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SEEDSTICK Is a Master Regulator of Development and Metabolism in Arabidopsis Seed Coat

ABSTRACT: Purpose: The goals of this study are to compare NGS-derived transcriptome profiling (RNA-seq) from the SEEDSTICK mutant in Arabidopsis with a wild type, to unveil the role of this transcription factor in seed development and decipher the impact of this factor in PAs metabolism Methods: Total RNA was extracted from two biological replicates from both wild-type and stk mutant inflorescences and siliques until 5 DAP with the Qiagen Kit according to the manufacturer's instructions. DNA contaminations were removed using the PROMEGA RQ1 RNase-Free DNase according to the manufacturer's instructions. RNA quality integrity was analyzed by electrophoresis gel and was validated on a Bioanalyzer 2100 (Aligent, Santa Clara, CA); RNA Integrity Number (RIN) values were greater than 7 for all the samples. In order to confirm that in stk mutant samples STK was not expressed, STK expression was checked by real time PCR with primer RT 780 (5’-TGCGATGCAGAAGTTGCGCTC-3’) and RT 781 (5’-AGTACGCGGCATTGATTTCTTG-3’). Sequencing libraries were prepared according to the manufacturer’s instructions by TruSeq RNA Sample Prep kit (Illumina Inc.) and sequenced on Illumina HiSeq2000 in one lane single-read 50bp. The processing of fluorescent images into sequences, base-calling and quality value calculations were performed using the Illumina data processing pipeline (version 1.8). Raw reads were filtered to obtain high-quality reads by removing low-quality reads containing more than 30% bases with Q < 20. Finally, a quality control of the raw sequence data was performed using FastQC [http://www.bioinformatics.babraham.ac.uk/projects/fastqc/]. Results: A total of 102,278,242 reads passed a quality filter and 85% were mapped back to the Arabidopsis TAIR10 genome. Approximately 90% mapped uniquely to only one location and could be assigned to a single annotated TAIR10 gene. Normalization of expression values was performed using RPKM values. All other parameters were kept at default levels. The CLC Genomic Workbench was also further used to determine all differentially expressed transcripts found in each cDNA library. Baggerley's test and a FDR correction were used for statistical analysis of samples. Our analysis revealed that 156 genes were upregulated, whereas for 90 genes a reduction in their mRNA level was observed in the stk mutant when compared to wild-type . Data analysis revealed a significant enrichment for terms related to the phenylpropanoid metabolic process, flavonoid biosynthetic process as well as cellular amino acid derivative metabolic process. Conclusion: Our genome-wide transcriptomic analysis suggests that the ovule identity factor STK is involved in the regulation of several metabolic processes providing a strong connection between cell fate determination, development and metabolism. In particular we characterize, through phenotypic, genetic, biochemical and transcriptomic approaches, the role of STK in PAs biosynthesis. Our results indicate that STK exerts this role through the direct regulation of the gene encoding for BANYULS/ANTHOCYANIDIN REDUCTASE (BAN/ANR), which converts anthocyanidins into their corresponding 2,3-cis-flavan-3-ols. Our study also demonstrates that the levels of H3K9ac chromatin modification directly correlate with the active state of BAN in an STK-dependent way. This supports the theory that MADS-domain proteins control the expression of their target genes through the modification of the chromatin states. STK might recruit or negatively regulate histone modifying factors to control their activity. Moreover, we show that STK controls through a complex regulatory network not only directly BAN but also other regulators of this key gene in tannin production

ORGANISM(S): Arabidopsis thaliana

PROVIDER: GSE59637 | GEO | 2014/12/17

SECONDARY ACCESSION(S): PRJNA255799

REPOSITORIES: GEO

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Project description:Purpose: The goals of this study are to compare NGS-derived transcriptome profiling (RNA-seq) from the SEEDSTICK mutant in Arabidopsis with a wild type, to unveil the role of this transcription factor in seed development and decipher the impact of this factor in PAs metabolism Methods: Total RNA was extracted from two biological replicates from both wild-type and stk mutant inflorescences and siliques until 5 DAP with the Qiagen Kit according to the manufacturer's instructions. DNA contaminations were removed using the PROMEGA RQ1 RNase-Free DNase according to the manufacturer's instructions. RNA quality integrity was analyzed by electrophoresis gel and was validated on a Bioanalyzer 2100 (Aligent, Santa Clara, CA); RNA Integrity Number (RIN) values were greater than 7 for all the samples. In order to confirm that in stk mutant samples STK was not expressed, STK expression was checked by real time PCR with primer RT 780 (5â??-TGCGATGCAGAAGTTGCGCTC-3â??) and RT 781 (5â??-AGTACGCGGCATTGATTTCTTG-3â??). Sequencing libraries were prepared according to the manufacturerâ??s instructions by TruSeq RNA Sample Prep kit (Illumina Inc.) and sequenced on Illumina HiSeq2000 in one lane single-read 50bp. The processing of fluorescent images into sequences, base-calling and quality value calculations were performed using the Illumina data processing pipeline (version 1.8). Raw reads were filtered to obtain high-quality reads by removing low-quality reads containing more than 30% bases with Q < 20. Finally, a quality control of the raw sequence data was performed using FastQC [http://www.bioinformatics.babraham.ac.uk/projects/fastqc/]. Results: A total of 102,278,242 reads passed a quality filter and 85% were mapped back to the Arabidopsis TAIR10 genome. Approximately 90% mapped uniquely to only one location and could be assigned to a single annotated TAIR10 gene. Normalization of expression values was performed using RPKM values. All other parameters were kept at default levels. The CLC Genomic Workbench was also further used to determine all differentially expressed transcripts found in each cDNA library. Baggerley's test and a FDR correction were used for statistical analysis of samples. Our analysis revealed that 156 genes were upregulated, whereas for 90 genes a reduction in their mRNA level was observed in the stk mutant when compared to wild-type . Data analysis revealed a significant enrichment for terms related to the phenylpropanoid metabolic process, flavonoid biosynthetic process as well as cellular amino acid derivative metabolic process. Conclusion: Our genome-wide transcriptomic analysis suggests that the ovule identity factor STK is involved in the regulation of several metabolic processes providing a strong connection between cell fate determination, development and metabolism. In particular we characterize, through phenotypic, genetic, biochemical and transcriptomic approaches, the role of STK in PAs biosynthesis. Our results indicate that STK exerts this role through the direct regulation of the gene encoding for BANYULS/ANTHOCYANIDIN REDUCTASE (BAN/ANR), which converts anthocyanidins into their corresponding 2,3-cis-flavan-3-ols. Our study also demonstrates that the levels of H3K9ac chromatin modification directly correlate with the active state of BAN in an STK-dependent way. This supports the theory that MADS-domain proteins control the expression of their target genes through the modification of the chromatin states. STK might recruit or negatively regulate histone modifying factors to control their activity. Moreover, we show that STK controls through a complex regulatory network not only directly BAN but also other regulators of this key gene in tannin production mRNA profiles from both Arabidopsis wild-type and stk mutant inflorescences and siliques until 5 DAP were generated by deep sequencing, in duplicate according to the manufacturerâ??s instructions by TruSeq RNA Sample Prep kit (Illumina Inc.) and sequenced on Illumina HiSeq2000 in one lane single-read 50bp. In order to confirm that in stk mutant samples STK was not expressed, STK expression was checked by real time PCR with primer RT 780 (5â??-TGCGATGCAGAAGTTGCGCTC-3â??) and RT 781 (5â??-AGTACGCGGCATTGATTTCTTG-3â??).

Project description:Purpose: microRNA profiles were generated from NIH-3T3 cells control and thapsigargin treated, in duplicate. The goal of this study was to compare microRNA profiles of untreated and thapsigargin treated NIH-3T3 fibroblast cells. Methods: NIH-3T3 cells were grown to confluency and either untreated or treated with 500 nM thapsigargin in media for 24 hours. Cells were harvested with TriZol and RNA isolated according to manufacturers protocol Analysis Outline: Short reads in fastq format were assembled using BclToFastq.pl script from Illumina CASAVA 1.8.1 software pipeline.Read quality was examined using FastQC program (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Adapters were trimmed at the 3'end using Btrim prgram (PMID:21651976), only sequences equal to and longer than 18nt were retained, leading N base was trimmed at the 5' end. Unique reads were collapsed using Raw_data_parse program from miRExpress suite (PMID:19821977) (the result of this process is a file that contains unique sequences in one column and number of times this sequence was found in the library in another). They can be found in *.merge files in trimmed_reads directory. Collapsed reads were reformatted and uploaded into miRanalyzer web-based pipeline (http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php; PMID:21515631) and matched to known mature miRNA (miRBase vesion 16), RFAM database (version 15) of known non-coding RNAs and known gene transcripts. The purpose of miRAnalyzer analysis was to only detect known miRNAs, prediction of novel miRNAs was not performed; search parameters were kept at default. MiRanalyzer output is saved in miRanalyzer folder with detailed information about mapping to known miRNA. Known miRNAs were divided into mature, maturestar (star sequences), maturestarunobs (star sequences not in miRBase) and hairpin. For each of the libraries there are files with unique and ambiguous mappings. Differentional expression analysis was based on unique alignments to known miRNAs (mature_unique.txt file in miRanalyzer folder). Mature_unique.txt has following columns: name: mature miRNA ID from miRBase; #unique reads: number of unique reads mapped; readCount: number of reads mapped; norm_expressed_all: normalized to all reads; norm_expressed_mapped: normalized to mapped reads. miRNA expression profiling was performed using edgeR bioconductor package (PMID:20478825). For differential expression analysis, used TMM normalization and analysis using common disperion (using tagwise dispersion yielded the same results). FDR was calculated according to Hochberg-Benjamini procedure (PMID:2218183). Results of differential expression analysis were saved in diff_exp folder as diff_exp.txt. diff_exp.txt contains miRNA concentrations in log scale, log2 ratio of WT to KO; p-values and FDR corrected p-values. miRNAs were sorted by p-value. NIH-3T3 cells grown to confluency and treated with 500 nM thapsigargin in media for 24 hours

Project description:Purpose: Understand the transcriptional changes and alternative splicing defects associated to the pleiotropic defects observed in mdf-1 mutants. Methods: Total RNA was extracted from 3 different biological replicates containing 12-day-old seedlings of WT and mdf-1 using the innuPREP Plant RNA kit (Analytik Jena Bio solutions). RNA quality assessment, library preparation, sequencing and bioinformatic analyses were performed by Novogene Co. Integrity and quantitation of the extracted RNA were measured using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). With 1 µg RNA per sample as input material, sequencing libraries were subsequently generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB) following the manufacturer’s recommendations. Index codes were also added to attribute sequences to each sample. Clustering of the index-coded samples was carried out on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina) following manufacturer’s instructions. Afterwards, library preparations were sequenced on an Illumina NovaSeq 6000 platform and approximately 50 million 150 paired end reads per sample were generated. To ensure the high quality of the samples, only clean data was used for subsequent analyses. This was achieved after the removal of reads containing adapter and poly-N sequences and reads with low quality from the raw data. Mapping against the Arabidopsis TAIR10 reference genome was performed using the HISAT2 software. Reads Per Kilobase of exon model per Million mapped reads (RPKM) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analyses between mdf-1 and WT were carried out using the DESeq2 R package with the Benjamini and Hochberg’s approach for adjusting P values according to the False Discovery Rate (FDR). Genes with adjusted p-value < 0.05 were considered as differentially expressed. Alternative splicing (AS) analysis was performed with the rMATS software. Events with adjusted p-value < 0.05 were considered as alternatively spliced. Results: Loss of MDF function is associated with growth defects, impaired transition to mitosis, increased endoreduplication and spontaneous cell death in meristems. This was associated with strong upregulation of stress-induced genes and down-regulation of mitotic regulators and a subset of genes required for essential developmental processes. MDF is also required for the correct splicing of numerous transcripts including transcription factors and cell cycle associated genes.

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SEEDSTICK Is a Master Regulator of Development and Metabolism in Arabidopsis Seed Coat

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