Annotating and making use of the Avena sativa cv. Sang reference genome

Oats is an important cereal used for both food and feed. The topic of this thesis is the annotation of the genome of oat (Avena sativa) cv. Sang, as well as some of the things this genome and its annotation have been used for.The first part of the thesis provides a short background to oat genomic resources, genomic resources in other plant species, and assembly of cereal genomes. Following this, it goes through the pipeline used to annotate the oat genome, covering various tools used, mentioning annotation pipelines used for other plant genomes, and comparing the results of cv. Sang annotation to annotations of other released oat genomes. It also briefly discusses a couple of tools used for functional annotation and identification of homologous genes.The following chapter looks at how this annotation may be used. It describes the pipeline used to identify homologous genes and provides an overview of how this was used to identify genes involved in epicuticular wax biosynthesis and in detoxification of Fusarium mycotoxins.Use of genetic markers, including how they have already been used both to identify breeding barriers in oats, and to establish that this oat reference genome corresponds to cv. Sang are brought up in the next chapter. How the markers may be aligned to the genome and how they may be visualized are also discussed. Next, mapping-by-sequencing is discussed, providing more details regarding the work done to identify the genes AsCer-q and AsGSK2.1. The method is explained, including selection of the number of individuals to include in the sequenced pools as well as the choices made to filter variants and genes. A background on the identified genes is also provided, before concluding with some thoughts regarding future work

The mosaic oat genome gives insights into a uniquely healthy cereal crop

Cultivated oat (Avena sativa L.) is an allohexaploid (AACCDD, 2n = 6x = 42) thought to have been domesticated more than 3,000 years ago while growing as a weed in wheat, emmer and barley fields in Anatolia1,2. Oat has a low carbon footprint, substantial health benefits and the potential to replace animal-based food products. However, the lack of a fully annotated reference genome has hampered efforts to deconvolute its complex evolutionary history and functional gene dynamics. Here we present a high-quality reference genome of A. sativa and close relatives of its diploid (Avena longiglumis, AA, 2n = 14) and tetraploid (Avena insularis, CCDD, 2n = 4x = 28) progenitors. We reveal the mosaic structure of the oat genome, trace large-scale genomic reorganizations in the polyploidization history of oat and illustrate a breeding barrier associated with the genome architecture of oat. We showcase detailed analyses of gene families implicated in human health and nutrition, which adds to the evidence supporting oat safety in gluten-free diets, and we perform mapping-by-sequencing of an agronomic trait related to water-use efficiency. This resource for the Avena genus will help to leverage knowledge from other cereal genomes, improve understanding of basic oat biology and accelerate genomics-assisted breeding and reanalysis of quantitative trait studies

Prediktion av proteinkontakter med djupinlärningsarkitekturen Tiramisu

Experimentally determining protein structure is a hard problem, with applications in both medicine and industry. Predicting protein structure is also difficult. Predicted contacts between residues within a protein is helpful during protein structure prediction. Recent state-of-the-art models have used deep learning to improve protein contact prediction. This thesis presents a new deep learning model for protein contact prediction, TiramiProt. It is based on the Tiramisu deep learning architecture, and trained and evaluated on the same data as the PconsC4 protein contact prediction model. 228 models using different combinations of hyperparameters were trained until convergence. The final TiramiProt model performs on par with two current state-of-the-art protein contact prediction models, PconsC4 and RaptorX-Contact, across a range of different metrics. A Python package and a Singularity container for running TiramiProt are available at https://gitlab.com/nikos.t.renhuldt/TiramiProt.Att kunna bestämma proteiners struktur har tillämpningar inom både medicin och industri. Såväl experimentell bestämning av proteinstruktur som prediktion av densamma är svårt. Predicerad kontakt mellan olika delar av ett protein underlättar prediktion av proteinstruktur. Under senare tid har djupinlärning använts för att bygga bättre modeller för kontaktprediktion. Den här uppsatsen beskriver en ny djupinlärningsmodell för prediktion av proteinkontakter, TiramiProt. Modellen bygger på djupinlärningsarkitekturen Tiramisu. TiramiProt tränas och utvärderas på samma data som kontaktprediktionsmodellen PconsC4. Totalt tränades modeller med 228 olika hyperparameterkombinationer till konvergens. Mätt över ett flertal olika parametrar presterar den färdiga TiramiProt-modellen resultat i klass med state-of-the-art-modellerna PconsC4 och RaptorX-Contact. TiramiProt finns tillgängligt som ett Python-paket samt en Singularity-container via https://gitlab.com/nikos.t.renhuldt/TiramiProt

Prediktion av proteinkontakter med djupinlärningsarkitekturen Tiramisu

Experimentally determining protein structure is a hard problem, with applications in both medicine and industry. Predicting protein structure is also difficult. Predicted contacts between residues within a protein is helpful during protein structure prediction. Recent state-of-the-art models have used deep learning to improve protein contact prediction. This thesis presents a new deep learning model for protein contact prediction, TiramiProt. It is based on the Tiramisu deep learning architecture, and trained and evaluated on the same data as the PconsC4 protein contact prediction model. 228 models using different combinations of hyperparameters were trained until convergence. The final TiramiProt model performs on par with two current state-of-the-art protein contact prediction models, PconsC4 and RaptorX-Contact, across a range of different metrics. A Python package and a Singularity container for running TiramiProt are available at https://gitlab.com/nikos.t.renhuldt/TiramiProt.Att kunna bestämma proteiners struktur har tillämpningar inom både medicin och industri. Såväl experimentell bestämning av proteinstruktur som prediktion av densamma är svårt. Predicerad kontakt mellan olika delar av ett protein underlättar prediktion av proteinstruktur. Under senare tid har djupinlärning använts för att bygga bättre modeller för kontaktprediktion. Den här uppsatsen beskriver en ny djupinlärningsmodell för prediktion av proteinkontakter, TiramiProt. Modellen bygger på djupinlärningsarkitekturen Tiramisu. TiramiProt tränas och utvärderas på samma data som kontaktprediktionsmodellen PconsC4. Totalt tränades modeller med 228 olika hyperparameterkombinationer till konvergens. Mätt över ett flertal olika parametrar presterar den färdiga TiramiProt-modellen resultat i klass med state-of-the-art-modellerna PconsC4 och RaptorX-Contact. TiramiProt finns tillgängligt som ett Python-paket samt en Singularity-container via https://gitlab.com/nikos.t.renhuldt/TiramiProt

Prediktion av proteinkontakter med djupinlärningsarkitekturen Tiramisu

Experimentally determining protein structure is a hard problem, with applications in both medicine and industry. Predicting protein structure is also difficult. Predicted contacts between residues within a protein is helpful during protein structure prediction. Recent state-of-the-art models have used deep learning to improve protein contact prediction. This thesis presents a new deep learning model for protein contact prediction, TiramiProt. It is based on the Tiramisu deep learning architecture, and trained and evaluated on the same data as the PconsC4 protein contact prediction model. 228 models using different combinations of hyperparameters were trained until convergence. The final TiramiProt model performs on par with two current state-of-the-art protein contact prediction models, PconsC4 and RaptorX-Contact, across a range of different metrics. A Python package and a Singularity container for running TiramiProt are available at https://gitlab.com/nikos.t.renhuldt/TiramiProt.Att kunna bestämma proteiners struktur har tillämpningar inom både medicin och industri. Såväl experimentell bestämning av proteinstruktur som prediktion av densamma är svårt. Predicerad kontakt mellan olika delar av ett protein underlättar prediktion av proteinstruktur. Under senare tid har djupinlärning använts för att bygga bättre modeller för kontaktprediktion. Den här uppsatsen beskriver en ny djupinlärningsmodell för prediktion av proteinkontakter, TiramiProt. Modellen bygger på djupinlärningsarkitekturen Tiramisu. TiramiProt tränas och utvärderas på samma data som kontaktprediktionsmodellen PconsC4. Totalt tränades modeller med 228 olika hyperparameterkombinationer till konvergens. Mätt över ett flertal olika parametrar presterar den färdiga TiramiProt-modellen resultat i klass med state-of-the-art-modellerna PconsC4 och RaptorX-Contact. TiramiProt finns tillgängligt som ett Python-paket samt en Singularity-container via https://gitlab.com/nikos.t.renhuldt/TiramiProt

Prediktion av proteinkontakter med djupinlärningsarkitekturen Tiramisu

Experimentally determining protein structure is a hard problem, with applications in both medicine and industry. Predicting protein structure is also difficult. Predicted contacts between residues within a protein is helpful during protein structure prediction. Recent state-of-the-art models have used deep learning to improve protein contact prediction. This thesis presents a new deep learning model for protein contact prediction, TiramiProt. It is based on the Tiramisu deep learning architecture, and trained and evaluated on the same data as the PconsC4 protein contact prediction model. 228 models using different combinations of hyperparameters were trained until convergence. The final TiramiProt model performs on par with two current state-of-the-art protein contact prediction models, PconsC4 and RaptorX-Contact, across a range of different metrics. A Python package and a Singularity container for running TiramiProt are available at https://gitlab.com/nikos.t.renhuldt/TiramiProt.Att kunna bestämma proteiners struktur har tillämpningar inom både medicin och industri. Såväl experimentell bestämning av proteinstruktur som prediktion av densamma är svårt. Predicerad kontakt mellan olika delar av ett protein underlättar prediktion av proteinstruktur. Under senare tid har djupinlärning använts för att bygga bättre modeller för kontaktprediktion. Den här uppsatsen beskriver en ny djupinlärningsmodell för prediktion av proteinkontakter, TiramiProt. Modellen bygger på djupinlärningsarkitekturen Tiramisu. TiramiProt tränas och utvärderas på samma data som kontaktprediktionsmodellen PconsC4. Totalt tränades modeller med 228 olika hyperparameterkombinationer till konvergens. Mätt över ett flertal olika parametrar presterar den färdiga TiramiProt-modellen resultat i klass med state-of-the-art-modellerna PconsC4 och RaptorX-Contact. TiramiProt finns tillgängligt som ett Python-paket samt en Singularity-container via https://gitlab.com/nikos.t.renhuldt/TiramiProt

HPAEC-PAD analysis for determination of the amino acid profiles in protein fractions from oat flour combined with correction of amino acid loss during hydrolysis

Current derivatization-dependent approaches for amino acid composition analysis of cereal proteins have significant variability due to lack of direct analysis opportunities and loss of amino acids during protein-hydrolysis. To tackle these drawbacks, a novel direct, derivatization-free approach was successfully introduced, using HPAEC-PAD, and applied for analysis of hydrolyzed defatted oat flour and extracted flour protein fractions. The approach ensured reliable detection of amino acids, including L-tryptophan, as well as oxidation products of L-cysteine and L-methionine. A time course study, analysed by nonlinear least-square regression to determine rates of hydrolysis and loss of each amino acid, allowed comparison of the original mass fraction (AA0) of the respective amino acid in the oat flour mixture with the mass fraction obtained after 24 h hydrolysis (AA24). The difference between (AA0) and (AA24) was less than 0.05%, except for L-arginine (0.61%), glycine (0.14%), L-isoleucine (0.27%), and L-tryptophan (0.17%). The (AA0)s obtained corresponded to literature-data, and fitted with the amino acid composition estimated from deduced proteins encoded in the oat genome, except for L-arginine (27%) and L-glutamic acid/L-glutamine (10%). The amino acid composition estimation from sequence data indirectly confirmed that the high presence of L-arginine observed was a result of co-elution with unknown flour components

Identification and Functional Characterisation of Two Oat UDP-Glucosyltransferases Involved in Deoxynivalenol Detoxification

Oat is susceptible to several Fusarium species that cause contamination with different trichothecene mycotoxins. The molecular mechanisms behind Fusarium resistance in oat have yet to be elucidated. In the present work, we identified and characterised two oat UDP-glucosyltransferases orthologous to barley HvUGT13248. Overexpression of the latter in wheat had been shown previously to increase resistance to deoxynivalenol (DON) and nivalenol (NIV) and to decrease disease the severity of both Fusarium head blight and Fusarium crown rot. Both oat genes are highly inducible by the application of DON and during infection with Fusarium graminearum. Heterologous expression of these genes in a toxin-sensitive strain of Saccharomyces cerevisiae conferred high levels of resistance to DON, NIV and HT-2 toxins, but not C4-acetylated trichothecenes (T-2, diacetoxyscirpenol). Re-combinant enzymes AsUGT1 and AsUGT2 expressed in Escherichia coli rapidly lost activity upon purification, but the treatment of whole cells with the toxin clearly demonstrated the ability to convert DON into DON-3-O-glucoside. The two UGTs could therefore play an important role in counteracting the Fusarium virulence factor DON in oat

The dynamics of touch-responsive gene expression in cereals

Wind, rain, herbivores, obstacles, neighbouring plants, etc. provide important mechanical cues to steer plant growth and survival. Mechanostimulation to stimulate yield and stress resistance of crops is of significant research interest, yet a molecular understanding of transcriptional responses to touch is largely absent in cereals. To address this, we performed whole-genome transcriptomics following mechanostimulation of wheat, barley, and the recent genome-sequenced oat. The largest transcriptome changes occurred ±25 min after touching, with most of the genes being upregulated. While most genes returned to basal expression level by 1–2 h in oat, many genes retained high expression even 4 h post-treatment in barley and wheat. Functional categories such as transcription factors, kinases, phytohormones, and Ca2+ regulation were affected. In addition, cell wall-related genes involved in (hemi)cellulose, lignin, suberin, and callose biosynthesis were touch-responsive, providing molecular insight into mechanically induced changes in cell wall composition. Furthermore, several cereal-specific transcriptomic footprints were identified that were not observed in Arabidopsis. In oat and barley, we found evidence for systemic spreading of touch-induced signalling. Finally, we provide evidence that both the jasmonic acid-dependent and the jasmonic acid-independent pathways underlie touch-signalling in cereals, providing a detailed framework and marker genes for further study of (a)biotic stress responses in cereals

The mosaic oat genome gives insights into a uniquely healthy cereal crop

Cultivated oat (Avena sativa L.) is an allohexaploid (AACCDD, 2n = 6x = 42) thought to have been domesticated more than 3,000 years ago while growing as a weed in wheat, emmer and barley fields in Anatolia1,2. Oat has a low carbon footprint, substantial health benefits and the potential to replace animal-based food products. However, the lack of a fully annotated reference genome has hampered efforts to deconvolute its complex evolutionary history and functional gene dynamics. Here we present a high-quality reference genome of A. sativa and close relatives of its diploid (Avena longiglumis, AA, 2n = 14) and tetraploid (Avena insularis, CCDD, 2n = 4x = 28) progenitors. We reveal the mosaic structure of the oat genome, trace large-scale genomic reorganizations in the polyploidization history of oat and illustrate a breeding barrier associated with the genome architecture of oat. We showcase detailed analyses of gene families implicated in human health and nutrition, which adds to the evidence supporting oat safety in gluten-free diets, and we perform mapping-by-sequencing of an agronomic trait related to water-use efficiency. This resource for the Avena genus will help to leverage knowledge from other cereal genomes, improve understanding of basic oat biology and accelerate genomics-assisted breeding and reanalysis of quantitative trait studies