Browsing by Person "Longin, Friedrich"

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    Deciphering the potential of large-scale proteomics to improve product quality and nutritional value in different wheat species
    (2022) Afzal, Muhammad; Longin, Friedrich
    Wheat (Triticum aestivum) is one of the most important staple crops globally, which provides on average ~20% of the dietary intake of protein, starch and further important ingredients like fiber, minerals, vitamins, and essential amino acids for humans. Besides common wheat, there exist further wheat species with global to only local importance, i.e., durum, spelt, emmer and einkorn. Common wheat and durum are relatively widely cultivated whereas the other three species are cultivated only in specific regions. Apart from other functions, wheat proteins largely influence the end-use quality of products such as bread and pasta quality. Furthermore, wheat proteins can induce inflammatory reactions in humans such as celiac disease, wheat allergy and non-celiac wheat sensitivity. Thus, proteome profiles of different wheat species and cultivars within these species are of high relevance for stakeholders along the wheat supply chain. Proteomic technology has made breakthrough advancements in the recent times capable of quantifying thousands of proteins in 1.5–2 hours. Also, the wheat reference genome has been published and extended recently. These developments are extremely helpful in studying the wheat proteome at a high resolution. However, the modern large-scale proteomics has yet neither been applied to perform comparative investigation of the proteomes of different wheat species nor to study the proteomes of different types of breads and flours nor to study its application in the context of plant breeding. Therefore, we utilized modern large-scale proteomics to fill these gaps within the framework of this PhD work. First of all, an optimized data analysis pipeline was designed to deal with big proteomics data. This was necessary to estimate a multitude of quantitative genetics parameters for each protein and perform a comparative investigation of the proteomes. Optimization included implementation of data filtering based on the quantification of a protein in a given proportion of the samples, cultivars and environments. Different tests such as test for normal distribution of each protein in the context of statistical modelling and test to check the equality of variance between groups to apply the appropriate t-test were incorporated into a semi-automated workflow. In parallel, we adjusted and improved the lab methodology to deal with hundreds of samples within a short time period. We introduced a novel hybrid liquid chromatography-mass spectrometry (LC-MS) approach that combines quantification concatamer (QconCAT) technology with short microflow LC gradients and data-independent acquisition (DIA). The proposed approach measures the proteome by label-free quantification (LFQ) while concurrently providing accurate QconCAT-based absolute quantification of the key amylase/trypsin inhibitors (ATIs). These methods were then applied to compare different wheat species based on dozens of cultivars grown at multiple locations. First, we compared common wheat and spelt and identified 3,050 proteins overall. Of total proteins, 1,555 proteins in spelt and 1,166 in common wheat were only detected in a subset of the field locations. There were 1,495 and 1,604 proteins in spelt and common wheat, respectively, which were consistently expressed across all test locations in at least one cultivar. Finally, there were 84 and 193 unique proteins for spelt and common wheat, respectively, as well as 396 joint proteins, which were significantly differentially expressed between the two species. Using potentially allergenic proteins – annotated as amylase/trypsin inhibitors, serpins, and wheat germ agglutinin – we calculated an equally weighted “allergen index” that largely varied across cultivars ranging from –13.32 to 10.88 indicating the potential to select for cultivars with favorable proteome profiles. Next, we examined the proteomes of six different flours (wholegrain and superfine flours) and 14 different bread types (yeast and sourdough fermented breads and common wheat breads plus/minus bread improver) from common wheat, spelt and rye. Proteins that could cause allergies were functionally classified and comparatively measured by LFQ in flours and breads. Our findings showed that allergenic proteins were more prevalent in common wheat and spelt than rye and were not specifically degraded during bread manufacturing. In terms of abundance of the allergenic proteins, there was almost no difference between spelt and common wheat and the type of grain is likely more important for allergenicity than milling or traditional fermentation techniques. In a further study, we generated the flour reference proteomes for five wheat species, identifying at least 2,540 proteins in each species. More than 50% of the proteins significantly differed between species. Particularly, einkorn expressed 5.4 and 7.2 times less allergens and amylase/trypsin inhibitors than common wheat, respectively, emerging as a potential alternative cereal crop for people with sensitivities to cereal allergens. Lastly, we studied the application of large-scale proteomics for plant breeding. We found a significant impact of the environmental factors on protein expression. Only a fraction of proteins was stably expressed in all environments in at least one cultivar. Environmental influence was observed not only in the form of absolute expression or suppression of a certain protein at one or more environments but also in the form of low heritability (H2). High coefficients of variation across wheat cultivars indicate that the protein profiles of different cultivars vary considerably. Although, heritability was low for many proteins, we were able to identify hundreds of proteins with H²>0.5 – including key proteins for baking quality and human health. It should be possible to specifically manipulate the expression of functionally important proteins with high heritability by selecting and breeding for superior wheat cultivars along the wheat supply chain. Nevertheless, a successful implementation in plant breeding programs needs an improvement in the speed of protein quantification methods and in the validation of protein functions and annotations. In a nutshell, high number of proteins can be quantified in cereal grains utilizing cutting-edge proteomics techniques, opening new avenues for their use in the wheat supply chain. We generated lists of intriguing candidate proteins for further investigations on wheat sensitivity, and proteins with high heritability and important biological functions. Current research work has significant implications for the scientific and business communities across multiple disciplines including breeding, agriculture, cereal technology, nutritional science, health, and medicine. Political decision-makers and stakeholders in the food supply chain can benefit from the findings of this PhD project.
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    Genetic architecture of quality traits in wheat
    (2021) Rapp, Matthias; Longin, Friedrich
    Quality traits in wheat are of great importance, as they are required for the production of a wide range of food products. In Europe, bread wheat (Triticum aestivum ssp. aestivum) for human consumption is primarily used in pastries. For durum wheat (Triticum turgidum ssp. durum) that is used almost exclusively for pasta production, quality traits are at least as important as in bread wheat. In Central Europe, the bread wheat subspecies spelt (Triticum aestivum ssp. spelta) is characterized by a different quality compared to bread wheat. In addition, it is produced for a niche market with a particular focus on the final product quality. The high number of demanded quality traits of a wheat variety represents a great challenge for wheat breeders. Thus, knowledge about the genetic architecture and interrelation of quality traits is of high value for wheat breeding. Due to the long list of quality traits in wheat, we focused on currently important quality traits in each of the three wheat species. In durum wheat, I was interested in traits with a high importance for durum millers and pasta producers. The protein content and the sedimentation volume are of high importance for pasta producers as they influence the firmness of cooked pasta, better known as “al dente”. A low falling number may lead to brown instead of light yellow pasta, which goes back to an increased maillard reaction during pasta production and drying. The vitreousity, representing the glassy appearance of durum grains, and the thousand kernel mass influence the semolina yield and are therefore of great interest for durum millers. In the genome-wide association mapping, I identified several putative QTL for these quality traits. For the sedimentation volume, a genomic region on chromosome 1B appeared to be important. A BLAST search against the reference genomes of emmer and bread wheat revealed the Glu-B3 gene as a likely candidate. For vitreousity, genomic regions on chromosome 7A explained a larger proportion of the genotypic variance. One of these QTL, possibly related to the Pinb-2 locus, also slightly influenced the protein content. Thus, this genomic region might be a genomic reason for the positive correlation between vitreousity and protein content. For TKM we detected a putative QTL, which explained a large proportion of the genetic variance, but could not be attributed to a known gene. Besides a good performance for quality traits, a modern durum wheat variety should be complemented by a good agronomic performance, in particular a high grain yield. This poses a great challenge for plant breeders, since grain yield and protein content are negatively correlated. With regard to simultaneously improving grain yield and protein content, the protein yield or the grain protein deviation (GPD) were proposed. We evaluated those and further selection indices for their potential to be utilized for the simultaneous improvement of grain yield and protein content. Our results indicated that a simultaneous improvement of the two traits grain yield and protein content by means of an index seems possible. However, its efficiency largely depends on the weighting of the single traits. The selection for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve the grain yield. Nevertheless, a combination of different indices allows balancing this selection. Compared to the primary traits grain yield and protein content, the selection indices did not essentially differ in the complexity of their genetic architecture. In bread wheat, we focused on the acrylamide precursor asparagine. Acrylamide is formed in potentially harmful concentrations when cereals are treated with high temperatures over a long period during the processing to food products. A promising strategy to reduce the acrylamide formation would be to decrease the precursors in the raw material. The wide range of variation for asparagine content showed that variety selection might have a large influence on the occurrence of acrylamide in the final product. In addition, the moderately high heritability suggested that successful breeding for lower asparagine content is possible. This conclusion is supported by the observation of no strong negative correlations between asparagine content and a number of other important traits. The genome-wide association mapping resulted in the detection of eight putative QTL, which jointly explained 78.5% of the genetic variance. A putative QTL on chromosome 7B explained with, 18.4%, the highest proportion of the genetic variance for a single marker. For spelt wheat, we assessed a high number of quality traits but placed a special emphasis on the flavor and odor of bread produced from 30 different varieties. Interestingly, we observed a significant genetic variation for bread flavor and a heritability estimate of moderate magnitude. This suggests that even for bread flavor a successful selection appears possible. Taken together, for most traits the genome-wide association mapping resulted in the detection of a high number of putative QTL. This indicates a complex genetic architecture, typical for predominantly quantitatively inherited traits. However, few of the putative QTL explained a large proportion of the genetic variance, so that they might have the potential to be used in marker-assisted selection. In order to examine the potential of genomic selection, I performed a five-fold cross validation for the different quality traits. I could confirm previous findings that the integration of QTL information as fixed effects in the genomic prediction model increased the prediction abilities considerably. The average prediction abilities for most traits suggested a high potential for genomic selection in breeding programs. In conclusion or results form a good basis for further research but more importantly already deliver valuable knowledge that can be used as guideline to advance wheat breeding programs for improved quality.
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    Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications
    (2023) Nagel‐Held, Johannes; El Hassouni, Khaoula; Longin, Friedrich; Hitzmann, Bernd
    Background and Objectives: Quality assessment of bread wheat is time-consuming and requires the determination of many complex characteristics. Because of its simplicity, protein content prediction using near-infrared spectroscopy (NIRS) serves as the primary quality attribute in wheat trade. To enable the prediction of more complex traits, information from Raman and fluorescence spectra is added to the NIR spectra of whole grain and extracted flour. Model robustness is assessed by predictions across cultivars, locations, and years. The prediction error is corrected for the measurement error of the reference methods. Findings: Successful prediction, robustness testing, and measurement error correction were achieved for several parameters. Predicting loaf volume yielded a corrected prediction error RMSECV of 27.5 mL/100 g flour and an R² of 0.86. However, model robustness was limited due to data distribution, environmental factors, and temporal influences. Conclusions: The proposed method was proven to be suitable for applications in the wheat value chain. Furthermore, the study provides valuable insights for practical implementations. Significance and Novelty With up to 1200 wheat samples, this is the largest study on predicting complex characteristics comprising agronomic traits; dough rheological parameters measured by Extensograph, micro-doughLAB, and GlutoPeak; baking trial parameters like loaf volume; and specific ingredients, such as grain protein content, sugars, and minerals.