FIT (FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR) is the central regulator of iron uptake in Arabidopsis thaliana roots. We performed transcriptome analyses of six day-old seedlings and roots of six week-old plants using wild type, a fit knock-out mutant and a FIT over-expression line grown under iron-sufficient or iron-deficient conditions. We compared genes regulated in a FIT-dependent manner depending on the developmental stage of the plants. We assembled a high likelihood dataset which we used to perform co-expression and functional analysis of the most stably iron deficiency-induced genes. 448 genes were found FIT-regulated. Out of these, 34 genes were robustly FIT-regulated in root and seedling samples and included 13 novel FIT-dependent genes. Three hundred thirty-one genes showed differential regulation in response to the presence and absence of FIT only in the root samples, while this was the case for 83 genes in the seedling samples. We assembled a virtual dataset of iron-regulated genes based on a total of 14 transcriptomic analyses of iron-deficient and iron-sufficient wild-type plants to pinpoint the best marker genes for iron deficiency and analyzed this dataset in depth. Co-expression analysis of this dataset revealed 13 distinct regulons part of which predominantly contained functionally related genes. We could enlarge the list of FIT-dependent genes and discriminate between genes that are robustly FIT-regulated in roots and seedlings or only in one of those. FIT-regulated genes were mostly induced, few of them were repressed by FIT. With the analysis of a virtual dataset we could filter out and pinpoint new candidates among the most reliable marker genes for iron deficiency. Moreover, co-expression and functional analysis of this virtual dataset revealed iron deficiency-induced and functionally distinct regulons.

Cellular homeostasis of the micronutrient iron is highly regulated in plants and responsive to nutrition, stress, and developmental signals. Genes for iron management encode metal and other transporters, enzymes synthesizing chelators and reducing substances, transcription factors, and several types of regulators. In transcriptome or proteome datasets, such iron homeostasis-related genes are frequently found to be differentially regulated. A common method to detect whether a specific cellular pathway is affected in the transcriptome data set is to perform Gene Ontology (GO) enrichment analysis. Hence, the GO database is a widely used resource for annotating genes and identifying enriched biological pathways in Arabidopsis thaliana. However, iron homeostasis-related GO terms do not consistently reflect gene associations and levels of evidence in iron homeostasis. Some genes in the existing iron homeostasis GO terms lack direct evidence of involvement in iron homeostasis. In other aspects, the existing GO terms for iron homeostasis are incomplete and do not reflect the known biological functions associated with iron homeostasis. This can lead to potential errors in the automatic annotation and interpretation of GO term enrichment analyses. We suggest that applicable evidence codes be used to add missing genes and their respective ortholog/paralog groups to make the iron homeostasis-related GO terms more complete and reliable. There is a high likelihood of finding new iron homeostasis-relevant members in gene groups and families like the ZIP, ZIF, ZIFL, MTP, OPT, MATE, ABCG, PDR, HMA, and HMP. Hence, we compiled comprehensive lists of genes involved in iron homeostasis that can be used for custom enrichment analysis in transcriptomic or proteomic studies, including genes with direct experimental evidence, those regulated by central transcription factors, and missing members of small gene families or ortholog/paralog groups. As we provide gene annotation and literature alongside, the gene lists can serve multiple computational approaches. In summary, these gene lists provide a valuable resource for researchers studying iron homeostasis in A. thaliana, while they also emphasize the importance of improving the accuracy and comprehensiveness of the Gene Ontology.

RNA-seq analysis is complex, requiring the integration of experimental biology with computational skills, highlighting the need for interdisciplinary expertise. To address this, I present PlugNSeq, a streamlined mRNA-Seq data analysis pipeline. The first part of the protocol is executed via sequential execution of bash scripts in a Linux or Mac OS terminal, or batch scripts in the Windows command prompt. It includes decompression, adapter and quality trimming, quality control, alignment, and transcript quantification, requiring only basic knowledge. This makes it accessible to both novice and senior scientists that are inexperienced in bioinformatics. The second part is executed in RStudio, and automates statistical analyses, producing key tables and figures, that form an excellent starting point for further exploration. This protocol is easy to execute and yet, important parameters of the statistical analyses can easily be adapted. Hence, this pipeline enables efficient but still flexible RNA-seq analysis of a well-annotated organism with minimal bioinformatics expertise. This pipeline is compatible with Linux, Mac OS and Windows. Workflow of the PlugNSeq pipeline After downloading and extracting the provided directory structure, the user only needs to follow four basic steps in the protocol (Figure 1). First, the user copies the required reference transcriptome, GFF3, RNA-Seq reads and adapter sequence files into dedicated folders. Then, four consecutive scripts are executed by the user. After the scripts have been successfully executed, the user edits the provided and partly newly created sample configuration file (required) and R configuration file (optional), and saves them in their dedicated folder. Finally, the provided R script is run by the user in RStudio. That's it. The results include PCA scores plots with accompanying tables, a volcano plot for each pairwise comparison, a main horizontally and vertically clustered heatmap (based on z-scores of the group means) with all genes that were significantly regulated in at least one of the pairwise comparisons, including the accompanying table. Additionally, a heatmap of fold-changes in the pairwise comparisons will be generated, which also comes with an accompanying table. Furthermore, the user receives the "mothertable", a spreadsheet that contains the locus and transcript ID's, annotations, all sample expression values and group mean expression values as well as corrected p-values, log2-fold-changes and regulation patterns of each gene for all pairwise comparisons performed. There will also be a heatmap of the replicate samples, allowing for quick assessment of sample quality. This heatmap has no accompanying table since it is only meant for visual inspection. Optionally, the user may provide custom gene lists before the R analysis starts, and then gets dedicated heatmaps for each of the provided gene lists, including the respective accompanying table. These heatmaps include ones that are created using z-scores of the group means, and ones that are created based on the fold-changes in all pairwise comparisons performed. All important heatmaps are created in two customizable three-step color palettes and in several sizes.

Cellular homeostasis of the micronutrient iron is highly regulated in plants and responsive to nutrition, stress and developmental signals. Genes for iron management encode metal and other transporters, enzymes synthesizing chelators and reducing substances, transcription factors and several types of regulators. In transcriptome or proteome datasets, such iron homeostasis-related genes are frequently found to be differentially regulated. A common method to detect whether a specific cellular pathway is affected in the transcriptome data set is to perform Gene Ontology (GO) enrichment analysis. Hence, the GO database is a widely used resource for annotating genes and identifying enriched biological pathways in Arabidopsis thaliana. However, iron homeostasis-related GO terms do not consistently reflect gene associations and levels of evidence in iron homeostasis. Some genes of the existing iron homeostasis GO terms lack direct evidence of involvement in iron homeostasis. In other aspects, the existing GO terms of iron homeostasis are incomplete and do not reflect the known biological functions associated with iron homeostasis. This can lead to potential errors in automatic annotation and interpretation of GO term enrichment analyses. We suggest that applicable evidence codes should be used to add missing genes and their respective ortholog/paralog groups to make the iron homeostasis-related GO terms more complete, and reliable. There is high likelihood of finding new iron homeostasis-relevant members in gene groups and families like the ZIP, ZIF, ZIFL, MTP, OPT, MATE, ABCG, PDR, HMA, and HMP. Hence, we compiled comprehensive lists of genes involved in iron homeostasis that can be used for custom enrichment analysis in transcriptomic or proteomic studies, including genes with direct experimental evidence, those regulated by central transcription factors, and missing members of small gene families or ortholog/paralog groups. As we provide gene annotation and literature alongside, the gene lists can serve in multiple computational approaches. In summary, these gene lists provide a valuable resource for researchers studying iron homeostasis in A. thaliana, while they also emphasize the importance of improving the accuracy and comprehensiveness of the Gene Ontology.

RNA-seq analysis is complex, requiring the integration of experimental biology with computational skills, highlighting the need for interdisciplinary expertise. To address this, I present PlugNSeq, a streamlined mRNA-Seq data analysis pipeline. The first part of the protocol is executed via sequential execution of bash scripts in a Linux or Mac OS terminal, or batch scripts in the Windows command prompt. It includes decompression, adapter and quality trimming, quality control, alignment, and transcript quantification, requiring only basic knowledge. This makes it accessible to both novice and senior scientists that are inexperienced in bioinformatics. The second part is executed in RStudio, and automates statistical analyses, producing key tables and figures, that form an excellent starting point for further exploration. This protocol is easy to execute and yet, important parameters of the statistical analyses can easily be adapted. Hence, this pipeline enables efficient but still flexible RNA-seq analysis of a well-annotated organism with minimal bioinformatics expertise. This pipeline is compatible with Linux, Mac OS and Windows. Workflow of the PlugNSeq pipeline After downloading and extracting the provided directory structure, the user only needs to follow four basic steps in the protocol (Figure 1). First, the user copies the required reference transcriptome, GFF3, RNA-Seq reads and adapter sequence files into dedicated folders. Then, four consecutive scripts are executed by the user. After the scripts have been successfully executed, the user edits the provided and partly newly created sample configuration file (required) and R configuration file (optional), and saves them in their dedicated folder. Finally, the provided R script is run by the user in RStudio. That's it. The results include PCA scores plots with accompanying tables, a volcano plot for each pairwise comparison, a main horizontally and vertically clustered heatmap (based on z-scores of the group means) with all genes that were significantly regulated in at least one of the pairwise comparisons, including the accompanying table. Additionally, a heatmap of fold-changes in the pairwise comparisons will be generated, which also comes with an accompanying table. Furthermore, the user receives the "mothertable", a spreadsheet that contains the locus and transcript ID's, annotations, all sample expression values and group mean expression values as well as corrected p-values, log2-fold-changes and regulation patterns of each gene for all pairwise comparisons performed. There will also be a heatmap of the replicate samples, allowing for quick assessment of sample quality. This heatmap has no accompanying table since it is only meant for visual inspection. Optionally, the user may provide custom gene lists before the R analysis starts, and then gets dedicated heatmaps for each of the provided gene lists, including the respective accompanying table. These heatmaps include ones that are created using z-scores of the group means, and ones that are created based on the fold-changes in all pairwise comparisons performed. All important heatmaps are created in two customizable three-step color palettes and in several sizes.