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Determination of endosperm protein secondary structure in hard wheat breeding lines using synchrotron infrared microspectroscopy
Vibrational Spectroscopy 48 (2008) 76–81
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Determination of endosperm protein secondary structure in hard wheat breeding lines using synchrotron infrared microspectroscopy Emily S. Bonwell a, Tiffany L. Fisher a, Allan K. Fritz b, David L. Wetzel a,* a b
Microbeam Molecular Spectroscopy Laboratory, Shellenberger Hall, Kansas State University, Manhattan, KS 66506, United States Departments of Grain Science and Agronomy, Kansas State University, Manhattan, KS 66506, United States
A R T I C L E I N F O
A B S T R A C T
Article history: Received 16 July 2007 Received in revised form 5 April 2008 Accepted 7 April 2008 Available online 16 April 2008
One molecular aspect of mature hard wheat protein quality for breadmaking is the relative amount of endosperm protein in the a-helix form compared with that in other secondary structure forms including b-sheet. Modeling of a-helix and b-sheet absorption bands that contribute to the amide I band at 1650 cm 1 was applied to more than 1500 spectra in this study. The microscopic view of wheat endosperm is dominated by many large starch granules with protein in between. The spectrum produced from in situ microspectroscopy of this mixture is dominated by carbohydrate bands from the large starch granules that fill up the field. The high spatial resolution achievable with synchrotron infrared microspectroscopy enables revealing good in situ spectra of the protein located interstitially. Synchrotron infrared microspectroscopic mapping of 4 mm thick frozen sections of endosperm in the subaleurone region provides spectra from a large number of pixels. Pixels with protein-dominated spectra are sorted out from among adjacent pixels to minimize the starch absorption and scattering contributions. Subsequent data treatment to extract information from the amide I band requires a high signal to noise ratio. Although spectral interference of the carbohydrate band on the amide band is not a problem, the scattering produced by the large starch granules diminishes the signal to noise ratio throughout the spectrum. High density mapping was done on beamlines U2B and U10B at the National Synchrotron Light Source at Brookhaven National Laboratory, Upton, NY. Mapping with a single masked spot size of 5.5 mm diameter or confocal 5 mm 5 mm spot size, respectively, on the two beamlines used produced spectra for new breeding lines under current consideration. Appropriate data treatment allows calculation of a numerical estimate of the a-helix population relative to other secondary protein structures from the position and shape of the amide I absorption band. Current breeding lines show a substantial variance in this feature and its determination allows the prediction of relative quality for breadmaking to be taken into consideration for subsequent steps in the wheat breeding process. Data treatments include deconvolution, modeling of the individual resulting bands that contribute to the amide I band to enable measurement of the relative amounts of both forms. Results with specimens representing multiple crop years of hard winter wheat breeding are reported. It is evident that a range is available for the breeder to choose from, that allows including this protein molecular structural attribute in the selection process. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Synchrotron infrared microspectroscopy Wheat protein secondary structure Wheat Chemical imaging
1. Introduction An important molecular structural feature that distinguishes the quality of a wheat cultivar for breadmaking is the predominance of a-helix secondary protein structure compared to the b-sheet form. This was reported by Piot and co-workers [1,2] who used Raman microspectroscopy to establish the distinction between hard and soft wheat classes at maturity. Assistance to the plant breeding program is possible by ranking
* Corresponding author. E-mail address: [email protected] (D.L. Wetzel). 0924-2031/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.vibspec.2008.04.002
breeding lines according to their a-helix to b-sheet ratio to allow informed selection in regard to this important protein quality factor. In the petroleum industry, the extent of branch chain molecular structure that determines octane is analyzed spectroscopically. This is analogous to the approach that we are taking. In wheat breeding, the relative extent of a-helix molecular secondary structure can favorably influence wheat protein quality for breadmaking. In a previous Vibrational Spectroscopy article using synchrotron infrared microscopy, we reported a range of ratios (1.4–2.0) within the hard wheat class. Within the soft wheat class, the much lower approximately 1:1 ratio that we found was similar to that reported by Piot. Microspectroscopic single point probing of
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microtomed frozen wheat sections provides localized analysis from which the secondary protein structure can be determined from the amide I band position and shape. Infrared microspectroscopic mapping has been previously used to localize b-amyloid forms of folded protein in a number of diseases [3]. These include localization of b-amyloid in the plaque of a brain of an Alzheimer’s disease victim. A number of diseases involve folded protein where the a-helix form is diminished rendering an abnormal and eventually fatal result. These diseases include Alzheimers, Huntingtons, Parkinsons, Mad Cow, Scrapie, and Creutzfeldt–Jakob disease. Scrapie disease, the sheep equivalent of mad cow disease, has been the subject of recent synchrotron infrared microspectroscopic studies [4]. In this instance we are using the same tools that are used for diseased mammalian tissue to study the genetically caused distribution of a-helix to other protein secondary structure at maturity among hard wheat breeding lines. The nature of the endosperm matrix in which the protein secondary structure is to be determined provides an analytical challenge because although endosperm may be described as starch granules in a sea of protein, the region of protein between the large starch granules is small. This is shown by the photomicrographs in Fig. 1. High spatial resolution is required to probe the areas with predominately protein absorption to avoid those which have their signal to noise compromised by light scattering caused by the granules of starch. Use of synchrotron infrared microspectroscopy in a mapping experiment with a
Fig. 1. (a) Brightfield photomicrograph of wheat endosperm section showing numerous starch granules from 30 to 5 mm in size. The scattering of light by the many starch granules attenuates the microbeam and reduces S/N of the spectrum that compromises interpretation of data. The pixels from the interstitial protein between the starch granules were cherry picked for good baseline, low noise and a reasonable amide I intensity band in comparison to the 1025 cm 1 starch band as described under experimental section. (b) SEM photomicrograph courtesy of Y.C. Shi of wheat slice inward from the edge where there is a row of aleurone cells. Starch granules shown inward from the aleurone layer are closely packed together.
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pinhole image plane mask that produces a 5.5 mm spot size provides spectra from pixels with predominantly protein that are not affected by light scatter from large starch granules. These spectra have a sufficiently high SNR to be worthy of deconvolution and modeling of the amide I band. Among new hard wheat lines from which future released varieties of wheat ultimately result a number of quality factors for end use are given consideration along with the usual agronomic factors. Chemical testing for the quantity of protein in the last quarter of the 20th century played a major role in boosting the protein level of Kansas Agricultural Experiment Station released varieties 2.5% absolute without sacrificing yield. Thus the opportunity for bread quality hard wheat production was enhanced. Analytical research has provided the tools for measurement of other molecular factors that affect wheat quality for bread making. Two molecular tests for protein quality and another for desirable lipid content provide the opportunity for genetic incorporation of desirable quality factors. The instrumental capability for FT-IR microspectroscopy has advanced from its introduction in 1986. We reported mapping of a single aleurone cell using double apertures of 6 mm 7 mm as early as 1993 [5]. The first successful synchrotron infrared microspectroscopy was accomplished in late 1992. Early in 1993, Kansas wheat specimens were probed at a temporary experimental microspectroscopy setup described [6,7] at infrared beamline U2B at the National Synchrotron Light Source (NSLS), Brookhaven National Laboratory (BNL) in Upton, NY. Numerous applications and reviews of FT-IR microspectroscopy to biological materials can be found [8–18]. The high spatial resolution of synchrotron infrared microspectroscopy is used to obtain spectra representing many pixels within wheat endosperm in the subaleurone region. There are three reasons why synchrotron infrared microspectroscopy is superior to the same technique using a conventional globar source. The synchrotron radiation is approximately 1000 times brighter. There is no thermal noise and light emitted relativistically is highly directional unlike that from the thermal source. Thus the penalty for masking is very minor resulting in maximum radiation flux on the smallest possible area to achieve high spatial resolution without the necessity of excessive coaddition of scans. FT-IR microspectroscopy requires thin sections which precludes analysis of grain prior to maturity. The confocal Raman microspectroscopy with laser excitation done by Piot et al. in Professor Manfait’s laboratory at the University of Reims did not have this limitation. With Raman microspectroscopy 50 mm thick specimens were used and even immature seeds could be probed. These researchers were able to follow the maturation process prior to harvest and study the chemical effects of seed maturation as it occurs in the field. With spatial resolution, many of the pixels for a mapping procedure were of pure protein in the endosperm without significant light scattering and the corresponding SNR loss. Spectra from each FT-IR mapping procedure were examined one at a time and those with high carbohydrate content from starch granules were excluded. In the Raman experiment, the effect of residual amounts of starch in the spectra from the remaining pixels was reduced by spectral subtraction of starch. Fourier self-deconvolution was used to separate any shoulders on the amide I band that would allow distinction between the a-helix and other forms of secondary protein structure. Bands appearing in the deconvolved spectra were modeled using curve fitting schemes. Areas of the models were compared, and the ratio of a-helix to b-sheet secondary structure was calculated.
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2. Experimental 2.1. Instrumentation Infrared microspectroscopic data were obtained from infrared microspectrometers permanently installed at beamlines U2b and U10b of the vacuum ultraviolet (VUV) storage ring at the National Synchrotron Light Source (NSLS) of Brookhaven National Laboratory (BNL) at Upton, NY. From the original two infrared beamlines in 1992 capability has been increased to include six infrared beamlines. The reconstruction of the original beamline and enhancement of infrared beamline capability has been discussed by Carr et al. [19]. The U2b beamline used in collecting the data in this report was equipped with a commercial infrared microspectrometer (Thermo Nicolet, Madison, WI), which consisted of a NicPLAN1 infrared microscope interfaced directly to a Magna 860 model FT-IR spectrometer. Synchrotron radiation from the VUV storage ring at beamline U2b entered the interferometer via the instrument port designed for use for infrared emission spectroscopy. The point of entry to the spectrometer was approximately 6 m from the ring. Soft X-ray and VUV rays were absorbed by a water-cooled copper laser mirror. The longer wavelength radiation is transmitted via Bremsstrahlung shielding optics through a slightly wedged diamond window that separates the high vacuum system from the 1 atm nitrogen purged mirror box from where the synchrotron beam is focused downline to the microspectrometer. The Magna 860 interferometer was equipped with a KBr beamsplitter. The built-in dedicated detector of the infrared microscope was a liquid nitrogen-cooled MCT with a 250 mm diameter cross-section and focusing Schwartzschild 32 objective and 10 condenser were used. A single pinhole mask manually positioned before the objective projected a 5.5 mm diameter area onto the image plane. A second large pinhole image plane mask placed after the condenser served to maintain purge, but its circular projected image onto the stage did not spatially restrict the transmitted rays to affect double aperturing. Some of the specimens were mapped at beamline U10b equipped with a different infrared microscope, the Continumm1 Model (ThermoElectron, Madison, WI), operating with a model 860 Magna bench. The difference between this instrument and the one at U2b previously described is that it is equipped with matching 32 infinity corrected Schwartzschild objective and condenser. It has a 50 mm diameter MCT detector and a single mask, double pass (before the objective and after the condenser) optical path which dictates that all spectra are obtained with double masking. A 5 mm 5 mm mask size was chosen. 2.2. Infrared focusing and operation Prior to collecting the mapping data the infrared focus of the NicPLAN1 microscope was adjusted to maximize the count (signal) from the detector preamplifier before spectra were recorded. The arrangement of the focusing mechanism of this model infrared microscope causes the condenser to track the objective as the focus knob is rotated. The vertical displacement of the stage is read from gradations on the knob. The data collection sequence requires visible focus for image capture followed subsequently by restoring the previously determined vertical displacement required for infrared focus just prior to initiating the automated mapping sequence. For the maps, 64 scans were coadded, and a resolution of 8 cm 1 was used. A ‘‘preview’’ feature of the Continumm1 microscope allowed simultaneous real time scanning of the spectrum while viewing. This feature was used to set the infrared focus to produce the highest single beam intensity
at the spectral region of greatest interest. In this case, 1650– 1550 cm 1 was maximized for the amide I and II band measurements. 2.2.1. Sample preparation and mapping Frozen sections of wheat kernels 4–6 mm thick thaw mounted onto 1 mm thick 13 mm diameter BaF2 windows were mapped in a transmission mode. The step size used in the raster scanning procedure was no more than 6 mm and no less than 3 mm. Rectangles with sides as great as 120 mm or small as 60 mm were mapped. Spectra were obtained from 81 to 150 pixels. From the available spectra those were chosen that were predominant in protein without being overwhelmed by carbohydrate. Because of the scattering in the starchy endosperm, a visual choice coupled with test spectra when setting up a mapping sequence were required to avoid parts of the specimen with insufficient transmittance. Those would produce non-linear as well as noisy data. A mapping procedure was used to produce high spatial resolution of synchrotron infrared microspectroscopy, but not with the purpose of producing a map or image. A raster scan mapping procedure is used in a tight pattern to collect spectra of wheat endosperm from a reasonable number of pixels to be analyzed individually so that spectroscopic conclusions can be made regarding their chemical structure. Because coaddition of the spectra from all the pixels of a given map collected from automated mapping would produce a single composite spectrum, that approach was unacceptable. A composite spectrum would contain amide bands from the protein but, because of the presence of many large starch granules in the matrix of protein, in the wheat endosperm, the contribution of starch would be disproportionately high. It would cause scattering, and details of the protein infrared spectra would be difficult to observe. 2.3. Pixel selection for protein analysis The selection process of useful pixels for protein analysis was to limit consideration to subaleurone endosperm and to maximize the protein contribution to the spectrum by displaying false color images of individual pixels based on a maximum protein: carbohydrate comparison (1650 cm 1:1025 cm 1) locally baseline corrected peak area ratio. Spectra from pixels that were off scale or had distorted carbohydrate bands in the 1800–920 cm 1 region were eliminated to avoid excessive scatter and reduced SNR. After reducing the wavenumber range on the display to 1780– 1490 cm 1 the shape of the individual amide I bands was viewed. Those spectra with a similar band shape were grouped together. A mean spectrum was calculated and superimposed onto the individual spectra displayed on a common scale. With each spectrum brought to a common baseline at 1780 cm 1 near the amide I band, any differences were readily observed. From that display several individual spectra representative of the mean spectrum were chosen for further data treatment and quantitation to measure secondary structural contributions to the composite 1650 cm 1 band. Because the object of this experiment was to determine the relative amounts of a-helix and b-sheet secondary structure protein it was necessary to produce a band shape of each peak resolved by deconvolution that would allow peak area determination as a way of quantitative analysis. A three-stage process of data treatment was used to determine the fraction of a-helix compared to other secondary forms. The steps were peak shape definition, deconvolution, and modeling by peak fitting to achieve areas of individual protein forms. For a detailed discussion of the procedure used, the reader is referred to our previous article [20].
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The maximum likelihood deconvolution routine of GRAMS ‘‘RAZOR’’ (ThermoGalactic, Salem, NH) was used to define the peak shape. The Lorentzian bandwidth setting was 20 cm 1 and maximum likelihood ‘‘normal’’ smoothing with 15 iterations was selected. Classic maximum likelihood deconvolution was performed with 60 iterations. For the modeling operation the baseline type selected was Gaussian plus Lorentzian sum (based on Bayesian/maximum entropy). The Bayesian/maximum likelihood peak picker was specified and the peak fitting was by maximum likelihood also. The wavenumber of each peak picked and its percent based on area were included in the report generated by the software. In general the composite amide I band nominally designated as 1650 cm 1 was composed of a major peak at 1658 and 1660 cm 1 with a shoulder on either side that were modeled as peaks at ca. 1680 and 1630 cm 1, respectively. The reported results of quantitating modeled peak areas represent a consensus obtained from calculations for individual spectra that are close to the mean spectrum of the group. Each individual spectrum was taken through the data treatment process previously described to produce simple area percent a-helix and b-sheet values. 3. Results and discussion Our previous synchrotron infrared spectroscopic work was done on released varieties of hard wheats and soft wheats. Results showed a nearly 1:1 ratio of a-helix to b-sheet for mature soft wheats. The same procedure used on hard wheat varieties from diverse sources resulted in ratios from 1.4–2.0. The experimental results of this study are from hard winter wheat class breeding lines that represent nurseries operated by the Kansas Agricultural Experiment Station and include specimens from five successive
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crop years. Fig. 2 illustrates how the position and shape of the amide I band differs depending on the contribution to that band from folded secondary protein structure forms as well as that of the a-helix structure. The second derivative spectra of the same region are also shown in Fig. 2, to illustrate differing populations. From examination of amide I band spectra (from numerous pixels) in highly spatially resolved mapping experiments of wheat protein endosperm, it is evident that different protein characteristics represent heterogeneity within the wheat subaleurone endosperm of a single kernel. Each protein domain within the endosperm matrix may contain its own molecular structural contribution to the shape and position of the amide I band. The high spatial resolution of synchrotron infrared microspectroscopy has enabled observation of that fact. Nevertheless it is good spectral resolution which enables calculation of the secondary structure composition pixel by pixel. We recognize the heterogeneity of protein secondary structures that exist within each map of wheat endosperm. However, our purpose has been to establish a consensus for the specimen of a particular wheat breeding line while recognizing that variance occurs from domain to domain within the endosperm matrix. The approach to formulating a consensus was described under the experimental section involving pixel selection. In this study, the results are reported on 127 different experimental wheats involving at least 15,000 spectra. In each case, spectra from 10 to 25 pixels were selected from an 81 to 150 pixel mapping procedure. Modeling of a-helix and b-sheet absorption bands that contribute to the amide I band at approximately 1650 cm 1 was done for more than 1500 spectra. The a:b ratios of individual breeding lines are shown in Table 1. Table 1 a:b ratios of experimental lines from successive years Crop year 2002
Fig. 2. (a) and (b) Differences in absorption spectra (solid line) second derivatives (dotted line) of the 1750–1550 cm 1 amide I region that result from the presence of different amounts of a-helix and folded protein secondary structures including bsheet. Note the relative intensity of second derivative peaks at 1658 cm 1 and in the 1640–1630 cm 1 region.
1.72 1.62 1.6 1.6 1.53 1.48 1.47 1.46 1.45 1.45 1.45 1.45 1.41 (1.39)b 1.38 1.37 1.37 1.35 1.32 1.32 1.3 1.27 1.27 1.24 1.23 1.23 1.15 a b
2003
1.92 1.89 1.75 1.66 1.6 1.53 1.51 1.5 1.5 1.48 1.47 (1.46)b 1.45 1.45 1.4 1.4 1.36 1.32 1.28 1.27 1.24 1.24 1.2
2004
1.8 1.78 1.77 1.76 1.7 1.7 1.66 1.65 1.65 1.65 1.62 1.61 1.6 (1.57)b 1.57 1.54 1.53 1.53 1.51 1.5 1.47 1.47 1.44 1.38 1.37 1.35 1.32 1.11
2005
2006
2.5 1.93 1.89 1.87 1.61 1.59 1.55 1.54 1.54 1.51 1.49 1.48 (1.47)b 1.46 1.43 1.41 1.39 1.38 1.37 1.36 1.33 1.31 1.31 1.31 1.3 1.2
1.81 1.77 1.76 1.75 1.72 1.67 1.67 1.67 1.66 1.66 1.62 1.62 1.59 1.55 (1.54)b 1.5 1.5 1.48 1.48 1.47 1.46 1.45 1.39 1.39 1.38 1.38 1.36 1.31 1.3 1.26
Indicates values are less reliable due to field problems. Designates median ratios.
2007a
1.65 1.62 1.62 1.6 (1.54)b 1.38 1.36 1.34 1.28
E.S. Bonwell et al. / Vibrational Spectroscopy 48 (2008) 76–81
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Fig. 3. The bar graph is in descending order by a-helix to b-sheet ratio from left to right representing hard winter wheat specimens from the same nursery in a single crop year. Solid bars are a-helix and open bars represent b-sheet.
All of the data reported are from hard winter wheat lines grown in Kansas Agricultural Experiment Station breeding program nurseries. It is interesting to note that even within the hard wheat class grown in the same area within a given year where the environmental factors are constant, significant variance is in evidence. Although the same growing location was used for the immediate prior year or the second previous year, the weather conditions during the critical period before harvest as the protein structures were being formed were not necessarily reproducible from year to year. Because of this factor, a ranking of a-helix to bsheet ratios within one year is meaningful; however, any expectations of having the same numerical value occur from year to year are unwarranted. A similar ranking, even under a different environment, would infer a certain degree of genetic influence on the a-helix to b-sheet ratio and on one of the protein quality end use traits of importance in the baking industry. It is these molecular secondary structural identifiable genetic features that we want to take advantage of. Fig. 3 displays the actual values in bar graph form for integrated model areas (solid a-helix, open b-sheet) for experimental lines in a single crop year. Note that higher actual integrated areas merely reflect the sample density or thickness whereas the population of a-helix relative to b-sheet is the molecular measurement that relates to endosperm protein structure [1,2]. The structural gradation within the hard wheat class that nature provides and that microspectroscopy reveals allows informed plant breeders to select the lines that will produce the desired quality of wheat for a particular end use. Results on six breeding lines from the successive crop years are shown in Table 2. Note that among the seven reported, only A shows lack of consistency in reference to either rank or numerical value. Several well known released hard wheat varieties, previously characterized by physical dough testing and experimental baking
were tested successive years as a control to which experimental lines are compared. These are listed in Table 3. Note that good agreement was found in three successive years for Jagger and Jagalene. Santa Fe and Overley were in agreement for two successive years. Results for Overley differ in the third year. The controls (Table 3) grown provide a yearly basis of comparison for the experimental lines reported in Table 2. Wheat variety A was higher or equal to control varieties Jagger, Jagalene, Overley and Santa Fe for 2 successive crop years, while variety F consistently was lower than all four. Wheat varieties B and C were consistently higher than control varieties Jagger, Jagalene and Santa Fe. For two successive crop years varieties D and E were either lower or equal to Jagalene. (Note: Synchrotron infrared microspectroscopy was used except for the crop year 2006 wheat.) Without scheduled beamtime, an experimental attempt was made to obtain spectra from predominantly protein populated pixels, using array detection (PerkinElmer Spotlight 300TM) at KSU with a globar source. Although, with limited spatial resolution and without confocal operation, the yield of pixels with predominantly protein was lower than with the synchrotron, acquiring spectra from many more pixels produced sufficient spectra worthy of amide I analysis. In previously reported [20] experimentation occurring over a three year period preceding this study, soft wheats analyzed had a very narrow range of 0.98–1.05 for a-helix to b-sheet ratios. Released hard wheat varieties chosen for analysis were from a very genetically diverse origin including hard red winter, hard red spring, and hard white wheats that were grown in diverse geographical regions. From these it was possible to demonstrate a broad enough range in the a-helix to b-sheet ratio to warrant application of the analytical procedure to breeding lines. In the present study, the variable of growing locations has been removed. However, the genetic pool included in the current KAES breeding lines represents a substantial variance within a single class that is grown at essentially the same geographical location.
Table 2 Ratios and ranks for two successive years Wheat cultivars
Relative a
Rank agreement
Ratios (‘05, ‘06)
Rank (‘05, ‘06)
A B C D E F
Highest 2nd highest 3rd highest Mid Low Low
Different Close Close Similar Similar Similar
2.50, 1.69, 1.55, 1.39, 1.20, 1.38,
1, 2, 3, 4, 6, 5,
1.66 1.75 1.72 1.67 1.62 1.30
4 1 2 3 5 6
Table 3 Results for released varieties of wheat over four years Cultivar
2004
2005
2006
2007a
Jagger Jagalene Overley Santa Fe
1.32 1.44 1.78
1.49 1.54 1.89 1.48
1.48 1.67 1.31 1.45
1.6 1.38 1.34 1.62
a
Indicates values are less reliable due to field problems.
E.S. Bonwell et al. / Vibrational Spectroscopy 48 (2008) 76–81
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The exception is that of different environmental conditions from year to year that affect later stages of development as maturation is achieved.
varieties in production. We believe this supports the importance of early generation testing.
4. Summary
Acknowledgements
The molecular structural distinction between different classes that have different end use is a convenient quality feature that is objective. Within the hard wheat class that is the choice for breadmaking, we have observed a broad range in the ratio of ahelix to the b-sheet form among experimental lines. From a single nursery, within a given crop year, the genetic factor is the variable observed. Breeding bread quality wheat for end use is served by analysis at early generations. Proper selection from experimental cultivars each year is done to advance the desirable trait among the experimental cultivar of the subsequent crop year. There are multiple quality traits involved in addition to the agronomic traits. Ultimately the functional quality is judged by bakers using the flour that is the endosperm physically separated from the rest of the wheat kernel by dry milling. For example, wheat characterized by a higher a-helix to the b-sheet ratio is desirable for frozen dough production. Endosperm with a mid-range 1.5 to 1.2 ratio will be suitable for hearth breads and pan bread. Our experimentation from multiple experimental crop years provides evidence that secondary protein structure determination of endosperm hardness among hard wheats that occurs genetically within a given season of growing conditions is attainable from spatially resolved FT-IR microspectroscopy. This allows ranking to assist selection. The value of such quality parameters in the breeding program is evident because early generation results impact the selection process by removing the guesswork in advancing the desirable quality trait. Analytically it is possible to have a seasonal test of a carefully selected group of perhaps 30–50 samples. At the present time, the severe limiting factor of pixel selection and one spectrum at a time data treatment limits immediate use for wholesale screening. In the future, it is anticipated that this limitation will be removed to enable its broader usage. In the mean time, the technique that we developed is in progress for select screening at the Kansas Agriculture Experimental Station. It will be included in the next year’s experimental design for end use quality. Working with experimental cultivars, we found a greater diversity in the protein secondary structure than we previously reported among released
The authors are indebted to Ned Marinkovic and Randy Smith of NSLS who facilitated successful use of infrared beamlines U2B and U10B, respectively. Mandy Phillips of KSU provided technical assistance sectioning wheat kernels on one BNL visit. The NSLS is operated by the US Department of Energy under contract BEAC0298CH10886 as a user facility. Funding is acknowledged from the KSU Microbeam Molecular Spectroscopy Laboratory and the Kansas Agricultural Experiment Station. Contribution No. 08-6-J Kansas Agricultural Experiment Station, Manhattan. References [1] O. Piot, J. Autran, M. Manfait, Cereal Sci. 32 (1) (2000) 57–71. [2] O. Piot, Ph.D. Dissertation, University of Reims, Reims, France, 2000. [3] L.-P. Choo, D.L. Wetzel, W.C. Halliday, M. Jackson, S.M. LeVine, H.H. Mantsch, Biophys. J. 71 (1996) 1672–1679. [4] J. Kneipp, L.M. Miller, M. Joncic, M. Kittel, P. Lasch, M. Beekes, D. Naumann, Biochim. Biophys. Acta 1639 (2003) 152–158. [5] D.L. Wetzel, in: G. Charalambous (Ed.), Food Flavors, Ingredients, and Composition, Elsevier, Amsterdam, 1993, pp. 679–728. [6] J.A. Reffner, G.L. Carr, S. Sutton, R.J. Hemley, G.P. Williams, Synchrot. Radiat. News 7 (2) (1994) 30–37. [7] G.L. Carr, J.A. Reffner, G.P. Williams, Rev. Sci. Instrum. 66 (1995) 1490–1492. [8] D.L. Wetzel, J.A. Reffner, G.P. Williams, Proc. Microsc. Soc. Am. (1996) 206–207. [9] D.L. Wetzel, S.M. LeVine, in: H.U. Gremlich, B. Yan (Eds.), Infrared and Raman Spectroscopy of Biological Materials, Marcel Dekker, New York, 2000, pp. 101– 142. [10] D.L. Wetzel, in: H.G. Charalambous (Ed.), Food Flavors, Generation, Analysis, Process Influence, Elsevier, Amsterdam, 1995, pp. 2039–2108. [11] D.L. Wetzel, A.J. Eilert, L.N. Pietrzak, S.S. Miller, J.A. Sweat, Cell. Mol. Biol. 44 (1) (1998) 145–168. [12] D.L. Wetzel, J.A. Reffner, G.P. Williams, Mikrochim. Acta 14 (Suppl.) (1997) 353– 355. [13] S.M. LeVine, D.L. Wetzel, Appl. Spectrosc. Rev. 28 (1993) 385. [14] V.K. Kalasinksy, Appl. Spectrosc. Rev. 31 (1996) 193–249. [15] D.L. Wetzel, S.M. LeVine, Science 285 (1999) 1224–1225. [16] D.L. Wetzel, J.A. Reffner, Chem. Ind. 9 (2000) 308–313. [17] C.A. Marcott, R.C. Reeder, J.A. Sweat, D.D. Panzer, D.L. Wetzel, Vib. Spectrosc. 19 (1998) 123–129. [18] D.L. Wetzel, S.M. LeVine (eds.) Cell Mol. Biol. 44(1) (1998) 270 pp. (26 articles). [19] L. Carr, P. Dumas, C.J. Hirschmugl, G.P. Williams II, Nirovo. Cimento. 20 (4) (1998) 375–395. [20] D.L. Wetzel, P. Srivarin, J.R. Finney, Vib. Spectrosc. 31 (2003) 109–114.
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Determination of endosperm protein secondary structure in hard wheat breeding lines using synchrotron infrared microspectroscopy Emily S. Bonwell a, Tiffany L. Fisher a, Allan K. Fritz b, David L. Wetzel a,* a b
Microbeam Molecular Spectroscopy Laboratory, Shellenberger Hall, Kansas State University, Manhattan, KS 66506, United States Departments of Grain Science and Agronomy, Kansas State University, Manhattan, KS 66506, United States
A R T I C L E I N F O
A B S T R A C T
Article history: Received 16 July 2007 Received in revised form 5 April 2008 Accepted 7 April 2008 Available online 16 April 2008
One molecular aspect of mature hard wheat protein quality for breadmaking is the relative amount of endosperm protein in the a-helix form compared with that in other secondary structure forms including b-sheet. Modeling of a-helix and b-sheet absorption bands that contribute to the amide I band at 1650 cm 1 was applied to more than 1500 spectra in this study. The microscopic view of wheat endosperm is dominated by many large starch granules with protein in between. The spectrum produced from in situ microspectroscopy of this mixture is dominated by carbohydrate bands from the large starch granules that fill up the field. The high spatial resolution achievable with synchrotron infrared microspectroscopy enables revealing good in situ spectra of the protein located interstitially. Synchrotron infrared microspectroscopic mapping of 4 mm thick frozen sections of endosperm in the subaleurone region provides spectra from a large number of pixels. Pixels with protein-dominated spectra are sorted out from among adjacent pixels to minimize the starch absorption and scattering contributions. Subsequent data treatment to extract information from the amide I band requires a high signal to noise ratio. Although spectral interference of the carbohydrate band on the amide band is not a problem, the scattering produced by the large starch granules diminishes the signal to noise ratio throughout the spectrum. High density mapping was done on beamlines U2B and U10B at the National Synchrotron Light Source at Brookhaven National Laboratory, Upton, NY. Mapping with a single masked spot size of 5.5 mm diameter or confocal 5 mm 5 mm spot size, respectively, on the two beamlines used produced spectra for new breeding lines under current consideration. Appropriate data treatment allows calculation of a numerical estimate of the a-helix population relative to other secondary protein structures from the position and shape of the amide I absorption band. Current breeding lines show a substantial variance in this feature and its determination allows the prediction of relative quality for breadmaking to be taken into consideration for subsequent steps in the wheat breeding process. Data treatments include deconvolution, modeling of the individual resulting bands that contribute to the amide I band to enable measurement of the relative amounts of both forms. Results with specimens representing multiple crop years of hard winter wheat breeding are reported. It is evident that a range is available for the breeder to choose from, that allows including this protein molecular structural attribute in the selection process. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Synchrotron infrared microspectroscopy Wheat protein secondary structure Wheat Chemical imaging
1. Introduction An important molecular structural feature that distinguishes the quality of a wheat cultivar for breadmaking is the predominance of a-helix secondary protein structure compared to the b-sheet form. This was reported by Piot and co-workers [1,2] who used Raman microspectroscopy to establish the distinction between hard and soft wheat classes at maturity. Assistance to the plant breeding program is possible by ranking
* Corresponding author. E-mail address: [email protected] (D.L. Wetzel). 0924-2031/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.vibspec.2008.04.002
breeding lines according to their a-helix to b-sheet ratio to allow informed selection in regard to this important protein quality factor. In the petroleum industry, the extent of branch chain molecular structure that determines octane is analyzed spectroscopically. This is analogous to the approach that we are taking. In wheat breeding, the relative extent of a-helix molecular secondary structure can favorably influence wheat protein quality for breadmaking. In a previous Vibrational Spectroscopy article using synchrotron infrared microscopy, we reported a range of ratios (1.4–2.0) within the hard wheat class. Within the soft wheat class, the much lower approximately 1:1 ratio that we found was similar to that reported by Piot. Microspectroscopic single point probing of
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microtomed frozen wheat sections provides localized analysis from which the secondary protein structure can be determined from the amide I band position and shape. Infrared microspectroscopic mapping has been previously used to localize b-amyloid forms of folded protein in a number of diseases [3]. These include localization of b-amyloid in the plaque of a brain of an Alzheimer’s disease victim. A number of diseases involve folded protein where the a-helix form is diminished rendering an abnormal and eventually fatal result. These diseases include Alzheimers, Huntingtons, Parkinsons, Mad Cow, Scrapie, and Creutzfeldt–Jakob disease. Scrapie disease, the sheep equivalent of mad cow disease, has been the subject of recent synchrotron infrared microspectroscopic studies [4]. In this instance we are using the same tools that are used for diseased mammalian tissue to study the genetically caused distribution of a-helix to other protein secondary structure at maturity among hard wheat breeding lines. The nature of the endosperm matrix in which the protein secondary structure is to be determined provides an analytical challenge because although endosperm may be described as starch granules in a sea of protein, the region of protein between the large starch granules is small. This is shown by the photomicrographs in Fig. 1. High spatial resolution is required to probe the areas with predominately protein absorption to avoid those which have their signal to noise compromised by light scattering caused by the granules of starch. Use of synchrotron infrared microspectroscopy in a mapping experiment with a
Fig. 1. (a) Brightfield photomicrograph of wheat endosperm section showing numerous starch granules from 30 to 5 mm in size. The scattering of light by the many starch granules attenuates the microbeam and reduces S/N of the spectrum that compromises interpretation of data. The pixels from the interstitial protein between the starch granules were cherry picked for good baseline, low noise and a reasonable amide I intensity band in comparison to the 1025 cm 1 starch band as described under experimental section. (b) SEM photomicrograph courtesy of Y.C. Shi of wheat slice inward from the edge where there is a row of aleurone cells. Starch granules shown inward from the aleurone layer are closely packed together.
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pinhole image plane mask that produces a 5.5 mm spot size provides spectra from pixels with predominantly protein that are not affected by light scatter from large starch granules. These spectra have a sufficiently high SNR to be worthy of deconvolution and modeling of the amide I band. Among new hard wheat lines from which future released varieties of wheat ultimately result a number of quality factors for end use are given consideration along with the usual agronomic factors. Chemical testing for the quantity of protein in the last quarter of the 20th century played a major role in boosting the protein level of Kansas Agricultural Experiment Station released varieties 2.5% absolute without sacrificing yield. Thus the opportunity for bread quality hard wheat production was enhanced. Analytical research has provided the tools for measurement of other molecular factors that affect wheat quality for bread making. Two molecular tests for protein quality and another for desirable lipid content provide the opportunity for genetic incorporation of desirable quality factors. The instrumental capability for FT-IR microspectroscopy has advanced from its introduction in 1986. We reported mapping of a single aleurone cell using double apertures of 6 mm 7 mm as early as 1993 [5]. The first successful synchrotron infrared microspectroscopy was accomplished in late 1992. Early in 1993, Kansas wheat specimens were probed at a temporary experimental microspectroscopy setup described [6,7] at infrared beamline U2B at the National Synchrotron Light Source (NSLS), Brookhaven National Laboratory (BNL) in Upton, NY. Numerous applications and reviews of FT-IR microspectroscopy to biological materials can be found [8–18]. The high spatial resolution of synchrotron infrared microspectroscopy is used to obtain spectra representing many pixels within wheat endosperm in the subaleurone region. There are three reasons why synchrotron infrared microspectroscopy is superior to the same technique using a conventional globar source. The synchrotron radiation is approximately 1000 times brighter. There is no thermal noise and light emitted relativistically is highly directional unlike that from the thermal source. Thus the penalty for masking is very minor resulting in maximum radiation flux on the smallest possible area to achieve high spatial resolution without the necessity of excessive coaddition of scans. FT-IR microspectroscopy requires thin sections which precludes analysis of grain prior to maturity. The confocal Raman microspectroscopy with laser excitation done by Piot et al. in Professor Manfait’s laboratory at the University of Reims did not have this limitation. With Raman microspectroscopy 50 mm thick specimens were used and even immature seeds could be probed. These researchers were able to follow the maturation process prior to harvest and study the chemical effects of seed maturation as it occurs in the field. With spatial resolution, many of the pixels for a mapping procedure were of pure protein in the endosperm without significant light scattering and the corresponding SNR loss. Spectra from each FT-IR mapping procedure were examined one at a time and those with high carbohydrate content from starch granules were excluded. In the Raman experiment, the effect of residual amounts of starch in the spectra from the remaining pixels was reduced by spectral subtraction of starch. Fourier self-deconvolution was used to separate any shoulders on the amide I band that would allow distinction between the a-helix and other forms of secondary protein structure. Bands appearing in the deconvolved spectra were modeled using curve fitting schemes. Areas of the models were compared, and the ratio of a-helix to b-sheet secondary structure was calculated.
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2. Experimental 2.1. Instrumentation Infrared microspectroscopic data were obtained from infrared microspectrometers permanently installed at beamlines U2b and U10b of the vacuum ultraviolet (VUV) storage ring at the National Synchrotron Light Source (NSLS) of Brookhaven National Laboratory (BNL) at Upton, NY. From the original two infrared beamlines in 1992 capability has been increased to include six infrared beamlines. The reconstruction of the original beamline and enhancement of infrared beamline capability has been discussed by Carr et al. [19]. The U2b beamline used in collecting the data in this report was equipped with a commercial infrared microspectrometer (Thermo Nicolet, Madison, WI), which consisted of a NicPLAN1 infrared microscope interfaced directly to a Magna 860 model FT-IR spectrometer. Synchrotron radiation from the VUV storage ring at beamline U2b entered the interferometer via the instrument port designed for use for infrared emission spectroscopy. The point of entry to the spectrometer was approximately 6 m from the ring. Soft X-ray and VUV rays were absorbed by a water-cooled copper laser mirror. The longer wavelength radiation is transmitted via Bremsstrahlung shielding optics through a slightly wedged diamond window that separates the high vacuum system from the 1 atm nitrogen purged mirror box from where the synchrotron beam is focused downline to the microspectrometer. The Magna 860 interferometer was equipped with a KBr beamsplitter. The built-in dedicated detector of the infrared microscope was a liquid nitrogen-cooled MCT with a 250 mm diameter cross-section and focusing Schwartzschild 32 objective and 10 condenser were used. A single pinhole mask manually positioned before the objective projected a 5.5 mm diameter area onto the image plane. A second large pinhole image plane mask placed after the condenser served to maintain purge, but its circular projected image onto the stage did not spatially restrict the transmitted rays to affect double aperturing. Some of the specimens were mapped at beamline U10b equipped with a different infrared microscope, the Continumm1 Model (ThermoElectron, Madison, WI), operating with a model 860 Magna bench. The difference between this instrument and the one at U2b previously described is that it is equipped with matching 32 infinity corrected Schwartzschild objective and condenser. It has a 50 mm diameter MCT detector and a single mask, double pass (before the objective and after the condenser) optical path which dictates that all spectra are obtained with double masking. A 5 mm 5 mm mask size was chosen. 2.2. Infrared focusing and operation Prior to collecting the mapping data the infrared focus of the NicPLAN1 microscope was adjusted to maximize the count (signal) from the detector preamplifier before spectra were recorded. The arrangement of the focusing mechanism of this model infrared microscope causes the condenser to track the objective as the focus knob is rotated. The vertical displacement of the stage is read from gradations on the knob. The data collection sequence requires visible focus for image capture followed subsequently by restoring the previously determined vertical displacement required for infrared focus just prior to initiating the automated mapping sequence. For the maps, 64 scans were coadded, and a resolution of 8 cm 1 was used. A ‘‘preview’’ feature of the Continumm1 microscope allowed simultaneous real time scanning of the spectrum while viewing. This feature was used to set the infrared focus to produce the highest single beam intensity
at the spectral region of greatest interest. In this case, 1650– 1550 cm 1 was maximized for the amide I and II band measurements. 2.2.1. Sample preparation and mapping Frozen sections of wheat kernels 4–6 mm thick thaw mounted onto 1 mm thick 13 mm diameter BaF2 windows were mapped in a transmission mode. The step size used in the raster scanning procedure was no more than 6 mm and no less than 3 mm. Rectangles with sides as great as 120 mm or small as 60 mm were mapped. Spectra were obtained from 81 to 150 pixels. From the available spectra those were chosen that were predominant in protein without being overwhelmed by carbohydrate. Because of the scattering in the starchy endosperm, a visual choice coupled with test spectra when setting up a mapping sequence were required to avoid parts of the specimen with insufficient transmittance. Those would produce non-linear as well as noisy data. A mapping procedure was used to produce high spatial resolution of synchrotron infrared microspectroscopy, but not with the purpose of producing a map or image. A raster scan mapping procedure is used in a tight pattern to collect spectra of wheat endosperm from a reasonable number of pixels to be analyzed individually so that spectroscopic conclusions can be made regarding their chemical structure. Because coaddition of the spectra from all the pixels of a given map collected from automated mapping would produce a single composite spectrum, that approach was unacceptable. A composite spectrum would contain amide bands from the protein but, because of the presence of many large starch granules in the matrix of protein, in the wheat endosperm, the contribution of starch would be disproportionately high. It would cause scattering, and details of the protein infrared spectra would be difficult to observe. 2.3. Pixel selection for protein analysis The selection process of useful pixels for protein analysis was to limit consideration to subaleurone endosperm and to maximize the protein contribution to the spectrum by displaying false color images of individual pixels based on a maximum protein: carbohydrate comparison (1650 cm 1:1025 cm 1) locally baseline corrected peak area ratio. Spectra from pixels that were off scale or had distorted carbohydrate bands in the 1800–920 cm 1 region were eliminated to avoid excessive scatter and reduced SNR. After reducing the wavenumber range on the display to 1780– 1490 cm 1 the shape of the individual amide I bands was viewed. Those spectra with a similar band shape were grouped together. A mean spectrum was calculated and superimposed onto the individual spectra displayed on a common scale. With each spectrum brought to a common baseline at 1780 cm 1 near the amide I band, any differences were readily observed. From that display several individual spectra representative of the mean spectrum were chosen for further data treatment and quantitation to measure secondary structural contributions to the composite 1650 cm 1 band. Because the object of this experiment was to determine the relative amounts of a-helix and b-sheet secondary structure protein it was necessary to produce a band shape of each peak resolved by deconvolution that would allow peak area determination as a way of quantitative analysis. A three-stage process of data treatment was used to determine the fraction of a-helix compared to other secondary forms. The steps were peak shape definition, deconvolution, and modeling by peak fitting to achieve areas of individual protein forms. For a detailed discussion of the procedure used, the reader is referred to our previous article [20].
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The maximum likelihood deconvolution routine of GRAMS ‘‘RAZOR’’ (ThermoGalactic, Salem, NH) was used to define the peak shape. The Lorentzian bandwidth setting was 20 cm 1 and maximum likelihood ‘‘normal’’ smoothing with 15 iterations was selected. Classic maximum likelihood deconvolution was performed with 60 iterations. For the modeling operation the baseline type selected was Gaussian plus Lorentzian sum (based on Bayesian/maximum entropy). The Bayesian/maximum likelihood peak picker was specified and the peak fitting was by maximum likelihood also. The wavenumber of each peak picked and its percent based on area were included in the report generated by the software. In general the composite amide I band nominally designated as 1650 cm 1 was composed of a major peak at 1658 and 1660 cm 1 with a shoulder on either side that were modeled as peaks at ca. 1680 and 1630 cm 1, respectively. The reported results of quantitating modeled peak areas represent a consensus obtained from calculations for individual spectra that are close to the mean spectrum of the group. Each individual spectrum was taken through the data treatment process previously described to produce simple area percent a-helix and b-sheet values. 3. Results and discussion Our previous synchrotron infrared spectroscopic work was done on released varieties of hard wheats and soft wheats. Results showed a nearly 1:1 ratio of a-helix to b-sheet for mature soft wheats. The same procedure used on hard wheat varieties from diverse sources resulted in ratios from 1.4–2.0. The experimental results of this study are from hard winter wheat class breeding lines that represent nurseries operated by the Kansas Agricultural Experiment Station and include specimens from five successive
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crop years. Fig. 2 illustrates how the position and shape of the amide I band differs depending on the contribution to that band from folded secondary protein structure forms as well as that of the a-helix structure. The second derivative spectra of the same region are also shown in Fig. 2, to illustrate differing populations. From examination of amide I band spectra (from numerous pixels) in highly spatially resolved mapping experiments of wheat protein endosperm, it is evident that different protein characteristics represent heterogeneity within the wheat subaleurone endosperm of a single kernel. Each protein domain within the endosperm matrix may contain its own molecular structural contribution to the shape and position of the amide I band. The high spatial resolution of synchrotron infrared microspectroscopy has enabled observation of that fact. Nevertheless it is good spectral resolution which enables calculation of the secondary structure composition pixel by pixel. We recognize the heterogeneity of protein secondary structures that exist within each map of wheat endosperm. However, our purpose has been to establish a consensus for the specimen of a particular wheat breeding line while recognizing that variance occurs from domain to domain within the endosperm matrix. The approach to formulating a consensus was described under the experimental section involving pixel selection. In this study, the results are reported on 127 different experimental wheats involving at least 15,000 spectra. In each case, spectra from 10 to 25 pixels were selected from an 81 to 150 pixel mapping procedure. Modeling of a-helix and b-sheet absorption bands that contribute to the amide I band at approximately 1650 cm 1 was done for more than 1500 spectra. The a:b ratios of individual breeding lines are shown in Table 1. Table 1 a:b ratios of experimental lines from successive years Crop year 2002
Fig. 2. (a) and (b) Differences in absorption spectra (solid line) second derivatives (dotted line) of the 1750–1550 cm 1 amide I region that result from the presence of different amounts of a-helix and folded protein secondary structures including bsheet. Note the relative intensity of second derivative peaks at 1658 cm 1 and in the 1640–1630 cm 1 region.
1.72 1.62 1.6 1.6 1.53 1.48 1.47 1.46 1.45 1.45 1.45 1.45 1.41 (1.39)b 1.38 1.37 1.37 1.35 1.32 1.32 1.3 1.27 1.27 1.24 1.23 1.23 1.15 a b
2003
1.92 1.89 1.75 1.66 1.6 1.53 1.51 1.5 1.5 1.48 1.47 (1.46)b 1.45 1.45 1.4 1.4 1.36 1.32 1.28 1.27 1.24 1.24 1.2
2004
1.8 1.78 1.77 1.76 1.7 1.7 1.66 1.65 1.65 1.65 1.62 1.61 1.6 (1.57)b 1.57 1.54 1.53 1.53 1.51 1.5 1.47 1.47 1.44 1.38 1.37 1.35 1.32 1.11
2005
2006
2.5 1.93 1.89 1.87 1.61 1.59 1.55 1.54 1.54 1.51 1.49 1.48 (1.47)b 1.46 1.43 1.41 1.39 1.38 1.37 1.36 1.33 1.31 1.31 1.31 1.3 1.2
1.81 1.77 1.76 1.75 1.72 1.67 1.67 1.67 1.66 1.66 1.62 1.62 1.59 1.55 (1.54)b 1.5 1.5 1.48 1.48 1.47 1.46 1.45 1.39 1.39 1.38 1.38 1.36 1.31 1.3 1.26
Indicates values are less reliable due to field problems. Designates median ratios.
2007a
1.65 1.62 1.62 1.6 (1.54)b 1.38 1.36 1.34 1.28
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Fig. 3. The bar graph is in descending order by a-helix to b-sheet ratio from left to right representing hard winter wheat specimens from the same nursery in a single crop year. Solid bars are a-helix and open bars represent b-sheet.
All of the data reported are from hard winter wheat lines grown in Kansas Agricultural Experiment Station breeding program nurseries. It is interesting to note that even within the hard wheat class grown in the same area within a given year where the environmental factors are constant, significant variance is in evidence. Although the same growing location was used for the immediate prior year or the second previous year, the weather conditions during the critical period before harvest as the protein structures were being formed were not necessarily reproducible from year to year. Because of this factor, a ranking of a-helix to bsheet ratios within one year is meaningful; however, any expectations of having the same numerical value occur from year to year are unwarranted. A similar ranking, even under a different environment, would infer a certain degree of genetic influence on the a-helix to b-sheet ratio and on one of the protein quality end use traits of importance in the baking industry. It is these molecular secondary structural identifiable genetic features that we want to take advantage of. Fig. 3 displays the actual values in bar graph form for integrated model areas (solid a-helix, open b-sheet) for experimental lines in a single crop year. Note that higher actual integrated areas merely reflect the sample density or thickness whereas the population of a-helix relative to b-sheet is the molecular measurement that relates to endosperm protein structure [1,2]. The structural gradation within the hard wheat class that nature provides and that microspectroscopy reveals allows informed plant breeders to select the lines that will produce the desired quality of wheat for a particular end use. Results on six breeding lines from the successive crop years are shown in Table 2. Note that among the seven reported, only A shows lack of consistency in reference to either rank or numerical value. Several well known released hard wheat varieties, previously characterized by physical dough testing and experimental baking
were tested successive years as a control to which experimental lines are compared. These are listed in Table 3. Note that good agreement was found in three successive years for Jagger and Jagalene. Santa Fe and Overley were in agreement for two successive years. Results for Overley differ in the third year. The controls (Table 3) grown provide a yearly basis of comparison for the experimental lines reported in Table 2. Wheat variety A was higher or equal to control varieties Jagger, Jagalene, Overley and Santa Fe for 2 successive crop years, while variety F consistently was lower than all four. Wheat varieties B and C were consistently higher than control varieties Jagger, Jagalene and Santa Fe. For two successive crop years varieties D and E were either lower or equal to Jagalene. (Note: Synchrotron infrared microspectroscopy was used except for the crop year 2006 wheat.) Without scheduled beamtime, an experimental attempt was made to obtain spectra from predominantly protein populated pixels, using array detection (PerkinElmer Spotlight 300TM) at KSU with a globar source. Although, with limited spatial resolution and without confocal operation, the yield of pixels with predominantly protein was lower than with the synchrotron, acquiring spectra from many more pixels produced sufficient spectra worthy of amide I analysis. In previously reported [20] experimentation occurring over a three year period preceding this study, soft wheats analyzed had a very narrow range of 0.98–1.05 for a-helix to b-sheet ratios. Released hard wheat varieties chosen for analysis were from a very genetically diverse origin including hard red winter, hard red spring, and hard white wheats that were grown in diverse geographical regions. From these it was possible to demonstrate a broad enough range in the a-helix to b-sheet ratio to warrant application of the analytical procedure to breeding lines. In the present study, the variable of growing locations has been removed. However, the genetic pool included in the current KAES breeding lines represents a substantial variance within a single class that is grown at essentially the same geographical location.
Table 2 Ratios and ranks for two successive years Wheat cultivars
Relative a
Rank agreement
Ratios (‘05, ‘06)
Rank (‘05, ‘06)
A B C D E F
Highest 2nd highest 3rd highest Mid Low Low
Different Close Close Similar Similar Similar
2.50, 1.69, 1.55, 1.39, 1.20, 1.38,
1, 2, 3, 4, 6, 5,
1.66 1.75 1.72 1.67 1.62 1.30
4 1 2 3 5 6
Table 3 Results for released varieties of wheat over four years Cultivar
2004
2005
2006
2007a
Jagger Jagalene Overley Santa Fe
1.32 1.44 1.78
1.49 1.54 1.89 1.48
1.48 1.67 1.31 1.45
1.6 1.38 1.34 1.62
a
Indicates values are less reliable due to field problems.
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The exception is that of different environmental conditions from year to year that affect later stages of development as maturation is achieved.
varieties in production. We believe this supports the importance of early generation testing.
4. Summary
Acknowledgements
The molecular structural distinction between different classes that have different end use is a convenient quality feature that is objective. Within the hard wheat class that is the choice for breadmaking, we have observed a broad range in the ratio of ahelix to the b-sheet form among experimental lines. From a single nursery, within a given crop year, the genetic factor is the variable observed. Breeding bread quality wheat for end use is served by analysis at early generations. Proper selection from experimental cultivars each year is done to advance the desirable trait among the experimental cultivar of the subsequent crop year. There are multiple quality traits involved in addition to the agronomic traits. Ultimately the functional quality is judged by bakers using the flour that is the endosperm physically separated from the rest of the wheat kernel by dry milling. For example, wheat characterized by a higher a-helix to the b-sheet ratio is desirable for frozen dough production. Endosperm with a mid-range 1.5 to 1.2 ratio will be suitable for hearth breads and pan bread. Our experimentation from multiple experimental crop years provides evidence that secondary protein structure determination of endosperm hardness among hard wheats that occurs genetically within a given season of growing conditions is attainable from spatially resolved FT-IR microspectroscopy. This allows ranking to assist selection. The value of such quality parameters in the breeding program is evident because early generation results impact the selection process by removing the guesswork in advancing the desirable quality trait. Analytically it is possible to have a seasonal test of a carefully selected group of perhaps 30–50 samples. At the present time, the severe limiting factor of pixel selection and one spectrum at a time data treatment limits immediate use for wholesale screening. In the future, it is anticipated that this limitation will be removed to enable its broader usage. In the mean time, the technique that we developed is in progress for select screening at the Kansas Agriculture Experimental Station. It will be included in the next year’s experimental design for end use quality. Working with experimental cultivars, we found a greater diversity in the protein secondary structure than we previously reported among released
The authors are indebted to Ned Marinkovic and Randy Smith of NSLS who facilitated successful use of infrared beamlines U2B and U10B, respectively. Mandy Phillips of KSU provided technical assistance sectioning wheat kernels on one BNL visit. The NSLS is operated by the US Department of Energy under contract BEAC0298CH10886 as a user facility. Funding is acknowledged from the KSU Microbeam Molecular Spectroscopy Laboratory and the Kansas Agricultural Experiment Station. Contribution No. 08-6-J Kansas Agricultural Experiment Station, Manhattan. References [1] O. Piot, J. Autran, M. Manfait, Cereal Sci. 32 (1) (2000) 57–71. [2] O. Piot, Ph.D. Dissertation, University of Reims, Reims, France, 2000. [3] L.-P. Choo, D.L. Wetzel, W.C. Halliday, M. Jackson, S.M. LeVine, H.H. Mantsch, Biophys. J. 71 (1996) 1672–1679. [4] J. Kneipp, L.M. Miller, M. Joncic, M. Kittel, P. Lasch, M. Beekes, D. Naumann, Biochim. Biophys. Acta 1639 (2003) 152–158. [5] D.L. Wetzel, in: G. Charalambous (Ed.), Food Flavors, Ingredients, and Composition, Elsevier, Amsterdam, 1993, pp. 679–728. [6] J.A. Reffner, G.L. Carr, S. Sutton, R.J. Hemley, G.P. Williams, Synchrot. Radiat. News 7 (2) (1994) 30–37. [7] G.L. Carr, J.A. Reffner, G.P. Williams, Rev. Sci. Instrum. 66 (1995) 1490–1492. [8] D.L. Wetzel, J.A. Reffner, G.P. Williams, Proc. Microsc. Soc. Am. (1996) 206–207. [9] D.L. Wetzel, S.M. LeVine, in: H.U. Gremlich, B. Yan (Eds.), Infrared and Raman Spectroscopy of Biological Materials, Marcel Dekker, New York, 2000, pp. 101– 142. [10] D.L. Wetzel, in: H.G. Charalambous (Ed.), Food Flavors, Generation, Analysis, Process Influence, Elsevier, Amsterdam, 1995, pp. 2039–2108. [11] D.L. Wetzel, A.J. Eilert, L.N. Pietrzak, S.S. Miller, J.A. Sweat, Cell. Mol. Biol. 44 (1) (1998) 145–168. [12] D.L. Wetzel, J.A. Reffner, G.P. Williams, Mikrochim. Acta 14 (Suppl.) (1997) 353– 355. [13] S.M. LeVine, D.L. Wetzel, Appl. Spectrosc. Rev. 28 (1993) 385. [14] V.K. Kalasinksy, Appl. Spectrosc. Rev. 31 (1996) 193–249. [15] D.L. Wetzel, S.M. LeVine, Science 285 (1999) 1224–1225. [16] D.L. Wetzel, J.A. Reffner, Chem. Ind. 9 (2000) 308–313. [17] C.A. Marcott, R.C. Reeder, J.A. Sweat, D.D. Panzer, D.L. Wetzel, Vib. Spectrosc. 19 (1998) 123–129. [18] D.L. Wetzel, S.M. LeVine (eds.) Cell Mol. Biol. 44(1) (1998) 270 pp. (26 articles). [19] L. Carr, P. Dumas, C.J. Hirschmugl, G.P. Williams II, Nirovo. Cimento. 20 (4) (1998) 375–395. [20] D.L. Wetzel, P. Srivarin, J.R. Finney, Vib. Spectrosc. 31 (2003) 109–114.