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\ No newline at end of file
diff --git a/README.md b/README.md
index 68cfb0c2d14d8f8b49a77b7783a8d79e31ab9708..ae4b3108a608c3c939c7bee0ab978c0e2e250e7a 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,28 @@
-# PPD-H1Improves Stress Resistance and Energy Metabolism to Boost Spike Fertility under High Ambient Temperatures
+# PPD-H1 Improves Stress Resistance and Energy Metabolism to Boost Spike Fertility under High Ambient Temperatures
 
-### Description
+### Original Publication
 
-High ambient temperature (HT) impairs reproductive development and grain yield in temperate crops. To ensure reproductive success under HT, plants must maintain developmental stability. However, the mechanisms integrating plant development and temperature resilience are largely unknown. Here, we demonstrate that PHOTOPERIOD 1 (PPD-H1), homologous to PSEUDO RESPONSE REGULATOR genes of the Arabidopsis circadian clock, controls developmental stability in response to HT in barley. We analyzed HT responses in independent introgression lines with either the ancestral wild-type Ppd-H1 allele or the natural ppd-h1 variant, selected in spring varieties to delay flowering and enhance yield under favourable conditions. HT delayed inflorescence development and reduced grain number in ppd-h1 mutant lines, while the wild-type Ppd-H1 genotypes accelerated reproductive development and showed a stable grain set under HT. Using a CRISPR/Cas9-induced ppd-h1 mutant, we confirmed that the CCT domain of Ppd-H1 controls developmental stability, but not clock gene expression. Transcriptome and phytohormone analyses in developing leaves and inflorescences revealed increased stress gene expression and abscisic acid levels in the leaf and inflorescence of the natural and induced mutant ppd-h1 lines. Furthermore, the mutant ppd-h1 lines downregulated photosynthesis-and energy metabolism-related genes, and reduced auxin and cytokinin levels in the inflorescence, which impaired anther and pollen development. By contrast, in the wild-type Ppd-H1 plants, the transcriptome and phytohormone levels and anther and pollen development remained stable under HT. Our findings suggest that Ppd-H1 enhances stress resistance and energy metabolism, thereby stabilizing reproductive development, floret fertility and grain set under HT.
+PPD-H1 Improves Stress Resistance and Energy Metabolism to Boost Spike Fertility under High Ambient Temperatures
+Tianyu Lan, Agatha Walla, Kumsal Ecem Çolpan Karışan, Gabriele Buchmann, Vera Wewer, Sabine Metzger, Isaia Vardanega, Einar Baldvin Haraldsson, Gesa Helmsorig, Venkatasubbu Thirulogachandar, Rüdiger Simon, Maria von Korff
+bioRxiv 2024.11.04.621966; doi: https://doi.org/10.1101/2024.11.04.621966
 
-## Table of Contents 
+### Abstract
 
+High ambient temperature (HT) impairs reproductive development and grain yield in temperate crops. To ensure reproductive success under HT, plants must maintain developmental stability. However, the mechanisms integrating plant development and temperature resilience are largely unknown. Here, we demonstrate that *PHOTOPERIOD 1* (*PPD-H1*), homologous to *PSEUDO RESPONSE REGULATOR* genes of the Arabidopsis circadian clock, controls developmental stability in response to HT in barley. We analyzed HT responses in independent introgression lines with either the ancestral wild-type *Ppd-H1* allele or the natural *ppd-h1* variant, selected in spring varieties to delay flowering and enhance yield under favourable conditions. HT delayed inflorescence development and reduced grain number in *ppd-h1* mutant lines, while the wild-type *Ppd-H1* genotypes accelerated reproductive development and showed a stable grain set under HT. Using a CRISPR/Cas9-induced *ppd-h1* mutant, we confirmed that the CCT domain of *Ppd-H1* controls developmental stability, but not clock gene expression. Transcriptome and phytohormone analyses in developing leaves and inflorescences revealed increased stress gene expression and abscisic acid levels in the leaf and inflorescence of the natural and induced mutant *ppd-h1* lines. Furthermore, the mutant ppd-h1 lines downregulated photosynthesis-and energy metabolism-related genes, and reduced auxin and cytokinin levels in the inflorescence, which impaired anther and pollen development. By contrast, in the wild-type *Ppd-H1* plants, the transcriptome and phytohormone levels and anther and pollen development remained stable under HT. Our findings suggest that *Ppd-H1* enhances stress resistance and energy metabolism, thereby stabilizing reproductive development, floret fertility and grain set under HT.
 
- 1. Studies 
+### License
 
-     - [plant-samples](#study--plant-samples)
+The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
 
- 2. Assays 
-
-     - [Salmon_quantification](#assay--salmon_quantification)
-     - [RNAseq_raw_data](#assay--rnaseq_raw_data)
-     - [FASTQC](#assay--fastqc)
-
- ## Relationships between Assays and Studies 
+ ### Relationships between Assays and Studies 
  
 ```mermaid
----
-title: Effects_of_Ppd-H1_and_High_Ambient_Temperatures_in_Barley
----
-
 flowchart TD
     classDef investigationStyle fill:#6c7885,color:#2d3e50,fontWeight:bold;
     classDef studyStyle fill:#62d4c1,color:#2d3e50,fontWeight:bold;
     classDef assayStyle fill:#ffd34d,color:#2d3e50,fontWeight:bold;
     classDef processStyle fill:#D46275,color:#2d3e50;
-    id_0["PPD-H1Improves Stress Resistance and Energy Metabolism to Boost Spike Fertility under High Ambient Temperatures"]
+    id_0["PHOTOPERIOD 1 enhances stress tolerance and energy metabolism to promote spike fertility in barley under high ambient temperatures"]
     class id_0 investigationStyle;
     id_0-->STUDY_id_1
     class STUDY_id_1 studyStyle;
@@ -37,114 +30,33 @@ flowchart TD
         id_2[plant-characteristics]
         class id_2 processStyle;
     end
-    subgraph ASSAY_id_3[Assay: Salmon_quantification]
-        id_4[salmon]
+    subgraph ASSAY_id_3[Assay: FASTQC]
+        id_4[FastQC]
         class id_4 processStyle;
     end
     class ASSAY_id_3 assayStyle;
-    subgraph ASSAY_id_5[Assay: RNAseq_analysis_of_MSA]
+    subgraph ASSAY_id_5[Assay: RNAseq_raw_data]
+        id_6[illumina]
+        class id_6 processStyle;
     end
     class ASSAY_id_5 assayStyle;
-    subgraph ASSAY_id_6[Assay: RNAseq_raw_data]
-        id_7[illumina]
-        class id_7 processStyle;
+    subgraph ASSAY_id_7[Assay: Salmon_quantification]
+        id_8[salmon]
+        class id_8 processStyle;
     end
-    class ASSAY_id_6 assayStyle;
-    subgraph ASSAY_id_8[Assay: RNAseq_analysis_of_leaf]
-    end
-    class ASSAY_id_8 assayStyle;
-    subgraph ASSAY_id_9[Assay: FASTQC]
-        id_4[salmon]
-        class id_4 processStyle;
+    class ASSAY_id_7 assayStyle;
+    subgraph ASSAY_id_9[Assay: ThreeDRNAseq]
+        id_10[TPM_leaf]
+        class id_10 processStyle;
+        id_11[CPM_leaf]
+        class id_11 processStyle;
     end
     class ASSAY_id_9 assayStyle;
-    id_2-->|188|id_7
-    id_7-->|382|id_4
-```
-
- 
-### Additional details
-| Meta Data | Description |
-| --------- | ----------- |
-| Submission Date  | tba |
-| Public Release Date | tba |
-| Study identifiers | plant-samples |
-| Study Count | 1 |
-| Assay identifiers | Salmon_quantification , RNAseq_analysis_of_MSA , RNAseq_raw_data , RNAseq_analysis_of_leaf , FASTQC |
-| Assay Count | 5 |
-
-## Contacts 
- 
-| Names | Email | Address | Affiliation | ORCID |
-| ----- | ----- | ------- | ----------- | ----- |
-| Tianyu  Lan | tianyu.lan@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany |  | 
- | Agatha  Walla | Walla@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences “SMART Plants for Tomorrow’s Needs”, 40225 Düsseldorf, Germany |  | 
- | Kumsal Ecem  Çolpan Karışan | kumsal.colpan@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences “SMART Plants for Tomorrow’s Needs”, 40225 Düsseldorf, Germany |  | 
- | Gabriele  Buchmann | gabriele.buchmann@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany |  | 
- | Vera  Wewer | vwewer@uni-koeln.de |  | CEPLAS Plant Metabolism and Metabolomics Facility, Institute for Plant Sciences, University of Cologne, 50674 Cologne, Germany; Cluster of Excellence on Plant Sciences “SMART Plants for Tomorrow’s Needs”, 40225 Düsseldorf, Germany |  | 
- | Sabine  Metzger | s.metzgar@uni-koeln.de |  | CEPLAS Plant Metabolism and Metabolomics Facility, Institute for Plant Sciences, University of Cologne, 50674 Cologne, Germany; Cluster of Excellence on Plant Sciences “SMART Plants for Tomorrow’s Needs”, 40225 Düsseldorf, Germany |  | 
- | Isaia  Vardanega | isaia.vardanega@hhu.de |  | Institute of Developmental Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany |  | 
- | Einar Baldvin  Haraldsson | einar.haraldsson@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany |  | 
- | Gesa  Helmsorig | gesa.helmsorig@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany |  | 
- | Venkatasubbu  Thirulogachandar | thirulogachandar.venkatasubbu@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany |  | 
- | Rüdiger  Simon | ruediger.simon@hhu.de |  | Institute of Developmental Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences “SMART Plants for Tomorrow’s Needs”, 40225 Düsseldorf, Germany |  | 
- | Maria  von Korff | maria.korff.schmissing@hhu.de |  | Institute of Plant Genetics, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences “SMART Plants for Tomorrow’s Needs”, 40225 Düsseldorf, Germany |  |
+    id_2-->|188|id_6
+    id_4-->|1|id_4
+    id_6-->|382|id_4
+    id_6-->|382|id_8
+    id_8-->|84|id_10
+    id_8-->|84|id_11
 
-
-## Study: _plant-samples_
-### Description
- tba 
-### Additional details
-| Meta Data | Description |
-| --------- | ----------- |
-| Table Count | 1 |
-| Table Names | plant-characteristics |
-| Sample Count | 188 |
-| Data File Count | 0 |
-| Associated assays | RNAseq_raw_data |
-| Biological replicates | 0 |
-### Annotation headers
-
-**Characteristics**: [organism](https://bioregistry.io/OBI:0100026),[genotype](https://bioregistry.io/EFO:0000513),[biological replicate](https://bioregistry.io/EFO:0002091),[plant structure](https://www.ebi.ac.uk/ols4/ontologies/po/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FPO_0009011)
-
-**Factors**: `temperature treatment`,`waddington stage`
-
-## Assay: _Salmon_quantification_
-### Additional details
-| Meta Data | Description |
-| --------- | ----------- |
-| Table Count | 1 |
-| Table Names | NewTable0 |
-| Sample Count | 388 |
-| Data File Count | 388 |
-| Associated studies |  |
-### Annotation headers
-
-## Assay: _RNAseq_raw_data_
-### Additional details
-| Meta Data | Description |
-| --------- | ----------- |
-| Measurement Type | RNA-Seq |
-| Technology Type | Illumina NovaSeq 6000 |
-| Table Count | 1 |
-| Table Names | illumina |
-| Sample Count | 388 |
-| Data File Count | 388 |
-| Associated studies | plant-samples |
-### Annotation headers
-
-**Parameters**: [library strategy](http://purl.org/nfdi4plants/ontology/dpbo/DPBO_0000035),[library preparation kit](http://purl.obolibrary.org/obo/GENEPIO_0000085),[library preparation kit version](http://purl.obolibrary.org/obo/GENEPIO_0000149),[adapter sequence](http://purl.obolibrary.org/obo/GENEPIO_0000083),[next generation sequencing instrument model](http://purl.org/nfdi4plants/ontology/dpbo/DPBO_0000040),[base-calling software](http://purl.org/nfdi4plants/ontology/dpbo/DPBO_0000017),[base-calling software version](http://purl.org/nfdi4plants/ontology/dpbo/DPBO_0000018),[base-calling software parameters](http://purl.org/nfdi4plants/ontology/dpbo/DPBO_0000019),[Raw data file format](http://purl.org/nfdi4plants/ontology/dpbo/DPBO_0000021)
-
-## Assay: _FASTQC_
-### Additional details
-| Meta Data | Description |
-| --------- | ----------- |
-| Technology Type | FASTQC |
-| Table Count | 1 |
-| Table Names | NewTable0 |
-| Sample Count | 840 |
-| Data File Count | 0 |
-| Associated studies |  |
-### Annotation headers
-
-**Characteristics**: [Format](https://bioregistry.io/NCIT:C42761)
+```
diff --git a/assays/FASTQC/isa.assay.xlsx b/assays/FASTQC/isa.assay.xlsx
index 8c6cbe803c4d2563dcecab7251a97f2b33840cd8..de65ce6e108d11967850d0d08186429572f2515a 100644
Binary files a/assays/FASTQC/isa.assay.xlsx and b/assays/FASTQC/isa.assay.xlsx differ
diff --git a/assays/RNAseq_analysis_of_MSA/README.md b/assays/RNAseq_analysis_of_MSA/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/assays/RNAseq_analysis_of_MSA/isa.assay.xlsx b/assays/RNAseq_analysis_of_MSA/isa.assay.xlsx
deleted file mode 100644
index 7474e358941e41affd97caa5b331e4c69eac29d0..0000000000000000000000000000000000000000
Binary files a/assays/RNAseq_analysis_of_MSA/isa.assay.xlsx and /dev/null differ
diff --git a/assays/RNAseq_analysis_of_leaf/dataset/.gitkeep b/assays/RNAseq_analysis_of_leaf/dataset/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/assays/RNAseq_analysis_of_leaf/isa.assay.xlsx b/assays/RNAseq_analysis_of_leaf/isa.assay.xlsx
deleted file mode 100644
index a6cb9802593d55d9595700ef0e69187deb808ced..0000000000000000000000000000000000000000
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diff --git a/assays/RNAseq_raw_data/isa.assay.xlsx b/assays/RNAseq_raw_data/isa.assay.xlsx
index 7f7a789473b725f8241cd32cde673e16f6cdf0ca..41d9bda5a17b04210b5f329e11ca6d52bbbf73b7 100644
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diff --git a/assays/RNAseq_raw_data/protocols/WholeTranscriptomeSequencing.md b/assays/RNAseq_raw_data/protocols/WholeTranscriptomeSequencing.md
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index 0000000000000000000000000000000000000000..cc597f70c6e16ce92aeadd57079163a9c71796b7
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+++ b/assays/RNAseq_raw_data/protocols/WholeTranscriptomeSequencing.md
@@ -0,0 +1,5 @@
+## Whole Transcriptome Sequencing
+
+To detect changes in the global leaf and MSA transcriptomes in response to HT, we harvested at ZT14 the leaves and developing MSAs of GP and GP-fast grown under LD either under CT or HT at four developmental stages, W1.0, W2.0, W3.5, and W6.0 for RNA-sequencing. Each replicate of leaf samples was collected by pooling the latest fully elongated leaf on the main culm from three plants. Each replicate of MSA samples was collected by pooling ca. 30, 20, 15, and 10 MSAs from plants at stages W1.0, W2.0, W3.5, and W6.0, respectively. All MSA samples were collected under a stereo microscope in the environment where the plant grew. A total of three to four biological replicates of MSA and leaf samples were used for RNA-sequencing.
+
+Total RNA was extracted from MSA and leaf samples using the RNeasy Plant Mini Kit (Qiagen, Germany) with beta-mercaptoethanol added, following the manufacturer’s instructions, and followed by the digestion with DNase I (Qiagen, Germany). RNA samples passing a cutoff of RNA Integrity Number (RIN) ≥ 6 were used for mRNA library preparation with poly(A)-enrichment. Paired-end 150 bp sequencing was performed on NovaSeq 6000 sequencing platform, and at least 5G of clean reads data per sample were generated by Novogene Co., Ltd (UK). All samples passed the assessment of the adaptor and GC contents by FastQC (Andrews 2010).
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diff --git a/assays/Salmon_quantification/isa.assay.xlsx b/assays/Salmon_quantification/isa.assay.xlsx
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diff --git a/assays/Salmon_quantification/protocols/TranscriptQuantification.md b/assays/Salmon_quantification/protocols/TranscriptQuantification.md
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+++ b/assays/Salmon_quantification/protocols/TranscriptQuantification.md
@@ -0,0 +1,3 @@
+## Transcript Quantification with Salmon
+
+To quantify transcripts, all cleaned reads were mapped to the BaRT1 reference (Rapazote-Flores et al., 2019) using Salmon (v. 0.14.1) (Patro et al., 2017), and the mapping rates were estimated with an average of ∼93%.
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diff --git a/assays/RNAseq_analysis_of_leaf/README.md b/assays/ThreeDRNAseq/README.md
similarity index 98%
rename from assays/RNAseq_analysis_of_leaf/README.md
rename to assays/ThreeDRNAseq/README.md
index cbf762352c6bbe819864f42a1a0ec2cb2111535e..4aa060d925a359e3a43f0d5c70eee252684cf17a 100644
--- a/assays/RNAseq_analysis_of_leaf/README.md
+++ b/assays/ThreeDRNAseq/README.md
@@ -1,6 +1,6 @@
-RNAseq analysis was conducted on the leaf and main shoot apex (MSA) samples separately.
-
-The tables of Count and Transcript per Million (TPM) were obtained using ThreeDRNAseq R package.
-The tables of differentially expressed genes were obtained using EdgeR R package.
-Gene Ontology analysis was performed using the Triticeae-Gene Tribe database (http://wheat.cau.edu.cn/TGT/m15/?navbar=GOEnrichment).
-WCGNA analysis was performed using the WGCNA R package.
+RNAseq analysis was conducted on the leaf and main shoot apex (MSA) samples separately.
+
+The tables of Count and Transcript per Million (TPM) were obtained using ThreeDRNAseq R package.
+The tables of differentially expressed genes were obtained using EdgeR R package.
+Gene Ontology analysis was performed using the Triticeae-Gene Tribe database (http://wheat.cau.edu.cn/TGT/m15/?navbar=GOEnrichment).
+WCGNA analysis was performed using the WGCNA R package.
diff --git a/assays/RNAseq_analysis_of_MSA/dataset/.gitkeep b/assays/ThreeDRNAseq/dataset/.gitkeep
similarity index 100%
rename from assays/RNAseq_analysis_of_MSA/dataset/.gitkeep
rename to assays/ThreeDRNAseq/dataset/.gitkeep
diff --git a/assays/RNAseq_analysis_of_leaf/dataset/Count_leaf/counts_gene_leaf.csv b/assays/ThreeDRNAseq/dataset/Count_leaf/counts_gene_leaf.csv
similarity index 100%
rename from assays/RNAseq_analysis_of_leaf/dataset/Count_leaf/counts_gene_leaf.csv
rename to assays/ThreeDRNAseq/dataset/Count_leaf/counts_gene_leaf.csv
diff --git a/assays/RNAseq_analysis_of_leaf/dataset/Count_leaf/counts_transcript_leaf.csv b/assays/ThreeDRNAseq/dataset/Count_leaf/counts_transcript_leaf.csv
similarity index 100%
rename from assays/RNAseq_analysis_of_leaf/dataset/Count_leaf/counts_transcript_leaf.csv
rename to assays/ThreeDRNAseq/dataset/Count_leaf/counts_transcript_leaf.csv
diff --git a/assays/RNAseq_analysis_of_leaf/dataset/TPM_leaf/TPM_genes_leaf.csv b/assays/ThreeDRNAseq/dataset/TPM_leaf/TPM_genes_leaf.csv
similarity index 100%
rename from assays/RNAseq_analysis_of_leaf/dataset/TPM_leaf/TPM_genes_leaf.csv
rename to assays/ThreeDRNAseq/dataset/TPM_leaf/TPM_genes_leaf.csv
diff --git a/assays/RNAseq_analysis_of_leaf/dataset/TPM_leaf/TPM_transcripts_leaf.csv b/assays/ThreeDRNAseq/dataset/TPM_leaf/TPM_transcripts_leaf.csv
similarity index 100%
rename from assays/RNAseq_analysis_of_leaf/dataset/TPM_leaf/TPM_transcripts_leaf.csv
rename to assays/ThreeDRNAseq/dataset/TPM_leaf/TPM_transcripts_leaf.csv
diff --git a/assays/ThreeDRNAseq/isa.assay.xlsx b/assays/ThreeDRNAseq/isa.assay.xlsx
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diff --git a/assays/RNAseq_analysis_of_MSA/protocols/.gitkeep b/assays/ThreeDRNAseq/protocols/.gitkeep
similarity index 100%
rename from assays/RNAseq_analysis_of_MSA/protocols/.gitkeep
rename to assays/ThreeDRNAseq/protocols/.gitkeep
diff --git a/assays/ThreeDRNAseq/protocols/NormalizationWithThreeDRNAseq.md b/assays/ThreeDRNAseq/protocols/NormalizationWithThreeDRNAseq.md
new file mode 100644
index 0000000000000000000000000000000000000000..dec8f6102cd7da953729f530c2654ec481679d34
--- /dev/null
+++ b/assays/ThreeDRNAseq/protocols/NormalizationWithThreeDRNAseq.md
@@ -0,0 +1,3 @@
+## Normalization with ThreeDRNAseq R package
+
+We kept transcripts with a minimum of 1 CPM (counts per million) in at least three samples. Normalization of TPM (transcript per million) was conducted using the ‘ThreeDRNAseq’ R package (v2.0.1) (Guo et al., 2021). For TPM values, see Supplementary Dataset 2 and 3. As the samples were collected at different DAEs and RNA extraction was conducted in several batches, we removed the batch effect in the gene count by using the ThreeDRNAseq’ R package (v2.0.1) (Guo et al., 2021).
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diff --git a/studies/plant-samples/isa.study.xlsx b/studies/plant-samples/isa.study.xlsx
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diff --git a/studies/plant-samples/protocols/PlantMaterialsAndGrowthConditions.md b/studies/plant-samples/protocols/PlantMaterialsAndGrowthConditions.md
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+++ b/studies/plant-samples/protocols/PlantMaterialsAndGrowthConditions.md
@@ -0,0 +1,5 @@
+## Plant Materials and Growth Conditions
+
+In this study, we selected two genotypes carrying a wild-type *Ppd-H1* allele and three genotypes with mutant *ppd-h1* allele to study the interaction between *Ppd-H1* and HT on shoot growth and inflorescence development. Golden Promise (GP) and Scarlett are spring barley cultivars carrying a natural mutation in the CCT domain of *PPD-H1* (G>T at CDS position 1969), according to gene model HORVU.MOREX.r3.2HG0107710.1 (Supplementary Dataset 1), causing a Gly-to-Trp amino acid exchange, and resulting in a late flowering time under long-day (LD; 16 h/8 h, day/night) conditions. GP-fast and S42-IL107 are two near-isogenic lines (NILs) derived from GP and Scarlett, respectively, carrying wild-type Ppd-H1 alleles from winter barley Igri and wild barley (*Hordeum vulgare ssp. spontaneum*) accession ISR42-8 (Supplementary Dataset 1). These lines are early flowering under LD (Schmalenbach et al., 2011; Digel, Pankin and von Korff, 2015; Gol, Haraldsson and Von Korff, 2021).
+
+Plants were sown in a 96-well growing tray with ED73 soil (Einheitserde Werkverband e.V., Germany) mixed with 7% sand and 4 g/L Osmocote Exact Hi.End 3-4M (ICL Group Ltd.). Seeds were stratified for three days at 4 °C in the soil for even germination and then transferred to controlled growth chambers (PAR 300 µM/m2s, humidity 60%) with inductive LD conditions (16 h/8 h, day/night) set to control (CT; 20 °C/16 °C, day/night) or high ambient temperature (HT; 28 °C/24 °C, day/night).
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