TITLE: Analysis of co-expression networks among Populus spp. involved in the response to phosphate deficiency and aluminium toxicity
AUTHORS: Thiago Bergamo Cardoso, Renan Terassi Pinto, Luciano Vilela Paiva

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block"><div class = "justify" style = "text-align:left"><span>It is reasonable that some molecular responses to aluminium toxicity and phosphate starvation stresses may be co-opted, as these stresses co-exist in the same environment and the most directly affected organ is the root in both cases .Some findings pointing to co-opted responses have already been reported, like the exudation of organic acids by root tips, that chelates Al</span><span style = "vertical-align:super;">3+</span><span> ions and at the same time increase phosphate availability, activation of the same transcriptional regulator triggered by both stresses and possibilities of hormone signalling crosstalk.</span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span>There are already reports in the literature of applications of molecular knowledge about adaptation to acid soils in commercially relevant crops. Here we focus on studyng the broad transcriptomic response to aluminium and phosphate starvation stresses in </span><span style = "font-style:italic;">Populus ssp.</span><span> as a model plant for woody crops, due to its genetic, genomic and transcriptomic data availability.</span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span>In view of the above, this work highlights the transcriptional responses to aluminum toxicity and phosphate starvation in </span><span style = "font-style:italic;">Populus</span><span>spp. by the use of public microarray data and points to connections between the responses to the two stresses through  gene co-expression networks. This approach is justified by the importance of understanding the relationship between the responses of these two major coexisting stresses in acidic soils and the lack of studies that address this relationship, as well as the responses to each stress in a specific way, in broad transcriptomic data.</span></div></div></div>

[STEPS]
?. [Analysis of data from microarray experiments]
The first step was to download the data and perform the necessary analyzes to determine which genes were differentially expressed.
?. [Analysis of data from microarray experiments]
The microarray data used in this work were obtained from the NCBI database using accession numbers GSE19297 and E-MTAB-3934
?. [Analysis of data from microarray experiments]
Microarray analyses were carried out with the Affymetrix GeneChip poplar genome array (v1.1 from the P. trichocarpagenome project),data were analysed using the free statistical software R (R version 3.3.2) following the protocol described by Janz. The R package "affy" was used to normalize the probes using the "rma" function, available in the Bioconductor software. The log2expression values of transcripts that were present in all replicates, obtained by the "mas5calls" function, in at least one of the conditions were used for the subsequent analyses.
?. [Analysis of data from microarray experiments]
Student’s t-test (p
?. [Analysis of data from microarray experiments]
For annotation of differentially expressed transcripts according to the Populus trichocarpa genome, the Blastx tool was used to align the probes with the CDS database of P. trichocarpa. An e-value
?. [Construction of gene co-expression networks]
?. [Construction of gene co-expression networks]
To identify the proximity of the gene expression profile in the different treatments, the biological coefficient of variability (BCV) was determined for each treatment and an MDS plot was plotted using the EdgeR package.
?. [Construction of gene co-expression networks]
To verify the carrelation of the expression profile of the genes two analyses were performed, a Spearman correlation to identify the relationship between genes and the p-value of this number to determine if this correlation was statistically significant. The p-value was determine using the formula in the excel P = IDIST{ABS[r/SQRT({1-r*r}/{n-2})],[n-2],2} as describe by Usadel, where the number of samples is indicated by n, and r is the observed co-expression score, only correlations with p-value
?. [Construction of gene co-expression networks]
Spearman’s correlation with ρ≥0.8 was selected because unlike the value used in the literature, ρ≥0.7, the goal was to focus only in the genes that showed the strongest correlation.
?. [Enrichment of gene co-expression networks]
To establish the type of regulation in relation to stress for each dataset, the following procedures were used: (1) for the aluminium stress-related libraries, the mean values of the biological triplicates of each gene in the different libraries were estimated, and subsequently, the values of the aluminium stress-related libraries were divided by the values of the library for the same stress exposure time but in the control condition; (2) for the phosphate-related libraries, again, the mean expression values for each gene among the biological triplicates of all libraries were estimated, and then the values of the medium phosphate concentration library were divided by the values of the high phosphate concentration library (to characterize the stress), and likewise, the low phosphate concentration values were divided by the high phosphate concentration values (severe stress).
?. [Enrichment of gene co-expression networks]
Using the values obtained in the calculations described above, it was possible to generate the filters used in the work: positive regulation (coefficient > 1), negative regulation (coefficient
?. [Enrichment of gene co-expression networks]
For each gene co-expression network constructed, the P. trichocarpa proteins corresponding to the probes used in the respective network were analysed in the TF prediction server software of the PlantTFDB database for predicting possible transcription factors present in the network in addition to the family of transcription factors to which it belongs. In addition, the Blast2GO software was used to identify enriched Gene Ontology classes in the data for the distinct groups of gene co-expression networks.
?. [Construction of gene co-expression networks]
Only genes that satisfy the two previous characteristics were used.