Integrative phenotyping approaches to unmask the Phyb-PIF4 pathway in Arabidopsis thaliana reproductive organs at high ambient temperatures

Integrative phenotyping approaches to unmask the Phyb-PIF4 pathway in Arabidopsis thaliana reproductive organs at high ambient temperatures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative phenotyping approaches to unmask the Phyb-PIF4 pathway in Arabidopsis thaliana reproductive organs at high ambient temperatures Shekufeh Ebrahimi Naghani, Ján Šmeringai, Barbora Pleskačová, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4223427/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2024 Read the published version in BMC Plant Biology → Version 1 posted 10 You are reading this latest preprint version Abstract Background The increasing ambient temperature significantly impacts plant growth, development, and reproduction. Uncovering the temperature-regulating mechanisms in plants is of high importance, not only for boosting our plant biology knowledge but also for assisting plant breeders in improving plant resilience to these stress conditions. Numerous studies on the molecular mechanisms by which plants regulate temperature responses revealed that plants employ distinct transcription factors to regulate thermomorphogenesis specific to each tissue type. A significant discovery in this field was the identification of PHYTOCHROME-INTERACTING FACTORs (PIFs) as key regulators of thermomorphogenesis during vegetative growth. PIF4, a regulator of auxin-mediated signaling pathways, is crucial in controlling high-temperature responses. Results In this study, we screened the temperature responses of the wild type and several PhyB-PIF4 pathway Arabidopsis mutant lines in combined and integrative phenotyping platforms for root in soil, shoot, inflorescence, and seed. We demonstrated that high ambient temperature differentially impacts vegetative and reproductive organs through this pathway. Suppression of the PhyB-PIF4 components mimics the response to a high ambient temperature in wild-type plants. We also identified correlative responses to high ambient temperature between shoot and root tissues. This integrative and automated phenotyping was complemented by monitoring the changes in transcript levels in reproductive organs. Transcriptomic profiling of the pistils from plants grown under high ambient temperature identified key elements that may provide clues to the molecular mechanisms behind temperature-induced reduced fertilization rate, such as a downregulation of auxin metabolism, upregulation of genes involved auxin signalling, miRNA156 and miRN160 pathways, pollen tube attractants. Conclusions Thermomorphogenesis is uniquely controlled in the different plant tissues at different developmental stages. We have identified key elements that may help to determine the response to high ambient temperatures during reproduction processes. Arabidopsis Automatic Phenotyping PIF4 pistils PhyB pollen tube guidance seeds thermomorphogenesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Abiotic stressors affect plant growth through physiological, morphological, biochemical, and molecular changes. Among these stressors, warm ambient temperatures affect plant life differently at different growth stages [1, 2]. With the Intergovernmental Panel on Climate Change (IPCC) predicting a global temperature increase of 1.8 °C to 4 °C by 2100 [3], understanding the physiological responses of plants to warmth and the molecular mechanisms involved is crucial for improving high temperature tolerance. Temperatures above the critical threshold temperature (about 30 °C for most temperate crops) are considered heat stress [4]. High ambient temperatures (hAT), or warmth, below this critical threshold temperature induce responses in Arabidopsis thaliana similar to shade avoidance syndrome, resulting in morphological changes collectively referred to as thermomorphogenesis, such as stem elongation, hyponastic leaves, reduced biomass, and accelerated flowering, which help plants dissipate heat and move organs to cooler environments [5, 6]. Thermomorphogenesis is an adaptive mechanism involving transcriptional changes, hormonal reactions, and developmental modifications. The bHLH PHYTOCHROME-INTERACTING FACTOR (PIF) transcription factors, particularly PIF4, play a crucial role in thermomorphogenesis and act as a central hub coordinating signaling pathways and facilitating the plant's adjustment to environmental conditions [7, 8]. The heat stress response deals with the reaction of high temperatures above the critical threshold temperature that could impair cellular functions (membrane fluidity, oxidative stress, protein folding) and induce cellular death. The heat stress response is regulated by the expression of heat stress-responsive genes through the activity of HEAT SHOCK FACTOR (Hsf) transcription factors [9]. While thermomorphogenesis and heat shock response are distinct thermal responses to a different temperature ranges, a crosstalk between these two processes has been identified with the finding that HsfA1 proteins are required for PIF4-mediated thermomorphogenesis in hAT [10]. The PIF protein amount and activity and PIF4 transcript levels are regulated by the plant photoreceptor Phytochrome B (PhyB), which also serves as a thermosensor [11–13]. Synthesized in its inactive form (Pr) in the cytoplasm, it converts to an active form (Pfr) upon absorption of red light and translocates to the nucleus, where it interacts with various transcription factors, including PIFs, to repress gene expression [14, 15]. hAT promotes the reversion of PhyB from Pfr to Pr [2, 16] and release its repression of PIFs. The influence of PhyB on PIF4 activity is evident in the phyB pif4 double mutant, which exhibits elongated hypocotyls in a dose-response to the ambient temperature [17]. PIF4 is critical for temperature-induced morphological responses, including hypocotyl and petiole elongation and leaf hyponasty. These responses are absent in pif4 mutant plants, except for early flowering [13, 18, 19]. PIF4 expression is then increased by warm temperature [20]. PIF4, in turn, activates the expression of warm temperature-responsive genes. Notably, pif4 mutants fail to induce the expression of warm temperature-responsive genes, such as the auxin biosynthetic gene YUCC8 ( YUC8 ) and the brassinosteroid biosynthetic gene DWARF4 [8, 13, 18, 20] . It highlights the importance of PIF4 in regulating thermomorphogenesis. Both hAT and heat shock affect different steps of plant reproduction, and consequently the production of viable seeds [21]. Heat shock impairs pollen viability and fertilization in pea [22], rice [23], and chickpea [24]. Heat shock reduces seed yield and quality in wheat [25], rice [26] and chickpea [27]. The long-term effects of hAT during reproduction have been studied in oilseed rape [28, 29] and few hints of molecular pathways activated to cope with hAT have been listed [30, 31], including response to heat stress, ROS production, and photosynthesis. We studied the thermomorphogenic response of A. thaliana in different tissues throughout the plant life cycle. Through detailed phenotyping techniques of wild-type (Col-0) plants and PhyB-PIF pathway mutant lines under normal (nAT) and high (hAT) ambient temperature conditions, we found that the PhyB-PIF4 signaling pathway is a potential player in regulating the plant response to hAT in several tested developmental processes (shoot, root and reproductive organs). This study uniquely combines multiple phenotyping approaches: seeds with Boxeed, ovules and embryos with microscopy, roots with rhizotron, and seedlings and plants with PlantScreen. We complemented this phenotyping by examining the transcriptomic response in pistils of wild-type, phyb , and 35S::PIF4 plants grown in nHT and hAT to identify the key regulatory pathways that could explain the reduced fertilization rate of the wild-type plants under hAT and whether the Phyb-PIF4 pathway could be involved in this response. Methods Plant Materials and Growing Conditions Arabidopsis thaliana seeds from Col-0, homozygous mutant lines of pif3-7 ( N66042 ) , pif4-2 ( N66043 , sail_1288_E07 ) , pif7-1 (N68809), pif7-2 (N71656, sail_622_G02), pif3-3 pif7-1 (N68810), pifq (N66049; pif1-1 (sail_256_G07) pif3-7 pif4-2 pif5-3 (N66044, salk_087012), phyb-9 (N6217) , 35S::PIF4 (kindly provided by Zhi-Yong Wang), and YUC4::3nGFP , YUC8::GUS-GFP , and TAA1::GFP-TAA1 reporter lines were used for this study [32–37]. Seeds were sterilized with 20 % bleach, washed twice in sterile distilled water, and vernalized at 4 °C for 24 h. Plants were either germinated directly in soil (mixture of 2/3 peat moss Substrate 3 [Klasmann-Deilmann GmbH, Germany] and 1/3 vermiculite) or on plates containing MS medium. In plates, plants were grown for ten days at 21 °C with a 16-h light/8-h dark photoperiod and 150 μmol.m -2 .s -1 LED illumination before transfer to soil. For all the measurements, plants were grown in a walk-in Fytoscope growth chamber (FS-WI, Plant Systems Instruments (PSI), Czech Republic) under growth conditions with a long-day regime (16 h light/8 h dark), LED illumination with an intensity of 150 μmol.m -2 .s -1 , and 35%–45% humidity. For normal conditions (nAT), the temperature was set at 21 °C during the day and 18 °C at night. For high ambient temperature conditions (hAT), the temperature wat set at 28 °C during the day and 24 °C at night. Root Phenotyping Seeds from wild-type Col-0, pif4, phyb, and 35S::PIF4 lines were sterilized, vernalized, and sown in PlantScreen TM rhizotron systems (PSI, Czech Republic). The rhizotrons (203 x 293 x 29.5 mm, H x W x D) with a transparent glass plate and a light-protected black sheet cover were filled with soil (peat moss Substrate 3 [Klasmann-Deilmann GmbH, Germany]) and tilted at 45 o with the glass plate facing downwards. After ten days of plant cultivation in nAT, half of the rhizotrons continued in nAT, and the other half in hAT. The soil temperature was measured with a soil temperature sensor Pt1000 with datalogger Microlog T3 (Environmental Measuring Systems Ltd, Czech Republic). The soil is about 1 °C less than the air in all conditions. Regular root phenotyping was performed three times a week using the PlantScreen TM SC System (PSI, Czech Republic) equipped with a bottom-side root imaging unit (GigE PSI BW - 12.36 megapixel camera with 1.1” CMOS sensor) with LED-based light source. Experiments were conducted in triplicate, with the first replicate consisting of five biological replicates and the following two replicates consisting of eight biological replicates for each genotype/condition. Rhizotron weights were measured prior to watering, and an equal amount of water was added to each tray. Subsequent watering occurred after the system had lost the weight of the added water. Raw data were automatically stored and processed using the PlantScreen TM SC Root Tester software (PSI, Czech Republic). Parameters such as primary root length, lateral root density, and length of the four longest lateral roots were evaluated manually using ImageJ. The Relative Growth Rate (RGR) is calculated as follow: (length T2 -length T1 )/(T2-T1). Shoot Phenotyping For shoot phenotyping, we examined nine A. thaliana lines, including Col-0, pif3, pif4, pif7-1, pif7-2, pif3 pif7, pifq, phyb, and 35S::PIF4 , in two experimental conditions (nAT and hAT). Each experiment consisted of 18 replicates per line. After sterilization and vernalization, seeds were directly sown in pots (70 mm × 70 mm × 65 mm, Poppelman TEKU, Germany) containing 65 g of freshly sieved soil (Substrate 2, Klasmann-Deilmann GmbH, Germany), watered with 10 ml of water per pot, and grown in nAT for 10 days. All plants were then transferred to a climate-controlled growth chamber (FS-WI, PSI, Czech Republic). The trays were designed to contain identical genotypes at the same positions for the two replicates. Growth conditions for day/night temperature were set at 21/18 °C for nAT and 28/24 °C for hAT. At least 17 plants of each genotype were monitored daily for 50 days in nAT and 42 days in hAT. The phenotyping protocol included multiple analyses, including photosynthesis-related traits using kinetic chlorophyll fluorescence imaging, morphological traits using RGB imaging, and VNIR hyperspectral imaging for reflectance profiling (400-850 nm). The PlantScreen TM Compact System [38] facilitated the daily transport of trays for phenotypic analyses on conveyor belts from the dark/light acclimation chamber to the light-isolated imaging cabinets and the weighing and watering station, where plants were automatically weighed and watered daily to maintain the soil at a relative water content of 44 % field capacity. Photosynthetic performance was assessed using a light curve protocol (as described in [38]), which quantified the rate of photosynthesis at four different photon irradiances with 60 s intervals of cool white actinic light at 140, 270, 410, and 540 mmol.m -2 .s -1 corresponding to L1, L2, L3, and L4, respectively. Raw data were automatically processed using the PlantScreen TM Analyzer software (PSI, Czech Republic). Reproductive tissues and embryo phenotyping Col-0, pif4, pifq , and phyb plants were analyzed to assess reproductive organs and seeds. After sterilization and vernalization, all seeds were germinated and grown on plates for approximately two weeks. They were then transferred to soil and grown under nAT until the development of the first flowering bud. Half of the plants of each genotype were then grown in hAT until the end of their life cycle. Anthers were dissected from flower buds one day before anthesis, mounted in Alexander’s staining solution [39], incubated overnight at 50 °C, and observed under a Zeiss Axioscope light microscope. For the ovule analysis, flowers were emasculated one day before anthesis. Two days later, mature ovules (FG7 stage) were dissected from the pistil, stained with 10 μg/mL propidium iodide for five minutes on microscope slides, and observed under a Zeiss LSM 700 laser scanning confocal microscope. Seeds containing embryos of different developmental stages were isolated from the silique and cleared using the ClearSee method [40], stained with Renaissance SR2200, and observed with a Zeiss LSM 700 laser scanning confocal microscope. Gene expression pattern in ovules by confocal microscopy For auxin biosynthetic gene expression pattern, flowers were emasculated one day before anthesis. Two days later, mature ovules (FG7 stage) were dissected from the pistil, stained with 10 μg/mL propidium iodide on microscope slides for five minutes. Fluorescent signals were observed using a ZEISS 700 microscope equipped with a 25x magnification objective. GFP imaging was performed using 488 nm laser lines. Dry seed phenotyping Non-invasive seed phenotyping analysis was performed using the Boxeed robot (Labdeers, Czech Republic). The seed sorting mode was used to understand the distribution of individual phenotypic traits in the progeny of nAT and hAT grown plants. In this mode, 1,000 seeds were randomly analyzed in two biological replicates. The parameters of individual seeds were analyzed from two orientations with an angular position of the nozzle at 0° and 90°. Seed analysis was performed using the Boxeed software for various seed morphometric parameters, including the seed size (mm 2 ), shape (ratio of seed length to area), length (mm), and width (mm). An average of two measurements for each seed was used to calculate seed characteristics. Data Analysis The results (multiple pairwise comparisons between different conditions and temperatures) were analyzed using Exact Fisher’s and two-way ANOVA followed by Tukey’s post hoc test using Python (Python Software Foundation, https://www.python.org/) in the Pycharm environment (https://www.jetbrains.com/pycharm/). The level of statistical significance was set at p ≤ 0.05 for all tests. Statistical analysis is described in the Additional File 2. Graphs for the representation of the phenotyping data were prepared using SuperPlotsOfData (https://huygens.science.uva.nl/SuperPlotsOfData/) [41]. The relative response to hAT for each tissue and parameter was evaluated as a percentage. The Pearson correlation method in Python was used to calculate the correlations between different parameters, resulting in a correlation matrix. To maintain the integrity of the analysis, a significance threshold of a p -value of 0.005 (5 %) was set. This threshold ensured that only correlations with p -values below this level were considered significant and included in the graph. The resulting matrix provides valuable insight into the intricate relationships between different tissues and parameters in response to hAT. RNA extraction, library construction and RNAseq Gynoecium samples from flowers at stages 11 and 12 (before anthesis) were collected from wild-type, phyb and 35S::PIF4 plants grown at nAT and hAT. Total RNA was extracted from 100 mg of pistils using the RNeasy Plant Mini Kit (Qiagen) following the manufacturer’s protocol. RNA isolates were treated with rDNAse Macherey-Nagel) to remove traces of contaminant DNA and purified using a RNeasy MinElute Cleanup Kit (Qiagen). RNA quality was assessed using a NanoDrop2000 spectrophotometer and agarose gel electrophoresis. Samples, four biological replicates each, were sent to the Novogene Genomic Sequencing Labs (Cambridge Sequencing Center) for sequencing. All samples passed Novogene's quality control threshold for library preparation and RNA-seq. mRNA-Seq libraries were constructed by Novogene, starting with 100 ng of high-quality RNA per sample. mRNA purification was performed using oligo(dT)-attached magnetic beads, followed by fragmentation and first-strand cDNA synthesis. Second-strand cDNA synthesis, end repair, adapter ligation, and size selection were performed. PCR enrichment yielded in the final cDNA library. Sequencing was conducted on the Illumina NovaSeq platform, generating 150-bp/150-bp paired-end reads. The sequence data have been deposited in the Genbank database under the BioProject PRJNA1091589. Clean reads were generated by removing adaptor sequences and low-quality reads using fqtools. The reads were mapped to the Arabidopsis genome using Araport11 (TAIR10, http://www.arabidopsis.org/). FeatureCounts was used to determine read count for each gene in each sample. The FPKM values were calculated to provide a measure of gene expression levels in each sample. Differential gene expression (DE) analysis Differential gene expression analysis was analyzed by Bioconductor package DESeq2 v1.34.0 [42]. Data generated by DESeq2 with independent filtering were selected for the differential gene expression analysis due to its conservative features and to avoid potential false positives. Genes were considered to be differentially expressed based on a cut-off of adjusted p -value < 0.05 and log2(fold-change) ≥1 or ≤-1 and a false discovery rate (FDR) < 0.05. Gene Ontology and hierarchical clustering Gene ontology annotation was retrieved from EnsemblPlants, Ensembl BioMarts [43]. Gene enrichment was performed using the R package clusterProfiler [44] on the differentially expressed genes (genes with adjusted p -value <0.05) and separated in up- and down-regulated set. Visualizations were made using ggplot2 [45]. Hierarchical clusters were generated from selected top differentially regulated genes using R package pheatmap v1.0.12 1 , volcano plots were produced using ggplot2 v3.3.5 package [45] and MA plots were generated using ggpubr v0.4.0 package 2 . Results High ambient temperature alters the root system architecture with a reduction in the number of emerged lateral roots compensated by their increased elongation Studying root development in petri dishes has limitations, including a limited growth area, root illumination, and the absence of soil. To overcome these challenges in in vitro plant cultivation, we used a light-isolated rhizotron system that allows plants to grow in soil for non-invasive, image-based root phenotyping. To investigate the effects of temperature on root morphology and the role of the PhyB-PIF4 pathway, we phenotyped Col-0, phyb, 35S::PIF4, and pif4 plants. Lateral and adventitious roots, total root length, primary root length, maximum length of the four longest lateral roots, and root area were measured throughout growth. Root growth patterns responded differently to warm temperatures among genotypes ( Fig . 1a ). hAT significantly promoted root elongation in Col-0 and pif4 , but had little effect on phyb and 35S::PIF4 plants ( Additional file 1 - Fig. S1 ). All plants initially showed increased relative root elongation at hAT ( Fig. 1b ). However, by the third week of development, root growth was slower at hAT compared to nAT in all genotypes ( Fig. 1b ). Overall, no significant differences were detected at the final time point ( Additional file 1 - Fig. S1 ), consistent with previous work using a TGRooZ device that mimics natural conditions [46]. hAT had no significant effect on the overall root growth rate of the genotypes studied. In hAT, 35S::PIF4 showed a slightly lower root growth rate of 1.29 cm/day, in contrast to the other genotypes (1.41 to 1.6 cm/day) during the entire measurement period. This difference in growth rates resulted in a smaller average root depth for 35S::PIF4 throughout its growth ( Table 1 ; Additional File 1 - Fig. S2 ). Moreover, during the initial growth phase (between 10 and 16 days after sowing), at nAT, no significant difference in the relative root growth rate were observed among the genotypes. At hAT, 35S::PIF4 showed a lowest significant root growth rate of 1.42 cm/day. During the second growth phase (between 18 and 21 days after sowing), 35S::PIF4 roots were growing significantly slower than Col-0 at both nAT (1.39 cm/day vs. 1.88 cm/day) and hAT (1.81 cm/day vs. 2.39 cm/day) ( Fig. 1b; Additional File 3 – Table S2 ). Lateral root formation was inhibited at hAT ( Fig. 1c ). Wild-type, phyb , and pif4 plants showed reduced lateral root density (number of emerged lateral roots per cm of primary root) at hAT. However, lateral root density was not affected in 35S::PIF4 plants at hAT ( Fig. 1c ; Additional File 2 - Table S1 ). At nAT, Col-0 and pif4 plants had 2.2 and 2.11 lateral roots per cm of primary root, respectively. In contrast, phyb and 35S::PIF4 plants produced fewer lateral roots, averaging 1.5 and 1.7 lateral roots per cm of primary root, respectively. At hAT, the number of lateral roots in wild-type plants was similar to that of phyb and 35S::PIF4 at nAT. Although the plants had fewer emerged lateral roots, hAT promoted their elongation in all the genotypes ( Fig. 1d ). There may be a trade-off between the number of lateral roots and their length. All the genotypes increased the average length of the four longest lateral roots with Col-0 and pif4 seedlings being the most affected and 35S::PIF4 and phyb being the least sensitive to hAT ( Fig . 1a,d ; Additional File 2 - Table S2 ). The opposite effects of hAT on the number of lateral roots and their length did not significantly affect the total root length and root area between nAT and hAT ( Additional File 1 – Figs. S2 and S3 ). However, these values were significantly lower for phyb and 35S::PIF4 genotypes under both conditions, resulting in a reduced root system compared to wild-type plants. Furthermore, temperature increase promoted the induction of adventitious roots in all the genotypes studied. 19% of the wild-type and phyb plants produced adventitious roots at hAT, while this value decreased to about 5% for the PIF4 -modified genotypes. These changes in (lateral) root length and number alter the root system architecture of plants grown at hAT. These results indicate that phyb and 35S::PIF4 plants under both conditions have a reduced root system compared to wild-type plants. They also suggest that the suppression of PhyB at nAT mimics the effects of hAT on the number of emerged lateral roots. Suppression of PhyB mimics the effects of high ambient temperatures on Arabidopsis shoot architecture To understand how hAT affects shoot development and photosynthetic efficiency through the PhyB-PIF4 pathway, we studied eight lines: 35S::PIF4, phyb-9, pif3-7, pif4-2, pif7-1, pif7-2, pif3-3 pif7-1, and pifq (pif1-1 pif3-7 pif4-2 pif5-3 ) mutants. We quantified the effects of hAT on plant growth by measuring the rosette area from 9 to 39 days after sowing, when the plants reached their final rosette size ( Fig . 2, Additional File 1 - Figs. S4, S5 ). The phyB and 35S::PIF4 plants exhibited delayed rosette expansion, starting at 26 days after sowing, while the other genotypes expanded from 22 days after sowing ( Fig. 2a, Additional File 1 - Figs. S5 ). At nAT, wild-type plants had the largest area (40 cm²), whereas 35S::PIF4 and phyb plants were smaller (20 cm² and 10 cm², respectively) ( Additional File 1 - Figs. S4, S5 ). Other genotypes ( pif3, pif7, and pifq ) produced plants with intermediate rosette areas. This is a consequence of a significant reduced growth rate between 20 and 27, and between 28 and 33, days after sowing in 35S::PIF4 and phyb plants. It is noteworthy that the phyb plants stopped expanding after 27 days ( Fig. 2b; Additional File 1 - Fig. S5 ). Wild-type plants were significantly sensitive to hAT, with a reduced growth rate and a final area of 15 cm² ( Additional File 1 - Fig. S5 ). In contrast, 35S::PIF4 , pif3 , pif4 , and pifq maintained a stable growth rate between 20 and 27 days ( Fig. 2a ) but pif3 , pif4 , and pifq slowed down their growth rate after 28 days ( Fig. 2b ). The final rosette area was about 20 cm² for pif4, pif7-1, pif7-2, and pif3 pif7 plants. The phyb and 35S::PIF4 plants showed the smallest area with only 5 cm². The wild type, pif3 , and pifq showed an intermediate size of 15 cm² ( Additional File 1 - Fig. S5 ). To analyze the effects of hAT on shoot branching, we measured the number of primary branches emerging from rosette leaves in all genotypes under both growth conditions. While most genotypes produced an average of about five branches under normal conditions, phyb and 35S::PIF4 plants produced an average of three branches. When exposed to hAT, branch production decreased in almost all genotypes. The phyb and 35S::PIF4 plants produced an average of two branches, while the other genotypes produced an average of three branches, mirroring the performance of phyb and 35S:PIF4 under nAT. Notably, the pif3 pif7 and pif7 plants appeared to be resilient to the hAT, roughly maintaining their branch production. At hAT, they outperformed other genotypes, producing an average of 4 branches ( Table 2 ). The inflorescence growth pattern was affected in hAT ( Fig. 3; Additional File 1 - Fig. S4 ). In nAT, phyb plants flowered at 25 days, earlier than the other lines (29 days). The first flowers of the primary inflorescence stem opened between 25 and 29 days in phyb and between 29 and 36 days in the other genotypes. Consequently, phyb inflorescence stems were longer than Col-0 stems during their growth period, e.g., until 36 days, when both genotypes reached a comparable height. However, the growth rate of the phyb primary inflorescence stem was significantly lower than that of the wild-type stem ( Figs. 3c and 3d; Additional File 3 - Table S3 ). The phyb mutant also stopped flowering earlier, at 44 days, compared to the other genotypes, which continued flowering until 49 days ( Fig. 3a ). All genotypes grew to a total height of 35 cm by the last observation point at 49 days ( Fig. 3a ). hAT stimulated early initiation of inflorescence stem elongation in all genotypes at 23 days, similar to that observed in phyb plants grown under nAT ( Figs. 3a, 3b ). In the primary inflorescence stem, flowers opened around 27-30 days in hAT. Plants reached their maximum growth earlier, at 41 days, as indicated by the significantly reduced growth rate in hAT in wild-type, pif7-1 , and pif3 pif7 plants ( Figs. 3c, 3d; Additional File 3 - Table S3 ). This resulted in a shorter final height ranging from 13-38.9 cm ( 35S::PIF4 stems being the shortest) at hAT, while this value corresponds to 18-43.8 cm at nAT ( Figs. 3a, 3b ). The main inflorescence stem growth rate was insensitive to temperature changes throughout the entire flowering period in phyb , 35S::PIF4 , pif3 , pif4 , pif7-2 , pif3 pif7 , and pifq plants ( Fig. 3d ), and only at the start of the flowering period in pif7-1 plants ( Fig. 3c ). Ambient temperature has a moderate impact on plant health but modulates photosynthetic parameters Since hAT affects different characteristics of plant growth, we wondered how these changes would affect the reflectance profile and pigment content of the plant. Hyperspectral imaging in the visible and near infrared (350-900 nm wavelength, VNIR) measures the light reflectance of plant leaves. It is an important indicator of plant health status [47, 48]. In our study, we measured VNIR parameters, including the Normalized Difference Vegetation Index (NDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index (PRI), Modified Chlorophyll Absorption Ratio Index 1 (MCARI1), Structure Insensitive Pigment Index (SIPI), and Plant Senescence Reflectance Index (PSRI). In nAT, NDVI increased with age until 29 days after sowing for all genotypes and remained stable until the end of the measurements at 34 days ( Additional File 1 - Fig. S6a ; Additional File 3 - Table S6 ). hAT reduced the NDVI in all genotypes ranging from 0.68 to 0.78, especially at later growth stages (22-28 days after sowing) ( Additional File 1 - Fig. S6a ). In both nAT and hAT, NDVI had lower values for phyb and 35S::PIF4 with values in nAT (an average of 0.74) being comparable to NVDI values (an average of 0.82) of the other genotypes in hAT. OSAVI, which is designed to mitigate the effects of soil on NDVI, mirrored the trends observed in NDVI ( Additional File 1 - Fig. S6b ; Additional File 3 - Table S6 ). These two parameters are indicators of plant vegetative health [49, 50]. Therefore, it can be concluded that both the suppression of PhyB activity and hAT affect the vegetative vitality of the plant. PRI and PSRI parameters were mostly not significantly affected by the different ambient temperatures for all genotypes ( Additional File 1 - Figs. S6c, S6d ; Additional File 3 - Table S6 ). PRI values decreased with the plant age, whereas the opposite was observed for PSRI, which measures plant senescence based on the ratio of carotenoids to chlorophyll. Again, phyb and 35S::PIF4 plants had lower PSRI values than wild type at nAT and hAT. The SIPI parameter is sensitive to chlorophyll and carotenoid content [51] and MCARI1 parameter is associated with the chlorophyll content in plant leaves [52]. Both values increased as the plants aged at nAT and hAT ( A dditional File 1 - Figs. S6e, S6f ; Additional File 3 - Table S6 ). All other genotypes, except 35S::PIF4 and phyb , had reduced SIPI values at hAT. The 35S::PIF4 and phyb plants had lower SIPI values at nAT and did not respond to hAT. A similar trend was observed for the MCARI1 parameter. We applied chlorophyll fluorescence imaging to assess the efficiency of the plants to use the light energy for photosynthesis in the studied genotypes at nAT and hAT. The parameter QY-max (F V /F M ) indicates the maximum quantum efficiency of the photosystem II (PSII) photochemistry. QY-max of wild-type plants increased steadily with age, with values ranging from 0.79-0.84 for nAT and 0.79-0.82 for hAT (significant difference only between 14 and 32 days after sowing). In nAT, the QY-max values for phyb and 35S::PIF4 plants were lower than in the wild type. Interestingly, phyb recovered to wild-type QY-max values after two weeks of cultivation at nAT ( A dditional File 1 - Fig. S7a ). At hAT, QY-max values increased with age for all genotypes, except 35S::PIF4 and pif3 ( A dditional File 1 - Fig. S7a) . Photosynthetic efficiency was also measured in light-adapted plants. In particular, the parameters QY-Lss (PSII operating efficiency), and qP (photochemical quenching coefficient) [53] displayed significantly higher values at hAT for all the genotypes, corresponding to those of 39-day-old plants grown at nAT for both low and high light saturation point (Lss1 and Lss4) ( A dditional File 1 - Fig. S7c-f ). For the two light intensities at hAT, the age of the plants did not impact the values of the two parameters. Non-photochemical quenching (NPQ) assesses the damage to photosystems caused by various environmental stressors [54]. All the genotypes exhibited lower NPQ values at hAT, indicating the negative impact of the high ambient temperature on the photosystem activity ( A dditional File 1 - Figs. S7g, S7h ). Compared to the wild type, the phyb and 35S::PIF4 plants showed elevated NPQ values at nAT and hAT. A correlative response to hAT in vegetative organs was observed To explore potential correlations for the response to hAT among different plant organs, we utilized correlation matrices ( Fig. 4 ). These matrices display correlations with p -values below the significance threshold of 0.05, indicating statistically significant relationships between the relative responses to hAT in the different organs. During vegetative growth ( Fig. 4a ), a positive correlation (0.96) was observed between the NDVI parameter and the length of the inflorescence stem, highlighting the effectiveness of the NDVI parameter in indicating vegetative growth dynamics. A robust positive correlation (0.94) was also noted between inflorescence stem growth rate and rosette area for their response to hAT, suggesting mutual interdependence between these traits. Notably, a negative correlation (-0.62) was observed between lateral root density and lateral root length, hinting at a potential trade-off mechanism governing root development. PhyB influences the response of reproductive tissues to hAT We have used Col-0, phyb , pif4 , pifq , and 35S::PIF4 plants to investigate whether the PhyB-PIF4 pathway regulates thermomorphogenesis during reproductive development. To ensure a similar fitness of the plants at the reproductive stage, plants were exposed to hAT after the first flower bud appearance and maintained at hAT until the end of their growth. Effects of hAT on anthers Anthers were collected at 7 and 9 days after the development of the first flower (DAFD) on the primary inflorescence stem. In nAT, we did not observe any abnormality in the different lines. In hAT, the wild type, phyb , and 35S::PIF4 lines were affected to different degrees. At 7 DAFD, 4.65 % of the wild-type anthers were aborted, while this percentage reached 23.40 % and 11.43 % for the 35S::PIF4 and phyb lines, respectively. Interestingly, these percentages increased to 7.81 %, 34.82 %, and 29.27 %, respectively, at 9 DAFD when plants were subjected to prolonged hAT. Notably, only the phyb mutant showed a highly significant increase in this trend ( Table 3 ). This observation suggests that the phyb plants may become increasingly sensitive to hAT as they progress through later developmental stages. Additionally, we observed that pif4 and pifq anthers were more resistant to hAT than wild type, with abortion rates of only 1.11 % and 1.44 %, respectively, at 9 DAFD. Our results suggest that suppression of PhyB , resulting in PIF4 activation, worsens the negative effect of hAT on anther development. Effects of hAT on mature ovules The same plants were analyzed to determine the effect of hAT on ovules. In nAT, 17.9 % and 16.1 % of phyb and 35S::PIF4 ovules, respectively, were defective, whereas the other lines had between 5.4 % and 9.4 % defective ovules ( Fig. 5 ; Table 4 ; Additional File 2 – Table S4 ). Notably, only phyb and 35S::PIF4 lines were defective in the fusion of the central cell nuclei ( Fig. 5c ; Additional File 2 – Table S4 ). At hAT, all genotypes exhibited the same types of defects, predominantly a collapsed embryo sac (lacking synergid, egg cell, and central cell structures), collapsed synergids, and unfused central cell nuclei ( Fig . 5a-d ). Although the types of ovule defects were consistent across genotypes, the percentage of these defects varied ( Additional File 2 – Table S4 ). 35S::PIF4 and phyB ovules were hypersensitive to hAT, producing 84.3 % and 62.6 % defective ovules, respectively ( Table 4 ). In contrast, these percentages were only 30.6 % and 27.6 % in the wild-type and pif4 lines, respectively. Interestingly, more ovules (45.9 %) were defective in pifq than in pif4 (27.6 %), suggesting that other PIFs (such as PIF3, PIF5, or PIF7) may play a synergistic role in this response in ovules. Based on these results, we hypothesize that repressing PhyB expression mimics the temperature effects observed in the wild type during ovule development. To better understand what would be the molecular mechanism behind the physiological response of Arabidopsis ovules to hAT, we performed a transcriptomic analysis of the gynoecium from 7 DAFD flowers at stage 11-12 (pre-anthesis, ovules at FG7) of Col-0, phyb and 35S::PIF4 plants grown under nAT and hAT. More than 40 million reads were obtained from each sample ( Additional File 2 – Table S5 ), with an average of 45 % GC content. RNA-seq data received a high quality score by the Phred of 98 for Q20 and 94 for Q30 in average. An overlapping transcriptional response is observed between hAT wild-type pistils and nAT-grown phyb and 35S::PIF4 pistils The analysis of differentially expressed genes (DEGs) in the different samples and conditions revealed different patterns. In response to hAT, the wild-type pistils had 8,485 DEGs (5,032 up-regulated and 3,453 down-regulated). A lower number of DEGs in phyb and 35S::PIF4 pistils, 1,862 and 2,612 genes, respectively, were distributed as 1037 and 2062 up-regulated genes, and 825 and 550 down-regulated genes, respectively ( Additional File 1 - Fig. S8a; Additional File 4 – Table S1 ). Notably, all genotypes responded to hAT with upregulation of gene expression. The phenotyping analysis indicated that the phyb and 35S::PIF4 plants at nAT behaved as Col-0 at hAT. Therefore, we compared the DEG patterns of the wild-type pistils in response to hAT with those of phyb and 35S::PIF4 pistils at nAT. The number of up- and downregulated DEGs in these comparisons was very similar ( Additional File 1 - Fig. S8b; Additional File 4 – Table S1 ). Venn diagrams analyze the overlap of the up and down DEGs in the same comparisons. In response to hAT, 10 % (542 genes) of the upregulated genes from wild-type pistils were also upregulated in 35S::PIF4 and phyb pistils at hAT, whereas only 3 % (121 genes) of the downregulated genes from wild-type pistils were also downregulated at hAT in the two mutants ( Additional File 1 - Figs. S8c, S8d; Additional File 4 – Table S1 ). However, only 3.7% of the genes upregulated in the wild-type pistils in response to hAT were also upregulated in both phyb and 35S::PIF4 pistils at nAT. The majority of the upregulated DEGs (62 %) in wild-type pistils at hAT were also found to be upregulated in 35S::PIF4 pistils at nAT ( Additional File 1 - Fig. S8e; Additional File 4 – Table S1 ). In addition , almost half of the genes downregulated in the wild-type pistils at hAT (46 %) were downregulated genes in phyb and 35S::PIF4 pistils at nAT ( Additional File 1 - Fig. S8f; Additional File 4 – Table S1 ). Wild-type Arabidopsis pistils (and ovules) developed at hAT showed pronounced transcriptional changes, mostly as upregulation, with a substantial overlapping regulation with phyb and 35S::PIF4 pistils developed at nAT. This suggests that the Arabidopsis response to hAT during pistil development may involve signaling pathways dependent on the PhyB and PIF regulators. Gene ontology analysis identified biological processes affected by hAT and PhyB-PIF4 signalling in pistils Gene Ontology (GO) functional annotation analysis using was performed for up- and downregulated DEGs in wild-type pistils from plants grown on hAT, and 35S::PIF4 and phyb pistils from plants grown on nAT to determine whether the hAT response in wild-type pistils shares GO patterns with the response in pistils from plants defective in the PhyB pathway ( Fig. 6; Additional File 4 – Table S2 ). Cell division rate is known to be dependent on ambient temperature [55]. Several GO terms related to cell division, cell cycle, DNA replication, and mRNA processing were enriched among the commonly upregulated DEGs. These processes are known to be critical during pistil and ovule development. Indeed, GO terms associated with megagametogenesis, ovule, embryo sac, and flower development and the transition to the reproductive phase in the meristem were among the commonly upregulated DEGs ( Fig. 6a ). Among the GO terms related to fertilization and reproduction, recognition of pollen, (regulation of) pollen growth and pollen development were enriched. Genes involved in pollen tube growth were specifically upregulated by hAT in the wild-type pistils, whereas genes involved in pollen germination were enriched only in 35S::PIF4 pistils ( Fig. 6a ). This suggests that both hAT and the PhyB-PIF4 pathway may influence the expression of genes involved in ovule development as observed in Fig. 5 , and that fertilization processes dependent on pollen tube growth and guidance may be specifically affected by hAT in wild-type pistils. Surprisingly, GO terms related to the responses to hormones and abiotic stresses were found to be downregulated ( Fig. 6b ). Responses to auxin and ethylene were downregulated in all sample comparisons. However, GO terms associated with brassinosteroid, gibberellin, abscisic acid, and jasmonic acid were exclusively downregulated in 35S::PIF4 pistils, which may explain the more pronounced phenotypic response of 35S::PIF4 pistils to hAT during ovule development ( Table 4 ; Additional File 2 - Table S4 ) . Furthermore, GO terms related to cold and light stress responses, photosynthesis, protein translation, and metabolism were generally enriched among the downregulated genes in all three samples ( Fig. 6b ). The expression profile of the phyb and 35S::PIF4 pistils at nAT for auxin signaling and miRNA processing genes is comparable to that of wild-type pistils at hAT Hierarchical clustering analysis of the expressed genes identified two major clusters among the top 100 DEGs in Col-0 hAT pistils compared to Col-0 nAT pistils ( Additional File 1 - Figure S10; Additional File 4 – Table S3 ), the DEGs involved in the auxin signaling pathway ( Fig. 7a; Additional File 4 – Table S3 ) and in miRNA biogenesis ( Fig, 7b; Additional File 4 – Table S3 ). One cluster consists exclusively of the wild-type pistils from plants grown in nAT. The second cluster includes the pistils from phyb and 35S::PIF4 plants grown in nAT and hAT, as well as from wild-type plants grown in hAT. Similar to what was observed during our phenotyping analysis, these results indicate that the response to hAT and to the PhyB-PIF4 pathway share a gene regulatory network. PIF4 binds to the promoters of several miR156 genes to repress their expression, resulting in the accumulation of the miR156 target transcripts, the SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE ( SPL ) genes [56]. SPL will then regulate plant growth in response to shade and warm temperature. The module miR156/ SPL9 regulates the thermomorphogenetic response of the hypocotyl by mitigating its sensitivity of auxin [57]. Several SMALL AUXIN UP RNA ( SAUR ) and Aux/IAA genes, as well as AUXIN RESPONSE FACTOR ARF10 and ARF19 are upregulated in the second cluster ( Fig. 7a ). We also identified MIR156 , MIR160 , and the miRNA processing AGO1 , DCL1 genes to be upregulated in the same cluster, while the MIR156 targets SPL5 and SPL9 were slightly down-regulated ( Fig. 7b ). Pollen tube attractants are upregulated at hAT We also performed a hierarchical clustering for genes related to pollen tube guidance, an enriched GO term category ( Fig. 6a ; Fig. 7c; Additional File 4 – Table S3 ). Again, two distinct clusters related to the hAT response were identified. Genes encoding the defensin-like pollen tube attractants CYSTEINE-RICH PEPTIDE (CRP) AtLURE1s and XIUQIU, EMBRYO SURROUNDING FACTORS 1.3 (ESF1.3), EGG CELL SPECIFCs (ECSs), and MYB98, a transcription factor controlling their expression [58, 59], were upregulated in the cluster comprising all pistil samples from plants grown in hAT ( Fig. 7c ), regardless of genotype. Changes in YUCCA and TAA1 expression levels in hAT in mature ovules suggest a role for auxin biosynthesis in the response to high ambient temperature In seedlings, hAT-activated PIF4 enhances the expression of the TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS ( TAA1 ), YUCCA 8 ( YUC8 ) and SAUR genes in the leaves and hypocotyls [8, 20]. TAA1 , YUC4 and YUC8 are also expressed in mature ovules at the micropyle cells surrounding the embryo sac [60]. To evaluate the effects of hAT on auxin homeostasis in mature ovules, we analyzed the expression pattern of the three auxin biosynthetic genes. TAA1 is expressed in the micropylar cells in nAT and its expression is altered in hAT ( Fig. 8a-c ). The TAA1 fluorescence signal was not detected in 49 % of the ovules and was weak in the remaining samples in hAT ( Figs. 8b, 8c ). YUC4 was strongly expressed in the integuments of mature nAT ovules ( Fig. 8d ). Different levels of the fluorescence signal intensity were observed for YUC4 in hAT ovules: same expression pattern with reduced signal intensity (19.4 %; Fig 8e ), restricted expression domain at the chalazal integuments with weak signal intensity (66.6 %; Fig. 8f ), and no signal (13.8 %; Fig. 8g ). YUC8 showed no (95.4 %; Fig. 8h ) to weak expression in the micropylar cells (4.6 %) in nAT ovules. However, in hAT, YUC8 was highly expressed in the micropylar cells ( Fig. 8i ). YUC8 is known to be upregulated in hAT in other tissues [8], which is consistent with our observations in ovules. The contrasting expression behavior of YUC4 and YUC8 at hAT suggests an intricate and complex regulatory mechanism in the response to hAT in the ovules. Effects of hAT on early embryo development Given the effects of hAT on ovules and the transcriptional changes associated with pollen guidance and its impact on fertilization, we investigated the effects of hAT on seed and embryo development in the same genotypes. Seeds bearing embryos from early developmental stages (one-cell to late globular) were analyzed for embryo patterning defects. In nAT, no significant differences were observed between the different genotypes ( Table 5 ). In hAT, however, all genotypes were significantly affected. No statistically significant differences in the percentage of defective embryos were observed between wild type (40.77 %), pif4 (44.23 %), pifq (41.56 %), and phyb (30.85 %). Only 35S::PIF4 appeared to be resistant to growth at hAT with a significantly lower embryonic defect rate of 21.95 % ( Table 5 ). A variety of embryonic defects have been observed, including an excess of cell divisions within the proper embryo or suspensor, irregularities in the size of the hypophysis cell, and a reduction in the length of the suspensor ( Figs . 5f-h ; Additional File 2 - Table S6 ). A shorter suspensor was observed in all the genotypes for hAT ( Fig. 5h ). In nAT, the suspensor of the 35S::PIF4 embryos was longer (111 μm) than the wild-type suspensor (97.18 μm). However, this difference disappeared in hAT, suggesting that the 35S::PIF4 embryos were the most affected by temperature variation for suspensor growth ( Fig . 5h ; Additional File 2 – Table S6 ). These results suggest that ectopic overexpression of PIF4 may confer a minor temperature resistance during embryogenesis. hAT-induced changes in seed traits Dry seeds harvested from the same plants flowering at nAT and hAT were phenotyped using the Boxeed robot. We focused on four seed traits: number of seeds produced per silique, seed shape, seed size, and seed weight ( Fig. 9; Additional File 3 – Tables S8 and S9 ). Elevated ambient temperatures led to an increase in seed area in all the genotypes, with the production of larger viable seeds and smaller misshapen seeds ( Fig. 9a ). Seed area increased by 34.74 % in Col-0, 31.73 % in 35S::PIF4 , 47.83 % in phyb, 25.20 % in pif4 , and 47.67 % in pifq ( Fig. 9b ; Additional File 2 - Table S7 ). Additionally, seeds produced under warmer conditions were rounder across various genotypes, as assessed by the ratio of the seed length to the seed area. The phyb seeds were the most affected by shape changes in hAT ( Fig. 9c ; Additional File 2 – Table S8 ). Evaluation of the number of seeds per silique showed that all genotypes produced fewer but heavier seeds per silique at hAT in all the genotypes ( Figs. 9d, 9e ; Additional File 2 – Tables S9 and S10 ). Interestingly, at nAT, phyb seeds were by 25% heavier than wild-type seeds ( Fig. 9e ; Additional File 2 – Table S10 ). The higher seed weight observed in seeds developed at hAT suggests a possible adaptive strategy in which plants may favor the production of nutrient-rich seeds rather than a greater number of seeds. However, phyb plants grown on nAT and wild-type plants grown on hAT produced a comparable number of seeds, precisely 42.14 and 47.75 seeds per silique for a comparable weight, 2.33 mg and 2.26 mg per 100 seeds, respectively. The correlation of the hAT response in reproductive tissues A correlative analysis of the effects of hAT on reproduction showed that seed number and the increased number of embryo defects and pollen defects were significantly negatively correlated (-0.92 and -0.63, respectively). Seed number and seed weight were also significantly negatively correlated (-0.71). Surprisingly, pollen defects were positively correlated (0.87) with increased seed weight. Discussion Plants have adapted to ambient growth temperatures through various molecular mechanisms, including the PhyB-PIF4 pathway [13, 61]. The aim of this study was to understand the response of Arabidopsis thaliana to high ambient temperatures during vegetative and reproductive growth and to investigate the role of the PhyB-PIF4 pathway, with a focus on seed production. We performed a comprehensive morphological analysis of different organs during both vegetative and reproductive growth stages using automated phenotyping solutions, with the phyb mutant and PIF4 overexpression lines to elucidate the influence of this pathway on these responses. We uncovered how suppression of the PhyB-PIF4 pathway differentially induces thermomorphogenesis at different developmental stages, thereby affecting the plant's resistance to temperature changes. Additionally, we investigated the correlation of the response to hAT between different organs. And we studied the transcriptional changes in pistils that may help to overcome the hAT-reduced fertilization rate. Research on the impact of hAT on root system growth has yielded mixed results, with some studies reporting decreased root growth and others reporting increased root growth [62–65]. In our study, hAT initially enhances root elongation in all genotypes. This is consistent with previous findings showing that overexpression of PIF4 hinders the thermal response of roots, similar to the phenotypes of hy5 and phya phyb mutants. Reduction of root meristem size in hAT is dependent on PhyA and PhyB [66, 67]. With a different temperature settings, Song et al.(2017) observed that a short-term heat shock at 37 °C inhibited primary root elongation in wild type and phyb and phya mutants, with a more pronounced effect in mutants. This is similar to what we observed with a prolonged growth at hAT ( Fig. 1b ). However, phyb resisted the inhibition of lateral root growth after the heat shock at 37 °C, whereas this trait was enhanced in phyb under our growth conditions. The plant root system consists of primary, lateral, and adventitious roots. Primary roots form during Arabidopsis embryogenesis, whereas adventitious and lateral roots emerge post-embryonically. Adventitious root formation serves as a crucial plant strategy to cope with environmental stresses [68]. We found that hAT induced adventitious root formation in all studied lines, except when PIF4 expression was altered. It is worth noting that in nature, the soil temperature is gradually decreases with depth [46] , which mitigates the thermal response of the root system. Interestingly, our results highlight the divergent response of shoot and root development to high temperatures. While hAT inhibits shoot elongation, it does not affect the final root length. However, when plants are exposed to hAT, initial growth acceleration and reduced branching are common in both tissues. It seems that both the root and shoot prioritize initial elongation at hAT, likely as a strategy to distance themselves from the warm soil surface. This prioritized elongation, particularly evident in the root, comes at the expense of nutrient uptake, as indicated by the observed reduction in the number of emerged lateral roots. This trade-off underscores the dynamic adjustments that plants make in response to environmental stress and highlights the intricate balance between growth and resource allocation. Notably, PIF4 overexpression abolishes the temperature response of both root and shoot branching, suggesting a potential function of this transcription factor in shoot and root development at hAT. Flowering time in plants is regulated by environmental signals that affect gene expression in the shoot apical meristem. Notably, ambient temperature modulates the expression of FLOWERING LOCUS T (FT) [69]. hAT generally leads to earlier flowering responses in most plants (reviewed by [70]). PIF4 emerges as a pivotal player in temperature-induced early flowering in Arabidopsis , exerting its influence by binding to the FT promoter in a temperature-dependent manner [61]. We have shown that exposing plants to hAT results in the premature cessation of rosette growth, leading to a reduced rosette area ( Fig. 2 ). These plants appear to prioritize energy conservation for the reproductive phase, which ultimately means reduced branching. Initially, plants hastened shoot elongation to distance flower buds from the warm soil surface, resulting in earlier flowering ( Fig. 3 ). Most of the temperature effects were observed in the phyb mutant line under normal conditions, suggesting that the suppression of PhyB simulates the effects of hAT during shoot development. Furthermore, in agreement with [71], our investigation showed that the studied spectral vegetation indices exhibited increased responsiveness to hAT during later stages of development. This suggests their potential utility as reliable non-destructive indicators of temperature stress. Plant reproductive development, especially pollen, is highly sensitive to environmental stress [72, 73]. Growing Arabidopsis at 27 °C affects pollen development, resulting in male sterility with a 22 % reduction in pollen viability, through processes such as meiosis disruption, premature development, and altered hormone regulation [74, 75]. We observed a mild effect of hAT on pollen viability with pif4 and pifq plants being resistant to hAT for the production of viable pollen grains. Our observations revealed a robust phenotypic response to hAT in ovules, highlighting their sensitivity to temperature changes. We demonstrated that the 35S::PIF4 plants in nAT effectively mimic the effects of hAT, highlighting the critical role of this pathway in thermomorphogenesis in female reproductive organs. To investigate the molecular mechanisms involved, we performed transcriptome analyses of wild-type, 35S::PIF4 , and phyb pistils from plants grown at nAT and hAT. This comprehensive approach allowed us to compare the transcriptomic responses of these genotypes in response to hAT and understand how the suppression of the PhyB pathway mimics the expression profile and phenotypes of wild-type pistils exposed to hAT. DEG analysis revealed that wild-type plants show significant up- and downregulation in response to hAT, while this response is milder in phyb and 35S::PIF4 pistils. The DEG profiles of phyb and 35S::PIF4 at nAT were similar to the response to hAT in the wild type, with an overlap especially in downregulated genes. We identified that hAT influenced the expression of specific microRNAs , particularly MIR156. MIR156 has been implicated in Arabidopsis hypocotyl elongation in response to hAT and is upregulated in our transcriptomic data [56, 57]. Consistently, heat stress during cotton pollen development regulates the expression of 6281 genes, among which miR167 and miR396 are associated with pollen fertility by targeting genes involved in auxin signaling and metabolism pathways. Additionally, heat-induced jasmonic acid (JA) signaling activates genes associated with auxin synthesis, ultimately leading to pollen abortion [76]. Furthermore, miR167 downregulates the expression of ARF6 and ARF8 genes in Arabidopsis ovules, facilitating integument growth. In anthers, miR167 affects gene expression in connective cells and locules, thereby influencing pollen release. The regulatory function of miR167 underscores its essential role in patterning during the development of reproductive organs [77]. These findings suggest that miRNAs play crucial roles in reproduction and response to hAT, potentially acting as mediators linking high-temperature signaling pathways to hormone signaling pathways during reproductive organ development. The impact of hAT on plant reproductive development involves complex regulatory mechanisms. While elevated temperature has been reported to activate auxin biosynthesis in vegetative plant tissues, such as the hypocotyl, it has opposite effects on auxin levels and biosynthetic genes during anther development in barley and Arabidopsis . Specifically, elevated temperature repressed the expression of YUCCA auxin biosynthetic genes, resulting in reduced endogenous auxin levels in developing anthers [78–80]. Similarly, our transcriptome analysis reveals that at hAT, auxin biosynthetic genes are downregulated at hAT during ovule development, which we confirmed using fluorescent reporters ( Fig, 8 ). Furthermore, Gene Ontology terms associated with the "auxin-activated signaling pathway" and "response to auxin" are suppressed at hAT. Despite a more pronounced impact on male processes, it is important to note that female tissues and post-fertilization development are also highly sensitive to temperature variation (reviewed by [81]). Elevated temperatures significantly influence seed production and overall plant yield. Despite extensive research on temperature effects on pollen and seed development, the underlying molecular mechanisms remain unclear. hAT affects both the total number of ovule/seeds and the number of mature ovule/seeds per silique. In the Arabidopsis Burren ecotype, warm temperatures resulted in up to 43 % unfertilized ovules, leading to shorter siliques and reduced seed yield while promoting larger seeds [82]. A 7 °C increase in temperature (reaching 30 °C) negatively affects multiple reproductive traits in Arabidopsis , including fewer ovules per pistil, fewer anthers and pollen grains per flower, and an increased incidence of improperly developed ovules leading to abortion [83]. In our study, hAT affected sexual reproductive organs and seed-related processes, influencing overall seed yield. Phenotyping with Boxeed identified larger and heavier seeds in hAT, possibly compensating for the reduced seed set ( Fig. 9 ). Suppressing PhyB enhanced PIF4 activation, heightening plant sensitivity to elevated temperatures during both male and female reproduction. Surprisingly, this mechanism improves plant resistance to hAT during embryogenesis, suggesting a versatile molecular pathway across developmental stages. Conclusion Our study provides an in-depth look at how plants respond to thermal stress, covering both their vegetative and reproductive stages through a comprehensive combination of automated phenotyping approaches and image analysis. We found that high ambient temperatures alter the timing of events like flowering and affect basic growth patterns, such as shoot and root system architecture. This suggests that plants prioritize reproduction under challenging conditions, a shift underscored by different temperature sensitivities at different developmental stages. Key among our findings is the role of the PhyB-PIF4 pathway, especially in regulating the development of reproductive tissues. However, its influence is less pronounced during embryogenesis. Overall, our research highlights the complex interplay between plant development and environmental temperatures, with the PhyB-PIF4 pathway playing a significant role in plant thermomorphogenesis. Abbreviations ARF AUXIN RESPONSE FACTOR CRP CYSTEIN-RICH PEPTIDE DAFD Days after the development of the first flower DEG Differentially expressed genes ECS EGG CELL SPECIFIC ESF EMBRYO SURROUNDING FACTOR GO Gene ontology hAT High ambient temperature MCARI1 Modified chlorophyll absorption ratio index 1 nAT Normal ambient temperature NDVI Normalized difference vegetation index NPQ Non-photochemical quenching OSAVI Optimized soil-adjusted vegetation index PhyB PHYTOCHROME B PIF PHYTOCHROME-INTERACTING FACTORs PRI Photochemical reflectance index PSII Photosystem II PSRI Plant senescence reflectance index qP photochemical quenching coefficient SAUR SMALL AUXIN UP RNA SIPI Structure insensitive pigment index SPL SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE TAA1 TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1 VNIR Visible and near infrared YUC YUCCA Declarations Ethics approval and consent to participate No specific permit was required for the samples analyzed in this study. The authors comply with relevant institutional, national, and international guidelines and legislation for plant studies. Plants were cultured and sampled in the greenhouses of the CEITEC Plant Sciences core facility, Brno, Czech Republic. Consent for publication Not applicable Availability of data and materials The dataset supporting the conclusions of this article is deposited to the NCBI repository (BioProject accession number PRJNA1091589). Competing interests The authors declare no competing interests. Funding This work was financially supported by the European Regional Development Fund-Project “SINGING PLANT” (No. CZ.02.1.01/0.0/0.0/16_026/0008446). Acknowledgments The authors thank Tereza Rumlerová (PSI) for assistance with the Root Tester software and Nicolas Blavet (CEITEC Bioinformatics CF) for assistance with the transcriptomic data analysis. The authors acknowledge for their technical support the following Core Facilities of Masaryk university: Bioinformatics (supported by the NCMG research infrastructure [LM2023067], funded by MEYS CR), CELLIM (supported by the Czech-BioImaging large RI project [LM2023050] funded by MEYS CR), Biological Data Management and Analysis (funded by ELIXIR CZ research infrastructure [LM2023055] funded by MEYS CR), and Plant Sciences. The authors acknowledge PSI Research Center phenotyping and cultivation facility. We would like to thank the NASC seed stock center and Zhi-Yong Wang for donating seeds used in this study. Author contributions S.E.N., M.P., K.P., and H.R.B. designed the research; S.E.N., J.Š., B.P., and T.D. performed experiments; S.E.N., J.Š., B.P., M.P., T.D., K.P., and H.S.R. analyzed the data; S.E.N. and H.R.B. wrote the paper; S.E.N., J.Š., B.P., T.D., K.P., M.P., and H.S.R. reviewed the paper and agreed for its publication. References Wolkovich EM, Cook BI, Allen JM, Crimmins TM, Betancourt JL, Travers SE, et al. Warming experiments underpredict plant phenological responses to climate change. Nature. 2012;485:494–7. Legris M, Nieto C, Sellaro R, Prat S, Casal JJ. Perception and signalling of light and temperature cues in plants. The Plant Journal. 2017;90:683–97. Sánchez B, Rasmussen A, Porter JR. Temperatures and the growth and development of maize and rice: a review. Glob Chang Biol. 2014;20:408–17. Luo Q. Temperature thresholds and crop production: a review. Climatic Change. 2011;109:583–98. Hayes S, Schachtschabel J, Mishkind M, Munnik T, Arisz SA. Hot topic: thermosensing in plants. Plant, Cell Environ. 2021;44:2018–33. Patel D, Franklin KA. Temperature-regulation of plant architecture. Plant Signal Behav. 2009;4:577–9. Nozue K, Harmer SL, Maloof JN. Genomic analysis of circadian clock-, light-, and growth-correlated genes reveals PHYTOCHROME-INTERACTING FACTOR5 as a modulator of auxin signaling in Arabidopsis. Plant Physiol. 2011;156:357–72. Franklin KA, Lee SH, Patel D, Kumar SV, Spartz AK, Gu C, et al. PHYTOCHROME-INTERACTING FACTOR 4 (PIF4) regulates auxin biosynthesis at high temperature. PNAS. 2011;108:20231–5. Ohama N, Sato H, Shinozaki K, Yamaguchi-Shinozaki K. Transcriptional regulatory network of plant heat stress response. Trends in Plant Science. 2017;22:53–65. Li B, Jiang S, Gao L, Wang W, Luo H, Dong Y, et al. HEAT SHOCK FACTOR A1s are required for phytochrome‐interacting factor 4‐mediated thermomorphogenesis in Arabidopsis. J Integr Plant Biol. 2023. https://doi.org/10.1111/jipb.13579. Nozue K, Covington MF, Duek PD, Lorrain S, Fankhauser C, Harmer SL, et al. Rhythmic growth explained by coincidence between internal and external cues. Nature. 2007;448:358–61. Legris M, Klose C, Burgie ES, Rojas CCR, Neme M, Hiltbrunner A, et al. PHYTOCHROME B integrates light and temperature signals in Arabidopsis. Science. 2016;354:897–900. Koini MA, Alvey L, Allen T, Tilley CA, Harberd NP, Whitelam GC, et al. High temperature-mediated adaptations in plant architecture require the bHLH transcription factor PIF4. Current biology. 2009;19:408–13. Trupkin SA, Legris M, Buchovsky AS, Rivero MBT, Casal JJ. PHYTOCHROME B nuclear bodies respond to the low red to far-red ratio and to the reduced irradiance of canopy shade in Arabidopsis. Plant Physiol. 2014;165:1698–708. Galvão RM, Li M, Kothadia SM, Haskel JD, Decker PV, Buskirk EKV, et al. Photoactivated phytochromes interact with HEMERA and promote its accumulation to establish photomorphogenesis in Arabidopsis. Genes Dev. 2012;26:1851–63. Casal JJ, Balasubramanian S. Thermomorphogenesis. Annu Rev Plant Biol. 2019;70:1–26. Foreman J, Johansson H, Hornitschek P, Josse E, Fankhauser C, Halliday KJ. Light receptor action is critical for maintaining plant biomass at warm ambient temperatures. Plant J. 2011;65:441–52. Gray WM, Ostin A, Sandberg G, Romano CP, Estelle M. High temperature promotes auxin-mediated hypocotyl elongation in Arabidopsis. PNAS. 1998;95:7197–202. Kumar SV, Lucyshyn D, Jaeger KE, Alós E, Alvey E, Harberd NP, et al. PHYTOCHROME INTERACTING FACTOR 4 controls the thermosensory activation of flowering. Nature. 2012;484:242–5. Sun J, Qi L, Li Y, Chu J, Li C. PIF4-mediated activation of YUCCA8 expression integrates temperature into the auxin pathway in regulating Arabidopsis hypocotyl growth. PLoS Genetics. 2012;8:e1002594–e1002594. Resentini F, Orozco-Arroyo G, Cucinotta M, Mendes MA. The impact of heat stress in plant reproduction. Front Plant Sci. 2023;14:1271644. Jiang Y, Lahlali R, Karunakaran C, Warkentin TD, Davis AR, Bueckert RA. Pollen, ovules and pollination in pea: success, failure and resilience in heat. Plant, Cell & Environment. 2019;42:354–72. Das S, Krishnan P, Nayak M, Ramakrishnan B. High temperature stress effects on pollens of rice ( Oryza sativa L.) genotypes. Environmental and Experimental Botany. 2014;101:36–46. Devasirvatham V, Gaur PM, Mallikarjuna N, Raju TN, Trethowan RM, Tan DKY. Reproductive biology of chickpea response to heat stress in the field is associated with the performance in controlled environments. Field Crops Research. 2013;142:9–19. Talukder ASMHM, McDonald GK, Gill GS. Effect of short-term heat stress prior to flowering and early grain set on the grain yield of wheat. Field Crops Research. 2014;160:54–63. Lin C-J, Li C-Y, Lin S-K, Yang F-H, Huang J-J, Liu Y-H, et al. Influence of high temperature during grain filling on the accumulation of storage proteins and grain quality in rice ( Oryza sativa L.). Journal of agricultural and food chemistry. 2010;58:10545–52. Jumrani K, Bhatia VS. Impact of elevated temperatures on growth and yield of chickpea ( Cicer arietinum L.). Field Crops Research. 2014;164:90–7. Mácová K, Prabhullachandran U, Štefková M, Spyroglou I, Pěnčík A, Endlová L, et al. Long-term high-temperature stress impacts on embryo and seed development in Brassica napus . Front Plant Sci. 2022;13:844292. Elferjani R, Soolanayakanahally R. Canola responses to drought, heat, and combined stress: shared and specific effects on carbon assimilation, seed yield, and oil composition. Frontiers in Plant Science. 2018;9:1224. Jedličková V, Hejret V, Demko M, Jedlička P, Štefková M, Robert HS. Transcriptome analysis of thermomorphogenesis in ovules and during early seed development in Brassica napus . BMC Genomics. 2023;24:236. Canales J, Verdejo JF, Calderini DF. Transcriptome and physiological analysis of rapeseed tolerance to post-flowering temperature increase. Int J Mol Sci. 2023;24:15593. Scholl RL, May ST, Ware DH. Seed and molecular resources for Arabidopsis. Plant Physiol. 2000;124:1477–80. Leivar P, Monte E, Al-Sady B, Carle C, Storer A, Alonso JM, et al. The Arabidopsis PHYTOCHROME-INTERACTING FACTOR PIF7, together with PIF3 and PIF4, regulates responses to prolonged red light by modulating PhyB levels. Plant cell. 2008;20:337–52. Oh S, Warnasooriya SN, Montgomery BL. Downstream effectors of light- and phytochrome-dependent regulation of hypocotyl elongation in Arabidopsis thaliana . Plant Mol Biol. 2013;81:627–40. Robert HS, Grones P, Stepanova AN, Robles LM, Lokerse AS, Alonso JM, et al. Local auxin sources orient the apical-basal axis in Arabidopsis embryos. Curr Biol. 2013;23:2506–12. Stepanova AN, Robertson-Hoyt J, Yun JJ, Benavente LM, Xie D-Y, Dolezal K, et al. TAA1-mediated auxin biosynthesis is essential for hormone crosstalk and plant development. Cell. 2008;133:177–91. Oh E, Zhu J-Y, Wang Z-Y. Interaction between BZR1 and PIF4 integrates brassinosteroid and environmental responses. Nat Cell Biol. 2012;14:802–9. Awlia M, Nigro A, Fajkus J, Schmoeckel SM, Negrão S, Santelia D, et al. High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana . Front plant Sci. 2016;7:1414. Alexander MP. Differential staining of aborted and nonaborted pollen. Stain technology. 1969;44:117–22. Attuluri VPS, López JFS, Maier L, Paruch K, Robert HS. Comparing the efficiency of six clearing methods in developing seeds of Arabidopsis thaliana . Plant Reprod. 2022;35:279–93. Postma M, Goedhart J. PlotsOfData—A web app for visualizing data together with their summaries. PLoS Biol. 2019;17:e3000202. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. Kinsella RJ, Kähäri A, Haider S, Zamora J, Proctor G, Spudich G, et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database. 2011;2011:bar030. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. 2021;2:100141. Wickham H. ggplot2, Elegant Graphics for Data Analysis. R. 2016. https://doi.org/10.1007/978-3-319-24277-4. González-García MP, Conesa CM, Lozano-Enguita A, Baca-González V, Simancas B, Navarro-Neila S, et al. Temperature changes in the root ecosystem affect plant functionality. Plant Commun. 2023;4:100514. Ong P, Jian J, Li X, Yin J, Ma G. Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration. Spectrochim acta Part A, Mol Biomol Spectrosc. 2023;305:123477. Liu L, Zareef M, Wang Z, Li H, Chen Q, Ouyang Q. Monitoring chlorophyll changes during Tencha processing using portable near-infrared spectroscopy. Food Chem. 2023;412:135505. Li C, Li H, Li J, Lei Y, Li C, Manevski K, et al. Using NDVI percentiles to monitor real-time crop growth. Comput Electron Agric. 2019;162:357–63. Payares LKA, Tarquis AM, Peralo RH, Cano J, Cámara J, Nowack J, et al. Multispectral and thermal sensors onboard UAVs for heterogeneity in merlot vineyard detection: contribution to zoning maps. Remote Sens. 2023;15:4024. Peñuelas J, Filella I, Gamon JA. Assessment of photosynthetic radiation‐use efficiency with spectral reflectance. N Phytol. 1995;131:291–6. Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ. 2004;90:337–52. Murchie EH, Lawson T. Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. J Exp Bot. 2013;64:3983–98. Ruban AV. Evolution under the sun: optimizing light harvesting in photosynthesis. J Exp Bot. 2015;66:7–23. Yang X, Dong G, Palaniappan K, Mi G, Baskin TI. Temperature‐compensated cell production rate and elongation zone length in the root of Arabidopsis thaliana . Plant, Cell Environ. 2017;40:264–76. Xie Y, Liu Y, Wang H, Ma X, Wang B, Wu G, et al. PHYTOCHROME-INTERACTING FACTORs directly suppress MIR156 expression to enhance shade-avoidance syndrome in Arabidopsis. Nat Commun. 2017;8:348. Sang Q, Fan L, Liu T, Qiu Y, Du J, Mo B, et al. MicroRNA156 conditions auxin sensitivity to enable growth plasticity in response to environmental changes in Arabidopsis. Nat Commun. 2023;14:1449. Takeuchi H, Higashiyama T. A species-specific cluster of defensin-like genes encodes diffusible pollen tube attractants in Arabidopsis. PLoS Biol. 2012;10:e1001449. Costa LM, Marshall E, Tesfaye M, Silverstein KAT, Mori M, Umetsu Y, et al. Central cell-derived peptides regulate early embryo patterning in flowering plants. Science. 2014;344:168–72. Robert HS, Park C, Gutièrrez CL, Wójcikowska B, Pěnčík A, Novák O, et al. Maternal auxin supply contributes to early embryo patterning in Arabidopsis. Nat Plants. 2018;4:548–53. Kumar SV, Lucyshyn D, Jaeger KE, Alós E, Alvey E, Harberd NP, et al. Transcription factor PIF4 controls the thermosensory activation of flowering. Nature. 2012;484:242–5. Song J, Liu Q, Hu B, Wu W. Photoreceptor PhyB involved in Arabidopsis temperature perception and heat-tolerance formation. International Journal of Molecular Sciences. 2017;18:1194. Liu J, Liu Y, Wang S, Cui Y, Yan D. Heat stress reduces root meristem size via induction of plasmodesmal callose accumulation inhibiting phloem unloading in Arabidopsis. Int J Mol Sci. 2022;23:2063. Wang R, Zhang Y, Kieffer MM, Yu H, Kepinski S, Estelle M. HSP90 regulates temperature-dependent seedling growth in Arabidopsis by stabilizing the auxin co-receptor F-box protein TIR1. Nature communications. 2016;7:10269–10269. Parveen S, Rahman A. Actin isovariant ACT7 modulates root thermomorphogenesis by altering intracellular auxin homeostasis. Int J Mol Sci. 2021;22:7749. Gaillochet C, Burko Y, Platre MP, Zhang L, Simura J, Willige BC, et al. HY5 and phytochrome activity modulate shoot-to-root coordination during thermomorphogenesis in Arabidopsis. Development. 2020;147:dev192625. Lee S, Wang W, Huq E. Spatial regulation of thermomorphogenesis by HY5 and PIF4 in Arabidopsis. Nat Commun. 2021;12:3656. Li Q-Q, Zhang Z, Zhang C-X, Wang Y-L, Liu C-B, Wu J-C, et al. PHYTOCHROME-INTERACTING FACTORs orchestrate hypocotyl adventitious root initiation in Arabidopsis. Development. 2022;149:dev200362. Franklin KA. Light and temperature signal crosstalk in plant development. Curr Opin Plant Biol. 2009;12:63–8. Jagadish SVK, Bahuguna RN, Djanaguiraman M, Gamuyao R, Prasad PVV, Craufurd PQ. Implications of high temperature and elevated CO2 on flowering time in plants. Front Plant Sci. 2016;7:913. Cao Z, Yao X, Liu H, Liu B, Cheng T, Tian Y, et al. Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat. Agric For Meteorol. 2019;265:121–36. Barnabás B, Jäger K, Fehér A. The effect of drought and heat stress on reproductive processes in cereals. Plant, Cell & Environment. 2008;31:11–38. Zhang S-S, Yang H, Ding L, Song Z-T, Ma H, Chang F, et al. Tissue-specific transcriptomics reveals an important role of the unfolded protein response in maintaining fertility upon heat stress in Arabidopsis. The Plant cell. 2017;29:1007–23. Lohani N, Singh MB, Bhalla PL. High temperature susceptibility of sexual reproduction in crop plants. Journal of Experimental Botany. 2020;71:555–68. Storme ND, Geelen D. High temperatures alter cross-over distribution and induce male meiotic restitution in Arabidopsis thaliana. Commun Biol. 2020;3:187. Zhang M, Zhang X, Wang R, Zang R, Guo L, Qi T, et al. Heat-responsive microRNAs participate in regulating the pollen fertility stability of CMS-D2 restorer line under high-temperature stress. Biol Res. 2023;56:58. Wu M-F, Tian Q, Reed JW. Arabidopsis microRNA167 controls patterns of ARF6 and ARF8 expression, and regulates both female and male reproduction. Development. 2006;133:4211–8. Oshino T, Miura S, Kikuchi S, Hamada K, Yano K, Watanabe M, et al. Auxin depletion in barley plants under high‐temperature conditions represses DNA proliferation in organelles and nuclei via transcriptional alterations. Plant, Cell & Environment. 2011;34:284–90. Sakata T, Oshino T, Miura S, Tomabechi M, Tsunaga Y, Higashitani N, et al. Auxins reverse plant male sterility caused by high temperatures. PNAS. 2010;107:8569–74. Higashitani A. High temperature injury and auxin biosynthesis in microsporogenesis. Frontiers in Plant Science. 2013;4:47. Irenaeus T, Mitra SK. Understanding the pollen and ovule characters and fruit set of fruit crops in relation to temperature and genotype – a review. J Appl Bot Food Qual. 2014;87. Huang Z, Footitt S, Finch-Savage WE. The effect of temperature on reproduction in the summer and winter annual Arabidopsis thaliana ecotypes Bur and Cvi. Ann Bot. 2014;113:921–9. Whittle CA, Otto SP, Johnston MO, Krochko JE. Adaptive epigenetic memory of ancestral temperature regime in Arabidopsis thalianaThis paper is one of a selection of papers published in a Special Issue from the National Research Council of Canada Plant Biotechnology Institute. Botany. 2009;87:650–7. Tables Table 1. Overall root growth rates for different genotypes at nAT and hAT Genotype Conditions Growth Rate (cm/day) Statistical groups Col-0 nAT 1.60 a, c 35S::PIF4 nAT 1.43 b phyb nAT 1.61 a pif4 nAT 1.62 a Col-0 hAT 1.6 a, c 35S::PIF4 hAT 1.29 b, c phyb hAT 1.41 c pif4 hAT 1.62 a The mean growth rate of 13 plants per genotype under nAT and hAT was determined using root growth regression lines. A two-way ANOVA assessed differences among genotypes and conditions. Post-hoc Tukey's test identified non-significant differences between genotypes with the same letter. Table 2. Number of primary inflorescence branches for different genotypes at nAT and hAT Genotype Condition Average number of branches SE Statistical groups Col_0 nAT 4.7 0.152 a phyb nAT 3 0.100 b, c 35S::PIF4 nAT 3.5 0.166 b, d pif3 nAT 4.4 0.276 a pif4 nAT 5.3 0.276 a pif7-1 nAT 5.3 0.221 a pif7-2 nAT 4.9 0.163 a pif3 pif7 nAT 4.8 0.266 a, e pifq nAT 4.7 0.314 a Col_0 hAT 3.1 0.100 c phyb hAT 2.1 0.298 d 35S::PIF4 hAT 2.1 0.133 d pif3 hAT 3.7 0.200 c pif4 hAT 3.7 0.221 c pif7-1 hAT 4.5 0.339 e pif7-2 hAT 4 0.314 c pif3 pif7 hAT 4.3 0.213 e pifq hAT 3.6 0.163 c n = 10 plants per genotype were analyzed to assess differences among genotypes and between nAT and hAT using a two-way ANOVA. Post-hoc Tukey's test identified non-significant differences between genotypes with the same letter. Table 3. Anther abortion rate for different genotypes in nAT and hAT 7 DAFD 9 DAFD Defective Normal n % Defects Defective Normal n % Defects Col-0 nAT 0 57 57 0 0 68 68 0 Col-0 hAT 2 41 43 4.65 5 59 64 7.81 * phyb nAT 0 93 93 0 0 87 0 0 phyb hAT 4 31 35 11.43 * 39 73 112 34.82 *** ### ^^^ 35S::PIF4 nAT 0 87 87 0 0 92 0 0 35S::PIF4 hAT 11 36 47 23.40 *** # 12 29 41 29.27 *** ## pif4 nAT 0 89 89 0 0 106 0 0 pif4 hAT 0 90 90 0 1 89 90 1.11 pifq nAT 0 103 103 0 0 91 91 0 pifq hAT 0 81 81 0 2 137 139 1.44 # The anthers were assessed at 7 and 9 days after flowering development (DAFD). Fisher's Exact Test analyzed comparisons, with anthers from each genotype and condition examined across three replicates for result reliability. Significance indicators are: * (temperature), # (genotype), and ^ (time). P-values are represented as: * # (0.05-0.01), ## (0.009-0.0001), and *** ### ^^^ (0.00009-0.000000). Details are provided in Additional File 2 – Table S3. Table 4. Ovule defective phenotypes for the different genotypes at nAT and hAT Genotype Growth conditions Normal Defective n % of defects P-values Col-0 nAT 122 7 129 5.4 Col-0 hAT 102 45 147 30.6 *** phyb nAT 78 17 95 17.9 # phyb hAT 31 52 83 62.6 *** ### 35S::PIF4 nAT 115 22 137 16.1 ## 35S::PIF4 hAT 16 86 102 84.3 *** ### pif4 nAT 90 8 98 8.2 pif4 hAT 55 21 76 27.6 ** pifq nAT 77 8 85 9.4 pifq hAT 37 31 68 45.6 *** # The data per phenotype categories are detailed in Additional File 2 – Table S4. The statistical analysis of these comparisons utilized Fisher's Exact Test. To ensure the reliability of our results, ovules from each genotype and condition were examined across three replicates. The significance levels in the results are denoted as follows: * significant temperature effect. # significant genotype effect. The p -value ranges are specified as # for p -values between 0.05 and 0.01 and *** for p -values ranging from 0.00009 to 0.000000. Table 5. Embryonic defects in seeds grown at nAT and hAT Genotype Growth conditions Normal Defective n % Defects Col-0 nAT 121 3 124 2.42 Col-0 hAT 61 42 103 40.77 *** phyb nAT 111 4 116 3.4 phyb hAT 65 29 94 30.85 *** 35S::PIF4 nAT 127 5 132 3.79 35S::PIF4 hAT 32 9 41 21.95 *** # pif4 nAT 90 4 96 4.1 pif4 hAT 29 23 52 44.23 *** pifq nAT 145 8 153 5.23 pifq hAT 45 32 77 41.56 *** Fisher's Exact Test was used for statistical analysis of these comparisons. To ensure result reliability, anthers from each genotype and condition were examined across three replicates. Significance indicators include * for a significant temperature effect and # for a significant genotype effect. P-values are denoted as # (0.05-0.01) and *** (0.00009-0.000000). Additional Declarations No competing interests reported. Supplementary Files AdditionalFile120240327.pdf AdditionalFile2Supplementarytables.pdf Additionalfile3TableS1RootLengthstats.xlsx Additionalfile3TableS2Datasourcerootphenotyping.xlsx Additionalfile3TableS3Datasourceshootphenotyping.xlsx Additionalfile3TableS4rosetteareatimeseriesstats.xlsx Additionalfile3TableS5Shootgrowthtimeseriesstats.xlsx Additionalfile3TableS6VNIRParametersstats.xlsx Additionalfile3TableS7Chlorophyllparameters.xlsx Additionalfile3TableS8DatasourcedryseedtraitsnAT.xlsx Additionalfile3TableS9DatasourceDrySeedtraitshAT.xlsx AdditionalFIle4TableS1ListofGenes.xlsx AdditionalFIle4TableS2GOterms.xlsx AdditionalFIle4TableS3Clusters.xlsx ADDITIONALFILES.docx Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 31 May, 2024 Reviews received at journal 30 May, 2024 Reviewers agreed at journal 19 May, 2024 Reviews received at journal 27 Apr, 2024 Reviewers agreed at journal 23 Apr, 2024 Reviewers agreed at journal 18 Apr, 2024 Reviewers invited by journal 16 Apr, 2024 Editor assigned by journal 10 Apr, 2024 Submission checks completed at journal 10 Apr, 2024 First submitted to journal 05 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4223427","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289827475,"identity":"481b6940-270b-4f2a-8aba-0ae20631c601","order_by":0,"name":"Shekufeh Ebrahimi Naghani","email":"","orcid":"","institution":"CEITEC MU—Central European Institute of Technology, Masaryk University","correspondingAuthor":false,"prefix":"","firstName":"Shekufeh","middleName":"Ebrahimi","lastName":"Naghani","suffix":""},{"id":289827476,"identity":"ca8b18d2-1197-4fee-a14e-0966fd4b4008","order_by":1,"name":"Ján Šmeringai","email":"","orcid":"","institution":"CEITEC MU— Central European Institute of Technology, Masaryk University","correspondingAuthor":false,"prefix":"","firstName":"Ján","middleName":"","lastName":"Šmeringai","suffix":""},{"id":289827477,"identity":"d8051946-4a9e-4357-91ef-541e63036023","order_by":2,"name":"Barbora Pleskačová","email":"","orcid":"","institution":"PSI - Photon Systems Instruments","correspondingAuthor":false,"prefix":"","firstName":"Barbora","middleName":"","lastName":"Pleskačová","suffix":""},{"id":289827478,"identity":"290a3806-9a0b-444c-bd00-5f2bbd3ae554","order_by":3,"name":"Tereza Dobisová","email":"","orcid":"","institution":"Labdeers s.r.o","correspondingAuthor":false,"prefix":"","firstName":"Tereza","middleName":"","lastName":"Dobisová","suffix":""},{"id":289827479,"identity":"ed4b51a8-8376-4ed1-95e2-491573379728","order_by":4,"name":"Klára Panzarová","email":"","orcid":"","institution":"PSI - Photon Systems Instruments","correspondingAuthor":false,"prefix":"","firstName":"Klára","middleName":"","lastName":"Panzarová","suffix":""},{"id":289827480,"identity":"6cd8c0d2-1f8c-44bf-ad08-badb77329a1d","order_by":5,"name":"Markéta Pernisová","email":"","orcid":"","institution":"Masaryk University","correspondingAuthor":false,"prefix":"","firstName":"Markéta","middleName":"","lastName":"Pernisová","suffix":""},{"id":289827481,"identity":"a0a3c9b4-afc5-4b7e-a94c-e382b409cf4c","order_by":6,"name":"Hélène S. Robert","email":"data:image/png;base64,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","orcid":"","institution":"CEITEC MU—Central European Institute of Technology, Masaryk University","correspondingAuthor":true,"prefix":"","firstName":"Hélène","middleName":"S.","lastName":"Robert","suffix":""}],"badges":[],"createdAt":"2024-04-05 13:51:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4223427/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4223427/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05394-w","type":"published","date":"2024-07-29T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54681286,"identity":"53e01e84-bdb2-45e5-9087-cf5f7a0226e3","added_by":"auto","created_at":"2024-04-15 07:57:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":764583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemperature suppresses lateral root formation and promotes root elongation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Root morphological phenotype of 21-day-old plants of Col-0, \u003cem\u003e35S::PIF4\u003c/em\u003e, \u003cem\u003ephyb, \u003c/em\u003eand \u003cem\u003epif4\u003c/em\u003e at nAT (top row) and hAT (bottom row). Scale bars represent 5 cm. (\u003cstrong\u003eb\u003c/strong\u003e) Relative growth rate (RGR) of the primary root (in mm per day) between 10 and 16 days after germination (left) and 18 and 21 days after germination (right). Data for the primary root length over time in nAT and hAT are shown in Fig. S1. (\u003cstrong\u003ec\u003c/strong\u003e) Quantification of lateral root density, expressed as the number of lateral roots per centimeter of the primary root for each genotype. n = 16 plants per genotype per condition in triplicate. (\u003cstrong\u003ed\u003c/strong\u003e) Length of the four longest lateral roots at maturity. n = 16 plants per genotype. Data for nAT in green and hAT in red. Statistical analysis and data source are provided in Additional File 3 – Tables S1 and S2.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/d42d2d715a486b7bc3ab4548.png"},{"id":54681279,"identity":"862865cb-f40b-4965-8e7c-835184bc51c6","added_by":"auto","created_at":"2024-04-15 07:57:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemperature-induced reduction in rosette area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003ea, b\u003c/strong\u003e) Relative growth rate of the rosette area from 20 to 27 days after sowing (\u003cstrong\u003ea\u003c/strong\u003e) and from 28 to 35 days after sowing (\u003cstrong\u003eb\u003c/strong\u003e). Green represents data for nAT and red for hAT. Data source and statistical analysis are provided in Additional File 3 – Tables S3 and S4. Plants are presented in Additional File 1 – Fig. S4. The time series for individual genotypes is presented in Additional File 1 – Fig. S5.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/d65a6e7c524ed08cb5b05a65.png"},{"id":54681277,"identity":"6f888f4b-e1ad-4027-a115-4560ec27fb7b","added_by":"auto","created_at":"2024-04-15 07:57:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemperature-induced early flowering and decreased branch length\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003ea, b\u003c/strong\u003e) Time series of the progression of primary inflorescence stem elongation from 20 to 49 days after sowing for nAT (\u003cstrong\u003ea\u003c/strong\u003e) and up to 41 days after sowing for nAT (\u003cstrong\u003eb\u003c/strong\u003e). (\u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003eb\u003c/strong\u003e) n = 10 plants per genotype per condition. (\u003cstrong\u003ec, d\u003c/strong\u003e) Relative growth rate of the stem during the full measurement period (29 days to 49 days after sowing at nAT, and 27 days to 41 days after sowing at hAT) (\u003cstrong\u003ec\u003c/strong\u003e) and from 29 to 39 days after sowing for nAT or from 27 and 27 days for hAT (\u003cstrong\u003ed\u003c/strong\u003e). (\u003cstrong\u003ee\u003c/strong\u003e) Color legend for a-d is provided. Data source is provided in Additional File 3 – Table S3. Plants are presented in Additional File 1 – Fig. S4.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/dd911c28e64201befbb3a1c6.png"},{"id":54681284,"identity":"deb53b5e-4a00-4ae1-99f6-5bbe3b42e599","added_by":"auto","created_at":"2024-04-15 07:57:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of the hAT response in vegetative and reproductive tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelative analysis of the response to hAT in vegetative (\u003cstrong\u003ea\u003c/strong\u003e) and reproductive (\u003cstrong\u003eb\u003c/strong\u003e) tissues for the different measured parameters.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/65f7f657ed9c81376ec97725.png"},{"id":54681289,"identity":"3aaa505d-6910-478b-8252-31cacbb2d292","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":641814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of hAT on ovules and embryo patterning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003ea-d\u003c/strong\u003e) Ovule phenotypes observed at the FG7 developmental stage observed at hAT: (\u003cstrong\u003ea\u003c/strong\u003e) \u0026nbsp;normal ovule observed at nAT with the egg cell, one visible synergid cell and the fused nuclei in the central cell, (\u003cstrong\u003eb\u003c/strong\u003e) ovule with a collapsed synergid (black mass), (\u003cstrong\u003ec\u003c/strong\u003e) ovules with unfused central cell nuclei, and (\u003cstrong\u003ed\u003c/strong\u003e) ovule with a collapsed embryo sac. Scale bars represent 20 µm. The quantification of the phenotypes is provided as Table 4 and Additional File 2 – Table S4. n \u0026gt; 50 ovules per genotype per each condition, in triplicate. (\u003cstrong\u003ee-g\u003c/strong\u003e) Embryo phenotypes observed at hAT in the seeds of the different genotypes: normal embryo (\u003cstrong\u003ee\u003c/strong\u003e), embryo with a dwarf suspensor (\u003cstrong\u003ef\u003c/strong\u003e), embryo exhibiting excessive cell divisions within the proper embryo region (\u003cstrong\u003eg\u003c/strong\u003e). Scale bars represent 20 µm. (\u003cstrong\u003eh\u003c/strong\u003e) Quantification of the suspensor length at nAT and hAT. n = appr. 20 suspensors per genotype per condition in triplicates. The quantification of the phenotypes is presented in Additional File 2 – Table S6.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/e4fbc62db0b259cab93397e7.png"},{"id":54681302,"identity":"df51edd1-57f5-42fd-b0a1-6a9670fcc284","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":422503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology analysis of the enriched biological processes in pistils of Col-0, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ephyb\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e35S::PIF4\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and common genes to the three genotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of Gene Ontology (GO)\u003cstrong\u003e \u003c/strong\u003efunctional annotation of the enriched biological processes was performed for up- (\u003cstrong\u003ea\u003c/strong\u003e) and downregulated (\u003cstrong\u003eb\u003c/strong\u003e) DEGs in wild-type pistils from plants grown on hAT, and \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyb\u003c/em\u003epistils from plants grown on nAT. Data source is provided in Additional File 4 – Table S2.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/35dc943a3aea612a8e5ca186.png"},{"id":54681275,"identity":"29b1503c-8883-4b6f-8d85-247c02d6a2d2","added_by":"auto","created_at":"2024-04-15 07:57:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":298428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster analysis of DEGs related to auxin (a), MIR biogenesis (b) and pollen tube attractants (c)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe color code of the samples is provided. Data source is provided in Additional File 4 – Table S3.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/2c9992b6f90e7c1279c02328.png"},{"id":54681288,"identity":"51969512-5ba4-422c-8054-7772810b8904","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1543647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression pattern of auxin biosynthetic genes is altered in ovules at hAT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpression pattern of \u003cem\u003eTAA1 \u003c/em\u003e(\u003cstrong\u003ea-c\u003c/strong\u003e), \u003cem\u003eYUC4 \u003c/em\u003e(\u003cstrong\u003ed-g\u003c/strong\u003e) and \u003cem\u003eTAA1 \u003c/em\u003e(\u003cstrong\u003eh, i\u003c/strong\u003e) in mature ovules from plants grown at nAT (\u003cstrong\u003ea, d, h\u003c/strong\u003e) and hAT (\u003cstrong\u003eb, c, e, f, g, i\u003c/strong\u003e). The green fluorescence signal of \u003cem\u003eTAA1::GFP-TAA1\u003c/em\u003e, \u003cem\u003eYUC4::nls3xGFP \u003c/em\u003eand \u003cem\u003eYUC8::GFP-GUS \u003c/em\u003eis seen as magenta, Scale bars represent 20 µm.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/bf9657f4bd1e0f9ce241fb3e.png"},{"id":54682480,"identity":"0a3b174d-0b22-4621-9564-fb6347763a86","added_by":"auto","created_at":"2024-04-15 08:13:10","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":467854,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemperature exposure leads to fewer, larger, and rounder seeds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Dry seed phenotype from Col-0, \u003cem\u003e35S::PIF4\u003c/em\u003e,\u003cem\u003e phyb, pif4,\u003c/em\u003e and \u003cem\u003epifq \u003c/em\u003eplants grown at nAT and hAT. The scale bar represents 0.5 mm. (\u003cstrong\u003eb\u003c/strong\u003e, \u003cstrong\u003ec\u003c/strong\u003e) Seed size in mm\u003csup\u003e2\u003c/sup\u003e (\u003cstrong\u003eb\u003c/strong\u003e) and seed shape (length to seed area ratio) (\u003cstrong\u003ec\u003c/strong\u003e) evaluations. The surface area and shape (seed length/ seed aera) of 1 000 seeds for each genotype were analyzed in triplicate from plants grown at nAT and hAT. (\u003cstrong\u003ed\u003c/strong\u003e) The number of seeds produced per silique is calculated in at least 12 mature siliques for each genotype/condition in triplicates. (\u003cstrong\u003ee\u003c/strong\u003e) The weight of 100 seeds from each genotype is measured in triplicates. Data source are provided in Additional File 3 – Tables S8 and S9.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/4f0a9c6e64ec8bfb76751fd2.png"},{"id":61793933,"identity":"d025cf7a-11cf-45ad-9f79-dc007ecb9b91","added_by":"auto","created_at":"2024-08-05 16:16:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6973719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/5e0dff61-20fa-4566-82ed-017be80a149d.pdf"},{"id":54681301,"identity":"e8e6fefc-77d4-4102-acd7-ec6e0ecc616d","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6325756,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile120240327.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/1e3dc093a65bcae4f39d40a1.pdf"},{"id":54681303,"identity":"58cb9818-9f2d-4f38-ad76-902e4ce4d6fb","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":207124,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile2Supplementarytables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/46ed4c4f78affe8c65519cb7.pdf"},{"id":54681276,"identity":"76788235-1986-467c-900b-863c7d55f017","added_by":"auto","created_at":"2024-04-15 07:57:07","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":61002,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS1RootLengthstats.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/a6fbcdf55abbf00ba7ee199a.xlsx"},{"id":54681773,"identity":"e3c912b3-0f92-4f3a-acaa-0331336f9934","added_by":"auto","created_at":"2024-04-15 08:05:09","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":130576,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS2Datasourcerootphenotyping.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/9bcc046322c97d642aca0abe.xlsx"},{"id":54681283,"identity":"942a61a0-1e45-44f6-b30c-399af09ca04c","added_by":"auto","created_at":"2024-04-15 07:57:08","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1336498,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS3Datasourceshootphenotyping.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/d07e12b3b2513473c6af2855.xlsx"},{"id":54681293,"identity":"db28ec10-6291-4e43-ae4e-fb93a9a37c7e","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":152968,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS4rosetteareatimeseriesstats.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/2d3080274ab15c54bf75f3da.xlsx"},{"id":54681292,"identity":"1b29ead7-b0c2-4dc8-8ac6-c2442508f43c","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":28653,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS5Shootgrowthtimeseriesstats.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/06d10199471b79fbe6a8de21.xlsx"},{"id":54681278,"identity":"5f97088d-e472-473e-be42-af67f9f02712","added_by":"auto","created_at":"2024-04-15 07:57:07","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":67639,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS6VNIRParametersstats.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/804591a79a99e1dcc53b2256.xlsx"},{"id":54681306,"identity":"25de4e5b-6df8-4a80-b59a-706409712d42","added_by":"auto","created_at":"2024-04-15 07:57:10","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":108183,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS7Chlorophyllparameters.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/d687138851ad99815c4be593.xlsx"},{"id":54681296,"identity":"758f026f-ed27-49f5-acd6-e2c21d8c9921","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1909143,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS8DatasourcedryseedtraitsnAT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/fc86ba63695fc2a6f39295e8.xlsx"},{"id":54681309,"identity":"92fe80c6-c7a9-4228-aaf4-2fc58b48ca05","added_by":"auto","created_at":"2024-04-15 07:57:10","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1343950,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS9DatasourceDrySeedtraitshAT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/c7f64900678d76dda0cbb824.xlsx"},{"id":54681304,"identity":"9a246b0b-20ae-42f1-92ea-13022bc25820","added_by":"auto","created_at":"2024-04-15 07:57:10","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":3325007,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFIle4TableS1ListofGenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/e98324510ef90f20bb671f1f.xlsx"},{"id":54681285,"identity":"bb1a8d6d-7b29-4eda-9ab6-7b269005fd62","added_by":"auto","created_at":"2024-04-15 07:57:08","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":205347,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFIle4TableS2GOterms.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/08f741b1906e12481e1b20c8.xlsx"},{"id":54681299,"identity":"8572fae9-11d7-4bca-a7de-6679d94582ec","added_by":"auto","created_at":"2024-04-15 07:57:09","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":17529,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFIle4TableS3Clusters.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/e57f008e203ef042c648cf35.xlsx"},{"id":54681775,"identity":"3a1a624e-8e74-47c8-b464-0fee1c64a606","added_by":"auto","created_at":"2024-04-15 08:05:09","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":13497,"visible":true,"origin":"","legend":"","description":"","filename":"ADDITIONALFILES.docx","url":"https://assets-eu.researchsquare.com/files/rs-4223427/v1/e1e46cb75c6b6016e8b3ffe1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative phenotyping approaches to unmask the Phyb-PIF4 pathway in Arabidopsis thaliana reproductive organs at high ambient temperatures","fulltext":[{"header":"Background","content":"\u003cp\u003eAbiotic stressors affect plant growth through physiological, morphological, biochemical, and molecular changes. Among these stressors, warm ambient temperatures affect plant life differently at different growth stages [1, 2]. With the Intergovernmental Panel on Climate Change (IPCC) predicting a global temperature increase of 1.8 \u0026deg;C to 4 \u0026deg;C by 2100 [3], understanding the physiological responses of plants to warmth and the molecular mechanisms involved is crucial for improving high temperature tolerance.\u003c/p\u003e\n\u003cp\u003eTemperatures above the critical threshold temperature (about 30 \u0026deg;C for most temperate crops) are considered heat stress [4]. High ambient temperatures (hAT), or warmth, below this critical threshold temperature induce responses in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e similar to shade avoidance syndrome, resulting in morphological changes collectively referred to as thermomorphogenesis, such as stem elongation, hyponastic leaves, reduced biomass, and accelerated flowering, which help plants dissipate heat and move organs to cooler environments [5, 6]. Thermomorphogenesis is an adaptive mechanism involving transcriptional changes, hormonal reactions, and developmental modifications. The bHLH PHYTOCHROME-INTERACTING FACTOR (PIF) transcription factors, particularly PIF4, play a crucial role in thermomorphogenesis and act as a central hub coordinating signaling pathways and facilitating the plant\u0026apos;s adjustment to environmental conditions [7, 8]. The heat stress response deals with the reaction of high temperatures above the critical threshold temperature that could impair cellular functions (membrane fluidity, oxidative stress, protein folding) and induce cellular death. The heat stress response is regulated by the expression of heat stress-responsive genes through the activity of HEAT SHOCK FACTOR (Hsf) transcription factors [9]. While thermomorphogenesis and heat shock response are distinct thermal responses to a different temperature ranges, a crosstalk between these two processes has been identified with the finding that HsfA1 proteins are required for PIF4-mediated thermomorphogenesis in hAT [10].\u003c/p\u003e\n\u003cp\u003eThe PIF protein amount and activity and \u003cem\u003ePIF4\u0026nbsp;\u003c/em\u003etranscript levels are regulated by the plant photoreceptor Phytochrome B (PhyB), which also serves as a thermosensor [11\u0026ndash;13]. Synthesized in its inactive form (Pr) in the cytoplasm, it converts to an active form (Pfr) upon absorption of red light and translocates to the nucleus, where it interacts with various transcription factors, including PIFs, to repress gene expression [14, 15]. hAT promotes the reversion of PhyB from Pfr to Pr [2, 16] and release its repression of PIFs. The influence of PhyB on PIF4 activity is evident in the \u003cem\u003ephyB\u003c/em\u003e \u003cem\u003epif4\u0026nbsp;\u003c/em\u003edouble mutant, which exhibits elongated hypocotyls in a dose-response to the ambient temperature [17]. PIF4 is critical for temperature-induced morphological responses, including hypocotyl and petiole elongation and leaf hyponasty. These responses are absent in \u003cem\u003epif4\u003c/em\u003e mutant plants, except for early flowering [13, 18, 19]. \u003cem\u003ePIF4\u003c/em\u003e expression is then increased by warm temperature [20]. PIF4, in turn, activates the expression of warm temperature-responsive genes. Notably, \u003cem\u003epif4\u003c/em\u003e mutants fail to induce the expression of warm temperature-responsive genes, such as the auxin biosynthetic gene \u003cem\u003eYUCC8\u0026nbsp;\u003c/em\u003e(\u003cem\u003eYUC8\u003c/em\u003e) and the brassinosteroid biosynthetic gene \u003cem\u003eDWARF4 [8, 13, 18, 20]\u003c/em\u003e. It highlights the importance of PIF4 in regulating thermomorphogenesis.\u003c/p\u003e\n\u003cp\u003eBoth hAT and heat shock affect different steps of plant reproduction, and consequently the production of viable seeds [21]. Heat shock impairs pollen viability and fertilization in pea [22], rice [23], and chickpea [24]. Heat shock reduces seed yield and quality in wheat [25], rice [26] and chickpea [27]. The long-term effects of hAT during reproduction have been studied in oilseed rape [28, 29] and few hints of molecular pathways activated to cope with hAT have been listed [30, 31], including response to heat stress, ROS production, and photosynthesis.\u003c/p\u003e\n\u003cp\u003eWe studied the thermomorphogenic response of \u003cem\u003eA. thaliana\u003c/em\u003e in different tissues throughout the plant life cycle. Through detailed phenotyping techniques of wild-type (Col-0) plants and PhyB-PIF pathway mutant lines under normal (nAT) and high (hAT) ambient temperature conditions, we found that the PhyB-PIF4 signaling pathway is a potential player in regulating the plant response to hAT in several tested developmental processes (shoot, root and reproductive organs). This study uniquely combines multiple phenotyping approaches: seeds with Boxeed, ovules and embryos with microscopy, roots with rhizotron, and seedlings and plants with PlantScreen. We complemented this phenotyping by examining the transcriptomic response in pistils of wild-type, \u003cem\u003ephyb\u003c/em\u003e, and \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003eplants grown in nHT and hAT to identify the key regulatory pathways that could explain the reduced fertilization rate of the wild-type plants under hAT and whether the Phyb-PIF4 pathway could be involved in this response.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e\u003cstrong\u003ePlant Materials and Growing Conditions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eArabidopsis thaliana\u0026nbsp;\u003c/em\u003eseeds from Col-0, homozygous mutant lines of \u003cem\u003epif3-7\u0026nbsp;\u003c/em\u003e(\u003cem\u003eN66042\u003c/em\u003e)\u003cem\u003e, pif4-2\u0026nbsp;\u003c/em\u003e(\u003cem\u003eN66043\u003c/em\u003e, \u003cem\u003esail_1288_E07\u003c/em\u003e)\u003cem\u003e, pif7-1 (N68809), pif7-2 (N71656, sail_622_G02), pif3-3 pif7-1 (N68810), pifq (N66049; pif1-1 (sail_256_G07) pif3-7 pif4-2 pif5-3 (N66044, salk_087012), phyb-9 (N6217)\u003c/em\u003e, \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003e(kindly provided by Zhi-Yong Wang), and \u003cem\u003eYUC4::3nGFP\u003c/em\u003e, \u003cem\u003eYUC8::GUS-GFP\u003c/em\u003e, and \u003cem\u003eTAA1::GFP-TAA1\u003c/em\u003e reporter lines were used for this study [32\u0026ndash;37]. Seeds were sterilized with 20 % bleach, washed twice in sterile distilled water, and vernalized at 4 \u0026deg;C for 24\u0026thinsp;h. Plants were either germinated directly in soil (mixture of 2/3 peat moss Substrate 3 [Klasmann-Deilmann GmbH, Germany] and 1/3 vermiculite) or on plates containing MS medium. In plates, plants were grown for ten\u0026thinsp;days at 21 \u0026deg;C with a 16-h light/8-h dark photoperiod and 150\u0026thinsp;\u0026mu;mol.m\u003csup\u003e-2\u003c/sup\u003e.s\u003csup\u003e-1\u003c/sup\u003e LED illumination before transfer to soil. For all the measurements, plants were grown in a walk-in Fytoscope growth chamber (FS-WI, Plant Systems Instruments (PSI), Czech Republic) under growth conditions with a long-day regime (16 h light/8\u0026thinsp;h dark), LED illumination with an intensity of 150\u0026thinsp; \u0026mu;mol.m\u003csup\u003e-2\u003c/sup\u003e.s\u003csup\u003e-1\u003c/sup\u003e, and 35%\u0026ndash;45% humidity. For normal conditions (nAT), the temperature was set at 21 \u0026deg;C during the day and 18 \u0026deg;C at night. For high ambient temperature conditions (hAT), the temperature wat set at 28 \u0026deg;C during the day and 24 \u0026deg;C at night.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eRoot Phenotyping\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSeeds from wild-type Col-0, \u003cem\u003epif4, phyb, and 35S::PIF4\u003c/em\u003e lines were sterilized, vernalized, and sown in PlantScreen\u003csup\u003eTM\u003c/sup\u003e rhizotron systems (PSI, Czech Republic). The rhizotrons (203 x 293 x 29.5 mm, H x W x D) with a transparent glass plate and a light-protected black sheet cover were filled with soil (peat moss Substrate 3 [Klasmann-Deilmann GmbH, Germany]) and tilted at 45\u003csup\u003eo\u003c/sup\u003e with the glass plate facing downwards. After ten days of plant cultivation in nAT, half of the rhizotrons continued in nAT, and the other half in hAT. The soil temperature was measured with a soil temperature sensor Pt1000 with datalogger Microlog T3 (Environmental Measuring Systems Ltd, Czech Republic). The soil is about 1 \u0026deg;C less than the air in all conditions. Regular root phenotyping was performed three times a week using the PlantScreen\u003csup\u003eTM\u003c/sup\u003e SC System (PSI, Czech Republic) equipped with a bottom-side root imaging unit (GigE PSI BW - 12.36 megapixel camera with 1.1\u0026rdquo; CMOS sensor) with LED-based light source.\u003c/p\u003e\n\u003cp\u003eExperiments were conducted in triplicate, with the first replicate consisting of five biological replicates and the following two replicates consisting of eight biological replicates for each genotype/condition. Rhizotron weights were measured prior to watering, and an equal amount of water was added to each tray. Subsequent watering occurred after the system had lost the weight of the added water. Raw data were automatically stored and processed using the PlantScreen\u003csup\u003eTM\u003c/sup\u003e SC Root Tester software (PSI, Czech Republic). Parameters such as primary root length, lateral root density, and length of the four longest lateral roots were evaluated manually using ImageJ. \u0026nbsp;The Relative Growth Rate (RGR) is calculated as follow: (length\u003csup\u003eT2\u003c/sup\u003e-length\u003csup\u003eT1\u003c/sup\u003e)/(T2-T1).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eShoot Phenotyping\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFor shoot phenotyping, we examined nine \u003cem\u003eA. thaliana\u003c/em\u003e lines, including Col-0, \u003cem\u003epif3, pif4, pif7-1, pif7-2, pif3 pif7, pifq, phyb,\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e, in two experimental conditions (nAT and hAT). Each experiment consisted of 18 replicates per line. After sterilization and vernalization, seeds were directly sown in pots (70 mm \u0026times; 70 mm \u0026times; 65 mm, Poppelman TEKU, Germany) containing 65 g of freshly sieved soil (Substrate 2, Klasmann-Deilmann GmbH, Germany), watered with 10 ml of water per pot, and grown in nAT for 10 days. All plants were then transferred to a climate-controlled growth chamber (FS-WI, PSI, Czech Republic). The trays were designed to contain identical genotypes at the same positions for the two replicates. Growth conditions for day/night temperature were set at 21/18 \u0026deg;C for nAT and 28/24 \u0026deg;C for hAT. At least 17 plants of each genotype were monitored daily for 50 days in nAT and 42 days in hAT. The phenotyping protocol included multiple analyses, including photosynthesis-related traits using kinetic chlorophyll fluorescence imaging, morphological traits using RGB imaging, and VNIR hyperspectral imaging for reflectance profiling (400-850 nm).\u003c/p\u003e\n\u003cp\u003eThe PlantScreen\u003csup\u003eTM\u003c/sup\u003e Compact System [38] facilitated the daily transport of trays for phenotypic analyses on conveyor belts from the dark/light acclimation chamber to the light-isolated imaging cabinets and the weighing and watering station, where plants were automatically weighed and watered daily to maintain the soil at a relative water content of 44 % field capacity. Photosynthetic performance was assessed using a light curve protocol (as described in [38]), which quantified the rate of photosynthesis at four different photon irradiances with 60 s intervals of cool white actinic light at 140, 270, 410, and 540 mmol.m\u003csup\u003e-2\u003c/sup\u003e.s\u003csup\u003e-1\u003c/sup\u003e corresponding to L1, L2, L3, and L4, respectively. Raw data were automatically processed using the PlantScreen\u003csup\u003eTM\u003c/sup\u003e Analyzer software (PSI, Czech Republic).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eReproductive tissues and embryo phenotyping\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCol-0, \u003cem\u003epif4, pifq\u003c/em\u003e, and \u003cem\u003ephyb\u003c/em\u003e plants were analyzed to assess reproductive organs and seeds. After sterilization and vernalization, all seeds were germinated and grown on plates for approximately two weeks. They were then transferred to soil and grown under nAT until the development of the first flowering bud. Half of the plants of each genotype were then grown in hAT until the end of their life cycle. Anthers were dissected from flower buds one day before anthesis, mounted in Alexander\u0026rsquo;s staining solution [39], incubated overnight at 50 \u0026deg;C, and observed under a Zeiss Axioscope light microscope. For the ovule analysis, flowers were emasculated one day before anthesis. Two days later, mature ovules (FG7 stage) were dissected from the pistil, stained with 10 \u0026mu;g/mL propidium iodide for five minutes on microscope slides, and observed under a Zeiss LSM 700 laser scanning confocal microscope. Seeds containing embryos of different developmental stages were isolated from the silique and cleared using the ClearSee method [40], stained with Renaissance SR2200, and observed with a Zeiss LSM 700 laser scanning confocal microscope.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene expression pattern in ovules by confocal microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor auxin biosynthetic gene expression pattern, flowers were emasculated one day before anthesis. Two days later, mature ovules (FG7 stage) were dissected from the pistil, stained with 10 \u0026mu;g/mL propidium iodide on microscope slides for five minutes. Fluorescent signals were observed using a ZEISS 700 microscope equipped with a 25x magnification objective. GFP imaging was performed using 488 nm laser lines.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDry seed phenotyping\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNon-invasive seed phenotyping analysis was performed using the Boxeed robot (Labdeers, Czech Republic). The seed sorting mode was used to understand the distribution of individual phenotypic traits in the progeny of nAT and hAT grown plants. In this mode, 1,000 seeds were randomly analyzed in two biological replicates. The parameters of individual seeds were analyzed from two orientations with an angular position of the nozzle at 0\u0026deg; and 90\u0026deg;. Seed analysis was performed using the Boxeed software for various seed morphometric parameters, including the seed size (mm\u003csup\u003e2\u003c/sup\u003e), shape (ratio of seed length to area), length (mm), and width (mm). An average of two measurements for each seed was used to calculate seed characteristics.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe results (multiple pairwise comparisons between different conditions and temperatures) were analyzed using Exact Fisher\u0026rsquo;s and two-way ANOVA followed by Tukey\u0026rsquo;s \u003cem\u003epost hoc\u003c/em\u003e test using Python (Python Software Foundation, https://www.python.org/) in the Pycharm environment (https://www.jetbrains.com/pycharm/). The level of statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 for all tests. Statistical analysis is described in the Additional File 2. Graphs for the representation of the phenotyping data were prepared using SuperPlotsOfData (https://huygens.science.uva.nl/SuperPlotsOfData/) [41].\u003c/p\u003e\n\u003cp\u003eThe relative response to hAT for each tissue and parameter was evaluated as a percentage. The Pearson correlation method in Python was used to calculate the correlations between different parameters, resulting in a correlation matrix. To maintain the integrity of the analysis, a significance threshold of a \u003cem\u003ep\u003c/em\u003e-value of 0.005 (5 %) was set. This threshold ensured that only correlations with \u003cem\u003ep\u003c/em\u003e-values below this level were considered significant and included in the graph. The resulting matrix provides valuable insight into the intricate relationships between different tissues and parameters in response to hAT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction, library construction and RNAseq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGynoecium samples from flowers at stages 11 and 12 (before anthesis) were collected from wild-type, \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eand \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003eplants grown at nAT and hAT. Total RNA was extracted from 100 mg of pistils using the RNeasy Plant Mini Kit (Qiagen) following the manufacturer\u0026rsquo;s protocol. RNA isolates were treated with rDNAse Macherey-Nagel) to remove traces of contaminant DNA and purified using a RNeasy MinElute Cleanup Kit (Qiagen). RNA quality was assessed using a NanoDrop2000 spectrophotometer and agarose gel electrophoresis. Samples, four biological replicates each, were sent to the Novogene Genomic Sequencing Labs (Cambridge Sequencing Center) for sequencing. All samples \u0026nbsp;passed Novogene\u0026apos;s quality control threshold for library preparation and RNA-seq. mRNA-Seq libraries were constructed by Novogene, starting with 100 ng of high-quality RNA per sample. mRNA purification was performed using oligo(dT)-attached magnetic beads, followed by fragmentation and first-strand cDNA synthesis. Second-strand cDNA synthesis, end repair, adapter ligation, and size selection were performed. PCR enrichment yielded in the final cDNA library. Sequencing was conducted on the Illumina NovaSeq platform, generating 150-bp/150-bp paired-end reads. The sequence data have been deposited in the Genbank database under the BioProject PRJNA1091589. Clean reads were generated by removing adaptor sequences and low-quality reads using fqtools. The reads were mapped to the \u003cem\u003eArabidopsis\u003c/em\u003e genome using Araport11 (TAIR10, http://www.arabidopsis.org/). FeatureCounts was used to determine read count for each gene in each sample. The FPKM values were calculated to provide a measure of gene expression levels in each sample.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene expression (DE) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis was analyzed by Bioconductor package DESeq2 v1.34.0 [42]. Data generated by DESeq2 with independent filtering were selected for the differential gene expression analysis due to its conservative features and to avoid potential false positives. Genes were considered to be differentially expressed based on a cut-off of adjusted \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 and log2(fold-change) \u0026ge;1 or \u0026le;-1 and a false discovery rate (FDR) \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology and hierarchical clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene ontology annotation was retrieved from EnsemblPlants, Ensembl BioMarts [43]. Gene enrichment was performed using the R package clusterProfiler [44] on the differentially expressed genes (genes with adjusted \u003cem\u003ep\u003c/em\u003e-value \u0026lt;0.05) and separated in up- and down-regulated set. Visualizations were made using ggplot2 [45]. Hierarchical clusters were generated from selected top differentially regulated genes using R package pheatmap v1.0.12 \u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e, volcano plots were produced using ggplot2 v3.3.5 package [45] and MA plots were generated using ggpubr v0.4.0 package \u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"ftn2\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eHigh ambient temperature alters the root system architecture with a reduction in the number of emerged lateral roots compensated by their increased elongation\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eStudying root development in petri dishes has limitations, including a limited growth area, root illumination, and the absence of soil. To overcome these challenges in \u003cem\u003ein vitro\u003c/em\u003e plant cultivation, we used a light-isolated rhizotron system that allows plants to grow in soil for non-invasive, image-based root phenotyping. To investigate the effects of temperature on root morphology and the role of the PhyB-PIF4 pathway, we phenotyped Col-0, \u003cem\u003ephyb, 35S::PIF4,\u003c/em\u003e and \u003cem\u003epif4\u003c/em\u003e plants. Lateral and adventitious roots, total root length, primary root length, maximum length of the four longest lateral roots, and root area were measured throughout growth. Root growth patterns responded differently to warm temperatures among genotypes (\u003cstrong\u003eFig\u003c/strong\u003e. \u003cstrong\u003e1a\u003c/strong\u003e). hAT significantly promoted root elongation in Col-0 and \u003cem\u003epif4\u003c/em\u003e, but had little effect on \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants (\u003cstrong\u003eAdditional file 1 - Fig. S1\u003c/strong\u003e). All plants initially showed increased relative root elongation at hAT (\u003cstrong\u003eFig. 1b\u003c/strong\u003e). However, by the third week of development, root growth was slower at hAT compared to nAT in all genotypes (\u003cstrong\u003eFig. 1b\u003c/strong\u003e). Overall, no significant differences were detected at the final time point (\u003cstrong\u003eAdditional file 1 - Fig. S1\u003c/strong\u003e), consistent with previous work using a TGRooZ device that mimics natural conditions [46]. hAT had no significant effect on the overall root growth rate of the genotypes studied. In hAT, \u003cem\u003e35S::PIF4\u003c/em\u003e showed a slightly lower root growth rate of 1.29 cm/day, in contrast to the other genotypes (1.41 to 1.6 cm/day) during the entire measurement period. This difference in growth rates resulted in a smaller average root depth for \u003cem\u003e35S::PIF4\u003c/em\u003e throughout its growth (\u003cstrong\u003eTable 1\u003c/strong\u003e; \u003cstrong\u003eAdditional File 1 - Fig. S2\u003c/strong\u003e). Moreover, during the initial growth phase (between 10 and 16 days after sowing), at nAT, no significant difference in the relative root growth rate were observed among the genotypes. At hAT, \u003cem\u003e35S::PIF4\u003c/em\u003e showed a lowest significant root growth rate of 1.42 cm/day. During the second growth phase (between 18 and 21 days after sowing), \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003eroots were growing significantly slower than Col-0 at both nAT (1.39 cm/day vs. 1.88 cm/day) and hAT (1.81 cm/day vs. 2.39 cm/day) (\u003cstrong\u003eFig. 1b; Additional File 3 \u0026ndash; Table S2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLateral root formation was inhibited at hAT (\u003cstrong\u003eFig. 1c\u003c/strong\u003e). Wild-type, \u003cem\u003ephyb\u003c/em\u003e, and \u003cem\u003epif4\u003c/em\u003e plants showed reduced lateral root density (number of emerged lateral roots per cm of primary root) at hAT. However, lateral root density was not affected in \u003cem\u003e35S::PIF4\u003c/em\u003e plants at hAT (\u003cstrong\u003eFig. 1c\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 - Table S1\u003c/strong\u003e). At nAT, Col-0 and \u003cem\u003epif4\u0026nbsp;\u003c/em\u003eplants had 2.2 and 2.11 lateral roots per cm of primary root, respectively. In contrast, \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eand \u003cem\u003e35S::PIF4\u003c/em\u003e plants produced fewer lateral roots, averaging 1.5 and 1.7 lateral roots per cm of primary root, respectively. At hAT, the number of lateral roots in wild-type plants was similar to that of \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e at nAT. Although the plants had fewer emerged lateral roots, hAT promoted their elongation in all the genotypes (\u003cstrong\u003eFig. 1d\u003c/strong\u003e). There may be a trade-off between the number of lateral roots and their length. All the genotypes increased the average length of the four longest lateral roots with Col-0 and \u003cem\u003epif4\u003c/em\u003e seedlings being the most affected and \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyb\u003c/em\u003e being the least sensitive to hAT (\u003cstrong\u003eFig\u003c/strong\u003e. \u003cstrong\u003e1a,d\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 - Table S2\u003c/strong\u003e). The opposite effects of hAT on the number of lateral roots and their length did not significantly affect the total root length and root area between nAT and hAT (\u003cstrong\u003eAdditional File 1 \u0026ndash; Figs. S2 and S3\u003c/strong\u003e). However, these values were significantly lower for \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e genotypes under both conditions, resulting in a reduced root system compared to wild-type plants. Furthermore, temperature increase promoted the induction of adventitious roots in all the genotypes studied. 19% of the wild-type and \u003cem\u003ephyb\u003c/em\u003e plants produced adventitious roots at hAT, while this value decreased to about 5% for the \u003cem\u003ePIF4\u003c/em\u003e-modified genotypes. These changes in (lateral) root length and number alter the root system architecture of plants grown at hAT. These results indicate that \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants under both conditions have a reduced root system compared to wild-type plants. They also suggest that the suppression of \u003cem\u003ePhyB\u0026nbsp;\u003c/em\u003eat nAT mimics the effects of hAT on the number of emerged lateral roots.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eSuppression of \u003cem\u003ePhyB\u003c/em\u003e mimics the effects of high ambient temperatures on \u003cem\u003eArabidopsis\u003c/em\u003e shoot architecture\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo understand how hAT affects shoot development and photosynthetic efficiency through the PhyB-PIF4 pathway, we studied eight lines: \u003cem\u003e35S::PIF4, phyb-9, pif3-7, pif4-2, pif7-1, pif7-2, pif3-3 pif7-1,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;pifq (pif1-1 pif3-7 pif4-2 pif5-3\u003c/em\u003e) mutants. We quantified the effects of hAT on plant growth by measuring the rosette area from 9 to 39 days after sowing, when the plants reached their final rosette size (\u003cstrong\u003eFig\u003c/strong\u003e. \u003cstrong\u003e2, Additional File 1 - Figs.\u003c/strong\u003e \u003cstrong\u003eS4, S5\u003c/strong\u003e). The \u003cem\u003ephyB\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants exhibited delayed rosette expansion, starting at 26 days after sowing, while the other genotypes expanded from 22 days after sowing (\u003cstrong\u003eFig. 2a, Additional File 1 - Figs.\u003c/strong\u003e \u003cstrong\u003eS5\u003c/strong\u003e). At nAT, wild-type plants had the largest area (40 cm\u0026sup2;), whereas \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyb\u003c/em\u003e plants were smaller (20 cm\u0026sup2; and 10 cm\u0026sup2;, respectively) (\u003cstrong\u003eAdditional File 1 - Figs.\u003c/strong\u003e \u003cstrong\u003eS4, S5\u003c/strong\u003e). Other genotypes (\u003cem\u003epif3, pif7,\u003c/em\u003e and \u003cem\u003epifq\u003c/em\u003e) produced plants with intermediate rosette areas. This is a consequence of a significant reduced growth rate between 20 and 27, and between 28 and 33, days after sowing in \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyb\u003c/em\u003e plants. It is noteworthy that the \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eplants stopped expanding after 27 days (\u003cstrong\u003eFig. 2b; Additional File 1 - Fig.\u003c/strong\u003e \u003cstrong\u003eS5\u003c/strong\u003e). Wild-type plants were significantly sensitive to hAT, with a reduced growth rate and a final area of 15 cm\u0026sup2; (\u003cstrong\u003eAdditional File 1 - Fig.\u003c/strong\u003e \u003cstrong\u003eS5\u003c/strong\u003e). In contrast, \u003cem\u003e35S::PIF4\u003c/em\u003e, \u003cem\u003epif3\u003c/em\u003e, \u003cem\u003epif4\u003c/em\u003e, and \u003cem\u003epifq\u003c/em\u003e maintained a stable growth rate between 20 and 27 days (\u003cstrong\u003eFig. 2a\u003c/strong\u003e) but \u003cem\u003epif3\u003c/em\u003e, \u003cem\u003epif4\u003c/em\u003e, and \u003cem\u003epifq\u003c/em\u003e slowed down their growth rate after 28 days (\u003cstrong\u003eFig. 2b\u003c/strong\u003e). The final rosette area was about 20 cm\u0026sup2; for \u003cem\u003epif4, pif7-1, pif7-2,\u003c/em\u003e and \u003cem\u003epif3 pif7\u003c/em\u003e plants. The \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants showed the smallest area with only 5 cm\u0026sup2;. The wild type, \u003cem\u003epif3\u003c/em\u003e, and \u003cem\u003epifq\u003c/em\u003e showed an intermediate size of 15 cm\u0026sup2; (\u003cstrong\u003eAdditional File 1 - Fig.\u003c/strong\u003e \u003cstrong\u003eS5\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo analyze the effects of hAT on shoot branching, we measured the number of primary branches emerging from rosette leaves in all genotypes under both growth conditions. While most genotypes produced an average of about five branches under normal conditions, \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants produced an average of three branches. When exposed to hAT, branch production decreased in almost all genotypes. The \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants produced an average of two branches, while the other genotypes produced an average of three branches, mirroring the performance of \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S:PIF4\u003c/em\u003e under nAT. Notably, the \u003cem\u003epif3 pif7\u003c/em\u003e and \u003cem\u003epif7\u0026nbsp;\u003c/em\u003eplants appeared to be resilient to the hAT, roughly maintaining their branch production. At hAT, they outperformed other genotypes, producing an average of 4 branches (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe inflorescence growth pattern was affected in hAT (\u003cstrong\u003eFig. 3; Additional File 1 - Fig. S4\u003c/strong\u003e). In nAT, \u003cem\u003ephyb\u003c/em\u003e plants flowered at 25 days, earlier than the other lines (29 days). The first flowers of the primary inflorescence stem opened between 25 and 29 days in \u003cem\u003ephyb\u003c/em\u003e and between 29 and 36 days in the other genotypes. Consequently, \u003cem\u003ephyb\u003c/em\u003e inflorescence stems were longer than Col-0 stems during their growth period, e.g., until 36 days, when both genotypes reached a comparable height. However, the growth rate of the \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eprimary inflorescence stem was significantly lower than that of the wild-type stem (\u003cstrong\u003eFigs. 3c and 3d; Additional File 3 - Table S3\u003c/strong\u003e). The \u003cem\u003ephyb\u003c/em\u003e mutant also stopped flowering earlier, at 44 days, compared to the other genotypes, which continued flowering until 49 days (\u003cstrong\u003eFig. 3a\u003c/strong\u003e). All genotypes grew to a total height of 35 cm by the last observation point at 49 days (\u003cstrong\u003eFig. 3a\u003c/strong\u003e). hAT stimulated early initiation of inflorescence stem elongation in all genotypes at 23 days, similar to that observed in \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eplants grown under nAT (\u003cstrong\u003eFigs. 3a, 3b\u003c/strong\u003e). In the primary inflorescence stem, flowers opened around 27-30 days in hAT. Plants reached their maximum growth earlier, at 41 days, as indicated by the significantly reduced growth rate in hAT in wild-type, \u003cem\u003epif7-1\u003c/em\u003e, and \u003cem\u003epif3 pif7\u0026nbsp;\u003c/em\u003eplants (\u003cstrong\u003eFigs. 3c, 3d; Additional File 3 - Table S3\u003c/strong\u003e). This resulted in a shorter final height ranging from 13-38.9 cm (\u003cem\u003e35S::PIF4\u003c/em\u003e stems being the shortest) at hAT, while this value corresponds to 18-43.8 cm at nAT (\u003cstrong\u003eFigs.\u003c/strong\u003e \u003cstrong\u003e3a, 3b\u003c/strong\u003e). The main inflorescence stem growth rate was insensitive to temperature changes throughout the entire flowering period in \u003cem\u003ephyb\u003c/em\u003e, \u003cem\u003e35S::PIF4\u003c/em\u003e, \u003cem\u003epif3\u003c/em\u003e, \u003cem\u003epif4\u003c/em\u003e, \u003cem\u003epif7-2\u003c/em\u003e, \u003cem\u003epif3 pif7\u003c/em\u003e, and \u003cem\u003epifq\u003c/em\u003e plants (\u003cstrong\u003eFig. 3d\u003c/strong\u003e), and only at the start of the flowering period in \u003cem\u003epif7-1\u0026nbsp;\u003c/em\u003eplants (\u003cstrong\u003eFig. 3c\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAmbient temperature has a moderate impact on plant health but modulates photosynthetic parameters\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSince hAT affects different characteristics of plant growth, we wondered how these changes would affect the reflectance profile and pigment content of the plant. Hyperspectral imaging in the visible and near infrared (350-900 nm wavelength, VNIR) measures the light reflectance of plant leaves. It is an important indicator of plant health status [47, 48]. In our study, we measured VNIR parameters, including the Normalized Difference Vegetation Index (NDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index (PRI), Modified Chlorophyll Absorption Ratio Index 1 (MCARI1), Structure Insensitive Pigment Index (SIPI), and Plant Senescence Reflectance Index (PSRI). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn nAT, NDVI increased with age until 29 days after sowing for all genotypes and remained stable until the end of the measurements at 34 days (\u003cstrong\u003eAdditional File 1 - Fig. S6a\u003c/strong\u003e; \u003cstrong\u003eAdditional File 3 - Table S6\u003c/strong\u003e).\u0026nbsp;hAT reduced the NDVI in all genotypes ranging from 0.68 to 0.78, especially at later growth stages (22-28 days after sowing) (\u003cstrong\u003eAdditional File 1 - Fig. S6a\u003c/strong\u003e). In both nAT and hAT, NDVI had lower values for \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e with values in nAT (an average of 0.74) being comparable to NVDI values (an average of 0.82) of the other genotypes in hAT. OSAVI, which is designed to mitigate the effects of soil on NDVI, mirrored the trends observed in NDVI (\u003cstrong\u003eAdditional File 1 - Fig. S6b\u003c/strong\u003e; \u003cstrong\u003eAdditional File 3 - Table S6\u003c/strong\u003e). These two parameters are indicators of plant vegetative health [49, 50]. Therefore, it can be concluded that both the suppression of PhyB activity and hAT affect the vegetative vitality of the plant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePRI and PSRI parameters were mostly not significantly affected by the different ambient temperatures for all genotypes (\u003cstrong\u003eAdditional File 1 - Figs. S6c, S6d\u003c/strong\u003e; \u003cstrong\u003eAdditional File 3 - Table S6\u003c/strong\u003e). PRI values decreased with the plant age, whereas the opposite was observed for PSRI, which measures plant senescence based on the ratio of carotenoids to chlorophyll. Again, \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants had lower PSRI values than wild type at nAT and hAT. The SIPI parameter is sensitive to chlorophyll and carotenoid content [51] and MCARI1 parameter is associated with the chlorophyll content in plant leaves [52]. Both values increased as the plants aged at nAT and hAT (\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003edditional File 1 - Figs. S6e, S6f\u003c/strong\u003e; \u003cstrong\u003eAdditional File 3 - Table S6\u003c/strong\u003e). All other genotypes, except \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003eand \u003cem\u003ephyb\u003c/em\u003e, had reduced SIPI values at hAT. The \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eplants had lower SIPI values at nAT and did not respond to hAT. A similar trend was observed for the MCARI1 parameter.\u003c/p\u003e\n\u003cp\u003eWe applied chlorophyll fluorescence imaging to assess the efficiency of the plants to use the light energy for photosynthesis in the studied genotypes at nAT and hAT. The parameter QY-max (F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e) indicates the maximum quantum efficiency of the photosystem II (PSII) photochemistry. QY-max of wild-type plants increased steadily with age, with values ranging from 0.79-0.84 for nAT and 0.79-0.82 for hAT (significant difference only between 14 and 32 days after sowing). In nAT, the QY-max values for \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants were lower than in the wild type. Interestingly, \u003cem\u003ephyb\u003c/em\u003e recovered to wild-type QY-max values after two weeks of cultivation at nAT (\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003edditional File 1 - Fig. S7a\u003c/strong\u003e). At hAT, QY-max values increased with age for all genotypes, except \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003epif3\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003edditional File 1 - Fig. S7a)\u003c/strong\u003e. Photosynthetic efficiency was also measured in light-adapted plants. In particular, the parameters QY-Lss (PSII operating efficiency), and qP (photochemical quenching coefficient) [53] displayed significantly higher values at hAT for all the genotypes, corresponding to those of 39-day-old plants grown at nAT for both low and high light saturation point (Lss1 and Lss4) (\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003edditional File 1 - Fig. S7c-f\u003c/strong\u003e). For the two light intensities at hAT, the age of the plants did not impact the values of the two parameters. Non-photochemical quenching (NPQ) assesses the damage to photosystems caused by various environmental stressors [54]. All the genotypes exhibited lower NPQ values at hAT, indicating the negative impact of the high ambient temperature on the photosystem activity\u0026nbsp;(\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003edditional File 1 - Figs. S7g, S7h\u003c/strong\u003e). Compared to the wild type, the \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants showed elevated NPQ values at nAT and hAT.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eA correlative response to hAT in vegetative organs was observed\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo explore potential correlations for the response to hAT among different plant organs, we utilized correlation matrices (\u003cstrong\u003eFig. 4\u003c/strong\u003e). These matrices display correlations with \u003cem\u003ep\u003c/em\u003e-values below the significance threshold of 0.05, indicating statistically significant relationships between the relative responses to hAT in the different organs. During vegetative growth (\u003cstrong\u003eFig. 4a\u003c/strong\u003e), a positive correlation (0.96) was observed between the NDVI parameter and the length of the inflorescence stem, highlighting the effectiveness of the NDVI parameter in indicating vegetative growth dynamics. A robust positive correlation (0.94) was also noted between inflorescence stem growth rate and rosette area for their response to hAT, suggesting mutual interdependence between these traits. Notably, a negative correlation (-0.62) was observed between lateral root density and lateral root length, hinting at a potential trade-off mechanism governing root development.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003ePhyB\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;influences the response of reproductive tissues to hAT\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe have used Col-0, \u003cem\u003ephyb\u003c/em\u003e, \u003cem\u003epif4\u003c/em\u003e, \u003cem\u003epifq\u003c/em\u003e, and \u003cem\u003e35S::PIF4\u003c/em\u003e plants to investigate whether the PhyB-PIF4 pathway regulates thermomorphogenesis during reproductive development. To ensure a similar fitness of the plants at the reproductive stage, plants were exposed to hAT after the first flower bud appearance and maintained at hAT until the end of their growth.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eEffects of hAT on anthers\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAnthers were collected at 7 and 9 days after the development of the first flower (DAFD) on the primary inflorescence stem. In nAT, we did not observe any abnormality in the different lines. In hAT, the wild type, \u003cem\u003ephyb\u003c/em\u003e, and \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003elines were affected to different degrees. At 7 DAFD, 4.65 % of the wild-type anthers were aborted, while this percentage reached 23.40 % and 11.43 % for the \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003eand \u003cem\u003ephyb\u003c/em\u003e lines, respectively. Interestingly, these percentages increased to 7.81 %, 34.82 %, and 29.27 %, respectively, at 9 DAFD when plants were subjected to prolonged hAT. Notably, only the \u003cem\u003ephyb\u003c/em\u003e mutant showed a highly significant increase in this trend (\u003cstrong\u003eTable 3\u003c/strong\u003e). This observation suggests that the \u003cem\u003ephyb\u003c/em\u003e plants may become increasingly sensitive to hAT as they progress through later developmental stages. Additionally, we observed that \u003cem\u003epif4\u003c/em\u003e and \u003cem\u003epifq\u003c/em\u003e anthers were more resistant to hAT than wild type, with abortion rates of only 1.11 % and 1.44 %, respectively, at 9 DAFD. Our results suggest that suppression of \u003cem\u003ePhyB\u003c/em\u003e, resulting in PIF4 activation, worsens the negative effect of hAT on anther development.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eEffects of hAT on mature ovules\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe same plants were analyzed to determine the effect of hAT on ovules. In nAT, 17.9 % and 16.1 % of \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e ovules, respectively, were defective, whereas the other lines had between 5.4 % and 9.4 % defective ovules (\u003cstrong\u003eFig. 5\u003c/strong\u003e; \u003cstrong\u003eTable 4\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 \u0026ndash; Table S4\u003c/strong\u003e). Notably, only \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e lines were defective in the fusion of the central cell nuclei (\u003cstrong\u003eFig. 5c\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 \u0026ndash; Table S4\u003c/strong\u003e). At hAT, all genotypes exhibited the same types of defects, predominantly a collapsed embryo sac (lacking synergid, egg cell, and central cell structures), collapsed synergids, and unfused central cell nuclei (\u003cstrong\u003eFig\u003c/strong\u003e. \u003cstrong\u003e5a-d\u003c/strong\u003e). Although the types of ovule defects were consistent across genotypes, the percentage of these defects varied (\u003cstrong\u003eAdditional File 2 \u0026ndash; Table S4\u003c/strong\u003e). \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyB\u0026nbsp;\u003c/em\u003eovules were hypersensitive to hAT, producing 84.3 % and 62.6 % defective ovules, respectively (\u003cstrong\u003eTable 4\u003c/strong\u003e). In contrast, these percentages were only 30.6 % and 27.6 % in the wild-type and \u003cem\u003epif4\u003c/em\u003e lines, respectively. Interestingly, more ovules (45.9 %) were defective in \u003cem\u003epifq\u003c/em\u003e than in \u003cem\u003epif4\u003c/em\u003e (27.6 %), suggesting that other PIFs (such as PIF3, PIF5, or PIF7) may play a synergistic role in this response in ovules. Based on these results, we hypothesize that repressing \u003cem\u003ePhyB\u003c/em\u003e expression mimics the temperature effects observed in the wild type during ovule development.\u003c/p\u003e\n\u003cp\u003eTo better understand what would be the molecular mechanism behind the physiological response of \u003cem\u003eArabidopsis\u003c/em\u003e ovules to hAT, we performed a transcriptomic analysis of the gynoecium from 7 DAFD flowers at stage 11-12 (pre-anthesis, ovules at FG7) of Col-0, \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003eplants grown under nAT and hAT. More than 40 million reads were obtained from each sample (\u003cstrong\u003eAdditional File 2 \u0026ndash; Table S5\u003c/strong\u003e), with an average of 45 % GC content. RNA-seq data received a high quality score by the Phred of 98 for Q20 and 94 for Q30 in average.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAn overlapping transcriptional response is observed between hAT wild-type pistils and nAT-grown \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eand \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003epistils\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of differentially expressed genes (DEGs) in the different samples and conditions revealed different patterns. In response to hAT, the wild-type pistils had 8,485 DEGs (5,032 up-regulated and 3,453 down-regulated). A lower number of DEGs in \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e pistils, 1,862 and 2,612 genes, respectively, were distributed as 1037 and 2062 up-regulated genes, and 825 and 550 down-regulated genes, respectively (\u003cstrong\u003eAdditional File 1 - Fig. S8a; Additional File 4 \u0026ndash; Table S1\u003c/strong\u003e). Notably, all genotypes responded to hAT with upregulation of gene expression.\u003c/p\u003e\n\u003cp\u003eThe phenotyping analysis indicated that the \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eand \u003cem\u003e35S::PIF4\u003c/em\u003e plants at nAT behaved as Col-0 at hAT. Therefore, we compared the DEG patterns of the wild-type pistils in response to hAT with those of \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003epistils at nAT. The number of up- and downregulated DEGs in these comparisons was very similar (\u003cstrong\u003eAdditional File 1 - Fig. S8b; Additional File 4 \u0026ndash; Table S1\u003c/strong\u003e). Venn diagrams analyze the overlap of the up and down DEGs in the same comparisons. In response to hAT, 10 % (542 genes) of the upregulated genes from wild-type pistils were also upregulated in \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyb\u003c/em\u003e pistils at hAT, whereas only 3 % (121 genes) of the downregulated genes from wild-type pistils were also downregulated at hAT in the two mutants (\u003cstrong\u003eAdditional File 1 - Figs. S8c, S8d; Additional File 4 \u0026ndash; Table S1\u003c/strong\u003e). However, only 3.7% of the genes upregulated in the wild-type pistils in response to hAT were also upregulated in both \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eand \u003cem\u003e35S::PIF4\u003c/em\u003e pistils at nAT. The majority of the upregulated DEGs (62 %) in wild-type pistils at hAT were also found to be upregulated in \u003cem\u003e35S::PIF4\u003c/em\u003e pistils at nAT (\u003cstrong\u003eAdditional File 1 - Fig. S8e; Additional File 4 \u0026ndash; Table S1\u003c/strong\u003e). In addition , almost half of the genes downregulated in the wild-type pistils at hAT (46 %) were downregulated genes in \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eand \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003epistils at nAT (\u003cstrong\u003eAdditional File 1 - Fig. S8f; Additional File 4 \u0026ndash; Table S1\u003c/strong\u003e). Wild-type \u003cem\u003eArabidopsis\u003c/em\u003e pistils (and ovules) developed at hAT showed pronounced transcriptional changes, mostly as upregulation, with a substantial overlapping regulation with \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e pistils developed at nAT. This suggests that the \u003cem\u003eArabidopsis\u003c/em\u003e response to hAT during pistil development may involve signaling pathways dependent on the PhyB and PIF regulators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene ontology analysis identified biological processes affected by hAT and PhyB-PIF4 signalling in pistils\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efunctional annotation analysis using was performed for up- and downregulated DEGs in wild-type pistils from plants grown on hAT, and \u003cem\u003e35S::PIF4\u003c/em\u003e and \u003cem\u003ephyb\u003c/em\u003e pistils from plants grown on nAT to determine whether the hAT response in wild-type pistils shares GO patterns with the response in pistils from plants defective in the PhyB pathway (\u003cstrong\u003eFig. 6; Additional File 4 \u0026ndash; Table S2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCell division rate is known to be dependent on ambient temperature [55]. Several GO terms related to cell division, cell cycle, DNA replication, and mRNA processing were enriched among the commonly upregulated DEGs. These processes are known to be critical during pistil and ovule development. Indeed, GO terms associated with megagametogenesis, ovule, embryo sac, and flower development and the transition to the reproductive phase in the meristem were among the commonly upregulated DEGs (\u003cstrong\u003eFig. 6a\u003c/strong\u003e). Among the GO terms related to fertilization and reproduction, recognition of pollen, (regulation of) pollen growth and pollen development were enriched. Genes involved in pollen tube growth were specifically upregulated by hAT in the wild-type pistils, whereas genes involved in pollen germination were enriched only in \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003epistils (\u003cstrong\u003eFig. 6a\u003c/strong\u003e). This suggests that both hAT and the PhyB-PIF4 pathway may influence the expression of genes involved in ovule development as observed in \u003cstrong\u003eFig. 5\u003c/strong\u003e, and that fertilization processes dependent on pollen tube growth and guidance may be specifically affected by hAT in wild-type pistils.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurprisingly, GO terms related to the responses to hormones and abiotic stresses were found to be downregulated (\u003cstrong\u003eFig. 6b\u003c/strong\u003e). Responses to auxin and ethylene were downregulated in all sample comparisons. However, GO terms associated with brassinosteroid, gibberellin, abscisic acid, and jasmonic acid were exclusively downregulated in \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003epistils, which may explain the more pronounced phenotypic response of \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003epistils to hAT during ovule development (\u003cstrong\u003eTable 4\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 - Table S4\u003c/strong\u003e)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e. Furthermore, GO terms related to cold and light stress responses, photosynthesis, protein translation, and metabolism were generally enriched among the downregulated genes in all three samples (\u003cstrong\u003eFig. 6b\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe expression profile of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and \u003cem\u003e35S::PIF4\u0026nbsp;\u003c/em\u003epistils at nAT for auxin signaling and miRNA processing genes is comparable to that of wild-type pistils at hAT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHierarchical clustering analysis of the expressed genes identified two major clusters among the top 100 DEGs in Col-0 hAT pistils compared to Col-0 nAT pistils (\u003cstrong\u003eAdditional File 1 - Figure S10; Additional File 4 \u0026ndash; Table S3\u003c/strong\u003e), the DEGs involved in the auxin signaling pathway (\u003cstrong\u003eFig. 7a; Additional File 4 \u0026ndash; Table S3\u003c/strong\u003e) and in miRNA biogenesis (\u003cstrong\u003eFig, 7b; Additional File 4 \u0026ndash; Table S3\u003c/strong\u003e).\u0026nbsp;One cluster consists exclusively of the wild-type pistils from plants grown in nAT. The second cluster includes the pistils from \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e plants grown in nAT and hAT, as well as from wild-type plants grown in hAT. Similar to what was observed during our phenotyping analysis, these results indicate that the response to hAT and to the PhyB-PIF4 pathway share a gene regulatory network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePIF4 binds to the promoters of several \u003cem\u003emiR156\u003c/em\u003e genes to repress their expression, resulting in the accumulation of the miR156 target transcripts, the \u003cem\u003eSQUAMOSA-PROMOTER BINDING PROTEIN-LIKE\u0026nbsp;\u003c/em\u003e(\u003cem\u003eSPL\u003c/em\u003e) genes [56]. SPL will then regulate plant growth in response to shade and warm temperature. The module miR156/\u003cem\u003eSPL9\u003c/em\u003e regulates the thermomorphogenetic response of the hypocotyl by mitigating its sensitivity of auxin [57]. Several \u003cem\u003eSMALL AUXIN UP RNA\u003c/em\u003e (\u003cem\u003eSAUR\u003c/em\u003e) and \u003cem\u003eAux/IAA\u003c/em\u003e genes, as well as \u003cem\u003eAUXIN RESPONSE FACTOR ARF10\u003c/em\u003e and \u003cem\u003eARF19\u003c/em\u003e are upregulated in the second cluster (\u003cstrong\u003eFig. 7a\u003c/strong\u003e). We also identified \u003cem\u003eMIR156\u003c/em\u003e, \u003cem\u003eMIR160\u003c/em\u003e, and the miRNA processing \u003cem\u003eAGO1\u003c/em\u003e, \u003cem\u003eDCL1\u003c/em\u003e genes to be upregulated in the same cluster, while the MIR156 targets \u003cem\u003eSPL5\u003c/em\u003e and \u003cem\u003eSPL9\u003c/em\u003e were slightly down-regulated (\u003cstrong\u003eFig. 7b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePollen tube attractants are upregulated at hAT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also performed a hierarchical clustering for genes related to pollen tube guidance, an enriched GO term category (\u003cstrong\u003eFig. 6a\u003c/strong\u003e; \u003cstrong\u003eFig. 7c; Additional File 4 \u0026ndash; Table S3\u003c/strong\u003e). Again, two distinct clusters related to the hAT response were identified. Genes encoding the defensin-like pollen tube attractants CYSTEINE-RICH PEPTIDE (CRP) AtLURE1s and XIUQIU, EMBRYO SURROUNDING FACTORS 1.3 (ESF1.3), EGG CELL SPECIFCs (ECSs), and MYB98, a transcription factor controlling their expression [58, 59], were upregulated in the cluster comprising all pistil samples from plants grown in hAT (\u003cstrong\u003eFig. 7c\u003c/strong\u003e), regardless of genotype.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in \u003cem\u003eYUCCA\u003c/em\u003e and \u003cem\u003eTAA1\u0026nbsp;\u003c/em\u003eexpression levels in hAT in mature ovules suggest a role for auxin biosynthesis in the response to high ambient temperature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn seedlings, hAT-activated PIF4 enhances the expression of the \u003cem\u003eTRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS\u003c/em\u003e (\u003cem\u003eTAA1\u003c/em\u003e), \u003cem\u003eYUCCA 8\u003c/em\u003e (\u003cem\u003eYUC8\u003c/em\u003e) and \u003cem\u003eSAUR\u003c/em\u003e genes in the leaves and hypocotyls [8, 20]. \u003cem\u003eTAA1\u003c/em\u003e, \u003cem\u003eYUC4\u003c/em\u003e and \u003cem\u003eYUC8\u003c/em\u003e are also expressed in mature ovules at the micropyle cells surrounding the embryo sac [60]. To evaluate the effects of hAT on auxin homeostasis in mature ovules, we analyzed the expression pattern of the three auxin biosynthetic genes. \u003cem\u003eTAA1\u003c/em\u003e is expressed in the micropylar cells in nAT and its expression is altered in hAT (\u003cstrong\u003eFig. 8a-c\u003c/strong\u003e). The \u003cem\u003eTAA1\u003c/em\u003e fluorescence signal was not detected in 49 % of the ovules and was weak in the remaining samples in hAT (\u003cstrong\u003eFigs. 8b, 8c\u003c/strong\u003e). \u003cem\u003eYUC4\u003c/em\u003e was strongly expressed in the integuments of mature nAT ovules (\u003cstrong\u003eFig. 8d\u003c/strong\u003e). Different levels of the fluorescence signal intensity were observed for \u003cem\u003eYUC4\u0026nbsp;\u003c/em\u003ein hAT ovules: same expression pattern with reduced signal intensity (19.4 %; \u003cstrong\u003eFig 8e\u003c/strong\u003e), restricted expression domain at the chalazal integuments with weak signal intensity (66.6 %; \u003cstrong\u003eFig. 8f\u003c/strong\u003e), and no signal (13.8 %; \u003cstrong\u003eFig. 8g\u003c/strong\u003e). \u003cem\u003eYUC8\u003c/em\u003e showed no (95.4 %; \u003cstrong\u003eFig. 8h\u003c/strong\u003e) to weak expression in the micropylar cells (4.6 %) in nAT ovules. However, in hAT, \u003cem\u003eYUC8\u003c/em\u003e was highly expressed in the micropylar cells (\u003cstrong\u003eFig. 8i\u003c/strong\u003e). \u003cem\u003eYUC8\u0026nbsp;\u003c/em\u003eis known to be upregulated in hAT in other tissues [8], which is consistent with our observations in ovules. The contrasting expression behavior of \u003cem\u003eYUC4\u003c/em\u003e and \u003cem\u003eYUC8\u0026nbsp;\u003c/em\u003eat hAT suggests an intricate and complex regulatory mechanism in the response to hAT in the ovules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of hAT on early embryo development\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the effects of hAT on ovules and the transcriptional changes associated with pollen guidance and its impact on fertilization, we investigated the effects of hAT on seed and embryo development in the same genotypes. Seeds bearing embryos from early developmental stages (one-cell to late globular) were analyzed for embryo patterning defects. In nAT, no significant differences were observed between the different genotypes (\u003cstrong\u003eTable 5\u003c/strong\u003e). In hAT, however, all genotypes were significantly affected. No statistically significant differences in the percentage of defective embryos were observed between wild type (40.77 %), \u003cem\u003epif4\u0026nbsp;\u003c/em\u003e(44.23 %), \u003cem\u003epifq\u0026nbsp;\u003c/em\u003e(41.56 %), and \u003cem\u003ephyb\u003c/em\u003e (30.85 %). Only \u003cem\u003e35S::PIF4\u003c/em\u003e appeared to be resistant to growth at hAT with a significantly lower embryonic defect rate of 21.95 % (\u003cstrong\u003eTable 5\u003c/strong\u003e). A variety of embryonic defects have been observed, including an excess of cell divisions within the proper embryo or suspensor, irregularities in the size of the hypophysis cell, and a reduction in the length of the suspensor (\u003cstrong\u003eFigs\u003c/strong\u003e. \u003cstrong\u003e5f-h\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 - Table S6\u003c/strong\u003e). A shorter suspensor was observed in all the genotypes for hAT (\u003cstrong\u003eFig. 5h\u003c/strong\u003e). In nAT, the suspensor of the \u003cem\u003e35S::PIF4\u003c/em\u003e embryos was longer (111 \u0026mu;m) than the wild-type suspensor (97.18 \u0026mu;m). However, this difference disappeared in hAT, suggesting that the \u003cem\u003e35S::PIF4\u003c/em\u003e embryos were the most affected by temperature variation for suspensor growth (\u003cstrong\u003eFig\u003c/strong\u003e. \u003cstrong\u003e5h\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 \u0026ndash; Table S6\u003c/strong\u003e). These results suggest that ectopic overexpression of \u003cem\u003ePIF4\u003c/em\u003e may confer a minor temperature resistance during embryogenesis.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003ehAT-induced changes in seed traits\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eDry seeds harvested from the same plants flowering at nAT and hAT were phenotyped using the Boxeed robot. We focused on four seed traits: number of seeds produced per silique, seed shape, seed size, and seed weight (\u003cstrong\u003eFig. 9; Additional File 3 \u0026ndash; Tables S8 and S9\u003c/strong\u003e). Elevated ambient temperatures led to an increase in seed area in all the genotypes, with the production of larger viable seeds and smaller misshapen seeds (\u003cstrong\u003eFig. 9a\u003c/strong\u003e). Seed area increased by 34.74 % in Col-0, 31.73 % in \u003cem\u003e35S::PIF4\u003c/em\u003e, 47.83 % in \u003cem\u003ephyb,\u003c/em\u003e 25.20 % in \u003cem\u003epif4\u003c/em\u003e, and 47.67 % in \u003cem\u003epifq\u0026nbsp;\u003c/em\u003e(\u003cstrong\u003eFig. 9b\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 - Table S7\u003c/strong\u003e). Additionally, seeds produced under warmer conditions were rounder across various genotypes, as assessed by the ratio of the seed length to the seed area. The \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eseeds were the most affected by shape changes in hAT (\u003cstrong\u003eFig. 9c\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 \u0026ndash; Table S8\u003c/strong\u003e). Evaluation of the number of seeds per silique showed that all genotypes produced fewer but heavier seeds per silique at hAT in all the genotypes (\u003cstrong\u003eFigs. 9d, 9e\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 \u0026ndash; Tables S9 and S10\u003c/strong\u003e). Interestingly, at nAT, \u003cem\u003ephyb\u003c/em\u003e seeds were by 25% heavier than wild-type seeds (\u003cstrong\u003eFig. 9e\u003c/strong\u003e; \u003cstrong\u003eAdditional File 2 \u0026ndash; Table S10\u003c/strong\u003e). The higher seed weight observed in seeds developed at hAT suggests a possible adaptive strategy in which plants may favor the production of nutrient-rich seeds rather than a greater number of seeds. However, \u003cem\u003ephyb\u003c/em\u003e plants grown on nAT and wild-type plants grown on hAT produced a comparable number of seeds, precisely 42.14 and 47.75 seeds per silique for a comparable weight, 2.33 mg and 2.26 mg per 100 seeds, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe correlation of the hAT response in reproductive tissues\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA correlative analysis of the effects of hAT on reproduction showed that seed number and the increased number of embryo defects and pollen defects were significantly negatively correlated (-0.92 and -0.63, respectively). Seed number and seed weight were also significantly negatively correlated (-0.71). Surprisingly, pollen defects were positively correlated (0.87) with increased seed weight.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePlants have adapted to ambient growth temperatures through various molecular mechanisms, including the PhyB-PIF4 pathway [13, 61]. The aim of this study was to understand the response of \u003cem\u003eArabidopsis thaliana\u0026nbsp;\u003c/em\u003eto high ambient temperatures during vegetative and reproductive growth and to investigate the role of the PhyB-PIF4 pathway, with a focus on seed production. We performed a comprehensive morphological analysis of different organs during both vegetative and reproductive growth stages using automated phenotyping solutions, with the \u003cem\u003ephyb\u003c/em\u003e mutant and \u003cem\u003ePIF4\u003c/em\u003e overexpression lines to elucidate the influence of this pathway on these responses. We uncovered how suppression of the PhyB-PIF4 pathway differentially induces thermomorphogenesis at different developmental stages, thereby affecting the plant\u0026apos;s resistance to temperature changes. Additionally, we investigated the correlation of the response to hAT between different organs. And we studied the transcriptional changes in pistils that may help to overcome the hAT-reduced fertilization rate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch on the impact of hAT on root system growth has yielded mixed results, with some studies reporting decreased root growth and others reporting increased root growth [62\u0026ndash;65]. In our study, hAT initially enhances root elongation in all genotypes. This is consistent with previous findings showing that overexpression of \u003cem\u003ePIF4\u003c/em\u003e hinders the thermal response of roots, similar to the phenotypes of \u003cem\u003ehy5\u003c/em\u003e and \u003cem\u003ephya phyb\u003c/em\u003e mutants. Reduction of root meristem size in hAT is dependent on PhyA and PhyB [66, 67]. With a different temperature settings, Song et al.(2017) observed that a short-term heat shock at 37 \u0026deg;C inhibited primary root elongation in wild type and \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003ephya\u003c/em\u003e mutants, with a more pronounced effect in mutants. This is similar to what we observed with a prolonged growth at hAT (\u003cstrong\u003eFig. 1b\u003c/strong\u003e). However, \u003cem\u003ephyb\u003c/em\u003e resisted the inhibition of lateral root growth after the heat shock at 37 \u0026deg;C, whereas this trait was enhanced in \u003cem\u003ephyb\u0026nbsp;\u003c/em\u003eunder our growth conditions. The plant root system consists of primary, lateral, and adventitious roots. Primary roots form during \u003cem\u003eArabidopsis\u003c/em\u003e embryogenesis, whereas adventitious and lateral roots emerge post-embryonically. Adventitious root formation serves as a crucial plant strategy to cope with environmental stresses [68]. We found that hAT induced adventitious root formation in all studied lines, except when \u003cem\u003ePIF4\u003c/em\u003e expression was altered. It is worth noting that in nature, the soil temperature is gradually decreases with depth [46] , which mitigates the thermal response of the root system.\u003c/p\u003e\n\u003cp\u003eInterestingly, our results highlight the divergent response of shoot and root development to high temperatures. While hAT inhibits shoot elongation, it does not affect the final root length. However, when plants are exposed to hAT, initial growth acceleration and reduced branching are common in both tissues. It seems that both the root and shoot prioritize initial elongation at hAT, likely as a strategy to distance themselves from the warm soil surface. This prioritized elongation, particularly evident in the root, comes at the expense of nutrient uptake, as indicated by the observed reduction in the number of emerged lateral roots. This trade-off underscores the dynamic adjustments that plants make in response to environmental stress and \u0026nbsp;highlights the intricate balance between growth and resource allocation. Notably, \u003cem\u003ePIF4\u0026nbsp;\u003c/em\u003eoverexpression abolishes the temperature response of both root and shoot branching, suggesting a potential function of this transcription factor in shoot and root development at hAT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFlowering time in plants is regulated by environmental signals that affect gene expression in the shoot apical meristem. Notably, ambient temperature modulates the expression of \u003cem\u003eFLOWERING LOCUS T (FT)\u003c/em\u003e [69]. hAT generally leads to earlier flowering responses in most plants (reviewed by [70]). PIF4 emerges as a pivotal player in temperature-induced early flowering in \u003cem\u003eArabidopsis\u003c/em\u003e, exerting its influence by binding to the \u003cem\u003eFT\u0026nbsp;\u003c/em\u003epromoter in a temperature-dependent manner [61]. We have shown that exposing plants to hAT results in the premature cessation of rosette growth, leading to a reduced rosette area (\u003cstrong\u003eFig. 2\u003c/strong\u003e). These plants appear to prioritize energy conservation for the reproductive phase, which ultimately means reduced branching. Initially, plants hastened shoot elongation to distance flower buds from the warm soil surface, resulting in earlier flowering (\u003cstrong\u003eFig. 3\u003c/strong\u003e). Most of the temperature effects were observed in the \u003cem\u003ephyb\u003c/em\u003e mutant line under normal conditions, suggesting that the suppression of \u003cem\u003ePhyB\u003c/em\u003e simulates the effects of hAT during shoot development. Furthermore, in agreement with [71], our investigation showed that the studied spectral vegetation indices exhibited increased responsiveness to hAT during later stages of development. This suggests their potential utility as reliable non-destructive indicators of temperature stress.\u003c/p\u003e\n\u003cp\u003ePlant reproductive development, especially pollen, is highly sensitive to environmental stress [72, 73]. Growing \u003cem\u003eArabidopsis\u003c/em\u003e at 27 \u0026deg;C affects pollen development, resulting in male sterility with a 22 % reduction in pollen viability, through processes such as meiosis disruption, premature development, and altered hormone regulation [74, 75]. We observed a mild effect of hAT on pollen viability with \u003cem\u003epif4\u0026nbsp;\u003c/em\u003eand \u003cem\u003epifq\u0026nbsp;\u003c/em\u003eplants being resistant to hAT for the production of viable pollen grains.\u0026nbsp;Our observations revealed a robust phenotypic response to hAT in ovules, highlighting their sensitivity to temperature changes. We demonstrated that the \u003cem\u003e35S::PIF4\u003c/em\u003e plants in nAT effectively mimic the effects of hAT, highlighting the critical role of this pathway in thermomorphogenesis in female reproductive organs. To investigate the molecular mechanisms involved, we performed transcriptome analyses of wild-type, \u003cem\u003e35S::PIF4\u003c/em\u003e, and \u003cem\u003ephyb\u003c/em\u003e pistils from plants grown at nAT and hAT. This comprehensive approach allowed us to compare the transcriptomic responses of these genotypes in response to hAT and understand how the suppression of the PhyB pathway mimics the expression profile and phenotypes of wild-type pistils exposed to hAT.\u0026nbsp;DEG analysis revealed that wild-type plants show significant up- and downregulation in response to hAT, while this response is milder in \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e pistils. The DEG profiles of \u003cem\u003ephyb\u003c/em\u003e and \u003cem\u003e35S::PIF4\u003c/em\u003e at nAT were similar to the response to hAT in the wild type, with an overlap especially in downregulated genes.\u003c/p\u003e\n\u003cp\u003eWe identified that hAT influenced the expression of specific \u003cem\u003emicroRNAs\u003c/em\u003e, particularly \u003cem\u003eMIR156. MIR156\u003c/em\u003e has been implicated in \u003cem\u003eArabidopsis\u0026nbsp;\u003c/em\u003ehypocotyl elongation in response to hAT and is upregulated in our transcriptomic data [56, 57]. Consistently, heat stress during cotton pollen development regulates the expression of 6281 genes, among which \u003cem\u003emiR167\u0026nbsp;\u003c/em\u003eand \u003cem\u003emiR396\u003c/em\u003e are associated with pollen fertility by targeting genes involved in auxin signaling and metabolism pathways. Additionally, heat-induced jasmonic acid (JA) signaling activates genes associated with auxin synthesis, ultimately leading to pollen abortion [76]. Furthermore, \u003cem\u003emiR167\u003c/em\u003e downregulates the expression of \u003cem\u003eARF6\u003c/em\u003e and \u003cem\u003eARF8\u003c/em\u003e genes in \u003cem\u003eArabidopsis\u003c/em\u003e ovules, facilitating integument growth. In anthers, \u003cem\u003emiR167\u003c/em\u003e affects gene expression in connective cells and locules, thereby influencing pollen release. The regulatory function of \u003cem\u003emiR167\u003c/em\u003e underscores its essential role in patterning during the development of reproductive organs [77]. These findings suggest that miRNAs play crucial roles in reproduction and response to hAT, potentially acting as mediators linking high-temperature signaling pathways to hormone signaling pathways during reproductive organ development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impact of hAT on plant reproductive development involves complex regulatory mechanisms. While elevated temperature has been reported to activate auxin biosynthesis in vegetative plant tissues, such as the hypocotyl, it has opposite effects on auxin levels and biosynthetic genes during anther development in barley and \u003cem\u003eArabidopsis\u003c/em\u003e. Specifically, elevated temperature repressed the expression of \u003cem\u003eYUCCA\u003c/em\u003e auxin biosynthetic genes, resulting in reduced endogenous auxin levels in developing anthers [78\u0026ndash;80]. Similarly, our transcriptome analysis reveals that at hAT, auxin biosynthetic genes are downregulated at hAT during ovule development, which we confirmed using fluorescent reporters (\u003cstrong\u003eFig, 8\u003c/strong\u003e). Furthermore, Gene Ontology terms associated with the \u0026quot;auxin-activated signaling pathway\u0026quot; and \u0026quot;response to auxin\u0026quot; are suppressed at hAT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite a more pronounced impact on male processes, it is important to note that female tissues and post-fertilization development are also highly sensitive to temperature variation (reviewed by [81]). Elevated temperatures significantly influence seed production and overall plant yield. Despite extensive research on temperature effects on pollen and seed development, the underlying molecular mechanisms remain unclear. hAT affects both the total number of ovule/seeds and the number of mature ovule/seeds per silique. In the \u003cem\u003eArabidopsis\u0026nbsp;\u003c/em\u003eBurren ecotype, warm temperatures resulted in up to 43 % unfertilized ovules, leading to shorter siliques and reduced seed yield while promoting larger seeds [82]. A 7 \u0026deg;C increase in temperature (reaching 30 \u0026deg;C) negatively affects multiple reproductive traits in \u003cem\u003eArabidopsis\u003c/em\u003e, including fewer ovules per pistil, fewer anthers and pollen grains per flower, and an increased incidence of improperly developed ovules leading to abortion [83]. In our study, hAT affected sexual reproductive organs and seed-related processes, influencing overall seed yield. Phenotyping with Boxeed identified larger and heavier seeds in hAT, possibly compensating for the reduced seed set (\u003cstrong\u003eFig. 9\u003c/strong\u003e). Suppressing \u003cem\u003ePhyB\u003c/em\u003e enhanced PIF4 activation, heightening plant sensitivity to elevated temperatures during both male and female reproduction. Surprisingly, this mechanism improves plant resistance to hAT during embryogenesis, suggesting a versatile molecular pathway across developmental stages.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides an in-depth look at how plants respond to thermal stress, covering both their vegetative and reproductive stages through a comprehensive combination of automated phenotyping approaches and image analysis. We found that high ambient temperatures alter the timing of events like flowering and affect basic growth patterns, such as shoot and root system architecture. This suggests that plants prioritize reproduction under challenging conditions, a shift underscored by different temperature sensitivities at different developmental stages. Key among our findings is the role of the PhyB-PIF4 pathway, especially in regulating the development of reproductive tissues. However, its influence is less pronounced during embryogenesis. Overall, our research highlights the complex interplay between plant development and environmental temperatures, with the PhyB-PIF4 pathway playing a significant role in plant thermomorphogenesis.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARF AUXIN RESPONSE FACTOR\u003c/p\u003e\n\u003cp\u003eCRP CYSTEIN-RICH PEPTIDE\u003c/p\u003e\n\u003cp\u003eDAFD Days after the development of the first flower\u003c/p\u003e\n\u003cp\u003eDEG Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eECS EGG CELL SPECIFIC\u003c/p\u003e\n\u003cp\u003eESF EMBRYO SURROUNDING FACTOR\u003c/p\u003e\n\u003cp\u003eGO Gene ontology\u003c/p\u003e\n\u003cp\u003ehAT High ambient temperature\u003c/p\u003e\n\u003cp\u003eMCARI1 Modified chlorophyll absorption ratio index 1\u003c/p\u003e\n\u003cp\u003enAT Normal ambient temperature\u003c/p\u003e\n\u003cp\u003eNDVI Normalized difference vegetation index\u003c/p\u003e\n\u003cp\u003eNPQ Non-photochemical quenching\u003c/p\u003e\n\u003cp\u003eOSAVI Optimized soil-adjusted vegetation index\u003c/p\u003e\n\u003cp\u003ePhyB PHYTOCHROME B\u003c/p\u003e\n\u003cp\u003ePIF PHYTOCHROME-INTERACTING FACTORs\u003c/p\u003e\n\u003cp\u003ePRI Photochemical reflectance index\u003c/p\u003e\n\u003cp\u003ePSII Photosystem II\u003c/p\u003e\n\u003cp\u003ePSRI Plant senescence reflectance index\u003c/p\u003e\n\u003cp\u003eqP photochemical quenching coefficient\u003c/p\u003e\n\u003cp\u003eSAUR SMALL AUXIN UP RNA\u003c/p\u003e\n\u003cp\u003eSIPI Structure insensitive pigment index\u003c/p\u003e\n\u003cp\u003eSPL SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE\u003c/p\u003e\n\u003cp\u003eTAA1 TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1\u003c/p\u003e\n\u003cp\u003eVNIR Visible and near infrared\u003c/p\u003e\n\u003cp\u003eYUC YUCCA\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific permit was required for the samples analyzed in this study. The authors comply with relevant institutional, national, and international guidelines and legislation for plant studies. Plants were cultured and sampled in the greenhouses of the CEITEC Plant Sciences core facility, Brno, Czech Republic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is deposited to the NCBI repository (BioProject accession number PRJNA1091589).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the European Regional Development Fund-Project \u0026ldquo;SINGING PLANT\u0026rdquo; (No. CZ.02.1.01/0.0/0.0/16_026/0008446).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Tereza Rumlerov\u0026aacute; (PSI) for assistance with the Root Tester software and Nicolas Blavet (CEITEC Bioinformatics CF) for assistance with the transcriptomic data analysis. The authors acknowledge for their technical support the following Core Facilities of Masaryk university: Bioinformatics (supported by the NCMG research infrastructure [LM2023067], funded by MEYS CR), CELLIM (supported by the Czech-BioImaging large RI project [LM2023050] funded by MEYS CR), Biological Data Management and Analysis (funded by ELIXIR CZ research infrastructure [LM2023055] funded by MEYS CR), and Plant Sciences. The authors acknowledge PSI Research Center phenotyping and cultivation facility. We would like to thank the NASC seed stock center and Zhi-Yong Wang for donating seeds used in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.E.N., M.P., K.P., and H.R.B. designed the research; S.E.N., J.\u0026Scaron;., B.P., and T.D. performed experiments; S.E.N., J.\u0026Scaron;., B.P., M.P., T.D., K.P., and H.S.R. analyzed the data; S.E.N. and H.R.B. wrote the paper; S.E.N., J.\u0026Scaron;., B.P., T.D., K.P., M.P., and H.S.R. reviewed the paper and agreed for its publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWolkovich EM, Cook BI, Allen JM, Crimmins TM, Betancourt JL, Travers SE, et al. Warming experiments underpredict plant phenological responses to climate change. Nature. 2012;485:494\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLegris M, Nieto C, Sellaro R, Prat S, Casal JJ. Perception and signalling of light and temperature cues in plants. The Plant Journal. 2017;90:683\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez B, Rasmussen A, Porter JR. Temperatures and the growth and development of maize and rice: a review. Glob Chang Biol. 2014;20:408\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eLuo Q. Temperature thresholds and crop production: a review. Climatic Change. 2011;109:583\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eHayes S, Schachtschabel J, Mishkind M, Munnik T, Arisz SA. Hot topic: thermosensing in plants. Plant, Cell Environ. 2021;44:2018\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003ePatel D, Franklin KA. Temperature-regulation of plant architecture. Plant Signal Behav. 2009;4:577\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eNozue K, Harmer SL, Maloof JN. Genomic analysis of circadian clock-, light-, and growth-correlated genes reveals PHYTOCHROME-INTERACTING FACTOR5 as a modulator of auxin signaling in Arabidopsis. Plant Physiol. 2011;156:357\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eFranklin KA, Lee SH, Patel D, Kumar SV, Spartz AK, Gu C, et al. PHYTOCHROME-INTERACTING FACTOR 4 (PIF4) regulates auxin biosynthesis at high temperature. PNAS. 2011;108:20231\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eOhama N, Sato H, Shinozaki K, Yamaguchi-Shinozaki K. Transcriptional regulatory network of plant heat stress response. Trends in Plant Science. 2017;22:53\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eLi B, Jiang S, Gao L, Wang W, Luo H, Dong Y, et al. HEAT SHOCK FACTOR A1s are required for phytochrome‐interacting factor 4‐mediated thermomorphogenesis in Arabidopsis. J Integr Plant Biol. 2023. https://doi.org/10.1111/jipb.13579.\u003c/li\u003e\n\u003cli\u003eNozue K, Covington MF, Duek PD, Lorrain S, Fankhauser C, Harmer SL, et al. Rhythmic growth explained by coincidence between internal and external cues. Nature. 2007;448:358\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eLegris M, Klose C, Burgie ES, Rojas CCR, Neme M, Hiltbrunner A, et al. PHYTOCHROME B integrates light and temperature signals in Arabidopsis. Science. 2016;354:897\u0026ndash;900.\u003c/li\u003e\n\u003cli\u003eKoini MA, Alvey L, Allen T, Tilley CA, Harberd NP, Whitelam GC, et al. High temperature-mediated adaptations in plant architecture require the bHLH transcription factor PIF4. Current biology. 2009;19:408\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eTrupkin SA, Legris M, Buchovsky AS, Rivero MBT, Casal JJ. PHYTOCHROME B nuclear bodies respond to the low red to far-red ratio and to the reduced irradiance of canopy shade in Arabidopsis. Plant Physiol. 2014;165:1698\u0026ndash;708.\u003c/li\u003e\n\u003cli\u003eGalv\u0026atilde;o RM, Li M, Kothadia SM, Haskel JD, Decker PV, Buskirk EKV, et al. Photoactivated phytochromes interact with HEMERA and promote its accumulation to establish photomorphogenesis in Arabidopsis. Genes Dev. 2012;26:1851\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eCasal JJ, Balasubramanian S. Thermomorphogenesis. Annu Rev Plant Biol. 2019;70:1\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eForeman J, Johansson H, Hornitschek P, Josse E, Fankhauser C, Halliday KJ. Light receptor action is critical for maintaining plant biomass at warm ambient temperatures. Plant J. 2011;65:441\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eGray WM, Ostin A, Sandberg G, Romano CP, Estelle M. High temperature promotes auxin-mediated hypocotyl elongation in Arabidopsis. PNAS. 1998;95:7197\u0026ndash;202.\u003c/li\u003e\n\u003cli\u003eKumar SV, Lucyshyn D, Jaeger KE, Al\u0026oacute;s E, Alvey E, Harberd NP, et al. PHYTOCHROME INTERACTING FACTOR 4 controls the thermosensory activation of flowering. Nature. 2012;484:242\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eSun J, Qi L, Li Y, Chu J, Li C. PIF4-mediated activation of \u003cem\u003eYUCCA8\u003c/em\u003e expression integrates temperature into the auxin pathway in regulating Arabidopsis hypocotyl growth. PLoS Genetics. 2012;8:e1002594\u0026ndash;e1002594.\u003c/li\u003e\n\u003cli\u003eResentini F, Orozco-Arroyo G, Cucinotta M, Mendes MA. The impact of heat stress in plant reproduction. Front Plant Sci. 2023;14:1271644.\u003c/li\u003e\n\u003cli\u003eJiang Y, Lahlali R, Karunakaran C, Warkentin TD, Davis AR, Bueckert RA. Pollen, ovules and pollination in pea: success, failure and resilience in heat. Plant, Cell \u0026amp; Environment. 2019;42:354\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eDas S, Krishnan P, Nayak M, Ramakrishnan B. High temperature stress effects on pollens of rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) genotypes. Environmental and Experimental Botany. 2014;101:36\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eDevasirvatham V, Gaur PM, Mallikarjuna N, Raju TN, Trethowan RM, Tan DKY. Reproductive biology of chickpea response to heat stress in the field is associated with the performance in controlled environments. Field Crops Research. 2013;142:9\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eTalukder ASMHM, McDonald GK, Gill GS. Effect of short-term heat stress prior to flowering and early grain set on the grain yield of wheat. Field Crops Research. 2014;160:54\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eLin C-J, Li C-Y, Lin S-K, Yang F-H, Huang J-J, Liu Y-H, et al. Influence of high temperature during grain filling on the accumulation of storage proteins and grain quality in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.). Journal of agricultural and food chemistry. 2010;58:10545\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eJumrani K, Bhatia VS. Impact of elevated temperatures on growth and yield of chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). Field Crops Research. 2014;164:90\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eM\u0026aacute;cov\u0026aacute; K, Prabhullachandran U, \u0026Scaron;tefkov\u0026aacute; M, Spyroglou I, Pěnč\u0026iacute;k A, Endlov\u0026aacute; L, et al. Long-term high-temperature stress impacts on embryo and seed development in \u003cem\u003eBrassica napus\u003c/em\u003e. Front Plant Sci. 2022;13:844292.\u003c/li\u003e\n\u003cli\u003eElferjani R, Soolanayakanahally R. Canola responses to drought, heat, and combined stress: shared and specific effects on carbon assimilation, seed yield, and oil composition. Frontiers in Plant Science. 2018;9:1224.\u003c/li\u003e\n\u003cli\u003eJedličkov\u0026aacute; V, Hejret V, Demko M, Jedlička P, \u0026Scaron;tefkov\u0026aacute; M, Robert HS. Transcriptome analysis of thermomorphogenesis in ovules and during early seed development in \u003cem\u003eBrassica napus\u003c/em\u003e. BMC Genomics. 2023;24:236.\u003c/li\u003e\n\u003cli\u003eCanales J, Verdejo JF, Calderini DF. Transcriptome and physiological analysis of rapeseed tolerance to post-flowering temperature increase. Int J Mol Sci. 2023;24:15593.\u003c/li\u003e\n\u003cli\u003eScholl RL, May ST, Ware DH. Seed and molecular resources for Arabidopsis. Plant Physiol. 2000;124:1477\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eLeivar P, Monte E, Al-Sady B, Carle C, Storer A, Alonso JM, et al. The Arabidopsis PHYTOCHROME-INTERACTING FACTOR PIF7, together with PIF3 and PIF4, regulates responses to prolonged red light by modulating PhyB levels. Plant cell. 2008;20:337\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eOh S, Warnasooriya SN, Montgomery BL. Downstream effectors of light- and phytochrome-dependent regulation of hypocotyl elongation in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Plant Mol Biol. 2013;81:627\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eRobert HS, Grones P, Stepanova AN, Robles LM, Lokerse AS, Alonso JM, et al. Local auxin sources orient the apical-basal axis in Arabidopsis embryos. Curr Biol. 2013;23:2506\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eStepanova AN, Robertson-Hoyt J, Yun JJ, Benavente LM, Xie D-Y, Dolezal K, et al. TAA1-mediated auxin biosynthesis is essential for hormone crosstalk and plant development. Cell. 2008;133:177\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eOh E, Zhu J-Y, Wang Z-Y. Interaction between BZR1 and PIF4 integrates brassinosteroid and environmental responses. Nat Cell Biol. 2012;14:802\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eAwlia M, Nigro A, Fajkus J, Schmoeckel SM, Negr\u0026atilde;o S, Santelia D, et al. High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Front plant Sci. 2016;7:1414.\u003c/li\u003e\n\u003cli\u003eAlexander MP. Differential staining of aborted and nonaborted pollen. Stain technology. 1969;44:117\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eAttuluri VPS, L\u0026oacute;pez JFS, Maier L, Paruch K, Robert HS. Comparing the efficiency of six clearing methods in developing seeds of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Plant Reprod. 2022;35:279\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003ePostma M, Goedhart J. PlotsOfData\u0026mdash;A web app for visualizing data together with their summaries. PLoS Biol. 2019;17:e3000202.\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.\u003c/li\u003e\n\u003cli\u003eKinsella RJ, K\u0026auml;h\u0026auml;ri A, Haider S, Zamora J, Proctor G, Spudich G, et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database. 2011;2011:bar030.\u003c/li\u003e\n\u003cli\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. 2021;2:100141.\u003c/li\u003e\n\u003cli\u003eWickham H. ggplot2, Elegant Graphics for Data Analysis. R. 2016. https://doi.org/10.1007/978-3-319-24277-4.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Garc\u0026iacute;a MP, Conesa CM, Lozano-Enguita A, Baca-Gonz\u0026aacute;lez V, Simancas B, Navarro-Neila S, et al. Temperature changes in the root ecosystem affect plant functionality. Plant Commun. 2023;4:100514.\u003c/li\u003e\n\u003cli\u003eOng P, Jian J, Li X, Yin J, Ma G. Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration. Spectrochim acta Part A, Mol Biomol Spectrosc. 2023;305:123477.\u003c/li\u003e\n\u003cli\u003eLiu L, Zareef M, Wang Z, Li H, Chen Q, Ouyang Q. Monitoring chlorophyll changes during Tencha processing using portable near-infrared spectroscopy. Food Chem. 2023;412:135505.\u003c/li\u003e\n\u003cli\u003eLi C, Li H, Li J, Lei Y, Li C, Manevski K, et al. Using NDVI percentiles to monitor real-time crop growth. Comput Electron Agric. 2019;162:357\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003ePayares LKA, Tarquis AM, Peralo RH, Cano J, C\u0026aacute;mara J, Nowack J, et al. Multispectral and thermal sensors onboard UAVs for heterogeneity in merlot vineyard detection: contribution to zoning maps. Remote Sens. 2023;15:4024.\u003c/li\u003e\n\u003cli\u003ePe\u0026ntilde;uelas J, Filella I, Gamon JA. Assessment of photosynthetic radiation‐use efficiency with spectral reflectance. N Phytol. 1995;131:291\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eHaboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ. 2004;90:337\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eMurchie EH, Lawson T. Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. J Exp Bot. 2013;64:3983\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eRuban AV. Evolution under the sun: optimizing light harvesting in photosynthesis. J Exp Bot. 2015;66:7\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eYang X, Dong G, Palaniappan K, Mi G, Baskin TI. Temperature‐compensated cell production rate and elongation zone length in the root of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Plant, Cell Environ. 2017;40:264\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eXie Y, Liu Y, Wang H, Ma X, Wang B, Wu G, et al. PHYTOCHROME-INTERACTING FACTORs directly suppress MIR156 expression to enhance shade-avoidance syndrome in Arabidopsis. Nat Commun. 2017;8:348.\u003c/li\u003e\n\u003cli\u003eSang Q, Fan L, Liu T, Qiu Y, Du J, Mo B, et al. MicroRNA156 conditions auxin sensitivity to enable growth plasticity in response to environmental changes in Arabidopsis. Nat Commun. 2023;14:1449.\u003c/li\u003e\n\u003cli\u003eTakeuchi H, Higashiyama T. A species-specific cluster of defensin-like genes encodes diffusible pollen tube attractants in Arabidopsis. PLoS Biol. 2012;10:e1001449.\u003c/li\u003e\n\u003cli\u003eCosta LM, Marshall E, Tesfaye M, Silverstein KAT, Mori M, Umetsu Y, et al. Central cell-derived peptides regulate early embryo patterning in flowering plants. Science. 2014;344:168\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eRobert HS, Park C, Guti\u0026egrave;rrez CL, W\u0026oacute;jcikowska B, Pěnč\u0026iacute;k A, Nov\u0026aacute;k O, et al. Maternal auxin supply contributes to early embryo patterning in Arabidopsis. Nat Plants. 2018;4:548\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eKumar SV, Lucyshyn D, Jaeger KE, Al\u0026oacute;s E, Alvey E, Harberd NP, et al. Transcription factor PIF4 controls the thermosensory activation of flowering. Nature. 2012;484:242\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eSong J, Liu Q, Hu B, Wu W. Photoreceptor PhyB involved in Arabidopsis temperature perception and heat-tolerance formation. International Journal of Molecular Sciences. 2017;18:1194.\u003c/li\u003e\n\u003cli\u003eLiu J, Liu Y, Wang S, Cui Y, Yan D. Heat stress reduces root meristem size via induction of plasmodesmal callose accumulation inhibiting phloem unloading in Arabidopsis. Int J Mol Sci. 2022;23:2063.\u003c/li\u003e\n\u003cli\u003eWang R, Zhang Y, Kieffer MM, Yu H, Kepinski S, Estelle M. HSP90 regulates temperature-dependent seedling growth in Arabidopsis by stabilizing the auxin co-receptor F-box protein TIR1. Nature communications. 2016;7:10269\u0026ndash;10269.\u003c/li\u003e\n\u003cli\u003eParveen S, Rahman A. Actin isovariant ACT7 modulates root thermomorphogenesis by altering intracellular auxin homeostasis. Int J Mol Sci. 2021;22:7749.\u003c/li\u003e\n\u003cli\u003eGaillochet C, Burko Y, Platre MP, Zhang L, Simura J, Willige BC, et al. HY5 and phytochrome activity modulate shoot-to-root coordination during thermomorphogenesis in Arabidopsis. Development. 2020;147:dev192625.\u003c/li\u003e\n\u003cli\u003eLee S, Wang W, Huq E. Spatial regulation of thermomorphogenesis by HY5 and PIF4 in Arabidopsis. Nat Commun. 2021;12:3656.\u003c/li\u003e\n\u003cli\u003eLi Q-Q, Zhang Z, Zhang C-X, Wang Y-L, Liu C-B, Wu J-C, et al. PHYTOCHROME-INTERACTING FACTORs orchestrate hypocotyl adventitious root initiation in Arabidopsis. Development. 2022;149:dev200362.\u003c/li\u003e\n\u003cli\u003eFranklin KA. Light and temperature signal crosstalk in plant development. Curr Opin Plant Biol. 2009;12:63\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eJagadish SVK, Bahuguna RN, Djanaguiraman M, Gamuyao R, Prasad PVV, Craufurd PQ. Implications of high temperature and elevated CO2 on flowering time in plants. Front Plant Sci. 2016;7:913.\u003c/li\u003e\n\u003cli\u003eCao Z, Yao X, Liu H, Liu B, Cheng T, Tian Y, et al. Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat. Agric For Meteorol. 2019;265:121\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eBarnab\u0026aacute;s B, J\u0026auml;ger K, Feh\u0026eacute;r A. The effect of drought and heat stress on reproductive processes in cereals. Plant, Cell \u0026amp; Environment. 2008;31:11\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eZhang S-S, Yang H, Ding L, Song Z-T, Ma H, Chang F, et al. Tissue-specific transcriptomics reveals an important role of the unfolded protein response in maintaining fertility upon heat stress in Arabidopsis. The Plant cell. 2017;29:1007\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eLohani N, Singh MB, Bhalla PL. High temperature susceptibility of sexual reproduction in crop plants. Journal of Experimental Botany. 2020;71:555\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eStorme ND, Geelen D. High temperatures alter cross-over distribution and induce male meiotic restitution in Arabidopsis thaliana. Commun Biol. 2020;3:187.\u003c/li\u003e\n\u003cli\u003eZhang M, Zhang X, Wang R, Zang R, Guo L, Qi T, et al. Heat-responsive microRNAs participate in regulating the pollen fertility stability of CMS-D2 restorer line under high-temperature stress. Biol Res. 2023;56:58.\u003c/li\u003e\n\u003cli\u003eWu M-F, Tian Q, Reed JW. Arabidopsis microRNA167 controls patterns of \u003cem\u003eARF6\u003c/em\u003e and \u003cem\u003eARF8\u003c/em\u003e expression, and regulates both female and male reproduction. Development. 2006;133:4211\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eOshino T, Miura S, Kikuchi S, Hamada K, Yano K, Watanabe M, et al. Auxin depletion in barley plants under high‐temperature conditions represses DNA proliferation in organelles and nuclei via transcriptional alterations. Plant, Cell \u0026amp; Environment. 2011;34:284\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eSakata T, Oshino T, Miura S, Tomabechi M, Tsunaga Y, Higashitani N, et al. Auxins reverse plant male sterility caused by high temperatures. PNAS. 2010;107:8569\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eHigashitani A. High temperature injury and auxin biosynthesis in microsporogenesis. Frontiers in Plant Science. 2013;4:47.\u003c/li\u003e\n\u003cli\u003eIrenaeus T, Mitra SK. Understanding the pollen and ovule characters and fruit set of fruit crops in relation to temperature and genotype \u0026ndash; a review. J Appl Bot Food Qual. 2014;87.\u003c/li\u003e\n\u003cli\u003eHuang Z, Footitt S, Finch-Savage WE. The effect of temperature on reproduction in the summer and winter annual Arabidopsis thaliana ecotypes Bur and Cvi. Ann Bot. 2014;113:921\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eWhittle CA, Otto SP, Johnston MO, Krochko JE. Adaptive epigenetic memory of ancestral temperature regime in Arabidopsis thalianaThis paper is one of a selection of papers published in a Special Issue from the National Research Council of Canada Plant Biotechnology Institute. Botany. 2009;87:650\u0026ndash;7. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Overall root growth rates for different genotypes at nAT and hAT\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth Rate (cm/day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol-0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003ea, c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol-0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003ea, c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003eb, c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.791044776119403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01492537313433%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.35820895522388%\" valign=\"top\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.83582089552239%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe mean growth rate of 13 plants per genotype under nAT and hAT was determined using root growth regression lines. A two-way ANOVA assessed differences among genotypes and conditions. Post-hoc Tukey\u0026apos;s test identified non-significant differences between genotypes with the same letter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Number of primary inflorescence branches for different genotypes at nAT and hAT\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"726\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage number of branches\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol_0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003eb, c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003eb, d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif7-1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif7-2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif3 pif7\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ea, e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol_0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif7-1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif7-2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif3 pif7\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52892561983471%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.05785123966942%\" valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.396694214876034%\" valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.31404958677686%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003en = 10 plants per genotype were analyzed to assess differences among genotypes and between nAT and hAT using a two-way ANOVA. Post-hoc Tukey\u0026apos;s test identified non-significant differences between genotypes with the same letter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Anther abortion rate for different genotypes in nAT and hAT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"692\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.751445086705202%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"43.20809248554913%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7 DAFD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.040462427745666%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9 DAFD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Defects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Defects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol-0 nAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol-0 hAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e7.81 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003enAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;hAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e11.43 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e34.82 *** ### ^^^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;nAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;hAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e23.40 *** #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e29.27 *** ##\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;nAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;hAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;nAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.706051873198847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;hAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.951008645533141%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.798270893371757%\" valign=\"top\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.645533141210375%\" valign=\"top\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.832853025936599%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.38328530259366%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37463976945245%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.916426512968299%\" valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39193083573487%\" valign=\"top\"\u003e\n \u003cp\u003e1.44 #\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe anthers were assessed at 7\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand 9 days after flowering development (DAFD). Fisher\u0026apos;s Exact Test analyzed comparisons, with anthers from each genotype and condition examined across three replicates for result reliability. Significance indicators are: * (temperature), # (genotype), and ^ (time). P-values are represented as: * # (0.05-0.01), ## (0.009-0.0001), and *** ### ^^^ (0.00009-0.000000). Details are provided in Additional File 2 \u0026ndash; Table S3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e \u003cstrong\u003eOvule defective phenotypes for the different genotypes at nAT and hAT\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% of defects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003eCol-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003eCol-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e62.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e*** ###\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e##\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e84.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e*** ###\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.191419141914192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.442244224422442%\" valign=\"top\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.871287128712872%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.920792079207921%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"top\"\u003e\n \u003cp\u003e*** #\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe data per phenotype categories are detailed in Additional File 2 \u0026ndash; Table S4. The statistical analysis of these comparisons utilized Fisher\u0026apos;s Exact Test. To ensure the reliability of our results, ovules from each genotype and condition were examined across three replicates. The significance levels in the results are denoted as follows: * significant temperature effect. # significant genotype effect. The \u003cem\u003ep\u003c/em\u003e-value ranges are specified as # for \u003cem\u003ep\u003c/em\u003e-values between 0.05 and 0.01 and *** for \u003cem\u003ep\u003c/em\u003e-values ranging from 0.00009 to 0.000000.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Embryonic defects in seeds grown at nAT and hAT\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"544\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Defects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol-0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCol-0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e40.77 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ephyb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e30.85 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35S::PIF4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e21.95 *** #\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epif4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e44.23 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003enAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.323529411764707%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003epifq\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003ehAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257352941176471%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.683823529411764%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.220588235294116%\"\u003e\n \u003cp\u003e41.56 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFisher\u0026apos;s Exact Test was used for statistical analysis of these comparisons. To ensure result reliability, anthers from each genotype and condition were examined across three replicates. Significance indicators include * for a significant temperature effect and # for a significant genotype effect. P-values are denoted as # (0.05-0.01) and *** (0.00009-0.000000).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Arabidopsis, Automatic Phenotyping, PIF4, pistils, PhyB, pollen tube guidance, seeds, thermomorphogenesis","lastPublishedDoi":"10.21203/rs.3.rs-4223427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4223427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe increasing ambient temperature significantly impacts plant growth, development, and reproduction. Uncovering the temperature-regulating mechanisms in plants is of high importance, not only for boosting our plant biology knowledge but also for assisting plant breeders in improving plant resilience to these stress conditions. Numerous studies on the molecular mechanisms by which plants regulate temperature responses revealed that plants employ distinct transcription factors to regulate thermomorphogenesis specific to each tissue type. A significant discovery in this field was the identification of PHYTOCHROME-INTERACTING FACTORs (PIFs) as key regulators of thermomorphogenesis during vegetative growth. PIF4, a regulator of auxin-mediated signaling pathways, is crucial in controlling high-temperature responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we screened the temperature responses of the wild type and several PhyB-PIF4 pathway \u003cem\u003eArabidopsis \u003c/em\u003emutant lines in combined and integrative phenotyping platforms for root in soil, shoot, inflorescence, and seed. We demonstrated that high ambient temperature differentially impacts vegetative and reproductive organs through this pathway. Suppression of the PhyB-PIF4 components mimics the response to a high ambient temperature in wild-type plants. We also identified correlative responses to high ambient temperature between shoot and root tissues. This integrative and automated phenotyping was complemented by monitoring the changes in transcript levels in reproductive organs. Transcriptomic profiling of the pistils from plants grown under high ambient temperature identified key elements that may provide clues to the molecular mechanisms behind temperature-induced reduced fertilization rate, such as a downregulation of auxin metabolism, upregulation of genes involved auxin signalling, \u003cem\u003emiRNA156\u003c/em\u003eand \u003cem\u003emiRN160\u003c/em\u003e pathways, pollen tube attractants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThermomorphogenesis is uniquely controlled in the different plant tissues at different developmental stages. We have identified key elements that may help to determine the response to high ambient temperatures during reproduction processes.\u003c/p\u003e","manuscriptTitle":"Integrative phenotyping approaches to unmask the Phyb-PIF4 pathway in Arabidopsis thaliana reproductive organs at high ambient temperatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-15 07:57:00","doi":"10.21203/rs.3.rs-4223427/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-31T19:22:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-30T05:27:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175477370946351374678850963687091647338","date":"2024-05-20T01:14:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-27T18:49:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1815a23a-ea75-41a1-a72d-b2b2663342d6","date":"2024-04-23T09:09:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7daf347d-1df1-4ef3-8044-50bdc918c40e","date":"2024-04-18T22:43:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-16T15:26:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-10T17:56:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-10T17:56:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-04-05T13:50:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a01b532-c3f7-4256-bd0a-d7d666824b75","owner":[],"postedDate":"April 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:09:10+00:00","versionOfRecord":{"articleIdentity":"rs-4223427","link":"https://doi.org/10.1186/s12870-024-05394-w","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-07-29 15:57:41","publishedOnDateReadable":"July 29th, 2024"},"versionCreatedAt":"2024-04-15 07:57:00","video":"","vorDoi":"10.1186/s12870-024-05394-w","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05394-w","workflowStages":[]},"version":"v1","identity":"rs-4223427","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4223427","identity":"rs-4223427","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}