dataesr/scientific-paragraphs-categorization huggingface dataset:
For calculations of the C, N and O 1s core-level binding energies, we utilized two methods based on density functional theory total energy differences, as implemented in the grid-based projector-augmented waves (PAW) simulation package GPAW  69, 70  : a standard delta Kohn-Sham (Δ KS) calculation with a projector-augmented wave dataset including an explicit core-hole on an atom of interest  71  , and a novel all-electron extension of the delta self-consistent field (Δ SCF) method  72. In the latter, the core electrons are included in the valence, enabling an explicit all-electron calculation within the PAW scheme  73. Exchange and correlation for these calculations were estimated by the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation  74  , yielding significantly more accurate core level binding energies than the LDA  72.
Acknowledgements We would like to acknowledge the following funding bodies and computing resources: (D. E. )  TUBITAK-2219 Abroad Post-Doctoral Research Funding Programme  ( 1059B191301289 ); (C. B. , M. S. ) the  Belgian Fund for Scientific Research (FRS-FNRS)  under FRFC contract  2. 4577. 11 (Chemographene); (T. S. ) the  Vienna Scientific Cluster  and the  Austrian Science Fund (FWF)  grants  P 28322-N36  and M  1497-N19 ; (G. R. , N. T. ) the  Greek General Secretariat for Research and Technology  and the  European Commission  through the  European Fund for Regional Development , NSRF 2007-2013 action " Development of Research Centers -Κ Ρ Η Π Ι Σ " (POLYNANO) , the  European Commission  under contract  CALIPSO Transnational Access , and grants  642742  ( Enabling Excellence ), 612577 (NanoCF), 226716 (ELISA) and 312284 (CALIPSO).
TXM measurements were performed at the  Bessy-II facility , which is financially supported by the  Helmholtz-Zentrum Berlin für Materialien und Energie GmbH. We thank  HZB  for the allocation of synchrotron radiation beamtime at the undulator beamline U41-TXM at the BESSY II electron storage ring, Berlin. STXM measurements were performed at the  Canadian Light Source , which is funded by the  Canada Foundation for Innovation , the  Natural Sciences and Engineering Research Council of Canada , the  National Research Council Canada , the  Canadian Institutes of Health Research , the  Government of Saskatchewan ,  Western Economic Diversification Canada , and the  University of Saskatchewan. STXM measurements were also performed at the  Advanced Light Source , which is supported by the  Director of the Office of Science, Department of Energy , under Contract No.
Background: 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 ironsufficient 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. Results: 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. Conclusions: 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.
Using CATMA twocolor microarrays we performed seven pairwise comparisons with three biological and two technical replicates, respectively. In three pairwise comparisons we measured the transcriptomic changes upon iron deficiency within the lines. These include wild type -Fe vs. +Fe, fit -Fe vs. +Fe and HA-FIT -Fe vs. +Fe. We refer to these comparisons as 'intra-line comparisons'. The four other pairwise comparisons monitor transcriptomic differences between the lines at a given iron status. They include the comparisons fit vs. wild type and HA-FIT vs. wild type both at +Fe and -Fe, respectively. We refer to them as 'inter-line comparisons'. To validate the seedling data, we performed RT-qPCR with selected iron homeostasisrelated genes (Additional file 2: Figure  S2 ). The root data were previously validated by  [29].
Gene regulation in roots from six-week-old Arabidopsis plants First, when gene expression in roots of six-week-old wild type plants was analyzed, a total number of 7402 genes was found regulated in at least one out of the seven comparisons (Additional file 3: Dataset 1). Four thousand one hundred genes were found regulated in the intra-line comparisons (Fig. 1a ). Out of these, 2287 were up-regulated (Fig. 1b ) and 2361 were down-regulated (Fig. 1c ) at -Fe in at least one of the comparisons. The least number of regulated genes at +Fe versus -Fe was found in fit. Four hundred fifty-four genes were induced and 438 genes repressed in fit. The number of induced and repressed genes in wild type was 1256 and 1418, respectively, while in HA-FIT 1303 genes were induced and 1555 genes repressed under -Fe.
Gene regulation in 6-day-old Arabidopsis seedlings In six-day-old seedlings 3802 genes were found regulated in at least one of the seven comparisons (Additional file 3: Dataset 2). With 2769 genes the number of regulated genes in the inter-line comparisons of six-day-old seedlings (Fig. 1g ) is 32 % less than in roots of six week-old plants. Out of these, 1435 genes were found induced (Fig. 1h ) and 1393 genes were found repressed (Fig. 1i ) under -Fe. The overlap between the up-and downregulated genes was only 2 % (59 genes). This suggests that gene regulation upon iron deficiency is very specific to the investigated lines. In contrast to the six-week-old roots we could not observe a large difference in the number of regulated genes that overlap between fit, wild type and HA-FIT seedlings in the intra-line comparisons.
Hierarchical clustering of the microarray results in iron homeostasis-enriched gene clusters To detect regulatory patterns we performed hierarchical clustering with the datasets from the six-week-old Arabidopsis roots (Additional file 3: Dataset 1), the six-day-old seedlings (Additional file 3: Dataset 2) and the combination of both (Additional file 3: Dataset 3). We highlighted clusters in which the two confirmed FIT-regulated marker genes IRT1 and FRO2  [12] , AT3G13610, AT3G07720, MTPA2 and COPT2  [11]  were present. In the dataset generated from the six-day-old seedlings the FIT-regulated marker genes appeared in five clusters of which four were directly adjacent and contained a total of 65 genes (Fig. 2a ). The fifth cluster was distinct from the others and contained 30 genes (Fig. 2b ).
In the dataset from the six-week-old roots, the marker genes were found in one cluster containing 33 genes (Fig. 2c ). Finally, in the combined dataset of both roots and seedlings, the marker genes were found in a single cluster containing 65 genes (Fig. 2d , Additional file 4: Figure  S3 ). When focusing on the genes that clustered with FIT-regulated marker genes, 14 genes were found co-regulated in a robust manner in three clusters. Thirty-three genes were found in two of three clusters and 85 genes were present in only one out of the three Fig. 2  Hierarchical clustering of the genes that were differentially regulated in six-day-old seedlings (a and b). Roots of six-week-old plants (c) and in the combined analysis (d). The respectively compared lines and conditions are indicated by numbers.
Stringent expression pattern filtering revealed robustly FIT-dependent genes To detect novel FIT targets, we performed stringent filtering of the 9048 regulated genes in the combined dataset of roots and seedlings (Additional file 3: Dataset 3). At first, we reduced the list of all genes to those genes that were found regulated in at least one comparison in seedlings and roots, thereby reducing the number to 2156 genes (intersection in Fig. 1m , Additional file 3: Dataset 4). Next, we performed a consecutive four-step filtering of genes by scatter plot analysis. In the first step we selected 99 genes that were induced by -Fe in the wild type in the comparisons WT -Fe vs. WT +Fe in roots and seedlings (Fig. 3a , Additional file 5: Table  S2 ). In the second step we selected genes that were down-regulated under iron deficiency in the fit knock-out mutant compared to wild type (Fig.
Expression pattern analysis in six-day-old seedlings and six-week-old roots reveals distinct sets of FIT-dependent genes We used the same filtering as above to detect genes regulated in a FIT-dependent manner only in six-day-old seedlings or only in six-week-old roots, respectively. In seedlings, out of the 3802 input genes (red circle in Fig. 1m , Additional file 3: Dataset 2) that were found regulated in one of the comparisons, 285 were expressed at a higher level in WT -Fe vs. +Fe and expressed at a lower level in fit -Fe vs. WT -Fe (Fig. 5a ). Out of these 285 genes, 96 were expressed at a higher level in the comparison HA-FIT -Fe vs. HA-FIT +Fe and not expressed at a higher level in the comparison fit -Fe vs. fit +Fe (Fig. 5b ). Among these 96 genes there were all the 32 previously found FIT-regulated genes, as expected.
Hence, 64 genes were regulated in a FITdependent manner exclusively in six-day-old seedlings (Additional file 5: Table  S3 ). In roots of six-week-old plants, out of 7402 total regulated genes (blue circle in Fig. 1m , Additional file 3: Dataset 1) 840 were expressed at a higher level in WT -Fe vs. +Fe and expressed at a lower level in fit -Fe vs. WT -Fe (Fig. 5c ). Out of these 840 genes 299 were expressed at a higher level in HA-FIT -Fe vs. HA-FIT +Fe and not expressed at a higher level in fit -Fe vs. fit +Fe (Fig. 5d ). Also these genes comprised the 32 previously determined FIT-regulated genes, so that finally, 267 genes were found regulated in a FITdependent manner exclusively in six-week-old roots (Additional file 5: Table  S4 ). Among the 64 genes that were regulated in a FITdependent manner specifically in seedlings there were 15 genes that have been previously associated with FIT  [11]  (Additional file 5: Table  S3 ) and those that were regulated in a FIT-dependent manner specifically in sixweek-old roots comprised 9 previously FIT-associated genes (Additional file 5: Table  S3 ).
Validation of FIT-dependent genes by assembly of a virtual dataset We assembled a virtual dataset using our own expression data of genes together with the transcriptomic data from 9 previous studies in which Arabidopsis wild type roots or seedlings were tested for transcriptomic adaptations upon iron deficiency  [11, 13, 19, 20, 24, 25, [63] [64] [65]. From time course experiments we used the 24 h  [24] , 48 h and 72 h data  [64]. The reconstructed data from two publications  [11, 20]  were incomplete since they only contained induced genes. Together, 14 transcriptomic analyses from 9 studies and our own data have been taken into account (Additional file 3: Dataset 5). From the collected data we assembled a virtual iron regulation dataset (Additional file 3: Dataset 6) in which the genes were filtered by the number of occurrences among the regulated genes and by the uniformity of their regulation.
From 5851 genes that were found regulated in at least one of the studies, 598 genes met the requirements of which 437 genes were induced and 161 were repressed under -Fe. Out of the 32 FIT-regulated genes all but seven genes (AT1G14182, AT1G32380, AT1G53635, AT4G17680 AT5G4510, AT5G46060 and AT5G55250) were among the induced genes in this virtual dataset. A closer look at these seven genes showed that five of them (AT1G14182, AT1G53635, AT4G17680, AT5G45105 and AT5G46060) had not been included in the Affymetrix ATH1 chips used in the published work. The two other genes, AT1G32380 and AT5G55250, were barely 1. 5 and 2-fold up-regulated under -Fe, which makes them prone to be filtered by the oftentimes used two-fold detection threshold. One observation that we made during the assembly of the virtual dataset was the very variable number of genes detected as regulated in the distinct analyses.
The highest number of genes that was found regulated in wild type upon iron deficiency was 2673 (this study). A comparable number of genes was for example also detected by Long et al. [25]  in the 48 h and 72 h time points of the time course analysis. The lowest number of genes that were found regulated upon iron deficiency in wild type roots was 14  [65]  while in transcriptomic analyses of other studies this number ranged from roughly 150 to 1000 genes. The average number is ca. 800. Hence, it is not surprising that potentially important genes were often not detected and this might have contributed to the fact that AT5G55250 did not make it into the virtual dataset and that some of the newly FIT-associated genes were not found as such. A reason for the great variability of the number of detected differentially expressed genes could be inconsistent growth conditions which may lead to a high variance between the biological replicates and consequently to insignificant regulation.
Interestingly, even the central regulator of iron uptake, FIT, has only been found regulated in 7 of 14 analyses. This might be due to the fact that FIT is relatively weakly up-regulated under iron deficiency but might also be due to the fact that this gene is only present on two of four often used microarrays to this time point. Hence, detection of some important genes might also depend on the microarrays used to perform the transcriptomic analysis. The comparison of the 598 genes in the virtual dataset with our own data showed that we found 293 of these genes regulated while 305 genes in the virtual dataset were not found regulated in our analyses. The fact that the average VIRT absolute value was 0. 36 shows that our analyses with ca. 49 % covered an above-average number of these genes.
Robust marker genes for iron deficiency Our virtual dataset of the transcriptomic response of wild type to iron deficiency was constructed so that the genes were not only ranked by the number of occurrences in all analyses but also according to the uniformity of their regulation across multiple analyses. Setting thresholds in the process of constructing the dataset enabled us to filter the regulatory noise and pinpoint those genes that are most reliably induced upon iron deficiency in wild type Arabidopsis roots and whole seedlings. According to this procedure, 598 out of 5847 genes that were found regulated in at least one of the 14 analyses of WT -Fe vs. WT +Fe in this study and all the other considered studies  [11, 13, 19, 20, 24, 25, [63] [64] [65]  are present in the virtual dataset (Additional file 3: Dataset 6).
However, due to its ranking the FER1 AT5G01600 -1 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 -0. 83 ATABC1 AT4G04770 -1 0 -1 -1 -1 -1 -1 -1 0 -1 -1 0 -0. 75 unknown AT2G36885 -1 0 0 0 -1 -1 -1 -1 -1 -1 -1 0 -0. 67 PSAF AT1G31330 -1 0 -1 -1 0 -1 -1 0 0 -1 -1 0 -0. 58 uncharacterized protein family (UPF0016) AT1G68650 -1 0 0 0 -1 -1 -1 -1 -1 0 -1 0 -0. 58 PER21 AT2G37130 -1 0 -1 -1 0 -1 -1 0 0 -1 -1 0 -0. 58 FER4 AT2G40300 -1 0 0 -1 -1 -1 -1 -1 -1 0 0 0 -0. 58 LAC7 AT3G09220 -1 -1 0 0 -1 -1 -1 0 0 -1 -1 0 -0. 58 SAPX AT4G08390 -1 0 -1 -1 0 -1 -1 0 0 -1 -1 0 -0. 58 unknown AT5G59400 -1 0 -1 -1 -1 -1 -1 -1 0 0 0 0 -0. 58 HEMA1 AT1G58290 0 0 0 0 -1 -1 -1 -1 0 -1 -1 0 -0. 50 FSD1 AT4G25100 -1 -1 0 -1 -1 0 0 -1 0 0 0 -1 -0. 50 peroxidase, putative AT5G64100 0 -1 0 -1 0 -1 -1 0 0 -1 -1 0 -0. 50 Up-regulated genes are represented by the value 1.
Co-expression and functional analysis of the virtual dataset functionally enriched regulons Out of the 598 genes in the virtual dataset 437 were found induced. This is concordant with the general observation that under iron deficiency more genes are induced than repressed. We used these genes to create co-expression networks using the String version 10 tool  [27]. Genes resulting in singlet nodes and networks with ≤ 3 nodes were disregarded. One hundred sixtynine genes grouped into networks with ≥4 nodes (Fig. 7 , Additional file 6: Figure  S4 ). After rearranging the nodes we could detect a total of 13 networks with 4 to 43 nodes. Six networks (Fig. 7 , regulons 1, 2, 3, 11, 12 and 13) had no connection to other networks. Seven networks (Fig. 7 , regulons 4-10) were connected with each other by sharing few nodes.
One closed regulon (Fig. 7 , regulon 1) contained genes of the FIT target network  [13] , namely IRT1, MTPA2, FRO2 and CYP82C4 (Additional file 5: Table  S7 ). Among the input genes of the virtual dataset further members of this regulon (AT1G34760, AT1G73120, AT1G 74770, AT3G07720, AT3G12820, AT3G12900, AT3G 50740, AT3G58810, AT4G19680, AT4G19690, AT4G 30120, AT4G31940 and AT5G38820) were present. Another closed regulon (Fig. 7 , regulon 2) was mainly composed of known members of the iron homeostasis PYE-BTS regulon  [13]. Only 3 genes of the original Fig. 7  Co-expression network built from the genes induced under -Fe in the virtual dataset: Regulon 1: contains members of the FIT target network  [13]. Regulon 2: consists of members of the iron homeostasis network  [13]. Regulon 3: is largely composed of genes involved in phenylpropanoid metabolism.
Regulon 4: mainly comprises genes that participate in the pentose phosphate pathway, glycolysis and gluconeogenesis. Regulon 5: is mostly composed of genes that are involved in RNA processing and translation. Regulon 6: contains mitochondrial proteins. Regulon 7: is heterogeneous but contains comparably many chaperons. Regulon 8: is enriched in genes involved in amino acid metabolism. Regulon 9: is also heterogeneous but enriched in genes that participate in plant-pathogen interaction. Regulon 10: shows no enrichment of molecular functions. Regulon 11: mainly contains genes that participate in purine, lipid and aromatic compound metabolism. Regulon 12: is composed of genes involved in the response to low sulfur. Regulon 13 shows no enrichment of molecular functions. The network has been created with the String version 10 protein interaction database  [27].
The confidence was set to 'medium' (0. 400) and no genes were added. The 437 genes induced under -Fe in the virtual dataset were used as input. Singlet nodes have been removed and only networks with 4 or more nodes are shown. The resulting network image contains 169 genes (Additional file 5: Table  S7 ). For a high resolution image see Additional file 6: Figure  S4  regulon were missing, namely PP2-A9, the gene of an unknown protein (AT2G30760) and IPT3. Hence, this regulon is very consistently and almost entirely induced upon deficiency. Interestingly, this regulon contained 5 additional members compared to the original network, namely IREG3, BHLH38, DJC77, PGR5-LIKE A and CGLD27. Thus, the PYE-BTS regulon could be extended by 5 members. The third closed regulon (Fig. 7 , regulon 3) was mainly composed of genes that are involved in phenylpropanoid metabolism.
Increase of phenylpropanoid biosynthesis has been previously observed at the proteomic level  [42]. Among others, PAL1, PAL2, 4CL1 and 4CL2, which catalyze the very first steps in the phenylpropanoid pathway, were members of this regulon (Additional file 5: Table  S7 ) and the respective proteins were also found induced upon iron deficiency. This network did not contain genes of enzymes that synthesize the final conversions. However, F6'H1 and another gene that has been hypothesized to also participate in coumarin biosynthesis (AT3G12900) as well as the gene of the ABC transporter PDR9 (AT3G53480) which could be responsible for coumarin secretion into the rhizosphere were among the consistently iron deficiency-induced genes (Additional file 3: Dataset 6) but not directly connected to one of the regulons in our graph (Fig.
Twenty-five of the FIT target genes were contained in the virtual dataset and used as input genes. Only three of them were present in the resulting co-expression network. Since we intentionally did not add any genes, the respective bridging nodes were missing so they appeared as singlet nodes or networks with <4 nodes and were removed. For the same reason the FIT target network and the iron homeostasis PYE-BTS-regulon were not connected. However, the deeper analysis of the virtual dataset showed a comparably high concordance with previous observations at the proteomic level. Although the actual overlap between distinct genes and proteins in the transcriptomic and proteomic analyses is comparably low and single comparisons between the proteomic and transcriptomic regulation under iron deficiency showed a pronounced discrepancy between gene and protein regulation, the general adaptations of some metabolic and regulatory pathways that were observed at the protein level  [41, 42]  are mirrored at the transcriptome level.
The construction of a virtual dataset based on 14 distinct transcriptomic analyses allowed for removing a great portion of regulatory noise and revealed a total of 598 genes that are stably regulated under iron deficiency in Arabidopsis roots and seedlings. Four hundred thirty-seven of them were found stably induced and 161 stably repressed under iron deficiency with a probability of ≥ 0. 25. From the induced genes in this dataset we performed co-expression analysis and found a total of 13 regulons with ≥ 4 nodes. Some of these regulons were enriched with functionally related genes among which parts of the previously known FIT target network and the iron homeostasis PYE-BTS regulon could be identified. The PYE-BTS regulon was almost completely present and could be extended by further genes.
Direct comparisons demonstrated large discrepancies between the proteomic and transcriptomic regulation  [29]  and remodeling the ribosomal composition has been proposed to cause biased translation  [74]. The analysis of our virtual dataset appears to confirm such remodeling processes. However, the data in the virtual dataset display considerable overlap with combined proteomic data  [42]  at least at the functional level. Taken together this study not only provides new insight into the effects of FIT abundance on gene expression but also points out the importance of redundant analyses.
Microarray analysis Two hundred nanogram of original total RNA were used per hybridization for the microarray analysis. The analysis was performed using CATMA microarrays. Three independent biological replicates were produced. For each biological replicate, RNA samples were prepared and analyzed in two technical replicates as previously described  [29]. We analyzed gene expression in roots of six-week-old plants that were grown on +Fe ¼-strength liquid Hoagland medium for five weeks and then transferred to +Fe or -Fe for one week. We also analyzed gene expression in six-day-old whole seedlings that were grown on +Fe or -Fe Hoagland agar for six days. Probes with a p value of ≤ 0. 05 and a fold change of ≥1. 5 were considered differentially expressed. The microarray data are publicly available at CATdb (http://urgv.