{"paper_id":"0600311a-0d63-4cce-bace-8f92a866b3bd","body_text":"Genes associated with translation and oxidative phosphorylation as components of the translational response in nodulated and water-restricted soybean | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genes associated with translation and oxidative phosphorylation as components of the translational response in nodulated and water-restricted soybean Mauro Martínez-Moré, Carla V. Filippi, Guillermo Eastman, Gastón Quero, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7686372/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Soybean primarily acquires nitrogen through symbiosis with nitrogen-fixing bacteria. Water deficit (WD) is a major stress limiting crop yield. Nodulation may enhance drought tolerance in legumes by modulating nitrogen and hormone metabolism, osmotic adjustment, and antioxidant defenses. However, the molecular mechanisms underlying the differing WD responses in nodulated (N-fix) versus non-nodulated (N-fed) plants remain unclear. Translational control of gene expression is a key regulatory mechanism during stress. Results Here, we compared the transcriptome and translatome of soybean roots from N-fix and N-fed plants exposed to WD, analyzing four combined treatments. Our results showed that N-fix plants under WD exhibited more complex responses in terms of total differentially expressed genes (DEGs) compared to N-fed plants. This complexity was also evident in DEGs subject to translational regulation and in differentially expressed transcription factors. Co-expression analysis revealed modules associated with core biological processes, encompassing nodulation, water deficit, and most interestingly, their interplay. Conclusions Our research reveals that translational regulation of genes involved in oxidative phosphorylation and translation initiation emerged as a key response in N-fix plants under WD. These findings highlight distinct molecular adaptations in nodulated soybean roots under WD, with translational control playing a central role. We also identified promising transcription factor candidate genes under translational regulation in N-fix roots—for which no role in nodulation has been described—offering potential targets for improving drought tolerance in legumes once validated functionally. translational control soybean oxidative phosphorylation translation root metabolism drought tolerance symbiotic nitrogen fixation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights N-fix plants keep stomata open longer than N-fed ones in water-restricted conditions. The integration of WGCNA and DEG analysis enhances the interpretation of complex biological data. Translational control is key in shaping the response of nodulated soybean plants to water restriction. Translational control of OXPHOS and ribosomal proteins supports ATP demand in N-fix plants. TFs under translational control emerge as key targets for functional studies. 1. Introduction Water deficit (WD) stress, which occurs when water supply does not meet plant water demand for growth, is the most important abiotic factor limiting crop yields globally (Vadez et al., 2013 ; Zhu, 2016 ). Changes in phenological stages, root architecture, seed production, biomass, harvest index, and germination rate are among the significant effects of drought on yield (Sánchez-Bermúdez et al., 2022 ). Stress-exposed plants eventually reach a soil moisture level where the root system cannot meet the full transpirational demand; at this point, the plants must initiate stomatal closure to prevent shoot desiccation (Vadez et al., 2013 ). Soybean genotypes vary significantly in their sensitivity and response to WD conditions. Genotypes that begin to partially close their stomata at relatively high soil water content may be advantageous in cases of prolonged stress or intermittent water stress with long intervals between rains. However, in a short-term WD scenario, a genotype that keeps its stomata open and maintains high photosynthetic rates for longer periods, even when leaf water potential decreases, may be advantageous since it won't penalize its yield (Sade et al., 2012 ; Vadez et al., 2013 ). One relevant trait in soybean, regarding yield increase under water-limited conditions, is the enhanced drought tolerance for nitrogen fixation (Sinclair et al., 2010 ). In this regard, it has been widely shown in different legume species grown under symbiotic nitrogen fixation (N-fix) that these plants are more protected against WD stress if compared to non-nodulated fertilized plants (N-fed); in other words, the nodulation condition of a legume (i.e., nodulated or non-nodulated) affects the way the plant responds to WD (Álvarez-Aragón et al., 2023 ; Liu et al., 2022 ; Lodeiro et al., 2000 ; López et al., 2023 ; Staudinger et al., 2016 ). It remains unclear, however, which molecular mechanisms are responsible for the differential response. The differential response of N-fix and N-fed plants to water restriction could result from the differential regulation of gene expression, including the regulation of different gene sets or the variation in the levels at which regulation is achieved, or a combination of both phenomena. Although for most genes, the regulation of their transcription is the primary and main level of regulation, there is also a level of post-transcriptional regulation that encompasses translational and post-translational events (Orphanides and Reinberg, 2002 ). These post-transcriptional regulatory events explain the poor or variable levels of correlation between transcript and protein levels reported in different organisms (Becker et al., 2018 ; Lei et al., 2015 ; Piccirillo et al., 2014 ; Traubenik et al., 2020 ). Translational control, in particular, has proven relevant in plants exposed to various stressors such as WD (Kawaguchi et al., 2004 ; Lei et al., 2015 ). Plants, and organisms in general, benefit from this step of gene expression regulation—which does not require de novo messenger RNA (mRNA) synthesis but rather refers to the efficiency with which mRNAs already present in cells are translated—since it allows them to respond rapidly thus conferring flexibility and adaptability (Lee and Bailey-serres, 2019 ; Urquidi Camacho et al., 2020 ). Thus, the analysis of the translatome (the subset of mRNAs that are being translated) allows for a more precise and comprehensive measurement of cell gene expression, as opposed to only analyzing steady-state mRNA levels (the transcriptome) (Sablok et al., 2017 ). A typical response of organisms subjected to environmental constraints that imply a reduction in energy availability (e.g., WD, hypoxia) or nutrient shortage is the general repression of translation—occurring mainly at the initiation level—impacting most cellular mRNAs. Still, in the just-mentioned conditions, specific mRNAs, such as from dehydration-inducible genes, increase their association with polysomes (Juntawong et al., 2014 ; Kawaguchi et al., 2004 ; Lei et al., 2015 ; Mustroph et al., 2009 ). However, the literature regarding examples of genes subjected to translational control in nodulated and water-restricted plants is scarce. Two exciting examples we can mention are the work recently published by our group, in which we reported, on the one hand, that some members of the thioredoxin and glutaredoxin systems and, on the other, that the metabolism of several hormones —abscisic acid, ethylene, auxin, and cytokinin— are regulated at the translational level in the roots of nodulated soybean plants subjected to water-deficit stress (Sainz et al., 2024 , 2022 ). Building on this, our current research shows that genes coding for ribosomal proteins and proteins involved in the mitochondrial oxidative phosphorylation (OXPHOS) system are among the most prominent examples of genes that are mainly regulated (particularly up-regulated) at the translational level in nodulated and water-restricted roots of the analysed soybean genotype. Identifying translational control as a critical step in regulating ribosomal and OXPHOS-related protein gene expression in plants is novel. Ribosomal proteins and ribosomal RNAs are structural components of ribosomes and, hence, essential for ribosome biogenesis, which is the foundation for cell growth and proliferation. The mitochondrial electron transport chain utilizes a series of electron transfer reactions to generate cellular ATP through OXPHOS; this system produces most of the energy required by non-photosynthetic cells (Kremer and Rehling, 2024 ; Nolfi-Donegan et al., 2020 ). Since translation is a highly energy-demanding process, it makes sense to coordinate the induction of ribosome biogenesis and OXPHOS in roots. However, it is well-known that mitochondrial respiration is generally inhibited due to the imposition of water restriction on plants (Atkin and Macherel, 2009 ). Hence, what is also novel is that the nodulation condition of those water-restricted roots is the determinant cue for the switch in the expression status of the aforementioned genes. In this work, we investigated the responses to water restriction at the transcriptional and translational levels in Génesis 5601, a commercial soybean genotype in Uruguay, cultivated under N-fix and N-fed conditions. Compared to N-fed plants, we found that the response of nodulated and water-restricted plants was more complex in terms of total differentially expressed genes (DEGs), as well as DEGs that code for transcription factors and DEGs with translational regulation. Furthermore, through a weighted gene co-expression network analysis (WGCNA) followed by a differential expression analysis, we identified gene modules associated with pertinent biological processes in the plant's differential responses. As mentioned above, the translational regulation of genes involved in OXPHOS and translation initiation was key in the nodulated and water-restricted plants. We uncovered robust candidate genes under translational control in the roots of nodulated soybean plants facing WD, opening new avenues for their functional characterization. 2. Materials and methods 2.1 Plant growth conditions, nodulation, and drought assay The trial was carried out in a growth chamber having the following environmental conditions: 620 µmoles photons m − 2 s − 1 (137 W m − 2 ) of a white light emitted from metal halide lamps, a 16/8 hours light/darkness photoperiod, a light/darkness temperature cycle of 28/20°C, and a relative humidity of 39.5 ± 7.7% during all the growth period. Bottles of 0.5 L containing a 1:1 mixture of sand:vermiculite as substrate were used as pots. The soybean [ Glycine max (L.) Merr.] genotype evaluated was “Génesis 5601” (G5601), developed in a local breeding program where the main trait for selection was yield stability (Quero et al., 2021 ; Simondi et al., 2022 ), and provided by Sergio Ceretta from Instituto Nacional de Investigaciones Agropecuarias (INIA – La Estanzuela, Uruguay). Three seeds per pot were sown, and the more vigorous seedling was selected in each pot after the cotyledons had fully developed. In addition, developmental homogeneity was taken into account when selecting plants to eschew the potential effects of differential developmental stages. For rhizobium inoculation, the U1302 Bradyrhizobium elkanii strain was used (Vincent, 1970 ), and inoculation was done on the day of seeding and repeated after three days. Eventually, each bottle was covered with a plastic lid—with a hole for the plant to grow through—to minimize water evaporation from the substrate. The trial was conducted using a completely randomized design with four treatments, each with five biological replicates, resulting in 20 individuals. The treatments combined nodulation (N) and no nodulation (NN) conditions, as well as well-watered (WW) and water-restricted (WR) conditions. In the case of WW plants, the substrate was held at field capacity throughout the trial since field capacity represents a soil state with no water constraints. Thus, WW was considered the control treatment for water conditions. As a result, the four treatments consisted of non-nodulated and well-watered plants (NN + WW), non-nodulated and water-restricted plants (NN + WR), nodulated and well-watered plants (N + WW), and nodulated and water-restricted plants (N + WR) (Fig. 1 A). In this way, the biological contrasts studied were i ) N + WR vs N + WW, ii ) N + WR vs NN + WR, iii ) NN + WR vs NN + WW, and iv ) N + WW vs NN + WW (Fig. 1 B). During the first 19 days after sowing (V3 developmental stage), all plants were watered with B&D-medium supplemented with KNO 3 (0.5 mM and 5 mM final concentration for nodulated and non-nodulated plants, respectively). As of day 20, water was withdrawn only to WR plants, considering this moment the day 0 of the WD period. The substrate water content was measured on a daily basis by gravimetry (water gravimetric content) during either the growth or WD period. Stomatal conductance (g sw ), measured with a SC-1 Porometer (Decagon Device), was the variable used to monitor WD. Daily measurements were performed at WW and WR plants during the WD period. WR plants were harvested when the g sw value reached approximately 50% of the value obtained on day 0, while WW plants were randomly harvested with WR plants. For N plants, nodules and roots were separately stored at -80°C; however, only the roots were analyzed in this work. For NN plants, roots were kept at -80°C until further processing, too (Fig. 1 A). 2.2 Purification of polysomal fraction The polysomal fraction was purified by sucrose cushion centrifugation, as described by (DiPaolo et al., 2020 ; Sainz et al., 2024 , 2022 ; Smircich et al., 2015 ). A volume of 2 mL of pulverized frozen roots was homogenized in 4 mL of polysome extraction buffer (Sainz et al., 2022 ). The homogenates were kept on ice until the samples were processed and clarified by centrifugation at 16000 g for 15 min. After this, the samples were filtered, and centrifugation was repeated. A 500 µL volume of the supernatant was separated for total RNA (TOTAL) isolation (Fig. 1 C). Sucrose cushions made of 12% and 33.5% sucrose layers were prepared in 13.2 mL tubes (UltraClear, Beckman Coulter, United States, 344059), and 2 mL of the remaining supernatant were loaded onto them. An S-class Beckman L-100K ultracentrifuge (W40 Ti swinging bucket rotor) was used to centrifuge the tubes at 35000 rpm for 2 h at 4°C. After centrifugation, the polysomal fraction was retrieved from the pellet for each sample and resuspended in 200 µL of polysome resuspension buffer (Sainz et al., 2022 ). The resuspended polysomes were kept at 4°C for 30 min to finally perform a regular RNA purification and obtain the polysome-associated mRNA fraction (PAR) (Fig. 1 C). 2.3 RNA extraction and sequencing The RNA extraction from TOTAL and PAR fractions was done by homogenizing them in 750 µL TRizol LS (Invitrogen, United States of America, 10296-028), following Sainz et al. ( 2022 ). The concentration and integrity of each RNA were determined using a BioAnalyzer Agilent 2100 (Agilent Technologies, Inc., United States of America). Samples with a concentration over 1.0 µg and an RNA integrity number greater than 7.0 were shipped to Macrogen Inc. (South Korea) for library preparation and sequencing. Sequencing was performed on the Illumina Novaseq 6000 high-throughput sequencing platforms. For each treatment, samples of both TOTAL and PAR fractions of roots from three biological replicates were sent for sequencing. 2.4 Data analysis 2.4.1 Sequencing read processing The quality of each sample was visually inspected using FastQC ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). Trimmomatic was used to remove adapters and trim low-sequencing-quality bases (v 0.39, Bolger et al., 2014 ). Trimmed sequences with a length over 80 bp and an overall quality greater than 30 were retained for further analysis. Transcript-level gene expression was quantified using Salmon in quasi-mapping-based mode (v1.8.0, Patro et al., 2017 ). All Salmon parameters were set to their default values, except for the GC bias correction parameter, which was enabled. The map index was built from the fourth version of the Glycine max transcriptome retrieved from NCBI (GCF_000004515.6, Glycine max v4.0, excluding rRNA). Transcript read counts were then aggregated to gene level using the tximport R package (v 1.26.1, Soneson et al., 2016 ). Initial and post-trimmed read descriptions and mapping rate are presented in Table S1 . The sequencing data is available in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA1284746. 2.4.2 WGCNA analysis A weighted gene co-expression network analysis was performed using the WGCNA R package (version 1.72-5, Langfelder and Horvath, 2008 ). The gene-level count data were used as input, normalized using the DESeq2 R package (v1.38.3, Love et al., 2016 ). Since low-expressed features tend to increase noise, genes with a low coefficient of variation and/or low counts (defined as having fewer than 50 counts in more than 50% of the samples) were removed. After that, soft threshold power was selected using the pickSoftThreshold function. The net was built using the blockwiseModules function, setting the arguments power at 9 (Figure S1 ), maxBlockSize at 27000 (genes number after filtering), mergeCutHeight at 0.20, networkType as unsigned, TOMtype as signed (to preserve the continuity of correlation), and all the remaining arguments at default. The module eigengene (ME) was calculated for each module using the moduleEigenges function to determine whether modules were associated with treatments. A heatmap was built using the pheatmap R package (v 1.0.12, Kolde, 2015) to visualize ME-treatment relationships. Also, for visualization purposes, the MEs were grouped by hierarchical clustering using 1-correlation as a distance measure (Zhang and Horvath, 2005 ). The average link was used as the clustering criterion for dendrogram creation (Dudek, 2020 ). The number of clusters was determined using the Silhouette index (Dudek, 2020 ) and the NbClust R package (Charrad et al., 2014 ). The network visualizations were done using the network R package (Butts, 2008 ), setting an edge adjacency of 0.15. Modules were plotted with different colors to distinguish them visually. In addition, the intramodular connectivity was estimated for each gene using the intramodular Connectivity WGCNA function, allowing to determine the 10% most within-connected genes of each module as hub genes (Seo et al., 2009 ). 2.4.3 DEG analysis A principal component analysis (PCA) of the samples and a heatmap of distances between samples were performed to observe clustering patterns and distances among replicates and treatments. The R packages FactoMineR (v. 2.1, Le et al., 2008 ), pheatmap (v. 1.02, Kolde, 2015), and ggplot2 (v. 3.5.1, Wickham, 2016) were used for these purposes. Genes that presented at least one count in the sum of all samples were used, and a variance-stabilizing transformation of the data was applied following the recommendations of Love et al., ( 2016 ). Additionally, a Gene Ontology (GO) biological process enrichment analysis was conducted using the topGO R package (v 2.50.0, Alexa and Rahnenfuhrer, 2021 ), focusing on the 50 genes that contributed the most to principal components 1 and 2, to identify which processes had the most significant influence on data variability. The differential expression statistical analysis was computed using the DESeq function of the DESeq2 R package (v 1.38.3, Love et al., 2016 ). DEGs were defined as those with |log2FC| >1 and Benjamini–Hochberg adjusted p-value (padj) < 0.05. DEGs lists of each contrast and RNA fraction were intersected to identify common and unique DEGs. From the TOTAL and PAR lists, intersections of genes with main transcriptional regulation (only TOTAL), main translational regulation (only PAR), or combined (transcriptional and translational) regulation (TOTAL + PAR) could be identified. Additionally, DEGs lists of pairs of contrasts were intersected to remove, for a contrast of interest, those DEGs that might be due to the effect of a biological treatment and not to the effect of the combined treatment. In one case, contrast i (N + WR vs N + WW) and contrast iii (NN + WR vs NN + WW) DEGs lists were intersected, so DEGs common to WR, and genes that are specifically altered when the plant experiences WD, but is also nodulated, are obtained. Likewise, contrast ii (N + WR vs NN + WR) and contrast iv (N + WW vs NN + WW) DEGs lists were intersected, obtaining common DEGs due to nodulation, and DEGs which are due to water restriction in nodulation plants. 2.4.4 WGCNA + DEG DEGs in each fraction and contrast between combined treatments [ i ) N + WR vs N + WW, ii ) N + WR vs NN + WR, iii ) NN + WR vs NN + WW, iv ) N + WW vs NN + WW] were co-localized in the co-expression network modules. DEGs were colored in the network according to their regulation status, i.e. up- or down-regulated or stable. As a result, modules with an enriched presence of DEGs were identified. 2.4.5 Functional Enrichment and Protein-Protein Interactions Analyses Annotation of GO terms for the soybean genome was conducted using PANNZER (Törönen and Holm, 2022 ). GO biological processes (GO_BP) enrichment analyses were performed on the modules with high DEGs co-localization, using Fisher’s exact test and the weight01 method to construct the GO graph structure. A GO term was considered significantly enriched when it had a false discovery rate (FDR) of less than 0.05. Analyses were done using the topGO R package (v 2.50.0, Alexa and Rahnenfuhrer, 2021 ). GO enrichment plots were built using ggplot2 (v 3.5.1, Wickham, 2016). Protein-protein interactions (PPI) network analyses were performed to search for potential interactions between proteins encoded by DEGs. The networks were constructed using the DEGs that were co-localized in the selected modules for analysis. CytoScape (Smoot et al., 2011 ) and STRING (Szklarczyk et al., 2021 ) were used to construct the networks. GO_BP and KEGG pathway enrichment analyses were done on the PPI networks, in both cases considering a term significantly enriched when it had a FDR of less than 0.05. 2.4.6 Motifs Discovery and Enrichment Analysis in 5’-UTRs The transcripts’ 5’-UTRs were retrieved from the genomic annotation GTF file (GCF_000004515.6, Glycine max v4.0) using an R public script ( https://github.com/saketkc/gencode_regions ). The bioinformatic tool seqtk ( https://github.com/lh3/seqtk ) was used to obtain the 5’-UTRs fasta files, utilizing the bed file obtained in the previous step as input. The motifs discovery in the 5’-UTR was conducted through MEME (version 5.5.7, Bailey and Elkan, 1994 ), targeting a maximum of 10 motifs of between 6 and 50 nucleotide length, utilizing the classic discovery mode, and setting zero or one motif occurrence per sequence. The motifs enrichment analysis was performed using SEA (Simple Enrichment Analysis) (Bailey et al., 2015 ). All search parameters were set to their default: E-value lower than 10, zero Markov order for shuffling sequences and for the background model, and center sequences alignment for site positional diagrams. 2.4.6 Candidate Transcription Factors Selection The transcription factors that exhibited differential expression at the translational regulation level (PAR or TOTAL + PAR) in contrasts involving the N + WR combined treatment —contrasts i and ii — were initially selected as candidates for further analysis. Subsequently, one of the two following criteria was applied to narrow down the list: first, being a hub gene in the co-expression modules selected during previous steps of the analysis; second, having putative targets that share the same regulatory state (up- or down-regulation), with these targets regulated at the transcriptional level (TOTAL or TOTAL + PAR). The list of putative targets for each transcription factor was retrieved from PlantTFDB (Jin et al., 2017 ). 3. Results In this study, the assessment of the differential responses of the roots of nodulated soybean plants to WD comprised an experimental design that combined two nodulation conditions and two hydric conditions, i.e., nodulated (N) and non-nodulated (NN) plants were subjected to water restriction (WR) or maintained well-watered (WW) throughout the WD period. Therefore, four combined treatments were obtained (as detailed in 2.1 and depicted in Fig. 1 ), and the following four biological contrasts were studied: i ) N + WR vs N + WW, ii ) N + WR vs NN + WR, iii ) NN + WR vs NN + WW, and iv ) N + WW vs NN + WW. Although our main objective was to gain knowledge of the molecular mechanisms governing the response of nodulated (or N-fix) plants to WD (analysed in contrasts i and ii ), the information gathered from all four contrasts is distinctive and relevant. It allows for the analysis of the plant responses to the nodulation and WD conditions, both jointly and individually, within the same experiment. This way, the response of nodulated plants to WR is highlighted in the N + WR vs N + WW ( i ) contrast, whereas in ii ) N + WR vs NN + WR, the effect of water restriction is studied under different nodulation contexts. The NN + WR vs NN + WW ( iii ) contrast shows non-nodulated (or N-fed) plant's responses to water restriction, and the N + WW vs NN + WW ( iv ) contrast evidence the plant responses resulting from the nodulation process without WR. Central to our study was the inclusion of translational control as an additional layer of information, complementing transcriptional regulation. This approach was driven by the significance of this gene expression regulatory step in plants under environmental stress, and by the potential of integrating data from multiple layers to reveal novel, biologically interpretable associations (VanDam et al., 2018 ). To achieve this, the four contrasts between combined treatments were analysed at the transcriptome (total RNA fraction; TOTAL) and the Polysome-Associated RNA level (PAR fraction), focusing attention on the variations in the polysome association of mRNA compared to total RNA levels. This way, we could assess regulation at the TOTAL and PAR levels as well as the combined responses (TOTAL + PAR). 3.1 RNA-seq data validation Since our RNA-seq experiment was performed in parallel with two genotypes: G5601—whose data is presented in this manuscript—and Don Mario (DM6.8i)—whose data has been already published (Sainz et al., 2024 )—, we validate the RNA-seq data by analysing the common DEGs to water restriction (contrasts i and iii ) on the one hand, and the common DEGs to the nodulation process (contrasts ii and iv ) on the other, of both genotypes. We analysed expression data (fold change values) of those genes and found high positive Pearson correlation coefficients between the common genes among genotypes (Figure S2 ), confirming the reliability of the RNA-seq results used in this study. In Figure S2 , we show the fold change of 29 genes common to contrasts i and iii , and 106 genes common to contrasts ii and iv , which were present in both genotypes. 3.2 Nodulation delays average stomata closure rate in G5601 soybean plants The WD condition—applied to WR plants—was established through water withdrawal; water substrate content was measured daily by gravimetry, and stomatal conductance was the WD monitoring variable. In our plant-pot-substrate system (Figure S3 ), water loss from the substrate is solely due to transpiration, as evaporation is negligible (thanks to the lids), and since the plants were grown at field capacity until the WD period, there is no percolation. The median stomatal conductance of N + WW and NN + WW plants was 220 mmol m − 2 s − 1 and 152 mmol m − 2 s − 1 , respectively (Fig. 2 B). Regarding WR plants, it was observed that N + WR plants held their initial stomatal conductance (g sw0 , that of day 0 of the WD period) for an average of four days throughout the WD period (Fig. 2 D). On the other hand, NN + WR plants maintained their g sw0 only until day one of the WD period, on average (Fig. 2 D). Hence, in this study, by using the stomatal conductance as the trait to determine the timing of WR plants harvesting, it was observed that nodulated plants reached 50% of g sw0 two days later than non-nodulated plants, on average (Fig. 2 E). This finding evidences that N-fix plants can keep their stomata as open as in WW conditions for a longer time compared to N-fed ones. Figure 2 A and C demonstrate that WW and WR plants are held at field capacity or subjected to WD, respectively. 3.3 Water restriction has a stronger impact on gene expression than nodulation, which mainly upregulates genes. Exploring the RNA-seq data using distance matrix analysis and principal component analysis (PCA) showed that all biological replicates clustered together (Figure S4 ). In the heatmap (Figure S4 , A), two large clusters formed, with all the samples corresponding to the N + WR treatment grouped on one side and the rest in another group. The PCA evidenced that dimension 1 (Dim1) explained 58.4%, separating the samples by the nodulation condition. Dimension 2 (Dim2)—explaining 21.2% of the variance—separates the samples by the hydric condition. This approach resulted in four well-defined groups, corresponding to the four combined treatments. As expected, each sample's TOTAL and PAR fractions were found to be very close to each other (Figure S4 , B; circles and triangles, respectively). These results indicate that the data are suitable for further analysis (replicates of the same treatment and RNA fractions of the same biological replicate were similar). Therefore, differential gene expression was analyzed between the combined treatments. Initially, contrasts were examined to assess how the nodulation context (N vs NN) and the hydric condition (WR vs WW) altered the global gene expression at both the transcriptome and translatome levels (Fig. 3 , A-D). To summarize, water restriction had a more significant impact on gene expression at both the transcriptome and translatome levels compared to nodulation. In the N vs NN comparison, the majority of DEGs were upregulated, whereas in the WR vs WW comparison, more DEGs were downregulated at both the TOTAL and PAR levels (Fig. 3 , A-D). Subsequently, the lists of DEGs—down-regulated on one hand and up-regulated on the other—for each contrast's TOTAL and PAR fractions were intersected, thus obtaining the gene identities that presented mainly transcriptional or translational regulation and those with combined regulation. Additionally, the DEG lists were intersected between contrasts ( i and iii ; ii and iv ) to discriminate in each list the genes that were differentially expressed due to the biological effect of one condition or treatment (e.g., water restriction) rather than the combined treatment (Fig. 3 , E-H). To begin with, the lists of contrast i , which exhibit the response of nodulated plants to water restriction, were intersected with the lists of contrast iii , which show the response of non-nodulated plants to water restriction, to obtain the DEGs that were due to the effect of water restriction independently of the nodulation context and also the preferential DEGs of each contrast (Fig. 3 , E, F). Interestingly, it can be observed that under water restriction, nodulated plants not only show a greater number of DEGs but also exhibit a higher amount of DEGs with mainly translational regulation than non-nodulated plants (Fig. 3 , E, F). Almost 30% (672 out of 2523) of the DEGs had translational regulation in nodulated plants under water restriction, whereas in non-nodulated plants, 15% (100 out of 653) of the DEGs showed regulation at the translational level under water restriction (Fig. 3 , E, F). Next, the Venn diagrams depicted in Fig. 3 , G, H visualize the same logical relationship between the lists of contrast ii , which shows the particular response to water restriction of nodulated plants with respect to non-nodulated plants, with those of contrast iv , which shows the plant responses due to the nodulation process without involving water restriction. This allowed for the discrimination between DEGs common to nodulation (N + WR and N + WW) and DEGs due to each combined treatment (N + WR, on the one hand, and N + WW, on the other). 3.4 Coordinated gene expression in nodulated soybean roots under water restriction: insights from weighted gene co-expression network analysis. A gene co-expression network was constructed to identify gene modules, hub genes, and, especially, candidate genes that could explain the plant responses to the different treatments—with special emphasis on the responses of nodulated plants to WD. To perform WGCNA, 24 samples comprising the three replicates from the four combined treatments ( i , ii , iii , and iv ) and the two mRNA fractions (TOTAL and PAR) were utilized. A total of 26,356 genes were included in the analysis, resulting in 33 gene co-expression modules and an additional module (module 0) that groups the genes that could not be classified into any of the other modules (Fig. 4 , A). The number of genes in each module ranged from 30 to 5,494, with module 1 having the highest and module 33 the lowest number of genes (Fig. 4 , A). The modules were then grouped into clusters based on the expression profiles of their eigengenes (ME), resulting in a total of nine clusters defined by the Silhouette index (Figure S5 ). Module 0 (M0) formed a single cluster (cluster 1) since the expression profile of its eigengene (ME0) did not group with any other module. The cluster comprising ME10, ME29, ME32, ME19, and ME31 (cluster 2) was formed based on the expression of their genes in relation to the nodulation context since the expression of these MEs was higher in the combined treatments N + WW and N + WR compared to the non-nodulation treatments. A third cluster was formed by ME5, ME26, ME17, and ME18, which was mainly detected in the PAR samples of all treatments, with the highest detection in non-nodulated plants subjected to water restriction (NN + WR). Cluster 5 (ME15, ME4, ME7, ME6, ME27, ME21, and M22) exhibits the lowest and highest transcript detection in water-restricted plants (NN + WR and N + WR) and well-watered plants (NN + WW and N + WW), respectively. This last observation is especially accurate for the PAR fraction. In contrast to the previous cluster, a sixth cluster composed of ME8, ME20, ME9, and ME24 included genes preferentially detected in the treatments comprising water restriction conditions (NN + WR and N + WR). This is graphically presented in Fig. 4 , A. The topological representation of the expression modules of the WGCNA network is shown in Fig. 4 , B). All modules, except for M28, exhibited edge adjacency values exceeding the threshold (0.15), indicating their presence in the network. Consequently, the network comprised 12,848 nodes—representing genes—and 3,951,052 edges—representing the connections between genes. The edges were not graphed due to computational limitations (Fig. 4 , B). 3.5 The integration of WGCNA and DEGs analysis allows the identification of specific co-expressed modules associated with nodulation, water restriction, or their interaction. To further investigate the responses of the G5601 soybean genotype to the different experimental conditions, the WGCNA and DEGs analysis information were integrated. This approach enabled the localization of DEGs associated with each contrast, considering their status (i.e., up- or down-regulated) and regulation level (i.e., TOTAL, PAR or TOTAL + PAR), within the different WGCNA modules. First, the localization was performed for the DEGs common to water restriction (those shared between contrast i and iii : N + WR vs N + WW and NN + WR vs NN + WW) and for the DEGs common to nodulation (those shared between contrast ii and iv : N + WR vs NN + WR and N + WW vs NN + WW) (Table S2 ). Most DEGs associated with WR were found in modules M4, M7, and M8 at the TOTAL + PAR level. Notably, the nodulation-related DEGs were found almost exclusively in M10 at the TOTAL + PAR level (Table S2 ). Thus, modules M4, M7, and M8 may be linked to the plant's responses to water restriction, while M10 is related to the nodulation process. Furthermore, the DEGs found in each of the four contrasts between combined treatments were localized in the co-expression modules (Table S3 ) and were mapped onto the co-expression network (Fig. 5 ). Most DEGs in contrast i (N + WR vs N + WW), which involved the response to water restriction of nodulated plants, were located in modules M1, M4, M7, M8, M20, and M21 (Table S3 ; Fig. 5 A). In the case of contrast ii (N + WR vs NN + WR), which showed how plants change their response to water restriction when nodulated, the majority of DEGs were located in modules M4, M7, and M19 (Table S3 ; Fig. 5 B). The DEGs identified in the contrast that shows non-nodulated plant's responses to water restriction (contrast iii : NN + WR vs NN + WW) were preferentially located in module M7 (Table S3 ; Fig. 5 C). Regarding the DEGs obtained in the contrast highlighting the plant responses resulting from the nodulation process without involving any water restriction (contrast iv : N + WW vs NN + WW), they were located in modules M7, M10, and M21 (Table S3 ; Fig. 5 D). Interestingly, M21 co-localized genes showing a reversal in their regulation status between two contrasts: up-regulated genes in contrast iv (N + WW vs NN + WW) were repressed in contrast i (N + WR vs N + WW). Thus, some genes responsive to nodulation were down-regulated upon establishing WD conditions (Table S3 ; Fig. 5 D, A). The previously DEGs-enriched mentioned modules, modules M4, M7, M8, and M10, were selected for further analysis of the DEGs common to water restriction or nodulation (Table 1 ) and modules M1, M4, M7, M8, M19, M20, and M21 were selected for further analysis of the DEGs from each contrast (Table 2 ). In both cases, the regulation level (TOTAL, PAR, TOTAL + PAR) of the DEGs was assessed. Most DEGs common to water restriction were located in M4 and M7, presented combined regulation, and were down-regulated (Table 1 ). Almost all DEGs common to nodulation also showed combined regulation, were up-regulated, and were located in M10 (Table 1 ). Regarding the DEGs identified for each contrast, several aspects are noteworthy. First, M7 contained mostly down-regulated DEGs in all four contrasts. Second, M4 preferentially accommodated up- and down-regulated DEGs of contrast i and ii of all regulation levels. Third, DEGs in M8 and M20 were mainly derived from contrast i (N + WR vs N + WW) and were up-regulated, with TOTAL + PAR regulation. Fourth, M19 contained up-regulated DEGs almost exclusively of contrast ii . Fifth, M21 presented 83 up-regulated DEGs in contrast iv and 67 down-regulated DEGs in contrast i . Of these, 55 genes reverted their regulation status (down- or up-), regardless of the regulation level, in the mentioned contrasts ( i and iv ). Lastly, and most notably, 70% of the DEGs in M1 (228 out of 338) presented mainly translational regulation (PAR) and corresponded to up-regulated genes from contrast i . This data is presented in Table 2 . Another interesting piece of information shown (between parentheses) in Table 1 and Table 2 (and also in Table S2 and Table S3 ) is the number of DEGs that are also the module's hub genes. We defined hub genes as the 10% most connected (see 2.4). Since highly connected genes often play a more crucial role in the functionality of networks than other nodes, we reasoned it was interesting to investigate whether any of the DEGs were also hub genes. All four contrasts presented hub genes within their DEGs, with the N + WR vs NN + WR contrast ( ii ) exhibiting the highest number of hub genes among its DEGs (32%: 146 out of 458), followed by the iv (N + WW vs NN + WW) contrast with 28% (60 out of 216) of hub genes among its DEGs. These results indicate that while nodulation influenced the differential expression of certain hub genes, a more significant percentage of hub genes were impacted by the interaction between nodulation and WD (relative to non-nodulation). Table 1 Weighted Gene Co-expression Network Analysis (WGCNA) Modules (M) co-localizing the greater number of differentially expressed genes (DEGs) common to contrasts i) and iii) and contrasts ii) and iv). Contrasts Status Regulation level M4 M7 M8 M10 i) N + WR vs N + WW and iii) NN + WR vs NN + WW Downregulated TOTAL 17 7 2 0 PAR 5 (1) 3 1 0 TOTAL + PAR 144 (84) 50 (21) 4 (2) 0 Upregulated TOTAL 6 0 0 0 PAR 1 0 1 0 TOTAL + PAR 34 (14) 1 16 (7) 0 ii) N + WR vs NN + WR and iv) N + WW vs NN + WW Downregulated TOTAL 2 (1) 2 0 2 PAR 0 0 0 1 TOTAL + PAR 4 (4) 9 (4) 0 5 Upregulated TOTAL 2 1 1 6 (1) PAR 0 0 0 1 TOTAL + PAR 1 0 0 113 (63) Whether the DEGs were down- or up-regulated is depicted in the Status column. The regulation level (TOTAL, PAR, TOTAL + PAR) is also shown. The number of DEGs that are also modules’ hub genes is indicated in parentheses. Hub genes were defined as the 10% of genes with the highest intramodular connectivity. Table 2 Gene co-expression Modules (M) obtained from Weighted Gene Co-expression Network Analysis (WGCNA) selected for having the greatest number of differentially expressed genes (DEGs) across the four contrasts analyzed ( i , ii , iii , and iv ). Condition DEG Status Regulation level M1 M4 M7 M8 M19 M20 M21 i) N + WR vs N + WW Downregulated TOTAL 8 60 (2) 21 (1) 19 0 8 (1) 15 (1) PAR 2 70 (4) 19 (6) 11 0 8 13 (2) TOTAL + PAR 3 240 (57) 43 (19) 20 (3) 0 21 (1) 39 (18) Upregulated TOTAL 4 49 2 33 (5) 0 6 (1) 0 PAR 228 (99) 63 (1) 0 22 (5) 0 18 3 TOTAL + PAR 31 (1) 158 (20) 3 120 (42) 0 51 (23) 1 ii) N + WR vs NN + WR Downregulated TOTAL 5 82 (31) 16 (2) 6 (1) 0 1 0 PAR 2 14 (5) 6 (1) 0 0 0 0 TOTAL + PAR 1 126 (61) 35 (8) 2 (1) 0 0 0 Upregulated TOTAL 14 (1) 28 (4) 2 (1) 6 27 (11) 1 (1) 0 PAR 2 14 (3) 1 3 (1) 7 (1) 0 0 TOTAL + PAR 4 31 (9) 4 7 (1) 11 (4) 1 0 iii) NN + WR vs NN + WW Downregulated TOTAL 4 9 25 (2) 4 0 2 0 PAR 0 4 15 (3) 2 1 0 0 TOTAL + PAR 0 8 51 (10) 0 0 0 0 Upregulated TOTAL 3 6 7 3 0 0 0 PAR 6 2 1 7 0 2 0 TOTAL + PAR 1 2 3 2 0 0 0 iv) N + WW vs NN + WW Downregulated TOTAL 5 7 12 (4) 6 (1) 0 3 (1) 4 PAR 4 0 17 (1) 1 0 2 3 TOTAL + PAR 3 4 28 (10) 4 (1) 0 2 1 Upregulated TOTAL 6 6 5 2 8 1 22 (1) PAR 1 1 2 0 5 0 8 (2) TOTAL + PAR 1 0 7 0 2 0 53 (19) Whether the DEGs were down- or up-regulated is depicted in the Status column. The regulation level (TOTAL, PAR, TOTAL + PAR) is also shown. The number of DEGs that are also modules’ hub genes is indicated in parentheses. Hub genes were defined as the 10% of genes with the highest intramodular connectivity. 3.6 Enrichment analysis of biological processes and metabolic pathways in the selected co-expression modules. Gene Ontology biological process enrichment analysis and KEGG were conducted on specific co-expression modules (those listed in Table 1 and Table 2 ) based on the characteristics of the DEGs previously described, to capture co-expression responses and understand which processes exhibited similar responses across treatments. M10, where nearly all DEGs common to nodulation co-localized, was overrepresented with higher significant gene counts in: “response to stimuli”, “transmembrane transport”, “regulation of nucleobase-containing compounds”, and “nodulation” for GO_BP, as well as “biosynthesis of cofactors”, “purine metabolism”, and “zeatin biosynthesis” for pathways (Fig. 6 D). M7 mainly comprised down-regulated DEGs across the three regulation levels (TOTAL, PAR, and TOTAL + PAR) and all four contrasts analyzed. Therefore, this module can be understood as a group of genes whose expression and/or association with polysomes is preferentially repressed when either of the stimuli assessed in this study—nodulation and water restriction—is present since each contrast studies the effect of a stimulus on a condition that does not present it. In contrast iv (N + WW vs NN + WW), the stimulus was the biotic effect of nodulation; in contrast i (N + WR vs N + WW) and iii (NN + WR vs NN + WW), the stimulus was the abiotic effect of water restriction (in different nodulation contexts). Meanwhile, in contrast ii (N + WR vs NN + WR), the differential stimulus was nodulation since the plants were subjected to water restriction in both treatments. When functional enrichment was analyzed over the entire module, it was found to be primarily enriched in biological processes and pathways of “stress response”, “transcription regulation”, “protein phosphorylation”, and “biosynthesis of flavonoids and phenylpropanoids” (Fig. 6 B). M4, which allocated DEGs from contrasts i and ii , i.e., genes that are being differentially expressed in a nodulated plant subjected to water restriction, was highly related to the GO_BP “response to stimulus” (more than 600 significant gene counts), followed by the GO terms “protein phosphorylation” and “transmembrane transport”. Regarding the pathways enrichment, “plant-pathogen interaction”, “MAPK signaling pathway”, and “phenylpropanoid biosynthesis” were the ones with the highest gene counts (Fig. 6 A). M8, which mainly co-localized contrast i DEGs, both up- and down-regulated ones, and at all levels of regulation, showed enrichment for the GO_BP terms “organic substance biosynthetic process”, “response to stress”, “response to endogenous stimuli”, among others. “Glutathione metabolism”, “phenylpropanoid biosynthesis”, and “pyruvate metabolism” were among the pathways with the highest gene counts (Fig. 6 C). M20, which also co-localized DEGs from contrast i , was strongly associated with the GO_BP term “regulation of transcription” (Figure S6 ). The enrichment analysis of the other two selected modules (M19 and M21) is shown in Figure S6 . 3.7 Module 1 is enriched in up-regulated PAR-level DEGs from contrast i , particularly related to translation and oxidative phosphorylation. Further analysis of M1 was particularly interesting to us because of its DEGs characteristics. This module was strongly associated with “phosphorylation” and “translation”, with over 500 gene counts, for GO_BP enrichment, as well as “spliceosome”, “oxidative phosphorylation”, and “ubiquitin-mediated proteolysis” for KEGG enrichment (Fig. 7 A). Also, we constructed a PPI network using the 228 up-regulated genes at the PAR level of contrast i (Fig. 7 B). Notably, within the network, we identified highly connected regions—or subnetworks—enriched in “eukaryotic translation initiation” and “oxidative phosphorylation” pathways, which exhibited the highest connectivity. Pathways associated with “mitochondrial protein import”, “detoxification of oxidant species”, and “amino acid regulate mTORC1\" were also found. The overrepresented functional terms identified in this PPI network illustrate the primary processes that changed due to upregulation, particularly at the PAR level, in N-fix plants exposed to water restriction (Fig. 7 B). The nodes (proteins) associated with the “translation initiation” pathway primarily include ribosome structural proteins (RPs) from both the large (RPLs) and small (RPSs) subunits. This suggests that under WD conditions, N-fix plants would enhance the translation of proteins involved in the translational machinery, thereby promoting the translation process in the abovementioned conditions. Among the proteins associated with the term “oxidative phosphorylation” were acyl carrier proteins (ACP; involved in fatty acid synthesis), two subunits of the cytochrome b-c1 complex, and three subunits of the NADH dehydrogenase (ubiquinone) 1 alpha subcomplex. This indicates a strong demand for ATP in N + WR plants, driven by an increase in the association with polysomes of certain components of its synthesis pathway. Furthermore, four distinct subunits of the vacuolar-type proton ATPase (v-ATPase) were among the proteins associated with both \"oxidative phosphorylation\" and \"amino acids regulate mTORC1\" in the Reactome database. This suggests that the mTORC1 (mechanistic target of rapamycin complex 1) signaling pathway, along with energy metabolism, may be regulated at the translational level and could be involved in how nodulated plants respond to water restriction conditions (Fig. 7 B). 3.8 Transcription factors differentially expressed at the translational level could be candidate genes for understanding how nodulated plants respond to water deficit. Transcription factors (TFs) and their intricate interactions play a crucial role in guiding specific genetic programs, including responses to environmental stresses. Here, we conducted an exploratory analysis to identify which TFs families were predominant in the different plant responses at the translational regulation level (either only PAR or combined TOTAL + PAR) across all four contrasts analysed. Contrast i (N + WR vs N + WW) presented 144 TFs DEGs, being the contrast with the highest number of differentially expressed TFs, followed by contrasts ii (N + WR vs NN + WR) and iv (N + WW vs NN + WW) with 56 and 53 TFs DEGs, respectively. Contrast iii (NN + WR vs NN + WW) showed a total of 37 differentially expressed TFs (Figure S8 ). The WRKY, NAC, MYB, ERF, and bHLH families stand out in contrast i . Regarding WRKY, most members were down-regulated; conversely, up-regulation of NAC and MYB family members was predominant. The ERF and bHLH families comprised a similar number of up- and down-regulated members. In contrast ii , which presented the same number of up- and down-regulated DEGs, the more representative TF families were GRAS, C2H2, ERF, and bHLH. NAC, LBD, ERF, and bHLH were the families with the highest number of members among the TFs DEGs of contrast iii , similar to contrast i , except for the WRKY family. The contrast iv mainly consisted of up-regulated TFs, with the MYB, ERF, and bHLH families being the most representative (Figure S8 ). Given the crucial role of TFs in regulating gene programs in response to diverse stimuli, the importance of translational control, and our focus on how N-fix plants respond to water restriction (analysed in contrast i and ii ), we selected candidate TFs based on their differential expression at the translational regulation level (PAR or TOTAL + PAR) and one of the following two criteria: being a hub gene in the co-expression module where they co-localized, or having putative targets with the same regulation state (up or down). In the latter, as candidate selection concerns TFs, the list was further filtered to retain targets with transcriptional regulation (whether exclusive, TOTAL, or combined with translational regulation, TOTAL + PAR). This data is shown in Table 3 . Table 3 A roster of robust candidate transcription factors involved in drought tolerance in nodulated plants for further functional studies. GlymaID (version 4) TF Family Module (M) Contrast Regulation Level Status Selection criterium Glyma_04G039300 bZIP TRAB1 bZIP M4 i TOTAL + PAR Up Putative targets with the same status at transcriptional (TOTAL) or mixed (TOTAL + PAR) regulation level Glyma_13G279900 NAC27 NAC M4 i TOTAL + PAR Glyma_16G021000 ATHB-12 HD-ZIP M4 i TOTAL + PAR Glyma_16G043200 NAC12 NAC M4 i TOTAL + PAR Glyma_14G152700 NAC21 NAC M8 i TOTAL + PAR Glyma_18G040700 MYB20 MYB M20 i TOTAL + PAR Up Hub Glyma_17G240100 ERF RAP2-1 ERF M20 i TOTAL + PAR Glyma_10G010300 MYB78 MYB M20 i TOTAL + PAR Glyma_11G127100 BBX24 DBB M20 i TOTAL + PAR Glyma_05G109500 HAT22 HD-ZIP M20 i TOTAL + PAR Glyma_07G052100 HOMEOBOX HD-ZIP M4 i TOTAL + PAR Glyma_19G180300 NAC NAC M4 ii TOTAL + PAR Down Glyma_05G029000 WRKY 72A WRKY M4 ii TOTAL + PAR The selection criteria are listed in the rightmost column. TF : transcription factor. M : module. Up : up-regulated. Down : down-regulated. Hub genes were defined as the 10% of genes with the highest intramodular connectivity. Regarding contrast i , one TF was found to co-localize in M1, five in M4, one in M8, and one in M20—belonging to the bZIP, HD-ZIP, NAC, and MYB families—all of which were up-regulated, with 121 putative targets also up-regulated and distributed across the aforementioned modules (Table S4 ). The TFs selected for the hub gene criterion, also in contrast i , comprised eight members from different families, all of which were up-regulated. In contrast ii , two down-regulated TFs from the NAC and WRKY families, which co-localized in M4, were selected for the hub gene criterion. The candidate TFs listed in Table 3 are strong candidates for future functional studies, which can be conducted using a combined approach of gene knockout and overexpression. This allows validation of results by comparing the effects of gene loss-of-function with gene gain-of-function. 4. Discussion Plants use various strategies to cope with water scarcity. The sensitivity of different soybean genotypes to water shortages—specifically, when they begin to sense lower soil water content and respond accordingly—determines their performance in terms of yield. This outcome heavily depends on the intensity and duration of the WD conditions (Sade et al., 2012 ; Sinclair et al., 2010 ; Vadez et al., 2013 ). Another important trait in this context is the nodulation status of the plant –whether soybean or other legumes–as several authors suggest that rhizobial symbiosis induces drought tolerance (Álvarez-Aragón et al., 2023 ; Liu et al., 2022 ; López et al., 2023 ; Staudinger et al., 2016 ). Given the likely evolution of legumes on N-poor soils in a symbiosis-dependent manner, it is plausible to assume that nodulated plants should be the form best adapted to various stresses (Álvarez-Aragón et al., 2023 ; Liu et al., 2020 ). In this work, we showed that nodulation delays the plant's harvest time by an average of two days compared to N-fed plants (Fig. 2 , D-E). Since we defined harvest time as the day when the plants reached 50% of the initial conductance (the value at day 0 of the WD period), this result suggests that the nodulation condition allows stomata to remain open for a longer period, thereby maintaining CO 2 uptake for photosynthesis. We previously found that this also occurs in another soybean genotype (Sainz et al., 2024 ), suggesting that this phenomenon may be widespread in soybean. Several studies have investigated the impact of water restriction on N-fed soybean plants (Song et al., 2016 ; VanHa et al., 2015 ; Wang et al., 2022 ), including this work, since contrast iii (NN + WR vs NN + WW) shows the response of N-fed plants to WR (Fig. 1 B). However, the effects of WR on N-fix soybean at the molecular level have not been extensively addressed, even though it is known that the response differs from that of N-fed plants (Álvarez-Aragón et al., 2023 ; López et al., 2023 ; Sainz et al., 2024 , 2022 ; Staudinger et al., 2016 ), resembling a stress priming phenomenon where nodulation modulates the WD-stress response of the plants. The two contrasts that addressed this in our study were contrast i (N + WR vs N + WW) and ii (N + WR vs NN + WR) (Fig. 1 B). Specifically, in contrast i , one key conclusion is that the response of nodulated plants to water restriction is more complex in terms of the number of DEGs (2523 vs 653 in contrast iii ) (Fig. 3 E-F; Fig. 5 A-C; Table S3 ; Table 2 ). Additionally, they exhibit a higher number of DEGs (almost 30% vs. 15% in N-fed plants) with translational regulation (Fig. 3 E-F; Table S3 ; Table 2 ), indicating that this step of gene expression regulation is a relevant one in shaping N-fix G5601 soybean plant responses to WD. This was particularly noticeable for the DEGs co-localized in M1 of the co-expression analysis (WGCNA), as 70% of them presented mainly translational regulation (Table 2 ). Although plants under environmental stress benefit from translational control, as it enables rapid responses, few examples in the literature evidence this in N + WR plants (Sainz et al., 2024 , 2022 ); the current study is another example denoting that the attribute of being regulated at the translational level (either PAR or TOTAL + PAR) is of relevance when screening for candidate genes to be involved in the response of N-fix plants to WD. The WGCNA has proven helpful for interpreting our data because it reduces the complexity of the RNA-seq data, identifying modules (or clusters of modules) that potentially uncover functional relationships between genes and their association with the biological processes underlying plant responses in the different scenarios, such as cluster 2 associated with nodulation, cluster 3 related to translational control, and cluster 5 and 6 representative of genes with the lowest and highest transcript detection in WD conditions, respectively (Fig. 4 ). Furthermore, by integrating co-expression and DEGs analysis (Li et al., 2020 ; Sánchez-Baizán et al., 2022 ; Sferra et al., 2023 ), we identified modules enriched with DEGs associated with the nodulation process, WR, or specifically with the response of N-fix plants to WR, allowing us to better interpret the biological information (Fig. 5 ; Table S2 ; Table S3 ). Regarding the DEGs associated with nodulation (those common to contrasts ii and iv , mainly up-regulated), M10 is definitely one of relevance (Table S2 ; Table 1 ), with the biological process of transmembrane transport as one of the most representative (Fig. 6 ). O’Rourke et al. ( 2014 ) pointed out that the regulation of nitrogen transporter expression plays a crucial role in the nodulation process. As nitrogen fixation involves transforming and transporting nitrogen into other biological forms, such as ureides, transporters must be adjusted to accommodate changes in nitrogen metabolism. Concerning the DEGs associated with the plant's responses to WD (the ones common to contrasts i and iii ; mainly down-regulated), M4 and M7 were the most notable ones (Table S2 ; Table 1 ). In this case, the GO_BP terms with the highest gene counts were “response to stimulus” and “response to stress”, respectively (Fig. 6 ). KEGG analysis showed that those responses could be mainly associated with secondary metabolism pathways, specifically with the phenylpropanoid biosynthesis (Fig. 6 ). Dalal et al. ( 2018 ) observed a down-regulation of phenylpropanoid biosynthesis in wheat roots under drought stress. As for the response of N-fix plants to WD (contrast i ), the most representative modules were also M4 and M7, but also M8 and, particularly, M1 (Table S3 ; Table 2 ), with the latter having very high gene counts for “phosphorylation” and “translation” GO_BP terms (Fig. 7 A). Further analysis of the subset of contrast i up-regulated DEGs at the PAR level that co-localized in M1 through a PPI network was very illustrative of the relevance of translational control in specific key cellular processes in the context of a nodulated and WR plant (Fig. 7 B). The up-regulation of genes associated with OXPHOS does not align with previously reported findings, where mitochondrial respiration is inhibited in N-fed roots upon the imposition of WD (Atkin and Macherel, 2009 ). Therefore, we suggest that N-fix plants have a high demand for ATP, and their translational regulation of OXPHOS-related proteins helps ensure this supply. Other genes related to OXPHOS that were also up-regulated at the PAR level in N + WR roots included different subunits of a v-ATPase (Fig. 7 B), a key player of the consensus model of amino acid signaling for mTORC1 activation (Takahara et al., 2020 ; Zoncu et al., 2011 ). mTOR, an evolutionarily conserved single-gene-encoded protein in higher eukaryotes, is a kinase that, in plants, impinges growth and development in response to the plant's energy status (Yokawa and Baluška, 2016 ). One of the key functions of mTORC1 is to promote anabolism, particularly the synthesis of proteins, lipids, and purines. Through the eukaryotic initiation factor 4E binding protein 1 (4E-BP1), mTORC1 stimulates the translation of a subset of mRNAs possessing a 5´ terminal oligopyrimidine (5’ TOP) motif, such as mRNAs encoding ribosomal proteins (Takahara et al., 2020 ). Since the other highly connected subnetwork in the PPI network was enriched in ribosomal proteins (Fig. 7 B), we looked for overrepresented motifs in the 5’ untranslated regions (5’-UTR) of the 228 DEGs used to build the network (MEME tool; Figure S7 ; Table S5 ). Interestingly, we found that 46% of the 228 transcripts presented the 5’ TOP motif, which is known to enhance the translation of ribosomal proteins, as previously mentioned, but also translation initiation factors, and elongation factors, among other proteins. Furthermore, 21 of these transcripts encode ribosomal proteins. This indicates that the 5’ TOP motif may enable cells to rapidly adjust the expression of proteins involved in ribosomal biogenesis, thereby sustaining and enhancing the translational machinery of N-fix plants under water restriction. The final focus of our study—aimed to identified robust candidate gene for future functional analysis—was on TFs subjected to translational control due to their relevance in gene reprogramming and the coordination of biological processes and metabolic pathways (Weidemüller et al., 2021 ), as well as the importance of translational control in shaping the plant responses to different stimuli (Kawaguchi et al., 2004 ; Lee and Bailey-serres, 2019 ; Lei et al., 2015 ; Urquidi Camacho et al., 2020 ). Interestingly, the bHLH, ERF, MYB, NAC, and WRKY families stand out with a high number of DEGs associated with the responses of N + WR plants (contrasts i and ii ) (Figure S8 ). While this aligns with existing research indicating that most TFs involved in the response to WD belong to the aforementioned families (Wan et al., 2022 ), their association with the nodulation process has not been reported before. The TFs identified (Table 3 ) play distinct roles in responding to abiotic stress, especially WD, such as activating or repressing processes like hormone metabolism, antioxidant production, and stress-related protein synthesis (Manna et al., 2021 ). Additionally, two other criteria—being a hub gene in the co-expression analysis or having putative targets (according to the PlantTFDB database; Jin et al., 2017 ) with the same regulatory state—were combined for more robust candidate selection (Table 3 ). Therefore, this list, comprising 13 TFs differentially expressed in N + WR plants, could be valuable for future functional analysis of the specific responses of N-fix plants under water-restricted conditions, a scenario that is increasingly common in the current climate change context. 5. Conclusion This study shows that nodulation modulates soybean responses to water restriction at both physiological and molecular levels, with translational control playing a central role in the response of nodulated and water-restricted plants. This is particularly true for the DEGs grouped in module 1 of the co-expression analysis, mainly involved in translation and oxidative phosphorylation, suggesting a high energy demand under these conditions. This way, we suggest that the translational regulation of OXPHOS and ribosomal protein-related genes supports ATP demand in N-fix plants. Several transcription factors from the NAC, MYB, WRKY, and bHLH families—with known roles in the plant responses to abiotic stress—were differentially expressed at the translational level in nodulated and water-restricted plants. Their association with the nodulation process is novel. Besides being DEG in N+WR plants, two additional criteria—being hub genes or having predicted target genes with concordant regulatory patterns—were used to refine the list, resulting in robust candidates for future functional studies—through knockout via CRISPR and overexpression—and potential use in breeding programs. Abbreviations 5’TOP 5’ terminal oligopyrimidine 5’UTR 5’ untranslated region ACP acyl carrier protein DEG differential expressed gene FDR false discovery rate GO gene ontology GO_BP GO biological process KEGG Kyoto Encyclopedia of Genes and Genomes ME module eigengene mRNA messenger RNA mTORC1 mechanistic target of rapamycin complex 1 N nodulation N-fed fertilized plants N-fix symbiotic nitrogen-fixing plants NN non-nodulation OXPHOS oxidative phosphorylation PAR polysome-associated mRNA fraction PCA principal component analysis PPI protein-protein interaction RPLs large subunit ribosomal protein RPs ribosome structural proteins RPSs small subunit ribosomal protein TF transcription factor TOTAL total RNA fraction WD water deficit WGCNA weighted gene co-expression network analysis WR water-restricted WW well-watered Declarations Ethics approval and consent to participate Not applicable. Clinical trial number: not applicable. Consent for publication Not applicable. Data availability All datasets supporting the results of this study are included within the article and its supplementary information. The sequencing data is available in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA1284746. Competing interests The authors declare no competing interests. Funding This work was supported by CSIC I+D 2022 Grant No. 22520220100256UD, CSIC I+D 2020 Grant No. 282, FVF 2017 Grant No. 210 (María Martha Sainz), Programa de Desarrollo de las Ciencias Básicas (PEDECIBA) (María Martha Sainz, Carla Valeria Filippi, Guillermo Eastman, Mariana Sotelo-Silveira, Omar Borsani, José Sotelo-Silveira), and Red Nacional de Biotecnología Agrícola: RTS_1_2014_1-ANII (Omar Borsani). Author contributions M.M.S., J.S.-S. and O.B. conceived the study; M.M-M, M.M.S., C.V.F., G.E., G.Q., and S.P-P performed the experiments; M.M-M, M.M.S., C.V.F., G.E., M.S.-S. and J.S.-S. analyzed the data; M.M.S and M.M-M wrote the original draft. All authors have reviewed and edited the manuscript. Acknowledgments We thank Stefan de Folter (UGA-LANGEBIO) for helpful comments on the manuscript. We also thank Sistema Nacional de Investigadores (ANII) (María Martha Sainz, Carla Valeria Filippi, Guillermo Eastman, Mariana Sotelo-Silveira, José Sotelo-Silveira, Omar Borsani). Mauro Martínez-More was the recipient of an MSc fellowship from Comisión Académica de Posgrado, CSIC, UdelaR. Selene Píriz-Pezzuto is a Ph.D fellow of Comisión Académica de Posgrado, CSIC, UdelaR. References Alexa, A., Rahnenfuhrer, J., 2021. topGO: Enrichment Analysis for Gene Ontology. https://doi.org/10.18129/B9.bioc.topGO Álvarez-Aragón, R., Palacios, J.M., Ramírez-Parra, E., 2023. Rhizobial symbiosis promotes drought tolerance in Vicia sativa and Pisum sativum. Environ. Exp. Bot. 208. https://doi.org/10.1016/j.envexpbot.2023.105268 Atkin, O.K., Macherel, D., 2009. The crucial role of plant mitochondria in orchestrating drought tolerance. Ann. 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Differentially expressed genes (DEGs) common to water restriction (contrasts i ) N+WR vs N+WW and iii ) NN+WR vs NN+WW) for the TOTAL and PAR regulation levels. B. Differentially expressed genes (DEGs) common to nodulation (contrasts ii ) N+WR vs NN+WR and iv ) N+WW vs NN+WW) for the TOTAL and PAR regulation levels. G5601: Génesis 5601 soybean genotype. DM6.8i: Don Mario 6.8i soybean genotype. FigureS3.pdf Figure S3. Schematic illustration of the water restriction assay. Each plant was grown at field capacity for 20 days, after which the water restriction period began. The PPS – Plant Pot Substrate – system was weighed every day during the six days of water deficit (dwd). FigureS4.pdf Figure S4. RNA-seq data descriptive analysis. A Heatmap of euclidean distance among replicates. Each of the three biological replicates of each combined treatment (NN+WW, NN+WR, N+WW, N+WR), considering the TOTAL and PAR RNA fractions, are included. B Principal Component Analysis. Dim1 and Dim2 are the first and second dimensions, respectively. TOT: total mRNA fraction. PAR: polysome-associated mRNA fraction. Both in A and B the raw count matrix was used as input data. FigureS5.pdf Figure S5. Module Eigengenes (ME) hierarchical clustering. A Pairwise distance among MEs calculated as 1 - pairwise correlation among MEs (Zhang and Horvath, 2005). Average linkage was used for clustering. The number of clusters was determined using the Silhouette index (Dudek 2020). B Silhouette plot. Silhouette index calculated using the R package NbClust (Charrad et al. 2014). FigureS6.pdf Figure S6. Gene Ontology (GO) biological process (BP) enrichment analysis of the selected co-expression modules M19, M20, and M21. The top ten significant GO-BP terms for each module are shown. FigureS7..jpg Figure S7. Identified motif in the transcript 5’-UTR of up-regulated genes at the translational level in the contrast i (N+WR vs N+WW) and co-localized in the co-expression module 1 of WGCNA. E-value of 2,0x10 -229 with 156 sites of nucleotide bases contributing to the motif generation. FigureS8.pdf Figure S8. Transcription factors number per family with mixed (TOTAL+PAR) or translational (PAR) regulation in the four analyzed contrasts. If transcription factors were down- or up-regulated it is shown in blue and red, respectively. SupplementaryTableswithcaptions.xlsx Table S1. Descriptive statistics of sequencing data processing for each biological replicate and its corresponding RNA fraction (TOTAL and PAR). #: number. PE: paired-end. # kept reads: after processing via Trimmomatic (versión 0.39, Bolger et al. 2014). Mapping against reference transcriptome depleted of rRNA (GCF_000004515.6, Glycine max v4.0, without rRNA). Table S2. Weighted gene co-expression network analysis (WGCNA) with co-localized Differentially Expressed Genes (DEGs) common to contrasts i ) and iii ) and contrasts ii ) and iv ) at the TOTAL, PAR, or TOTAL+PAR level. All 34 modules (M) are depicted, as well as their status (up- or down- regulated). The number of DEGs that are also modules’ hub genes is indicated in parentheses. Table S3. Number of Differentially Expressed Genes (DEGs) co-localized within the 33 Modules (M) of the Weighted Gene Co-expression Network Analysis (WGCNA) across the four contrasts analyzed (i, ii, iii, and iv). All 34 modules (M) are depicted. The four different contrasts (i, ii, iii, and iv) in which the DEG condition was assessed are shown, as well as the status (up- or down-regulated) and the regulation level (TOTAL, PAR, or TOTAL+PAR). The number of DEGs that are also modules’ hub genes is indicated in parentheses. Table S4. Putative targets of selected candidate transcription factors’ description. Transcription factors were selected for being up-regulated at the translational level (TOTAL+PAR or PAR) in the contrast i) N+WR vs N+WW and for their putative targets being up-regulated at the transcriptional level (TOTAL or TOTAL+PAR). This follows the natural molecular function of transcriptional regulation in which one would expect the putative target to have higher transcripts (TOTAL) after the regulator transcription factors had potentially higher levels of protein expression (PAR). Putative targets were retrieved from PlantTFDB. The co-expression modules in which either the transcription factors and the putative targets co-localized are shown. Whether a connection exists between a transcription factor and its putative targets in the plotted Weighted Gene Co-expression Network is also shown. Table S5. 5’-TOP THHYYYYYTYCTCTYTYTYTYTCTYYNTY motif enrichment analysis in contrast i (N+WR vs N+WW). M1: Gene co-expression Module 1. PAR: Polysome-Associated mRNA fraction. TOTAL: total RNA fraction. Not Up-PAR: non-translational-up-regulated genes, i.e., stable genes, down-regulated genes at the translational level, or down- or up-regulated genes at the transcriptional level. Comparisons having strong evidence against the null hypothesis (E-values < 0.01) are shown under bold text. 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17:01:27\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":139872,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSchematic outline of the approach used to obtain combined treated soybean roots, along with the comparisons among the combined treatments and the RNA fractions obtained for the transcriptomic and translatomic analysis. A \\u003c/strong\\u003ePlant growth conditions, nodulation, and water restriction assay pipeline. WR plants were harvested when the stomatal conductance (g\\u003csub\\u003esw\\u003c/sub\\u003e) was 50% of the g\\u003csub\\u003esw\\u003c/sub\\u003e at day 0 (g\\u003csub\\u003esw0\\u003c/sub\\u003e) of the water deficit period. \\u003cstrong\\u003eB \\u003c/strong\\u003eContrasts between combined treatments. \\u003cstrong\\u003eWW\\u003c/strong\\u003e: well-watered. \\u003cstrong\\u003eWR\\u003c/strong\\u003e: water-restricted. \\u003cstrong\\u003eN\\u003c/strong\\u003e: nodulated. \\u003cstrong\\u003eNN\\u003c/strong\\u003e: non-nodulated. \\u003cstrong\\u003eC \\u003c/strong\\u003eRNA fractions (TOTAL and PAR)\\u003cstrong\\u003e \\u003c/strong\\u003efor the transcriptome and translatome analysis. \\u003cstrong\\u003eTOTAL\\u003c/strong\\u003e: Total RNA. \\u003cstrong\\u003ePAR\\u003c/strong\\u003e: polysome associated RNA. BioRender was used to build this plot. V2-3 developmental stage according to Fehr and Caviness (1977).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/95e3b2dd64c3cfe58dd3bd81.png\"},{\"id\":93425164,\"identity\":\"78ca41fd-6114-47bf-8698-980a36e6fa27\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:37:27\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":81502,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSoil water content, stomatal conductance, and time of harvest of N-fed and N-fix experimental units throughout the water restriction assay. A \\u003c/strong\\u003eand \\u003cstrong\\u003eB \\u003c/strong\\u003edepict the soil water content (WC) and the average stomatal conductance (g\\u003csub\\u003esw\\u003c/sub\\u003e), respectively, of well-watered (WW) nodulated (N) and non-nodulated (NN) plants, up and down in each plot, respectively. \\u003cstrong\\u003eC \\u003c/strong\\u003eand \\u003cstrong\\u003eD \\u003c/strong\\u003eshow the soil WC and the gsw percentage regarding the water restriction period (WR) initial day (g\\u003csub\\u003esw0\\u003c/sub\\u003e), respectively, of N+WR and NN+WR. g\\u003csub\\u003esw\\u003c/sub\\u003e was measured on the uppermost fully expanded leaf. The dashed line at 40% on the WC plots (\\u003cstrong\\u003eA\\u003c/strong\\u003e and \\u003cstrong\\u003eC\\u003c/strong\\u003e) indicates the field capacity level. The dashed line at 50% on \\u003cstrong\\u003eD \\u003c/strong\\u003edepicts the g\\u003csub\\u003esw\\u003c/sub\\u003e threshold at which each plant was harvested. Solid circles represent means, and translucent circles represent observations. \\u003cstrong\\u003eE. \\u003c/strong\\u003eWater restricted (WR) plants day of harvesting. Experimental units whose roots RNA were sequenced are in yellow. The vertical line indicates the average day of harvesting between both N and NN plants. Lollipops represent the average day of harvesting within nodulation or non-nodulation treatments. dwd: days of water deficit.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/e7461f1d49fb801358b74f79.png\"},{\"id\":93427293,\"identity\":\"517b227b-945e-418f-be1e-1543645847ae\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 17:01:27\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":260465,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTranscriptional and translational responses as the number of differentially expressed genes (DEGs) under treatments and combined treatments. A \\u003c/strong\\u003eand\\u003cstrong\\u003eB \\u003c/strong\\u003eVolcano plots\\u003cstrong\\u003e \\u003c/strong\\u003eshowing stable genes as well as down- and up-regulated DEGs between nodulated (N) and non-nodulated (NN) plants at the transcriptional (TOTAL) and translational (PAR) levels, respectively. \\u003cstrong\\u003eC \\u003c/strong\\u003eand\\u003cstrong\\u003e D \\u003c/strong\\u003edepict the same as A and B, but between water-restricted (WR) and well-watered (WW) plants. \\u003cstrong\\u003eE \\u003c/strong\\u003eand\\u003cstrong\\u003e F \\u003c/strong\\u003eVenn diagrams intersecting DEGs lists at the TOTAL and PAR levels of down- and up-regulated DEGs, respectively, between contrasts \\u003cem\\u003ei\\u003c/em\\u003e) and \\u003cem\\u003eiii\\u003c/em\\u003e). Highlighted in bold are the preferential DEGs of each contrast at the different regulation levels. \\u003cstrong\\u003eG \\u003c/strong\\u003eand\\u003cstrong\\u003e H \\u003c/strong\\u003eVenn diagrams intersecting DEGs lists at the TOTAL and PAR levels of down- and up-regulated DEGs, respectively, between contrasts \\u003cem\\u003eii\\u003c/em\\u003e) and \\u003cem\\u003eiv\\u003c/em\\u003e). Highlighted in bold are the preferential DEGs of each contrast at the different regulation levels.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/1a54a4cf5a7189ca5b52f7bc.png\"},{\"id\":93425166,\"identity\":\"2057e9fb-ed53-4e74-850b-12a88f774560\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:37:27\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":182732,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eWeighted gene co-expression network of soybean plants under four different treatments combining nodulation and hydric conditions and total RNA (TOTAL) and polysome-associated mRNA (PAR) samples. A \\u003c/strong\\u003eHeatmap of module eigengene (ME) expression profiles. Colors represent normalized average gene expression of each ME across all replicates. The number of each biological replicate, as well as the number of total genes in the module that each ME belongs to, is indicated. Cluster identities are also shown. \\u003cstrong\\u003eWW\\u003c/strong\\u003e: well-watered. \\u003cstrong\\u003eWR\\u003c/strong\\u003e: water-restricted. \\u003cstrong\\u003eN\\u003c/strong\\u003e: nodulated. \\u003cstrong\\u003eNN\\u003c/strong\\u003e: non-nodulated. \\u003cstrong\\u003eB. \\u003c/strong\\u003eVisualization of the weighted gene co-expression network. Module 28 was not plotted in the network due to a low edge adjacency value. The network comprised 12,848 nodes –representing genes– and 3,951,102 edges –representing the connections between genes. Edges were not plotted due to computational limitations.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/b9821ffe809bd857a2ace0e5.png\"},{\"id\":93426788,\"identity\":\"fbbedc92-bb5a-463e-b7b5-8f6ca7fca818\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:53:27\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":237392,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCo-localization on the Weighted Gene Co-Expression Network of the Differentially Expressed Genes (DEGs) at the four studied contrasts. A \\u003c/strong\\u003eContrast \\u003cem\\u003ei\\u003c/em\\u003e) N+WR vs N+WW. \\u003cstrong\\u003eB \\u003c/strong\\u003eContrast \\u003cem\\u003eii\\u003c/em\\u003e) N+WR vs NN+WR. \\u003cstrong\\u003eC \\u003c/strong\\u003eContrast \\u003cem\\u003eiii\\u003c/em\\u003e) NN+WR vs NN+WW. \\u003cstrong\\u003eD \\u003c/strong\\u003eContrast \\u003cem\\u003eiv\\u003c/em\\u003e) N+WW vs NN+WW. Down- and up-regulated genes are depicted in blue and red, respectively. All DEGs within TOTAL and PAR fractions are plotted.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/c8537ebae0d0892d57a0711a.png\"},{\"id\":93425174,\"identity\":\"6f4da1e7-cc5f-4932-877e-2f02c0dd6d1d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:37:27\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":161648,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGene Ontology (GO) biological process (BP) enrichment and KEGG pathway enrichment analyses of the selected modules. A \\u003c/strong\\u003eM4. \\u003cstrong\\u003eB \\u003c/strong\\u003eM7. \\u003cstrong\\u003eC \\u003c/strong\\u003eM8. \\u003cstrong\\u003eD \\u003c/strong\\u003eM10. The top ten significant GO-BP terms for each module are shown in the plots on the left. All the significant KEGGs terms were held for each module (plots on the right).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/53b4f801b8fe1f3ac0c925e9.png\"},{\"id\":93426791,\"identity\":\"537c13f2-11a7-4626-b2e5-2f8c497da86f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:53:28\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":343076,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGene Co-expression Module 1 functional and pathway analyses. A \\u003c/strong\\u003eGene Ontology biological process enrichment and KEGG pathway enrichment analyses. \\u003cstrong\\u003eB \\u003c/strong\\u003eProtein-Protein Interaction network among the 228 up-regulated genes in the contrast \\u003cem\\u003ei\\u003c/em\\u003e) N+WR vs N+WW at the translational level (PAR) that co-localized in M1. Enrichment analysis on them and the network construction were performed through StringDB.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/8f7c52a39257585d94bb620c.png\"},{\"id\":93428075,\"identity\":\"f1646dbc-cd05-4ecf-ba5b-a407ef42887e\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 17:09:29\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3302867,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/3b2ec641-5034-49d2-a638-0f173f82624a.pdf\"},{\"id\":93425163,\"identity\":\"a7cab0b2-b557-48d0-a15e-597042e84946\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:37:27\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":41638,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary information\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFigure S1. Network topology analysis for selecting soft-thresholding power for the Weighted Gene Co-expression Network construction. A \\u003c/strong\\u003eScale-free fit index as a function of power. The red line indicates the minimum signed R\\u003csup\\u003e2\\u003c/sup\\u003e for network construction (Zhang and Horvath, 2005). \\u003cstrong\\u003eB \\u003c/strong\\u003eMean connectivity as a function of power.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/c27b08d957d0f10aa3a52df2.pdf\"},{\"id\":93426120,\"identity\":\"ecb14648-eb30-469f-9b77-ddd68610785f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:45:27\",\"extension\":\"pdf\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":71116,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S2. Scatter plots of the correlation between the two soybean genotypes whose gene expression was evaluated in parallel. A. \\u003c/strong\\u003eDifferentially expressed genes (DEGs) common to water restriction (contrasts \\u003cem\\u003ei\\u003c/em\\u003e) N+WR vs N+WW\\u003cem\\u003e \\u003c/em\\u003eand \\u003cem\\u003eiii\\u003c/em\\u003e) NN+WR vs NN+WW) for the TOTAL and PAR regulation levels. \\u003cstrong\\u003eB. \\u003c/strong\\u003eDifferentially expressed genes (DEGs) common to nodulation (contrasts \\u003cem\\u003eii\\u003c/em\\u003e) N+WR vs NN+WR\\u003cem\\u003e \\u003c/em\\u003eand \\u003cem\\u003eiv\\u003c/em\\u003e) N+WW vs NN+WW) for the TOTAL and PAR regulation levels. G5601: Génesis 5601 soybean genotype. DM6.8i: Don Mario 6.8i soybean genotype.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/a7aa7407e030fb5719e0b582.pdf\"},{\"id\":93426790,\"identity\":\"5ada5eca-27d4-4774-a32f-816054c4bb3a\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:53:28\",\"extension\":\"pdf\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":90986,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S3. Schematic illustration of the water restriction assay. \\u003c/strong\\u003eEach plant was grown at field capacity for 20 days, after which the water restriction period began. The PPS – Plant Pot Substrate – system was weighed every day during the six days of water deficit (dwd).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS3.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/7f4a4d7d941030b27a7d1da1.pdf\"},{\"id\":93426121,\"identity\":\"a06c1ca9-91c0-4dce-b437-d922814cc43d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:45:27\",\"extension\":\"pdf\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":49894,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S4. RNA-seq data descriptive analysis. A \\u003c/strong\\u003eHeatmap\\u003cstrong\\u003e \\u003c/strong\\u003eof euclidean distance among replicates. Each of the three biological replicates of each combined treatment (NN+WW, NN+WR, N+WW, N+WR), considering the TOTAL and PAR RNA fractions, are included. \\u003cstrong\\u003eB \\u003c/strong\\u003ePrincipal Component Analysis. Dim1 and Dim2 are the first and second dimensions, respectively. TOT: total mRNA fraction. PAR: polysome-associated mRNA fraction. Both in\\u003cstrong\\u003e A \\u003c/strong\\u003eand \\u003cstrong\\u003eB \\u003c/strong\\u003ethe raw count matrix was used as input data.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS4.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/94be88741880c143a689abe9.pdf\"},{\"id\":93425172,\"identity\":\"a873db94-5519-43c7-9a51-c7126542271d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:37:27\",\"extension\":\"pdf\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":50354,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S5. Module Eigengenes (ME) hierarchical clustering. A \\u003c/strong\\u003ePairwise distance among MEs calculated as 1 - pairwise correlation among MEs (Zhang and Horvath, 2005). Average linkage was used for clustering. The number of clusters was determined using the Silhouette index (Dudek 2020). \\u003cstrong\\u003eB \\u003c/strong\\u003eSilhouette plot. Silhouette index calculated using the R package \\u003cem\\u003eNbClust\\u003c/em\\u003e (Charrad \\u003cem\\u003eet al.\\u003c/em\\u003e 2014).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS5.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/85f7f1d28da646eea7f7f76e.pdf\"},{\"id\":93426125,\"identity\":\"87e8ca34-1066-49d8-a1bb-0f59e99016cd\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:45:27\",\"extension\":\"pdf\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":62256,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S6. Gene Ontology (GO) biological process (BP) enrichment analysis of the selected co-expression modules M19, M20, and M21. \\u003c/strong\\u003eThe top ten significant GO-BP terms for each module are shown.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS6.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/7babac0fee23fe9c8337010c.pdf\"},{\"id\":93426126,\"identity\":\"93c21ea2-7b1d-41fc-a6ff-6040fe3e819a\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:45:27\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":28942,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S7.\\u0026nbsp;Identified motif in the transcript 5’-UTR of up-regulated genes at the translational level in the contrast i (N+WR vs N+WW) and co-localized in the co-expression module 1 of WGCNA. \\u003c/strong\\u003eE-value of 2,0x10\\u003csup\\u003e-229\\u003c/sup\\u003e with 156 sites of nucleotide bases contributing to the motif generation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS7..jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/d3f021986adbb8b1ac1eb9cb.jpg\"},{\"id\":93425178,\"identity\":\"51697b7a-0aaa-4523-919c-3cb10d9e4d11\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:37:28\",\"extension\":\"pdf\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":47541,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S8. Transcription factors number per family with mixed (TOTAL+PAR) or translational (PAR) regulation in the four analyzed contrasts. \\u003c/strong\\u003eIf transcription factors were down- or up-regulated it is shown in blue and red, respectively.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS8.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/aec78df266f785bfce5125ab.pdf\"},{\"id\":93426131,\"identity\":\"2fa6aab5-7b45-45f1-b0f2-237669a6be01\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:45:28\",\"extension\":\"xlsx\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":59129,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable S1. Descriptive statistics of sequencing data processing for each biological replicate and its corresponding RNA fraction (TOTAL and PAR).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e#\\u003c/strong\\u003e: number. \\u003cstrong\\u003ePE\\u003c/strong\\u003e: paired-end. \\u003cstrong\\u003e# kept reads\\u003c/strong\\u003e: after processing via Trimmomatic (versión 0.39, Bolger et al. 2014). Mapping against reference transcriptome depleted of rRNA (GCF_000004515.6, Glycine max v4.0, without rRNA).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S2. Weighted gene co-expression network analysis (WGCNA) with co-localized Differentially Expressed Genes (DEGs) common to contrasts \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003ei\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e) and \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eiii\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e) and contrasts \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eii\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e) and \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eiv\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e) at the TOTAL, PAR, or TOTAL+PAR level.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll 34 modules (M) are depicted, as well as their status (up- or down- regulated). The number of DEGs that are also modules’ hub genes is indicated in parentheses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S3. Number of Differentially Expressed Genes (DEGs) co-localized within the 33 Modules (M) of the Weighted Gene Co-expression Network Analysis (WGCNA) across the four contrasts analyzed (i, ii, iii, and iv).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll 34 modules (M) are depicted. The four different contrasts (i, ii, iii, and iv) in which the DEG condition was assessed are shown, as well as the status (up- or down-regulated) and the regulation level (TOTAL, PAR, or TOTAL+PAR). The number of DEGs that are also modules’ hub genes is indicated in parentheses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S4. Putative targets of selected candidate transcription factors’ description.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTranscription factors were selected for being up-regulated at the translational level (TOTAL+PAR or PAR) in the contrast i) N+WR vs N+WW and for their putative targets being up-regulated at the transcriptional level (TOTAL or TOTAL+PAR). This follows the natural molecular function of transcriptional regulation in which one would expect the putative target to have higher transcripts (TOTAL) after the regulator transcription factors had potentially higher levels of protein expression (PAR). Putative targets were retrieved from PlantTFDB. The co-expression modules in which either the transcription factors and the putative targets co-localized are shown. Whether a connection exists between a transcription factor and its putative targets in the plotted Weighted Gene Co-expression Network is also shown.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S5. 5’-TOP THHYYYYYTYCTCTYTYTYTYTCTYYNTY motif enrichment analysis in contrast i (N+WR vs N+WW).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eM1:\\u003c/strong\\u003e Gene co-expression Module 1. \\u003cstrong\\u003ePAR:\\u003c/strong\\u003e Polysome-Associated mRNA fraction. \\u003cstrong\\u003eTOTAL:\\u003c/strong\\u003e total RNA fraction. \\u003cstrong\\u003eNot Up-PAR: \\u003c/strong\\u003enon-translational-up-regulated genes, i.e., stable genes, down-regulated genes at the translational level, or down- or up-regulated genes at the transcriptional level. Comparisons having strong evidence against the null hypothesis (E-values \\u0026lt; 0.01) are shown under bold text.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryTableswithcaptions.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/75f71eaf6cb2b50452ceb762.xlsx\"},{\"id\":93426130,\"identity\":\"12ba6887-ad4e-473b-a313-906207dbe8db\",\"added_by\":\"auto\",\"created_at\":\"2025-10-13 16:45:28\",\"extension\":\"pdf\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1599710,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"GraphicalAbstract.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7686372/v1/fe5c0e5768994cd9b2562074.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Genes associated with translation and oxidative phosphorylation as components of the translational response in nodulated and water-restricted soybean\",\"fulltext\":[{\"header\":\"Highlights\",\"content\":\"\\u003cul\\u003e\\n \\u003cli\\u003eN-fix plants keep stomata open longer than N-fed ones in water-restricted conditions.\\u003c/li\\u003e\\n \\u003cli\\u003eThe integration of WGCNA and DEG analysis enhances the interpretation of complex biological data.\\u003c/li\\u003e\\n \\u003cli\\u003eTranslational control is key in shaping the response of nodulated soybean plants to water restriction.\\u003c/li\\u003e\\n \\u003cli\\u003eTranslational control of OXPHOS and ribosomal proteins supports ATP demand in N-fix plants.\\u003c/li\\u003e\\n \\u003cli\\u003eTFs under translational control emerge as key targets for functional studies.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eWater deficit (WD) stress, which occurs when water supply does not meet plant water demand for growth, is the most important abiotic factor limiting crop yields globally (Vadez et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Zhu, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Changes in phenological stages, root architecture, seed production, biomass, harvest index, and germination rate are among the significant effects of drought on yield (S\\u0026aacute;nchez-Berm\\u0026uacute;dez et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Stress-exposed plants eventually reach a soil moisture level where the root system cannot meet the full transpirational demand; at this point, the plants must initiate stomatal closure to prevent shoot desiccation (Vadez et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Soybean genotypes vary significantly in their sensitivity and response to WD conditions. Genotypes that begin to partially close their stomata at relatively high soil water content may be advantageous in cases of prolonged stress or intermittent water stress with long intervals between rains. However, in a short-term WD scenario, a genotype that keeps its stomata open and maintains high photosynthetic rates for longer periods, even when leaf water potential decreases, may be advantageous since it won't penalize its yield (Sade et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Vadez et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). One relevant trait in soybean, regarding yield increase under water-limited conditions, is the enhanced drought tolerance for nitrogen fixation (Sinclair et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). In this regard, it has been widely shown in different legume species grown under symbiotic nitrogen fixation (N-fix) that these plants are more protected against WD stress if compared to non-nodulated fertilized plants (N-fed); in other words, the nodulation condition of a legume (i.e., nodulated or non-nodulated) affects the way the plant responds to WD (\\u0026Aacute;lvarez-Arag\\u0026oacute;n et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Liu et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Lodeiro et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e; L\\u0026oacute;pez et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Staudinger et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). It remains unclear, however, which molecular mechanisms are responsible for the differential response.\\u003c/p\\u003e\\u003cp\\u003eThe differential response of N-fix and N-fed plants to water restriction could result from the differential regulation of gene expression, including the regulation of different gene sets or the variation in the levels at which regulation is achieved, or a combination of both phenomena. Although for most genes, the regulation of their transcription is the primary and main level of regulation, there is also a level of post-transcriptional regulation that encompasses translational and post-translational events (Orphanides and Reinberg, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e). These post-transcriptional regulatory events explain the poor or variable levels of correlation between transcript and protein levels reported in different organisms (Becker et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Lei et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Piccirillo et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Traubenik et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Translational control, in particular, has proven relevant in plants exposed to various stressors such as WD (Kawaguchi et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Lei et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Plants, and organisms in general, benefit from this step of gene expression regulation\\u0026mdash;which does not require de novo messenger RNA (mRNA) synthesis but rather refers to the efficiency with which mRNAs already present in cells are translated\\u0026mdash;since it allows them to respond rapidly thus conferring flexibility and adaptability (Lee and Bailey-serres, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Urquidi Camacho et al., \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Thus, the analysis of the translatome (the subset of mRNAs that are being translated) allows for a more precise and comprehensive measurement of cell gene expression, as opposed to only analyzing steady-state mRNA levels (the transcriptome) (Sablok et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eA typical response of organisms subjected to environmental constraints that imply a reduction in energy availability (e.g., WD, hypoxia) or nutrient shortage is the general repression of translation\\u0026mdash;occurring mainly at the initiation level\\u0026mdash;impacting most cellular mRNAs. Still, in the just-mentioned conditions, specific mRNAs, such as from dehydration-inducible genes, increase their association with polysomes (Juntawong et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Kawaguchi et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Lei et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Mustroph et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). However, the literature regarding examples of genes subjected to translational control in nodulated and water-restricted plants is scarce. Two exciting examples we can mention are the work recently published by our group, in which we reported, on the one hand, that some members of the thioredoxin and glutaredoxin systems and, on the other, that the metabolism of several hormones \\u0026mdash;abscisic acid, ethylene, auxin, and cytokinin\\u0026mdash; are regulated at the translational level in the roots of nodulated soybean plants subjected to water-deficit stress (Sainz et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Building on this, our current research shows that genes coding for ribosomal proteins and proteins involved in the mitochondrial oxidative phosphorylation (OXPHOS) system are among the most prominent examples of genes that are mainly regulated (particularly up-regulated) at the translational level in nodulated and water-restricted roots of the analysed soybean genotype.\\u003c/p\\u003e\\u003cp\\u003eIdentifying translational control as a critical step in regulating ribosomal and OXPHOS-related protein gene expression in plants is novel. Ribosomal proteins and ribosomal RNAs are structural components of ribosomes and, hence, essential for ribosome biogenesis, which is the foundation for cell growth and proliferation. The mitochondrial electron transport chain utilizes a series of electron transfer reactions to generate cellular ATP through OXPHOS; this system produces most of the energy required by non-photosynthetic cells (Kremer and Rehling, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Nolfi-Donegan et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Since translation is a highly energy-demanding process, it makes sense to coordinate the induction of ribosome biogenesis and OXPHOS in roots. However, it is well-known that mitochondrial respiration is generally inhibited due to the imposition of water restriction on plants (Atkin and Macherel, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Hence, what is also novel is that the nodulation condition of those water-restricted roots is the determinant cue for the switch in the expression status of the aforementioned genes.\\u003c/p\\u003e\\u003cp\\u003eIn this work, we investigated the responses to water restriction at the transcriptional and translational levels in G\\u0026eacute;nesis 5601, a commercial soybean genotype in Uruguay, cultivated under N-fix and N-fed conditions. Compared to N-fed plants, we found that the response of nodulated and water-restricted plants was more complex in terms of total differentially expressed genes (DEGs), as well as DEGs that code for transcription factors and DEGs with translational regulation. Furthermore, through a weighted gene co-expression network analysis (WGCNA) followed by a differential expression analysis, we identified gene modules associated with pertinent biological processes in the plant's differential responses. As mentioned above, the translational regulation of genes involved in OXPHOS and translation initiation was key in the nodulated and water-restricted plants. We uncovered robust candidate genes under translational control in the roots of nodulated soybean plants facing WD, opening new avenues for their functional characterization.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Plant growth conditions, nodulation, and drought assay\\u003c/h2\\u003e\\u003cp\\u003eThe trial was carried out in a growth chamber having the following environmental conditions: 620 \\u0026micro;moles photons m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (137 W m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e) of a white light emitted from metal halide lamps, a 16/8 hours light/darkness photoperiod, a light/darkness temperature cycle of 28/20\\u0026deg;C, and a relative humidity of 39.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.7% during all the growth period. Bottles of 0.5 L containing a 1:1 mixture of sand:vermiculite as substrate were used as pots. The soybean [\\u003cem\\u003eGlycine max\\u003c/em\\u003e (L.) Merr.] genotype evaluated was \\u0026ldquo;G\\u0026eacute;nesis 5601\\u0026rdquo; (G5601), developed in a local breeding program where the main trait for selection was yield stability (Quero et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Simondi et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), and provided by Sergio Ceretta from Instituto Nacional de Investigaciones Agropecuarias (INIA \\u0026ndash; La Estanzuela, Uruguay). Three seeds per pot were sown, and the more vigorous seedling was selected in each pot after the cotyledons had fully developed. In addition, developmental homogeneity was taken into account when selecting plants to eschew the potential effects of differential developmental stages. For rhizobium inoculation, the U1302 \\u003cem\\u003eBradyrhizobium elkanii\\u003c/em\\u003e strain was used (Vincent, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e1970\\u003c/span\\u003e), and inoculation was done on the day of seeding and repeated after three days. Eventually, each bottle was covered with a plastic lid\\u0026mdash;with a hole for the plant to grow through\\u0026mdash;to minimize water evaporation from the substrate.\\u003c/p\\u003e\\u003cp\\u003eThe trial was conducted using a completely randomized design with four treatments, each with five biological replicates, resulting in 20 individuals. The treatments combined nodulation (N) and no nodulation (NN) conditions, as well as well-watered (WW) and water-restricted (WR) conditions. In the case of WW plants, the substrate was held at field capacity throughout the trial since field capacity represents a soil state with no water constraints. Thus, WW was considered the control treatment for water conditions. As a result, the four treatments consisted of non-nodulated and well-watered plants (NN\\u0026thinsp;+\\u0026thinsp;WW), non-nodulated and water-restricted plants (NN\\u0026thinsp;+\\u0026thinsp;WR), nodulated and well-watered plants (N\\u0026thinsp;+\\u0026thinsp;WW), and nodulated and water-restricted plants (N\\u0026thinsp;+\\u0026thinsp;WR) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). In this way, the biological contrasts studied were \\u003cem\\u003ei\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW, \\u003cem\\u003eii\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR, \\u003cem\\u003eiii\\u003c/em\\u003e) NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW, and \\u003cem\\u003eiv\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eDuring the first 19 days after sowing (V3 developmental stage), all plants were watered with B\\u0026amp;D-medium supplemented with KNO\\u003csub\\u003e3\\u003c/sub\\u003e (0.5 mM and 5 mM final concentration for nodulated and non-nodulated plants, respectively). As of day 20, water was withdrawn only to WR plants, considering this moment the day 0 of the WD period. The substrate water content was measured on a daily basis by gravimetry (water gravimetric content) during either the growth or WD period. Stomatal conductance (g\\u003csub\\u003esw\\u003c/sub\\u003e), measured with a SC-1 Porometer (Decagon Device), was the variable used to monitor WD. Daily measurements were performed at WW and WR plants during the WD period. WR plants were harvested when the g\\u003csub\\u003esw\\u003c/sub\\u003e value reached approximately 50% of the value obtained on day 0, while WW plants were randomly harvested with WR plants. For N plants, nodules and roots were separately stored at -80\\u0026deg;C; however, only the roots were analyzed in this work. For NN plants, roots were kept at -80\\u0026deg;C until further processing, too (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Purification of polysomal fraction\\u003c/h2\\u003e\\u003cp\\u003eThe polysomal fraction was purified by sucrose cushion centrifugation, as described by (DiPaolo et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Sainz et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Smircich et al., \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). A volume of 2 mL of pulverized frozen roots was homogenized in 4 mL of polysome extraction buffer (Sainz et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The homogenates were kept on ice until the samples were processed and clarified by centrifugation at 16000 g for 15 min. After this, the samples were filtered, and centrifugation was repeated. A 500 \\u0026micro;L volume of the supernatant was separated for total RNA (TOTAL) isolation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). Sucrose cushions made of 12% and 33.5% sucrose layers were prepared in 13.2 mL tubes (UltraClear, Beckman Coulter, United States, 344059), and 2 mL of the remaining supernatant were loaded onto them. An S-class Beckman L-100K ultracentrifuge (W40 Ti swinging bucket rotor) was used to centrifuge the tubes at 35000 rpm for 2 h at 4\\u0026deg;C. After centrifugation, the polysomal fraction was retrieved from the pellet for each sample and resuspended in 200 \\u0026micro;L of polysome resuspension buffer (Sainz et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The resuspended polysomes were kept at 4\\u0026deg;C for 30 min to finally perform a regular RNA purification and obtain the polysome-associated mRNA fraction (PAR) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 RNA extraction and sequencing\\u003c/h2\\u003e\\u003cp\\u003eThe RNA extraction from TOTAL and PAR fractions was done by homogenizing them in 750 \\u0026micro;L TRizol LS (Invitrogen, United States of America, 10296-028), following Sainz et al. (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The concentration and integrity of each RNA were determined using a BioAnalyzer Agilent 2100 (Agilent Technologies, Inc., United States of America). Samples with a concentration over 1.0 \\u0026micro;g and an RNA integrity number greater than 7.0 were shipped to Macrogen Inc. (South Korea) for library preparation and sequencing. Sequencing was performed on the Illumina Novaseq 6000 high-throughput sequencing platforms. For each treatment, samples of both TOTAL and PAR fractions of roots from three biological replicates were sent for sequencing.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Data analysis\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.4.1 Sequencing read processing\\u003c/h2\\u003e\\u003cp\\u003eThe quality of each sample was visually inspected using FastQC (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e).\\u003c/span\\u003e Trimmomatic was used to remove adapters and trim low-sequencing-quality bases (v 0.39, Bolger et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Trimmed sequences with a length over 80 bp and an overall quality greater than 30 were retained for further analysis. Transcript-level gene expression was quantified using Salmon in quasi-mapping-based mode (v1.8.0, Patro et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). All Salmon parameters were set to their default values, except for the GC bias correction parameter, which was enabled. The map index was built from the fourth version of the \\u003cem\\u003eGlycine max\\u003c/em\\u003e transcriptome retrieved from NCBI (GCF_000004515.6, Glycine max v4.0, excluding rRNA). Transcript read counts were then aggregated to gene level using the tximport R package (v 1.26.1, Soneson et al., \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Initial and post-trimmed read descriptions and mapping rate are presented in Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e. The sequencing data is available in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA1284746.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.4.2 WGCNA analysis\\u003c/h2\\u003e\\u003cp\\u003eA weighted gene co-expression network analysis was performed using the WGCNA R package (version 1.72-5, Langfelder and Horvath, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). The gene-level count data were used as input, normalized using the DESeq2 R package (v1.38.3, Love et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Since low-expressed features tend to increase noise, genes with a low coefficient of variation and/or low counts (defined as having fewer than 50 counts in more than 50% of the samples) were removed. After that, soft threshold power was selected using the pickSoftThreshold function. The net was built using the blockwiseModules function, setting the arguments power at 9 (Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e), maxBlockSize at 27000 (genes number after filtering), mergeCutHeight at 0.20, networkType as unsigned, TOMtype as signed (to preserve the continuity of correlation), and all the remaining arguments at default.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe module eigengene (ME) was calculated for each module using the moduleEigenges function to determine whether modules were associated with treatments. A heatmap was built using the pheatmap R package (v 1.0.12, Kolde, 2015) to visualize ME-treatment relationships. Also, for visualization purposes, the MEs were grouped by hierarchical clustering using 1-correlation as a distance measure (Zhang and Horvath, \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). The average link was used as the clustering criterion for dendrogram creation (Dudek, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The number of clusters was determined using the Silhouette index (Dudek, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) and the NbClust R package (Charrad et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe network visualizations were done using the network R package (Butts, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e), setting an edge adjacency of 0.15. Modules were plotted with different colors to distinguish them visually. In addition, the intramodular connectivity was estimated for each gene using the intramodular Connectivity WGCNA function, allowing to determine the 10% most within-connected genes of each module as hub genes (Seo et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.4.3 DEG analysis\\u003c/h2\\u003e\\u003cp\\u003eA principal component analysis (PCA) of the samples and a heatmap of distances between samples were performed to observe clustering patterns and distances among replicates and treatments. The R packages FactoMineR (v. 2.1, Le et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e), pheatmap (v. 1.02, Kolde, 2015), and ggplot2 (v. 3.5.1, Wickham, 2016) were used for these purposes. Genes that presented at least one count in the sum of all samples were used, and a variance-stabilizing transformation of the data was applied following the recommendations of Love et al., (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Additionally, a Gene Ontology (GO) biological process enrichment analysis was conducted using the topGO R package (v 2.50.0, Alexa and Rahnenfuhrer, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), focusing on the 50 genes that contributed the most to principal components 1 and 2, to identify which processes had the most significant influence on data variability. The differential expression statistical analysis was computed using the DESeq function of the DESeq2 R package (v 1.38.3, Love et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). DEGs were defined as those with |log2FC| \\u0026gt;1 and Benjamini\\u0026ndash;Hochberg adjusted p-value (padj)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\\u003cp\\u003eDEGs lists of each contrast and RNA fraction were intersected to identify common and unique DEGs. From the TOTAL and PAR lists, intersections of genes with main transcriptional regulation (only TOTAL), main translational regulation (only PAR), or combined (transcriptional and translational) regulation (TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) could be identified. Additionally, DEGs lists of pairs of contrasts were intersected to remove, for a contrast of interest, those DEGs that might be due to the effect of a biological treatment and not to the effect of the combined treatment. In one case, contrast \\u003cem\\u003ei\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW) and contrast \\u003cem\\u003eiii\\u003c/em\\u003e (NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW) DEGs lists were intersected, so DEGs common to WR, and genes that are specifically altered when the plant experiences WD, but is also nodulated, are obtained. Likewise, contrast \\u003cem\\u003eii\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR) and contrast \\u003cem\\u003eiv\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW) DEGs lists were intersected, obtaining common DEGs due to nodulation, and DEGs which are due to water restriction in nodulation plants.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.4.4 WGCNA\\u0026thinsp;+\\u0026thinsp;DEG\\u003c/h2\\u003e\\u003cp\\u003eDEGs in each fraction and contrast between combined treatments [\\u003cem\\u003ei\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW, \\u003cem\\u003eii\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR, \\u003cem\\u003eiii\\u003c/em\\u003e) NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW, \\u003cem\\u003eiv\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW] were co-localized in the co-expression network modules. DEGs were colored in the network according to their regulation status, i.e. up- or down-regulated or stable. As a result, modules with an enriched presence of DEGs were identified.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.4.5 Functional Enrichment and Protein-Protein Interactions Analyses\\u003c/h2\\u003e\\u003cp\\u003eAnnotation of GO terms for the soybean genome was conducted using PANNZER (T\\u0026ouml;r\\u0026ouml;nen and Holm, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). GO biological processes (GO_BP) enrichment analyses were performed on the modules with high DEGs co-localization, using Fisher\\u0026rsquo;s exact test and the weight01 method to construct the GO graph structure. A GO term was considered significantly enriched when it had a false discovery rate (FDR) of less than 0.05. Analyses were done using the topGO R package (v 2.50.0, Alexa and Rahnenfuhrer, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). GO enrichment plots were built using ggplot2 (v 3.5.1, Wickham, 2016).\\u003c/p\\u003e\\u003cp\\u003eProtein-protein interactions (PPI) network analyses were performed to search for potential interactions between proteins encoded by DEGs. The networks were constructed using the DEGs that were co-localized in the selected modules for analysis. CytoScape (Smoot et al., \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) and STRING (Szklarczyk et al., \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) were used to construct the networks. GO_BP and KEGG pathway enrichment analyses were done on the PPI networks, in both cases considering a term significantly enriched when it had a FDR of less than 0.05.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.4.6 Motifs Discovery and Enrichment Analysis in 5\\u0026rsquo;-UTRs\\u003c/h2\\u003e\\u003cp\\u003eThe transcripts\\u0026rsquo; 5\\u0026rsquo;-UTRs were retrieved from the genomic annotation GTF file (GCF_000004515.6, \\u003cem\\u003eGlycine max\\u003c/em\\u003e v4.0) using an R public script (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://github.com/saketkc/gencode_regions\\u003c/span\\u003e\\u003cspan address=\\\"https://github.com/saketkc/gencode_regions\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The bioinformatic tool seqtk (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://github.com/lh3/seqtk\\u003c/span\\u003e\\u003cspan address=\\\"https://github.com/lh3/seqtk\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) was used to obtain the 5\\u0026rsquo;-UTRs fasta files, utilizing the bed file obtained in the previous step as input. The motifs discovery in the 5\\u0026rsquo;-UTR was conducted through MEME (version 5.5.7, Bailey and Elkan, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e), targeting a maximum of 10 motifs of between 6 and 50 nucleotide length, utilizing the classic discovery mode, and setting zero or one motif occurrence per sequence. The motifs enrichment analysis was performed using SEA (Simple Enrichment Analysis) (Bailey et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). All search parameters were set to their default: E-value lower than 10, zero Markov order for shuffling sequences and for the background model, and center sequences alignment for site positional diagrams.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.4.6 Candidate Transcription Factors Selection\\u003c/h2\\u003e\\u003cp\\u003eThe transcription factors that exhibited differential expression at the translational regulation level (PAR or TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) in contrasts involving the N\\u0026thinsp;+\\u0026thinsp;WR combined treatment \\u0026mdash;contrasts \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eii\\u003c/em\\u003e\\u0026mdash; were initially selected as candidates for further analysis. Subsequently, one of the two following criteria was applied to narrow down the list: first, being a hub gene in the co-expression modules selected during previous steps of the analysis; second, having putative targets that share the same regulatory state (up- or down-regulation), with these targets regulated at the transcriptional level (TOTAL or TOTAL\\u0026thinsp;+\\u0026thinsp;PAR). The list of putative targets for each transcription factor was retrieved from PlantTFDB (Jin et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eIn this study, the assessment of the differential responses of the roots of nodulated soybean plants to WD comprised an experimental design that combined two nodulation conditions and two hydric conditions, i.e., nodulated (N) and non-nodulated (NN) plants were subjected to water restriction (WR) or maintained well-watered (WW) throughout the WD period. Therefore, four combined treatments were obtained (as detailed in 2.1 and depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), and the following four biological contrasts were studied: \\u003cem\\u003ei\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW, \\u003cem\\u003eii\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR, \\u003cem\\u003eiii\\u003c/em\\u003e) NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW, and \\u003cem\\u003eiv\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW. Although our main objective was to gain knowledge of the molecular mechanisms governing the response of nodulated (or N-fix) plants to WD (analysed in contrasts \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eii\\u003c/em\\u003e), the information gathered from all four contrasts is distinctive and relevant. It allows for the analysis of the plant responses to the nodulation and WD conditions, both jointly and individually, within the same experiment. This way, the response of nodulated plants to WR is highlighted in the N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW (\\u003cem\\u003ei\\u003c/em\\u003e) contrast, whereas in \\u003cem\\u003eii\\u003c/em\\u003e) N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR, the effect of water restriction is studied under different nodulation contexts. The NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW (\\u003cem\\u003eiii\\u003c/em\\u003e) contrast shows non-nodulated (or N-fed) plant's responses to water restriction, and the N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW (\\u003cem\\u003eiv\\u003c/em\\u003e) contrast evidence the plant responses resulting from the nodulation process without WR.\\u003c/p\\u003e\\u003cp\\u003eCentral to our study was the inclusion of translational control as an additional layer of information, complementing transcriptional regulation. This approach was driven by the significance of this gene expression regulatory step in plants under environmental stress, and by the potential of integrating data from multiple layers to reveal novel, biologically interpretable associations (VanDam et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). To achieve this, the four contrasts between combined treatments were analysed at the transcriptome (total RNA fraction; TOTAL) and the Polysome-Associated RNA level (PAR fraction), focusing attention on the variations in the polysome association of mRNA compared to total RNA levels. This way, we could assess regulation at the TOTAL and PAR levels as well as the combined responses (TOTAL\\u0026thinsp;+\\u0026thinsp;PAR).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1 RNA-seq data validation\\u003c/h2\\u003e\\u003cp\\u003eSince our RNA-seq experiment was performed in parallel with two genotypes: G5601\\u0026mdash;whose data is presented in this manuscript\\u0026mdash;and Don Mario (DM6.8i)\\u0026mdash;whose data has been already published (Sainz et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u0026mdash;, we validate the RNA-seq data by analysing the common DEGs to water restriction (contrasts \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eiii\\u003c/em\\u003e) on the one hand, and the common DEGs to the nodulation process (contrasts \\u003cem\\u003eii\\u003c/em\\u003e and \\u003cem\\u003eiv\\u003c/em\\u003e) on the other, of both genotypes. We analysed expression data (fold change values) of those genes and found high positive Pearson correlation coefficients between the common genes among genotypes (Figure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e), confirming the reliability of the RNA-seq results used in this study. In Figure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e, we show the fold change of 29 genes common to contrasts \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eiii\\u003c/em\\u003e, and 106 genes common to contrasts \\u003cem\\u003eii\\u003c/em\\u003e and \\u003cem\\u003eiv\\u003c/em\\u003e, which were present in both genotypes.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 Nodulation delays average stomata closure rate in G5601 soybean plants\\u003c/h2\\u003e\\u003cp\\u003eThe WD condition\\u0026mdash;applied to WR plants\\u0026mdash;was established through water withdrawal; water substrate content was measured daily by gravimetry, and stomatal conductance was the WD monitoring variable. In our plant-pot-substrate system (Figure \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e), water loss from the substrate is solely due to transpiration, as evaporation is negligible (thanks to the lids), and since the plants were grown at field capacity until the WD period, there is no percolation.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe median stomatal conductance of N\\u0026thinsp;+\\u0026thinsp;WW and NN\\u0026thinsp;+\\u0026thinsp;WW plants was 220 mmol m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 152 mmol m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). Regarding WR plants, it was observed that N\\u0026thinsp;+\\u0026thinsp;WR plants held their initial stomatal conductance (g\\u003csub\\u003esw0\\u003c/sub\\u003e, that of day 0 of the WD period) for an average of four days throughout the WD period (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD). On the other hand, NN\\u0026thinsp;+\\u0026thinsp;WR plants maintained their g\\u003csub\\u003esw0\\u003c/sub\\u003e only until day one of the WD period, on average (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD). Hence, in this study, by using the stomatal conductance as the trait to determine the timing of WR plants harvesting, it was observed that nodulated plants reached 50% of g\\u003csub\\u003esw0\\u003c/sub\\u003e two days later than non-nodulated plants, on average (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE). This finding evidences that N-fix plants can keep their stomata as open as in WW conditions for a longer time compared to N-fed ones. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and C demonstrate that WW and WR plants are held at field capacity or subjected to WD, respectively.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3 Water restriction has a stronger impact on gene expression than nodulation, which mainly upregulates genes.\\u003c/h2\\u003e\\u003cp\\u003eExploring the RNA-seq data using distance matrix analysis and principal component analysis (PCA) showed that all biological replicates clustered together (Figure \\u003cspan refid=\\\"MOESM4\\\" class=\\\"InternalRef\\\"\\u003eS4\\u003c/span\\u003e). In the heatmap (Figure \\u003cspan refid=\\\"MOESM4\\\" class=\\\"InternalRef\\\"\\u003eS4\\u003c/span\\u003e, A), two large clusters formed, with all the samples corresponding to the N\\u0026thinsp;+\\u0026thinsp;WR treatment grouped on one side and the rest in another group. The PCA evidenced that dimension 1 (Dim1) explained 58.4%, separating the samples by the nodulation condition. Dimension 2 (Dim2)\\u0026mdash;explaining 21.2% of the variance\\u0026mdash;separates the samples by the hydric condition. This approach resulted in four well-defined groups, corresponding to the four combined treatments. As expected, each sample's TOTAL and PAR fractions were found to be very close to each other (Figure \\u003cspan refid=\\\"MOESM4\\\" class=\\\"InternalRef\\\"\\u003eS4\\u003c/span\\u003e, B; circles and triangles, respectively).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThese results indicate that the data are suitable for further analysis (replicates of the same treatment and RNA fractions of the same biological replicate were similar). Therefore, differential gene expression was analyzed between the combined treatments. Initially, contrasts were examined to assess how the nodulation context (N vs NN) and the hydric condition (WR vs WW) altered the global gene expression at both the transcriptome and translatome levels (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, A-D). To summarize, water restriction had a more significant impact on gene expression at both the transcriptome and translatome levels compared to nodulation. In the N vs NN comparison, the majority of DEGs were upregulated, whereas in the WR vs WW comparison, more DEGs were downregulated at both the TOTAL and PAR levels (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, A-D).\\u003c/p\\u003e\\u003cp\\u003eSubsequently, the lists of DEGs\\u0026mdash;down-regulated on one hand and up-regulated on the other\\u0026mdash;for each contrast's TOTAL and PAR fractions were intersected, thus obtaining the gene identities that presented mainly transcriptional or translational regulation and those with combined regulation. Additionally, the DEG lists were intersected between contrasts (\\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eiii\\u003c/em\\u003e; \\u003cem\\u003eii\\u003c/em\\u003e and \\u003cem\\u003eiv\\u003c/em\\u003e) to discriminate in each list the genes that were differentially expressed due to the biological effect of one condition or treatment (e.g., water restriction) rather than the combined treatment (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, E-H). To begin with, the lists of contrast \\u003cem\\u003ei\\u003c/em\\u003e, which exhibit the response of nodulated plants to water restriction, were intersected with the lists of contrast \\u003cem\\u003eiii\\u003c/em\\u003e, which show the response of non-nodulated plants to water restriction, to obtain the DEGs that were due to the effect of water restriction independently of the nodulation context and also the preferential DEGs of each contrast (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, E, F). Interestingly, it can be observed that under water restriction, nodulated plants not only show a greater number of DEGs but also exhibit a higher amount of DEGs with mainly translational regulation than non-nodulated plants (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, E, F). Almost 30% (672 out of 2523) of the DEGs had translational regulation in nodulated plants under water restriction, whereas in non-nodulated plants, 15% (100 out of 653) of the DEGs showed regulation at the translational level under water restriction (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, E, F). Next, the Venn diagrams depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, G, H visualize the same logical relationship between the lists of contrast \\u003cem\\u003eii\\u003c/em\\u003e, which shows the particular response to water restriction of nodulated plants with respect to non-nodulated plants, with those of contrast \\u003cem\\u003eiv\\u003c/em\\u003e, which shows the plant responses due to the nodulation process without involving water restriction. This allowed for the discrimination between DEGs common to nodulation (N\\u0026thinsp;+\\u0026thinsp;WR and N\\u0026thinsp;+\\u0026thinsp;WW) and DEGs due to each combined treatment (N\\u0026thinsp;+\\u0026thinsp;WR, on the one hand, and N\\u0026thinsp;+\\u0026thinsp;WW, on the other).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3.4 Coordinated gene expression in nodulated soybean roots under water restriction: insights from weighted gene co-expression network analysis.\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eA gene co-expression network was constructed to identify gene modules, hub genes, and, especially, candidate genes that could explain the plant responses to the different treatments\\u0026mdash;with special emphasis on the responses of nodulated plants to WD. To perform WGCNA, 24 samples comprising the three replicates from the four combined treatments (\\u003cem\\u003ei\\u003c/em\\u003e, \\u003cem\\u003eii\\u003c/em\\u003e, \\u003cem\\u003eiii\\u003c/em\\u003e, and \\u003cem\\u003eiv\\u003c/em\\u003e) and the two mRNA fractions (TOTAL and PAR) were utilized. A total of 26,356 genes were included in the analysis, resulting in 33 gene co-expression modules and an additional module (module 0) that groups the genes that could not be classified into any of the other modules (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, A). The number of genes in each module ranged from 30 to 5,494, with module 1 having the highest and module 33 the lowest number of genes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, A). The modules were then grouped into clusters based on the expression profiles of their eigengenes (ME), resulting in a total of nine clusters defined by the Silhouette index (Figure \\u003cspan refid=\\\"MOESM5\\\" class=\\\"InternalRef\\\"\\u003eS5\\u003c/span\\u003e). Module 0 (M0) formed a single cluster (cluster 1) since the expression profile of its eigengene (ME0) did not group with any other module. The cluster comprising ME10, ME29, ME32, ME19, and ME31 (cluster 2) was formed based on the expression of their genes in relation to the nodulation context since the expression of these MEs was higher in the combined treatments N\\u0026thinsp;+\\u0026thinsp;WW and N\\u0026thinsp;+\\u0026thinsp;WR compared to the non-nodulation treatments. A third cluster was formed by ME5, ME26, ME17, and ME18, which was mainly detected in the PAR samples of all treatments, with the highest detection in non-nodulated plants subjected to water restriction (NN\\u0026thinsp;+\\u0026thinsp;WR). Cluster 5 (ME15, ME4, ME7, ME6, ME27, ME21, and M22) exhibits the lowest and highest transcript detection in water-restricted plants (NN\\u0026thinsp;+\\u0026thinsp;WR and N\\u0026thinsp;+\\u0026thinsp;WR) and well-watered plants (NN\\u0026thinsp;+\\u0026thinsp;WW and N\\u0026thinsp;+\\u0026thinsp;WW), respectively. This last observation is especially accurate for the PAR fraction. In contrast to the previous cluster, a sixth cluster composed of ME8, ME20, ME9, and ME24 included genes preferentially detected in the treatments comprising water restriction conditions (NN\\u0026thinsp;+\\u0026thinsp;WR and N\\u0026thinsp;+\\u0026thinsp;WR). This is graphically presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, A.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe topological representation of the expression modules of the WGCNA network is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, B). All modules, except for M28, exhibited edge adjacency values exceeding the threshold (0.15), indicating their presence in the network. Consequently, the network comprised 12,848 nodes\\u0026mdash;representing genes\\u0026mdash;and 3,951,052 edges\\u0026mdash;representing the connections between genes. The edges were not graphed due to computational limitations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, B).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3.5 The integration of WGCNA and DEGs analysis allows the identification of specific co-expressed modules associated with nodulation, water restriction, or their interaction.\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo further investigate the responses of the G5601 soybean genotype to the different experimental conditions, the WGCNA and DEGs analysis information were integrated. This approach enabled the localization of DEGs associated with each contrast, considering their status (i.e., up- or down-regulated) and regulation level (i.e., TOTAL, PAR or TOTAL\\u0026thinsp;+\\u0026thinsp;PAR), within the different WGCNA modules. First, the localization was performed for the DEGs common to water restriction (those shared between contrast \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eiii\\u003c/em\\u003e: N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW and NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW) and for the DEGs common to nodulation (those shared between contrast \\u003cem\\u003eii\\u003c/em\\u003e and \\u003cem\\u003eiv\\u003c/em\\u003e: N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR and N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW) (Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Most DEGs associated with WR were found in modules M4, M7, and M8 at the TOTAL\\u0026thinsp;+\\u0026thinsp;PAR level. Notably, the nodulation-related DEGs were found almost exclusively in M10 at the TOTAL\\u0026thinsp;+\\u0026thinsp;PAR level (Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Thus, modules M4, M7, and M8 may be linked to the plant's responses to water restriction, while M10 is related to the nodulation process.\\u003c/p\\u003e\\u003cp\\u003eFurthermore, the DEGs found in each of the four contrasts between combined treatments were localized in the co-expression modules (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e) and were mapped onto the co-expression network (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Most DEGs in contrast \\u003cem\\u003ei\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW), which involved the response to water restriction of nodulated plants, were located in modules M1, M4, M7, M8, M20, and M21 (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). In the case of contrast \\u003cem\\u003eii\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR), which showed how plants change their response to water restriction when nodulated, the majority of DEGs were located in modules M4, M7, and M19 (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). The DEGs identified in the contrast that shows non-nodulated plant's responses to water restriction (contrast \\u003cem\\u003eiii\\u003c/em\\u003e: NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW) were preferentially located in module M7 (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). Regarding the DEGs obtained in the contrast highlighting the plant responses resulting from the nodulation process without involving any water restriction (contrast \\u003cem\\u003eiv\\u003c/em\\u003e: N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW), they were located in modules M7, M10, and M21 (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD).\\u003c/p\\u003e\\u003cp\\u003eInterestingly, M21 co-localized genes showing a reversal in their regulation status between two contrasts: up-regulated genes in contrast \\u003cem\\u003eiv\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW) were repressed in contrast \\u003cem\\u003ei\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW). Thus, some genes responsive to nodulation were down-regulated upon establishing WD conditions (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD, A). The previously DEGs-enriched mentioned modules, modules M4, M7, M8, and M10, were selected for further analysis of the DEGs common to water restriction or nodulation (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) and modules M1, M4, M7, M8, M19, M20, and M21 were selected for further analysis of the DEGs from each contrast (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). In both cases, the regulation level (TOTAL, PAR, TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) of the DEGs was assessed. Most DEGs common to water restriction were located in M4 and M7, presented combined regulation, and were down-regulated (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Almost all DEGs common to nodulation also showed combined regulation, were up-regulated, and were located in M10 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Regarding the DEGs identified for each contrast, several aspects are noteworthy. First, M7 contained mostly down-regulated DEGs in all four contrasts. Second, M4 preferentially accommodated up- and down-regulated DEGs of contrast \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eii\\u003c/em\\u003e of all regulation levels. Third, DEGs in M8 and M20 were mainly derived from contrast \\u003cem\\u003ei\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW) and were up-regulated, with TOTAL\\u0026thinsp;+\\u0026thinsp;PAR regulation. Fourth, M19 contained up-regulated DEGs almost exclusively of contrast \\u003cem\\u003eii\\u003c/em\\u003e. Fifth, M21 presented 83 up-regulated DEGs in contrast \\u003cem\\u003eiv\\u003c/em\\u003e and 67 down-regulated DEGs in contrast \\u003cem\\u003ei\\u003c/em\\u003e. Of these, 55 genes reverted their regulation status (down- or up-), regardless of the regulation level, in the mentioned contrasts (\\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eiv\\u003c/em\\u003e). Lastly, and most notably, 70% of the DEGs in M1 (228 out of 338) presented mainly translational regulation (PAR) and corresponded to up-regulated genes from contrast \\u003cem\\u003ei\\u003c/em\\u003e. This data is presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eAnother interesting piece of information shown (between parentheses) in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e (and also in Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e and Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e) is the number of DEGs that are also the module's hub genes. We defined hub genes as the 10% most connected (see 2.4). Since highly connected genes often play a more crucial role in the functionality of networks than other nodes, we reasoned it was interesting to investigate whether any of the DEGs were also hub genes. All four contrasts presented hub genes within their DEGs, with the N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR contrast (\\u003cem\\u003eii\\u003c/em\\u003e) exhibiting the highest number of hub genes among its DEGs (32%: 146 out of 458), followed by the \\u003cem\\u003eiv\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW) contrast with 28% (60 out of 216) of hub genes among its DEGs. These results indicate that while nodulation influenced the differential expression of certain hub genes, a more significant percentage of hub genes were impacted by the interaction between nodulation and WD (relative to non-nodulation).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eWeighted Gene Co-expression Network Analysis (WGCNA) Modules (M) co-localizing the greater number of differentially expressed genes (DEGs) common to contrasts \\u003cem\\u003ei)\\u003c/em\\u003e and \\u003cem\\u003eiii)\\u003c/em\\u003e and contrasts \\u003cem\\u003eii)\\u003c/em\\u003e and \\u003cem\\u003eiv).\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eContrasts\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eStatus\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eRegulation level\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eM7\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eM8\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eM10\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei) N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW and iii) NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eDownregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e144 (84)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e50 (21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4 (2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eUpregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34 (14)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e16 (7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eii) N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR and iv) N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eDownregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4 (4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9 (4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eUpregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e6 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e113 (63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eWhether the DEGs were down- or up-regulated is depicted in the Status column. The regulation level (TOTAL, PAR, TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) is also shown. The number of DEGs that are also modules\\u0026rsquo; hub genes is indicated in parentheses. Hub genes were defined as the 10% of genes with the highest intramodular connectivity.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eGene co-expression Modules (M) obtained from Weighted Gene Co-expression Network Analysis (WGCNA) selected for having the greatest number of differentially expressed genes (DEGs) across the four contrasts analyzed (\\u003cem\\u003ei\\u003c/em\\u003e, \\u003cem\\u003eii\\u003c/em\\u003e, \\u003cem\\u003eiii\\u003c/em\\u003e, and \\u003cem\\u003eiv\\u003c/em\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"10\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCondition DEG\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eStatus\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eRegulation level\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM1\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eM7\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eM8\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eM19\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003eM20\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eM21\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei) N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eDownregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e60 (2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e21 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e8 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e15 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e70 (4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e19 (6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e13 (2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e240 (57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e43 (19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e20 (3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e21 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e39 (18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eUpregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e33 (5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e6 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e228 (99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e63 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e22 (5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e51 (23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eii) N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eDownregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e82 (31)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e126 (61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e35 (8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eUpregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e28 (4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e27 (11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14 (3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e3 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e7 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e31 (9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e7 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e11 (4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eiii) NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eDownregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e25 (2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e15 (3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e51 (10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eUpregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eiv) N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eDownregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e12 (4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e6 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e3 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e17 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e28 (10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eUpregulated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e22 (1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e8 (2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e53 (19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eWhether the DEGs were down- or up-regulated is depicted in the Status column. The regulation level (TOTAL, PAR, TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) is also shown. The number of DEGs that are also modules\\u0026rsquo; hub genes is indicated in parentheses. Hub genes were defined as the 10% of genes with the highest intramodular connectivity.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.6 Enrichment analysis of biological processes and metabolic pathways in the selected co-expression modules.\\u003c/h2\\u003e\\u003cp\\u003eGene Ontology biological process enrichment analysis and KEGG were conducted on specific co-expression modules (those listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) based on the characteristics of the DEGs previously described, to capture co-expression responses and understand which processes exhibited similar responses across treatments.\\u003c/p\\u003e\\u003cp\\u003eM10, where nearly all DEGs common to nodulation co-localized, was overrepresented with higher significant gene counts in: \\u0026ldquo;response to stimuli\\u0026rdquo;, \\u0026ldquo;transmembrane transport\\u0026rdquo;, \\u0026ldquo;regulation of nucleobase-containing compounds\\u0026rdquo;, and \\u0026ldquo;nodulation\\u0026rdquo; for GO_BP, as well as \\u0026ldquo;biosynthesis of cofactors\\u0026rdquo;, \\u0026ldquo;purine metabolism\\u0026rdquo;, and \\u0026ldquo;zeatin biosynthesis\\u0026rdquo; for pathways (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD).\\u003c/p\\u003e\\u003cp\\u003eM7 mainly comprised down-regulated DEGs across the three regulation levels (TOTAL, PAR, and TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) and all four contrasts analyzed. Therefore, this module can be understood as a group of genes whose expression and/or association with polysomes is preferentially repressed when either of the stimuli assessed in this study\\u0026mdash;nodulation and water restriction\\u0026mdash;is present since each contrast studies the effect of a stimulus on a condition that does not present it. In contrast \\u003cem\\u003eiv\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW), the stimulus was the biotic effect of nodulation; in contrast \\u003cem\\u003ei\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW) and \\u003cem\\u003eiii\\u003c/em\\u003e (NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW), the stimulus was the abiotic effect of water restriction (in different nodulation contexts). Meanwhile, in contrast \\u003cem\\u003eii\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR), the differential stimulus was nodulation since the plants were subjected to water restriction in both treatments. When functional enrichment was analyzed over the entire module, it was found to be primarily enriched in biological processes and pathways of \\u0026ldquo;stress response\\u0026rdquo;, \\u0026ldquo;transcription regulation\\u0026rdquo;, \\u0026ldquo;protein phosphorylation\\u0026rdquo;, and \\u0026ldquo;biosynthesis of flavonoids and phenylpropanoids\\u0026rdquo; (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). M4, which allocated DEGs from contrasts \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eii\\u003c/em\\u003e, i.e., genes that are being differentially expressed in a nodulated plant subjected to water restriction, was highly related to the GO_BP \\u0026ldquo;response to stimulus\\u0026rdquo; (more than 600 significant gene counts), followed by the GO terms \\u0026ldquo;protein phosphorylation\\u0026rdquo; and \\u0026ldquo;transmembrane transport\\u0026rdquo;. Regarding the pathways enrichment, \\u0026ldquo;plant-pathogen interaction\\u0026rdquo;, \\u0026ldquo;MAPK signaling pathway\\u0026rdquo;, and \\u0026ldquo;phenylpropanoid biosynthesis\\u0026rdquo; were the ones with the highest gene counts (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). M8, which mainly co-localized contrast \\u003cem\\u003ei\\u003c/em\\u003e DEGs, both up- and down-regulated ones, and at all levels of regulation, showed enrichment for the GO_BP terms \\u0026ldquo;organic substance biosynthetic process\\u0026rdquo;, \\u0026ldquo;response to stress\\u0026rdquo;, \\u0026ldquo;response to endogenous stimuli\\u0026rdquo;, among others. \\u0026ldquo;Glutathione metabolism\\u0026rdquo;, \\u0026ldquo;phenylpropanoid biosynthesis\\u0026rdquo;, and \\u0026ldquo;pyruvate metabolism\\u0026rdquo; were among the pathways with the highest gene counts (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC). M20, which also co-localized DEGs from contrast \\u003cem\\u003ei\\u003c/em\\u003e, was strongly associated with the GO_BP term \\u0026ldquo;regulation of transcription\\u0026rdquo; (Figure \\u003cspan refid=\\\"MOESM6\\\" class=\\\"InternalRef\\\"\\u003eS6\\u003c/span\\u003e). The enrichment analysis of the other two selected modules (M19 and M21) is shown in Figure \\u003cspan refid=\\\"MOESM6\\\" class=\\\"InternalRef\\\"\\u003eS6\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3.7 Module 1 is enriched in up-regulated PAR-level DEGs from contrast\\u003c/b\\u003e \\u003cb\\u003ei\\u003c/b\\u003e, \\u003cb\\u003eparticularly related to translation and oxidative phosphorylation.\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eFurther analysis of M1 was particularly interesting to us because of its DEGs characteristics. This module was strongly associated with \\u0026ldquo;phosphorylation\\u0026rdquo; and \\u0026ldquo;translation\\u0026rdquo;, with over 500 gene counts, for GO_BP enrichment, as well as \\u0026ldquo;spliceosome\\u0026rdquo;, \\u0026ldquo;oxidative phosphorylation\\u0026rdquo;, and \\u0026ldquo;ubiquitin-mediated proteolysis\\u0026rdquo; for KEGG enrichment (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA). Also, we constructed a PPI network using the 228 up-regulated genes at the PAR level of contrast \\u003cem\\u003ei\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB). Notably, within the network, we identified highly connected regions\\u0026mdash;or subnetworks\\u0026mdash;enriched in \\u0026ldquo;eukaryotic translation initiation\\u0026rdquo; and \\u0026ldquo;oxidative phosphorylation\\u0026rdquo; pathways, which exhibited the highest connectivity. Pathways associated with \\u0026ldquo;mitochondrial protein import\\u0026rdquo;, \\u0026ldquo;detoxification of oxidant species\\u0026rdquo;, and \\u0026ldquo;amino acid regulate mTORC1\\\" were also found. The overrepresented functional terms identified in this PPI network illustrate the primary processes that changed due to upregulation, particularly at the PAR level, in N-fix plants exposed to water restriction (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003eThe nodes (proteins) associated with the \\u0026ldquo;translation initiation\\u0026rdquo; pathway primarily include ribosome structural proteins (RPs) from both the large (RPLs) and small (RPSs) subunits. This suggests that under WD conditions, N-fix plants would enhance the translation of proteins involved in the translational machinery, thereby promoting the translation process in the abovementioned conditions. Among the proteins associated with the term \\u0026ldquo;oxidative phosphorylation\\u0026rdquo; were acyl carrier proteins (ACP; involved in fatty acid synthesis), two subunits of the cytochrome b-c1 complex, and three subunits of the NADH dehydrogenase (ubiquinone) 1 alpha subcomplex. This indicates a strong demand for ATP in N\\u0026thinsp;+\\u0026thinsp;WR plants, driven by an increase in the association with polysomes of certain components of its synthesis pathway. Furthermore, four distinct subunits of the vacuolar-type proton ATPase (v-ATPase) were among the proteins associated with both \\\"oxidative phosphorylation\\\" and \\\"amino acids regulate mTORC1\\\" in the Reactome database. This suggests that the mTORC1 (mechanistic target of rapamycin complex 1) signaling pathway, along with energy metabolism, may be regulated at the translational level and could be involved in how nodulated plants respond to water restriction conditions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3.8 Transcription factors differentially expressed at the translational level could be candidate genes for understanding how nodulated plants respond to water deficit.\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eTranscription factors (TFs) and their intricate interactions play a crucial role in guiding specific genetic programs, including responses to environmental stresses. Here, we conducted an exploratory analysis to identify which TFs families were predominant in the different plant responses at the translational regulation level (either only PAR or combined TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) across all four contrasts analysed. Contrast \\u003cem\\u003ei\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW) presented 144 TFs DEGs, being the contrast with the highest number of differentially expressed TFs, followed by contrasts \\u003cem\\u003eii\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR) and \\u003cem\\u003eiv\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WW vs NN\\u0026thinsp;+\\u0026thinsp;WW) with 56 and 53 TFs DEGs, respectively. Contrast \\u003cem\\u003eiii\\u003c/em\\u003e (NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW) showed a total of 37 differentially expressed TFs (Figure \\u003cspan refid=\\\"MOESM8\\\" class=\\\"InternalRef\\\"\\u003eS8\\u003c/span\\u003e). The WRKY, NAC, MYB, ERF, and bHLH families stand out in contrast \\u003cem\\u003ei\\u003c/em\\u003e. Regarding WRKY, most members were down-regulated; conversely, up-regulation of NAC and MYB family members was predominant. The ERF and bHLH families comprised a similar number of up- and down-regulated members. In contrast \\u003cem\\u003eii\\u003c/em\\u003e, which presented the same number of up- and down-regulated DEGs, the more representative TF families were GRAS, C2H2, ERF, and bHLH. NAC, LBD, ERF, and bHLH were the families with the highest number of members among the TFs DEGs of contrast \\u003cem\\u003eiii\\u003c/em\\u003e, similar to contrast \\u003cem\\u003ei\\u003c/em\\u003e, except for the WRKY family. The contrast \\u003cem\\u003eiv\\u003c/em\\u003e mainly consisted of up-regulated TFs, with the MYB, ERF, and bHLH families being the most representative (Figure \\u003cspan refid=\\\"MOESM8\\\" class=\\\"InternalRef\\\"\\u003eS8\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eGiven the crucial role of TFs in regulating gene programs in response to diverse stimuli, the importance of translational control, and our focus on how N-fix plants respond to water restriction (analysed in contrast \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eii\\u003c/em\\u003e), we selected candidate TFs based on their differential expression at the translational regulation level (PAR or TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) and one of the following two criteria: being a hub gene in the co-expression module where they co-localized, or having putative targets with the same regulation state (up or down). In the latter, as candidate selection concerns TFs, the list was further filtered to retain targets with transcriptional regulation (whether exclusive, TOTAL, or combined with translational regulation, TOTAL\\u0026thinsp;+\\u0026thinsp;PAR). This data is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eA roster of robust candidate transcription factors involved in drought tolerance in nodulated plants for further functional studies.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlymaID (version 4)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTF\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFamily\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eModule (M)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eContrast\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eRegulation Level\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eStatus\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eSelection criterium\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_04G039300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ebZIP TRAB1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ebZIP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eUp\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003ePutative targets with the same status at transcriptional (TOTAL) or mixed (TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) regulation level\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_13G279900\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNAC27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNAC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_16G021000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eATHB-12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHD-ZIP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_16G043200\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNAC12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNAC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_14G152700\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNAC21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNAC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_18G040700\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMYB20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMYB\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003eUp\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e\\u003cp\\u003eHub\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_17G240100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eERF RAP2-1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eERF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_10G010300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMYB78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMYB\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_11G127100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBBX24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDBB\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_05G109500\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHAT22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHD-ZIP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_07G052100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHOMEOBOX\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHD-ZIP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_19G180300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNAC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNAC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eii\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eDown\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlyma_05G029000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWRKY 72A\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eWRKY\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eM4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eii\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTOTAL\\u0026thinsp;+\\u0026thinsp;PAR\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c8\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eThe selection criteria are listed in the rightmost column. \\u003cb\\u003eTF\\u003c/b\\u003e: transcription factor. \\u003cb\\u003eM\\u003c/b\\u003e: module. \\u003cb\\u003eUp\\u003c/b\\u003e: up-regulated. \\u003cb\\u003eDown\\u003c/b\\u003e: down-regulated. Hub genes were defined as the 10% of genes with the highest intramodular connectivity.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eRegarding contrast \\u003cem\\u003ei\\u003c/em\\u003e, one TF was found to co-localize in M1, five in M4, one in M8, and one in M20\\u0026mdash;belonging to the bZIP, HD-ZIP, NAC, and MYB families\\u0026mdash;all of which were up-regulated, with 121 putative targets also up-regulated and distributed across the aforementioned modules (Table \\u003cspan refid=\\\"MOESM4\\\" class=\\\"InternalRef\\\"\\u003eS4\\u003c/span\\u003e). The TFs selected for the hub gene criterion, also in contrast \\u003cem\\u003ei\\u003c/em\\u003e, comprised eight members from different families, all of which were up-regulated. In contrast \\u003cem\\u003eii\\u003c/em\\u003e, two down-regulated TFs from the NAC and WRKY families, which co-localized in M4, were selected for the hub gene criterion. The candidate TFs listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e are strong candidates for future functional studies, which can be conducted using a combined approach of gene knockout and overexpression. This allows validation of results by comparing the effects of gene loss-of-function with gene gain-of-function.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003ePlants use various strategies to cope with water scarcity. The sensitivity of different soybean genotypes to water shortages\\u0026mdash;specifically, when they begin to sense lower soil water content and respond accordingly\\u0026mdash;determines their performance in terms of yield. This outcome heavily depends on the intensity and duration of the WD conditions (Sade et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Sinclair et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Vadez et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Another important trait in this context is the nodulation status of the plant \\u0026ndash;whether soybean or other legumes\\u0026ndash;as several authors suggest that rhizobial symbiosis induces drought tolerance (\\u0026Aacute;lvarez-Arag\\u0026oacute;n et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Liu et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; L\\u0026oacute;pez et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Staudinger et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Given the likely evolution of legumes on N-poor soils in a symbiosis-dependent manner, it is plausible to assume that nodulated plants should be the form best adapted to various stresses (\\u0026Aacute;lvarez-Arag\\u0026oacute;n et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Liu et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In this work, we showed that nodulation delays the plant's harvest time by an average of two days compared to N-fed plants (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, D-E). Since we defined harvest time as the day when the plants reached 50% of the initial conductance (the value at day 0 of the WD period), this result suggests that the nodulation condition allows stomata to remain open for a longer period, thereby maintaining CO\\u003csub\\u003e2\\u003c/sub\\u003e uptake for photosynthesis. We previously found that this also occurs in another soybean genotype (Sainz et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), suggesting that this phenomenon may be widespread in soybean.\\u003c/p\\u003e\\u003cp\\u003eSeveral studies have investigated the impact of water restriction on N-fed soybean plants (Song et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; VanHa et al., \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Wang et al., \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), including this work, since contrast \\u003cem\\u003eiii\\u003c/em\\u003e (NN\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WW) shows the response of N-fed plants to WR (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). However, the effects of WR on N-fix soybean at the molecular level have not been extensively addressed, even though it is known that the response differs from that of N-fed plants (\\u0026Aacute;lvarez-Arag\\u0026oacute;n et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; L\\u0026oacute;pez et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Sainz et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Staudinger et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), resembling a stress priming phenomenon where nodulation modulates the WD-stress response of the plants. The two contrasts that addressed this in our study were contrast \\u003cem\\u003ei\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs N\\u0026thinsp;+\\u0026thinsp;WW) and \\u003cem\\u003eii\\u003c/em\\u003e (N\\u0026thinsp;+\\u0026thinsp;WR vs NN\\u0026thinsp;+\\u0026thinsp;WR) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). Specifically, in contrast \\u003cem\\u003ei\\u003c/em\\u003e, one key conclusion is that the response of nodulated plants to water restriction is more complex in terms of the number of DEGs (2523 vs 653 in contrast \\u003cem\\u003eiii\\u003c/em\\u003e) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE-F; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA-C; Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Additionally, they exhibit a higher number of DEGs (almost 30% vs. 15% in N-fed plants) with translational regulation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE-F; Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), indicating that this step of gene expression regulation is a relevant one in shaping N-fix G5601 soybean plant responses to WD. This was particularly noticeable for the DEGs co-localized in M1 of the co-expression analysis (WGCNA), as 70% of them presented mainly translational regulation (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Although plants under environmental stress benefit from translational control, as it enables rapid responses, few examples in the literature evidence this in N\\u0026thinsp;+\\u0026thinsp;WR plants (Sainz et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e); the current study is another example denoting that the attribute of being regulated at the translational level (either PAR or TOTAL\\u0026thinsp;+\\u0026thinsp;PAR) is of relevance when screening for candidate genes to be involved in the response of N-fix plants to WD.\\u003c/p\\u003e\\u003cp\\u003eThe WGCNA has proven helpful for interpreting our data because it reduces the complexity of the RNA-seq data, identifying modules (or clusters of modules) that potentially uncover functional relationships between genes and their association with the biological processes underlying plant responses in the different scenarios, such as cluster 2 associated with nodulation, cluster 3 related to translational control, and cluster 5 and 6 representative of genes with the lowest and highest transcript detection in WD conditions, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Furthermore, by integrating co-expression and DEGs analysis (Li et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; S\\u0026aacute;nchez-Baiz\\u0026aacute;n et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Sferra et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), we identified modules enriched with DEGs associated with the nodulation process, WR, or specifically with the response of N-fix plants to WR, allowing us to better interpret the biological information (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e; Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e; Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). Regarding the DEGs associated with nodulation (those common to contrasts \\u003cem\\u003eii\\u003c/em\\u003e and \\u003cem\\u003eiv\\u003c/em\\u003e, mainly up-regulated), M10 is definitely one of relevance (Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), with the biological process of transmembrane transport as one of the most representative (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). O\\u0026rsquo;Rourke et al. (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) pointed out that the regulation of nitrogen transporter expression plays a crucial role in the nodulation process. As nitrogen fixation involves transforming and transporting nitrogen into other biological forms, such as ureides, transporters must be adjusted to accommodate changes in nitrogen metabolism. Concerning the DEGs associated with the plant's responses to WD (the ones common to contrasts \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eiii\\u003c/em\\u003e; mainly down-regulated), M4 and M7 were the most notable ones (Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). In this case, the GO_BP terms with the highest gene counts were \\u0026ldquo;response to stimulus\\u0026rdquo; and \\u0026ldquo;response to stress\\u0026rdquo;, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). KEGG analysis showed that those responses could be mainly associated with secondary metabolism pathways, specifically with the phenylpropanoid biosynthesis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Dalal et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) observed a down-regulation of phenylpropanoid biosynthesis in wheat roots under drought stress. As for the response of N-fix plants to WD (contrast \\u003cem\\u003ei\\u003c/em\\u003e), the most representative modules were also M4 and M7, but also M8 and, particularly, M1 (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), with the latter having very high gene counts for \\u0026ldquo;phosphorylation\\u0026rdquo; and \\u0026ldquo;translation\\u0026rdquo; GO_BP terms (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA).\\u003c/p\\u003e\\u003cp\\u003eFurther analysis of the subset of contrast \\u003cem\\u003ei\\u003c/em\\u003e up-regulated DEGs at the PAR level that co-localized in M1 through a PPI network was very illustrative of the relevance of translational control in specific key cellular processes in the context of a nodulated and WR plant (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB). The up-regulation of genes associated with OXPHOS does not align with previously reported findings, where mitochondrial respiration is inhibited in N-fed roots upon the imposition of WD (Atkin and Macherel, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Therefore, we suggest that N-fix plants have a high demand for ATP, and their translational regulation of OXPHOS-related proteins helps ensure this supply. Other genes related to OXPHOS that were also up-regulated at the PAR level in N\\u0026thinsp;+\\u0026thinsp;WR roots included different subunits of a v-ATPase (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB), a key player of the consensus model of amino acid signaling for mTORC1 activation (Takahara et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zoncu et al., \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). mTOR, an evolutionarily conserved single-gene-encoded protein in higher eukaryotes, is a kinase that, in plants, impinges growth and development in response to the plant's energy status (Yokawa and Baluška, \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). One of the key functions of mTORC1 is to promote anabolism, particularly the synthesis of proteins, lipids, and purines. Through the eukaryotic initiation factor 4E binding protein 1 (4E-BP1), mTORC1 stimulates the translation of a subset of mRNAs possessing a 5\\u0026acute; terminal oligopyrimidine (5\\u0026rsquo; TOP) motif, such as mRNAs encoding ribosomal proteins (Takahara et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Since the other highly connected subnetwork in the PPI network was enriched in ribosomal proteins (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB), we looked for overrepresented motifs in the 5\\u0026rsquo; untranslated regions (5\\u0026rsquo;-UTR) of the 228 DEGs used to build the network (MEME tool; Figure \\u003cspan refid=\\\"MOESM7\\\" class=\\\"InternalRef\\\"\\u003eS7\\u003c/span\\u003e; Table \\u003cspan refid=\\\"MOESM5\\\" class=\\\"InternalRef\\\"\\u003eS5\\u003c/span\\u003e). Interestingly, we found that 46% of the 228 transcripts presented the 5\\u0026rsquo; TOP motif, which is known to enhance the translation of ribosomal proteins, as previously mentioned, but also translation initiation factors, and elongation factors, among other proteins. Furthermore, 21 of these transcripts encode ribosomal proteins. This indicates that the 5\\u0026rsquo; TOP motif may enable cells to rapidly adjust the expression of proteins involved in ribosomal biogenesis, thereby sustaining and enhancing the translational machinery of N-fix plants under water restriction.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe final focus of our study\\u0026mdash;aimed to identified robust candidate gene for future functional analysis\\u0026mdash;was on TFs subjected to translational control due to their relevance in gene reprogramming and the coordination of biological processes and metabolic pathways (Weidem\\u0026uuml;ller et al., \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), as well as the importance of translational control in shaping the plant responses to different stimuli (Kawaguchi et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Lee and Bailey-serres, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Lei et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Urquidi Camacho et al., \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Interestingly, the bHLH, ERF, MYB, NAC, and WRKY families stand out with a high number of DEGs associated with the responses of N\\u0026thinsp;+\\u0026thinsp;WR plants (contrasts \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003eii\\u003c/em\\u003e) (Figure \\u003cspan refid=\\\"MOESM8\\\" class=\\\"InternalRef\\\"\\u003eS8\\u003c/span\\u003e). While this aligns with existing research indicating that most TFs involved in the response to WD belong to the aforementioned families (Wan et al., \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), their association with the nodulation process has not been reported before. The TFs identified (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) play distinct roles in responding to abiotic stress, especially WD, such as activating or repressing processes like hormone metabolism, antioxidant production, and stress-related protein synthesis (Manna et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Additionally, two other criteria\\u0026mdash;being a hub gene in the co-expression analysis or having putative targets (according to the PlantTFDB database; Jin et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) with the same regulatory state\\u0026mdash;were combined for more robust candidate selection (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Therefore, this list, comprising 13 TFs differentially expressed in N\\u0026thinsp;+\\u0026thinsp;WR plants, could be valuable for future functional analysis of the specific responses of N-fix plants under water-restricted conditions, a scenario that is increasingly common in the current climate change context.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study shows that nodulation modulates soybean responses to water restriction at both physiological and molecular levels, with translational control playing a central role in the response of nodulated and water-restricted plants. This is particularly true for the DEGs grouped in module 1 of the co-expression analysis, mainly involved in translation and oxidative phosphorylation, suggesting a high energy demand under these conditions. This way, we suggest that the translational regulation of OXPHOS and ribosomal protein-related genes supports ATP demand in N-fix plants. Several transcription factors from the NAC, MYB, WRKY, and bHLH families\\u0026mdash;with known roles in the plant responses to abiotic stress\\u0026mdash;were differentially expressed at the translational level in nodulated and water-restricted plants. Their association with the nodulation process is novel. Besides being DEG in N+WR plants, two additional criteria\\u0026mdash;being hub genes or having predicted target genes with concordant regulatory patterns\\u0026mdash;were used to refine the list, resulting in robust candidates for future functional studies\\u0026mdash;through knockout via CRISPR and overexpression\\u0026mdash;and potential use in breeding programs.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003e5’TOP\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;5’ terminal oligopyrimidine\\u003c/p\\u003e\\n\\u003cp\\u003e5’UTR\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;5’ untranslated region\\u003c/p\\u003e\\n\\u003cp\\u003eACP\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;acyl carrier protein\\u003c/p\\u003e\\n\\u003cp\\u003eDEG\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;differential expressed gene\\u003c/p\\u003e\\n\\u003cp\\u003eFDR\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;false discovery rate\\u003c/p\\u003e\\n\\u003cp\\u003eGO\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;gene ontology\\u003c/p\\u003e\\n\\u003cp\\u003eGO_BP\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;GO biological process\\u003c/p\\u003e\\n\\u003cp\\u003eKEGG\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\\u003c/p\\u003e\\n\\u003cp\\u003eME\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;module eigengene\\u003c/p\\u003e\\n\\u003cp\\u003emRNA\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;messenger RNA\\u003c/p\\u003e\\n\\u003cp\\u003emTORC1\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;mechanistic target of rapamycin complex 1\\u003c/p\\u003e\\n\\u003cp\\u003eN\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;nodulation\\u003c/p\\u003e\\n\\u003cp\\u003eN-fed\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;fertilized plants\\u003c/p\\u003e\\n\\u003cp\\u003eN-fix\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;symbiotic nitrogen-fixing plants\\u003c/p\\u003e\\n\\u003cp\\u003eNN\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;non-nodulation\\u003c/p\\u003e\\n\\u003cp\\u003eOXPHOS\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;oxidative phosphorylation\\u003c/p\\u003e\\n\\u003cp\\u003ePAR\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;polysome-associated mRNA fraction\\u003c/p\\u003e\\n\\u003cp\\u003ePCA\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;principal component analysis\\u003c/p\\u003e\\n\\u003cp\\u003ePPI\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;protein-protein interaction\\u003c/p\\u003e\\n\\u003cp\\u003eRPLs\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;large subunit ribosomal protein\\u003c/p\\u003e\\n\\u003cp\\u003eRPs\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;ribosome structural proteins\\u003c/p\\u003e\\n\\u003cp\\u003eRPSs\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;small subunit ribosomal protein\\u003c/p\\u003e\\n\\u003cp\\u003eTF\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;transcription factor\\u003c/p\\u003e\\n\\u003cp\\u003eTOTAL\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;total RNA fraction\\u003c/p\\u003e\\n\\u003cp\\u003eWD\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;water deficit\\u003c/p\\u003e\\n\\u003cp\\u003eWGCNA\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;weighted gene co-expression network analysis\\u003c/p\\u003e\\n\\u003cp\\u003eWR\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;water-restricted\\u003c/p\\u003e\\n\\u003cp\\u003eWW \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; well-watered\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eClinical trial number: not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll datasets supporting the results of this study are included within the article and its supplementary information. The sequencing data is available in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA1284746.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by CSIC I+D 2022 Grant No. 22520220100256UD, CSIC I+D 2020 Grant No. 282, FVF 2017 Grant No. 210 (María Martha Sainz), Programa de Desarrollo de las Ciencias Básicas (PEDECIBA) (María Martha Sainz, Carla Valeria Filippi, Guillermo Eastman, Mariana Sotelo-Silveira, Omar Borsani, José Sotelo-Silveira), and Red Nacional de Biotecnología Agrícola: RTS_1_2014_1-ANII (Omar Borsani).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eM.M.S., J.S.-S. and O.B. conceived the study; M.M-M, M.M.S., C.V.F., G.E., G.Q., and S.P-P performed the experiments; M.M-M, M.M.S., C.V.F., G.E., M.S.-S. and J.S.-S. analyzed the data; M.M.S and M.M-M wrote the original draft. All authors have reviewed and edited the manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank Stefan de Folter (UGA-LANGEBIO) for helpful comments on the manuscript. We also thank Sistema Nacional de Investigadores (ANII) (María Martha Sainz, Carla Valeria Filippi, Guillermo Eastman, Mariana Sotelo-Silveira, José Sotelo-Silveira, Omar Borsani). Mauro Martínez-More was the recipient of an MSc fellowship from Comisión Académica de Posgrado, CSIC, UdelaR. Selene Píriz-Pezzuto is a Ph.D fellow of Comisión Académica de Posgrado, CSIC, UdelaR.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAlexa, A., Rahnenfuhrer, J., 2021. topGO: Enrichment Analysis for Gene Ontology. https://doi.org/10.18129/B9.bioc.topGO\\u003c/li\\u003e\\n\\u003cli\\u003e\\u0026Aacute;lvarez-Arag\\u0026oacute;n, R., Palacios, J.M., Ram\\u0026iacute;rez-Parra, E., 2023. Rhizobial symbiosis promotes drought tolerance in Vicia sativa and Pisum sativum. Environ. Exp. Bot. 208. https://doi.org/10.1016/j.envexpbot.2023.105268\\u003c/li\\u003e\\n\\u003cli\\u003eAtkin, O.K., Macherel, D., 2009. The crucial role of plant mitochondria in orchestrating drought tolerance. Ann. Bot. 103, 581\\u0026ndash;597. https://doi.org/10.1093/aob/mcn094\\u003c/li\\u003e\\n\\u003cli\\u003eBailey, T.L., Elkan, C., 1994. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. 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Science (80-. ). 334, 678\\u0026ndash;683. https://doi.org/10.1126/science.1207056.mTORC1\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-plant-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pbio\",\"sideBox\":\"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pbio/default.aspx\",\"title\":\"BMC Plant Biology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"translational control, soybean, oxidative phosphorylation, translation, root metabolism, drought tolerance, symbiotic nitrogen fixation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7686372/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7686372/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cb\\u003eBackground\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eSoybean primarily acquires nitrogen through symbiosis with nitrogen-fixing bacteria. Water deficit (WD) is a major stress limiting crop yield. Nodulation may enhance drought tolerance in legumes by modulating nitrogen and hormone metabolism, osmotic adjustment, and antioxidant defenses. However, the molecular mechanisms underlying the differing WD responses in nodulated (N-fix) versus non-nodulated (N-fed) plants remain unclear. Translational control of gene expression is a key regulatory mechanism during stress.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eResults\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eHere, we compared the transcriptome and translatome of soybean roots from N-fix and N-fed plants exposed to WD, analyzing four combined treatments. Our results showed that N-fix plants under WD exhibited more complex responses in terms of total differentially expressed genes (DEGs) compared to N-fed plants. This complexity was also evident in DEGs subject to translational regulation and in differentially expressed transcription factors. Co-expression analysis revealed modules associated with core biological processes, encompassing nodulation, water deficit, and most interestingly, their interplay.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eConclusions\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eOur research reveals that translational regulation of genes involved in oxidative phosphorylation and translation initiation emerged as a key response in N-fix plants under WD. These findings highlight distinct molecular adaptations in nodulated soybean roots under WD, with translational control playing a central role. We also identified promising transcription factor candidate genes under translational regulation in N-fix roots\\u0026mdash;for which no role in nodulation has been described\\u0026mdash;offering potential targets for improving drought tolerance in legumes once validated functionally.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Genes associated with translation and oxidative phosphorylation as components of the translational response in nodulated and water-restricted soybean\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-13 16:37:22\",\"doi\":\"10.21203/rs.3.rs-7686372/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-10-23T21:08:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-22T15:45:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-21T10:43:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"162879175166290615925502808257379206274\",\"date\":\"2025-10-08T15:48:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"69964236173833279195845268143276432059\",\"date\":\"2025-10-07T18:30:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"62198524534543703219596665576059682599\",\"date\":\"2025-10-03T01:16:58+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-09-30T19:26:28+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-09-29T17:00:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-09-29T14:38:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-29T14:38:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Plant Biology\",\"date\":\"2025-09-22T15:17:57+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-plant-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pbio\",\"sideBox\":\"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pbio/default.aspx\",\"title\":\"BMC Plant Biology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"2ec783a8-e3b1-406d-9c41-09d092b759f8\",\"owner\":[],\"postedDate\":\"October 13th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-21T18:38:20+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-13 16:37:22\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7686372\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7686372\",\"identity\":\"rs-7686372\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}