be associated with drought responses, such as modifying the expression of drought related transcription factors or protein kinases, increasing the expression of genes associated with production of osmoprotectants or protection form reactive oxygen species (see Refs. [34,47]). The main groups of genes that have been investigated are summarised in Table 1. An alternative approach has been the systematic screen of groups or classes of genes in transgenics by overexpressing the candidate genes under constitutive or stress inducible promoters or through activational tagging of genes. This approach allows the identification of genes without any prior knowledge but it is labour intensive and expensive. Consequently this approach has been followed largely in the private sector but has led to the discovery of several interesting genes, including the cold shock protein deployed in Genuity1 DroughtGardTM released in maize by Monsanto. However only two drought tolerant GM crops have been approved for commercial production (Table 2).
Conclusion The problem is that we do not really know what makes a plant drought adapted beyond its ability to adapt its www.sciencedirect.com
Gene
Function
EcBetA, Rm BetA, Cold shock protein B
Osmoprotection, Glycine betaine production Preserves RNA stability and translation
phenology or extend its root system to avoid severe stress. We also lack comprehensive biophysical information about many drought environments worldwide, a problem that can be easily solved with adequate investment. It is encouraging that there is now a substantial effort in building information and resources around crop plant responses to drought. We have learnt that significant gain can be made by tackling some of the related and component traits of drought response and we are slowing improving the complexity of the phenotypic and genetic models. We have gone down some blind alleys in trying to transfer genomics information from non-crop and unadapted plant species across to crops and by assuming there are universal models for drought adaptation that will apply to all plants. We do need improved models that allow us to cope with the diversity of environmental factors and genetic characteristics of our crops. These models will, inevitably be very complex but through increased effort by many research groups they are starting to take shape [49,50]. The attraction is that the models can start with fairly simple scenarios and build in complexity and sophistication as our datasets and understanding grows.
Acknowledgements Funding support to ACPFG from the Australian Research Council, Grains Research and Development Corporation, Government of South Australia and the University of Adelaide is gratefully acknowledged. Current Opinion in Biotechnology 2015, 32:130–135
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32. Lopes MS, Rebetzke GJ, Reynolds M: Integration of phenotyping and genetic platforms for a better understanding of wheat performance under drought. J Exp Bot 2014, 65:61676177. 33. Salekdeh GH, Reynolds MP, Bennett J, Boyer J: Conceptual framework for drought phenotyping during molecular breeding. Trends Plant Sci 2009, 14:1360-1385. 34. Lawlor DW: Genetic engineering to improve plant performance under drought: physiological evaluation of achievements, limitations and possibilities. J Exp Bot 2013, 64:83-108. Many experiments with crop plants engineered for improved drought tolerance fail to consider the physiological mechanisms associated with the drought tolerant phenotype and most simply delay the onset of stress. If drought tolerant transgenic crop are to accepte physiological consideration and field relevance need to be included in the evaluation procedures. 35. Manes Y, Gomez HF, Puhl L, Reynolds M, Braun HJ, Trethowan R:: Genetic yield gains of the CIMMYT international semi-arid wheat yield trials from 1994 to 2010. Crop Sci 2012, 52:15431552. Results from over 500 international yield trials grown under various levels of drought from 1994 to 2010 demonstrated that conventional breeding achieved on average a 1% annual genetic gain in yield. 36. Reynolds M, Manes Y, Izanloo A, Langridge P: Phenotyping approaches for physiological breeding and gene discovery in wheat. Ann Appl Biol 2009, 155:309-320. 37. Thudi M, Upadhyaya HD, Rathore A, Gaur PM, Krishnamurthy L, Roorkiwal M, Nayak SN, Chaturvedi SK, Basu PS, Gangarao NVPR, Fikre A, Sharma PC, Sheshashayee MS, Tobita S, Kashiwagi J, Ito O, Killian A, Vashney RK: Genetic dissection of drought and heat tolerance in chickpea through genome-wide and candidate gene-based association mapping approaches. PLOS ONE 2014, 9:e96758. 38. Morris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD, Riera-Lizarazu O, Brown PJ, Acharya CB, Mitchell SE, Harriman J, Glaubitz JC, Buckler ES, Kresovich S: Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci U S A 2013, 110:453-458. The power of genome-wide association mapping is nicely demonstrated through a study of sorghum accessions from around the world. Selective sweeps for range of morphological traits including height maturity and inflorescence structure could be clearly identified. 39. Rebetzke GJ, Verbyla AP, Verbyla KL, Morell MK, Cavanagh CR: Use of a large multiparent wheat mapping population in genomic dissection of coleoptile and seedling growth. Plant Biotechnol J 2014, 12:219-230. A multiparent advanced generation intercross (MAGIC) population identified QTL for seedling vigor traits and show strong, positive genetic correlations for coleoptile and shoot lengths (rg = 0.85–0.90) suggest a common physiological and genetic basis. 40. Ferrer-Admetilla A, Lian M, Korneliussen T, Nielsen R: On detecting incomplete soft and hard selective sweeps using haplotype structure. Mol Biol Evol 2014, 31:1275-1291.
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Current Opinion in Biotechnology 2015, 32:130–135
ScienceDirect Genomic tools to assist breeding for drought tolerance Peter Langridge1 and Matthew P Reynolds2 Water deficit or drought stress is a major limitation to crop production globally. Plant breeders have used a wide range of technologies to successfully breed varieties that perform well under the growth conditions for their target environments but they are always seeking new opportunities to enhance rates of genetic gain. Under drought, yield is determined by the integration of variable levels of water deficit across the developmental life of the crop. Genomics technologies were seen as a path to understand the genetic and environmental complexity of drought stress. To be relevant to breeding programs, genomic studies must consider the nature of drought stress in the target environment and use plant material and phenotyping techniques that relate to field conditions. Addresses 1 Australian Centre for Plant Functional Genomics, University of Adelaide, Urrbrae SA 5064, Australia 2 International Maize and Wheat Improvement Centre, CIMMYT, El Batan 56130, Texcoco, Mexico Corresponding author: Langridge, Peter ([email protected])
Current Opinion in Biotechnology 2015, 32:130–135 This review comes from a themed issue on Plant biotechnology Edited by Inge Broer and George N Skaracis For a complete overview see the Issue and the Editorial Available online 19th December 2014 http://dx.doi.org/10.1016/j.copbio.2014.11.027 0958-1669/# 2014 Elsevier Ltd. All rights reserved.
The drought environment The term ‘drought stress’ hides great complexity and varies greatly between crops and environments. For example, maize yield may fail due to moderate stress whose timing delays anthesis-silking interval beyond a critical threshold [1]. Wheat on the other hand can show a linear response to water, and this plasticity permits it to be grown under highly unpredictable conditions, including severe moisture stress [2]. While rainfall is probably the least predictable environmental factor in most crop environments, breeding has been very effective at targeting lines to a wide range of water availabilities [3]. Consequently, for most major crops, farmers have access to varieties that perform well under the typical environmental conditions for their region. For wheat, farmers in the UK have varieties that can take advantage of highly favourable production conditions and routinely yield up Current Opinion in Biotechnology 2015, 32:130–135
to 15 t/ha while Australian farmers sow varieties that produce an average of 1.5 t/ha. However, both UK and Australian wheat breeders are seeking improved performance of their varieties under drought. In reality the problem they are trying to tackle is environmental variability rather than drought per se. Below average and unfavourable distribution of rainfall is the core problem. Therefore we need to ask how can we improve the ability of our crops to cope with unusually low rainfall and are the requirements the same for the UK as for the Australian varieties? If genomics tools are to be of value to breeding activities, we need to consider how they can help breeders tackle this question. Importantly, we need to show that these tools can add value over and above what can be achieved using existing breeding and selection techniques.
Genomics applications Broadly speaking genomics tools offer knowledge and information about single genes, pathways or gene networks, and genome structure and behaviour. This knowledge and information can be deployed in several ways. Where individual genes controlling the trait of interest are known, the gene knowledge can be used to identify, discover and tag individual alleles and to develop and deploy molecular markers to track the desired alleles in breeding programs [4]. Armed with gene knowledge, novel alleles can be sought in diverse germplasm pools, including wild relatives, expression variation can be studied and new alleles, both structural and expression, can be created either through genetic engineering or through the new genome editing techniques. At the genome structure level, genomics and whole genome analysis can help breeders design optimal recombination strategies and deploy some new breeding techniques, such as genomic selection [5]. Marker information can also be valuable in genetic dissection to distinguish between traits with an independent genetic basis that could, for example, be combined to achieve cumulative gene action versus trait expression associated with alternate alleles at a given loci that can only be traded-off against each other. The areas where genomics tools are being applied are where the genetic control of the target trait is clearly defined and, consequently simple, or where information on individual genes is not required and genome structural and predictive models can be used [6]. The area where genomics has struggled to have an impact is also the area where genomics was thought to offer the greatest potential, namely in describing and defining complex traits www.sciencedirect.com
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where genotype x environment interactions exert a major effect, such as drought tolerance [7]. The limitation may not lie with the genomics technologies but rather with our understanding of the genetic basis of target traits and the interactions with an environment that is in constant flux.
The nature and components of drought stress First we need to consider the nature of drought stress in more detail. Drought stress is not static; it can occur at any time during the crop growth, it can vary in severity and it frequently occurs in conjunction with other environment stresses, such as heat and salinity [8]. Different tissues or organs of the plant will respond differently to drought stress and the level of stress will fluctuate diurnally, high stress during peak photosynthetic periods and low stress overnight [9]. The nature of a plants’ response will also vary greatly depending on whether the plant is entering stress for the first time or after several exposures and whether they are recovering from stress after a rainfall or irrigation event [10]. Drought tolerance can take on several very different forms (Figure 1) including: the ability of the plant to access water-determined largely by the architecture and health of the root system [11,12,13]; the efficiency of water use by the plant-usually based on a series of compromises between the ability to access CO2 for photosynthesis while limiting water loss [14], the allocation of resources (roots versus shoots, carbon storage versus growth) [15];
protection against damage-especially to reproductive growth-resulting from dehydration and heat [16], including oxidative stress [17].
Defect elimination There are several areas where relatively simple traits, from a genetic perspective, can be directly tackled to enhance the performance of the crop plant under waterlimited condition [18]. Perhaps the most important area where this has been shown is related to root function where there are some very effective examples of significant advances through the applications of genomics. Root health and vigour can dramatically affect the ability of the plant to access water; nematodes, fungi and bacteria can all damage root systems, while nutrient deficiencies (particularly P, Zn and Fe) and toxicities (salt, Al, B) can limit root vigour (for example [19]). In some environments deep roots will help capture moisture [11,12,20] while in others, where deep water is absent or even toxic (e.g., saline or acid), strong surface roots may be the best option [21]. In many cases our best option for improving drought tolerance is to tackle these components or associated stresses since these are often under simple genetic control and can be rapidly and reliably phenotyped. For example, incorporating resistance to nematodes [22] or tolerance to toxic levels of boron [23] or salt [24] may offer significant improvements in drought tolerance. This strategy of defect elimination has been widely deployed and resulted in significant improvement in crop performance.
Phenotyping for drought tolerance Figure 1
YIELD = WU x WUE x HI Photo-Protection Leaf morphology • wax/pubescence • posture/rolling Pigments • chl a:b • carotenoids Antioxidants • various candidates
Transpiration Efficiency WUE of leaf photosynthesis • low 12/13C discrimination • PGR signals (ABA, ethylene, etc) Spike/awn photosynthesis
Water Uptake Partitioning (HI) Partitioning to stem carbohydrates Harvest index • Rht alleles • Avoid grain abortion (PGR signals)
Rapid ground cover • Leaf area • Coleoptile length/seed size Access to water by roots • Ψ leaf (spectrometry) • IR thermometry Dehydration avoidance • osmotic adjustment
Current Opinion in Biotechnology
A conceptual model of drought adaptive traits in wheat (adapted from Reynolds and Tuberosa, 2008); WU, WUE, and HI stand for water uptake, water use efficiency, and harvest index, respectively. www.sciencedirect.com
Following on from the above discussion, the most important drought tolerance phenotype is yield under conditions where water availability is below expectations for the target environment. How can this trait be measured, or more specifically how do we expose plants to a relevant drought stress and measure the impact of the stress on yield or component physiological, developmental, biochemical or genetic traits [25,20,26]? While some believe controlled facilities are the only way to achieve scientific precision, abstraction from reality can produced unacceptable levels of artefacts. These errors can be reduced if knowledge of the environmental and biological systems being simulated is used to design phenotyping platforms. Although we lack comprehensive biophysical information about many of the drought environments worldwide, some general rules of thumb can still be applied. For example, drought screens often fail to differentiate between water use efficiency (WUE) and water uptake (WU) as targets for improvement. While both have benefits in the right agronomic context, they are very different phenomena, and require distinct screening protocols. Another key limitation to controlled facilities is that crop plants normally grow as a community of genetically Current Opinion in Biotechnology 2015, 32:130–135
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identical, or very similar, individuals and the plants are likely to behave very differently when grow alone or in a small group relative to the field or production scenario. Indeed a major outcome of breeding has been improvements in tolerance to competition stress [27,28]. The field soil environment is also very different from pots and potting mixes used in greenhouses. Recently, several groups are moving to large pots or tubs in an effort to create more realistic controlled environment scenarios [29]. Even so, achieving depth and volume of soil may not be sufficient as plant growth is also sensitive to soil temperature and even temperature gradients [30]. Secondly, we can grow the plants under field conditions but closely monitor the field environment. In this approach plants would be grown in diverse environments [31]. The more environments tested, the greater the ability to detect and qualify useful variation. In this approach it is important not to deviate too far from the normal production environment for the lines, populations or varieties being tested [29]. There is little value in assessing lines developed for an environment where yield losses due to drought range are never below 20% or 30% in an environment where losses can exceed 80%, except where responsiveness to favourable seasons is an important factor in determining farmers’ income. Although we have described two options for assessing drought stress, it is also possible to create semi-controlled field conditions though the use of rainout shelters, irrigation, enclosures to simulate heat stress and various related strategies. These systems offer greater flexibility than standard field trials but are usually limited in scale [32]. The drought scenarios most widely applied in genomics experiments are conducted in controlled environment systems. Although the drought scenarios used tend to vary greatly, they usually bear little relationship to field situations [33]. There have been some studies that use field grown material and these tend to have greater credibility although they also generally show greater variation between replicates [34].
Opportunities for genetic gain As noted above, in considering the role of genomics tools in drought improvement, we need to be conscious of the impressive advances that continue to be made in breeding [35]. For crops that have been subject to breeding and selection for some time, many key traits related to adaptation to water stressed environments may already have been optimised. This is particularly the case for the cereals where phenology has usually been well matched to the target environments [36]. However for some crops that have been recently introduced into new production regions, such as many of the grain legumes, there may still be a good opportunity to optimise phenology [37]. Current Opinion in Biotechnology 2015, 32:130–135
Several association mapping studies have attempted to identify regions where particular haplotypes have resulted from selective sweeps linked to adaption to specific environments (for example, [13,38]). With some exceptions (for example, [11,39]) these studies have generally failed to identify novel regions (as opposed to the already know phenology loci) [37]. There are several possible reasons for this failure including the complexity and diversity of the different environmental factors, the likelihood of multiple genetic and biochemical mechanisms that can lead to a particular outcome and the genetic complexity and probable epistasis between the multiple adaptive loci. It is probable that all these factors play a part. However, based on recent modelling studies on detecting selective sweeps using haplotype structure [40,41], we may see resolution of this problem as the size of association mapping panels expand, the accuracy of the phenotyping improves and the density and reliability of genotyping information improves.
Omics for gene discovery and breeding — a resource for supporting other approaches Drought genetics and physiology has been dominated for several decades by the view that we can develop physiological or biochemical models for improved drought response and then design experiments and crossing strategies to test these approaches (Graphical abstract). This strategy has had some successes, including the application of carbon isotope discrimination to select for transpiration efficiency in Australia [14] and canopy temperature to select for deeper roots [36,11,12], an approach that has been adopted by many wheat breeders worldwide and led to large scale adoption of physiological breeding approaches [36,42]. The advent of whole genome, metabolome, proteome and related technologies offered a new and non-hypothesis driven approach, as well as providing a valuable tool to further dissect proven adaptive traits. It was generally hoped that if one generates a series of large ‘omics’ datasets from drought adapted versus unadapted lines under stressed and non-stressed conditions, a series of genes or pathways would emerge that could be associated with enhanced drought response [43]. To date these studies have essentially just confirmed the complexity of drought response and provide few novel insights. However, these datasets do now provide a resource that can be linked to other approaches. For example, if a major QTL related to yield is identified, the underlying genes can be scanned across the gene expression databases to identify those that are up or down regulated under drought stress, altered expression of a gene related to a stress pathway may be particularly relevant or there may be significant allelic variation across a germplasm panel [13,37]. Frequently we are seeing presence or absence polymorphisms for genes related to stress adaption so this can also be a clue to potential candidates [44,45,46]. www.sciencedirect.com
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Table 1 Classes of genes shown to have an effect on drought tolerance responses when overexpressed in transgenic plants (based on information in Hu and Xiong [47]). Gene class Protein kinases Transcription factors Protein degradation Protein modification Hormone metabolism Osmotic adjustment Dehydrins ROS Scavenging Amino acid metabolism
Gene source
Transgenics evaluated
Field trials
Rice, tobacco, Arabidopsis Rice, Arabidopsis, wheat, tomato, potato Rice Rice Rice, Arabidopsis Rice, E. coli, Phaseolus aconitiformis Rice, barley Rice, maize Rice
Rice Rice, wheat, barley, tomato, potato Rice Rice Rice, soybean, cotton Rice, wheat
Rice, wheat, maize Rice, wheat, barley, maize
Rice, maize, wheat Rice, maize, wheat Rice
Rice Rice Rice
Rice
Table 2 Drought tolerance crops approved for cultivation (information from ISAAA, [50]). Crop Sugarcane Maize
Developer PT Perkebunan Nusantara XI Monsanto
Approved for cultivation
Date of approval
Indonesia
2013
Canada, Japan, USA
2010, 2012, 2011
Genetic engineering drought tolerance Genetic engineering represents an important tool for delivery of gene information and knowledge to crop improvement. The most widely used approach for identifying potential useful genes, has been through targeting specific pathways or processes known to be associated with drought responses, such as modifying the expression of drought related transcription factors or protein kinases, increasing the expression of genes associated with production of osmoprotectants or protection form reactive oxygen species (see Refs. [34,47]). The main groups of genes that have been investigated are summarised in Table 1. An alternative approach has been the systematic screen of groups or classes of genes in transgenics by overexpressing the candidate genes under constitutive or stress inducible promoters or through activational tagging of genes. This approach allows the identification of genes without any prior knowledge but it is labour intensive and expensive. Consequently this approach has been followed largely in the private sector but has led to the discovery of several interesting genes, including the cold shock protein deployed in Genuity1 DroughtGardTM released in maize by Monsanto. However only two drought tolerant GM crops have been approved for commercial production (Table 2).
Conclusion The problem is that we do not really know what makes a plant drought adapted beyond its ability to adapt its www.sciencedirect.com
Gene
Function
EcBetA, Rm BetA, Cold shock protein B
Osmoprotection, Glycine betaine production Preserves RNA stability and translation
phenology or extend its root system to avoid severe stress. We also lack comprehensive biophysical information about many drought environments worldwide, a problem that can be easily solved with adequate investment. It is encouraging that there is now a substantial effort in building information and resources around crop plant responses to drought. We have learnt that significant gain can be made by tackling some of the related and component traits of drought response and we are slowing improving the complexity of the phenotypic and genetic models. We have gone down some blind alleys in trying to transfer genomics information from non-crop and unadapted plant species across to crops and by assuming there are universal models for drought adaptation that will apply to all plants. We do need improved models that allow us to cope with the diversity of environmental factors and genetic characteristics of our crops. These models will, inevitably be very complex but through increased effort by many research groups they are starting to take shape [49,50]. The attraction is that the models can start with fairly simple scenarios and build in complexity and sophistication as our datasets and understanding grows.
Acknowledgements Funding support to ACPFG from the Australian Research Council, Grains Research and Development Corporation, Government of South Australia and the University of Adelaide is gratefully acknowledged. Current Opinion in Biotechnology 2015, 32:130–135
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Current Opinion in Biotechnology 2015, 32:130–135