Union coexpression data in COXPRESdb ver 7.1. The coexpression index is MR (smaller value indicates stronger coexpression).
Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/

• Cel-u.c1-0 Cel-u.v18-10.G18947-S3326.combat_pca_subagging.mrgeo.d.zip
• Cfa-u.c1-0 Cfa-u.v18-10.G19530-S1030.combat_pca_subagging.mrgeo.d.zip
• Dme-u.c1-0 Dme-u.v18-10.G13856-S8805.combat_pca_subagging.mrgeo.d.zip
• Dre-u.c1-0 Dre-u.v18-10.G20643-S4472.combat_pca_subagging.mrgeo.d.zip
• Gga-u.c1-0 Gga-u.v18-10.G17621-S2200.combat_pca_subagging.mrgeo.d.zip
• Hsa-u.c1-0 Hsa-u.v18-10.G22004-S56567.combat_pca_subagging.mrgeo.d.zip
• Mcc-u.c1-0 Mcc-u.v18-10.G20198-S2211.combat_pca_subagging.mrgeo.d.zip
• Mmu-u.c1-0 Mmu-u.v18-10.G22278-S63643.combat_pca_subagging.mrgeo.d.zip
• Rno-u.c1-0 Rno-u.v18-10.G17352-S16119.combat_pca_subagging.mrgeo.d.zip
• Sce-u.c1-0 Sce-u.v18-10.G5711-S4798.combat_pca_subagging.mrgeo.d.zip
• Spo-u.c1-0 Spo-u.v18-10.G5713-S309.combat_pca_subagging.mrgeo.d.zip

Union coexpression is calculated by the average of the logit-transformed MR values of RNAseq and microarray coexpression; for gene pairs with only RNAseq coexpression, RNAseq coexpression values were converted to union values by linear regression.

Please also check our related publication for a plant coexpression database, ATTED-II:
Obayashi T, Hibara H, Kagaya Y, Aoki Y, Kinoshita K. (2022) ATTED-II v11: a plant gene coexpression database using a sample balancing technique by subagging of principal components. Plant Cell Physiology, 63: 869-881.

Human coexpression data provided in COXPRESdb. Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/

These are the gene coexpression data in Notation3 format, also provided in Virtuoso in ATTED-II. Details of the data can be found on the download page in ATTED-II. https://atted.jp/download/

Example:

@prefix m2r: . @prefix rdfs: . @prefix dcterms: . @prefix obo: . @prefix ncbigene: . @prefix sio: . @prefix atted: . @prefix attedo: . @prefix xsd: .

atted:Ath-u.c3-0 dcterms:identifier "Ath-u.c3-0" ; dcterms:issued "2022-04-07"^^xsd:date .

[] attedo:gene ncbigene:836761 ; attedo:gene ncbigene:2745723 ; attedo:lsmr_score 5.6464 ; attedo:dataset atted:Ath-u.c3-0 ; a attedo:CoExpressedGenePair .

[] attedo:gene ncbigene:836827 ; attedo:gene ncbigene:2745723 ; attedo:lsmr_score 5.2684 ; attedo:dataset atted:Ath-u.c3-0 ; a attedo:CoExpressedGenePair .

These are the gene coexpression data in Notation3 format, also provided in Virtuoso in COXPRESdb. Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/

Example:

@prefix m2r: <http://med2rdf.org/ontology/med2rdf#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix obo: <http://purl.obolibrary.org/obo/> .
@prefix ncbigene: <http://identifiers.org/ncbigene/> .
@prefix sio: <http://semanticscience.org/resource/> .
@prefix coxpresdb: <https://coxpresdb.jp/dataset/> .
@prefix coxpresdbo: <https://coxpresdb.jp/ontology/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

coxpresdb:Hsa-u.c4-0
dcterms:identifier "Hsa-u.c4-0" ;
dcterms:issued "2022-06-08"^^xsd:date .

[]
coxpresdbo:gene ncbigene:9 ;
coxpresdbo:gene ncbigene:10 ;
coxpresdbo:lsmr_score 6.33 ;
coxpresdbo:dataset coxpresdb:Hsa-u.c4-0 ;
a coxpresdbo:CoExpressedGenePair .

[]
coxpresdbo:gene ncbigene:41 ;
coxpresdbo:gene ncbigene:100 ;
coxpresdbo:lsmr_score 2.49 ;
coxpresdbo:dataset coxpresdb:Hsa-u.c4-0 ;
a coxpresdbo:CoExpressedGenePair .

With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation.

We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 “coexpression links” that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function.

Coexpression data provided in COXPRESdb ver 8.1. Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/

• Cel-m.c5-0 Cel-m.v21-06.G17256-S1357.combat_pca.subagging.ls.d.zip
• Cel-r.c3-0 Cel-r.v21-10.G14532-S5785.combat_pca.subagging.ls.d.zip
• Cel-u.c3-1 Cel-u.v22-05.G14532-S7142.combat_pca.subagging.z.d.zip
• Cfa-m.c4-0 Cfa-m.v21-06.G16214-S619.combat_pca.subagging.ls.d.zip
• Cfa-r.c3-0 Cfa-r.v21-10.G15365-S1361.combat_pca.subagging.ls.d.zip
• Cfa-u.c3-1 Cfa-u.v22-05.G15365-S1980.combat_pca.subagging.z.d.zip
• Dme-m.c5-0 Dme-m.v21-06.G12626-S3401.combat_pca.subagging.ls.d.zip
• Dme-r.c4-0 Dme-r.v21-10.G12209-S13514.combat_pca.subagging.ls.d.zip
• Dme-u.c3-1 Dme-u.v22-05.G12209-S15610.combat_pca.subagging.z.d.zip
• Dre-m.c5-0 Dre-m.v21-06.G10112-S1321.combat_pca.subagging.ls.d.zip
• Dre-r.c3-0 Dre-r.v21-10.G20344-S10037.combat_pca.subagging.ls.d.zip
• Dre-u.c3-1 Dre-u.v22-05.G20344-S11358.combat_pca.subagging.z.d.zip
• Fca-r.c1-0 Fca-r.v21-11.G19735-S267.combat_pca.subagging.z.d.zip
• Gga-m.c5-0 Gga-m.v21-06.G13757-S1155.combat_pca.subagging.ls.d.zip
• Gga-r.c3-0 Gga-r.v21-10.G15998-S3333.combat_pca.subagging.ls.d.zip
• Gga-u.c3-1 Gga-u.v22-05.G15998-S4488.combat_pca.subagging.z.d.zip
• Hsa-m.c7-0 Hsa-m.v21-06.G20283-S25362.combat_pca.subagging.ls.d.zip
• Hsa-m2.c4-0 Hsa2-m.v21-06.G20199-S10511.combat_pca.subagging.ls.d.zip
• Hsa-r.c6-0 Hsa-r.v22-05.G16651-S235187.combat_pca.subagging.z.d.zip
• Hsa-u.c4-0 Hsa-u.v22-05.G16651-S245698.combat_pca.subagging.z.d.zip
• Mcc-m.c4-0 Mcc-m.v21-06.G15782-S590.combat_pca.subagging.ls.d.zip
• Mcc-r.c3-0 Mcc-r.v21-10.G16312-S5665.combat_pca.subagging.ls.d.zip
• Mcc-u.c3-1 Mcc-u.v22-05.G16312-S6255.combat_pca.subagging.z.d.zip
• Mmu-m.c5-0 Mmu-m.v21-06.G20962-S25087.combat_pca.subagging.ls.d.zip
• Mmu-r.c6-0 Mmu-r.v22-05.G17008-S214753.combat_pca.subagging.z.d.zip
• Mmu-u.c4-0 Mmu-u.v22-05.G17008-S239840.combat_pca.subagging.z.d.zip
• Rno-m.c5-0 Rno-m.v21-06.G13751-S7872.combat_pca.subagging.ls.d.zip
• Rno-r.c3-0 Rno-r.v21-10.G16521-S13267.combat_pca.subagging.ls.d.zip
• Rno-u.c3-1 Rno-u.v22-05.G16521-S21139.combat_pca.subagging.z.d.zip
• Sce-m.c4-0 Sce-m.v21-06.G4461-S3071.combat_pca.subagging.ls.d.zip
• Sce-r.c3-0 Sce-r.v21-11.G5718-S6225.combat_pca.subagging.ls.d.zip
• Sce-u.c3-1 Sce-u.v22-05.G5718-S8789.combat_pca.subagging.z.d.zip
• Spo-m.c4-0 Spo-m.v21-06.G4881-S166.combat_pca.subagging.ls.d.zip
• Spo-r.c3-0 Spo-r.v21-10.G5512-S556.combat_pca.subagging.ls.d.zip
• Spo-u.c3-1 Spo-u.v22-05.G5512-S722.combat_pca.subagging.z.d.zip

Union coexpression data (Xxx-u) in ATTED-II ver 11.1.

The coexpression value is provided as a z-score (larger value indicates stronger coexpression). Details of the data can be found on the download page in ATTED-II. https://atted.jp/download/

Ath-u.c3-0 Ath-u.v22-04.G18957-S27427.combat_pca.subagging.ls.d.zip

Gma-u.c3-0 Gma-u.v22-04.G33331-S2104.combat_pca.subagging.ls.d.zip

Mtr-u.c3-0 Mtr-u.v22-04.G22252-S1090.combat_pca.subagging.ls.d.zip

Osa-u.c3-0 Osa-u.v22-04.G22698-S2553.combat_pca.subagging.ls.d.zip

Ppo-u.c3-0 Ppo-u.v22-04.G21727-S1205.combat_pca.subagging.ls.d.zip

Sly-u.c3-0 Sly-u.v22-04.G20073-S1141.combat_pca.subagging.ls.d.zip

Vvi-u.c3-0 Vvi-u.v22-04.G17304-S1493.combat_pca.subagging.ls.d.zip

Zma-u.c3-0 Zma-u.v22-04.G26131-S5017.combat_pca.subagging.ls.d.zip

This data repository contains coexpression networks from publicly-available RNA-Seq datasets (obtained from the recount2 database) that were generated using the best workflows identified in the benchmarking study: Johnson KA, Krishnan A (2020) Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data. bioRxiv 10.1101/2020.09.22.308577.

GTEx coexpression networks
There are 62 coexpression networks built from 31 GTEx datasets (each dataset corresponding to one GTEx tissue) reconstructed using two different network-building workflows: i) CTF_CLR: Counts adjusted using TMM Factors followed by CLR transformation of the Pearson correlation coefficients; ii) CTF: Counts adjusted using TMM Factors (without any further transformation).

SRA coexpression networks
There are 256 coexpression networks built from 256 SRA datasets. Each dataset corresponds to a set of samples generated as part of the same transcriptome experiment from the same tissue. These networks are reconstructed using the top-performing workflow: CTF, Counts adjusted using TMM Factors.

Refer to the preprint for more details on the workflows and the steps used for obtaining the original datasets.

Increasing effects of anthropogenic stressors and those of natural origin on aquatic ecosystems have intensified the need for predictive and functional models of their effects. Here, we use gene expression patterns in combination with weighted gene coexpression networks and generalized additive models to predict effects on reproduction in the aquatic microcrustacean Daphnia. We developed models to predict effects on reproduction upon exposure to different cyanobacteria, different insecticides and binary mixtures of cyanobacteria and insecticides. Models developed specifically for groups of stressors (e.g., either cyanobacteria or insecticides) performed better than general models developed on all data. Furthermore, models developed using in silico generated mixture gene expression profiles from single stressor data were able to better predict effects on reproduction compared to models derived from the mixture exposures themselves. Our results highlight the potential of gene expression data to quantify effects of complex exposures at higher level organismal effects without prior mechanistic knowledge or complex exposure data.

A mechanistic understanding of community ecology requires tackling the nonadditive effects of multispecies interactions, a challenge that necessitates integration of ecological and molecular complexity-- namely moving beyond pairwise ecological interaction studies and the ‘gene at a time’ approach to mechanism. Here, we investigate the consequences of multispecies mutualisms for the structure and function of genome-wide coexpression networks for the first time, using the tractable and ecologically-important interaction between legume Medicago truncatula, rhizobia, and mycorrhizal fungi. First, we found that genes whose expression is affected nonadditively by multiple mutualists are more highly connected in gene networks than expected by chance and had 94% greater network centrality than genes showing additive effects, suggesting that nonadditive genes may be key players in the widespread transcriptomic responses to multispecies symbioses. Second, multispecies mutualisms substantially changed coexpression network structure of host plants and symbionts. Less than 50% of the plant and 10% of mycorrhizal fungi coexpression modules detected with rhizobia present were preserved in its absence, indicating that third-party mutualists can cause significant rewiring of plant and fungal molecular networks. Third, we identified unique sets of coexpressed genes that explain variation in plant performance only when multiple mutualists were present. Finally, an ‘across-symbiosis’ approach identified sets of coexpressed plant and mycorrhizal genes that were significantly associated with plant performance, were unique to the multiple mutualist context, and suggested coupled responses across the plant-mycorrhizal interaction to third-party mutualists. Taken together, these results show multispecies mutualism have substantial effects on the molecular interactions in host plants, microbes, and across symbiotic boundaries.

Marine organisms are commonly exposed to variable environmental conditions and many of them are under threat from increased sea temperatures caused by global climate change. Generating transcriptomic resources under different stress conditions are crucial for understanding molecular mechanisms underlying thermal adaptation. In this study, we conducted transcriptome-wide gene expression profiling of the scallop Chlamys farreri challenged by acute and chronic heat stress. Of the 13,953 unique tags, more than 850 were significantly differentially expressed at each time point after acute heat stress, which was more than the number of tags differentially expressed (320~350) under chronic heat stress. To obtain a systemic view of gene expression alterations during thermal stress, a weighted gene coexpression network was constructed. Six modules were identified as acute heat stress-responsive modules. Among them, four modules involved in apoptosis regulation, mRNA binding, mitochondrial envelope formation, and oxidation reduction were down-regulated. The remaining two modules were up-regulated. One was enriched with chaperone, and the other with microsatellite sequences, whose coexpression may originate from a transcription factor binding site. These results indicated that C. farreri triggered several cellular processes to acclimate to elevated temperature. No modules responded to chronic heat stress, suggesting that the scallops might have acclimated to elevated temperature within 3 days. This study represents the first sequencing-based gene network analysis in a non-model aquatic species and provides valuable gene resources for the study of thermal adaptation, which should assist in the development of heat-tolerant scallop lines for aquaculture.

It contains the Supplementary Material of the following article: Coexpression network analysis of the adult brain sheds light on the pathogenic mechanism of DDR1 in schizophrenia and bipolar disorder, which is aimed to know the biological function of DDR1 transcripts in adult brain dorsolateral prefrontal cortex. Particularly: Figure S1. Plot of principal components (PCs) of the whole sample matrices before (A) and after (B) adjusting for surrogate variables. Figure S2. (A) Variance explained by the identified surrogate variables (SVs). Figure S3. Analysis of the network topology under various soft-thresholding power values in the control (A) and whole-sample (B) networks. Figure S4. Quantification of DDR1 transcripts in healthy controls, patients with schizophrenia and patients with bipolar disorder. Figure S5. Correlations between the expression of DDR1 transcripts and cell type proportion estimates of astrocytes, OPCs, oligodendrocytes, neurons, microglia and endothelial cells. Table S1. List of the nominally associated p-values of the eQTL analysis. Table S2. ID and module of the genes in the control network. Table S3. Full list of the results of the cell type enrichment analyses in the control network. Table S4. Full list of the results of the GO enrichment analysis in the control network. Table S5. Correlation of the surrogate variables with the cell type proportion estimates. Table S6. Module distribution and transcript significance of DDR1 transcripts.

Supplementary material of the Article "Coexpression of the discoidin domain receptor 1 gene with oligodendrocyte-related and schizophrenia risk genes in the developing and adult human brain". It contains the data derived form a stuy aimed at identifying the genes that coexpressed with DDR1 in different stages of brain development, particularly: Figure S1. Spatiotemporal expression levels of DDR1, collagen, oligodendrocyte and myelin genes in human brain. Figure S2. Module eigengenes (ME) correlations with sample characteristics at each period studied. Figure S3. Gene overlap between each module identified and the pre-defined cell type modules according to Miller and cols (2010) in each developmental period. Figure S4. Cell type expression of DDR1 in prenatal and adult human brain. Table S1: Sample description. Table S2: Module correspondence. Table S3: List of genes in each DDR1 module according to time interval. Table S4: Module localization of oligodendrocyte, myelin, astrocyte, microglia and collagen genes. Table S5: Gene overlap with premade gene-lists from Cahoy et al. in the DDR1-modules. Table S6: Pathway enrichment in the DDR1-module in I-1. Table S7: Pathway enrichment in the DDR1-module in I-2. Table S8: Pathway enrichment in the DDR1-module in I-3. Table S9: Pathway enrichment in the DDR1-module in I-4. Table S10: Overlapp of genes between DDR1-module and SCZ-susceptibility genes according to time interval.

BACKGROUND: Differential coexpression is a change in coexpression between genes that may reflect 'rewiring' of transcriptional networks. It has previously been hypothesized that such changes might be occurring over time in the lifespan of an organism. While both coexpression and differential expression of genes have been previously studied in life stage change or aging, differential coexpression has not. Generalizing differential coexpression analysis to many time points presents a methodological challenge. Here we introduce a method for analyzing changes in coexpression across multiple ordered groups (e.g., over time) and extensively test its validity and usefulness. RESULTS: Our method is based on the use of the Haar basis set to efficiently represent changes in coexpression at multiple time scales, and thus represents a principled and generalizable extension of the idea of differential coexpression to life stage data. We used published microarray studies categorized by age to test the methodology. We validated the methodology by testing our ability to reconstruct Gene Ontology (GO) categories using our measure of differential coexpression and compared this result to using coexpression alone. Our method allows significant improvement in characterizing these groups of genes. Further, we examine the statistical properties of our measure of differential coexpression and establish that the results are significant both statistically and by an improvement in semantic similarity. In addition, we found that our method finds more significant changes in gene relationships compared to several other methods of expressing temporal relationships between genes, such as coexpression over time. CONCLUSION: Differential coexpression over age generates significant and biologically relevant information about the genes producing it. Our Haar basis methodology for determining age-related differential coexpression performs better than other tested methods. The Haar basis set also lends itself to ready interpretation in terms of both evolutionary and physiological mechanisms of aging and can be seen as a natural generalization of two-category differential coexpression. CONTACT: paul@bioinformatics.ubc.ca.

data-repositorycoexpression-conservation-between-human-and-mouse.zipTurning-PointThe list of turning points in coexpression conservation.Enriched-GO-TermsThe list of all enriched GO terms when SCS = 3, 4 and 5

Coexpression data (Xxx-u, Xxx-r) in ATTED-II ver 12.0.

The coexpression value is provided as a z-score (larger values indicate stronger coexpression).
Further details about the data are available on the ATTED-II Download page: https://atted.jp/download/

Species Codes:
- Ath: Arabidopsis
- Bra: Field mustard
- Gma: Soybean
- Hvu: Barley
- Mtr: Medicago
- Osa: Rice
- Ppo: Poplar
- Sly: Tomato
- Tae: Wheat
- Vvi: Grape
- Zma: Maize

Data Source Codes:
- Xxx-r: RNAseq-based coexpression
- Xxx-m: Microarray-based coexpression
- Xxx-u: Union of Xxx-r and Xxx-m; generally the recommended version in terms of quality and coverage

Cultivated sunflower (Helianthus annuus L.) exhibits numerous phenotypic and transcriptomic responses to drought. However, the ways in which these responses vary with differences in drought timing and severity are insufficiently understood. We used phenotypic and transcriptomic data to evaluate the response of sunflower to drought scenarios of different timing and severity in a common garden experiment. Using a semi-automated outdoor high-throughput phenotyping platform, we grew six oilseed sunflower lines under control and drought conditions. Our results reveal that similar transcriptomic responses can have disparate phenotypic effects when triggered at different developmental time points. Leaf transcriptomic responses, however, share similarities despite timing and severity differences (e.g., 523 differentially expressed genes (DEGs) were shared across all treatments), though increased severity elicits greater differences in expression, particularly during vegetative growth. Across tr..., Six sunflower maintainer (B) lines bred for oil-rich seeds from the SAM population (HA124, HA370, HA412HO, HA850, HAR4, and SF193, also known as XRQ) were grown at the Heliaphen outdoor high-throughput phenotyping platform at INRAE Toulouse (France) in 2018. Seeds were planted on 17 April 2018 in 10 L pots of Terreau Proveen PAM 2 substrate. Plants were fertilized with 300 mL of Peter’s Professional 17-07-27 and 200 mL of Hortrilon per pot on 5 May, 25 May, and 13 June, and were provided Ortiva Top fungicide on 28 May and 15 June. Pots were maintained at specified soil moisture levels by measuring the pot weights daily and refilling them with water to return the pot weights to target levels. Soil around the plants was covered with a silicone cover to limit soil evaporation and was protected from rain using a cone-shaped polystyrene skirt. Drought treatment regimes were imposed by permitting pot weights to drop to specified levels for set periods of time. Plants were grown under one of f...,

lake_whitefish_brain_transcriptome_rawlake_whitefish_brain_transcriptome_normalized

Phosphorus (P) is an essential plant nutrient, but its availability is often limited in soil. Here, we studied changes in the transcriptome and in nutrient element concentrations in leaves and roots of poplars (Populus × canescens) in response to P deficiency. P starvation resulted in decreased concentrations of S and major cations (K, Mg, Ca), in increased concentrations of N, Zn and Al, while C, Fe and Mn were only little affected. In roots and leaves >4,000 and >9,000 genes were differently expressed upon P starvation. These genes clustered in eleven co-expression modules of which seven were correlated with distinct elements in the plant tissues. One module (4.7% of all differentially expressed genes) was strongly correlated with changes in the P concentration in the plant. In this module the GO term “response to P starvation” was enriched with phosphoenolpyruvate carboxylase kinases, phosphatases and pyrophosphatases as well as regulatory domains such as SPX, but no phosphate transporters. The P-related module was also enriched in genes of the functional category “galactolipid synthesis”. Galactolipids substitute phospholipids in membranes under P limitation. Two modules, one correlated with C and N and the other with biomass, S and Mg, were connected with the P-related module by co-expression. In these modules GO terms indicating “DNA modification” and “cell division” as well as “defense” and “RNA modification” and “signaling” were enriched; they contained phosphate transporters. Bark storage proteins were among the most strongly upregulated genes in the growth-related module suggesting that N, which could not be used for growth, accumulated in typical storage compounds. In conclusion, weighted gene coexpression network analysis revealed a hierarchical structure of gene clusters, which separated phosphate starvation responses correlated with P tissue concentrations from other gene modules, which most likely represented transcriptional adjustments related to down-stream nutritional changes and stress.