WO2015179985A1 - Screening method for selection of disease-resistant plant cultivars - Google Patents

Abstract

Methods involving the combination of X-ray phase contrast imaging (PCI) with biomolecular imaging to provide a combined approach to screening plants for disease resistance. The combined approach can also be applied to detect biomarkers of disease-resistance in plants for use in screening protocols. More specifically, methods that offer a combined approach to screening commercial crop plants for disease-resistance are described.

Description

SCREENING METHOD FOR SELECTION OF DISEASE-RESISTANT PLANT

CULTIVARS

TECHNICAL FIELD

[0001] The present disclosure relates to the field of plant breeding and, in particular, to a screening method for selection of disease-resistant plants. More specifically, the disclosure relates to a method for screening commercial crop plants exhibiting structural and spectral biomarkers associated with disease-resistance.

BACKGROUND [0002] A wide range of plants are grown and harvested worldwide for profit or subsistence. These commercial crop plants generally fall into six categories: food crops for human consumption (e.g., cereal grains and pulses); feed crops for animal consumption (e.g., grasses and legumes); fibre crops (e.g., cotton and hemp); oilseed crops (e.g., cottonseed, corn, canola); ornamental crops (e.g., dogwood, azalea); and industrial and secondary crops (e.g., rubber and tobacco).

[0003] Commercial crop plants are subject to numerous diseases, including bacterial, viral and fungal diseases which can result in significant economic losses worldwide as well as compromise global food and industrial production. One of the most effective approaches to disease management is the use of genetically resistant plants and the propagation of such plants using plant breeding techniques. Various plant breeding techniques are known and commonly used to achieve the purposeful manipulation of plant species in order to create the desired genotypes and phenotypes for disease-resistance. This manipulation can involve either controlled pollination, genetic engineering, or both, followed by artificial selection of progeny. [0004] Of paramount importance to any breeding program is the availability of effective screening methods. For example, proteomic and genomic analysis have been used to screen for abiotic, biotic, and nutrition stress resistance in crops. Such proteomic and genomic analysis, however, is invasive and not conducive to high throughput screening methods for identifying resistant plants, for example. In addition to genomic and proteomic based techniques, phenotypic screening methods have been utilized. For example, microscopy methods including electron, confocal, and light microscopy methods have been used to determine structural differences between fungal resistant and susceptible cultivars of wheat and barley (2005, Jansen, C. et al, Infection patterns in barley and wheat spikes inoculated with wild-type and trichodiene synthase gene disrupted Fusarium graminearum. PNAS 102: 16892-16897). Such structural visualization techniques, however, tend to be invasive and oftentimes destructive thereby limiting the use of such techniques in the long term monitoring of the physiological changes and responses of a plant or plant part to disease.

[0005] Phenomics based techniques are emerging as effective tools for measuring physical and chemical parameters in a large number of samples. In this respect, a number of imaging techniques have been utilized for non-destructive measurement of plant physiological traits. For example, visible light imaging has been used to measure shoot area and inferred mass, plant height and width, canopy density, other morphometric data, leaf colour and senescence. Fluorescence imaging with blue light large field excitation (<500 nm) has been used to reveal variations associated with plant senescence, chlorophyll health and GFP expression. Near infrared (roots) imaging has been used to provide information about the distribution of moisture in the root column. Such phenomics based techniques offer promising platforms for capturing phenotypic data that can be used with modern breeding technologies for the improvement in crop performance. [0006] A continuing need, however, exists for a rapid, time and cost efficient, non-invasive screening method for screening plants, in particular highly valued crop plants for disease resistance.

[0007] This background information is provided for the purpose of making known information believed by the applicant to be of possible relevance to the present disclosure. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present disclosure.

SUMMARY

[0008] Disclosed herein are exemplary embodiments pertaining to a screening method for selection of disease-resistant plant cultivars. In accordance with one aspect of the disclosure, there is described a method for screening plants to identify plant cultivars having resistance to a disease, comprising: (a) subjecting a candidate plant or plant part to phase contrast X-ray imaging to visualize structural features of the plant or plant part; (b) comparing the structural features of the plant or plant part to the structural features of a reference plant or plant part having resistance to the disease, wherein structural identifiers of resistance to the disease are identified; (c) obtaining a sample from the plant or plant part identified in step (b) as exhibiting the structural identifiers of resistance to the disease; (d) determining a mid-infrared spectral profile for the sample; and (e) comparing the mid-infrared spectral profile of the sample to the mid-infrared spectral profile of a sample obtained from the reference plant or plant part, wherein spectral biomarkers of resistance to the disease are identified to confirm resistance of the candidate plant to the disease.

[0009] In accordance with another aspect of the disclosure, there is described a method for screening plants to identify plant cultivars having resistance to a disease, comprising: (a) subjecting a candidate plant or plant part to synchrotron-based phase contrast X-ray imaging to visualize structural features of the plant or plant part; (b) comparing the structural features of the plant or plant part to the structural features of a reference plant or plant part having resistance to disease, wherein structural identifiers of resistance to the disease are identified; (c) obtaining a sample from the plant or plant part identified in step (b) as exhibiting the structural identifiers of resistance to the disease; (d) determining a mid-infrared spectral profile for the sample; and (e) comparing the mid-infrared spectral profile of the sample to the mid-infrared spectral profile of a sample obtained from the reference plant or plant part, wherein spectral biomarkers of resistance to the disease are identified to confirm resistance of the candidate plant to the disease.

[0010] In accordance with a further aspect of the disclosure, there is described a method for detecting biomarkers of disease-resistance in plants for use in screening protocols, comprising: (a) providing a disease-resistant and a disease-susceptible plant, wherein each plant has been exposed to a disease-causing pathogen; (b) subjecting the disease-resistant and the disease-susceptible plant or a part of the plant to phase contrast X-ray imaging to visualize structural features of each plant or plant part; (c) comparing the structural features of the disease-resistant plant or plant part to the structural features of the disease-susceptible plant or plant part, wherein differences in compared structural features indicate structural identifiers of resistance to the disease-causing pathogen; (d) obtaining a sample from each plant part identified in step (c) wherein differences in the structural features between the disease-resistant and the disease-susceptible plant is identified; (e) determining a mid-infrared spectral profile for each sample; and (f) comparing the mid-infrared spectral profile of the samples taken from the disease-resistant and the disease-susceptible plants, wherein spectral biomarkers of resistance to the disease-causing pathogen are identified.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] These and other features of the disclosure will become more apparent in the following detailed description in which reference is made to the appended drawings.

[0012] Figure 1 is a graphical representation of the number of diseased spikelets of wheat cultivars following infection with Fusarium head blight at 4 and 10 days after inoculation (dai). Values are mean of triplicates ± errors. For each incubation period, the treatments having same letters are not significantly different (P < 0.05), as determined by using the least significant difference (LSD) test;

[0013] Figures 2A, IB, and 2C, show images of control and healthy florets in the spikelets of wheat cultivars using phase contrast X-ray imaging at 10 days after inoculation with FHB. The florets were mounted on a kapton tape and X-ray images were recorded at 18 keV using a 8.75 μηι resolution detector. Figure 2A (top and bottom) shows images for Sumai3, Figure 2B (top and bottom) shows images for FL62R1, and Figure 2C (top and bottom) shows images for Muchmore. Inclined bar indicates the limit of the first fertile floret and (a) indicates the rachilla; [0014] Figures 3A, 3B, and 3C, show images of diseased and healthy florets in the spikelets of wheat cultivars using phase contrast X-ray imaging at 4 days after inoculation with FHB. The spikelets were kept inside a 18 mm diameter falcon tube and X-ray images were recorded at 18 keV using a 8.75 μηι resolution detector. Figure 3 A shows images for Sumai3, Figure 3B shows images for FL62R1, and Figure 3C shows images for Muchmore; [0015] Figures 4A, 4B, and 4C (top and bottom), show phase contrast X-ray images of healthy (1) and infected rachis (2) of wheat cultivars at 4 days after inoculation with FHB. The spikelets were kept inside a 18 mm diameter falcon tube and X-ray images were recorded at 18 keV using a 8.75 μηι resolution detector. Figure 4A (top and bottom) shows images for Sumai3, Figure 4B (top and bottom) shows images for FL62R1, and Figure 4C (top and bottom) shows images for Muchmore.; [0016] Figures 5A and 5B, show averaged triplicates of mid infrared absorbance spectra of wheat florets and rachis at 4 days after inoculation with FHB;

[0017] Figures 6A and 6B, show averaged triplicates of mid infrared absorbance spectra of wheat florets and rachis at 10 days after inoculation with FHB;

[0018] Figures 7A, 7B, 7C, and 7D, show the PCA of the FTIR spectra in the 1800-800 cm^"1 region (A) of the florets of wheat cultivars Sumai3, FL62R1, and Muchmore in two experimental conditions (i.e. in the presence or absence of Fusarium head blight). Loadings plot (PCI, PC2, PC3, and PC4) of the florets of wheat cultivars using FTIR spectra (B). Each point of the plot is the projection of a spectrum in the principal components PCI- PC2 space (C) and PC3-PC4 (D). Empty symbols (o = non-inoculated with FHB, and□ = inoculated with FHB) represent the spectra from 4 DAI and filled ones for 10 DAI (· = non-inoculated with FHB), and■ = inoculated with FHB). The percentages between brackets represent the proportion of variance held in the principal components; and

[0019] Figures 8A, 8B, 8C, and 8D, show the PCA of the FTIR spectra in the 1800-800 cm^"1 region (A) of the rachises of wheat cultivars Sumai3, FL62R1, and Muchmore in two experimental conditions (i.e. in the presence or absence of Fusarium head blight). Loadings plots (PCI, PC2, PC3, and PC4) of the rachises of wheat cultivars using FTIR spectra (B). Each point of the plot is the projection of a spectrum in the principal components PCI- PC2 space (C) and PC3-PC4 (D). Empty symbols (o = non-inoculated with FHB, and□ = inoculated with FHB) represent the spectra from 4 DAI and filled ones for 10 DAI (· = non- inoculated with FHB), and■ = inoculated with FHB). The percentages between brackets represent the proportion of variance held in the principal components. DETAILED DESCRIPTION

Definitions

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. [0021] The term "spectral profile" refers to a series of peaks in the output from any type of spectral analysis instrument, and is known in the art, for example Fourier transform infrared (FTIR) spectroscopy. In a given analysis, the profile can include peaks that can represent one or more components in a sample.

[0022] The term "sample" refers to biological material isolated from a plant or plant part. The sample can contain any suitable biological material, but preferably comprises tissue obtained from a particular plant part. The sample can be isolated according to methods known in the art.

[0023] The term "biomarker" refers to one or more chemicals that are related to a physiological condition. The term further includes the term "spectral biomarker" to refer to spectral features derived from spectral analysis of a sample that correspond to a biomarker(s).

[0024] As used herein, the term "about" refers to an approximately + 10% variation from a given value. It is to be understood that such a variation is always included in any given value provided herein, whether or not it is specifically referred to.

[0025] The methods described herein involve the combination of phase contrast imaging (PCI) with biomolecular imaging in order to offer a combined approach to screening plants for disease resistance. According to certain embodiments, this combined approach can be further applied to detect biomarkers of disease-resistance in plants for use in screening protocols. In such embodiments, the combined approach can be used to target plant tissues exhibiting resistance properties in order to improve proteomic and genomic analysis of the plant, for example. In further embodiments, the present disclosure relates to a combined approach to screening commercial crop plants for disease-resistance. In particular embodiments, the present disclosure relates to a combined approach to screening plants for disease resistance that comprises a combination of synchrotron-based phase contrast imaging and FTIR.

[0026] The methods described herein involve the combination of phase contrast imaging (PCI), specifically X-ray phase contrast imaging, with biomolecular imaging, such as Fourier transform infrared (FTIR) spectroscopy, in order to offer a combined approach to screening plants for disease resistance. According to certain embodiments, the combined approach can be further applied to detect biomarkers of disease-resistance in plants for use in screening protocols. In further embodiments, the present disclosure relates to a combined approach to screening commercial crop plants for disease-resistance. In particular embodiments, the present disclosure relates to a combined approach to screening cereal grain plants for Fusarium head blight (FHB) resistance.

Determining Structural Identifiers of Disease-Resistance - X-Ray Phase Contrast Imaging (PCI)

[0027] X-ray PCI provides a non-destructive technique offering the spatial and temporal resolution, penetrating power and sensitivity to soft tissue that is required to visualize the internal structure of living plants or animals on the scale from millimeters to sub-microns. As such, X-ray PCI is applied to methods of the present disclosure to identify the physiology and internal biomechanical structures of plant samples. In particular, according to embodiments of the present disclosure, the methods involve the use of X-ray PCI to rapidly visualize the structural features of a plant in a non-invasive manner. In this way, comparative observations can be made to identify structural differences between plants.

[0028] According to certain embodiments, methods of the present disclosure comprise X- ray PCI to identify the structural differences between disease-resistant and disease- susceptible plants. Such structural differences can correspond to structural identifiers for disease-resistance. According to certain embodiments, the identification of such structural identifiers can be used to rapidly screen disease-resistant plants exhibiting the structural identifiers. According to other embodiments, such structural identifiers can be used to identify the parts of the plant exhibiting the structural identifiers that may contain biomarkers linked to the disease-resistance property. In this way, methods of the present disclosure offer a targeted approach to identifying biomarkers linked to disease-resistance. [0029] X-ray phase contrast imaging, according to embodiments of the present disclosure, allows the internal structure of plants and/or plant parts to be imaged at the micron or sub- micron scale without destroying the original specimen. X-ray phase contrast imaging techniques capable of achieving such resolution are known to those skilled in the art and may be used in the methods disclosed herein. For example, propagation-based phase contrast techniques, talbot interferometry, refraction-enhanced imaging, and X-ray interferometry, may be applied without limitation. The advantage of these techniques compared to normal absorption-contrast X-ray imaging is higher contrast that makes it possible to see smaller details. Such techniques may further involve synchrotron or microfocus X-ray sources, X-ray optics, and high resolution X-ray detectors in order to provide sufficient resolution. According to certain embodiments, the X-ray phase contrast imaging technique provides a refractive index that is smaller than 1.

[0030] According to certain embodiments of the present disclosure, synchrotron-based phase contrast X-ray imaging is used to visualize the structural features of a plant or plant part that may be distinctive of resistance. According to such embodiments, synchrotron- based phase contrast X-ray imaging is used to identify structural identifiers of resistance. According to further embodiments, synchrotron-based phase contrast X-ray imaging is used to identify plant tissue exhibiting structural identifiers of resistance.

Identifying Spectral Biomarkers of Disease-Resistance - Biomolecular Imaging [0031] The methods described herein further involve the use of biomolecular imaging of plants and/or plant parts that have been determined to exhibit structural identifiers of resistance, identified by X-ray phase contrast imaging, to further identify disease-resistance biomarkers that may be present in the plant exhibiting the structural identifiers. According to certain embodiments of the present disclosure, plants and/or plant parts, that have been determined by X-ray phase contrast imaging to exhibit structural identifiers, are further analyzed by Fourier transform infrared (FTIR) spectroscopy to identify disease-resistance biomarkers. FTIR generates a spectrum by the vibrations of bonds within chemical functional groups that can be considered as a biochemical or metabolic "fingerprint" of the sample. By assessing the infrared absorption peak width, position, and intensity, the configuration of molecular functional assemblies in a sample can be evaluated. [0032] By combining biomolecular imaging such as FTIR with X-ray phase contrast imaging, the methods according to the present disclosure offer a rapid, non-invasive, method for screening plants to identify resistance to a disease. In other embodiments, the combined approach offers a targeted approach to identifying possible biomarkers linked to disease- resistance.

Screening Method For Selection of Disease-Resistant Plant Cultivars

[0033] According to embodiments of the present disclosure, X-ray phase contrast imaging and biomolecular imaging are combined in a screening method for selection of disease- resistant plant cultivars. According to such methods, candidate plants or plant parts are subjected to phase contrast X-ray imaging to visualize structural features of the plant or plant part. According to certain embodiments of the present disclosure, synchrotron-based PCI is used. According to such embodiments, synchrotron-based PCI is carried out at energy levels ranging from 8 to 40 keV. According to some embodiments, the synchrotron PCI is carried out at an energy level ranging from 10 to 35 keV. According to other embodiments, the synchrotron PCI is carried out at an energy level ranging from 15 to 30 keV. According to further embodiments, the synchrotron PCI is carried out at an energy level ranging from 20 to 35 keV. According to other embodiments, the synchrotron PCI is carried out at an energy level of 18 keV.

[0034] From the PCI imaging, the structural features of the candidate plant or plant part can be compared to the corresponding structural features of a reference plant known to have resistance to the disease of interest. Comparison of the structural features of the candidate plant or plant part with the reference plant allows structural identifiers of resistance to the disease to be identified. In some embodiments, the structural features of the candidate plant or plant part can be compared to known reference structural identifier data. For example, structural identifiers can include structural features that have been determined to be characteristic of a plant known to be resistant to the disease of interest. Such structural identifiers may already be known in the art or may be elucidated using methods described herein. According to certain embodiments, the structural identifiers may be related to the floret of the plant. According to other embodiments, the structural identifiers may be related to the rachis of the plant. According to further embodiments, the structural identifiers may be related to both the florets and the rachis of the plant.

[0035] In certain embodiments, the structural identifiers of resistance to the disease of interest can include one or more structural identifiers selected from the group consisting of florets having high density internal structures, rachis having well-defined and closed intemode structures, and rachis having well-defined edges. As described herein, such structural identifiers correspond to the plant's resistance mechanism to the particular disease pathogen.

[0036] The candidate plant or plant part, according to embodiments of the present disclosure, can be any commercial crop plant of interest. For example, and without limitation, commercial crop plants of interest can include food crops (e.g., cereal grains and pulses); feed crops (e.g., grasses and legumes); fibre crops (e.g., cotton and hemp); oilseed crops (e.g., cottonseed, corn, canola); ornamental crops (e.g., dogwood, azalea); and industrial and secondary crops (e.g., rubber and tobacco).

[0037] According to some embodiments of the present disclosure, the candidate plant or plant part is a cereal grain including members of the grass family Poaceae. Such members can be grown for the edible starchy components of its grain and include, without limitation, plants such as wheat, barley, maize, oats, rice, rye, sorghum, and millet. According to further embodiments, the cereal grain plants include wheat, barley, rice, oats, and maize. In a preferred embodiment, the cereal grain plant is wheat.

[0038] According to other embodiments, the candidate plant or plant part is an oilseed crop grown for oil production and include, without limitation, soybean, peanut, canola, sunflower, safflower, and flax.

[0039] According to embodiments, methods of the present disclosure enable plants to be screened for resistance to a disease pathogen that can be fungal, viral, or bacterial. Examples of viral diseases include but are not limited to Agropyron mosaic, Barley stripe mosaic, Barley stripe mosaic, Barley yellow dwarf, Barley yellow streak mosaic, Barley yellow striate mosaic, Brome mosaic, Cereal northern mosaic, Cereal tillering, Chloris striate mosaic, Cocksfoot mottle, Eastern wheat striate, Enanismo, High plains disease, Maize streak, Northern cereal mosaic, Oat sterile dwarf, Rice black-streaked dwarf, Rice hoja blanca, Russian winter wheat mosaic, Seedborne wheat yellows, Tobacco mosaic, Wheat American striate mosaic, Wheat chlorotic streak, Wheat dwarf, Wheat European striate mosaic, Wheat soilborne mosaic, Wheat soilbome yellow mosaic, Wheat spindle streak mosaic, Wheat spot mosaic, Wheat streak mosaic, Wheat striate mosaic, Wheat yellow leaf, Wheat yellow mosaic.

[0040] Examples of bacterial diseases include but are not limited to Bacterial leaf blight {Pseudomonas syringae), Bacterial mosaic (Clavibacter michiganensis), Bacterial sheath rot {Pseudomonas fuscovaginae), Basal glume rot {Pseudomonas syringae), Black chaff {Xanthomonas campestris), Pink seed {Erwinia rhapontici), Spike blight {Rathayibacter tritici).

[0041] Examples of fungal diseases include but are not limited to Alternaria leaf blight

(Alternaria triticina), Anthracnose (Colletotrichum graminicola, Glomerella graminicola), Ascochyta leaf spot (Ascochyta tritici), Aureobasidium decay (Microdochium bolleyi, Aureobasidium bolleyi), Black head molds (Alternaria spp., Cladosporium spp., Epicoccum spp., Sporobolomyces spp., Stemphylium spp.), Cephalosporium stripe (Cephalosporium gramineum), Common bunt (Tilletia tritici, Tilletia laevis), Common root rot (Cochliobolus sativus, Bipolaris sorokiniana), Cottony snow mold (Coprinus psychromorbidus), Crown rot (Fusarium spp., Fusarium pseudograminearum, Gibber ella zeae, Fusarium graminearum Group II, Gibberella avenacea, Fusarium avenaceum, Fusarium culmorum), Dilophospora leaf spot (Dilophospora alopecuri), Downy mildew (Sclerophthora macrospora), Dwarf bunt (Tilletia controversa), Ergot (Claviceps purpurea, Sphacelia segetum), Eyespot (Tapesia yallundae, Ramulispora herpotrichoides , Tapesia acuformis, Ramulispora acuformis), False eyespot (Gibellina cerealis), Flag smut (Urocystis agropyri), Foot rot (Fusarium spp), Halo spot (Pseudoseptoria donacis), Karnal bunt (Tilletia indica), Leaf rust (Puccinia triticina), Leptosphaeria leaf spot (Phaeosphaeria herpotrichoides, Stagonospora sp.), Loose smut (Ustilago tritici, Ustilago segetum var. nuda, Ustilago segetum var. avenae), Microscopica leaf spot (Phaeosphaeria microscopica), Phoma spot (Phoma spp., Phoma glomerata, Phoma sorghina), Pink snow mold (Microdochium nivale, Fusarium nivale), Platyspora leaf spot (Clathrospora pentamera), Powdery mildew (Erysiphe graminis f.sp. tritici, Blumeria graminis, Oidium monilioides), Pythium root rot (Pythium aphanidermatum, Pythium arrhenomanes , Pythium graminicola, Pythium myriotylum, Pythium volutum), Rhizoctonia root rot (Rhizoctonia solani, Thanatephorus cucumeris), Ring spot (Wirrega blotch Pyrenophora seminiperda, Drechslera wirreganensis), Scab (Fusarium spp., Gibberella zeae, Fusarium graminearum Group II, Gibberella avenacea, Fusarium avenaceum, Fusarium culmorum, Microdochium nivale, Fusarium nivale, Monographella nivalis), Sclerotinia snow mold (Myriosclerotinia borealis), Sclerotium wilt (Sclerotium rolfsii, Athelia rolfsii), Septoria blotch (Septoria tritici), Sharp eyespot (Rhizoctonia cerealis), Snow rot (Pythium spp.), Southern blight (Sclerotium rolfsii), Speckled snow mold (Typhula idahoensis), Spot blotch (Cochliobolus sativus), Stagonospora blotch (Phaeosphaeria avenaria f.sp. triticae), Stem rust (Puccinia graminis), Stripe rust (Puccinia striiformis), Tan spot (Pyrenophora tritici-repentis), Tar spot (Phyllachora graminis), Wheat Blast (Magnaporthe grisea), Zoosporic root rot (Lagena radicicola).

[0042] According to a certain embodiment, methods of the present disclosure enable plants to be screened for resistance to Scab otherwise known as Fusarium head blight (FHB)

(Fusarium spp., Gibberella zeae, Fusarium graminearum, Gibberella avenacea, Fusarium avenaceum, Fusarium culmorum, Microdochium nivale, Fusarium nivale, Monographella nivalis).

[0043] Once the candidate plant or plant part has been determined by X-ray phase contrast imaging, for example synchrotron PCI, to exhibit structural identifiers linked to the disease- resistance of interest, a sample from the plant or plant part is taken and subjected to biomolecular imaging. According to embodiments of the present disclosure, X-ray phase contrast imaging is combined with FTIR spectroscopy. According to further embodiments, synchrotron PCI is combined with FTIR spectroscopy in the screening method of the present disclosure. According to other embodiments, synchrotron PCI is combined with FTIR spectroscopy in the screening method for selection of disease-resistant plant cultivars of the present disclosure.

[0044] According to embodiments in which FTIR spectroscopy is used, a mid-infrared spectral profile for the sample is compared to known reference spectral data for a comparable plant having disease resistance. According to certain embodiments, the plant is a commercial crop plant. Alternatively, the spectral profile of the sample can be compared to the spectral profile of a sample obtained from the reference plant or plant part. Identification of spectral biomarkers of resistance to the disease confirm resistance of the candidate plant to the disease.

[0045] According to embodiments of the present disclosure, a sample is taken from the plant or plant part exhibiting the structural identifiers determined by X-ray phase contrast imaging. According to certain embodiments, the sample is taken from the florets of the plant or plant part. According to other embodiments, the sample is taken from the rachis of the plant or plant part. According to further embodiments, the sample is taken from both the florets and the rachis of the plant or plant part.

[0046] The infrared spectrum of the sample is recorded in the mid-infrared range using FTIR. According to embodiments of the present disclosure, the infrared spectrum is recorded in the mid-infrared range of 4000-800 cm^"1 to confirm the presence of spectral biomarkers of resistance to the disease of interest. According to certain embodiments, a sample from the florets of the candidate plant is screened for the presence of any one or more spectral biomarkers corresponding to a very broad band in the region of 3650-3000 cm^"1 with peak near 3260 cm^"1, and a change of amide I a-helix peak (1655 cm^"1) to β-sheet (1634-37 cm^"1). According to further embodiments, a sample from the rachis of the candidate plant is screened for the presence of any one or more spectral biomarkers corresponding to a peak at 1530-1563 cm^"1 denoting amide II, an increased shift in peak of 1323 cm^"1 denoting cellulose, and an increased shift in peak of 1245 cm^"1 denoting hemi cellulose.

Method To Identify Spectral Biomarkers For Disease-Resistance

[0047] According to embodiments of the present disclosure, X-ray phase contrast imaging and biomolecular imaging are combined in a method for detecting and identifying biomarkers of disease-resistance in plants for use in screening protocols. According to certain embodiments of the present disclosure, synchrotron PCI is used and combined with biomolecular imaging to detect and identify biomarkers of disease-resistance. According to further embodiments, synchrotron PCI is combined with FTIR spectroscopy to detect and identify biomarkers of disease-resistance.

[0048] The method comprises exposing a disease-resistant and a disease-susceptible plant to a disease-causing pathogen using methods known in the art, and subjecting the exposed plants or part of the plants to X-ray phase contrast imaging, for example, synchrotron-based phase contrast X-ray imaging. In embodiments utilizing synchrotron PCI, such embodiments can be carried out at energy levels ranging from 8 to 40 keV. According to some embodiments, the synchrotron PCI is carried out at an energy level ranging from 10 to 35 keV. According to other embodiments, the synchrotron PCI is carried out at an energy level ranging from 15 to 30 keV. According to further embodiments, the synchrotron PCI is carried out at an energy level ranging from 20 to 35 keV. According to other embodiments, the synchrotron PCI is carried out at an energy level of 18 keV.

[0049] The structural features of each plant or plant part can then be visualized and compared in order to identify differences in structural features caused by the disease-causing pathogen. The structural differences can be inferred to indicate structural identifiers of resistance to the disease-causing pathogen.

[0050] According to certain embodiments of the present disclosure, the plant or plant part is a commercial crop plant. According to other embodiments of the present disclosure, the plant or plant part includes oilseed crop plants. According to further embodiments of the present disclosure, the plant or plant part is a cereal crop plant including members of the grass family Poaceae. Such members can be grown for the edible starchy components of its grain and include, without limitation, plants such as wheat, barley, maize, oats, rice, rye, sorghum, and millet. According to further embodiments, the cereal grain plants include wheat, barley, rice, oats, and maize. In a preferred embodiment, the cereal grain plant is wheat.

[0051] Biomarkers linked to disease resistance to a fungal, viral, or bacterial disease pathogen can be identified according to embodiments of the present disclosure. According to certain embodiments, the disease pathogen can be viral and includes but is not limited to Agropyron mosaic, Barley stripe mosaic, Barley stripe mosaic, Barley yellow dwarf, Barley yellow streak mosaic, Barley yellow striate mosaic, Brome mosaic, Cereal northern mosaic, Cereal tillering, Chloris striate mosaic, Cocksfoot mottle, Eastern wheat striate, Enanismo, High plains disease, Maize streak, Northern cereal mosaic, Oat sterile dwarf, Rice black- streaked dwarf, Rice hoja blanca, Russian winter wheat mosaic, Seedbome wheat yellows, Tobacco mosaic, Wheat American striate mosaic, Wheat chlorotic streak, Wheat dwarf, Wheat European striate mosaic, Wheat soilborne mosaic, Wheat soilbome yellow mosaic, Wheat spindle streak mosaic, Wheat spot mosaic, Wheat streak mosaic, Wheat striate mosaic, Wheat yellow leaf, Wheat yellow mosaic.

[0052] According to other embodiments, the disease pathogen can be bacterial and includes but is not limited to Bacterial leaf blight {Pseudomonas syringae), Bacterial mosaic (Clavihacter michiganensis), Bacterial sheath rot {Pseudomonas fuscovaginae), Basal glume rot {Pseudomonas syringae), Black chaff (Xanthomonas campestris), Pink seed {Erwinia rhapontici), Spike blight {Rathayibacter tritici).

[0053] According to further embodiments, the disease pathogen can be fungal and includes but is not limited to Alternaria leaf blight (Alternaria triticina), Anthracnose (Colletotrichum graminicola, Glomerella graminicola), Ascochyta leaf spot (Ascochyta tritici), Aureobasidium decay (Microdochium bolleyi, Aureobasidium bolleyi), Black head molds (Alternaria spp., Cladosporium spp., Epicoccum spp., Sporobolomyces spp., Stemphylium spp), Cephalosporium stripe (Cephalosporium gramineum), Common bunt (Tilletia tritici, Tilletia laevis), Common root rot (Cochliobolus sativus, Bipolaris sorokiniana), Cottony snow mold (Coprinus psychromorbidus), Crown rot (Fusarium spp., Fusarium pseudograminearum, Gibberella zeae, Fusarium graminearum Group II, Gibberella avenacea, Fusarium avenaceum, Fusarium culmorum), Dilophospora leaf spot (Dilophospora alopecuri), Downy mildew (Sclerophthora macrospora), Dwarf bunt (Tilletia controversa), Ergot (Claviceps purpurea, Sphacelia segetum), Eyespot (Tapesia yallundae, Ramulispora herpotrichoides , Tapesia acuformis, Ramulispora acuformis), False eyespot (Gibellina cerealis), Flag smut (Urocystis agropyri), Foot rot (Fusarium spp.), Halo spot (Pseudoseptoria donacis), Karnal bunt (Tilletia indica), Leaf rust (Puccinia triticina), Leptosphaeria leaf spot (Phaeosphaeria herpotrichoides, Stagonospora sp.), Loose smut (Ustilago tritici, Ustilago segetum var. nuda, Ustilago segetum var. avenae), Microscopica leaf spot (Phaeosphaeria microscopica), Phoma spot (Phoma spp., Phoma glomerata, Phoma sorghina), Pink snow mold (Microdochium nivale, Fusarium nivale), Platyspora leaf spot (Clathrospora pentamera), Powdery mildew (Erysiphe graminis f.sp. tritici, Blumeria graminis, Oidium monilioides), Pythium root rot (Pythium aphanidermatum, Pythium arrhenomanes, Pythium graminicola, Pythium myriotylum, Pythium volutum), Rhizoctonia root rot (Rhizoctonia solani, Thanatephorus cucumeris), Ring spot (Wirrega blotch Pyrenophora seminiperda, Drechslera wirreganensis), Scab (Fusarium spp., Gibberella zeae, Fusarium graminearum Group II, Gibberella avenacea, Fusarium avenaceum, Fusarium culmorum, Microdochium nivale, Fusarium nivale, Monographella nivalis), Sclerotinia snow mold (Myriosclerotinia borealis), Sclerotium wilt (Sclerotium rolfsii, Athelia rolfsii), Septoria blotch (Septoria tritici), Sharp eyespot (Rhizoctonia cerealis), Snow rot (Pythium spp.), Southern blight (Sclerotium rolfsii), Speckled snow mold (Typhula idahoensis), Spot blotch (Cochliobolus sativus), Stagonospora blotch (Phaeosphaeria avenaria f.sp. triticae), Stem rust (Puccinia graminis), Stripe rust (Puccinia striiformis), Tan spot (Pyrenophora tritici-repentis), Tar spot (Phyllachora graminis), Wheat Blast (Magnaporthe grised), Zoosporic root rot (Lagena radicicola).

[0054] According to a certain embodiment, the disease pathogen is Scab otherwise known as Fusarium head blight (FHB) {Fusarium spp., Gibberella zeae, Fusarium graminearum, Gibberella avenacea, Fusarium avenaceum, Fusarium culmorum, Microdochium nivale, Fusarium nivale, Monographella nivalis).

[0055] Samples are then obtained, according to known methods, from each plant part identified as having structural identifiers of resistance to the disease-causing pathogen. Biomolecular imaging, according to certain embodiments FTIR spectroscopy, is conducted on each sample and a mid-infrared spectral profile is determined for each sample. Comparison of the mid-infrared spectral profile of the samples taken from the disease- resistant and the disease-susceptible plants allows spectral biomarkers of resistance to the disease-causing pathogen to be identified.

[0056] According to embodiments of the present disclosure, the infrared spectrum is recorded in the mid-infrared range of 4000-800 cm^"1 to determine the presence of spectral biomarkers of resistance to the disease of interest.

[0057] It is contemplated that any embodiment discussed herein can be implemented with respect to any method or composition of the disclosure, and vice versa. Furthermore, compositions and kits of the disclosure can be used to achieve methods of the disclosure.

[0058] To gain a better understanding of the disclosure described herein, the following examples are set forth. It will be understood that these examples are intended to describe illustrative embodiments of the disclosure and are not intended to limit the scope of the disclosure in any way.

EXAMPLES

[0059] Fusarium graminearum Schw., is the most common causal agent of Fusarium head blight (FHB) in North America and many other parts of the world. This destructive disease, commonly known as scab, affects wheat, barley and other small grains including rice, oats, and maize. The pathogen poses a two-fold threat: first, infested cereals show significant reduction in seed quality and yield due to discolored, shriveled "tombstone" kernels, and secondly, scabby grain is often contaminated with trichothecene and estrogenic mycotoxins making it unsuitable for food or feed 1997, (McMullen, M. et al, Scab of wheat and barley: A re-emerging disease of devastating impact. Plant Disease 1997, 81 : 1340-1348).

[0060] Plant cultivars highly resistant to the disease or tolerant to the toxin currently are not available and the use of fungicides for controlling the disease is limited by cost, difficulty in efficient application to wheat heads and an incomplete understanding of factors that influence disease development. Consequently, it is difficult to implement Fusarium resistance into breeding programs.

[0061] For these reasons, FHB -resistance in wheat was selected as an exemplary candidate for testing the screening method described in the present disclosure. According to one aspect, the screening method of the present disclosure was used to compare structural and molecular changes in wheat resulting from Fusarium infection to determine the resistance mechanisms to FHB. In another aspect, the screening method of the present disclosure was used to identify spectral biomarkers for selecting resistance cultivars against FHB.

EXAMPLE 1: FUNGAL MATERIAL AND INOCULUM PREPARATION

[0062] Wild-type Fusarium gramineraum (isolate DAOM 180379 from the Canadian collection of fungal cultures, Ottawa, Ont), which was transformed to constitutively express Green Fluorescent Protein (GFP) in both macroconidia and hyphae was used in this study.

Fresh inoculum was taken from the stored type culture at monthly intervals. For the production of macroconidia, a plug of actively growing GFPFg (Green Fluorescent Protein

Fusarium gramineraum) was placed in the center of a petri dish containing Soft Nutrient Agar (SNA). Plates were placed under a combination of fluorescent and UV lights for 5 days at 23°C. Macroconidia were harvested by pouring a small amount of sterile water over the culture in the petri dish and then by either gently scraping the surface with a bent glass pipette or washing with a gentle stream of water, using a pipette. A working concentration of approximately 2500 macroconidia/mL was attained by concentrating the suspension or by dilution with sterile water as required. A concentration of 2 ^χ 10⁴ macroconidia/mL was used for inoculation.

EXAMPLE 2: PLANT MATERIAL AND INFECTION PROCEDURE

[0063] All wheat samples both infected and non-infected by FHB were generously donated by the researchers at the National Research Council of Saskatoon. All experiments were conducted in the greenhouse due to restrictions on inoculating with a transformed fungus in the field. Seeds of resistant 'Sumai3' (referred to as Sumai3 or SU), tolerant Canadian germplasm 'FL62R1 ' (or FL) and susceptible 'Muchmore' (or MM) wheat cultivars were sown in peat pots (diameter, 12.7 cm) and maintained in a growth cabinet at 20°C: 15°C day: night cycle, with 16 h of light per day until flowering. Pots were watered by hand at the base of the plants. When the heads were at the mid-flowering stage (approximately 60% of anthers extruded), 5 spikelets midway along the spike were point inoculated, inside the lemma, with a macroconidial suspension. After inoculation, the plants were moved to an enclosed greenhouse and maintained in the same day-night cycle duration and temperature. [0064] Following inoculation, wheat heads of all cultivars (Sumai3, FL62R1, and Muchmore) were assessed after 4 and 10 Days After Innoculation (DAI) for the number of diseased spikelets (Figure 1). At 4 DAI, the disease symptom was highly significant in the susceptible cultivar Muchmore compared to the resistant reference Sumai3, and the Canadian tolerant cultivar FL62R1. At 10 DAI, the number of infected spikelets increased in all cultivars but the highest number was still recorded for Muchmore as compared to Sumai3. FL62R1 seems to be less affected by the fungus than Muchmore and exhibits a resistance close to that observed for Sumai3.

[0065] Diseased spikelets were expressed as means ± standard error of three independent infected spikes. Data were analyzed by using one-way ANOVA test of the statistical analysis system (SAS Institute, version 9.1, Cary, NC, USA). Mean values were compared using Fisher's LSD test at statistical significance P=0.05.

EXAMPLE 3: SYNCHROTRON BASED PHASE CONTRAST X-RAY IMAGING (PCI) [0066] Synchrotron PCI was used to compare the structural differences among resistant cultivar Sumai3, tolerant or Canadian resistant germplasm FL62R1, and susceptible cultivar Muchmore.

[0067] Fresh spikelets were excised from the plants ~ 4 hours before X-ray imaging and the cut spikelets were stored in a plastic bag at room temperature. X-ray images of wheat spikelets were recorded using the phase contrast imaging technique at the Biomedical and Imaging Therapy (BMIT) beamline at the Canadian Light Source (44 Innovation boulevard, Saskatoon, SK, S7N 2V3, Canada). The X-ray energy was selected to be 18 keV, the lowest possible in the beamline and a 0.5 mm thick aluminum filter was used before the monochromator to reduce the heat load on the monochromator. An 8.75 μηι resolution detector was used and most images were recorded in less than a second exposure time. Two dimensional transmission images (projection images) were collected and the projection images were corrected for the dark signal from the detector (dark signal correction) and flat signal (flat-field correction) for imperfections from the monochromator and scintillator screens. The dark and flat images were collected at the beginning of the imaging session for each spikelet. The spikelets were kept inside a 18 mm diameter falcon tube during imaging to prevent any movement of the spikelet when collecting data along the length of the spikelets.

Structural Differences - Florets

[0068] Scanned wheat heads of three cultivars that contain artificially infected and non- infected florets in the same spikelets with FHB at 4 DAI are shown in Figures 2 and 3. Differences in mass densities and phase contrast signals between healthy and infected spikelets were observed. Healthy florets appear in white colors filled with internal structures while infected ones are largely empty and transparent, perhaps due to loss of water and floret tissues. [0069] The high X-ray energy (18 keV) and the low resolution detector (8.75 μηι) used here are not able to reveal any visible fungal structures such as mycelia of Fusarium graminearum. A total loss of cell viability in infected floret structures such as external and internal epidermis of glumes, and external and intemal epidermis of anthers is revealed by the X-ray images (Figure 4). This phenomenon was more pronounced in Muchmore cultivar compared to the other two cultivars.

[0070] The ovary in an infected Muchmore floret appears to be in a stressed state with the absence of anthers which may be destroyed by the fungus itself, indicating a loss of fertility in the infected spikelets of that cultivar. [0071] Overall, the synchrotron based PCI results have highlighted significant differences between infected and non-infected florets. However it was difficult to precisely characterize real difference between non-inoculated resistant and susceptible cultivars in term of floret structures due to the complexity of the internal structures.

Structural Differences - Rachis [0072] To further elucidate resistance mechanisms and structural differences between three tested germplasms in response to FHB, florets were removed from the healthy and infected spikes of the wheat and rachis alone were imaged (Figure 4). A slower development of necrosis due to FHB was observed in the rachis of Sumai3 followed by FL62R1 at 4 DAI. In contrast, the rachis of Muchmore was completely invaded and altered. [0073] In healthy rachis of different cultivars, phase contrast images show significant differences in the physical and intemal structures of the rachises. The healthy rachis of Sumai3 is more transparent than that of FL62R1 and Muchmore near the floret base, indicating less intemal structures and more cavitations in Sumai3. The intemodes of the rachis joints in resistant cultivars are closed with a well-defined wall (visible bright line). Interestingly, form and thickness of edge of the rachis are different from cultivar to cultivar. In the presence of the fungus, the phase contrast X-ray images show that structures intemal in rachis could be lost or altered. The cavitation (transparent area in phase contrast images and in which water movement occurs in the rachis) becomes thinner in infected rachises with FHB. These characteristic different structures in resistant cultivars compared to moderately resistant and susceptible ones may serve to limit the growth and spread of the fungal mycelium. The structural difference may also reduce the spread of fungal mycelilal mass along with water flow within the rachis, which is considered to be one of the causes of spike blight symptoms. Structural Identifiers of FHB-Resistant Wheat

[0074] The structural identifiers observed for FHB-resistant wheat can be identified as shown in Table 5. The ability to image the internal structural differences in FHB-resistant cultivars confirms the applicability of 2D PCI and 3D PCI as tools for fast screening of resistant cultivars against scab blight. Table 5: Structural identifiers of FHB-resistant wheat

EXAMPLE 4: FTIR SPECTROSCOPY

[0075] The mid-infrared spectrum of a chemical compound provides details of the fundamental vibrations of the groups of its component molecules. The IR spectrum of a biological sample is a weighted spectrum of individual chemical compounds present in that sample (2012, Peiris, K.H.S. et al, Infrared Spectral Properties of germ, pericarp, and endosperm section of Sound wheat Kernels and those damaged by Fusarium graminerum. Applied Spectroscopy 66: 1053-160).

[0076] All FTIR spectroscopy were collected at the mid infrared beamline (01 B 1-1) at the Canadian Light Source Inc., Saskatoon, Canada using the glowbar source (silicon carbide) as the infrared source. The Bruker - IFS 66V/S spectrophotometer (Bruker Optics, Ettlingen, Germany) with a Deuterated triglycine sulphate (DTGS) detector was used for the FTIR measurements.

[0077] The floret and rachis samples of infected and non-infected spikelets were prepared by the method described by Naumann et al. (1991, Naumann, D. et al, The characterization of microorganisms by Fourier -transform infrared spectroscopy (FTIR). IN Modern Techniques for Rapid Microbiological Analysis (ed.) Nelson, W.H. New York: VCH Publishers, 43-96). Floret and rachis samples were first dried using a freeze drier and ground to a fine powder. About 1 mg of freeze dried and powdered sample was homogenized with about 2.0 mg of dry potassium bromide (KBr) using pestle and mortar and made into a pellet. Transmission infrared spectrum was obtained from the finally prepared pellet for replicate samples. Each IR spectrum was recorded in the mid infrared range of 4000-800 crrf ¹ wavenumbers at a spectral resolution of 2 cm^-1. Each sample spectrum is an average of 64 scans and pure KBr spectra (average of 512 scans) was recorded for normalizing all sample spectra. The normalized spectra were base line corrected using the rubber band correction (64 points) and vector normalized using the OPUS software (version 7.0, Bruker Optics Inc., Billerica, MA). All FTIR spectra shown here are the average spectra from three replicates. The FTIR peaks cited in Tables 1 to 4 were determined using the Quick Peaks routine in OriginPro with the settings of local maximum at 0% threshold height, no baseline, and area at Y=0.

Biochemical Changes - Florets

[0078] Obvious spectral differences in the mid-infrared region (4000-800 cm^"1) between healthy and diseased florets and rachis of wheat spikelets of different germplasms are shown in Figure 4. The summary of the characteristic peaks and their assignments in reference to previous findings are shown in Tables 1 to 4 (2013, Szymanska-Chargot, M. et al, Use of FTIR spectra and PCA to the bulk characterization of cell wall residues of fruits and vegetables along a fraction process. Food Biophysics 8:29-42; 2012,Taoutaou, A. et al, New markers for potato late blight resistance and susceptibility using FTIR Spectroscopy. Not. Bot. Horti. Agrobo. 40: 150-154; 2010, Erukhimovitch, V. et al, Direct identification of Potato 's fungal phytopathogens by Fourier -transform infrared (FTIR) microscopy. Spectroscopy 24:609-619; 2009, Mann, D.G.J, et al, Rapid assessment of Lignin content and structure in switchgrass (Panicum virgatum L.) grown under different environmental conditions. Bioenerg. Res. 2:246-256; 2005, Martin, J.A. et al, Fourier transform-infrared spectroscopy as a new method for evaluating host resistance in the Dutch elm disease complex. Tree Physiol. 25: 1331-1338; 2001, Kacurakova, M. et al, Developments in mid- infrared FT -IR spectroscopy of selected carbohydrates. Carbohydrates Polymers 44:291-303; 2000, Kacurakova, M. et al, FT-IR study of plant cell wall model compounds :pectic polysaccharides and hemicelluloses . Carbohydrates polymers 43: 195-203; 1999, Bertoluzza, A. et al, Molecular Monitoring of horse chestnut leaves affected with biotic and abiotic disorders. Journal of Plant Pathology 81 : 89-94).

[0079] At 4 DAI, spectra of diseased and healthy floret of different wheat cultivars are shown in Figure 5A. Apart from the intense but unspecific stretching bands for OH groups (3394-3407 cm^"1) and alkyl C-H groups (-2921 cm^"1), the spectra showed a prominent peak with a maximum near 1049-1032 cm^"1 attributable to C-0 vibrations in the cellulose pyranoside units. For each cultivar, the groups of samples without inoculation (controls) were clearly differentiated from the samples inoculated with FHB. Finger print spectral regions showed intense peaks for carbonyl compounds C=0 groups (1733 cm^"1), C-H bending in alkyl groups (1420 cm^"1), and presence of proteins (amide I at about 1655 cm^"1) (2001, Dorado, J. et al, Infrared spectroscopy analysis of hemp (Cannabis sativa) after selective delignification by Bjerkandera sp. at different nitrogen levels. Enzyme Microb. Technol. 28:550-559).

[0080] A well-defined partem was obtained with characteristic peaks centered at about 1546-1566 and 1515cm^"1 for aromatic skeletal vibrations and additional peaks at 1420, 1375 and 1246 cm^"1 that coincide with the different methoxy phenolic substitutions in the aromatic units of lignin (2001, Fengel, D. et al., Wood, Chemistry, Ultrastructure, Reactions. Walter de Gruyter, Berlin, 1989:613p).

[0081] In the resistant cultivar Sumai3, the relatively large shifts in characteristic peaks of amide III, cellulose, and phosphate (1246.5 shift to 1256.8 cm^"1, 1038.9 shift to 1049.1 cm^"1, and 1158.1 shift to 1161.5 cm^"1) towards high wavenumbers after inoculation may reflect increased metabolic activity in the host compared with susceptible cultivar Muchmore in which peaks (1423.5 and 1052.5 cm^"1 are shifted to 1409.9 and 1049.1 cm^"1, respectively) of cellulose were shifted towards lower wavenumber. This increased metabolic activity in Sumai3 is probably linked with the formation of defense compounds, such as those involved in the reinforcement of the cell walls. [0082] In all cultivars, the amide II peaks at 1546-1566 cm^"1 disappeared in the presence of the fungus. The a-helix structure of amide I located around 1655 cm^"1 in the controls have changed in diseased plants to β-sheet (1634-37 cm^"1), indicating a change in proteins that may be used by the fungus for feeding for its survival. At the same time, no other difference was observed between healthy florets of three germplasms, except the peak located around 1540- 1570 cm^"1 which was more intense for Sumai3 (about 1566.5 cm^"1).

[0083] In the finger print spectral region for carbohydrate groups (1000-800 cm^"1), no significant changes were detected following pathogenic infection or between healthy florets of three cultivars.

[0084] At 10 DAI, an important shift in amide I peak was observed only for both Sumai3 (1631.2 to 1638 cm^"1) and Muchmore (1631.2 to 1659 cm^"1) (Figure 6A). As the disease progresses from 4 to 10 DAI, the amide I peak remained same in Sumai3 and it shifted (1634. 6 to 1631.2 cm^"1) in FL62R1 and Muchmore (1637. 9 to 1658.4 cm^"1). Similarly, an important shift for cellulose peak in Muchmore (1406.6 to 1423.5 cm^"1), Sumai3 (1403.1 to 1420.1 cm^{" l}), FL62R1 (1413.3 to 1406.5 cm^"1) was observed in infected floret. Also a shift of carbohydrate peak from 1055.9 to 1038.9 cm^"1 was observed in Muchmore while a slight shift towards high wavenumber was observed in FL62R1 for the same peak.

Biochemical Changes - Rachis

[0085] Figure 5B shows the FTIR spectra of a sample composed of infected and healthy rachis of wheat cultivars examined 4 DAI. The spectra have a trend similar to that of wheat floret characterized by the same absorption peaks as described above. The broad peak at about 3390-3407 cm^"1 is due to the stretching vibration of OH functional groups of water, alcohols, and phenols. The peak located at about 3002-3020 cm^"1 is attributed to C-H lipid groups and the doublet at about 2928-2850 cm^"1 is attributed to asymmetric stretching modes of the CH₂ methylene group, the common plant product. [0086] At 4 DAI, slight biochemical changes were observed between resistant, moderate resistant, and susceptible cultivars. The most important changes were: the disappearance of amide II peak at about 1550 cm^"1 in both cultivars, Muchmore and FL62R1 after pathogenic infection; the shift of amide II peak in Sumai3 (1566.5 to 1559.7 cm^"1); the shift of cellulose CH₂ symmetric bending peak from 1426 to 1423 cm^"1 in Muchmore; and the shift of amide III peak (1328.2 to 1331.6 cm^"1) in both Sumai3 and Muchmore. The peak at 1249.9 cm^"1 (linked to PO^"2 asymmetric phosphate vibration) shifted to higher wavenumber (1260.2 cm^"1) only in Sumai3 followed by FL62R1 (1249.9 to 1246.5 cm^"1) and no change was observed in Muchmore following fungus infection. [0087] At 10 DAI, the peak intensity of amide II (1563.1 cm^"1) was persistent in resistant cultivar Sumai3 even after pathogenic infection as observed at 4 DAI (Figure 6B). The amide III was shifted in Muchmore from 1331 cm^"1 (4 DAI) to 1321.4 (10 DAI). Other peaks were still persistent, even after 10 DAI in Sumai3 and other cultivars. An important difference after infection was the appearance of a peak at 1546.1 cm^"1 for both FL62R1 and Muchmore, and the peak intensity at 1192.1 cm^"1 only in FL62R1.

EXAMPLE 5: PRINCIPAL COMPONENT ANALYSIS (PCA)

[0088] Principal Component Analysis (PCA) is one of the most common methods used in IR spectroscopy to look at the spread of the data. By performing a PCA the variance-covariance structure of p variables through k linear combinations, called Principal Components (PC), can be explained. The purpose of PCA is to reduce the number of variables and provide an easy graphical representation on the spread of the data (2013, Szymanska-Chargot, M. et al, Use of FTIR spectra and PCA to the bulk characterization of cell wall residues of fruits and vegetables along a fraction process. Food Biophysics 8:29-42). Geometrically, a PCA is a vector space transform. It can be seen as a transformation applied to the coordinate axis rather than to the data set. The new coordinates are the principal components such that the first PC represents the direction of greatest variability, the second greatest variance lies on the second PC and so on. This method is especially useful in the interpretation of FTIR spectra, which show peak diversity and complication depending on the source of the sample. The Unscrambler 10.1 (Camo Software AS., Norway) was used for performing PCA. Each wavelength of FTIR was treated as an equally weighted variable in this analysis. Principal Component Analysis (PCA) - Components in Floret and Rachis

[0089] In all cases and independent of the length of after inoculation periods, PCA revealed a significant impact of FHB on floret and rachis of wheat cultivars and distinguished two clusters between infected and non-infected floret and rachis of each wheat cultivar. In most cases, PCI explained more variation between both clusters for each wheat cultivar (data not shown); suggesting that PCA coupled with infrared spectroscopy is able to discriminate between infected and non-infected samples at an early stage of the development of pathogen infection. As demonstrated previously, the important impact of FHB on floret and rachis of wheat cultivars was observed in the IR spectra ranging from 1800 to 800 cm^"1. Therefore, PCA was done in the spectral range from 1800 to 800 cm^"1 to discriminate cell wall compounds between cultivars and between infected or non-infected wheat with FHB, independent of timing of inoculation for both floret and rachis.

Discrimination of Components - Wheat Florets

[0090] Spectra from control and inoculated floret of Sumai3, FL62R1, and Muchmore after inoculation periods of 4 and 10 days, were compared using PCA (Figure 7). The negative peak at around 1384 cm^"1 in the spectra is due to the variation in the thickness of KBr pellets made as KBr has strong absorption peak at this wavenumber (Figure 7A). The total sample variation (73%) in wheat floret was explained by principal components 1 and 2 (Figure 7C). The score scatter plot of PCI vs. PC2 indicates that the infected florets are grouped along the PC2 axis and scattered along the PCI axis. The scores of both infected and non-infected Sumai3, and non-infected Muchmore are grouped along the positive side of PCI whereas those of non-infected FL62R1 are spread along positive and negative sides, suggesting important differences between these three wheat germplasms.

[0091] Independent of inoculation periods, score plot shows that both infected florets of FL62R1 and Muchmore are grouped in the negative side of PC2 and PCI, and significantly different from those of Sumai3. This suggests that both susceptible cultivars are affected by FHB more than the resistant cultivar, Sumai3. PCI loadings indicated that positive influence on floret scores had values which could be assigned to pectin (around 920, 855.4, and 816.8 cm^"1). The negative impact had values indicating pectin (1737.8, 1267.2, 969.2 cm^"1), amide I (1688.6 cm^"1), amide II (1586.8 and 1544.9 cm^"1), cellulose (1516, 1463.9, 1374.2, 1317.3, 1172.7, 1117.7, 1036.7, and 896.9 cm^"1), and xyloglucan (1066.6 cm^"1). These negative scores suggest that changes in infected cultivars of FL62R1 tend to be located in cell wall and polysaccharides groups which were negatively affected by the presence of the fungus.

[0092] PC2 explained a variability of 28%, which positively differentiated the non-infected floret at 4 days from that at 10 days, and the majority of infected florets with FHB. The loadings in the case of PC2 showed positive values for amide I (1785.1, 1686.7 cm^"1), cellulose (1459.1, 1207.4, 1155.3, and 998.1 cm^"1), xyloglucan (1085.9 cm^"1) (pectin ring and xyloglucan), and pectin (926.8 and 861.2 cm^"1). The negative PC2 loading underlines eleven peaks for hemicellulosic and cellulosic polysaccharides, and pectin (1373, 1319.3, 1601.8, 1508.3, 1473.6, 1451.4, 1401.2, 1260.4, 1068.5, 894.9, 819.7 cm^"1).

[0093] PC3 explained a variability of 12% on scores of wheat floret and separated scores into two clusters of 4 DAI in positive side and 10 DAI in negative side. The PC3 loading indicates that most of positive biochemical changes were found in pectin (1772.5, 1751.3, and 1596cm^"1) and amide I (1660.6 cm^"1) whereas negative changes were located for cellulose (1475.5, 1461, 1406, 1358.8, 1180.4, 1167.8, and 896.9 cm^"1), xyloglucan (1345.3, 1100.3, and 1067.6 cm^"1), pectin (1326.0, 1292.3, 1271.9, 1249.8, 966.3, 856.4, 843.8, and 822.6 cm^"1).

[0094] The PC4 explained only 4% of variability and differentiated the non-inoculated samples and inoculated Sumai3 at 4 and 10 DAI in positive side. The infected FL62R1 and Muchmore were regrouped in negative side of PC4. The positive influence had peaks for pectin (1737.8, 951.8 and 825.5 cm^"1), amide I (1654.9 cm^"1), cellulose (1318.3, 1182.3,

1117.7, 1097.4, 1068.5, and 893 cm^"1), whereas the peaks that implied negative influence were amide I (1680.8 cm^"1), amide II (1575.7 cm^"1), cellulose and hemicellulose (1485.1,

1440.8, 997.2), and pectin (1150.5, 1082.0, 923.9, and 862.1 cm^"1). Discrimination of Components - Wheat Rachis

[0095] The total sample variation (82%) in the rachis of the wheat cultivars was explained by principal components 1 and 2 (Figure 8). The PCI (66%) clearly distinguish infected rachis of Muchmore (4 and 10 DAI) and the infected rachis of FL62R1 and Sumai3 (10 DAI) in separate clusters in the negative side of PCI whereas non-infected rachis and infected rachis of FL62R1 and Sumai3 (4 DAI) are scattered in the positive side of PCI.

[0096] PCI loadings indicated that positive influence of FHB on rachis scores had values for pectin (1785, 1742.6, 1326.0, 930.6, 920, 870.8, 838.99 and 821.6 cm^"1), cellulose and hemicellulose (1475.5, 1460.1, 1428.2, 1365.5, 1254.6, 1164.9, 1125.4, 1110.9, 1056.9, 1033.8, 1019.3, 1002.9, 989.4, and 897.8 cm^"1).

[0097] PC2 explained a variability of 16% and the loadings in the case of PC2 showed positive values for pectin (850.6, 837.1, and 824.5 cm^"1). The most important peaks in the negative PC2 loading were for amide I (1673.2 cm^"1), amide II (1562.3, 1556.5, and 1524.7 cm^"1), cellulose, hemicellulose and pectin groups (1739.71493.8, 1453.3, 1423.4, 1395.4, 1375.2, 1334.7, 1323.1, 1288.4, 1276.8, 1265.2, 1173.6, 1141.8, 1109, 1059.8, 978.8, 897.8, and 873.7 cm^"1).

[0098] PC3 and PC4 explained a variability of 11% on scores of the rachis of wheat and showed two clusters. In the case of PC3, a positive impact on scores corresponds to wavenumbers for (1672.2 cm^"1), amide II (1524.6 cm^"1) and cell wall polysaccharides (1209.3, 1187.1, 1143.7, 1003.9, 977.9, 963.4, 949.9, 934.5, 921.9, 893.0, 864.1, and 816.8 cm^"1). The negative impact on the scores had wavenumbers indicating phenolic regions lignin and pectin (1793.7, 1779.3, 1768.6, and 1738.7 cm^"1), amide II (1577.7 and 1542.0 cm^"1), and cellulose, hemicellulose, and pectin (1464.9, 1433.0, 1413.8, 1337.6, 1305.8, 1294.2, 1280.7 and 1248.9, 1165.9, 1123.5, 1112.9, 1089.7, 1048.3, and 840.9 cm^"1).

[0099] The PC4 explained only 4% of variability of the impact of FHB on the rachis of the wheat cultivars. The PC4 loading obtained for every three sets of variable (Sumai3, FL62R1 and Muchmore) had significant positive value corresponding to pectin and phenolic groups (1786.9, 1736.8, and 1714.6 cm^"1), amide II (1594. land 1569.9 cm^"1) cellulose and hemicellulose (500.6, 1461.9, 1372.3, , 1168.8, 1135.1, 999.1, and 986.5, 897.8 cm^"1) and pectin (1327.9, 943.0, 873.7, and 840.9 cm^"1). Negative influence had wavenumbers around amide I (1674.1 cm^"1), amide II (1575.8 and 1540.1 cm^"1) and polysaccharides (1478.4,

1440.8, 1410.9, 1349.1, 1338.6, 1304.8, 1294.2, 1261.4, 1235.4, 1214.1, 1089.7, 1082.1,

1056.9, 1024.2, and 1021.3 cm^"1). Spectral Biomarkers for FHB Infection

[00100] Independent of the length of post-inoculation periods, PCA results showed significant differences between infected and non-infected florets of wheat cultivars. In the resistant cultivar Sumai3, the shifts in spectral peaks after inoculation were high (1375.9 tol382.7 cm^"1, 1246.5 tol256.8 cm^"1, 1038.9 to 1049.1 cm^"1, and 1158.1 to 1161.5 cm^"1). In Muchmore, the peaks at 1423.5, 1375.9, and 1052.5 cm^"1 were shifted to 1409.9, 1379.2, and 1049.1 cm^"1 respectively. For FL62R1 and other cultivars the disappearance of amide I a- helix peak (1546-66 cm^"1) and the change of amide I (1655 cm^"1) in control to β-sheet (1634- 37 cm^"1) in diseased plant when coupled with a change in the cellulose peak (1049.1 cm^"1) indicates that both amide I and cellulose peaks may be used as a marker of pathogenic infection with FHB.

[00101] Overall, the analysis of spectra of infected and non-infected florets supports a conclusion of increased metabolic activity in the host. The most important peaks representing amide I, amide II, cellulose, hemicellulose, and pectin may be linked with the formation of defense compounds, such as those involved in the reinforcement of the cell walls.

[00102] Additionally, the timing after infection had also influenced significantly the biochemical changes in the florets of wheat cultivars infected with or without FHB infection. For example, there was an important shift in 1631 cm^"1 and 1406.6 cm^"1 observed in Muchmore (to 1659 and 1420.1 cm^"1) and Sumai3 (to 1638 and 1420.1 cm^"1). In the carbohydrates region, a significant decrease of peak from 1055.9 to 1038.9 cm^"1 was recorded only for Muchmore.

[00103] The PCA has explained more than 73% of total variability principally due to the Fusarium infection. PCA showed that infected florets are grouped along PC2 axis and scattered along PCI axis. The florets from Sumai3 appeared genetically different from those from other cultivars, Muchmore and FL62R1 in terms of response to FHB infection. The positive loading of PCI was prominent in the spectra linked to carbohydrates 920, 855 and 816.8 cm^"1 which assigned to pectin and xyloglucan while the negative loadings is dominated by peaks belonging to pectin (1737.8, 1267.2, and 969.2 cm^"1), proteins (1688.6 and 1544.9 cm^"1), cellulose vibrations (1516, 1463.9, 1374.2, 1313.3, 117.7, and 896.9 cm^"1), and hemicellulose (1066.6 cm^"1), which may play an important role in host defense against the invasion by FHB and may be useful for fast routine screening methods of highly resistant cultivars against this devastating pathogenic fungus.

[00104] The spectrum of Fusarium has been largely studied and contains a very broad band in the region of 3650-3000 cm^"1 with a peak near 3260 cm^"1. The amide I and II bands (1700- 1485 cm^"1), which likely arise from fungal proteins, were prominent. The fingerprint region of FHB hyphae was dominated by a broad peak near 1035 cm^"1. Accordingly, the broad and strong absorption of this peak in the floret of infected cultivars in combination with a visible alteration in the protein region (a-helix turned to β-sheet) following pathogenic infection, maybe used as a signature marker for pathogenic infection with FHB. PCA applied to FTIR spectra underlines a substantial role of cell wall compounds in reaction to FHB as demonstrated by the relatively higher spectral peaks in Sumai3 after inoculation with FHB (1382.7, 1256, 1049.1 cm^"1), which may reflect increased metabolic activity linked with the formation of defense compounds such as those involved in the reinforcement of cell walls.

Table 6: Spectral Biomarkers Identified for FHB Infection

Spectral Biomarkers for FHB-Resistant Wheat

[00105] The only difference observed between the spectra of florets among the different cultivars is the peak around 1540-70 which was more intense for Sumai3 (-1566.5 cm^"1), suggesting that peak could be used as resistance marker for FHB.

[00106] Measurement of the spread of FHB within a spike has been recognized as a relatively reliable index of cultivar resistance (Bai et al, 1991; Schroeder et al., 1963). The spread of the disease within a spike was characterized by two distinct stages; spread into rachis and through the rachis into other florets via rachis internodes (Bai et al, 1996). These stages are mostly affected by resistance genes cultivars (Bai et al, 1996). The structure of the rachis was known to display a significant role in the resistance. Consequently, understanding the impact of FHB on chemical structure of the rachis in resistant and susceptible cultivars could help develop new selection strategies against this devastating pathogen.

[00107] FTIR spectra have shown the same absorption peaks as described above for the wheat florets and slight difference was recorded between resistant, moderate resistant, and susceptible cultivars. The peak 1515 cm^"1 related to lignin vibration disappeared in both cultivars, Muchmore and FL62R1 after pathogenic infection at 4 DAI and is still predominant in Sumai3, even after 10 DAI, suggesting the implication of lignin in resistance II of Sumai3 against FHB.

[00108] We noticed a shift of peak related to cellulose CH2 symmetric bending at about 1426 to 1423 cm^"1 in Muchmore, and the shift of peak at about 1328.2 to 1331.6 cm^"1 in both Sumai3 and Muchmore. The peak 1249.9 cm^"1 is linked to PO^"2 asymmetric phosphate vibration was increased only in Sumai3 following fungus infection. This indicates a potential role in resistance of Sumai3 against this pathogenic fungus. It was known that phosphorylated proteins are key elemental function in the activation of phenylpropanoids biosynthetic genes involved in the elaboration of lignin precursors, phytoalexins and the secondary signal salicylic acid as early responses to pathogen invasion. Therefore, the absorption peak at 1530-1563 cm^"1 (amide II, lignin), which was persistent in the rachis of Sumai3 and coupled with increased shift in absorption peaks of 1323 cm^"1 and 1245 cm^"1 in Sumai3 inoculated with FHB may be considered as a marker of resistance against this pathogenic fungus.

[00109] These conclusions could be of significance in developing early screening methods based on FTIR in selecting cultivars resistant against FHB and avoiding time consuming and costly screening techniques. By comparing both inoculation periods, the most important changes were a shift of peak 1331 to 1321.4 cm^"1 in the case of Muchmore and the appearance of peak 1546 cm^"1 for both cultivars Muchmore and FL62R1 as a reaction to pathogenic infection. The peak 1192.08 cm^"1 appeared only in FL62R1 and may related to the host immunity system response to the presence of pathogenic fungus.

[00110] The PC A of FTIR spectra underlined significant differences in rachis of the wheat cultivars and between those infected and non-infected by FHB. The total sample variation (82%) in the rachis of the wheat cultivars was explained by principal components 1 and 2. PCI (66%) clearly distinguish infected rachis of Muchmore (4 and 10 DAI) and the infected rachis of FL62R1 and Sumai3 (10 DAI) in separate clusters in the negative side of PCI whereas non-infected rachis and infected rachis of FL62R1 and Sumai3 (4dai) are scattered in the positive side of PCI. [00111] The positive influence of PCI on rachis scores had values around 1785, 1742.6, and 1326.0, 1254.6 cm^"1 (alkyl ester, pectin, lignin and phenolic vibrations), 1475.5, 1460.1, 1428.2, and 1365.5 cm^"1 (cellulose and lignin vibration), and polysaccharides regions including some wavenumbers denoting hemicellulose and pectin (1164.9, 1125.4, 1110.9, 1056.9, 1033.8, 1019.3, 1002.9, 989.4, 930.6, 920, 897.8, 870.8, 838.99 and 821.6 cm^"1). [00112] PC2 explained a variability of 16% and the loadings in the case of PC2 showed positive values for pectin and polysaccharides (850.6, 837.1, 824.5 cm^"1). The most important peaks in the negative PC2 loading were dominated by carbonyl compounds such as phenolics and lignin (1739.7 cm^"1), proteins (1673.2, 1562.3, 1556.5 and 1524.7), celluloses and pectin (1493.8, 1453.3, 1423.4, 1395.4, 1375.2, 1334.7, 1323.1, 1288.4, 1276.8, and 1265.2 cm^"1), and polysaccharides (1173.6, 1141.8, 1109, 1059.8, 978.8, 897.8, and 873.7 cm^"1). These results highlighted that pathogenic infection induced host resistance in three cultivars are represented by proteins and cell wall compounds.

Table 7: Spectral Biomarkers Identified for FHB Resistance

[00113] Although the disclosure has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art without departing from the spirit and scope of the disclosure. All such modifications as would be apparent to one skilled in the art are intended to be included within the scope of the following claims.

Claims

CLAIMS:

1. A method for screening plants to identify plant cultivars having resistance to a disease, comprising:

(a) subjecting a candidate plant or plant part to phase contrast X-ray imaging to visualize structural features of the plant or plant part;

(b) comparing the structural features of the plant or plant part to the structural features of a reference plant or plant part having resistance to the disease, wherein structural identifiers of resistance to the disease are identified;

(c) obtaining a sample from the plant or plant part identified in step (b) as exhibiting the structural identifiers of resistance to the disease;

(d) determining a mid-infrared spectral profile for the sample; and

(e) comparing the mid-infrared spectral profile of the sample to the mid-infrared spectral profile of a sample obtained from the reference plant or plant part, wherein spectral biomarkers of resistance to the disease are identified to confirm resistance of the candidate plant to the disease.

2. The screening method according to claim 1, wherein the phase contrast X-ray imaging is synchrotron-based phase contrast imaging.

3. The screening method according to claim 1 or 2, wherein the candidate plant is a commercial crop plant.

4. The screening method according to claim 3, wherein the commercial crop plant is selected from the group consisting of a food crop plant, a feed crop plant, a fibre crop plant, an oilseed crop plant, an ornamental crop plant, and an industrial or secondary crop plant.

5. The screening method according to claim 4, wherein the food crop plant is a cereal grain plant.

6. The screening method according to claim 5, wherein the cereal grain plant is selected from the group consisting of wheat, barley, rice, oats, and maize.

7. The screening method according to claim 6, wherein the cereal grain plant is wheat.

8. The screening method according to any one of claims 1 to 7, wherein the disease is Fusarium head blight (FHB).

9. The screening method according to claim 2, wherein X-ray energy of 18 keV is used in step (a).

10. The screening method according to any one of claims 1 to 9, wherein the infrared spectrum is recorded in step (d) in the mid-infrared range of 4000-800 cm^"1.

11. The screening method according to any one of claims 1 to 10, wherein the floret and rachis of the plant are analyzed.

12. The screening method according to claim 11, wherein the structural identifiers of resistance to the disease in step (b) comprise one or more structural identifiers selected from the group consisting of florets having high density internal structures, rachis having well- defined and closed intemode structures, and rachis having well-defined edges.

13. The screening method according to claim 11, wherein the spectral biomarkers of resistance to the disease in step (e) are identified in a sample taken from the florets of the candidate plant and comprise one or more spectral biomarkers selected from the group consisting of a broad band in the region of 3650-3000 cm^"1 with peak near 3260 cm^"1, and a change of amide I a-helix peak (1655 cm^"1) to β-sheet (1634-37 cm^"1).

14. The screening method according to claim 11, wherein the spectral biomarkers of resistance to the disease in step (e) are identified in a sample taken from the rachis of the candidate plant and comprise one or more spectral biomarkers selected from the group consisting of a peak at 1530-1563 cm^"1 denoting amide II, an increased shift in peak of 1323 cm^"1 denoting cellulose, and an increased shift in peak of 1245 cm^"1 denoting hemicellulose.

15. A method for screening plants to identify plant cultivars having resistance to a disease, comprising:

(a) subjecting a candidate plant or plant part to synchrotron-based phase contrast X-ray imaging to visualize structural features of the plant or plant part; (b) comparing the structural features of the plant or plant part to the structural features of a reference plant or plant part having resistance to disease, wherein structural identifiers of resistance to the disease are identified;

(c) obtaining a sample from the plant or plant part identified in step (b) as exhibiting the structural identifiers of resistance to the disease;

(d) determining a mid-infrared spectral profile for the sample; and

(e) comparing the mid-infrared spectral profile of the sample to the mid-infrared spectral profile of a sample obtained from the reference plant or plant part, wherein spectral biomarkers of resistance to the disease are identified to confirm resistance of the candidate plant to the disease.

16. The screening method according to claim 15, wherein the candidate plant is a commercial crop plant.

17. The screening method according to claim 16, wherein the commercial crop plant is selected from the group consisting of a food crop plant, a feed crop plant, a fibre crop plant, an oilseed crop plant, an ornamental crop plant, and an industrial or secondary crop plant.

18. The screening method according to claim 17, wherein the food crop plant is a cereal grain plant.

19. The screening method according to claim 18, wherein the cereal grain plant is selected from the group consisting of wheat, barley, rice, oats, and maize.

20. The screening method according to claim 19, wherein the cereal grain plant is wheat.

21. The screening method according to claim 15, wherein X-ray energy of 18 keV is used in step (a).

22. The screening method according to any one of claims 15 to 21, wherein the infrared spectrum is recorded in step (d) in the mid-infrared range of 4000-800 cm^"1.

23. The screening method according to any one of claims 15 to 22, wherein the disease is Fusarium head blight (FHB).

24. The screening method according to any one of claims 15 to 23, wherein the structural identifiers of resistance to the disease in step (b) comprise one or more structural identifiers selected from the group consisting of florets having high density internal structures, rachis having well-defined and closed internode structures, and rachis having well-defined edges.

25. The screening method according to any one of claims 15 to 23, wherein the spectral biomarkers of resistance to the disease in step (e) are identified in a sample taken from the florets of the candidate plant and comprise one or more spectral biomarkers selected from the group consisting of a broad band in the region of 3650-3000 cm^"1 with peak near 3260 cm^"1, and a change of amide I a-helix peak (1655 cm^"1) to β-sheet (1634-37 cm^"1).

26. The screening method according to any one of claims 15 to 23, wherein the spectral biomarkers of resistance to the disease in step (e) are identified in a sample taken from the rachis of the candidate plant and comprise one or more spectral biomarkers selected from the group consisting of a peak at 1530-1563 cm^"1 denoting amide II lignin, an increased shift in peak of 1323 cm^"1 denoting cellulose, and an increased shift in peak of 1245 cm^"1 denoting hemicellulose.

27. A method for detecting biomarkers of disease-resistance in plants for use in screening protocols, comprising:

(a) providing a disease-resistant and a disease-susceptible plant, wherein each plant has been exposed to a disease-causing pathogen;

(b) subjecting the disease-resistant and the disease-susceptible plant or a part of the plant to phase contrast X-ray imaging to visualize structural features of each plant or plant part;

(c) comparing the structural features of the disease-resistant plant or plant part to the structural features of the disease-susceptible plant or plant part, wherein differences in compared structural features indicate structural identifiers of resistance to the disease-causing pathogen; (d) obtaining a sample from each plant part identified in step (c) wherein differences in the structural features between the disease-resistant and the disease-susceptible plant is identified;

(e) determining a mid-infrared spectral profile for each sample; and

(f) comparing the mid-infrared spectral profile of the samples taken from the disease-resistant and the disease-susceptible plants, wherein spectral biomarkers of resistance to the disease-causing pathogen are identified.

28. The screening method according to claim 27, wherein the plant is a commercial crop plant.

29. The screening method according to claim 28, wherein the commercial crop plant is selected from the group consisting of a food crop plant, a feed crop plant, a fibre crop plant, an oilseed crop plant, an ornamental crop plant, and an industrial or secondary crop plant.

30. The screening method according to claim 29, wherein the food crop plant is a cereal grain plant.

31. The screening method according to claim 30, wherein the cereal grain plant is selected from the group consisting of wheat, barley, rice, oats, and maize.

32. The screening method according to claim 31, wherein the cereal grain plant is wheat.

33. The screening method according to any one of claims 27 to 32, wherein the phase contrast X-ray imaging is synchrotron-based phase contrast imaging.

34. The screening method according to claim 33, wherein X-ray energy of 18 keV is used in step (b).

35. The screening method according to any one of claims 27 to 34, wherein the infrared spectrum is recorded in step (e) in the mid-infrared range of 4000-800 cm^"1.