Unlocking the hidden potential of genetic diversity to improve durum wheat tolerant to heat stress

UNIVERSITA’ DEGLI STUDI DI PARMA Dottorato di Ricerca in BIOTECNOLOGIE E BIOSCIENZE CICLO XXXV Unlocking the hidden potential of genetic diversity to improve durum wheat tolerant to heat stress Coordinatore: Chiar.mo Prof. Marco Ventura Supervisore: Chiar.mo Prof. Nelson Marmiroli Dott.ssa Michela Janni Tutore: Chiar.ma Prof.ssa Elena Maestri Candidato: Nadia Palermo 2019/2020-2021/2022 UNIVERSITY OF PARMA Ph.D. in Biotechnology and Biosciences XXXV COURSE Unlocking the hidden potential of genetic diversity to improve durum wheat tolerant to heat stress Coordinator: Prof. Marco Ventura Supervisor: Prof. Nelson Marmiroli Dr. Michela Janni Tutor: Prof. Elena Maestri Candidate: Nadia Palermo 2019/2020-2021/2022 Index Abstract 1 1. INTRODUCTION 3 1.1 Greenhouse Effects and Global Warming ........................................................................ 3 1.1.1 Climate changes: global temperature rise .................................................................. 4 1.1.2 High temperature impact on agriculture .................................................................... 5 1.2 Wheat ................................................................................................................................ 7 1.2.1 Wheat production ...................................................................................................... 7 1.2.2 Wheat life cycle ......................................................................................................... 9 1.2.3 Wheat allopolyploidization...................................................................................... 10 1.3 Heat stress on plants ...................................................................................................... 12 1.3.1 Heat damage and ROS production .......................................................................... 12 1.3.2 Heat stress effects on wheat development ............................................................... 13 1.3.3 Plant adaptation strategies to heat stress.................................................................. 15 1.3.4 Heat resilience ......................................................................................................... 16 1.4 Molecular mechanisms of Heat tolerance ...................................................................... 19 1.4.1. Signal transduction ................................................................................................. 19 1.4.2 Heat Shock Factors (HSF) ....................................................................................... 19 1.4.3 Heat-Shock Proteins (HSPs), master Players for Heat Stress Tolerance ................ 21 1.4.3.1 HSP100s ........................................................................................................... 23 1.4.3.2 HSP90s ............................................................................................................. 24 1.4.3.3 HSP70s ............................................................................................................. 24 1.4.3.4 HSP60s ............................................................................................................. 25 1.4.3.5 Small HSPs ....................................................................................................... 25 1.4.3.6 sHSP26 ............................................................................................................. 27 1.5 Heat resilience ................................................................................................................ 28 1.5.1 Genetic diversity and breeding to cope climate changes and heat stress ................ 28 1.5.2 TILLING and EcoTILLING .................................................................................... 29 1.5.3 Phenotyping ............................................................................................................. 29 2. AIM OF THE PROJECT 33 3. MATERIAL AND METHODS 34 3.1 EcoTILLING and detection of natural variation in TdHsp26-A1 and TdHsp26-B1 in durum wheat genotypes ........................................................................................................ 34 3.1.1 Plant material ........................................................................................................... 34 3.1.2 DNA extraction and gene targeting ......................................................................... 35 3.1.3 Bioinformatics analysis of the NGS data ................................................................ 37 3.1.4 KASP Assay ............................................................................................................ 37 3.1.5 Promoter functional motifs in Silico analysis ......................................................... 39 3.2 Heat stress experiments .................................................................................................. 39 3.2.1 Plant growth and heat stress conditions of short-term HS ...................................... 39 Seedlings experiment .................................................................................................... 39 Tillering experiment ..................................................................................................... 39 Anthesis experiment ..................................................................................................... 40 3.3 Phenotypic analysis ........................................................................................................ 43 3.3.1 Morphological analysis ........................................................................................... 43 3.3.2 Biochemical analysis ............................................................................................... 45 Malondialdehyde (MDA) content ................................................................................ 45 Determination of Hydrogen peroxide H2O2 ................................................................. 45 3.3.3 Physiological measurements ................................................................................... 46 Photosynthesis: Photosystem II (PSII) efficiency: Fv/Fm ........................................... 46 Transpiration: Stomatal Resistance (rS) and stomatal conductance (gs) ....................... 46 Canopy temperature: Infrared Leaf Temperature (IR) and Canopy Depression Temperature (CTD) ...................................................................................................... 46 Relative Water Content (RWC) .................................................................................... 48 3.3.4 Yield traits ............................................................................................................... 48 3.3.5 Stress indices ........................................................................................................... 49 3.4 TdHsp26-A1 and TdHsp26-B1 gene expression ............................................................. 49 3.4.1 Sample collection .................................................................................................... 49 3.4.2 RNA extraction and cDNA retrotranscription ......................................................... 50 3.4.3 Quantitative Real Time PCR (qtPCR) ..................................................................... 50 3.5. Data analysis ................................................................................................................. 51 4. RESULTS AND DISCUSSION 52 4.1 Materials and Approaches used to discover novel natural variability to increase heat stress resilience .................................................................................................................... 52 4.2 EcoTILLING approach to discover the natural variance in TdHsp26-A1 and TdHsp26B1 in durum wheat genotypes ............................................................................................... 53 4.2.1 SNPs identification in TdHSp26 genes .................................................................... 53 4.2.2 SNPs localization and heat stress experiment ......................................................... 55 4.2.3 Effects of SNPs on TdHsp26 promoter ................................................................... 58 4.3 Does the genetic variation identified in TdHsp26 sequence impact on the heat stress response? .............................................................................................................................. 61 4.3.1 Heat stress response in seedling stage ..................................................................... 61 Morphological traits ..................................................................................................... 61 MDA content ................................................................................................................ 63 TdHsp26-A1 and -B1 gene expression during heat stress in seedlings stage ............... 66 4.3.2 The heat stress response in tillering stage ............................................................... 67 4.3.2.1 Morphological traits ......................................................................................... 67 4.3.2.2 Physiological traits ........................................................................................... 68 Stress indices ................................................................................................................ 71 4.3.2.3 Antioxidant activity and ROS accumulation .................................................... 72 4.3.2.4 TdHsp26-A1 and TdHsp26-B1 gene expression analysis during heat stress in tillering stage ................................................................................................................ 73 4.3.2.5 Yield traits ........................................................................................................ 74 4.3.3 SSD lines characterization for heat stress resilience ............................................... 75 4.3.4 Discussion on the heat stress response for durum wheat lines in seedling and tillering stage .................................................................................................................... 77 4.3.5 The heat stress response after prolonged heat stress in anthesis phase ................... 79 4.3.5.1 Morphological traits ......................................................................................... 79 4.3.5.2 Physiological traits ........................................................................................... 83 4.3.5.3 MDA content .................................................................................................... 88 4.3.5.4 TdHsp26-A1 and TdHsp26-B1 gene expression analysis during heat stress in anthesis stage ................................................................................................................ 89 4.3.5.5 Yield traits ........................................................................................................ 89 4.3.6 Discussion of the prolonged heat stress response in anthesis phase........................ 91 5. DISCUSSION AND CONCLUSIONS 95 6. SUPPLEMENTARY MATERIAL 98 7. ACKNOWLEDGMENTS 101 REFERENCES 102 1 Abstract According to the Intergovernmental Panel on Climate Change report, the average global temperature will increase by 0.5 to 4°C in the 21st century, leading to a reduction in crop yields. Wheat is one of the world's oldest and most widespread food crops and an important component of the human diet; it is very sensitive to heat stress (HS), and it is considered that for every 1°C increase in average temperature during the reproductive phase, wheat production is estimated to decrease by 6%. Moreover, the fast increases in food demand due to population growth must be considered. A strategy to overcome the ongoing climate change is to understand the morphological and physiological traits associated with tolerance to high temperatures to produce crops more tolerant to abiotic stresses. Possible approaches to succeed in maintaining high crop yields include (i) the exploitation of natural and induced mutations; (ii) the exploitation of available genetic resources to produce new genetic material that is more tolerant to HS and related secondary stresses; (iii) improve the ability to identify available sources of resilience; and (iv) developing new selection techniques. The involvement of sHsp26 in the heat stress response was analyzed, by dissecting the natural mutations of TdHsp26 in some durum genotypes belonging to a germplasm collection. A target enrichment PCR for NGS followed by KASP analysis was used to determine the SNPs present on the gene of interest in the different genotypes analyzed; a total of 17 haplotype combinations were found. A phenotyping analysis was performed on the target genotypes subjected to thermal stress in three different phases of wheat development: seedlings (Z10), accession (Z31) and anthesis (Z65) Biochemical, morphological, and physiological traits were recorded during the experiment and contrasting genotypes were identified and selected through the experiments. Cultivated varieties were also included in the trials. On the basis of the results obtained SSD69 and SSD397 were finally identified as putative tolerant and susceptible to heat stress. 2 These results support the importance of plant phenotyping of the identification of superior genotypes under heat stress. 3 1. INTRODUCTION 1.1 Greenhouse Effects and Global Warming Earth's atmosphere is a mixture of gases called greenhouse gasses (GHG), and it retains heat like the walls of a greenhouse. Solar radiation passes through the atmosphere unimpeded, heating the earth's surface. In turn, the energy is re-emitted as infrared, much of which is absorbed by CO2 and water vapor in the atmosphere. The latter acts as a "blanket" of the right thickness, absorbing the appropriate solar energy to maintain a global average temperature adequate for life. Without this natural greenhouse effect, the average surface temperature would fall to about -21°C, much less than the current 14°C (Anderson, Hawkins, and Jones 2016). Gasses that contribute to the greenhouse effect include: Carbon dioxide (CO2), methane (CH4), Nitrous oxide (NO2) and Chlorofluorocarbons (CFCs) (Kweku et al. 2018). Many of these come from natural processes, such as: - evaporation, which adds water vapor to the atmosphere; - respiration of animals or plants, which contributes carbon dioxide; - decomposition resulting in the release of methane (‘The Greenhouse Effect and Our Planet National Geographic Society’ 2022). The sharp increase in greenhouse gasses over the past 100 years has been caused by anthropogenic activities (Kazancoglu, Ozbiltekin-Pala, and Ozkan-Ozen 2021). Main players in increasing greenhouse gasses are fossil fuel combustion, deforestation, logging, and agricultural activities (Wang et al. 2021). 1) Fossil fuels, such as coal, oil, and natural gas burning are the main source of energy for electricity, heating, and transportation, as well as for the manufacture of a wide range of products, from steel to plastics. This process causes the release of greenhouse gasses (particularly CO2) at high rates. 2) Deforestation seriously contributes to the increase in greenhouse gasses. During photosynthesis, plants, and trees store carbon (C) and release oxygen (O2) into the air. Due to deforestation and logging, carbon is released as CO2. According to the Food and Agriculture 4 Organization of the United Nations (FAO, 2006), deforestation releases between 25–30% of GHGs annually (Al-Yasiri and Géczi 2021). 3) Agriculture plays a key role in greenhouse gas emission, the global food system responsible for 21-37% of annual emissions and generates around half of all anthropogenic methane emissions and around three-quarters of anthropogenic N2O (Lynch et al. 2021). The strong increase in greenhouse gasses due to all the above activities has resulted in the global warming effect that has been so far defined as “the increase in the surface average temperature of the earth” because of the increase in the concentration of greenhouse gasses (Al-Ghussain 2019).From 1990 to 2019, the increase in global warming is 45% (US EPA 2022). 1.1.1 Climate changes: global temperature rise Global warming has drastic consequences on changes in the world climate, leading to an increase in the frequency and intensity of heat waves and droughts, as well as other abiotic stress conditions such as flooding, salinity, and freezing (Bigot et al. 2018). In October 2018, the Intergovernmental Panel on Climate Change (IPCC) released a special report on global warming, highlighting that average global temperatures have increased by about 1Cº since pre-industrial times (Figure 1). Furthermore, in that report it was pointed out that average global temperatures are increasing by about 0.2°C every decade (‘IPCC — Intergovernmental Panel on Climate Change’ 2018). In addition, in the paper it is predicted that average global warming will likely reach 1.5°C between 2030 and 2052 considering the rate of current anthropogenic greenhouse gas (GHG) emissions (Ogunbode, Doran, and Böhm 2020). According to climate models (Driedonks et al. 2015) and the report from the Intergovernmental Panel on Climate Change, the world mean temperature will rise by 0.5 to 4°C in the twenty-first century (Hansen et al. 2016; Zandalinas, Fritschi, and Mittler 2021). 11 Durum wheat (DW), Triticum turgidum L. ssp. Durum (Desf.) Husn., genome BBAA, evolved from domesticated emmer wheat (DEW), T. turgidum ssp. dicoccum (Schrank ex Schübl.) Thell. DEW itself derived from wild emmer wheat (WEW), T. turgidum ssp. dicoccoides (Körn. ex Asch. & Graebn.) Thell., in the Fertile Crescent about 10,000 years ago. Although the earliest evidence of DW dates back to 6,500–7,500 years ago (Maccaferri et al. 2019). Instead, Triticum aestivum L. derived from a polyploidization event between Triticum turgidum ssp. durum (AABB genome) and Aegilops tauschii (DD genome) (Levy and Feldman 2022). Allopolyploidy of the wheat genome is one of the key factors in wheat success as a global staple crop. The presence of two or three copies of a gene may confer greater plasticity and thus allow adaptation to changing environmental conditions (The International Wheat Genome Sequencing Consortium (IWGSC) et al. 2014). The first step to exploit the genetic potential of wheat was the complete assembly of its genome, which took a long time to complete, as the large size of the genome (16 Gb for T. aestivum and 12 Gb for T. turgidum) and the high sequence similarity between subgenomes and the abundance of repetitive elements (about 85% of the genome) hampered early attempts at assembly (The International Wheat Genome Sequencing Consortium (IWGSC) et al. 2014). In 2017, both tetraploid and hexaploid wheat genomes were made available (Avni et al. 2017; Zimin et al. 2017). Next Generation Sequencing (NGS) technologies are also providing more opportunities to study gene structure and expression. Heritable variation in the genome that underlies important agronomic traits can be identified more rapidly and systematically (Jia et al. 2017). The comprehensive sequencing of these wheat genomes with the help of NGS technology is a milestone for wheat biology and provides resources for functional wheat genomics. However, to link phenotypic traits to functional genes, wheat researchers are working on additional platforms and technical resources, such as mutant libraries, complete cDNA clones, and SNP microarrays (Jia et al. 2017). 12 1.3 Heat stress on plants 1.3.1 Heat damage and ROS production Heat stress affects several physiological processes of the plant, such as: photosynthesis, respiration, transpiration, membrane thermostability, and osmotic regulation. Among all, membrane dysfunction is the main physiological consequence of plant exposure to HS. Under high temperature conditions, the kinetic energy and movement of biomolecules across membranes increases, which causes weakening of chemical bonds, leading to disintegration of membrane lipids resulting in increased fluidity (Jaconis et al. 2021). Photosynthesis is another process that is greatly impaired by heat, causing severe repercussions on the plant (Haworth et al. 2018). Under HS conditions, photochemical reactions in the thylakoid lamellae and carbon metabolism in the chloroplast stroma are subject to damage (Hu, Ding, and Zhu 2020). Heat stress causes thylakoid membranes to rupture, thereby inhibiting the activities of electron transporters and membrane-associated enzymes, reducing the rate of photosynthesis. Among chloroplast membrane protein complexes, PSII is the most sensitive target of heat stress. It can detach from the membrane, due to increased membrane fluidity, resulting in impairment of its integrity and affecting photosynthetic electron transfer (Mathur, Agrawal, and Jajoo 2014). Photosynthetic electron transport and ATP synthesis are severely impaired if PSII suffers severe thermal damage ( Wang et al. 2018, 2). Another component of the photosynthetic apparatus that is affected by thermal stress is the Rubisco enzyme, which, under non-stress conditions, is responsible for the fixation of about 1011 tons of atmospheric CO2 (Galmés et al. 2013). It acts in the Calvin-BensonBassham cycle, catalyzing the carboxylation of the 5-carbon-atom sugar ribulose-1,5bisphosphate (RuBP), ( Wang et al. 2018). At moderately elevated temperatures, Rubisco activase (RCA) activity is inhibited, resulting in thermal inactivation of Rubisco activity. Under high temperatures, the stability of the Rubisco activating chaperone enzyme decreases, resulting in the inhibition of photosynthesis (Perdomo et al. 2017). All these alterations lead to increased concentrations of reactive oxygen species (ROS). ROS are normally produced in plant cells, particularly in the chloroplast the following are formed: hydrogen peroxide (H2O2), superoxide, hydroxyl radicals (-OH) and 1O2 during 13 photosynthesis (Asada 2006; Khorobrykh et al. 2020). ROS production occurs during excitation energy transfer in the antenna complex of PSII and during electron transport in the reaction center of PSII (Suzuki et al. 2012; Pospíšil 2016). In particular, 1O2 is formed through excitation energy transfer, while the superoxide anion radical (O2 --), H2O2 and -OH are formed through electron transport (Pospíšil and Prasad 2014). Under stress conditions, due to malfunction of PSII and the Calvin cycle ROS synthesis increase, leading to lipid peroxidation and cell membrane damage (Nelson et al. 2014). Under non-stress conditions, a balance exists between ROS production and removal, known as redox homeostasis (Caverzan, Casassola, and Brammer 2016). When ROS production exceeds the cell ability to recover, the cell is subjected to a stress known as oxidative stress (Figure 5). The HS-induced increase in ROS production causes a change in membrane potential (depolarization), lipid peroxidation, protein oxidation, nuclear acid damage, obstruction of enzyme function, and activation of programmed cell death (Sarita Srivastava and Dubey 2011). Fig 5. Effects of heat stress on plants (reproduced from J. Zhao et al. 2020). 1.3.2 Heat stress effects on wheat development Wheat is very sensitive to heat stress (Akter and Rafiqul Islam 2017), and crop growth is significantly impaired depending on the severity and timing of the stress. High temperature affects almost all stages of wheat growth and development, including seed germination, root and leaf emergence, stem growth, flowering initiation, pollination, fertilization, seed yield and 14 seed quality (Buttar et al. 2020). Optimal temperatures for wheat growth and development are shown in Table 1. The reproductive and grain-filling stages are the most sensitive to heat stress, and plants are less likely to recover if stressed at this critical stage (Barlow et al. 2015; Aiqing et al. 2018). In fact, high temperatures during the reproductive stages can cause pollen sterility, sterile ovules, decreased fertilization, and aborted flowers (Prasad and Djanaguiraman 2014) which ultimately reduce grain numbers and yield. Grain size is also reduced due to shorter duration of the filing period and early senescence (Shirdelmoghanloo et al. 2016), whereas grain quality decreases (Nuttall et al. 2015; Ullah et al. 2020) with an increase in the percentage of shriveled/broken grains (also called screens) (Ferreira et al. 2012). For each 1°C increase in mean temperature during the reproductive phase, grain production is estimated to decrease by 6% ( Chen et al. 2020). Table 1. Optimal temperature requirements of wheat at different growth stages from (Khan et al. 2020) Stages Optimum Temperature (°C) Minimum Temperature (°C) Maximum Temperature (°C) Seed germination 20-25 ± 1.2 3.5-5.5 ± 0.44 35 ± 1.02 Root growth 17.2 ± 0.87 3.50 ± 0.73 24.0 ± 1.21 Shoot growth 18.5 ± 1.90 4.50 ± 0.76 20.1 ± 0.64 Leaf initiation 20.5 ± 1.25 1.50 ± 0.52 23.5 ± 0.95 Terminal spikelet 16.0 ± 2.30 2.50 ± 0.49 20.0 ±1.60 Anthesis 23.0 ± 1.75 10.0 ±1.12 26.0 ± 1.01 Grain filling duration 26.0 ± 1.53 13.0 ± 1.45 30.0 ± 2.13 However, even the early stages of growth can be severely affected by high temperatures. For example, growth and development begin with seed germination, which requires optimal temperature and humidity. The germination index and potential are reduced under high temperatures ( Liu et al. 2019). High temperature also heavily affects the rooting of wheat seedlings in the early vegetative stages of the crop; if the temperature is high, there is a reduction in root growth, shoots, green leaf area, and the number of effective shoots per plant ( Gupta et al. 2013). 15 A better understanding of the morphological and physiological traits associated with HS tolerance would allow defense measures against heat stress to be defined (Janni et al. 2018; Langridge and Reynolds 2021). 1.3.3 Plant adaptation strategies to heat stress Plants have three main adaptation strategies for heat resistance: heat tolerance (HT), heat escape (HE) and heat avoidance (HA) (Shanker, Bhanu, and Maheswari 2020); almost all of the adaptive traits that enable the plant to counteract stress are encapsulated in the term Heat Resilience (Figure 6). 1) Heat tolerance is the ability of the plant to resist and withstand continuous exposure to hot conditions with a varying degree of plant damage, the less the greater the tolerance. The term includes mobilization of heat shock proteins, activation of the antioxidant system, induction of membrane thermostability and photoprotective metabolites (Wahid et al. 2007). 2) Heat avoidance is the ability of plants to avoid the effects of heat by maintaining tissue homeostasis and enzyme activity thereby countering heat; short term avoidance includes leaf rolling or change in leaf position, change in lipidic membrane composition, and cooling as the result of transpiration and rapid water uptake (Sangeeta Srivastava et al. 2012). 3) Heat escape is the ability of the plant to escape heat-like conditions by adjusting its phenology, developmental plasticity, growth pattern so that its critical physiological stage that can be affected by heat escapes it (Shanker, Bhanu, and Maheswari 2020). 16 Fig 6. Heat resilience and the processes leading to heat stress in plants (from Shanker et al., 2020). 1.3.4 Heat resilience To achieve "zero hunger" among the sustainable development goals and ensure food security, crops must be improved especially under changing climatic conditions (Janni et al. 2020). To do this, scientists must be able to produce crops that are resistant to abiotic stresses; this involves the use of advanced technical techniques such as high-throughput genotyping and phenotyping and genome editing (Hickey et al. 2019). Conventional plant breeding strategies based on phenotypic selection and qualitative genetics were used in the past (Setia and Setia 2018). However, population growth resulting in increased demand for food has resulted in the search for elite cultivars that are more adaptable to ongoing changes. The mechanisms for responding to various stresses are very complex and understanding the mechanisms that regulate the various responses is crucial in order to continue with the search for cultivars that can adapt to stresses. Advances in "omics" technologies, particularly genomics, transcriptomics, proteomics, metabolomics, and phenomics allowed to monitor factors and traits that influence crop growth and yield in response to environmental threats ( Soda 2015;Setia and Setia 2018). - Genomics. Next-generation sequencing techniques have accelerated advances in crop functional genomics studies (Li et al. 2018). Numerous genes have been identified in plants that control key agronomic traits, especially under abiotic stresses ( Sharma et al. 2017; Wen et al. 2021). Genome-wide association study (GWAS) has been applied to link traits to their underlying genetics. Many association studies have been conducted on various crops, such as wheat under heat stress (Rong Zhou et al. 2022). - Transcriptomics. In recent years, RNA-seq (RNA sequencing) with next-generation sequencing techniques has enabled more accurate characterization of the transcriptome than microarray. MicroRNAs (miRNAs), circular RNAs (circRNAs) and long ncRNAs (lncRNAs) also play key roles in the response to abiotic stresses. These ncRNAs, including miRNA, circRNA and lncRNA, are considered an emerging target for crop improvement (Rong Zhou et al. 2022). It is of considerable importance to study plant transcriptome responses at individual cell level because different cell types are known to play different biological roles in plant growth and development. Single-cell RNA-sequencing (scRNA-seq) is a high-resolution 17 approach to study the functional genomes and transcriptional activity of plants at the singlecell level (Rich-Griffin et al. 2020), which helps scientists explore plant heterogeneity within cell types. A significant difference in some genes has been found among Arabidopsis thaliana (L.) Heynh. cell types under heat stress, although the heat shock protein response dominates gene expression in different cell types (Jean-Baptiste et al. 2019). - Proteomics. Proteins, like gene products, can directly play roles in crop response to heat stress. A number of proteins involved in crop heat tolerance have been identified using proteomics ( Zhao et al. 2016; Lu et al. 2017; Mu et al. 2017) as for example the study conducted by Lu et al. (2017) which led to the identification of 258 thermo-protein reactive in wheat that were playing a role in regulating redox, chlorophyll synthesis, protein turnover and carbon fixation (Lu et al. 2017). - Metabolomics. It provides an efficient method for characterizing heat tolerance in plants and metabolome composition and dynamics in various cultures to heat stress have been identified using metabolomics (Chebrolu et al. 2016; Paupière et al. 2017). Overall, metabolomics is required to complete knowledge of other omics and to develop models for the system as a whole (Paupière et al. 2017), which can contribute to heat tolerance lighting by the appearance that other omics cannot cover. - Phenomics. Over the past decade, plant phenomics has made impressive progress, developing novel sensors and imaging techniques for a wide range of traits, organs and situations (Tardieu 2017). Phenomics also characterized the plasticity of the plant phenome when exposed to a range of environmental conditions by capturing and interpreting a multidimensional matrix of functional and architectural variables measured at different scales (organ, plant, canopy), developmental stages and environmental scenarios (Danzi et al. 2021). The integration of genomics, transcriptomics, proteomics, metabolomics, and phenomics data not only allowed to explain various biological processes (Rong Zhou et al. 2022), but gave innovative tools to fight the growing challenges of climate change on crops (Janni et al. 2020), resulting in the development of new high-yield varieties with increased heat tolerance and adaptation to climate change. Multi-omics can facilitate technique-assisted breeding, genetic engineering, and genome editing and thus accelerate crop improvement. Current data indicate that at least two different genetic systems can protect plants from the otherwise lethal effects of heat stress: (i) a series of Mendelian HS genes acting possibly 18 in a dominant or semi-dominant way; and (ii) a small number of QTLs which allow plant growth and reproduction under different stress conditions (Janni et al. 2020). 19 1.4 Molecular mechanisms of Heat tolerance 1.4.1. Signal transduction The plasma membrane acts as the first sensor for thermal stress; the change in plasma membrane fluidity causes cyclic nucleotide calcium channels (CNGCs) controlled by nucleotide cyclases to open, causing Ca2+ to move into the cytosol from the nuclear membrane (Mittler, Finka, and Goloubinoff 2012). Ca2+ ions are associated with protein calmodulin 3 (CaM3) during HS; the Ca2+- CaM3 complex interacts with calcium/calmodulin-binding protein kinase 3 (CBK3) and phosphatase PP7 to transduce cytosol heat-stress response (HSR) signals from the cytosol to the nucleus by modulating phosphorylation and de-phosphorylation of HSFA1, respectively (Ohama et al. 2017). Heat Shock Factors (HSFs) are encoded by large gene families with variable expression and functions and are components of complex signaling systems that control responses not only to high temperatures, but also to a range of abiotic stresses such as cold, drought, hypoxic conditions, soil salinity, toxic minerals, strong irradiation, and pathogen threats (Andrási, Pettkó-Szandtner, and Szabados 2021). Signal transduction also occurs with increased levels of Inositol-1,4,5-triphosphate (IP3), through the phosphoinositide signaling pathway, which results in Ca2+ influx into the cytoplasm from intracellular Ca2+ pools such as the endoplasmic reticulum (ER) and vacuole during HS (Ohama et al. 2017). ROS also contribute to signal transduction with the accumulation of nitric oxide (NO), which induces CaM3 activation (Rengang Zhou et al. 2009). 1.4.2 Heat Shock Factors (HSF) As mentioned in the previous paragraph, signal transduction leads to the activation of Heat Shock Factors, HSFs. HSFs play a crucial role in the response to high temperatures in plants. They are the activators of production of heat shock proteins, which in turn are major players in the heat stress response (Liao et al. 2022). Generally, plant HSFs proteins share a well-conserved structure characterized by 3 main domains. The N-terminal DNA-binding domain (DBD) is characterized by a central helix-turn-helix motif that specifically binds to heat stress elements (HSEs) in target 20 promoters and subsequently activates transcription of stress-inducible genes (Scharf 2012). The oligomerization domain (OD) with a bipartite pattern of hydrophobic amino acid residues (HR-A/B region) is linked to the DBD by a flexible linker. The C-terminal activation domains of plant HSFs are characterized by short peptide motifs (AHA motifs), which in many cases are crucial for activator function (Meng et al. 2016). Despite many conserved features, HSF family members show a strong diversification of expression patterns and functions within the HSF family ( Chen et al. 2018). In wheat, 56 TaHsf members were identified, classified into classes A, B and C (Xue et al. 2014). When plants experience heat stress, HsfA1s induce the expression of HSPs by binding the HSE in the corresponding promoter DNA sequence. The HSEs are central trinucleotide sequences, 5′-nGAAn-3′ or 5′-nTTCn-3′, with alternating orientation, separated by two nucleotides (Arofatullah et al. 2018). In addition, HsfA1s activate TFs, triggering a transcriptional cascade composed of various TFs, including HsfAs (HsfA1e, HsfA2, HsfA3, HsfA7a and HsfA7b), HsfBs (HsfB1, HsfB2a and HsfB2b), DREB2A and MBF1c ( Liu and Charng 2013). The HSF interacts with the ROS system activated, not only by the signal cascade initiated by signal transduction, but also by the accumulation of hydrogen peroxide (H2O2) molecules (Kollist et al. 2019). The study by Perez-Salamo et al., showed that a subset of Hsfs genes is up-regulated by treatments that generate ROS (Pérez-Salamó et al. 2014). The mechanism of sensing is not well understood. Function of HSFs might be affected by the presence of H2O2, as the latter can stabilize HSF trimers through reversible oxidation of Cys residues and formation of Cys-Cys bonds ( Miller and Mittler 2006). In addition, several studies have shown that the HSE region of Hsfs does not interact only with Heat shock proteins (HSPs). Storozhenko et al. (1998) showed that HsfB1 cloned from tomato binds the HSE of the ascorbate peroxidase 1 (apx1) gene by activating its transcription under HS in arabidopsis (Storozhenko et al. 1998). An apx with a similar HSE motif was upregulated after exposure of rice seedlings to 42°C (Sato et al. 2001). Thus, Hsfs can drive the production of key regulatory elements in plant defense against heat stress: the HSPs and enzymes with scavenger activity such as: ascorbate peroxidase (APX), superoxide dismutase (SOD), catalase (CAT) (Driedonks et al. 2015). 27 1.4.3.6 sHSP26 The sHsp26 gene family in durum wheat was isolated and characterized by Comastri et al.2018, exploiting a TILLING approach. Four putative TaHsp26 genes, namely TaHsp26A1, -A2, -A3, and -B1 were retrieved in the durum wheat genome (Comastri et al. 2018). The three A genome loci are localized on the short arm of chromosome 4A, while the single B genome locus is mapped on 4BL. The gene's topography is shown in Figure 10 (Comastri et al. 2018). Fig 10. TdHsp26 gene structures. The conserved N-terminal MrD (Methionine-rich Domain) amphipathic a-helix is highlighted in light gray and the ACD in dark gray. The exon/intron junctions are indicated from (Comastri et al. 2018) . In silico analyses placed these proteins in the chloroplast, thanks to the presence of a chloroplast Transit Peptide (cTP). TdHsp26 genes were differently regulated upon direct heat stress and especially after acclimation. TdHsp26-A1 showed the highest upregulation following direct heat stress and TdHsp26-B1 the highest one when the heat stress was imposed after acclimation. This prompted us to focus on TdHsp26-A1 and -B1 genes in this research thesis. 28 1.5 Heat resilience 1.5.1 Genetic diversity and breeding to cope climate changes and heat stress Climate change is strongly affecting crop yields and the increasing world's population, the development of improved crops more tolerant to the ongoing changes is needed to ensure food security and safety (Kholová et al. 2021). Thus, understanding the mechanisms of heat tolerance, based on genetics and physiology, is critical to achieving this goal. The knowledge gained will be used to develop heat-tolerant cultivars suitable for sustainable growth under heat stress conditions (Shanmugavel et al. 2021). Possible approaches to maintain high crop yields are (i) exploiting natural and induced mutations; (ii) harnessing available genetic resources to produce new genetic material more tolerant to HS and related secondary stresses; (iii) improving the ability to screen and identify available sources of resilience; and (iv) developing new breeding techniques (Janni et al. 2020). Much of the genomic information is provided by sequencing, highlighting the presence of millions of Single nucleotide polymorphisms (SNPs), but resequencing of diverse germplasm (including wild species) also contributes. The SNPs are the most common form of DNA sequence variation between alleles, in several plant species. The discovery and application of SNPs increased our knowledge about genetic diversity and provided a better understanding on crop improvement (Morgil et al. 2020). Modern breeding selection methods have been based on improvement of elite lines which have a narrow genetic base; this limits the genetic pool which breeders can exploit for the production of new varieties ready to face the predicted climate changes or adaptation to new cultivation areas (Pignone et al. 2015). Germplasm resources could help mitigate the effects of climate change on agricultural production (Branlard et al. 2020). Here, we explored the natural genetic diversity within the sHsp26 gene family in a collection of durum wheat genotypes, mainly landraces, to identify novel sources of genetic diversity for breeding. The durum wheat germplasm collection was developed by Pignone et al, in 2015 in CNR to and consists of 452 genotypes originated in about 40 different world countries. (Pignone et al. 2015). From this collection, 33 genotypes were chosen for their 29 potential abiotic stress tolerance traits (Danzi et al. 2019; 2022) and qualities (Janni et al. 2018). Previous works have reported the success in exploiting natural diversity to discover novel useful traits and QTL for abiotic stress tolerance mainly drought. Allele mining approach was used to find SNPs in the sHsp26 gene. Allele mining is useful for identifying nucleotide variation in a genomic region (candidate gene) associated with phenotypic variation for a trait. In this way, the frequency, type and extent of the occurrence of new haplotypes and the resulting phenotypic variations can be assessed ( Kumar et al. 2010). 1.5.2 TILLING and EcoTILLING To meet the demand for increased production of food crops it is necessary to introduce new selection strategies that accelerate the development of plants that are adapted to the changing environment. The use of induced mutations, combined with modern genomics tools, is an effective strategy to identify and manipulate genes for crop improvement. The highthroughput TILLING (Targeting Induced Local Lesions IN Genomes) methodology identifies mutations in mutagenized populations, while EcoTILLING identifies SNPs within a natural population and associates these variations with traits of breeding interest. The main advantage of these techniques as a "reverse genetics" strategy is that they can be applied to any species, regardless of genome size and ploidy level (Barkley and Wang 2008). Germplasm collections can be useful resources for researchers for TILLING and EcoTILLING experiments, providing so much genetic information and/or to acquire the material needed for a study. On the other hand, TILLING could be applied in association/collaboration with germplasm repositories to develop mutant lines that have advantageous characteristics for breeders ( Wang et al. 2017). Previous work reported the efficacy of TILLING and ECOTILLING in producing and selecting novel material for the upcoming needs of wheat breeding (Sestili et al. 2010; Chen et al. 2012; Comastri et al. 2018; Irshad et al. 2020). 1.5.3 Phenotyping Plants respond to abiotic stressors in a dynamic and complicated manner, both reversibly and irreversibly. Plant abiotic reactions have been studied physiologically, 30 biochemically, cellularly, and molecularly, revealing intricate cellular responses to abiotic stressors. However, phenotyping remains a cornerstone of plant breeding. Despite advances in genetics and the application of molecular technologies in crop research (Reynolds et al. 2020), crop breeding still relies heavily on the expression of grain yield and a handful of agronomically important traits for making selections and defining commercial products. The phenotype is the functional body of a plant, formed during plant growth and development. It depends on the dynamic interaction between the genetic background and the physical world in which the plant develops. These interactions determine plant performance and productivity measured as accumulated biomass and commercial yield and resource use efficiency (Watt et al. 2020). The term "phenotyping" refers to the application of methodologies and protocols to measure a specific trait related to plant structure or function, with traits ranging from the cell level to the whole plant level (Carvalho et al. 2021). In recent decades, phenotyping has developed as an essential tool for characterizing an enormous amount of plant processes, functions, and structures, mostly by non-destructive image-based optical analysis of plant traits. Therefore, plant phenotyping has started to become a tool applicable to plant scientists to understand plant-environment interaction and to plant breeders to select desirable genotypes for their specific field of interest, such as tolerance to abiotic stresses (Watt et al. 2020). Technological improvement in the last decades in plant phenotyping has allowed the introduction of new non-invasive tools, ensuring a rapid and faithful structural parameters’ extraction (Zhang et al. 2016). In this context, crop phenotyping platforms are considered a valid solution (Chawade et al. 2016). Phenotyping platform-based approaches enable highthroughput, automated and simultaneous multiple-plant screening, resulting in saving processing time and improved accuracy in the analysis of plant traits (Acharjee et al. 2018). Automated platforms have been designed mainly for plant phenotyping in growth chambers or greenhouses and combine robotics, remote sensors and data analysis systems. The drawbacks of most time-consuming phenotyping methods in terms of throughput and standardization have been overcome, in recent years, using image-based data collection ( Araus, Buchaillot, and Kefauver 2022). Remote sensing technologies (Figure 11), with the respective controllers and data loggers that complement the imaging systems, are usually assembled into what are termed as phenotyping platforms ( Araus et al. 2018). The use of 31 these platforms allows for a more efficient and accurate phenotyping with stable error across all genotypes, whether as single plants or in micro-plots. However, currently many of these platforms are costly and/or not applicable on a wide scale. Therefore, there is a strongly expressed need by the crop breeding community to develop both state-of-the-art and costeffective, easy to use, and nonstationary (High Throughput Phenotyping Platforms (HTFP). Within the hand-held category of platforms, smartphones are becoming an alternative since they may carry out different imagers (e.g., RGB and thermal), data management activities and geo-referencing functions (Araus et al. 2018; Sanchez-Bragado et al. 2020). Fig 11. Different Categories of Ground and Aerial Phenotyping Platforms. Ground level: these include from Handheld sensors (in this case just a person holding a mobile), to Phenopoles, Phenomobiles, Stationary Platforms. From 10 to 100 m: Unmanned Aerial Vehicles, as drones of different sizes and compactness, fixed-wind drone. From 100 to 4000 m Manned Aerial Vehicles as airplanes or helicopters. In the near future different categories of satellites (Nanosatellite, Microsatellite and Satellites) from 50 to 700 km. (reproduced from Araus, Buchaillot, and Kefauver 2022). Identifying the key traits for phenotyping may result in convergent approaches and in particular the genetic advance in wheat for a wide range of environmental conditions, has been associated with a higher stomatal conductance (Roche 2015) and higher final biomass. 32 To this end remote sensing techniques such as infrared thermometry or thermography may be deployed as proxies for higher transpiration (Impollonia et al. 2022). Greater biomass is also considered as a key target trait for selection under heat stress, since harvest index is reaching theoretical maximum, an increase in biomass becomes a target. The most canonical way to assess biomass is by using LiDAR (Light Detection and Ranging) mounted in an aerial platform or in a “phenomobile” (Madec et al. 2017). However still today the most common way to assess green biomass is through vegetation indices, either multispectral or RGBderived, given the common perception these approaches being more affordable and easier to use than the LiDAR (Patrignani and Ochsner 2015, Figure 11). Recently, a different class of sensor, referred to as a “bioristor,” has been shown to be able to detect, in vivo and in real time, the changes in the composition of the plant sap in a growing tomato (Solanum lycopersicum L.) plant ( Coppedè et al. 2017; Janni et al. 2019) , without interfering with plant functions. Bioristor is an organic electrochemical based transistor (OECT) realized on textile thread (Coppedè et al. 2017) and enables measuring the changes in ion concentration in the plant sap. The pioneering application of bioristor in dicotyledonous plant species, such as tomato, successfully allowed detection of changes occurring in the plant sap composition following the day/night circadian cycle (Coppedè et al. 2017), as well as early detection of plant stress conditions under drought ( Janni et al. 2019; Finco et al. 2022) and saline stress in monocots (Janni et al. 2021). The correlations among bioristor sensor response, changes in relative humidity, and vapor pressure deficit have been recently demonstrated, proposing the bioristor as a novel sensor to improve water use efficiency (Vurro et al. 2019). 33 2. AIM OF THE PROJECT Climate change and the rise in temperature severely hamper crop growth and development. Several physiological drawbacks affect crop yields, also causing a modification in cultivated areas. At the same time, food production must double by 2050 to meet the demand of the world’s growing population and innovative strategies are needed to help combat hunger, which already affects more than 1 billion people in the world. Wheat is one of the major cereal crops worldwide, providing 20% of the total dietary calories and proteins worldwide. FAO has projected a 43% increase in global demand for cereals, including wheat, by 2050, mainly from developing countries. To address these needs, the availability of new genetic materials exploitable in breeding programs is mandatory to achieve these goals. The exploitation of the hidden in the genetic diversity contained in germplasm collections can be a key strategy to achieve this goal. Small Hsps represent a good target to improve heat resilience due to their strong involvement in the heat stress response in plants and in particular in wheat. Here, an innovative approach, based on cost-effective targeted resequencing coupled with low-cost phenotyping techniques was applied to a set of durum wheat landraces to assess and exploit a possible genetic variation within the sHsp26 gene family as a source of new improved heat resilience. Plants have been subjected to heat stress at different developmental stages and we associated the different haplotypes of TdHsp26 with the different responses to thermal stress, increasing the knowledge on the role of TdHsp26 in the response to thermal stress. 34 3. MATERIAL AND METHODS 3.1 EcoTILLING and detection of natural variation in TdHsp26-A1 and TdHsp26-B1 in durum wheat genotypes 3.1.1 Plant material This study was conducted on 33 durum wheat genotypes as a core set of a single seed descent (SSD) durum wheat collection, developed by IBBR-CNR in Bari (Pignone et al. 2015) and selected on the basis of a phenotyping selection (Danzi et al. 2019; 2022). In addition, 5 commercial varieties (Svevo, Saragolla, Cappelli, Colosseo and Kronos) were included as references in the experiments. A list of the genotypes and their geographic origin is provided in Table 2. 35 Table 2. List of genotypes and country of origin used for the allele mining approach. SSD Entry Origin 35 Algeria 44 Tunisia 64 Morocco 69 Morocco 92 USA-ND North Dakota 99 Ethiopia 109 Iraq 112 Iraq 116 Iraq 122 USA-ND North Dakota 135 Turkey 171 Peru 178 France 195 Saudi Arabia 244 Ethiopia 253 Cyprus 269 Iran 278 Bulgaria 322 Turkey 325 Syria 335 Iraq 343 Iran 397 Crete 409 Grece 415 Crete 416 Grece 441 Crete 451 Iraq 459 USA 487 Grece 494 Grece 499 511 Libya Colosseo Italy Kronos USA Cappelli Italy Saragolla Italy Svevo Italy Plants were grown in a growth chamber till seedlings stage (Zadoks 10) at 16°C with a photoperiod of 18/6 day/night. DNA was extracted as described in the next section (3.1.2). 3.1.2 DNA extraction and gene targeting Genomic DNA was extracted with the GenElute™ Plant Genomic DNA Miniprep Kit (Sigma-Aldrich) from leaves tissues of durum wheat genotypes and analyzed through allele 36 mining approach that combines targeted enrichment PCR with next generation sequencing as described in (Buffagni 2019). Genomic region corresponding to TdHsp26-A1 (LT220905) and TdHsp26-B1 (LT220911) (1171 bp and 2575 bp respectively) were amplified by PCR to identify SNPs in the selected genotypes listed in Table 2 (Comastri et al. 2018). All the DNA of the SSD genotypes was kindly provided by IBBR-CNR (Bari, Italy). TdHsp26-A1 and TdHsp26-B1 (total 3.74 kb Mb) were targeted by PCR with 3 homeologous-specific primer pairs (PPs), 1 for TdHsp26-A1 (PP1 F:5’- TGTTGGGCCTCCTGATCG-3’; R: 5’-AGCCTCAGATGCAGGGTAC) and 2 for TdHsp26B1 (PP2 F:5’-CAATTGGTTCGCACAAACAC-3’; R:5’-CCCTCCAGGCACGGATG-3’ and PP3 F:5’-GACACTCTCTCGTTTCAATTCTC-3’; R:5’-GTTATCAGCTTCTTCCGGG-3’). PCR was performed in 25 μL final volume, using TaqDNA Polymerase (New England BioLabs, Hitchin, UK), 10–20 ng template DNA, 0.2 μmol/L of each forward and reverse primers, 0.2 mmol/L of each dNTPs, 1X Standard Taq Buffer (New England BioLabs). PCR amplification with PP-2 was performed with touch-down PCR as follows: 1) initial denaturation at 95°C for 2 min 2) 10 cycles at 94 C for 25 s, annealing at 66°C for 30 s with a drop -0.5°C/cycle, elongation at 72°C for 1 min 30 s, 3) 35 cycles of 94°C for 25 s, annealing at 61°C for 45 s, elongation at 72°C for 1 min 30 s, 4) 1 cycle at 72°C for 10 min. PCR amplifications with PP1-1 and PP-3 were performed as follows: 1) initial denaturation at 95°C for 2 min 2) 35 cycles at 95°C for 30 s, annealing at 60°C for 60 s, elongation at 72°C for 2 min, 3) 1 cycle at 72°C for 10 min. PCR products were checked on TAE agarose gel to ensure specific amplification of the targeted region. PCR products were sequenced with BigDye™ Terminator v3.1 Cycle Sequencing Kit (Thermofisher Scientific, Waltham, MA) according to the manufacturer instructions. As described in (Buffagni et al. 2018) 3 PCR reaction for all 38 genotypes were performed in 96 wells and then pooled in 4 tubes as follows: • pool1 (SSD35, SSD92, SSD122, SSD171, SSD253, SSD269, SSD416, SSD487. SSD 494 and cv. Kronos), • pool2 (SSD44, SSD109, SSD178, SSD322, SSD343, SSD397, SSD415, SSD511, cv. Colosseo), 43 3.3 Phenotypic analysis The list of the phenotypic analyses is reported in the following scheme (Fig. 15). Fig 15. Phenotypic traits considered during the experimental phases in all developmental stages. 3.3.1 Morphological analysis In seedlings experiment the length of C1W and S1W seedlings of the various genotypes was measured using ImageJ Software (available at http://rsb.info.nih.gov/ij/ accessed on 20 September 2021; developed by Wayne Rasband, National Institutes of Health, Bethesda, MD, USA) (Figure 16). Three replications for each condition were considered. 44 Fig 16. Images of the seedlings one week after stressing a) control b) stressed. The images were analyzed using ImageJ software. In the tillering experiment, the plant height, number of culms, number of leaves one week post stress. Leaf area and canopy of 3 plants for both control and stress plants were recorded before the stress, 48hPS and 1WPS. The leaf area has been calculated manually using the following formula Leaf area = Length x width x 0.75 (Kuzmanović et al. 2014; Ahmad et al. 2015); while canopy (%) was measured using CANOPEO app (Patrignani et al. 2015). In the anthesis experiment, considering the prolonged stress, morphological traits were scored in tillering (Zadoks 31), stem elongation (Zadoks 54) and spiking (Zadoks 72) precisely at 30 DAT, 42 DAT and at 50 DAT. The number of leaves was detected only after 30 and 42 DAT, this is because at the end of elongation the arrangement of leaves changes with the death of some and the formation of new ones, which implies a loss of linearity that occurs in earlier stages instead. Plant Height, tiller number and leaves number were measured in 12 different plants, 3 plants for each pot for each line. 45 3.3.2 Biochemical analysis In seedlings and tillering, samples were collected at times: T0, 4hPS, 1hPR, C1W and S1W and 8 replicates were sampled for seedlings and four in tillering for each genotype: the pools were immediately frozen with liquid nitrogen. In anthesis sampling was performed one week after flowering (1WAF), in the morning at lower temperatures and in the afternoon at higher temperatures. Pools of 6 spikes and six leaves were made considering two leaves and two spikes from 3 plants of the same line were sampled and immediately placed in liquid nitrogen. Malondialdehyde (MDA) content The effect of heat stress on membrane lipid peroxidation was verified by quantifying the MDA content. To determine the concentration of malondialdehyde (MDA) the Thiobarbituric Acid (TBA) test was used following the protocol reported in Senthilkumar et al (2021)(Senthilkumar, Amaresan, and Sankaranarayanan 2021). 50 mg of leaves grounded in liquid nitrogen, 1mL 0.1% (w/v) trichloroacetic acid (TCA) has been added to precipitate the proteins. The homogenized samples were centrifuged at 14,000 g for 15 min. Subsequently, 250 µL of the supernatant was added to 1000 µL of 20% (w/v) TCA containing 0.67% (w/v) TBA. The mixture was boiled at 95°C for 30 min in a water bath and quickly cooled in an ice bath for 10 min to stop the reaction. The mixture has been centrifuged at 10,000 rpm for 5 min and the supernatant was collected. The absorbance was read at 532 and 600 nm and calculate the concentration of MDA-TBA concentration based on the Ɛ value using a Varian Cary 50 spectrophotometer. Where, Ɛ is the coefficient absorbance (1.55 mM - 1 cm -1). The amount of malondialdehyde is expressed as nmol mg−1 FW. (𝑀𝐷𝐴𝑠𝑡𝑟𝑒𝑠𝑠𝑒𝑑 − 𝑀𝐷𝐴𝑐𝑜𝑛𝑡𝑟𝑜𝑙) 𝑀𝐷𝐴𝑐𝑜𝑛𝑡𝑟𝑜𝑙 ×100 Determination of Hydrogen peroxide H2O2 The Hydrogen peroxide (H2O2) concentrations were determined, in tillering experiment, following Velikova et al. (2008) (Velikova, Fares, and Loreto 2008), with some modifications. Leaf tissues (50 mg) were homogenized in an ice bath with 1mL 0.1% (w/v) trichloroacetic acid (TCA) The homogenate was centrifuged at 12,000 g for 15 min at 4°C. Then 250 µL of the supernatant was added to 250 µL 10 mmol/L potassium phosphate buffer 46 (pH 7.0) and 500 µL 1mol/L KI and the supernatant held for 20 min at room temperature, after which the absorbance was read at 390 nm using a Varian Cary 50 spectrophotometer. H2O2 content was determined using the extinction coefficient 0.28 μM−1cm−1 (Dong et al. 2014). The amount was expressed as nmol mg−1 FW. 3.3.3 Physiological measurements Photosynthesis: Photosystem II (PSII) efficiency: Fv/Fm To evaluate the effect of high temperatures on photosystem II, Fv/Fm was measured using FluorPen FP110, in the anthesis experiment. One measurement per plant was made for a total of 9 measurements. The reading was taken after 15 min of darkness. The analyses were done at day 42 DAT (near flowering) and one week after flowering (1WAF), which was recorded for each genotype. For each day, measurements were taken at two different times of the day in the morning from 8 to 10:30 am and in the afternoon from 2 to 4:30 pm to assess the effects of high temperatures on the various parameters analyzed. Transpiration: Stomatal Resistance (rS) and stomatal conductance (gs) Stomatal resistance expressed (sec cm-1) and stomatal conductance (mmol m⁻² s⁻¹) were measured in the tillering and the anthesis experiment respectively by using AP4 porometer (Delta-T Devices). In tillering stomatal resistance was acquired at the following time points: T0 (before stress), 4hPS (4 hours post stress), 48hPS (48 hours post stress) and finally after one week post stress in C1W and S1W. Three flag leaves from three plants per plot were measured for stomatal resistance, six readings per leaf. In anthesis gs was measured. The measurements were done at day 42 DAT and 1WAF at two different times of the day, in the morning from 8 to 10:30 am and in the afternoon from 2:00 pm to 4:30 pm to assess the effects of high temperatures on the conductance. Canopy temperature: Infrared Leaf Temperature (IR) and Canopy Depression Temperature (CTD) Thermal imaging was applied using an infrared thermal camera FLIR E75 to detect the canopy temperature during the stress. The camera resolution is 320 x 240 pixels, thermal 47 sensitivity lower than 0.03° C, emissivity used was 0.99. three pictures. 3 photos per plant were acquired for each analysis. In tillering the measurements were taken at the following time points T0 (before stress), 4hPS (4 hours post stress) C1W and S1W. Three pictures of each plant were acquired. In anthesis the measurements were done at day 42 DAT (near flowering), and one week after flowering at two different times of the day, in the morning from 8 to 10:30 am and in the afternoon from 2 to 4:30 pm to assess the effects of high temperatures on the conductance. Two vessels per genotype were considered, for a total of 12 photos per genotype. The thermal images were analyzed using R software (R Core Team, 2022) and the Therm-image package (https://cran.r-project.org/web/packages/Thermimage/index.html). To segment vegetation from the background, pure vegetation pixels were classified by applying a k-means clustering algorithm on thermal images. The average canopy temperature for each plant was extracted from pure vegetation pixels. An example of a thermal image clustering during the experiment is shown in Figure 17. Once leaf temperature was known, we calculated canopy thermal depression (CTD), defined as the difference between plant canopy temperature and ambient temperature, has been recognized as key trait to evaluate/compare the response of genotypes to low water use, high temperatures and other environmental stresses. 48 Fig 17. a) RGB image, b) thermal image acquired with the thermal camera FLIR 075, c) thermal image with graduated scale, d) image clustered by the k-means algorithm for plant segmentation. Relative Water Content (RWC) The RWC content was measured in leaves following the protocol reported in Celik, (Çelik, Ayan, and Atak 2017). Three leaves were randomly collected to determine relative water contents within each group. After measuring the fresh weights (FW), leaves were placed into the distilled water for 12h in order to obtain turgid weight. (DW) Following the turgid weight (TW) measurement, leaves were heated in a dry heat incubator for 24h (80°C) to obtain dry weights. Relative water contents (RWC) were calculated according to the formula as reported by Smart (Smart and Bingham 1974): 𝑅𝑊𝐶 = (𝐹𝑊 −𝐷𝑊) (𝑇𝑊 −𝐷𝑊)∗100 3.3.4 Yield traits In the tillering experiment, the first spike of control and stressed plants of each genotype was analyzed for the: 49 - number of seeds per spike. - Kernel Yields Per Spike (KYPS), consists of weighing the seeds obtained from each ear, the weight is expressed in grams. In anthesis were measured: - Spike Length (SL), of all spikes from the main culm, was measured from the base of the rachis to the tip of the terminal spikelet, excluding the awns; - Grain Yield per plant, consists of weighing the seeds obtained from each plant, the weight is expressed in grams; - number of seeds per plant; - number of spikelets of all spikes arising from the main culm. 3.3.5 Stress indices To calculate common stress tolerance and susceptibility indices for various crop traits, the iPASTIC (The Plant Abiotic Stress Index Calculator) software (Pour-Aboughadareh et al. 2019) was used. The indices acquired through iPASTIC are the following: tolerance index (TOL), relative stress index (RSI), mean productivity (MP), harmonic mean (HM), yield stability index (YSI), geometric mean productivity (GMP), stress susceptibility index (SSI), stress tolerance index (STI) and yield index (YI). The program estimates an Average Sum Ranks (ASR) for all indices to select potentially superior genotypes; the lower is the ASR value, more tolerant is the genotype. The grain yield per plant, expressed as the number of seeds per plant, was used to run iPASTIC was the yield of stressed and not stressed plants for each genotype. 3.4 TdHsp26-A1 and TdHsp26-B1 gene expression 3.4.1 Sample collection For the short-term stress in seedlings and tillering, leaf material for gene expression analysis was collected at times T0, 4hPS and 1hPR. For long-term stress, sampling, again of leaf material, was done one week after flowering. The flowering time of each genotype was monitored, and sampling in target days. Seven days post the anthers emission, two samplings were done, one early in the morning and the second in the afternoon with the highest temperatures. 50 3.4.2 RNA extraction and cDNA retrotranscription For all experiments, RNA was extracted with RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) from all samples, and visualized on agarose gel (1,2% agarose w/v, 1X TAE buffer, GelRed™ as Nucleic Acid gel stain) to assess its integrity. For each condition, 1 μg of total RNA samples extracted from 100 mg of frozen tissue were retro-transcribed into cDNA with QuantiTect Reverse Transcription Kit (Qiagen) according to the manufacturer instructions. 3.4.3 Quantitative Real Time PCR (qtPCR) Real Time (RT) qPCR analysis was performed using CFX96 Touch Real-Time PCR Detection System (Biorad). PCR reactions were set up in 10μL containing 1μL of 1:10 dilution of cDNA, 0.25 nmol/L of gene-copy specific forward and reverse primers (Table 4) A1-PT31F/A1-PT31R, B1-PT10F/B1-PT10R (Comastri et al. 2018). TdACT gene (AB181991) was used as housekeeping. Table 4. List of primers used for the Real time qPCR Target Primer Forward Primer Reverse Forward 5’ – 3’ Reverse 5’ – 3’ Amplicon size (bp) TdHsp26-A1 A1– PT31F A1– PT31R CCAGGCCCAGAACGCT CCTCCTTcTCGTCCTCCATa 338 TdHsp26-B1 B1-PT10F B1-PT10R CGATGCGGCAGATGCTT TGACGAGCGCGTCGC 211 TdACT ACT-Fw ACT-Rev CTTGTATGCCAGCGGTCG AA TGAGGAAGCGTGTATCCCTC G 96-173 For the samples collected in seedlings and anthesis, we calculated expression level changes by ΔΔCT. Starting from we ΔCT as CTtarget gene - ΔCTinternal standard; ΔΔCT was calculated as CTtreatment - CTcontrol. For seedlings the CTcontrol is the sampling at T0, while for the anthesis experiment the CTcontrol is the sampling done in the morning S1W after flowering. Gene expression was considered relevant when fold-change was greater than 2 (FC ≥ 2). In the samples collected in the tillering experiment we applied semiquantitative RTPCR for Hsp26 gene expression. The amplicons were loaded on a 1.2% agarose gel. The 51 bands were analyzed by ImageJ software and normalized for the band of the housekeeping genes. 3.5. Data analysis For the experiment in seedlings: Student t-Test was applied to analyze the difference of plant height and for the analysis of data inherent to gene expression. For data analysis on MDA content, ANOVA test with p-value <0.05 was used using Past 4.03 software, the next post hoc used was Tukey's test. In the tillering experiment: Leaf Area, number culms, Plant height, stomatal resistance, CTD and gene expression analysis were analyzed by Student t-Test. Infrared Leaf Temperature, average sum of ranks, H2O2 and MDA were analyzed by ANOVA test with p-value <0.05 using Past 4.03 software, the next post hoc used was Tukey's test. Principal component analysis (PCA) was performed using the R statistical software version 4.2.1. In Anthesis experiment: morphological data and gene expression data were analyzed by ANOVA test with p-value<0.05 using Past 4.03 software the next post hoc used was Tukey's test. CTD, IR, MDA were analyzed with Student t-Test. Correlation analysis was done by was performed using the R statistical software version 4.2.1. 52 4. RESULTS AND DISCUSSION 4.1 Materials and Approaches used to discover novel natural variability to increase heat stress resilience In 2018 Comastri and coworkers isolated and characterized four members of the chloroplast-localized small heat shock proteins (sHSP) encoded by the sHsp26 gene family. A TILLING approach in vivo and in silico was used to identify novel alleles within this family. The choice of the target genes used in this work was based on the known protective role played by sHSPs such as HSP26 over photosynthesis and the synthesis and compartmentalization of key metabolites in plants challenged by high temperature stress (Maestri et al. 2002; Chauhan et al. 2012; Khurana, Chauhan, and Khurana 2013). The TdHsp26 family in durum wheat was composed of four functional genes, three mapping to the 4A chromosome and one to the 4B genome homoeologue. TdHsp26 genes were differently regulated upon direct heat stress and especially after acclimation. TdHsp26-A1 showed the highest upregulation following direct heat stress and TdHsp26-B1 the highest one when the heat stress was imposed after acclimation (Comastri et al., 2018). The in vitro transcriptomic analysis was extended to other tissues and to stress conditions by querying the in silico ExpVIP database. For instance, TdHsp26 genes were strongly up-regulated by high temperature but even more by a combination of high temperature and drought, suggesting a role of sHSPs in both stress responses(Al-Whaibi 2011; Rampino et al. 2012; Khurana, Chauhan, and Khurana 2013). Based on these results and considering the strong involvement of TdHsp26-A1 and TdHsp26-B1 genes in regulating the heat stress response, in this thesis, we exploited the natural variation represented in the durum wheat germplasm collection, targeting TdHsp26-A1 and -B1 genes by using an EcoTILLING approach to discover novel sources of variability to increase the heat tolerance in durum wheat. 59 Table 8. SNPs identified in the promoter region and their effects on the regulatory elements and function. The SSD genotypes carrying the mutation in the promoter region are listed. PROMOTER ANALYSIS SNP IN TDHSP26-B1 Reference sequence Mutant sequence Effects on Promoter Role Reference SSD SNP2 CAGGCA CATGCA Formation of RYREPEATBNN Required for seed specific expression Ezcurra et al., 2000 253 SNP4 GAAAAT AAAAAT Loss of GT1CONSENSUS Consensus GT-1 binding site in many lightregulated genes Villain et al., 1996 397,69 SNP6 GTGA GTGG Loss of GTGANTG10 Primary metabolism: tissue (late pollen) Rogers et al., 2021 All genotypes except for 322, 415 SNP25 CAAACTCG CACACTCG formation of DPBFCORED plant male reproductive tissue specific. Ramkumar et al., 2015 244 60 The effect of mutations on the protein function was also verified in silico through SIFT software (Ng and Henikoff 2003). It returns a number ranging from 0 to 1 and is linked to the possible damage of the protein function. Amino acid substitution is harmful when the score is < 0.05 and tolerated if the score is > 0.05. Within the SNPs retrieved, none of those caused deleterious mutations to the protein function, but different values were assigned to the SNPs. Of great interest are SNP5 found in exon I and SNP8 in exon II, both located in TdHsp26-A1, other SNP of interest is in exon II of the TdHsp26-B1gene, all SNPs cause missense mutation (Table 9). Of high interest the SNP8 causing G196D substitution in the αcrystallin domain, which is characterized by two highly conserved regions, (Bondino, Valle, and ten Have 2012), thus the presence of a SNP in this region suggests a possible deleterious effect on the protein function. SNP59 had the lowest tolerance value 0.10 followed by SNP5 which was assigned a value of 0.23, while SNP8 despite being in the α-crystallin domain is the one the software assigned value 1, thus tolerable. Table 9. List of SNPs in exons analyzed with SIFT EXONS ANALYSIS Gene Mutation type Effect Score Sift SSD TDHSP26-A1 SNP5 MISSENSE E73Q 0,23 64,69,92,112,116,122,135,171, 195, 253, 322, 325, 409, 415,441, 459,494,499 TDHSP26-A1 SNP8 MISSENSE G196D 1 64,69,92,112,116, 122, 135, 171, 195,322, 325, 415, 451, 459, 499 TDHSP26-B1 SNP59 MISSENSE M97V 0,10 112 Some of the analyzed genotypes possess many of the listed mutations: SSD69 shows SNPs 5,8 of TdHsp26-A1 gene, the SNPs 4,6 of TdHsp26-B1; SSD253 has SNPs 2,5 in TdHsp26-A1 and SNP6 in TdHsp26-B1; SSD112 has SNP5,8 in TdHsp26-A1 and SNP6, 59 in TdHsp26-B1. 61 The hypothesis of a possible correlation between haplotype combination in the TdsHSp26 genes and the different phenotypes in response to heat stress, was formulated. 4.3 Does the genetic variation identified in TdHsp26 sequence impact on the heat stress response? 4.3.1 Heat stress response in seedling stage Morphological traits Plant height after one week post stress was performed. Three different trends were observed SSD44, SSD64, SSD69, SSD109, SSD116, SSD171, SSD253, SSD278, SSD441, SSD451, SSD487, SSD511 and Kronos showed shorter plants respect the control, indicating a stronger effect of the heat stress; SSD35, SSD92, SSD99, SSD112, SSD122, SSD244, SSD269, SSD397, SSD409, SSD459, SSD494 and Svevo show no difference between stressed and control; SSD178, SSD195, SSD335, SSD415 and SSD322 showed higher stressed plants than controls (Figure 19). 62 Fig 19. Plant height (cm). Student’s t-test was applied for each control and stressed sample *P<0.05; **P <0.01; ***P <0.001 63 MDA content The Malondialdehyde (MDA) content was assessed in the selected lines being one of the main breakdown products of polyunsaturated fatty acids in cell membranes and as a good proxy of oxidative stress in plants subjected to heat stress conditions (Figure 20). Seedlings of all genotypes were analyzed for the MDA content. The MDA trend showed an unusual behavior in wheat plants exposed to heat stress that normally increased during and under the stress. Here, in several genotypes: SSD35, SSD441, SSD451 and Kronos showed a gradual reduction in MDA from T0 to 1hPR while all other genotypes showed a strong and rapid decrease after 4h of heat stress and a significant increase already after one hour post recovery. Conversely, significant increase, consistent with the literature, in the MDA was observed in SSD 64 and SSD397 which respectively have an increase of 133,4% and 169,75%. SSD69 showed a limited and not significant increase in MDA content at 1hPR of 11,59%, in comparison with T0. 64 Fig 20. Plot of MDA content (nmol/FW(g)) in seedlings after heat stress exposure. SE is included. The different alphabetical letters in superscript indicate the significant differences with the ANOVA test, p-value < 0.05. 65 To better evaluate the behavior of the MDA trend, the MDA ratio was calculated (Table 10). SSD92, SSD 112, SSD178, SSD253, SSD325, SSD335, SSD494 showed a significant variation in MDA content with a consistent accumulation of MDA at 1hPR (Table 10). SSD35 showed a strong variation in MDA during the phases of heat stress with almost a full recovery after one week. Kronos showed a progressive decrease in MDA reaching the maximum at S1W, while SSD69 and Svevo, interestingly, showed no significant variation in MDA immediately after the stress and after the recovery (1hPR and S1W), supporting their tolerance to heat stress and a possible role link with the MDA variation (Table 10). Table 10. MDA ratio calculated as control/stress. SSD %T0/4HPS %T0/1HPR %C1W/S1W 35 -19.86% -75.02% 9% 64 -23.22% -14.54% 133.4% 69 -18.43% -2.30% 11.59% 92 1.02% 49.28% 144.03% 112 -15.47% 10.66% 9.15% 178 -26.80% 7.47% -0.31% 195 -44.73% -31.14% 7.93% 244 -82.33% -50.53% 2.67% 253 -5.22% 78.36% -27.38% 335 0.23% 23.78% -60.53% 397 -28.85% -10.43% 169.75% 415 -76.86% -60.27% 1.56% 441 -35.31% -59.95%. 28.28% 451 -5.59% -15.88% 26.26% 494 -1.12% 22.85% 38.02% SVEVO -38.52% -6.95% 36.72% KRONOS -25.24% -34.47% 25.30% 66 TdHsp26-A1 and -B1 gene expression during heat stress in seedlings stage Based on the results of plant height and MDA content together with the haplotype and SNPs position in the gene, SSD397 and SSD69 were chosen for TdHsp26-A1 and -B1 gene expression analysis. Both TdHsp26-A1 and TdHsp26-B1 genes were significantly upregulated in SSD69 following heat stress with a range from 10.62 fold at 4hPR to 6.62 fold at 1hPR for TdHsp26A1 and from 9.32 fold at 4hPR to 5.61 at 1hPR for TdHsp26-B1 (Figure 21). SSD397 showed a remarkable lower gene expression of both genes. TdHsp26-A1 gene expression drops from 4hPR to 1hPR from 2.84 to 1.2 fold change. TdHsp26B1 was from 2.62 to -0.28 folds from 4hPS to 1hPR. Both lines show a higher expression of the TdHsp26-A1 gene than the TdHsp26-B1 gene, in accordance with what was previously reported (Comastri et al. 2018). Fig 21. Expression analysis of TdHsp26-A1 and TdHsp26-B1 in SSD69 and SSD397 genotypes. The induction levels are measured as the fold change (RQ) of the treated samples in respect to the controls and reported as log2(RQ) in the chart. Bars indicate the standard error. Student’s t-test was applied between 4hPS and 1hPR of each group. ***p <0.001. Summarizing, in this first experiment, the effect of short (4h) heat stress (38°C) on plants morphologically (plant height) and biochemically (MDA content) was verified, and from the data obtained, two genotypes with contrasting phenotypes for gene expression analysis of TdHsp26-A1 and -B1 were selected. 67 4.3.2 The heat stress response in tillering stage Based on the results obtained in seedlings stage and the position of SNPs in the gene of interest 7 genotypes, corresponding to 5 haplotypes were chosen for applying the heat stress in tillering stage (Table 7). Two contrasting cultivars, Svevo and Kronos, were also included as reference varieties. 4.3.2.1 Morphological traits To perform the morphological analysis, the leaves area was measured at 48hPS and after 1 week post stress since 4hPS it was not possible to detect any morphological difference, while the number of culms and plant height were measured 1 week post stress (Figure 22). Significant differences are observed in leaf area between stressed and controls at S1W in SSD415 and between C48h/S48 and C1W/S1W in Kronos, with a reduction in area in stressed compared with controls (Figure 22a). At S1W stressed SSD244 and SSD397 showed a significant reduction (P< 0,05 and P< 0,01) in plant height (Figure 22c). At S1W Kronos showed a reduced number of culms, while all other genotypes do not show any significant variation in the number of culms between stressed and control plants (Figure 22b). Fig 22. Morphological traits analyzed in the tillering experiment a) Leaf Area, b) number culms, c) Plant height. Student’s t-test was applied. Error bars are reported and represent the standard error. *P <0.05, **P <0.01, ***P <0.001. 68 4.3.2.2 Physiological traits Plant transpiration The stomatal resistance (rs, sec x cm-1), is an efficient trait to estimate the gas exchange such as CO2 absorption thoroughly to the leaves and water loss with transpiration depending on stomata pore. Under heat stress the stomatal resistance is reduced under heat stress. At 4hPS under heat stress all genotypes showed a reduction in stomatal resistance indicative of increased conductance (Figure 23a), at 48hPS all genotypes show resistance equal to that of the control plants, except for SSD397, which still showed a reduction in stomatal resistance (Figure 23b). At S1W both genotype 397 and Svevo have lower resistance in the stressed plants than in the control (Figure 23c) 75 Fig 29. Plot of the correlation of the number of seeds and the kernel yield per spike (g). 4.3.3 SSD lines characterization for heat stress resilience A principal components analysis (PCA) was performed to determine the most contributive agro-morphological traits explaining the difference encountered in heat resilience between lines. All variables and stages collected before stress and after 4h at 38°C and S1W were considered in the analysis (Figure 30). The first two principal components explained 57.7% of the total variation. In 4hPS controls and stressed genotypes are correctly clustered in two separate groups highlighting the effect of the imposed heat treatment, but in S1W, a different clusterization was observed. While control and stressed samples of SSD69 and SSD178 clustered really close to each other supporting a possible heat tolerance for this genotype, line SSD397 are separate in the plot suggesting a higher susceptibility. Moreover, RWC, SR and CTD are highly correlated at 4hPS, while morphological traits such as canopy and leaf area are shown to be strongly correlated (Figure 30). The cluster of morphological traits (number of culms, leaf area, plant height and number of leaves) separated by the physiological traits (SR, infrared temperature) at S1W, further support the validity of the phenotyping methods applied to characterize the wheat stress resilience in durum wheat genotypes (Figure 30). In both PCA plots, infrared temperature and CTD are inversely correlated as expected, further validating the approach used (Figure 30). 76 Fig 30. Principal component analysis (PCA) of the effect of heat treatment in 7 SSD genotypes. a) Biplot at 4hPS; b) Biplot S1W. RWC, Relative Water Content, CTD, Canopy Temperature Depression, Infrared Leaf Temperature, SR, Stomatal Resistance, canopy, leaf area, plant height, number culms, number leaves (individual data can be viewed in the Supplementary Figure1). 77 4.3.4 Discussion on the heat stress response for durum wheat lines in seedling and tillering stage Two main objectives were the target for the heat stress experiment performed in tillering stage: i) to characterize the phenotype of the selected genotypes under heat stress and to verify, whether there were differences between seedlings and tillering; ii) to identify traits that could easily indicate whether a genotype is susceptible or tolerant to heat stress. Heat stress in plants is known to cause a reduction in plant height (Hassan et al. 2020); here we identified three different phenotypes comparing stressed and controls: plants with decrease height (41.93%), plants higher than controls (19.35%), and plants showing no significant differences under stress (38.71%). Plants exposed to heat stress often lead to the generation of destructive ROS, including singlet oxygen (1O2), superoxide radical (O2−), hydrogen peroxide (H2O2), and hydroxyl radical (OH−) responsible for generating oxidative stress (Marutani et al. 2012; Suzuki et al. 2012). Oxidative stress notably increased membrane peroxidation and decreased membrane thermo-stability in many plants including wheat (Savicka and Škute 2010) leading to the accumulation of MDA that is considered a good indicators of the damage of the cell membranes as reported in rice and maize (S. Kumar, Singh, and Nayyar 2012) and sorghum (Tan et al. 2011). In wheat (Savicka and Škute 2010), increased MDA content in all organs of wheat seedlings was observed. The expression of TdHsp26 seems to play a role in the heat response reducing MDA accumulation in seedlings of SSD69 indicating a low oxidative stress damage. Between the two genes, TdHsp26-A1, showed a strong increase in the relative expression at seedling stage while lower expression of TdHsp26-B1 variant was observed. An hypothesis of a link between gene expression, MDA content and heat tolerance was formulated. A sharp increase of 100% in MDA was observed in SSD397 and a total lack of expression of the TdHsp26-B1 gene. Conversely, SSD69, showed no significant variation in the MDA content a 1 week after stress (S1W) and a high expression of TdHsp26-B1. In seedling, all lines showed a decrease of MDA immediately after the stress (4hPS), while a strong increase was observed at S1W supporting the hypothesis of Savicka et al. (2010). Conventional behavior was observed in the tillering stage for all lines except for SSD69 and Svevo that showed decreased values in MDA and H2O2 content. 78 This led to hypothesize a direct role of the expression in SSD durum wheat genetic resources of TdHsp26-A1 and -B1 haplotypes in leading a redox perturbation in late stage of development as the tillering stage that was not observed in seedling stages. This was previously reported in barley (Taratima et al. 2022), where a lower accumulation of MDA was observed and can play a critical role in triggering heat stress signaling cascades and the transcriptional effects of stressful high temperatures have been shown to be at least partially mediated by elevated H2O2 levels. In tillering a reduced H2O2 accumulation and a corresponding reduction in MDA content was observed in putative heat tolerant genotype SSD69 while SSD397 showed conventional trends of H2O2 accumulation and MDA content after heat stress imposition. However, both genes were overexpressed in heat stress conditions. This unconventional behavior of MDA and H2O2 suggests a possible mechanism of action of the variants in TdHsp26. MDA and H2O2 traits are confirmed as good biochemical proxy of heat stress tolerance together with the canopy temperature. All together these results suggest for SSD69 a reduced oxidative damage thus an increased tolerance to heat stress. SSD69 showed a set of phenotyping traits supporting its increased heat tolerance. A reduced canopy temperature and increased CTD values, support that genotypes with higher CTD values and a cooler canopy temperature under drought stress use more available soil moisture to cool the canopy by transpiration that have concrete beneficial effects also on heat tolerance. On the contrary, SSD397 showed higher values of canopy temperature and lower CTD, supporting their inability to promote the activation of the transpiration process to reduce the effects of heat stress. This was also demonstrated in the PCA (Figure 30), where CTD and canopy temperature vectors are inversely correlated. All together the results obtained in seedlings and tillering stages under short term stress allowed us to hypothesize a possible link between the expression of TdHsp26-A1 and - B1 variants and the cascade of physiological phenomena triggered during the stress (Figure 31). However, the results obtained were not sufficient to correlate the phenotyping effects observed with the specific SNPs. Only speculations can be done in terms of heat stress response with the geographical origin of the two genotypes (Janni et al. 2018). SSD69 79 originated in Morocco while SSD397 originated in Crete and showed different heat tolerance together with different HMW glutenins composition (Janni et al. 2018). Fig 31. Hypothesize mechanism of the role of TdHsp26 in the ROS cascade. Under heat stress, the ROS and HSP cascade is activated. H+, hydrogen molecule, adenosine diphosphate; ATP, adenosine triphosphate; H2O, water; ETC, electron transport chain; e-, electron; Fd, ferredoxin; PSII, photosystem II; O2, oxygen molecule; O2•−, superoxide radical; SOD, superoxide dismutase; CAT, catalase; GPX, glutathione peroxidase; H2O2, hydrogen peroxide; (•-OH), hydroxyl radical. 4.3.5 The heat stress response after prolonged heat stress in anthesis phase 4.3.5.1 Morphological traits To concretely demonstrate the increased resilience of SSD69 and tolerance of SSD397 and to further demonstrate the efficacy of plant phenotyping in characterizing heat stress resilience, heat stress experiments were performed in the anthesis phase. Two cultivars were included as reference tests (Iurlaro et al. 2016). The growth stage at which heat stress events occur has a significant impact on wheat grain yield (Djanaguiraman et al. 2020). Effects of high temperature stress during anthesis and grain filling periods on photosynthesis, lipids, and grain yield in wheat. A heat stress at 32.0◦C reduced yield per plant by 29.0% and 44.0% at anthesis and during the grain filling period, respectively. Due to the threat of food insecurity, there is a growing imperative to 80 identify high-yielding wheat cultivars that display resilience to the impacts of rising temperatures and maintain nutritional and end-use quality (Djanaguiraman et al. 2020). Here, a prolonged and extremely severe heat stress was applied to mime the normal environmental events occurring in open fields in the final stages of wheat development. Based on previous results in seedling and tillering the plant height and leaves number were scored (Figure 32). The dynamic of plant growth was significantly different between lines (Figure 33). SSD 69 and Svevo showed a comparable trend of development. At 25 DAT SSD 69 and Svevo showed a lower plant canopy, plant height, reduced number of leaves and a shorter development cycle (Figure 33b, d). Yield traits depend mainly on tillering and flowering time both affecting the number of seeds and grain yield (Table 11). Fig 32. Plots of morphological traits scored in the anthesis phase. a), d), g) plant height on days 30, 42, 50 DAT; b), e), h) the numbers of culms on days 30, 42, 50 DAT; c), f) the numbers of leaves 30 and 42 DAT. Values shown mean ± SE. The different alphabetical letters in superscript indicate the significant differences with the ANOVA test t at a p-value< 0.05. 81 Fig 33. Growth and plant development stages expressed in Zadoks stages of SSD397, SSD69, Kronos and Svevo. DAT, Days after Transplant 13DAT 13DAT 30DAT 3 30DAT 34DAT 3 34DAT 42DAT 42DAT 5 50DAT 50DAT 5 Svevo Svevo Svevo a) b) 13DAT 13DAT 30DAT 30DAT 3 34DAT 3 34DAT 42DAT 42DAT 50DAT 50DAT c) d) 82 Table 11 Morphological traits and temperatures recorded in the anthesis experiment. Kronos showed complete ear emergence at 34 DAT followed by Svevo at 37 DAT, SSD 69 at 42 DAT and SSD 397 at 48DAT. In terms of flowering time SSD69 and Svevo showed an earlier flowering time (48, 49 DAT). Kronos showed a reduced flowering time of about 2 days (46 DAT, Figure 33a). SSD 397 showed a delay in flowering time of about 2 days (51 DAT, Figure 33c, Table 11). Moreover, SSD397 plants showed a denser canopy, bigger number of leaves and culms with a slower growth dynamic. For example, plant growth (in terms of height) was slow up to 42 DAT when it rapidly increased from 36.42 ± 1.23 to 59.67 ± 1.44 at 50DAT (Figure 32). Kronos had the fastest development cycle and maturation process compared to the other lines. Kronos and Svevo showed similar length of time between ear emergence and anthesis (12 days) while SSD 397 showed the fastest days to flowering (3days), SSD69 intermediate length with 6 days (Tab. 11). For SSD69 a staygreen phenotype under heat stress (Figure 34) was hypothesized. The staygreen phenotype is an antagonist to senescence, chlorophyll, and photosynthetic capacity of leaves were maintained or prolonged, and is considered an indicator of heat tolerance (Fokar, Blum, and Nguyen 1998) and the plant continues to produce tillers also when the first ear reaches maturity (Figure 34). Compared with the staygreen varieties, the yield and inferior grains of no-staygreen cultivar were much more influenced by high-temperature stress (Yang et al. 2014). 83 Fig 34. Staygreen phenotype in SSD69 (90 DAT, Z99,30 August 2022). 4.3.5.2 Physiological traits Under heat stress, the monitoring of the regulation of the transpiration mechanisms is a possible strategy for selecting heat-tolerant varieties. The stomatal conductance (gs) has been recorded. Plants under high temperatures cool their canopies through transpiration, which occurs through stomata when soil water is available. Efficient reproductive development requires higher gas exchanges, which occur through open stomata, putting plants in a constant struggle for survival against harsh environments (Huggins et al. 2018). Higher gs is associated with better performance of tolerant genotypes under stress conditions (Saeidi and Abdoli 2015). Zhang and coworkers in their study pointed out the effects of different percentages of air humidity on winter wheat and found that their most productive genotypes had in common a strong stomatal regulatory ability in response to changing air humidity (Zhang et al. 2020). The plot shows the temperatures recorded during the measurements, acquired in two different days, morning and afternoon plotted with the stomatal conductance (Figure 35). The highest temperatures were recorded, as expected, during the afternoon. SSD69 showed a reduction in stomatal conductance between the first two points, although there is not a great variation in temperature. The high conductance recorded at 42 DAT is attributable to the high humidity recorded. During those surveys, the relative humidity 84 was about 48.3 %, the highest ever recorded during our analysis. Several studies have shown an increased stomatal conductance with increased humidity (Zhang et al. 2020). SSD69 and Svevo showed firstly a large variation in the gs trend and an increase in correspondence of increasing temperature, whereas SSD397 and Kronos showed opposite trend in the stomatal conductance, supporting the occurrence of higher transpiration to mitigate the effects of increased temperature and allow to ensure good yields and quality. Fig 35. Stomatal conductance measured in the four genotypes under investigation. Measurements were made on two different days (50 DAT and one week after flowering specific for each SSD) morning and afternoon; conductance was correlated with temperatures recorded at the time of the survey. Values shown mean ± SE. The analysis of the stomatal conductance in contrasting genotypes (SSD69 and SSD397) and varieties (Svevo and Kronos) shows that the analysis of a physiological trait as the variation in gs is critical to tolerate the prolonged heat stress. The tolerance of SSD 69 and susceptibility of SSD 397 is further supported by the analysis of key physiological processes affected by heat stress in plants. The chlorophyll fluorescence parameter Fv/Fm reflects the maximum quantum efficiency of PSII photochemistry and has been widely used for early stress detection in plants (D. K. Sharma et al. 2015). 91 4.3.6 Discussion of the prolonged heat stress response in anthesis phase Here, the natural environmental summer conditions, where the heat stress is often prolonged during the entire length of the durum wheat developmental stages, was exploited in pots mimicking the conditions observed in the field. Plants were subjected for 90 days (from transplant to seed maturity) to a prolonged heat stress above 35°C were recorded for the entire length of the experiment. The objective of this study was to firstly identify the effect of a prolonged heat stress during the entire length of the plant growth cycle from seedling (Zadoks 10) to seed maturity (Zadoks 99) on SSD genotypes on yield components. Photosynthesis is the most sensitive physiological event leading to poor growth performance in wheat (Feng et al. 2014) as also confirmed in this experiment. A major effect of heat stress is the reduction in photosynthesis resulting from decreased plant height, impaired photosynthetic machinery, reduced transpiration and reduction in wheat production (Ashraf and Harris 2013; Mathur, Agrawal, and Jajoo 2014). Heat stress firstly damages the complex phenomena of PSII and secondly, changes the photosynthetic behavior. This is manifested when temperatures exceeding 40°C dissociate the light harvesting complex-II Chl a/b-proteins from the PSII (Iwai et al. 2010). This was constantly reached for the entire length of the experiment, mostly in the afternoon. The index Fv/Fm was appropriate in selecting most efficient genotypes in our study, in fact, SSD69 and Svevo did not show a drop in Fv/Fm values also in severe heat stress conditions, supporting that higher Fv/Fm values indicated increased tolerance to heat stress (Nagar et al. 2015; Rong Zhou et al. 2015; D. K. Sharma et al. 2017). Previous study conducted by Sharma et al. (2015), on T. aestivum used Fv/Fm as a selection criteria for heat tolerance ( Sharma et al. 2015). CTD although reporting low values for SSD69 that has been described as a staygreen phenotype under heat stress as previously reported by (Kumari et al. 2012), does not seem to be highly effective in the test environmental conditions for genotype selection. Plants under high temperatures cool their canopies through transpiration, which occurs through stomata when soil water is available. Efficient reproductive development requires higher gas exchanges, which occur through open stomata, putting plants in a constant struggle for survival against harsh environments (Huggins et al. 2018). This has been validated also in these severe conditions, in fact, gs showed a good correlation with the temperature, higher in 92 the afternoon, suggesting that in presence of heat stress, an increased transpiration was operated to maintain plant functions. Higher gs observed in the afternoon with almost 50°C was observed in SSD69 and can be associated with better performance of tolerant genotypes under stress conditions (Saeidi and Abdoli 2015) leading to hypothesize that SSD69 and Svevo had in common a strong stomatal regulatory ability in response to increasing temperatures. In fact, line SSD69 exhibits large variations in gs attributable to both temperature and moisture, and we observe the same behavior in Svevo. In lines SSD397 and Kronos they did not show these traits, incentivizing the potential susceptibility of these genotypes. Miller et al. 2009 found that heat stress increased O2production in root by 68% and MDA content in leaf by 27% at the early stages, and 58% at the later stage of seedling development (Fernie et al. 2022). In terms of yield, which are the final traits to be considered, the ability of SSD69 to overcome the severe heat stress, despite the slow and low overall plant growth, was confirmed. SSD69 and Svevo showed an increased estimated fertility that corresponds to an increased grain yield and number of seeds when compared with SSD397 that promoted high and fast plant biomass growth but at the end failed in the reproductive stage leading to low yields and reduced seed set. The severity of grain yield depletion in wheat crops is highly dependent on the growth stage at which heat stress occurs. Although all growth stages are susceptible, heat stress during the reproductive phase (anthesis and grain filling) can particularly hinder grain development, causing reproductive sterility and significant reduction in grain number and yield (Fernie et al. 2022). SSD397 showed the most delayed anthesis in comparison with the other genotypes tested. This coupled with the extremely high temperatures reached ( min 21°C and max 58°C) severely lowered grain yield by reducing grain number per plant as a consequence of a marked floret sterility (Kaur and Behl 2010). As observed in almost all lines, moreover, day/night high temperatures of 31/20°C may also cause shrinking of grains resulting from changing structures of the aleurone layer and cell endosperm, as resulted in this experiment (data not shown) (Dias, Bagulho, and Lidon 2008). 93 SSD69 showed, in extremely severe heat stress conditions, better thermotolerance than SSD397 probably because of its increases in preventing protein degradation and PSII repair, and oxidative stress response. Based on the real-time results, a high difference between the two genotypes have been observed. As Svevo, known as tolerant variety, SSD69 showed an higher expression of TdHsp26-A1 with respect to TdHsp26-B1. SSD 397 that showed a heat susceptible phenotype under heat stress, showed no expression of TdHsp26-A1 but good expression levels of TdHsp26-B1. In SSD 69 the downregulation of TdHsp26-B1 is linked with the dynamic accumulation of MDA in SSD 69 leaf samples, leading to hypothesize a correlation between TdHSP26-A1 and the accumulation of MDA under heat stress (Section 4.3.5), and its involvement in the membrane protection pathway. Based on these results, in Figure 42 we summarized the formulated hypothesis of HSP26 involvement in the heat stress response. Under heat stress conditions, HSP26, participate directly and indirectly in the protection of PSII, the stomata opening to maintain high levels of transpiration and corresponding reduction in temperature, and finally it is involved in protecting the cell from ROS resulting in the reduction of MDA content. 94 Fig 42. Proposed model of the interaction between sHSP26 and various physiological components. Red arrows mean decrease, and green arrows mean increase. 95 5. DISCUSSION AND CONCLUSIONS Increasing temperatures and consequent changes in climate adversely affect plant growth and development, resulting in devastating loss of wheat productivity. Increased global mean temperatures and frequency of extreme heat days pose a significant risk to the productivity of wheat cropping systems ( Sharma et al. 2022) that rely on optimal conditions between 12.0–22.0°C for favorable growth and development. For each degree rise in temperature, wheat production is estimated to reduce by 6%. A detailed overview of morpho-physiological responses of wheat to heat stress may help formulating appropriate strategies for heat-stressed wheat yield improvement. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed (Paux et al. 2022). The availability of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences concretely help in accelerating the average phenotypic values of crop plants. On one hand, the use of induced mutations coupled with modern genomics tools is an effective strategy for identifying and manipulating genes for crop improvement. A successful example is Highthroughput TILLING methodology, which detects mutations in mutagenized populations. On the other hand, such powerful tools are essential to perform genome-wide association studies, to implement genomic and phenomic selection, and to characterize the richness of natural worldwide diversity (Paux et al. 2022). In this frame, EcoTILLING identifies single nucleotide polymorphisms (SNPs) within a natural population and associates these variations with traits of breeding interest. The main advantage of this “reverse genetics” strategy is that they can be applied to any species regardless of genome size and ploidy level (Irshad et al. 2020). However, both techniques required several rounds of backcross in wheat to stabilize the genome by reducing redundant mutation (in TILLING) and the drawbacks traits characterized in genetic material as the landraces (in EcoTILLING). Here, the following question has been raised: how the genetic variation in the TdHsp26 genes influenced the performance of the SSD69 line during heat stress occurring in different developmental stages? 96 Validation of the relationship between gene variation and key mechanisms of plant development regulating final yield has been performed. Chlorophyll fluorescence parameter, canopy infrared temperature (IR or CTD) and plant performance under stress have been demonstrated to be effective phenotyping traits and good indicators enabling the characterization and selection of a complex environmental response as heat stress and in turn may directly or indirectly aid in crop improvement for stress tolerance in the context of global climate change. In this work, we applied a progressive selection of durum wheat landraces, in different key development stages, revealing in final round of selection, a possible resilience to heat stress for SSD 69 and, as expected for Svevo, and susceptibility for SSD 397 and for Kronos in the tested conditions. Heat shock proteins occur in response to high temperatures, binding to denatured proteins as protection from aggregation, before facilitating their reformation after high temperature ceases ( Kumar et al. 2022). Here in the frame of different developmental stages of different genotypes, we observed that TdHsp26-A1 and -B1 are genotypic and growth stage specific as hypothesized in previous studies performed in bread wheat ( Kumar et al. 2018). Especially in seedlings, we observed an alteration in the antioxidant pathway, letting hypothesize for the heat resilient genotype SSD69 a reduced production of H2O2 and the consequence lower production of MDA, that, in anthesis stage is not however linked with a decrease in yield. In SSD 397 the conventional mechanism of antioxidant is maintained in all stages, with the consequence of an increased and faster biomass production, anticipating the anthesis but a lower fertility encountered. SSD 69 put in place a set of defense mechanisms aimed at preserving final yields, under severe heat stress, in both tillering and anthesis. SSD 69 increased, in tillering experiment, the canopy temperature depression known to be an important physiological mechanism for sustaining grain yield after exposure to heat stress (Reynolds et al. 2020). In high temperature and low humidity conditions, thermotolerant cultivars expressed more efficient transpiration cooling, owing to greater stomatal conductance of the leaf. SSD69 also showed a staygreen phenotype indicating its ability to overcome heat stress and increased yield. 97 Concluding, in this thesis we have successfully applied a combined genotypic and phenotypic approach that leads to the identification of two contrasting durum wheat landraces, of which SSD69 showed superior heat resilience characteristics with increased yield performance also in extreme heat stress conditions. A set of phenotyping traits have been identified as key traits for the identification of heat resilience genotypes, constituting a toolbox useful for further analysis. 98 6. SUPPLEMENTARY MATERIAL Suppl. Table1 SNPs identified in the TdHsp26-A1 and TdHsp26-B1 sequence of the SSD genotypes, the wild type nucleotide is indicated. The position of the identified SNP on the gene elements is also reported. 99 EFFECTS GAG/CAG(E73Q) GAG/CAG(E73Q) GAG/CAG(E73Q GAG/CAG(E73Q) GCC/GAC(G196) GCC/GAC(G196) GCC/GAC(G196D) GCC/GAC(G196D) ATG/GTG(M97V) TYPE OF MUTATION MISSENSE MISSENSE MISSENSE MISSENSE MISSENSE MISSENSE MISSENSE MISSENSE MISSSENSE POSITION ON THE GENE PROMOTOR PROMOTOR EXON1 EXON1 EXON1 EXON1 EXON2 EXON2 EXON2 EXON2 PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR PROMOTOR 5UTR’ EXON2 ALLELE FREQUENCY 14,83 6,80 68,52 29,31 46,37 80,65 68,52 30,02 46,32 80,47 7,57 11,54 21,41 14,79 5,48 96,18 98,92 94,14 99,79 4,72 4,63 5,17 5,09 46,37 SSD SNP T C C C C C A A A A T T A A A G G G G G C C G G NUCLEOTIDE WILDTYPE G G G G G G G G G G G A G G G A A A A C G A A A REFERENCE POSITION 2086 2102 2469 2469 2469 2469 2929 2929 2929 2929 597 633 1212 1250 1250 1287 1287 1287 1287 1570 1596 1647 1667 2180 SNP ID SNP1 SNP2 SNP5 SNP5 SNP5 SNP5 SNP8 SNP8 SNP8 SNP8 SNP2 SNP3 SNP4 SNP5 SNP5 SNP6 SNP6 SNP6 SNP6 SNP18 SNP19 SNP25 SNP26 SNP59 POOL LIB4 LIB1 LIB1 LIB2 LIB3 LIB4 LIB1 LIB2 LIB3 LIB4 LIB1 LIB1 LIB3 LIB3 LIB2 LIB3 LIB1 LIB2 LIB4 LIB3 LIB3 LIB3 LIB3 LIB4 GENE TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-A1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 TdHsp26-B1 100 Suppl. 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