Transcriptome Analysis Unravels Diverse Response Mechanisms of Nodules to Phytic Acid Supply in Vicia faba

Transcriptome Analysis Unravels Diverse Response Mechanisms of Nodules to Phytic Acid Supply in Vicia faba | 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 Transcriptome Analysis Unravels Diverse Response Mechanisms of Nodules to Phytic Acid Supply in Vicia faba Frank Kwarteng Amoako, Michael Ackah, Ebenezer Kweku Ntiriakwa, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6417689/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Faba bean ( Vicia faba L.) is an essential crop that contributes enormously to nitrogen (N) input due to its ability to fix atmospheric nitrogen via symbiotic N fixation (SNF). During this process, the symbionts undergo cascades of gene expression perturbations and reprogramming in the organogenesis of nodules under nutritional stresses. Inorganic phosphorus (Pi) has been the ultimate source for nodulation, to the neglect of organic P (Po) in many SNF studies. To elucidate the role of Po in SNF, we used de novo RNA-seq tools and the results identified a total of 2,263 differentially expressed genes (DEGs) in nodules when Vicia faba plants were exposed to different P sources, viz., low Pi, high Pi, and Po when inoculated with Rhizobium leguminosarum bv. viciae 3841 in hydrponics experiment. The results consistently reveal that Po-induced nodules comparisons altered 1,144 and 811 DEGs, respectively, relative to the Pi-induced nodules comparison (308 DEGs), highlighting higher DEGs triggered by phytic acid supply. The results further reveal differential nodulation, transport and carbon metabolism mechanisms employed by the different P sources during N-fixation. The expression of these genes in Vicia faba will provide more insights into the functional characterization of these DEGs for breeding purposes, and also contribute enomously towards the ongoing genome annotation project and database of Vicia faba plants. Vicia faba nodule phytic acid symbiotic nitrogen fixation De novo RNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Key Message Exogenous phytic acid enhances carbon allocation for N₂-fixation and activates solute transport and nodule organogenesis genes in nodules of symbiotically grown Vicia faba relative to inorganic phosphorus. 1. INTRODUCTION Phosphorus (P) is an essential nutrient that plays a crucial role in plant growth and development. It exists predominantly as inorganic (Pi) and organic (Po), with the latter being proposed as a direct substitute for the widely patronized chemical P fertilizers, which are derived from rock phosphate (Amoako et al. 2023a, b). This is very likely because Po has generally been discovered to compose approximately 20–80% (Wasner et al. 2023) and, in other reports, up to 90% (McConnell et al. 2020) of the total P pool in the soil. Orthophosphate monoesters, orthophosphate diesters, organic polyphosphates, and phosphonates, RNA, and phospholipids, with the orthophosphate monoester inositol phosphate and its derivatives, are the most abundant and commonly found Po forms in soil fractions, accounting for approximately 50% of the soil total Po fraction due to their mineralization efficiencies (McConnell et al. 2020; Park et al. 2022). Phytic acid (myo-inositol hexakisphosphate, PA) is discovered as the predominant phosphorus (P) reservoir in cereals and legumes that supply the biosynthetic pathway and nutritional requirements of plants during germination to regulate cellular processes (Gulabani et al. 2022; Kumar et al. 2023). In soils, it is quantitatively the dominant and most significant inositol phosphate (Gerke 2015), with other stereoisomers and phosphorylated derivatives also discovered (Turner et al. 2012). For instance, over 50 million tonnes of phytate are known to be manufactured commercially in fruits and seeds of crops annually (Lott et al. 2000), and recent data suggest that about 67% of the global application rates of fertilizers in the form of P is quantified from phytate, signifying the quantitative relevance of phytate for P cycling in soil (Mullaney & Ullah 2007). Significant strides have been made in an effort to elucidate how plants respond to Po utilization when supplied as a fertilizer in crop production in the last decades. Even though Po, as a P source cannot be taken up directly by plants but are first mineralized into Pi via solubilization by P solublizing microorganisms. Of course, several studies point to the fact that Po acquisition and utilization in different plants such as cereals, pastures, and legumes have been shown to be very promising and support the growth and development of crops similar to Pi wih help of P solublizing microorganisms such as bacteria (Amoako et al. 2023a, b; Jillani et al. 2022). For example, Jillani et al. (2022) reported that the Vicia faba plant could utilize different P sources, including phytic acid, as competently as Pi substrates when inoculated with bacteria. Notwithstanding, Amoako et al. (2023a, b; 2024) reported a similar trend in both soil and hydroponics, where different Vicia faba varieties treated with phytic acid produced and accumulated similar biomasses like its counterpart, Pi, at equivalent application rates upon bacteria inoculation. This had earlier been reported by Tarafdar and Claassen (1988), who revealed that different plant species, viz., wheat, oat, barley, and clover, have the capacity to utilize Po and accumulate biomasses like Pi (Tarafdar & Claassen 1988). However, some contrasting reports indicate that some plant species cannot utilize Po. For instance, the supply of Po substrates could not support growth and biomass in Vicia villosa (Said-Pullicino et al. 2022) or in legumes and six pastures (Hayes et al. 2000). This was associated with the limited utilization of phytate by the species as a consequence of the low capacity of plant roots to mineralize Po and the adsorption of phytate to soil solid surfaces. It could be deduced from the above conflicting reports that the acquisition and utilization of Po by varying plants are associated with growth media, plant intraspecies, genotypic variations, soil type and right phosphatases, and bacteria inoculants (Amoako et al. 2023a, b; Jillani et al. 2022). Po has recently been reported to improve not only growth and biomass of Vicia faba but also promotes N fixation and biological nitrogen fixation (Amoako et al. 2023a, b; Jillani et al. 2022). P is largely needed for N 2 -fixation and limited P during the process hampers and terminates biological nitrogen fixation. Further studies to elucidate Po`s pivotal roles in legumes and N 2 -fixation is crucial. Biological nitrogen fixation (BNF) is the process by which microorganisms convert di-nitrogen (N 2 ) in the atmosphere into plant-usable form. It is an energetically expensive process that requires 16 ATP molecules to break down an N 2 molecule to form NH 3 , with 12 more ATP molecules needed for ammonium assimilation (Soumare et al. 2020) via the GS/GOGAT cycle. Studies have reported the deleterious effect of P deficiency on nodulation and BNF efficiency in crops such as Vicia faba (Amoako et al. 2023a, b; Jillani et al. 2022), Medicago truncatula (Sulieman & Tran 2017), Vicia villosa (Said-Pullicino et al. 2022), Astragalus sinicus (Sun et al. 2022), Glycine max (Wang et al. 2020; Xing et al. 2022), Cicer arietinum (Loucif et al. 2022), and Phaseolus vulgaris (Isidra-Arellano et al. 2018). For proper functioning of the nodule, a higher amount of P is required. Apart from Pi, which is predominantly used in most symbiotic and nodulation studies, Po has recently been reported to improve nodule number, biomass, and N 2 -fixation like Pi when plants are exclusively dependent on nodule symbiosis for nitrogen (Amoako et al. 2023a, b; Jillani et al. 2022). The formation of nodules in legumes is a very complex mechanism occurring through the establishment of a mutualistic relationship that begins with a molecular dialogue between the two partners, the host plant, and the nitrogen-fixing organism (rhizobia) (Soumare et al. 2020). The flavonoids and isoflavonoids exuded by the host plant trigger recognition, infection, differentiation of root hair cells, and nodule development (Mukherjee & Sen 2021). This involves a plethora of signals, programming, and reprogramming of the functioning nodules. In our recent publications, we reported the perturbations caused in the nodules of Vicia faba plants induced by phytic acid supply (Amoako et al. 2023b). We reported that the supply of phytic acid did not only improve the mineral and carbon metabolism adjustments of nodules, but also supplied the needed P and carbon for normal homeostasis and functioning of the nodules (Amoako et al. 2023b). Furthermore, the supply of phytic acid did not only alter the cationic-anionic balances in nodules (Amoako et al. 2023b), but also transcriptionally altered the expression of plasma membrane (PM) H + -ATPase gene isoforms in nodules (Amoako et al. 2024). Hence, it is quite tempting to hypothesize that the supply of phytic acid could perturb the molecular mechanisms that modulate (more genes and pathways) the programming and reprogramming of the nodules if genetic alterations and modifications are to be employed to enhance symbiotic nitrogen fixation (SNF) and crop productivity in the future. However, studies pertaining to Po signatures in nodule transcriptome and N 2 -fixation remains obscure. In recent times, the use of transcriptional reprogramming linked with nodulation has been suggested as one of the most powerful approaches to elucidate the genetic control of nodule formation under different conditions (Gao et al. 2022). This has been made possible via transcriptome profiling studies that have been conducted to characterize and identify the genes, molecular pathways, and cellular processes involved in nodulation and N 2 -fixation (Gao et al. 2022). Through transcriptomics and proteomics analyses, several transporter genes and proteins, such as phosphate transporter ( PHT1-4) (Nasr et al. 2017), vacuolar iron transporter 1 ( VIT1 ), zinc transporter ( ZIP ), copper transporter 5.1, potassium transporter 2, magnesium transporter ( CorA-like Mg 2+ transporter protein ), calcium-transporting ATPase, molybdenum transporter of the MFS superfamily, metal transporters ( NRAMP3 ), ABC family transporters, SWEET, amino acid permease ( Aap ) etc., have been identified in soybean (Clarke et al. 2015; Sakamoto et al. 2019) and other plants, and references therein, are reported to be localized to the symbiosome membrane (SM) and infected nodule cells. For example, Clarke et al. (2015) identified 197 proteins on the SM that were characterized to being involved in cellular processes such as metabolism, solute transport, and membrane trafficking in soybean nodules. Previous transcriptomic studies have significantly enhanced our insight into SNF; the majority of these studies have only profiled the transcriptional changes of root nodules strictly in connection with Pi as the source of P, to the total neglect of the organic counterpart. Even though the entire genome of Vicia faba (Faba bean) has not been entirely annotated, but through BLAST (de novo) from different databases, numerous genes have been annotated to transform our understanding of the transcriptome of Vicia faba nodules. Through transcriptome analysis, nodule-specific cysteine-rich (NCR) peptides, early and late nodulation genes have been reported to express solely in infected cells of M. truncatula nodules, which functions during nodule organogenesis and bacteroid differentiation (Guefrachi et al. 2014; Horváth et al. 2023; Nallu et al. 2013). The objective of this study was to identify genes involved and triggered during nodule growth (nodulation), transport (exchange and fluxes across the symbiont), and carbon metabolism in response to different P sources in symbiotically grown Vicia faba . To answer the above objective, we therefore, hypothesized that the differential nodulation, transport and carbon metabolism mechanisms exhibited by Vicia faba plants in response to phytic acid recruit diverse pathways and mechanisms during nodule organogenesis and N 2 -fixation relative to inorganic P. In this study, we employed de novo RNA-Seq analysis to identify differentially expressed genes (DEGs) involved in nodules responses to phytic acid supply in hydroponics during SNF of symbiotically grown Vicia faba plants. We generated RNA-Seq datasets across nine nodule samples. Our bioinformatics detected both known and unknown unigenes with varied expression patterns under varied levels of P sources. 2. MATERIALS AND METHODS 2.1 Plant Growth Conditions, Phosphorus Treatment and Rhizobia Inoculation Plant materials, growth conditions, and P treatments followed the same experimental procedures as described in our initial study (Amoako et al. 2023b, 2024). In short, Vicia faba L. (var. Hiverna) seeds were surface purified (NaClO 4 solution; 5–7% v/v) and cleansed with ddH 2 O (double deionized water) to discard contaminants. Afterwards, early germination was augmented by immersing the seeds in a 1 mM CaSO 4 solution for 2 days (d) at 20°C and were germinated using the sandwich method. Uniform seedlings were transplanted after 7–10 d of germination in 5 L plastic containers containing a 1/4th nutrient solution (NS) strength, with the same basal NS formulation as described. The seedlings were hydroponically grown in a climatic chamber under conditions of 14/10 h day/night cycle, 20/15°C day/night temperature, 60% relative humidity, and 300 µmol m − 2 s − 1 photosynthetic active radiation for a period of 30 days as previously. The NS concentrations were systematically and steadily increased to ½ on the second day, 3/4 on the third day, and full on the fourth day to circumvent osmotic stress. The NS was recycled every three days to freshen up the exhausted nutrients. The P treatments followed the same procedure as previously elucidated (Amoako et al. 2023b; 2024) and included inadequate and adequate P, which consisted of low-Pi (2.0 µM KH 2 PO 4 ; NoP), adequate-Pi (200 µM KH 2 PO 4 ; Pi), and Po (200 µM phytic acid; Po). Meanwhile, the low and adequate KH 2 PO 4 -Pi treatments were used as negative and positive controls, respectively, for better treatment comparisons. To circumvent nitrogen (N) deficiency at the early stages of growth, a small amount N (20 µM NH 4 NO 3 ) was supplied to the plants for a period of 10 d and was paused to ensure that chemical N does not inhibit nodulation and N 2 -fixation. After 14 d of transplant, seedlings were inoculated (inoculation assay) with Rhizobium leguminosarum bv. viciae 3841 broth. The rhizobium broth was prepared as described (Amoako et al. 2023b). The design of the experiment followed a completely randomized design (CRD), with 4 biological replicates, and plants were allowed to grow for a period of 30 d. The plants were harvested in 30 d, and tissues were fragmented into three, viz., leaves, roots, and nodules. The fresh nodule samples were instantly frozen in liquid N and stored at -80°C for RNA-seq and other analyses. For the RNA-seq analysis, three biological replicates, consisting of nine nodule samples, were used. All growth data, physiological parameters, analytical nutrient determinations, and biochemical measurements pertaining to this study have been reported (Amoako et al. 2023b; 2024). 2.2 RNA Extraction, Library Construction and Sequencing RNA was isolated from nine Vicia faba nodule samples of three biological replicates of P treatments, consisting of negative control (NoP), positive control (Pi), and phytic acid (Po), using the RNeasy Plus Mini Kit (Tiangen), according to the manufacturer’s protocols. The quality and quantity of each RNA extracted were determined using agarose gel electrophoresis and the Nanodrop 2500 (Thermo Fisher Scientific, US). For library preparation for transcriptome sequencing, a total amount of 1 µg RNA per sample was used for the RNA sample preparations. Briefly, the extracted mRNA was purified from total RNA using poly-T oligo-attached magnetic beads and fragmented using divalent cations under high temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). The first and second strand cDNAs were synthesized using random hexamer primer and M-MuLV Reverse Transcriptase and DNA Polymerase I, and RNase H, respectively. The remaining overhangs were changed into blunt ends with the help of exonuclease/polymerase. After 3’ ends adenylation of DNA fragments, NEBNext Adaptor, with hairpin loop structure was ligated to ensure hybridization. The library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA) to ensure that the cDNA fragments of 240 bp in length were selected. Before PCR, a 3 µl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C. PCR was performed with Phusion High-Fidelity DNA Polymerase, Universal PCR primers, and Index (X) Primer and the PCR products were finally purified (AMPure XP system), and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. Finally, the Illumina Hiseq 2000 platform by BIOMARKER TECH CO. LTD. (Beijing, China) based on sequencing by synthesis technology was used to sequence the resulting cDNA library to generate paired end reads. 2.3 Quality Control and De novo Transcriptome Assembly Analyses After the generation of the raw paired end reads, quality control was performed. Raw data (raw reads) in fastq format were initially processed via in-house perl scripts. To obtain quality clean reads, adapter, reads containing ploy N, and low-quality reads were removed from raw data using Fastqc (version 0.18.0). Clean data (clean reads) were obtained by removing the following reads: (a) reads with 5′ adapter,(b) reads without 3′ adapter or insert sequence, (c) reads with > 10% N, (d) reads with > 50% nucleotides with Qphred ≤ 20, and (e) reads with poly A/T/G/C. The Phred quality score, viz., Q20, Q30, GC-content, and sequence duplication level of clean data, were calculated to obtain the base calling accuracy. All the downstream analyses were based on clean data with a high-quality score. The transcriptome assembly was performed using Trinity v2.5.1 (Grabherr et al. 2011), with minimum kmer coverage set to 2 by default and all other parameters set to default. Assembly statistics and assess assembly quality was obtained using Trinity v2.5.1 and assembly completeness analysis was performed with QUAST v5.3.0. 2.4 Functional Annotation of Unigenes and Transcripts Analysis The annotation of transcripts was performed by juxtaposing or aligning the clean reads against the Non-redundant (Nr, http://www.ncbi.nlm.nih.gov/ , (Grabherr et al. 2011), Swissprot ( http://ftp.ebi.ac.uk/pub/databases/swissprot , (Buchfink et al. 2015), Clusters of Orthologous Groups (COG, http://www.ncbi.nlm.nih.goc/COG/ , (Apweiler et al. 2004), homologous protein family (Pfam, http://pfam.xfam.org ), eukaryotic orthologous groups (KOG, http://www.ncbi.nlm.nih.goc/KOG/ , (Tatusov et al. 2000), orthologous group of genes (eggNOG (v4.5), http://eggnog.embl.de/ , (Koonin et al. 2004), Translated European Molecular Biology Laboratory (TrEMBL, https://www.uniprot.org/TrEMBL ), Gene Ontology (GO, http://geneontology.org , (Deng et al. 2006), and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg , (Huerta-Cepas et al. 2016) databases using BLASTX. KEGG orthologs of unigenes were performed by KOBAS v3.0 software (Kanehisa et al. 2004). The amino acid sequences of unigenes were predicted, and the predicted sequences were annotated by searching against the Pfam (Xie et al. 2011) database and HMMER v3.1b2 executed utilizing a hidden Markov model (HMM) (Jones et al. 2014). The thresholds of BLAST E-value not larger than 1e-5 and HMMER E-value not larger than 1e-10 were used. 2.5 Quantification of Gene Expression Levels and Differential Expression Analysis Gene expression levels were estimated by RSEM v1.2.19 (Li & Dewey 2011) for each sample. This was carried out by mapping back clean data onto the assembled transcriptome, and the Readcount for each gene was obtained from the mapping. The fragments per kilobase of transcript per million fragments mapped (FPKM) were quantified using RSEM software and were used to juxtapose the expression levels of the transcripts. The sample comparisons consisted of NoP vs. Pi, NoP vs. Po, and Pi vs. Po. Differential expression analysis of two conditions/groups was performed using the DESeq R package (1.10.1) with the FPKM values. DESeq is used for determining differential expression in digital gene expression data based on the negative binomial distribution. The resulting P values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate (FDR). Unigenes and transcripts found by DESeq with an absolute fold change ≥ 2 and an FDR < 0.01 were adjudged differentially expressed. 2.6 GO Enrichment and KEGG Pathway Analyses For the Gene Ontology (GO) analysis, all DEGs were mapped to GO terms in the Gene Ontology database ( http://www.geneontology.org/ ) using the topGO R packages based on the Kolmogorov–Smirnov test. The KEGG pathway and enrichment analysis were performed using the KEGG database for determining the DEG pathways. The statistical enrichment of differential expression genes in the KEGG pathways was tested using the KOBAS v3.0 software. The p-value was calculated and underwent FDR correction at a threshold of FDR ≤ 0.05. 2.7 DEGs Validation Using RT-qPCR. Twelve P-responsive unigenes (i.e., 4 from each treatment) were arbitrarily chosen from the transcriptome data based on their expression levels and significant involvement in the KEGG pathways. To ascertain the authenticity of the transcriptome data, the relative expression levels of these unigenes were further assessed using RT-qPCR. The Applied Biosystem 7300 Real-Time PCR device was used for the authentication and performed in an optical 96-well plate. The synthesized cDNA from 1 µg of RNA was diluted 10-fold, and 4 µL of the diluted cDNA was used as a template for the RT-qPCR. Twelve primers were designed from the twelve selected sequences to detect the expression levels through RT-qPCR using SYBR Green RT-PCR (Roche, San Diego, CA, USA) and attached as a supplementary data 1 ( Table S1 ). Briefly, the reaction consisted of 10 µL SYBR qPCR Mix, 4 µL of cDNA, 1 µL of forward and reverse primers, respectively, and 4 µL ddH 2 O. Vicia faba Actin ( Vf actin 11) was used as an internal control gene for normalization. The RT-qPCR thermal cycling parameters were 95°C for 60 s, followed by 45 cycles of 95°C for 10 s, 50°C for 10 s and 70°C for 10 s. The relative gene expression levels were calculated using the 2 −ΔΔCt method. For each P-induced nodule sample, three biological replicates were used and determined in duplicates. 3. RESULTS 3.1 Nodulation of Vicia faba in Response to P Supplies Our previous works (Amoako et al. 2023a, b; Amoako et al. 2024) have already highlighted all the morpho-physiological, biochemical (phosphatases) and some molecular (ATPases genes) parameters related to this plant variety and that this current work is based on the premise of these previous studies. Nodule formation and development is hallmarks of leguminous species when the right Rhizobia species is inoculated. Vicia faba plants were treated with different P levels and sources for a 30-d growth period, and the responses of nodulation are displayed (Fig. 1 ). The result clearly shows the deleterious effect of low P supply on nodule formation, with low P-treated nodules exhibiting the smallest size and lowest number (Fig. 1 A). Conversely, when sufficient P was supplied, the formation and growth of nodules tremendously increased, with both P treatments (Pi vs. Po) exhibiting similar nodulation patterns (Fig. 1 B, C). Po- and Pi-induced nodules had several of their nodules clustering at a point compared with NoP-induced nodules. 3.2 De novo Assembly of Transcriptome and Functional Annotation of Unigenes In this analysis, the Po treatment was compared with both the positive (Pi) and negative (NoP) controls to ensure pragmatic and detailed comparisons. Notwithstanding, both the positive (Pi) and negative (NoP) controls were also compared in the analysis for more novel findings and to reveal the P-related stress genes during nodulation. Nine RNA-Seq libraries of three biological replicates for NoP, Pi, and Po were prepared and then paired-end sequenced to compare the molecular patterns and signatures in the nodules of Vicia faba plants. A total of 64.06 Gb was generated, with a minimum size of 6.30 Gb for each sample, and the percentage of bases with a quality score of 30 was higher than and within the range of 92.55–94.15% ( suppl. data 2; Table S1 ). The sequencing of the Vicia faba transcriptome generated a total of 214,390,209 clean reads from the 9 samples, with an average read of 23,821,134 per sample and base composition of 64,063,311,082, as depicted in Table S1 . The sequence reads averaged a GC content of more than 41% in each sample ( suppl. data 2; Table S1 ). Since the genome of the Vicia faba plant is not entirely annotated, the reads were mapped back with each other, and approximately 149,685,304 reads, with an average mapped ratio ranging from 68.86–72.01% of the clean reads, were uniquely mapped back to the transcripts ( suppl. data 2; Table S2 ). After sequence assembly, 52,274 unigenes and 559,047 transcripts were obtained in total, among which 20,508 unigenes were found with a length longer than 1 kb ( suppl. data 2; Table S3 ). A total of 20,342 unigenes, representing 38.91%, were found within the length range of 300–500, with most of the transcripts (105,515, representing 18.87%) found within the length range of 200–300 ( suppl. data 2; Table S3 ). Both unigenes and transcripts obtained a total length of 61,144,827 and 718,805,445, with mean lengths of 1169.70 and 1285.77, respectively. Additionally, unigenes and transcripts of N50 length obtained were 1,919 and 2,058, respectively ( suppl. data 2; Table S3 ). A correlation analysis was performed to evaluate how the samples were associated with each other ( suppl. data 2; Table S4 ). The results show a strong positive correlation among the samples, with 0.54 being the lowest correlation value ( suppl. data 2; Table S4 ). To annotate the obtained unigenes, they were searched (BLAST) against nine public databases, viz., Nr, COG, KOG, eggNOG, GO, KEGG, Pfam, TrEMBL and Swissprot for homologous sequences. A total of 29,511 unigenes were annotated in all databases, with 11,628 and 17,878 unigenes generated within 300 ≤ length < 1000 and length ≥ 1000, respectively (Table 1 ). Among the databases, Nr annotated a total of 28,260 unigenes, followed by TrEMBL (28,199), with COG database (8,558) annotating the least unigenes (Table 1 ). Table 1 Summary statistics of unigenes annotation databases Annotated Database Annotated number 300 ≤ length < 1000 Length ≥ 1000 COG 8,558 2,532 6,026 GO 24,103 8,723 15,375 KEGG 18,881 6,023 12,858 KOG 15,943 5,257 10,686 Pfam 21,658 6,985 14,673 Swissprot 18,332 5,463 12,869 TrEMBL 28,199 10,444 17,755 eggNOG 23,370 7,958 15,412 Nr 28,260 10,520 17,740 All Annotation 29,511 11,628 17,878 Annotated databases applied for BLAST. Annotated number: the number of unigenes annotated based on corresponding database. 300 ≤ length < 1000: the number of annotated unigenes with length ranges of 300 to 1000 bp. Length ≥ 1000: the number of annotated unigenes with length longer than or equal to 1000 bp. Further quantification was performed using FPKM. This was used to quantify unigenes expression based on the criteria p < 0.05 for adjusted p-value and greater than 0 for fold-change base log2. The gene expressions were normalized using a box and density plots to indicate an even distribution of reads in the individual treatments (Fig. 2 A, B). Interestingly, a clustering heatmap correlation analysis was performed among the samples (Fig. 2 C). The analysis shows clustering and sub-clustering of samples, with samples showing significantly positive and negative correlations among samples at the individual levels. A principal component (PC) analysis shows a significant separation of the transcriptomes of the treatment groups, with PC 1 and 2 explaining 52.1 and 16.9% variations, respectively (Fig. 2 D). 3.3 Differentially Expressed Genes (DEGs) Induced by Different P Supply Genes and unigenes differentially expressed in the P treatment groups were adjudged significantly expressed based on false discovery rate (FDR =2, as represented (Table 2 ; Fig. 3 ). From the results, a total of 2,263 genes were differentially expressed, of which 1,612 (71%) genes were up-regulated and 651 (29%) genes were down-regulated. The results consistently reveal that treatment groups NoP_vs._Po recorded a total of 1,144 DEGs, where 843 and 301 were up- and down-regulated, respectively (Table 2 ). The results show a total of 308 genes, with 167 and 141 genes being up- and down-regulated in NoP_vs._Pi, respectively. Meanwhile, the comparison between the two sufficient treatments (Pi_vs._Po) revealed a total of 811 genes, of which 602 and 209 were up- and down-regulated (Table 2 ). To identify the main functional groups of DEGs, we used a BLASTx search for the NCBI Nr, Swiss-Prot, GO, COG, KEGG, KOG, eggNOG, Pfam, and TrEMBL databases (Table 3 ). The results show that a total of 1,961 DEGs were annotated for all treatment groups, with each treatment group obtaining 258, 996, and 707 DEGs in NoP_vs._Pi, NoP_vs._Po and Pi_vs._Po, respectively (Table 2 ). The DEGs annotation statistics reveal that both the Nr and TrEMBL databases recorded the highest (1,916) and the same total number of DEGs (Table 2 ). Table 2 Summary of differentially expressed genes (DEGs) in response to different phosphorus DEG Set All DEGs Up regulated Down regulated NoP_vs._Pi 308 167 141 NoP_vs._Po 1,144 843 301 Pi_vs._Po 811 602 209 Total DEG 2,263 1,612 651 Table 3 Summary of DEGs annotation from databases comparisons between treatments DEG Set Annotated COG GO KEGG KOG Pfam Swissprot TrEMBL eggNOG Nr NoP vs. Pi 258 79 215 172 130 204 180 250 214 250 NoP vs. Po 996 368 830 673 542 835 720 974 855 974 Pi vs. Po 707 275 597 489 376 600 518 692 615 692 Total 1,961 722 1,642 1,334 1,048 1,639 1,418 1,916 1,684 1,916 The volcano plots reveal more off-centre point distributions, indicating a larger fold difference between DEGs. The closer the DEGs are to the top, the more significant variations are observed (Fig. 3 A-C). Subsequently, a cluster heatmap analysis was employed to evaluate the expression pattern of the DEGs in the three groups (Fig. 3 D-F), and the data shows contrasting expression patterns among treatments. To identify DEGs uniquely expressed by all treatment groups and those expressed within and between treatment groups, a Venn diagram was performed (Fig. 3 G). The result clearly shows that a total of 23 genes were uniquely expressed by all treatment groups (Fig. 3 G). The Venn diagram analysis indicates that 175, 656, and 385 genes were unique to NoP_vs._Pi, NoP_vs._Po and Pi_vs._Po, respectively. A total of 86 genes were expressed between the NoP_vs._Pi, and NoP_vs._Po groups, 24 between NoP_vs._Pi, and Pi_vs._Po, 379 between NoP_vs._Po, and Pi_vs._Po, respectively (Fig. 3 G). Raw sequence data have been deposited at NCBI SRA with accession number PRJNA1030752 and website https://www.ncbi.nlm.nih.gov/sra/PRJNA1030752 . The annotations from the COG, eggNOG and KOG databases reveal 98, 214, and 145 DEGs, respectively, and were grouped into 25 functional categories ( Figure S1 A-C ) under the NoP_vs._Pi treatment group. Under the NoP_vs._Po treatment group, the COG, eggNOG and KOG databases generated 421, 855, and 855 DEGs, respectively, and were categorized into 25 functional categories ( Figure S1 D-F ). For Pi_vs._Po, similar classifications were obtained, with the COG, eggNOG and KOG databases recording 310, 615, and 408 DEGs, respectively ( Figure S1 G-I ). The functional classifications under KOG and eggNOG generally reveal that most of the DEGs were classified under the function “unknown category”. However, the COG classification generally observed a higher number of genes under the carbohydrate transport and metabolism category ( Figure S1 A, D, G ). 3.4 Gene Ontology (GO) Annotation and KEGG Pathway Analyses of the DEGs We performed GO and KEGG enrichment analyses to explore the relevant pathways and biological functions, which enlightened us to understand the functional differences between the P treatment groups in Vicia faba nodules (Figs. 4 and 5 ). We evaluated the potential functions of the DEGs between the treatments, and DEGs with > 2-fold expression change were assigned to different GO categories, viz., biological process, molecular function, and cellular location, with 57 functional GO terms analyzed (Fig. 4 ). Among the categories, a total of 830 DEGs were involved in the treatment group of NoP_vs._Po for all three GO categories (Fig. 4 A), whereas 215 DEGs were involved in the GO term of the NoP_vs._Pi treatment group (Fig. 4 B). For treatment group Pi_vs._Po, a total of 597 DEGs were involved in the GO analysis (Fig. 4 C). For all three P treatment groups, the biological processes mainly associated with the DEGs were involved in metabolic processes, cellular processes, response to stimulus and single-organism processes, biological regulation, localization, multi-organism process and cellular component organization or biogenesis. The cellular components mainly included the cell, cell part and organelle, membrane part, macromolecular complex, and organelle. The main molecular functions of the DEGs were binding, catalytic activity, structural molecule activity, transporter activity, nucleic acid binding transcription factor, signal transducer activity, molecular transducer activity, molecular function regulator, and antioxidant activity. It was generally realized that most of the DEGs involved in these 57 functional GO terms were identified in treatment comparisons involving the Po (phytic acid) treatment relative to the comparison between the two inorganic P treatments (KH 2 PO 4 ). Based on the KEGG pathway enrichment analysis, our results consistently reveal that the majority of the DEGs were significantly enriched in most pathways in all treatment groups (Fig. 5 ). DEGs were found to be significantly involved in starch and sucrose metabolism (ko00500), MAPK signaling pathway-plant (ko04016), plant hormone signal transduction (ko04075) and plant-pathogen interaction (ko04626) in the treatment group of NoP_vs._Po, with a total of 367 DEGs involved in 114 pathways (Fig. 5 A). For the NoP_vs._Pi treatment group, a total of 91 DEGs were involved in the 61 KEGG pathways, with most of the DEGs significantly enriched and involved in pathways such as glycolysis/gluconeogenesis (ko00010), starch and sucrose metabolism (ko00500), plant hormone signal transduction (ko04075), and oxidative phosphorylation (ko00190) (Fig. 5 B). Likewise, alpha-linolenic acid metabolism (ko00592), cysteine and methionine metabolism (ko00270), MAPK signaling pathway-plant (ko04016), and plant hormone signal transduction (ko04075) were the most significant pathways, with 265 genes involved in the 107 pathways (Fig. 5 C). The DEGs involved in most of the pathways were up- and down-regulated. We went further to classify the DEGs in the KEGG according to the secondary metabolism classification, which includes cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems (Fig. 5 D-F). Intriguingly, the data consistently show that most of the DEGs were involved in metabolism and genetic information processing. In the treatment comparison group of NoP_vs._Po, under the metabolism classification pathway, starch and sucrose metabolism was the most dominant and significant pathway, with a total of 28 genes, representing 7.63% (Fig. 5 D), consistent with the KEGG enrichment pathway in Fig. 5 A. In the same treatment group, plant hormone signal transduction (34 genes, representing 9.26%), ribosome (37, 10.08%), and plant-pathogen interactions (38, 10.35%) were the significant pathways in environmental information processing, genetic information processing, and organismal systems classifications, respectively (Fig. 5 D). However, when the two inorganic P treatments were compared (NoP_vs._Pi), glycolysis/gluconeogenesis (11, 12.09%), starch and sucrose metabolism (9, 9.89%) and carbon metabolism (8, 8.79%) were the significant pathways involved in the metabolism classification (Fig. 5 E). Similarly, plant hormone signal transduction (12 genes, 13.19%) and plant-pathogen interactions (8, 8.79%) were the most enriched pathways in the environmental information processing and organismal systems classifications, respectively (Fig. 5 E). Consistent with the primary classification, starch and sucrose metabolism was the dominant and significantly enriched pathway, with 15 DEGs representing 5.66% of the metabolism classification in the Pi_vs._Po treatment group (Fig. 5 F). Remarkably, plant hormone signal transduction (24 genes, 9.06%), ribosome (25, 9.43%), and plant-pathogen interactions (26, 9.81%) were the significant pathways in the classification of environmental information processing, genetic information processing, and organismal systems, respectively (Fig. 5 F). The P treatment groups underwent five main (top 5 pathways) pathways in the KEGG. The specific genes that were significantly involved in pathways and their interactions are elaborated using gene and pathway network analyses (Fig. 6 ). From the network analysis, it was revealed that treatment group NoP_vs._Po was specifically involved in five main pathways which include cyanoamino acid metabolism, MAPK signaling pathway-plant, plant hormone signal transduction, plant-pathogen interaction, and starch and sucrose metabolism (Fig. 6 A). The results show that 7, 32, 34, 38, and 28 DEGs were involved in cyanoamino acid metabolism, the MAPK signaling pathway-plant, plant hormone signal transduction, plant-pathogen interaction, and starch and sucrose metabolism, respectively, with details of the specific genes attached as supplementary data ( suppl. data 2; Table S5 ). It should be noted that the genes have been attached to avoid crowding and redundancy, since so many genes were generated in response to this treatment comparison. Under NoP_vs._Pi, the pathways and DEGs were significantly involved in glycolysis/gluconeogenesis ( 11 ), oxidative phosphorylation ( 6 ), phenylpropanoid biosynthesis ( 6 ), plant hormone signal transduction ( 12 ), and starch and sucrose metabolism ( 9 ) pathways, respectively (Fig. 6 B). However, Pi_vs._Po group observed three unique pathways distinguishable from the other treatments (Fig. 6 C). Cysteine and methionine metabolism, linoleic acid metabolism, and alpha-linolenic acid metabolism were identified as unique, and 11, 11, and 4 DEGs, respectively, were observed to be significantly involved in these pathways. Interestingly, the MAPK signaling pathway-plant and plant hormone signal transduction were least observed to be common to all treatment groups. 3.5 Identification of DEGs Involved in Transport in Nodules of Vicia faba Several DEGs present in the study were revealed to be implicated in transport within the nodule compartment, as depicted in the barplots and clustering heatmap analysis (Fig. 7 ). A total of 61 DEGs participated in the transport of various minerals and solutes, with the majority of these DEGs expressing when phytic acid was applied (Fig. 7 ). Under NoP_vs._Pi treatment, the results reveal that BMK_Unigene_029164 and BMK_Unigene_032564 genes were specific to this group and observed to be coding for bidirectional sugar transporter SWEET4 and bidirectional sugar transporter N3 , respectively, with the former being down-regulated and the latter up-regulated (Fig. 7 A and D ). The BMK_Unigene_032815 coding for a metal transporter Nramp3 was up-regulated, whereas the BMK_Unigene_036844 involved in sodium/hydrogen exchanger 6 was observed to be down-regulated in NoP_vs._Pi (Fig. 7 A and D ). In comparison, NoP_vs._Po group had the majority of the DEGs involved in ABC transporters, zinc, magnesium and molybdate transporters (Fig. 7 A, C). We found BMK_Unigene_058922 , BMK_Unigene_054678 , and BMK_Unigene_060918 genes to be putatively coding for the ATP binding cassette (ABC) transporter, and BMK_Unigene_060469 , BMK_Unigene_129480 putatively coding for the ABC-2 type transporter in NoP_vs._Po (Fig. 7 A-C). Additionally, BMK_Unigene_136703 , BMK_Unigene_131646 , and BMK_Unigene_061734 were observed to encode putative for ZIP Zinc transporter, CorA-like Mg 2+ transporter protein, and Molybdate transporter of the MFS superfamily, respectively (Fig. 7 A-C). Similarly, these transporter genes ( BMK_Unigene_058922, BMK_Unigene_060469, BMK_Unigene_131646 , and BMK_Unigene_061734 ) were also found in Pi_vs._Po treatment group (Fig. 7 A, B). Interestingly, Pi_vs._Po treatment group observed copper, magnesium, calcium, iron, potassium, sodium and phosphorus related transporter genes and other genes, as depicted in the cluster heatmap analysis (Fig. 7 E) being significantly upregulated. For examples, the BMK_Unigene_081823, BMK_Unigene_062257, BMK_Unigene_063447, BMK_Unigene_064567, BMK_Unigene_054555 , and BMK_Unigene_034906 genes are putatively coded for copper transporter 5.1, inorganic phosphate transporter 1–4, calcium-transporting ATPase, potassium transporter 2, vacuolar iron transporter-like protein, and sodium-coupled neutral amino acid transporter 1-like protein, respectively, in Pi_vs._Po (Fig. 7 A, B, E) and were all found up-regulated. Interesting genes, viz., amino acid transporter AVT1A isoform X2 ( BMK_Unigene_133019 ), sodium-coupled neutral amino acid transporter 1-like protein ( BMK_Unigene_034906 ), probable polyamine transporter At1g31830 ( BMK_Unigene_059198 ), and nodulin 26-like intrinsic protein 1;1 ( BMK_Unigene_059591 ), were uniquely and significantly expressed exclusively in Pi_vs._Po (Fig. 7 A, B, E). 3.6 Identification of Transcription Factors in Nodules of Vicia faba The data reveal transcription factors (TFs) involved in gene expression in P-induced nodules (Fig. 8 ). The clustering heatmap analysis indicates the expression levels with respect to each treatment and is calculated based on FPKM. A total of 86 TFs genes were generated in this study. A total of 16 TFs genes were exclusively expressed in NoP_vs._Pi, with 12 and 4 being up- and down-regulated, respectively (Fig. 8 A, B, D). Under NoP_vs._Po, 48 TF genes were expressed in total, and 40 were up-regulated, with 8 observed to be down-regulated (Fig. 8 A-C). Furthermore, a total of 22 TF genes were expressed, with 17 and 5 being up- and down-regulated, respectively (Fig. 8 A, B, E) in Pi_vs._Po. The most abundant families were bHLH , WRKY , M-type , ARF and ERF families, followed by NAC , C2H2 , RAX2 , RAV2 , GATA and others as identified. Among the treatment comparisons, we observed higher TFs in NoP_vs._Po treatment group, with ERF ( 22 ), WRKY ( 9 ), MYB ( 7 ), bHLH ( 6 ), GATA ( 4 ), NAC ( 3 ), RAX2 ( 1 ) and RAV ( 1 ) TF genes being the most expressed (Fig. 8 ). For instance, BMK_Unigene_131668 encodes putative NAC transcription factor 47, BMK_Unigene_020831 coding for transcription factor bHLH51 , BMK_Unigene_029658 coding for a probable WRKY transcription factor 69 , BMK_Unigene_029647 coding for MYB transcription factor MYB70 , BMK_Unigene_061074 coding for GATA transcription factor 1, BMK_Unigene_025025 coding for a putative transcription factor C2H2 family, BMK_Unigene_134678 coding for AP2-like ethylene-responsive transcription factor ANT, BMK_Unigene_132837 coding for MADS-box transcription factor , and BMK_Unigene_132927 coding for bZIP transcription factor 11, with most of these TFs expressed upon Po treatment. Meanwhile, some of these TFs were also expressed in the other treatment groups in different isoforms (Fig. 8 A-C). However, BMK_Unigene_128791 coding for transcription factor KUA1 and BMK_Unigene_103216 coding for transcription factor RAX2 were uniquely expressed in NoP_vs._Pi, and Pi_vs._Po, respectively (Fig. 8 A, B, D, E). 3.7 DEGs Involved in Plant Hormone and Signal Transduction in Nodules of Vicia faba The possible impacts of P supply on Vicia faba resulted in the identification of different groups of genes implicated in signaling and signaling-related genes (Fig. 9 ). A total of 102 genes were identified, of which 80 and 22 were identified to be up- and down-regulated, respectively ( Figure. 9A, B ). The results indicate a total of 26 DEGs, with 14 up-regulated and 12 down-regulated in the NoP_vs._Pi group (Fig. 9 A, B, D), whereas the NoP_vs._Po group altered 48 DEGs in total, of which 41 were up-regulated and 7 were down-regulated (Fig. 9 A-C). Conversely, the two sufficient treatments (Pi_vs._Po) identified 28 DEGs in total, 25 of which were found to be up-regulated, with only 3 found to be down-regulated (Fig. 9 A, B, E). The results, however, indicated that the BMK_Unigene_062196 , BMK_Unigene_061185 , and BMK_Unigene_131117 genes were found to be putatively coding for calmodulin-binding transcription activator 4 isoform X1, calcium-dependent protein kinase 1, and calmodulin-binding receptor cytoplasmic kinase 1, respectively, and were exclusively found to be expressed in the NoP_vs._Po group (Fig. 9 B, C). We identified BMK_Unigene_063802 and BMK_Unigene_064212 coding, respectively, for lipopolysaccharide kinase and Galactoside-binding lectin in NoP_vs._Pi (Fig. 9 A, B, D). Additionally, genes such as BMK_Unigene_021451 , BMK_Unigene_090095 , and BMK_Unigene_077658 were uniquely identified only under Pi_vs._Po and putatively found to be coding for receptor-like cytoplasmic kinase 176, auxin-induced in root cultures protein 12-like and nudix hydrolase 8, respectively (Fig. 9 A, B, E). Interestingly, genes such as BMK_Unigene_131481 , BMK_Unigene_095015 , and BMK_Unigene_053704 were expressed in all treatments and identified to be coding for protein phosphatase 2C . 3.8 Nodulation-Related DEGs in Nodules of Vicia faba Nodule formation in response to different P levels and sources altered several genes involved in nodulation (Fig. 10 ). In general, a total of 24 genes were identified, of which only 8 were up-regulated and 16 were found to be down-regulated (Fig. 10 A-D). It was realized that most of these nodulation related genes were found to be coding for late nodulin proteins ( 14 ). Under the NoP_vs._Po group, most of these late nodulin proteins were down-regulated, with only one being up-regulated ( BMK_Unigene_114973 ). Similarly, BMK_Unigene_061444, BMK_Unigene_059591 , and BMK_Unigene_130549 are putatively encoding legume lectin domain and protein tyrosine and serine/threonine kinase, nodulin 26-like intrinsic protein 1;1, and early nodulin-16, respectively, in NoP_vs._Po (Fig. 10 A, B). However, BMK_Unigene_000106 putatively coding for Early nodulin 93 ENOD93 protein was found to be identified only in NoP_vs._Pi and was up-regulated (Fig. 10 A, C). The other late nodulin proteins were downregulated. Meanwhile, Pi_vs._Po uniquely induced BMK_Unigene_029218 , which codes for Nodule Cysteine-Rich (NCR) secreted peptide and was identified to be significantly up-regulated in nodules (Fig. 10 A, D). These genes ( BMK_Unigene_059591 and BMK_Unigene_065419 ) were significantly implicated and up-regulated in nodules and were putatively coding for nodulin 26-like intrinsic protein 1;1 and putative late nodulin (Fig. 10 A, D). 3.9 Amino Acid and Nitrogen Metabolism-Related DEGs in Nodules of Vicia faba Different amino acid and nitrogen metabolism related DEGs were induced in response to varied P levels and sources (Fig. 10 E-H). We identified a total of 50 DEGs, with 40 and 10 up- and down-regulated, respectively, and clustered differently as depicted ( Figure F, G ). Out of these genes, 6 genes, of which 3 each were found to be up- and down-regulated, respectively, in NoP_vs._Pi (Fig. 10 E, G). For NoP_vs._Po, 23 DEGs were identified and 21 were up-regulated, with only 2 found down-regulated (Fig. 10 E, F). Meanwhile, Pi_vs._Po treatment group identified 21 genes, of which 16 were up-regulated and 5 were down-regulated (Fig. 10 E, H). The individual treatment analysis shows that the BMK_Unigene_060395 gene was identified to code for nitrate transporter (protein NRT1/PTR FAMILY 1.1 ) and up-regulated only in NoP_vs._Po (Fig. 10 E, F). Genes such as BMK_Unigene_033352 , BMK_Unigene_001084 , and BMK_Unigene_061333 were identified as putatively coding for cationic amino acid transporter 4, gamma-glutamyl peptidase 5, and gamma-glutamylputrescine oxidoreductase, respectively, in NoP_vs._Po (Fig. 10 E, F). The NoP_vs._Pi treatment group identified BMK_Unigene_035727 and BMK_Unigene_068511 as being observed to encode serine carboxypeptidase-like isoform X1 and probable isoaspartyl peptidase/L-asparaginase 2, respectively (Fig. 10 E, G). Genes involved in nitrogen assimilation were only implicated when sufficient P was applied. The comparison between Pi and Po (Pi_vs._Po) identified genes involved in amino acid transport and nitrogen metabolism and included BMK_Unigene_063924 (nitrate reductase), BMK_Unigene_134721 (nitrate regulatory gene 2 protein), BMK_Unigene_026586 (2-oxoglutarate-dependent dioxygenase DAO), BMK_Unigene_062144 (probable 2-oxoglutarate-dependent dioxygenase At5g05600 ), and BMK_Unigene_133019 (transmembrane amino acid transporter protein, tryptophan/tyrosine permease family, transmembrane amino acid transporter protein). 3.10 Identification of DEGs Involved in P and Pi-Remobilization in Nodules of Vicia faba To identify P and Pi remobilization genes preferentially expressed during nodule development, we performed cluster analysis to compare the different P levels and sources (Fig. 11 ). We identified 96 DEGs in total, and 75 and 21 were up- and down-regulated, respectively (Fig. 11 ). Most of the DEGs were largely expressed in the NoP_vs._Po treatment group relative to the others. In the NoP_vs._Po, a gene involved in phytic acid biosynthesis ( BMK_Unigene_059958 ) was identified to code putatively for inositol 1,3,4-trisphosphate 5/6-kinase ATP-grasp domain (Fig. 11 B, C). The results identified genes implicated in Pi metabolism and remobilization and include BMK_Unigene_030805 ( PAP2 superfamily ), BMK_Unigene_022815 , and BMK_Unigene_033339 (endonuclease/exonuclease/phosphatase family), whereas genes such as BMK_Unigene_061323 , BMK_Unigene_057098 , and BMK_Unigene_130692 were observed to code for purple acid phosphatase in NoP_vs._Po (Fig. 11 A-C). Additionally, BMK_Unigene_048749 was putatively coding for the 14-3-3 protein and was uniquely expressed in NoP_vs._Po (Fig. 11 A, C). We observed that BMK_Unigene_035177 , and BMK_Unigene_025675 were found to encode phosphatidic acid phosphatase (PAP2) family protein and acid phosphatase 1 and were exclusively expressed in NoP_vs._Pi (Fig. 11 B, D). The Pi_vs._Po treatment identified genes exclusively expressed under high P supply. BMK_Unigene_052701 was found to code for a putative glycerol-3-phosphate transporter 1-like protein (Fig. 11 B, E). However, genes such as BMK_Unigene_057098 (putative extracellular phytase ) and BMK_Unigene_061323 (nucleotide pyrophosphatase/phosphodiesterase) were found to be commonly expressed in both Pi_vs._Po and NoP_vs._Po (Fig. 11 A, B, E). Meanwhile, the inorganic phosphate transporter 1–4 ( BMK_Unigene_062257 ) gene was upregulated and exclusively expressed in Pi_vs._Po (Fig. 11 B, E). 3.11 P-Responsive Lipids and Secondary Metabolisms-related Genes in Nodules of Vicia faba A total of 62 genes related to lipids and secondary metabolites were identified in response to P supply, as depicted in barplots and cluster analysis (Fig. 12 ). Out of this, 51 genes were significantly up-regulated in all treatment groups, and 11 genes were identified as down-regulated. The results show up-regulation of genes such as BMK_Unigene_063961 (coding for biotin-lipoyl like and biotin carboxylase C-terminal domain), BMK_Unigene_060394 (coding for alpha/beta hydrolase), and BMK_Unigene_056057 (coding for 2OG-Fe (II) oxygenase superfamily and non-haem dioxygenase in morphine synthesis N- terminal ) in only NoP_vs._Po (Fig. 12 A-C). However, genes ( BMK_Unigene_063566 and BMK_Unigene_128525 ) putatively coding for squalene epoxidase 1 and cytochrome P450 CYP73A100 , respectively, were found to be solely expressed under NoP_vs._Pi (Fig. 12 A, B, D). Under Pi_vs._Po, BMK_Unigene_029385 , BMK_Unigene_065309 , BMK_Unigene_132493 , and BMK_Unigene_032427 genes were uniquely identified and putatively coding for cytochrome P450 71A23, geraniol 8-hydroxylase, 4-coumarate-CoA ligase 7-like protein, and sphingolipid delta ( 4 )-desaturase DES1-like, respectively (Fig. 12 A, E). 3.12 Carbon and Carbohydrate Metabolisms in Nodules of Vicia faba Based on KEGG pathway analysis, DEGs identified in the present study were implicated in carbon and carbohydrate metabolism (Figs. 13 and 15 ). These DEGs mainly participated in starch and sucrose metabolism, glycolysis/gluconeogenesis, galactose metabolism, and pyruvate metabolism. We identified that these genes ( BMK_Unigene_062409, BMK_Unigene_033731, BMK_Unigene_048556, and BMK_Unigene_063126 ) were all up-regulated and putatively encoding different isoforms of glycosyl hydrolases such as glycosyl hydrolases family 1, 14, and 18 and glycosyl hydrolase family 3 N terminal domain and fibronectin type III-like domain in NoP_vs._Po (Fig. 13 A-C). Apart from these, genes such as BMK_Unigene_082109 , BMK_Unigene_030779 , BMK_Unigene_048610 , BMK_Unigene_110457, BMK_Unigene_062430 , and BMK_Unigene_027824 were found to putatively encode for aldose 1-epimerase, pfkB family carbohydrate kinase, glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate kinase, glucokinase and transaldolase/fructose-6-phosphate aldolase, respectively, in NoP_vs._Po (Figs. 13 A-C and 15 ). Comparatively, the NoP_vs._Pi identified genes implicated in pyruvate biosynthesis and include BMK_Unigene_065186 (encoding pyruvate decarboxylase 1 ), BMK_Unigene_134540 (encoding pyruvate decarboxylase 2), BMK_Unigene_129620 (encoding pyruvate kinase 1, cytosolic) and BMK_Unigene_048128 (encoding pyruvate kinase, cytosolic isozyme) and were all found to be up-regulated (Figs. 13 A, B, D and 15 ). Additionally, carbon metabolites ( BMK_Unigene_060394 coding for probable carboxylesterase 15 , BMK_Unigene_059780 coding for probable alpha-trehalose-phosphate synthase [UDP-forming] 9, and BMK_Unigene_033669 coding for probable trehalose-phosphate phosphatase J) and sugar transporter genes such as BMK_Unigene_029164 (bidirectional sugar transporter SWEET4 ) and BMK_Unigene_032564 (bidirectional sugar transporter N3) in NoP_vs._Pi (Fig. 13 A, B, D). Interestingly, BMK_Unigene_110457 and BMK_Unigene_031571 were putatively coding for hypothetical protein CDD82_7894 and hypothetical protein TSUD_01980, respectively, in Pi_vs._Po (Fig. 13 A, B, E). 3.13 Validation of Selected Genes using RT-qPCR We performed a regression analysis to evaluate and validate 12 selected genes from RNA-seq data using RT-qPCR (Fig. 14 ). The results generally indicate a significant correlation coefficient (R 2 = 0.86; y = 1.29 + 1.02x) between RNA-seq and RT-qPCR data as quantified in log 2 FC (Fig. 14 ). The RT-qPCR results validate the expression of the various genes randomly chosen from the RNA-seq pathways. For example, gene isoforms such as BMK_Unigene_021141 and BMK_Unigene_021141 were observed to up- and down-regulated both in RNA-seq and RT-qPCR, with BMK_Unigene_065447 being the most expressed gene. The relative expression of the 12 unigenes was consistent with the RNA-Seq data, signifying the reliability of our RNA-Seq results. 4. DISCUSSION Although the genome of Vicia faba is not fully annotated, through de novo RNA-seq analysis, we have comprehensively identified DEGs involved in cellular, biological, and molecular processes of nodule organogenesis and SNF. To the best of our knowledge, this is the first exhaustive report that has identified Po-induced-responsive genes and pathways involved in the possible molecular and metabolic reprogramming mechanisms in response to phytic acid (Po), which is proposed as an alternative P source to the inorganic P (Pi) as previously asserted (Amoako et al. 2023a, b; 2024). In this current study, nine nodule samples from Vicia faba plants exposed to negative control (low KH 2 PO 4 , tagged as NoP), positive control (high KH 2 PO 4 , tagged as Pi), and phytic acid (Po) were subjected to de novo RNA-seq analysis to comparatively identify DEGs and related metabolic pathways in nodules. Our results revealed that a total of 64.06 Gb of clean data was generated, and the Q30 was found to be greater than 93%, indicating highly referential and uncompromized analysis and highlighting the quality of the sequencing results ( suppl. data1; Table S1 ). Interestingly, a total of 1,961 DEGs were annotated for all treatment groups, where 258, 996, and 707 DEGs were identified in NoP_vs._Pi, NoP_vs._Po and Pi_vs._Po, respectively (Table 2 ). However, it was revealed that the supply of phytic acid to Vicia faba nodules altered significantly higher DEGs under NoP_vs._Po/Pi_vs._Po relative to inorganic P (NoP_vs._Pi), highlighting that the metabolic reprogramming of Vicia faba nodules was significantly altered and markedly induced higher gene expression in nodules. As stated earlier, the Vicia faba genome and transcriptome are not fully annotated; however, with the help of bioinformatics tools and databases, we identified a total of 1,916 DEGs that were annotated by both Nr and TrEMBL (Table 3 ). In this study, several P-triggered-related DEGs, pathways and mechanisms have been altered in nodule during SNF, however, DEGs and mechanisms crucial and implicated in nodule organogenesis, transport/fluxes across the symbionts and carbon metabolism and trade-offs have been elaborated in this discussion. 4.1 Po Supply Employs Different Nodulation and Nodule Organogenesis Mechanisms during SNF The modifications and terminal differentiation of bacteroids during legume symbiosis are associated with signals and transcriptional expression of nodule-specific peptides that are specific to bacteroids in the nodule symbiosome. For example, nodule-specific cysteine-rich (NCR) peptides and related genes have been reported to specifically express exclusively in infected cells of M. truncatula nodules and are known to be triggered during nodule organogenesis and bacteroid differentiation (Guefrachi et al. 2014; Horváth et al. 2023; Nallu et al. 2013). Our de novo RNA-seq analysis revealed differential expression of the BMK_Unigene_029218 gene, encoding the Nodule Cysteine-Rich (NCR) secreted peptide , which was exclusively found to be induced and significantly up-regulated in Pi_vs._Po treatment comparisons in nodules compared to the other treatments (Fig. 10 A, D). The significant up-regulation of the NCR gene being exclusively expressed in Pi_vs._Po signifies that the transcriptional expression of NCR was markedly higher in concomitant with higher P application and that terminal bacteroid differentiation (TBD) was highly enhanced under Po supply. This hypothesis means that the supply of phytic acid as a P source enhances swelling of bacteroids and endoreduplication of the genome for better N 2 -fixation and nodule biomass as well as biomass ratio (Roy et al. 2020), which is consistent with nodule number and biomass in this study (Fig. 1 B-C) and our previous studies (Amoako et al. 2023a, b; Amoako et al. 2024). A recent comparative RNA-seq analysis of two alfalfa cultivars (G3 and G9) inoculated with Ensifer meliloti strain LL2 identified 87 genes encoding NCR , and their upregulation ensured effective initiation of specific rhizobia–alfalfa symbioses, which led to higher and more efficient nitrogen fixation (Kang et al. 2023). Several plant-associated proteins have been identified to be involved in nodule symbiosis and are collectively called nodulins. They are categorized as early or late nodulins depending on the period of expression during nodule organogenesis (Olivares et al. 2011). It’s been widely revealed that the early nodulin genes are associated with earlier signaling, infection development, and nodule formation, with the late nodulin genes primarily involved in nodule metabolism and function (Olivares et al. 2011). Intriguingly, the results of this study revealed downregulation of genes putatively encoding late nodulin among the treatment comparisons, especially in NoP_vs._Po group (Fig. 10 A-D), implicating that Vicia faba plants under NoP_vs._Po predominantly employ downregulation mechanisms in their metabolic pathway during nodule symbiosis (Olivares et al. 2011). However, the BMK_Unigene_114973 gene, encoding late nodulin in NoP_vs._Po, was identified to be significantly upregulated and was involved in the metabolism and functioning of nodules. Indeed, it is quite tempting to suggest that a sufficient supply of P in the form of phytic acid enhances nodulation, confirming an earlier report that the supply of Po stimulates higher nodulation and N 2 -fixation efficiency in Vicia faba plants (Amoako et al. 2023a, b; Amoako et al. 2024). Additionally, an upregulation of a gene putatively coding for Early nodulin 93, ENOD93 ( BMK_Unigene_000106 ), was identified and exclusively expressed in NoP_vs._Pi, suggesting that the supply of inorganic P (KH 2 PO 4 ) could trigger initial signaling events, infection development, and nodule organogenesis better than Po (Olivares et al. 2011). This clearly indicates that both the inorganic and organic P sources employed different mechanisms in their nodulation processes. In agreement with our study, Medicago truncatula plants inoculated with purified nod factor (NF) for 4 and 24 h identified early nodulin genes such as MtENOD12 ( Medtr3g415650 ), MtENOD11 ( Medtr3g415670 ), MtENOD40-1 , and were involved in the formation of symbiotic structures in nodules, viz., nodule parenchyma, infection thread (IT) walls, and peribacteroid membrane (PBM) (Jardinaud et al. 2016). Interestingly, nodulin 26-like intrinsic protein 1;1, aquaporin genes ( BMK_Unigene_059591, and BMK_Unigene_065419 ) were upregulated and exclusively expressed in phytic acid-treated comparisons (NoP_vs._Po/Pi_vs._Po; Fig. 10 A, D) and are reported to trigger root hair swelling during early nodule formation (Jardinaud et al. 2016). Strikingly, NoP_vs._Po altered genes such as BMK_Unigene_061444, BMK_Unigene_059591 , and BMK_Unigene_130549 that encoded the legume lectin domain and tyrosine and serine/threonine kinases. The legume lectin domain, for instance, has been found to be involved in the binding of host-produced lectins to rhizobium cells during symbiosis (Hirsch 1999). The lectins bind to the rhizobium via surface polysaccharides such lipopolysaccharides (Hirsch 1999). It is quite plausible to highlight that the supply of Po induces other proteins and receptors that stimulate recognition, attachment, infection, differentiation, and nodule organogenesis in comparison to Pi during SNF. It could be concluded that the supply of phytic acid as a P source perturbs several nodulation genes and uses, in part, different metabolic and molecular mechanisms during nodulation, N 2 -fixation, and nodule reprogramming, supporting the hypothesis of this study. 4.2 P-induced Nodules Trigger Diverse DEGs Involved in Transports and Exchanges (Fluxes) during Nodulation and SNF Transport across the symbiosome membrane (SM) is mediated by plasma membrane H + -ATPases, which energize the SM to enhance influx and efflux of minerals and solutes. We reported in our previous study (Amoako et al. 2024) that the supply of Po causes significant upregulation of H + -ATPases gene isoforms in nodules relative to Pi. This signifies that many genes related to transport across the SM would be triggered to enhance effective transport between the plant and nodule cells when Po is exogenously applied. The SM is the physical barrier that engulfs the differentiated bacteroids and separates them from the cytoplasmic compartment of the plant (Clarke et al. 2014; Clarke et al. 2015). It is a selective permeable membrane that regulates, controls fluxes, and facilitates the movement of solutes and minerals between symbionts (Clarke et al. 2014; Clarke et al. 2015). Several transporters and channels have been identified on the SM that facilitate and coordinate the fluxes of various metabolites. Through non-gel proteomic techniques, Clarke et al. (2015) identified 197 proteins as constituents of the SM, and these proteins were associated with cascades of cellular processes such as metabolism, protein folding and degradation, membrane trafficking, and transport of solutes. Most of these proteins were found to be localized to the SM and peribacteroid space (PBS). The nodule is considered a preferential organ and serves as a P-sink. Its organogenesis has been reported to be very sensitive to P, since 16 ATP molecules are required for the nitrogenase enzyme to convert atmospheric N into ammonia. Our de novo RNA-seq analysis revealed upregulation of inorganic phosphate transporter 1–4 (BMK_Unigene_062257 ) exclusively expressed in Pi_vs._Po comparison (Fig. 7 A), suggesting a unique role in transporting the needed P from cytoplasmic compartment of the plant tto the nodule for nodule homeostasis and N 2 -fixation upon Po supply. Recently, Nasr et al. (2017) observed upregulation of the XM_004502090.2 gene encoding phosphate transporter 1–4 ( PHT1;4 ) in MmSWR19-Pd/MmSWR19-Ps and found to be associated with P acclimation during P starvation. However, in our study, the inorganic phosphate transporter 1–4 was uniquely expressed under sufficient P supply (Pi_vs._Po), signifying a dual role under both sufficient and deficient P conditions in nodules. The upregulation of this gene under Po is consistent with the accumulation of high Pi contents observed in nodules under phytic acid supply, as reported (Amoako et al. 2023a, b; Amoako et al. 2024). Iron and molybdenum are essential components of the nitrogenase complex and are required in larger amounts in the nodules to synthesize the nitrogenase complex (Clarke et al. 2014). Studies have identified several metal transporters, including iron and molybdenum transporters, that regulate the fluxes of metals between symbionts during SNF (Clarke et al. 2014; Clarke et al. 2015). For example, zinc, copper, iron, potassium, calcium, molybdenum, magnesium transporters, and metal transporters (Nramp3) have been identified on the SM to ensure metal homeostasis in the nodule. Our analysis revealed both up- and down-regulation of the above-mentioned metal transporters when Vicia faba nodules were treated with different P sources. The upregulation of the BMK_Unigene_032815 gene encoding the metal transporter Nramp3 in NoP_vs._Pi (Fig. 7 A and D ) indicates that a lot of metal transporters were altered and expressed in this study. It has previously been reported that the transport of ferrous and ferric iron in nodules has been demonstrated on the SM and GmDMT1 (Divalent Metal Transporter 1), which is a member of the NRAMP (Natural resistance-associated macrophage protein) family of transporters, was also identified to transport ferrous transporters and other metals such as zinc, magnesium, and copper in nodules (Clarke et al. 2014; Clarke et al. 2015; Jardinaud et al. 2016). Consistent with our results, the upregulation of genes (such as BMK_Unigene_081823, BMK_Unigene_063447, BMK_Unigene_064567, BMK_Unigene_054555 , and BMK_Unigene_034906) putatively encoding copper transporter 5.1, calcium-transporting ATPase, potassium transporter 2, vacuolar iron transporter-like protein, and sodium-coupled neutral amino acid transporter 1-like protein, respectively, were observed in Pi_vs._Po (Fig. 7 A, B, E), suggesting that transport are significantly improved when sufficient P is supplied to the nodules. Additionally, the treatment of Po altered CorA-like and Mg 2+ transporter proteins ( BMK_Unigene_061734 ), molybdate transporter 1-like protein and molybdate transporter of the MFS superfamily ( BMK_Unigene_131646 ), and zinc transporter 2 ( BMK_Unigene_136703 and BMK_Unigene_069675 ). The upregulation of these metals in only Po (Pi_vs._Po/NoP_vs._Po) comparisons suggests that Po-treated nodules observed improved transportation and fluxes of the above-mentioned metals, which subsequently ensured increased metal homeostasis relative to NoP_vs._Pi (Jardinaud et al. 2016) for efficient N 2 -fixation in this study. In accordance with our results, a Vacuolar Iron transporter (VIT1) family was found to be exclusively expressed in nodules of infected cells (Hakoyama et al. 2012). Kim et al. (2006) demonstrated in Arabidopsis that the VIT1 transporter gene transports and translocate ferrous iron into the vacuole, which was symmetrical to the transport across the SM (Kim et al. 2006). These metals are necessary for the synthesis of the nitrogenase enzyme, and the bacteroids only acquire it from the host plant, hence, observing significant upregulation of these metal transporters are not surprising in this study. The energization of the SM via the extrusion of protons (H + ions) by an H + -ATPase not only creates an electrochemical gradient across the SM to mediate the transport and exchange processes between symbionts, but also protonates the conversion of fixed ammonia (NH 3 ) to ammonium (NH 4 + ) for assimilation by plants after N 2 -fixation (Amoako et al. 2024). For instance, a transcriptome study has recently identified 13 H + -ATPase genes expressed in nodules (Severin et al. 2010), which are found in the family of ATP-binding cassette transporter families. This study identified several ABC (ATP-binding cassette) family transporters that play significant roles in the uptake of minerals and other solutes in the nodules. For example, our analysis identified four ABC family transporter gene isoforms, which were mostly upregulated in NoP_vs._Po, and included those encoding the ABC-2 type transporter ( BMK_Unigene_060918, BMK_Unigene_129480, and BMK_Unigene_060469 ), E1-E2 ATPase ( BMK_Unigene_132193 ), and ABC type transporter ( BMK_Unigene_058922 ), with the ABC transporter G family member 17 ( BMK_Unigene_060469 ) being expressed only in Pi_vs._Po (Fig. 7 A-E). The detection of these transporter types and families in Po-treatment comparisons (NoP_vs._Po/Pi_vs._Po) relative to Pi-treatment comparison (NoP_vs._Pi) indicates that the energization of the SM and transport of solutes in nodules were greatly improved, and, in part, significantly resulted in higher expression of H + -ATPase genes and activities of N assimilation enzymes observed in our previous study (Amoako et al. 2024). Clarke et al. (2015) identified five peptides with homology to the ABC superfamily transporters in the SM proteome, and GmABCA2 , GmABCA7 , and GmABCA11 had higher expression patterns exclusively in soybean nodules. These were found to perform bidirectional functions, with their activities driven by ATP hydrolysis. Nodulin 26 is a major intrinsic membrane protein that acts as a multifunctional aquaglyceroporin, and is known to facilitate the movement of glycerol and formamide (Sakamoto et al. 2019). Intriguingly, genes associated with nodulin 26, such as BMK_Unigene_059591 , encoding nodulin 26-like intrinsic protein 1;1, were significantly upregulated exclusively in Pi_vs._Po comparison (Fig. 7 B, E), suggesting that Po treatment altered the transport of water and glycerol during SNF. The nodulin 26 gene, Glyma08g12650 , was detected on the SM in soybean (Clarke et al. 2015) and in arbuscular mycorrhiza (AM)-induced nodulin 26 and nodulin 21 transcripts in soybean roots (Sakamoto et al. 2019). These genes were suggested to be localized to the peribacteroid and periarbuscular membranes and were implicated in osmoregulation in AM symbiosis and rhizobia symbiosis. The Po-induced genes were associated with membrane transports, including those encoding the nitrate and peptide transporter family (NRT1/PTR) , amino acid, and bidirectional sugar transporters (sugar will eventually enter transporter -SWEET ). The identification of nitrate transporters on the SM of soybean nodules has been reported (Vincill et al. 2005). It is postulated that these transporter genes ensure the regulation of ions and membrane potential via the transport of nitrate, which facilitates the regulation of symbiosis in legumes (Udvardi & Day 1989). This study identified the upregulation of the BMK_Unigene_060395 gene putatively encoding nitrate/peptide transporter proteins ( NRT1/PTR FAMILY 1.1 ) only in NoP_vs._Po (Fig. 10 E, F), suggesting that the supply of Po as a P source enhanced the fluxes of a wide range of nitrogen-based compounds between the symbionts. This is consistent with the upregulation of amino acid transporters, viz., amino acid transporter AVT1A isoform X2 ( BMK_Unigene_133019 ), sodium-coupled neutral amino acid transporter 1-like protein ( BMK_Unigene_034906 ), and probable polyamine transporter At1g31830 ( BMK_Unigene_059198 ) in Pi_vs._Po/NoP_vs._Po (Fig. 7 A, B, E) observed in this study. To support the claim or hypothesis that Po-induced nodules altered higher and upregulated amino acid transporters than Pi-induced nodules, two amino acid permease ( Aap ) genes ( BMK_Unigene_059198 and BMK_Unigene_081174 ) were uniquely expressed and significantly upregulated in only NoP_vs._Po/Pi_vs._Po comparisons (Figs. 7 B and 10 E), highlighting the crucial role of Po supplementation in amino acid metabolism and nitrogen distribution to the cytosol of the host plant during symbiosis for assimilation. This upregulation is consistent with the higher activities of N assimilation enzymes (GS/GOGAT, AAT) previously reported (Amoako et al. 2024). It’s been reported that DCAT1 belongs to the nitrate/peptide transporter (NRT/PTR) family (NPF) (Léran et al. 2014), and this family is associated with the transport of dicarboxylate in legumes. A transcriptome analysis has revealed that NPF -encoding genes are strongly altered in nodules (Severin et al. 2010). This means that the transportation of dicarboxylate (such as malate) that provides the carbon skeleton (energy) for N 2 -fixation in this study cannot be ruled out. This is because the NPF gene was significantly upregulated in Pi_vs._Po, affirming the hypothesis that the supply of phytic acid stimulated and provided the required carbon in the form of dicarboxylate (malate) that can be utilized by the bacteroids to enhance N 2 -fixation (Amoako et al. 2023b). It’s been widely known that bacteroids do not directly utilize sucrose and other hexoses as carbon sources during N 2 -fixation, but a class of sucrose transporters known as SWEET have been discovered in infected nodules, the meristem, invasion zone, and vasculature of nodules (Kryvoruchko et al. 2016). It was reported that two independent Tnt1-insertion sweet11 mutants did not compromize the SNF process, indicating that the MtSWEET11 observed in nodules of M. truncatula distributed sucrose within the nodules, but was not crucial during SNF (Kryvoruchko et al. 2016). Interestingly, our analysis revealed SWEET transporter genes been specifically expressed in Pi treatment comparison (NoP_vs._Pi) relative to Po (Fig. 7 A and D ). The downregulation of the BMK_Unigene_029164 gene encoding bidirectional sugar transporter SWEET4 in this study confirms that SWEET4 is identified in nodules, and even though identified to distribute sucrose within nodules, it is found not to be a critical component in efficient SNF process (Kryvoruchko et al. 2016). However, the nodule-specific gene ( BMK_Unigene_032564 ) encoding the bidirectional sugar transporter N3 in nodules was up-regulated (Fig. 7 A and D ), suggesting a dual functional role during SNF. It could be concluded that the observation of no SWEET transporter genes in the Po-treatment comparisons (NoP_vs._Po/Pi_vs._Po) indicates that the supply of phytic acid directs dicarboxylates to the nodule for SNF and instead of hexose or sucrose as previously reported (Amoako et al. 2023b). We can confidently assert and confirm that diverse P sources recruit different genes and mechanisms in the SM during nodule organogenesis and SNF, confirming the hypothesis of this study. Our previous study concluded that Po-treated plants stimulated higher organic acids (malate) relative to Pi-treated plants, so it is not surprising we did not observe any sucrose transporters in this transcriptome analysis (Amoako et al. 2023b). Even though the entire genome of Vicia faba is yet to be fully annotated, the findings of this study are not surprising because transporter genes analogous to metal and ion transporters, sugars, amino acids and peptide transporters have been reported to be significantly increased in nodules and are presumed to be localized on SM in infected cells of Lotus japonicus (Kouchi et al. 2004). 4.3 Organic and Inorganic P Sources Adopt Varied Carbon and Sucrose Metabolism Mechanism s in Nodules Organogenesis During SNF The general principle for SNF is the supply of reduced carbon (dicarboxylates) to the bacteroids by the host plant in exchange for reduced nitrogen (ammonia). The amount of carbon supplied by plants is equivalent to the nitrogen provided for assimilation, making carbon a very important and crucial resource for SNF in legume-rhizobia symbiosis. The source of carbon for SNF is sucrose supplied from the shoot via photosynthesis by the host plant (Amoako et al. 2023b; Liu et al. 2018). However, bacteroids cannot directly use sucrose as a substrate by the bacteroids to enhance SNF (Liu et al. 2018). Therefore, sucrose is split into glucose/fructose and fructose/UDP-glucose by alkaline invertase (INV) and sucrose synthase (SUS) (Liu et al. 2018), which subsequently enters the TCA cycle to provide the required dicarboxylates. Malate and succinate, preferably malate, are the main sources of dicarboxylates (organic acids) that can enter the SM to provide the energy and carbon skeleton required for efficient N 2 -fixation (Amoako et al. 2023b). Our analysis identified key genes that participated and directly involved in starch and sucrose metabolism to produce the precursors for the activation and synthesis of dicarboxylates needed for SNF (Figs. 13 and 15 ). Starch obtained from photosynthesis is degraded into sucrose for translocation into the cytosol of the host plant for further degradation. After the splitting of sucrose by INV and SUS, glucose or fructose enter glycolysis by the enzymes glucokinase and fructose-6-phosphatase aldolase. In this study, we found genes such as BMK_Unigene_027824 (encoding fructose-6-phosphate aldolase) and BMK_Unigene_062430 (putatively encoding glucokinase) to be downregulated and upregulated, respectively, only in NoP_vs._Po (Figs. 13 A-C and 15 ). This indicates that Vicia faba plants treated with phytic acid employed different pathways (i.e., glucokinase pathway) instead of the fructose-6-phosphate aldolase pathway in their glycolytic pathway and carbon metabolism mechanism during SNF. Interestingly, aldolase-1 epimerase ( BMK_Unigene_082109 ), which catalyzes the conversion of alpha-D-glucose to beta-D-glucose (galactose metabolism, Hucho & Wallenfels, 1971), was found to be exclusively upregulated and enriched in NoP_vs._Po (Figs. 13 A-C and 15 ), confirming that Po-treated plants used an alternative pathway in their sucrose metabolism. Meanwhile, two isoforms of glyceraldehyde-3-phosphate dehydrogenase genes ( BMK_Unigene_021856 and BMK_Unigene_048610) , which catalyzes the conversion of glyceraldehyde 3-phosphate to glycerate-1, 3-biphosphate, a process associated with the construction of NADH (Wang et al. 2020), were significantly upregulated in NoP_vs._Po/NoP_vs._Pi (Figs. 13 A-C and 15 ). However, upregulation of the phosphoglycerate kinase gene ( BMK_Unigene_110457 ), which reversibly transforms 1,3-bisphosphoglycerate (1,3-bPG) to generate 3-phosphoglycerate (3PG) and ATP (Yagi et al., 2021), was also found to be keenly involved in the starch/sucrose metabolism pathway strictly in Po-treatment comparisons (NoP_vs._Po/Pi_vs._Po). Indeed, this result suggests that Po treatments significantly enhanced sucrose metabolism and glycolysis via upregulation of this gene in Vicia faba nodules. The higher upregulation of glycolytic enzymes in this present study is consistent with the higher net photosynthesis observed in our previous study (Amoako et al. 2024). A report by Nasr et al. (2017) identified two pyruvate kinase genes ( XM_004496111.2 and XM_004489126.2 ) in MmSWR19-Pd/MmSWR19-Ps - and McCP-31-Pd/McCP-31-Ps -induced nodules in chickpea plants exposed to different P levels. Similarly, our results identified gene isoforms such as BMK_Unigene_065186 and BMK_Unigene_134540, ( putatively encoding pyruvate decarboxylase 1, and pyruvate decarboxylase 2 ) , and BMK_Unigene_129620 and BMK_Unigene_048128 ( encoding pyruvate kinase 1-cytosolic and pyruvate kinase, cytosolic isozyme ) , respectively, which were significantly upregulated in NoP_vs._Pi (Figs. 13 A and 15 ). These genes were found to be keenly implicated in the sucrose metabolism pathway in Pi treatment (NoP_vs._Pi), but not in Po treatment (NoP_vs._Po/Pi_vs._Po) comparisons, supporting what was observed previously (Nasr et al., 2017). This phenomenon suggests that Po treatment recruits pathways different from its corresponding Pi treatments in producing the precursors (such as PEP and pyruvate) for the synthesis of the dicarboxylates (activation of the TCA cycle enzymes) required for SNF. Furthermore, the results of this study have consistently revealed that Po-induced plants adopt different molecular and metabolic pathways in their glycolytic mechanisms and processes, as reported earlier. An obvious example was enolase, which is an enzyme involved in the reversible and catalytic transformation of D-2-phosphoglycerate (2-PGA) to phosphoenolpyruvate (PEP) during glycolysis and gluconeogenesis in plants (Avilán et al., 2011). Intriguingly, we observed upregulation of the BMK_Unigene_063190 gene encoding Enolase (C-terminal TIM barrel domain and N-terminal domain), which was solely enriched in NoP_vs._Po and participated significantly in the glycolytic pathway in this study. The significant upregulation of this gene specifically in NoP_vs._Po confirms that Po-induced nodules employ different pathway relative to Pi-induced nodules (Figs. 13 A-C and 15 ) in metabolizing sucrose for SNF. It is quite plausible to suggest that Po- and Pi-induced nodules adopt diverse carbon metabolic mechanisms and pathways in their starch/sucrose metabolism processes when plants are exclusively dependent on nodule symbiosis for nitrogen. 5. CONCLUSION Taken together, our findings reveal that Po-induced nodules triggered and altered a higher number of DEGs relative to Pi-induced nodules, highlighting its crucial role in nodule programming, SNF, and the possibility of Po serving as a strong alternative to Pi. It was demonstrated that both treatments (organic and inorganic P) employ diverse nodulation, transport, and carbon metabolism mechanisms in nodules when plants are exclusively dependent on nodule symbiosis for nitrogen, confirming our hypothesis that phytic acid-induced nodulation in Vicia faba plants employs different mechanistic and metabolic pathways relative to Pi. Thus, both host and bacteroid ensure fluxes of solutes, metabolites, and ions to enhance effective mutualistic relationships during SNF. We adopted both positive and negative controls to ensure comprehensive comparisons of DEGs via de novo RNA-seq transcriptome data analysis and thereby chronicling, for the first time, the perturbations occurring in Vicia faba nodules induced with different P sources under symbiotic conditions. This work is a very crucial step towards the ongoing genome annotation project of Vicia faba plants and the creation of its database. Declarations Authors and Affiliations Institute of Plant Nutrition and Soil Science, Kiel University, Hermann-Rodewald-Straße 2, 24118 Kiel, Germany. Frank Kwarteng Amoako, Amit Sagervanshi & Karl H. Mühling Jiangsu Key Laboratory of Sericulture Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang 212100, China. Michael Ackah & Weiguo Zhao Institut für Pflanzenwissenschaften und Mikrobiologie Pflanzenbiochemie und Infektionsbiologie Ohnhorststr. 18, 22609, University of Hamburg, Hamburg, Germany. Ebenezer Kweku Ntiriakwa Department of Molecular Biology and Biotechnology, School of Biological Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, PMB Ghana. Frank Kwarteng Amoako, Michael Ackah & Aaron Tettey Asare Authors Contributions Frank Kwarteng Amoako : Conceptualization, Methodology, Investigation, Supervision, Project administration, Funding, Formal analysis, Data curation, Software, Validation, Writing – original draft, Writing – review & editing. Ebenezer Ntiriakwa : Methodology, Investigation, Resources. Michael Ackah : Formal analysis, Data curation, Software, Writing – review & editing. Karl H. Mühling and Amit Sagervanshi: Conceptualization, Writing – review & editing. Weiguo Zhao: Supervision, Funding, Writing – review & editing. Aaron Tettey Asare: Supervision, Funding, Writing – review & editing. Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have influenced the findings chronicled in this manuscript. Data availability Raw sequence data are available on NCBI with accession number PRJNA1030752 and website https://www.ncbi.nlm.nih.gov/sra/PRJNA1030752 Acknowledgements We are grateful to Biomarker Tech. Co. Ltd (Beijing, China) for assisting in the RNA sequencing and bioinformatics analyses. Appendix A. Supplementary data References Amoako, F. K., Jillani, G., Sulieman, S., & Mühling, K. H. (2023a). Faba bean (Vicia faba L.) varieties reveal substantial and contrasting organic phosphorus use efficiencies (PoUE) under symbiotic conditions. Journal of Plant Nutrition and Soil Science , 186 (6), 673-692. https://doi.org/10.1002/jpln.202300198 Amoako, F. K., Sagervanshi, A., Hussain, M. A., Pitann, B., & Mühling, K. H. 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Plant Physiology , 137 (4), 1435-1444. https://doi.org/10.1104/pp.104.051953 Wang, S., Chen, H., Tang, X., Zhang, H., Hao, G., Chen, W., & Chen, Y. Q. (2020). The role of glyceraldehyde-3-phosphate dehydrogenases in NADPH supply in the oleaginous filamentous fungus Mortierella alpina. Frontiers in microbiology , 11 , 818. https://doi.org/10.3389/fmicb.2020.00818 Wang, Y., Yang, Z., Kong, Y., Li, X., Li, W., Du, H., & Zhang, C. (2020). GmPAP12 is required for nodule development and nitrogen fixation under phosphorus starvation in soybean. Frontiers in Plant Science , 11 , 450. https://doi.org/10.3389/fpls.2020.00450 Wasner, D., Prommer, J., Zezula, D., Mooshammer, M., Hu, Y., & Wanek, W. (2023). Tracing 33P-labelled organic phosphorus compounds in two soils: New insights into decomposition dynamics and direct use by microbes. Frontiers in Soil Science , 3 , 1097965. https://doi.org/10.3389/fsoil.2023.1097965 Xing, X., Du, H., Yang, Z., Li, X., Kong, Y., Li, W., & Zhang, C. (2022). GmSPX8, a nodule-localized regulator confers nodule development and nitrogen fixation under phosphorus starvation in soybean. BMC Plant Biology , 22 (1), 161. https://doi.org/10.1186/s12870-022-03556-2 Yagi, H., Kasai, T., Rioual, E., Ikeya, T., & Kigawa, T. (2021). Molecular mechanism of glycolytic flux control intrinsic to human phosphoglycerate kinase. Proceedings of the National Academy of Sciences , 118 (50), e2112986118. https://doi.org/10.1073/pnas.2112986118 Supplementary Files Supplementarydata1.docx Supplementarydata2.docx Supplementaryfigure1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6417689","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456702609,"identity":"6e8621e0-0844-4471-97b2-cab302b0d1ac","order_by":0,"name":"Frank Kwarteng Amoako","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-7910-036X","institution":"Kiel University: Christian-Albrechts-Universitat zu Kiel","correspondingAuthor":true,"prefix":"","firstName":"Frank","middleName":"Kwarteng","lastName":"Amoako","suffix":""},{"id":456702610,"identity":"4cb07b34-ee65-491c-a276-296408615709","order_by":1,"name":"Michael Ackah","email":"","orcid":"","institution":"Jiangsu University of Science and Technology: Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Ackah","suffix":""},{"id":456702611,"identity":"262485da-70a0-47af-aff2-9a0ca6d5a10b","order_by":2,"name":"Ebenezer Kweku Ntiriakwa","email":"","orcid":"","institution":"University of Hamburg: Universitat Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Ebenezer","middleName":"Kweku","lastName":"Ntiriakwa","suffix":""},{"id":456702612,"identity":"cb149479-601b-43ec-9171-38c39bf90f7c","order_by":3,"name":"Weiguo Zhao","email":"","orcid":"","institution":"Jiangsu University of Science and Technology: Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Zhao","suffix":""},{"id":456702613,"identity":"30c3e1f2-3bd5-4fc1-afc3-1ee53bb70caf","order_by":4,"name":"Aaron Tettey Asare","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"Tettey","lastName":"Asare","suffix":""},{"id":456702614,"identity":"c96cda01-712b-4339-a248-7803666192f1","order_by":5,"name":"Amit Sagervanshi","email":"","orcid":"","institution":"Kiel University: Christian-Albrechts-Universitat zu Kiel","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"Sagervanshi","suffix":""},{"id":456702615,"identity":"9476634e-ebc1-45c3-825c-201e08f066a1","order_by":6,"name":"Karl H. Mühling","email":"","orcid":"","institution":"Kiel University: Christian-Albrechts-Universitat zu Kiel","correspondingAuthor":false,"prefix":"","firstName":"Karl","middleName":"H.","lastName":"Mühling","suffix":""}],"badges":[],"createdAt":"2025-04-10 07:49:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6417689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6417689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82992484,"identity":"3d6eaa6a-03d1-4903-8134-891bd7dfc985","added_by":"auto","created_at":"2025-05-18 16:44:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2434671,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographic depiction of nodulation of \u003cem\u003eVicia faba\u003c/em\u003e in response to different P supply for 30 d in hydroponics. (A) low Pi (2 µM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e-NoP) (B) high Pi (200 µM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e-Pi) and (C) phytic acid (200 µM-Po). Note: Not drawn to scale.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/8001210fc972a5c3d6a428bd.png"},{"id":82992489,"identity":"ff04251d-a1c4-4818-83fb-789e43bfa76a","added_by":"auto","created_at":"2025-05-18 16:44:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1286541,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Sample boxplot of FPKM. (B) Density plot of FPKM. (C) Sample clustering correlation heatmap analysis. (D) Principal component analysis (PCA) of treatments. Points in PCA represent samples, different colors represent different groups. Ellipses represent confidence intervals of core regions. The triangles are quality control samples derived from the three samples, and each represent one treatment.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/26b8b25329f8ead62c28da49.png"},{"id":82992487,"identity":"f1e6686e-e7a1-46fb-8397-357ea878437c","added_by":"auto","created_at":"2025-05-18 16:44:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":805177,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot detailing significant gene patterns. (A) NoP_vs._Po (B) NoP_vs._Pi, and (C) Pi_vs._Po. Up-regulated genes are shown by red dots, down-regulated genes are shown by green dots. Cluster heatmap analysis of DEGs in \u003cem\u003eVicia faba\u003c/em\u003e nodules exposed to different P. (D) NoP_vs._Po (E) NoP_vs._Pi, and (F) Pi_vs._Po. Venn diagram of treatment unigenes expression in \u003cem\u003eVicia faba\u003c/em\u003e plants exposed to different P (G).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/6edfe6fe7a44c4c73e007b6e.png"},{"id":82992785,"identity":"28dbe88b-a8c3-4d8d-8212-43ae7754135c","added_by":"auto","created_at":"2025-05-18 16:52:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1440308,"visible":true,"origin":"","legend":"\u003cp\u003eGO annotations of unigenes in \u003cem\u003eVicia faba\u003c/em\u003e nodules exposed to different P. (A) NoP_vs._Po (B) NoP_vs._Pi and (C) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/b33c52f758c23cedff831efb.png"},{"id":82993095,"identity":"c32c5dcd-8357-45f6-a7dd-d2b2204fb2a6","added_by":"auto","created_at":"2025-05-18 17:00:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":252736,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of KEGG pathways. Bubble plots of enrichment analysis. (A) NoP_vs._Po, (B) NoP_vs._Pi and (C) Pi_vs._Po. KEGG secondary classification (D) NoP_vs._Po (E) NoP_vs._Pi and (F) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/4743689a9c763d82ee23c426.jpg"},{"id":82992787,"identity":"58723087-f9e8-4205-bfd9-f46dfbb25284","added_by":"auto","created_at":"2025-05-18 16:52:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":802543,"visible":true,"origin":"","legend":"\u003cp\u003eInteractive networks of DEGs related to different KEGG pathways. The size and color of the circle represent the number of edges and different gene expression, respectively. (A) NoP_vs._Po (B) NoP_vs._Pi and (C) Pi_vs._Po. \u003cstrong\u003eNOTE:\u003c/strong\u003e Genes involved in the figure A have been attached as supplementary data 2 (Table S5) to avoid cloudiness and overlapping due to huge number of genes implicated in this gene network analysis.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/bf511d277c5b34a55ffa377a.jpg"},{"id":82992790,"identity":"98d471c3-689c-489c-92b3-83761ea35df4","added_by":"auto","created_at":"2025-05-18 16:52:47","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":648769,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Bar plot of transporter gene family. Cluster heatmap analyses of transporter family genes of \u003cem\u003eVicia faba\u003c/em\u003e nodules exposed to P. (C) NoP_vs._Po (D) NoP_vs._Pi and (E) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/716eb1be6c212b73e470cc62.jpg"},{"id":82992499,"identity":"0136ec3d-c55b-47e7-80c8-a9c3a5ca284e","added_by":"auto","created_at":"2025-05-18 16:44:47","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":838462,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Bar plot of transcription factor (TF) family. Cluster heatmap analysis of TFs family in \u003cem\u003eVicia faba\u003c/em\u003eplants in response to different P. (C) NoP_vs._Po (D) NoP_vs._Pi and (E) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/963d5b51887c457472203a52.jpg"},{"id":82992789,"identity":"f60b495a-f997-4024-ad0c-2e616c8e453a","added_by":"auto","created_at":"2025-05-18 16:52:47","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":904427,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Bar plot of signaling and signal transduction gene family. Cluster heatmap analysis of Signaling and signal transduction family in \u003cem\u003eVicia faba\u003c/em\u003eplants in response to different P. (C) NoP_vs._Po (D) NoP_vs._Pi and (E) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/4f29e3ea71b759790be451f2.jpeg"},{"id":82993096,"identity":"ed3313d4-418f-4c81-a8c8-5147bba5cd53","added_by":"auto","created_at":"2025-05-18 17:00:47","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":605984,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot and cluster heatmap analysis of nodulation-related gene family (A-D). Bar plot and cluster heatmap analysis of nitrogen and amino acid metabolism-related genes in \u003cem\u003eVicia faba\u003c/em\u003e plants in response to different P (E-H). NoP_vs._Po, NoP_vs._Pi and Pi_vs._Po.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/4e977b06ef5fc40da3d0b9f6.jpg"},{"id":82992522,"identity":"16c6e76f-845e-4354-9085-599d83dc6f9e","added_by":"auto","created_at":"2025-05-18 16:44:47","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":788812,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Bar plot of P and Pi-remobilization genes. Cluster heatmap analysis of P and Pi remobilization gene family in \u003cem\u003eVicia faba\u003c/em\u003e plants in response to different P. (C) NoP_vs._Po (D) NoP_vs._Pi and (E) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/cc38c2f5f36c7c6f0873c459.jpeg"},{"id":82992532,"identity":"26dd8667-fa99-4cbb-ae8a-2b4cbe0f4e0e","added_by":"auto","created_at":"2025-05-18 16:44:47","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":741823,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Bar plot of lipids and secondary metabolism genes. Cluster heatmap analysis of lipids and secondary metabolism gene family in \u003cem\u003eVicia faba\u003c/em\u003e plants in response to different P. (C) NoP_vs._Po (D) NoP_vs._Pi and (E) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/e26e68f1e011dcb8952644e1.jpeg"},{"id":82992497,"identity":"896f2c7e-565c-4e58-b4f4-a58a8c3f79ca","added_by":"auto","created_at":"2025-05-18 16:44:47","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":908159,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Bar plot of carbon metabolism gene family. Cluster heatmap analysis of carbon metabolism gene family in \u003cem\u003eVicia faba\u003c/em\u003e plants in response to different P. (C) NoP_vs._Po (D) NoP_vs._Pi and (E) Pi_vs._Po.\u003c/p\u003e","description":"","filename":"13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/f35794c9d3a8a0ddeef53df9.jpeg"},{"id":82992791,"identity":"b1e43365-aa33-4e17-b3f0-af7e5636ac6f","added_by":"auto","created_at":"2025-05-18 16:52:47","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":316493,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis showing the validation of 12 genes from RNA-seq using RT-qPCR in \u003cem\u003eVicia faba\u003c/em\u003e plants exposed to P treatments, viz., low Pi (KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e), high Pi (KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e) and phytic acid.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/a6f1bb45eca00ac2c3b669c6.jpg"},{"id":82992495,"identity":"b04a00ef-9931-4f98-832a-8143fd319ca3","added_by":"auto","created_at":"2025-05-18 16:44:46","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":182277,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of starch/sucrose metabolic pathway. Red and green arrows and texts indicate up-and down-regulated genes involved in the starch/sucrose metabolic pathway, respectively.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/af4ff13c125ef0ad476ba90c.jpg"},{"id":84643201,"identity":"77f8a6c0-77fd-49c0-b96d-0a472a2b11d2","added_by":"auto","created_at":"2025-06-15 15:42:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15661832,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/86222f9a-8c3a-4edd-bc99-27b1ea942fc8.pdf"},{"id":82992500,"identity":"e75fc60a-a31c-435c-8755-85ab952d1b98","added_by":"auto","created_at":"2025-05-18 16:44:47","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16872,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/990c1c5a732aad080a40995c.docx"},{"id":82992788,"identity":"cfb2db16-5692-4842-a066-3dbbe6a573e8","added_by":"auto","created_at":"2025-05-18 16:52:46","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":26357,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/15addcfd1c2104edf0889bb0.docx"},{"id":82992505,"identity":"8f0f582e-d4bd-4c86-8427-867b738567ac","added_by":"auto","created_at":"2025-05-18 16:44:47","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":611323,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6417689/v1/9c2f116442c93026b6201c2e.docx"}],"financialInterests":"","formattedTitle":"Transcriptome Analysis Unravels Diverse Response Mechanisms of Nodules to Phytic Acid Supply in Vicia faba","fulltext":[{"header":"Key Message","content":"\u003cp\u003eExogenous phytic acid enhances carbon allocation for N₂-fixation and activates solute transport and nodule organogenesis genes in\u0026nbsp;nodules\u003cem\u003e\u0026nbsp;\u003c/em\u003eof\u003cem\u003e\u0026nbsp;\u003c/em\u003esymbiotically grown \u003cem\u003eVicia faba\u0026nbsp;\u003c/em\u003erelative to inorganic phosphorus.\u0026nbsp;\u003c/p\u003e"},{"header":"1. INTRODUCTION","content":" \u003cp\u003ePhosphorus (P) is an essential nutrient that plays a crucial role in plant growth and development. It exists predominantly as inorganic (Pi) and organic (Po), with the latter being proposed as a direct substitute for the widely patronized chemical P fertilizers, which are derived from rock phosphate (Amoako et al. 2023a, b). This is very likely because Po has generally been discovered to compose approximately 20\u0026ndash;80% (Wasner et al. 2023) and, in other reports, up to 90% (McConnell et al. 2020) of the total P pool in the soil. Orthophosphate monoesters, orthophosphate diesters, organic polyphosphates, and phosphonates, RNA, and phospholipids, with the orthophosphate monoester inositol phosphate and its derivatives, are the most abundant and commonly found Po forms in soil fractions, accounting for approximately 50% of the soil total Po fraction due to their mineralization efficiencies (McConnell et al. 2020; Park et al. 2022). Phytic acid (myo-inositol hexakisphosphate, PA) is discovered as the predominant phosphorus (P) reservoir in cereals and legumes that supply the biosynthetic pathway and nutritional requirements of plants during germination to regulate cellular processes (Gulabani et al. 2022; Kumar et al. 2023). In soils, it is quantitatively the dominant and most significant inositol phosphate (Gerke 2015), with other stereoisomers and phosphorylated derivatives also discovered (Turner et al. 2012). For instance, over 50\u0026nbsp;million tonnes of phytate are known to be manufactured commercially in fruits and seeds of crops annually (Lott et al. 2000), and recent data suggest that about 67% of the global application rates of fertilizers in the form of P is quantified from phytate, signifying the quantitative relevance of phytate for P cycling in soil (Mullaney \u0026amp; Ullah 2007).\u003c/p\u003e \u003cp\u003eSignificant strides have been made in an effort to elucidate how plants respond to Po utilization when supplied as a fertilizer in crop production in the last decades. Even though Po, as a P source cannot be taken up directly by plants but are first mineralized into Pi via solubilization by P solublizing microorganisms. Of course, several studies point to the fact that Po acquisition and utilization in different plants such as cereals, pastures, and legumes have been shown to be very promising and support the growth and development of crops similar to Pi wih help of P solublizing microorganisms such as bacteria (Amoako et al. 2023a, b; Jillani et al. 2022). For example, Jillani et al. (2022) reported that the \u003cem\u003eVicia faba\u003c/em\u003e plant could utilize different P sources, including phytic acid, as competently as Pi substrates when inoculated with bacteria. Notwithstanding, Amoako et al. (2023a, b; 2024) reported a similar trend in both soil and hydroponics, where different \u003cem\u003eVicia faba\u003c/em\u003e varieties treated with phytic acid produced and accumulated similar biomasses like its counterpart, Pi, at equivalent application rates upon bacteria inoculation. This had earlier been reported by Tarafdar and Claassen (1988), who revealed that different plant species, viz., wheat, oat, barley, and clover, have the capacity to utilize Po and accumulate biomasses like Pi (Tarafdar \u0026amp; Claassen 1988). However, some contrasting reports indicate that some plant species cannot utilize Po. For instance, the supply of Po substrates could not support growth and biomass in \u003cem\u003eVicia villosa\u003c/em\u003e (Said-Pullicino et al. 2022) or in legumes and six pastures (Hayes et al. 2000). This was associated with the limited utilization of phytate by the species as a consequence of the low capacity of plant roots to mineralize Po and the adsorption of phytate to soil solid surfaces. It could be deduced from the above conflicting reports that the acquisition and utilization of Po by varying plants are associated with growth media, plant intraspecies, genotypic variations, soil type and right phosphatases, and bacteria inoculants (Amoako et al. 2023a, b; Jillani et al. 2022). Po has recently been reported to improve not only growth and biomass of \u003cem\u003eVicia faba\u003c/em\u003e but also promotes N fixation and biological nitrogen fixation (Amoako et al. 2023a, b; Jillani et al. 2022). P is largely needed for N\u003csub\u003e2\u003c/sub\u003e-fixation and limited P during the process hampers and terminates biological nitrogen fixation. Further studies to elucidate Po`s pivotal roles in legumes and N\u003csub\u003e2\u003c/sub\u003e-fixation is crucial.\u003c/p\u003e \u003cp\u003eBiological nitrogen fixation (BNF) is the process by which microorganisms convert di-nitrogen (N\u003csub\u003e2\u003c/sub\u003e) in the atmosphere into plant-usable form. It is an energetically expensive process that requires 16 ATP molecules to break down an N\u003csub\u003e2\u003c/sub\u003e molecule to form NH\u003csub\u003e3\u003c/sub\u003e, with 12 more ATP molecules needed for ammonium assimilation (Soumare et al. 2020) via the GS/GOGAT cycle. Studies have reported the deleterious effect of P deficiency on nodulation and BNF efficiency in crops such as \u003cem\u003eVicia faba\u003c/em\u003e (Amoako et al. 2023a, b; Jillani et al. 2022), \u003cem\u003eMedicago truncatula\u003c/em\u003e (Sulieman \u0026amp; Tran 2017), \u003cem\u003eVicia villosa\u003c/em\u003e (Said-Pullicino et al. 2022), \u003cem\u003eAstragalus sinicus\u003c/em\u003e (Sun et al. 2022), \u003cem\u003eGlycine max\u003c/em\u003e (Wang et al. 2020; Xing et al. 2022), \u003cem\u003eCicer arietinum\u003c/em\u003e (Loucif et al. 2022), and \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e (Isidra-Arellano et al. 2018). For proper functioning of the nodule, a higher amount of P is required. Apart from Pi, which is predominantly used in most symbiotic and nodulation studies, Po has recently been reported to improve nodule number, biomass, and N\u003csub\u003e2\u003c/sub\u003e-fixation like Pi when plants are exclusively dependent on nodule symbiosis for nitrogen (Amoako et al. 2023a, b; Jillani et al. 2022). The formation of nodules in legumes is a very complex mechanism occurring through the establishment of a mutualistic relationship that begins with a molecular dialogue between the two partners, the host plant, and the nitrogen-fixing organism (rhizobia) (Soumare et al. 2020). The flavonoids and isoflavonoids exuded by the host plant trigger recognition, infection, differentiation of root hair cells, and nodule development (Mukherjee \u0026amp; Sen 2021). This involves a plethora of signals, programming, and reprogramming of the functioning nodules. In our recent publications, we reported the perturbations caused in the nodules of \u003cem\u003eVicia faba\u003c/em\u003e plants induced by phytic acid supply (Amoako et al. 2023b). We reported that the supply of phytic acid did not only improve the mineral and carbon metabolism adjustments of nodules, but also supplied the needed P and carbon for normal homeostasis and functioning of the nodules (Amoako et al. 2023b). Furthermore, the supply of phytic acid did not only alter the cationic-anionic balances in nodules (Amoako et al. 2023b), but also transcriptionally altered the expression of plasma membrane (PM) H\u003csup\u003e+\u003c/sup\u003e-ATPase gene isoforms in nodules (Amoako et al. 2024). Hence, it is quite tempting to hypothesize that the supply of phytic acid could perturb the molecular mechanisms that modulate (more genes and pathways) the programming and reprogramming of the nodules if genetic alterations and modifications are to be employed to enhance symbiotic nitrogen fixation (SNF) and crop productivity in the future. However, studies pertaining to Po signatures in nodule transcriptome and N\u003csub\u003e2\u003c/sub\u003e-fixation remains obscure.\u003c/p\u003e \u003cp\u003eIn recent times, the use of transcriptional reprogramming linked with nodulation has been suggested as one of the most powerful approaches to elucidate the genetic control of nodule formation under different conditions (Gao et al. 2022). This has been made possible via transcriptome profiling studies that have been conducted to characterize and identify the genes, molecular pathways, and cellular processes involved in nodulation and N\u003csub\u003e2\u003c/sub\u003e-fixation (Gao et al. 2022). Through transcriptomics and proteomics analyses, several transporter genes and proteins, such as phosphate transporter (\u003cem\u003ePHT1-4)\u003c/em\u003e (Nasr et al. 2017), vacuolar iron transporter 1 (\u003cem\u003eVIT1\u003c/em\u003e), zinc transporter (\u003cem\u003eZIP\u003c/em\u003e), copper transporter 5.1, potassium transporter 2, magnesium transporter (\u003cem\u003eCorA-like Mg\u003c/em\u003e\u003csup\u003e\u003cem\u003e2+\u003c/em\u003e\u003c/sup\u003e \u003cem\u003etransporter protein\u003c/em\u003e), calcium-transporting ATPase, molybdenum transporter of the MFS superfamily, metal transporters (\u003cem\u003eNRAMP3\u003c/em\u003e), ABC family transporters, SWEET, amino acid permease (\u003cem\u003eAap\u003c/em\u003e) etc., have been identified in soybean (Clarke et al. 2015; Sakamoto et al. 2019) and other plants, and references therein, are reported to be localized to the symbiosome membrane (SM) and infected nodule cells. For example, Clarke et al. (2015) identified 197 proteins on the SM that were characterized to being involved in cellular processes such as metabolism, solute transport, and membrane trafficking in soybean nodules. Previous transcriptomic studies have significantly enhanced our insight into SNF; the majority of these studies have only profiled the transcriptional changes of root nodules strictly in connection with Pi as the source of P, to the total neglect of the organic counterpart. Even though the entire genome of \u003cem\u003eVicia faba\u003c/em\u003e (Faba bean) has not been entirely annotated, but through BLAST (de novo) from different databases, numerous genes have been annotated to transform our understanding of the transcriptome of \u003cem\u003eVicia faba\u003c/em\u003e nodules. Through transcriptome analysis, nodule-specific cysteine-rich \u003cem\u003e(NCR)\u003c/em\u003e peptides, early and late nodulation genes have been reported to express solely in infected cells of \u003cem\u003eM. truncatula\u003c/em\u003e nodules, which functions during nodule organogenesis and bacteroid differentiation (Guefrachi et al. 2014; Horv\u0026aacute;th et al. 2023; Nallu et al. 2013).\u003c/p\u003e \u003cp\u003eThe objective of this study was to identify genes involved and triggered during nodule growth (nodulation), transport (exchange and fluxes across the symbiont), and carbon metabolism in response to different P sources in symbiotically grown \u003cem\u003eVicia faba\u003c/em\u003e. To answer the above objective, we therefore, hypothesized that the differential nodulation, transport and carbon metabolism mechanisms exhibited by \u003cem\u003eVicia faba\u003c/em\u003e plants in response to phytic acid recruit diverse pathways and mechanisms during nodule organogenesis and N\u003csub\u003e2\u003c/sub\u003e-fixation relative to inorganic P. In this study, we employed de novo RNA-Seq analysis to identify differentially expressed genes (DEGs) involved in nodules responses to phytic acid supply in hydroponics during SNF of symbiotically grown \u003cem\u003eVicia faba\u003c/em\u003e plants. We generated RNA-Seq datasets across nine nodule samples. Our bioinformatics detected both known and unknown unigenes with varied expression patterns under varied levels of P sources.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant Growth Conditions, Phosphorus Treatment and Rhizobia Inoculation\u003c/h2\u003e \u003cp\u003ePlant materials, growth conditions, and P treatments followed the same experimental procedures as described in our initial study (Amoako et al. 2023b, 2024). In short, \u003cem\u003eVicia faba\u003c/em\u003e L. (var. Hiverna) seeds were surface purified (NaClO\u003csub\u003e4\u003c/sub\u003e solution; 5\u0026ndash;7% v/v) and cleansed with ddH\u003csub\u003e2\u003c/sub\u003eO (double deionized water) to discard contaminants. Afterwards, early germination was augmented by immersing the seeds in a 1 mM CaSO\u003csub\u003e4\u003c/sub\u003e solution for 2 days (d) at 20\u0026deg;C and were germinated using the sandwich method. Uniform seedlings were transplanted after 7\u0026ndash;10 d of germination in 5 L plastic containers containing a 1/4th nutrient solution (NS) strength, with the same basal NS formulation as described. The seedlings were hydroponically grown in a climatic chamber under conditions of 14/10 h day/night cycle, 20/15\u0026deg;C day/night temperature, 60% relative humidity, and 300 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e photosynthetic active radiation for a period of 30 days as previously. The NS concentrations were systematically and steadily increased to \u0026frac12; on the second day, 3/4 on the third day, and full on the fourth day to circumvent osmotic stress. The NS was recycled every three days to freshen up the exhausted nutrients. The P treatments followed the same procedure as previously elucidated (Amoako et al. 2023b; 2024) and included inadequate and adequate P, which consisted of low-Pi (2.0 \u0026micro;M KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e; NoP), adequate-Pi (200 \u0026micro;M KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e; Pi), and Po (200 \u0026micro;M phytic acid; Po). Meanwhile, the low and adequate KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e-Pi treatments were used as negative and positive controls, respectively, for better treatment comparisons. To circumvent nitrogen (N) deficiency at the early stages of growth, a small amount N (20 \u0026micro;M NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e) was supplied to the plants for a period of 10 d and was paused to ensure that chemical N does not inhibit nodulation and N\u003csub\u003e2\u003c/sub\u003e-fixation. After 14 d of transplant, seedlings were inoculated (inoculation assay) with \u003cem\u003eRhizobium leguminosarum\u003c/em\u003e bv. \u003cem\u003eviciae\u003c/em\u003e 3841 broth. The rhizobium broth was prepared as described (Amoako et al. 2023b). The design of the experiment followed a completely randomized design (CRD), with 4 biological replicates, and plants were allowed to grow for a period of 30 d. The plants were harvested in 30 d, and tissues were fragmented into three, viz., leaves, roots, and nodules. The fresh nodule samples were instantly frozen in liquid N and stored at -80\u0026deg;C for RNA-seq and other analyses. For the RNA-seq analysis, three biological replicates, consisting of nine nodule samples, were used. All growth data, physiological parameters, analytical nutrient determinations, and biochemical measurements pertaining to this study have been reported (Amoako et al. 2023b; 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 RNA Extraction, Library Construction and Sequencing\u003c/h2\u003e \u003cp\u003eRNA was isolated from nine \u003cem\u003eVicia faba\u003c/em\u003e nodule samples of three biological replicates of P treatments, consisting of negative control (NoP), positive control (Pi), and phytic acid (Po), using the RNeasy Plus Mini Kit (Tiangen), according to the manufacturer\u0026rsquo;s protocols. The quality and quantity of each RNA extracted were determined using agarose gel electrophoresis and the Nanodrop 2500 (Thermo Fisher Scientific, US). For library preparation for transcriptome sequencing, a total amount of 1 \u0026micro;g RNA per sample was used for the RNA sample preparations. Briefly, the extracted mRNA was purified from total RNA using poly-T oligo-attached magnetic beads and fragmented using divalent cations under high temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). The first and second strand cDNAs were synthesized using random hexamer primer and M-MuLV Reverse Transcriptase and DNA Polymerase I, and RNase H, respectively. The remaining overhangs were changed into blunt ends with the help of exonuclease/polymerase. After 3\u0026rsquo; ends adenylation of DNA fragments, NEBNext Adaptor, with hairpin loop structure was ligated to ensure hybridization. The library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA) to ensure that the cDNA fragments of 240 bp in length were selected. Before PCR, a 3 \u0026micro;l USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37\u0026deg;C for 15 min followed by 5 min at 95\u0026deg;C. PCR was performed with Phusion High-Fidelity DNA Polymerase, Universal PCR primers, and Index (X) Primer and the PCR products were finally purified (AMPure XP system), and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer\u0026rsquo;s instructions. Finally, the Illumina Hiseq 2000 platform by BIOMARKER TECH CO. LTD. (Beijing, China) based on sequencing by synthesis technology was used to sequence the resulting cDNA library to generate paired end reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Quality Control and De novo Transcriptome Assembly Analyses\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAfter the generation of the raw paired end reads, quality control was performed. Raw data (raw reads) in fastq format were initially processed via in-house perl scripts. To obtain quality clean reads, adapter, reads containing ploy N, and low-quality reads were removed from raw data using Fastqc (version 0.18.0). Clean data (clean reads) were obtained by removing the following reads: (a) reads with 5\u0026prime; adapter,(b) reads without 3\u0026prime; adapter or insert sequence, (c) reads with \u0026gt;\u0026thinsp;10% N, (d) reads with \u0026gt;\u0026thinsp;50% nucleotides with Qphred\u0026thinsp;\u0026le;\u0026thinsp;20, and (e) reads with poly A/T/G/C. The Phred quality score, viz., Q20, Q30, GC-content, and sequence duplication level of clean data, were calculated to obtain the base calling accuracy. All the downstream analyses were based on clean data with a high-quality score. The transcriptome assembly was performed using Trinity v2.5.1 (Grabherr et al. 2011), with minimum kmer coverage set to 2 by default and all other parameters set to default. Assembly statistics and assess assembly quality was obtained using Trinity v2.5.1 and assembly completeness analysis was performed with QUAST v5.3.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional Annotation of Unigenes and Transcripts Analysis\u003c/h2\u003e \u003cp\u003eThe annotation of transcripts was performed by juxtaposing or aligning the clean reads against the Non-redundant (Nr, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (Grabherr et al. 2011), Swissprot (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ftp.ebi.ac.uk/pub/databases/swissprot\u003c/span\u003e\u003cspan address=\"http://ftp.ebi.ac.uk/pub/databases/swissprot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (Buchfink et al. 2015), Clusters of Orthologous Groups (COG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.goc/COG/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.goc/COG/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (Apweiler et al. 2004), homologous protein family (Pfam, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pfam.xfam.org\u003c/span\u003e\u003cspan address=\"http://pfam.xfam.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), eukaryotic orthologous groups (KOG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.goc/KOG/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.goc/KOG/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (Tatusov et al. 2000), orthologous group of genes (eggNOG (v4.5), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://eggnog.embl.de/\u003c/span\u003e\u003cspan address=\"http://eggnog.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (Koonin et al. 2004), Translated European Molecular Biology Laboratory (TrEMBL, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/TrEMBL\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/TrEMBL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Gene Ontology (GO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org\u003c/span\u003e\u003cspan address=\"http://geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (Deng et al. 2006), and Kyoto Encyclopedia of Genes and Genomes (KEGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (Huerta-Cepas et al. 2016) databases using BLASTX. KEGG orthologs of unigenes were performed by KOBAS v3.0 software (Kanehisa et al. 2004). The amino acid sequences of unigenes were predicted, and the predicted sequences were annotated by searching against the Pfam (Xie et al. 2011) database and HMMER v3.1b2 executed utilizing a hidden Markov model (HMM) (Jones et al. 2014). The thresholds of BLAST E-value not larger than 1e-5 and HMMER E-value not larger than 1e-10 were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Quantification of Gene Expression Levels and Differential Expression Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eGene expression levels were estimated by RSEM v1.2.19 (Li \u0026amp; Dewey 2011) for each sample. This was carried out by mapping back clean data onto the assembled transcriptome, and the Readcount for each gene was obtained from the mapping. The fragments per kilobase of transcript per million fragments mapped (FPKM) were quantified using RSEM software and were used to juxtapose the expression levels of the transcripts. The sample comparisons consisted of NoP vs. Pi, NoP vs. Po, and Pi vs. Po. Differential expression analysis of two conditions/groups was performed using the DESeq R package (1.10.1) with the FPKM values. DESeq is used for determining differential expression in digital gene expression data based on the negative binomial distribution. The resulting P values were adjusted using Benjamini and Hochberg\u0026rsquo;s approach for controlling the false discovery rate (FDR). Unigenes and transcripts found by DESeq with an absolute fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 and an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were adjudged differentially expressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 GO Enrichment and KEGG Pathway Analyses\u003c/h2\u003e \u003cp\u003eFor the Gene Ontology (GO) analysis, all DEGs were mapped to GO terms in the Gene Ontology database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the topGO R packages based on the Kolmogorov\u0026ndash;Smirnov test. The KEGG pathway and enrichment analysis were performed using the KEGG database for determining the DEG pathways. The statistical enrichment of differential expression genes in the KEGG pathways was tested using the KOBAS v3.0 software. The p-value was calculated and underwent FDR correction at a threshold of FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 DEGs Validation Using RT-qPCR.\u003c/h2\u003e \u003cp\u003eTwelve P-responsive unigenes (i.e., 4 from each treatment) were arbitrarily chosen from the transcriptome data based on their expression levels and significant involvement in the KEGG pathways. To ascertain the authenticity of the transcriptome data, the relative expression levels of these unigenes were further assessed using RT-qPCR. The Applied Biosystem 7300 Real-Time PCR device was used for the authentication and performed in an optical 96-well plate. The synthesized cDNA from 1 \u0026micro;g of RNA was diluted 10-fold, and 4 \u0026micro;L of the diluted cDNA was used as a template for the RT-qPCR. Twelve primers were designed from the twelve selected sequences to detect the expression levels through RT-qPCR using SYBR Green RT-PCR (Roche, San Diego, CA, USA) and attached as a \u003cb\u003esupplementary data 1\u003c/b\u003e (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Briefly, the reaction consisted of 10 \u0026micro;L SYBR qPCR Mix, 4 \u0026micro;L of cDNA, 1 \u0026micro;L of forward and reverse primers, respectively, and 4 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO. \u003cem\u003eVicia faba\u003c/em\u003e Actin (\u003cem\u003eVf\u003c/em\u003eactin 11) was used as an internal control gene for normalization. The RT-qPCR thermal cycling parameters were 95\u0026deg;C for 60 s, followed by 45 cycles of 95\u0026deg;C for 10 s, 50\u0026deg;C for 10 s and 70\u0026deg;C for 10 s. The relative gene expression levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. For each P-induced nodule sample, three biological replicates were used and determined in duplicates.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cb\u003eNodulation of Vicia faba in Response to P Supplies\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eOur previous works (Amoako et al. 2023a, b; Amoako et al. 2024) have already highlighted all the morpho-physiological, biochemical (phosphatases) and some molecular (ATPases genes) parameters related to this plant variety and that this current work is based on the premise of these previous studies. Nodule formation and development is hallmarks of leguminous species when the right Rhizobia species is inoculated. \u003cem\u003eVicia faba\u003c/em\u003e plants were treated with different P levels and sources for a 30-d growth period, and the responses of nodulation are displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The result clearly shows the deleterious effect of low P supply on nodule formation, with low P-treated nodules exhibiting the smallest size and lowest number (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Conversely, when sufficient P was supplied, the formation and growth of nodules tremendously increased, with both P treatments (Pi vs. Po) exhibiting similar nodulation patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C). Po- and Pi-induced nodules had several of their nodules clustering at a point compared with NoP-induced nodules.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cb\u003eDe novo Assembly of Transcriptome and Functional Annotation of Unigenes\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn this analysis, the Po treatment was compared with both the positive (Pi) and negative (NoP) controls to ensure pragmatic and detailed comparisons. Notwithstanding, both the positive (Pi) and negative (NoP) controls were also compared in the analysis for more novel findings and to reveal the P-related stress genes during nodulation. Nine RNA-Seq libraries of three biological replicates for NoP, Pi, and Po were prepared and then paired-end sequenced to compare the molecular patterns and signatures in the nodules of \u003cem\u003eVicia faba\u003c/em\u003e plants. A total of 64.06 Gb was generated, with a minimum size of 6.30 Gb for each sample, and the percentage of bases with a quality score of 30 was higher than and within the range of 92.55\u0026ndash;94.15% (\u003cb\u003esuppl. data 2; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The sequencing of the \u003cem\u003eVicia faba\u003c/em\u003e transcriptome generated a total of 214,390,209 clean reads from the 9 samples, with an average read of 23,821,134 per sample and base composition of 64,063,311,082, as depicted in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. The sequence reads averaged a GC content of more than 41% in each sample (\u003cb\u003esuppl. data 2; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Since the genome of the \u003cem\u003eVicia faba\u003c/em\u003e plant is not entirely annotated, the reads were mapped back with each other, and approximately 149,685,304 reads, with an average mapped ratio ranging from 68.86\u0026ndash;72.01% of the clean reads, were uniquely mapped back to the transcripts (\u003cb\u003esuppl. data 2; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAfter sequence assembly, 52,274 unigenes and 559,047 transcripts were obtained in total, among which 20,508 unigenes were found with a length longer than 1 kb (\u003cb\u003esuppl. data 2; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). A total of 20,342 unigenes, representing 38.91%, were found within the length range of 300\u0026ndash;500, with most of the transcripts (105,515, representing 18.87%) found within the length range of 200\u0026ndash;300 (\u003cb\u003esuppl. data 2; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). Both unigenes and transcripts obtained a total length of 61,144,827 and 718,805,445, with mean lengths of 1169.70 and 1285.77, respectively. Additionally, unigenes and transcripts of N50 length obtained were 1,919 and 2,058, respectively (\u003cb\u003esuppl. data 2; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). A correlation analysis was performed to evaluate how the samples were associated with each other (\u003cb\u003esuppl. data 2; Table S4\u003c/b\u003e). The results show a strong positive correlation among the samples, with 0.54 being the lowest correlation value (\u003cb\u003esuppl. data 2; Table S4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo annotate the obtained unigenes, they were searched (BLAST) against nine public databases, viz., Nr, COG, KOG, eggNOG, GO, KEGG, Pfam, TrEMBL and Swissprot for homologous sequences. A total of 29,511 unigenes were annotated in all databases, with 11,628 and 17,878 unigenes generated within 300\u0026thinsp;\u0026le;\u0026thinsp;length\u0026thinsp;\u0026lt;\u0026thinsp;1000 and length\u0026thinsp;\u0026ge;\u0026thinsp;1000, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the databases, Nr annotated a total of 28,260 unigenes, followed by TrEMBL (28,199), with COG database (8,558) annotating the least unigenes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSummary statistics of unigenes annotation databases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated Database\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnotated number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u0026thinsp;\u0026le;\u0026thinsp;length\u0026thinsp;\u0026lt;\u0026thinsp;1000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLength\u0026thinsp;\u0026ge;\u0026thinsp;1000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePfam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwissprot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrEMBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28,199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeggNOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23,370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Annotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAnnotated databases applied for BLAST. Annotated number: the number of unigenes annotated based on corresponding database. 300\u0026thinsp;\u0026le;\u0026thinsp;length\u0026thinsp;\u0026lt;\u0026thinsp;1000: the number of annotated unigenes with length ranges of 300 to 1000 bp. Length\u0026thinsp;\u0026ge;\u0026thinsp;1000: the number of annotated unigenes with length longer than or equal to 1000 bp.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther quantification was performed using FPKM. This was used to quantify unigenes expression based on the criteria p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for adjusted p-value and greater than 0 for fold-change base log2. The gene expressions were normalized using a box and density plots to indicate an even distribution of reads in the individual treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Interestingly, a clustering heatmap correlation analysis was performed among the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The analysis shows clustering and sub-clustering of samples, with samples showing significantly positive and negative correlations among samples at the individual levels. A principal component (PC) analysis shows a significant separation of the transcriptomes of the treatment groups, with PC 1 and 2 explaining 52.1 and 16.9% variations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cb\u003eDifferentially Expressed Genes (DEGs) Induced by Different P Supply\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eGenes and unigenes differentially expressed in the P treatment groups were adjudged significantly expressed based on false discovery rate (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and log2 fold change, |log2FC|\u0026gt;=2, as represented (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). From the results, a total of 2,263 genes were differentially expressed, of which 1,612 (71%) genes were up-regulated and 651 (29%) genes were down-regulated. The results consistently reveal that treatment groups NoP_vs._Po recorded a total of 1,144 DEGs, where 843 and 301 were up- and down-regulated, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results show a total of 308 genes, with 167 and 141 genes being up- and down-regulated in NoP_vs._Pi, respectively. Meanwhile, the comparison between the two sufficient treatments (Pi_vs._Po) revealed a total of 811 genes, of which 602 and 209 were up- and down-regulated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To identify the main functional groups of DEGs, we used a BLASTx search for the NCBI Nr, Swiss-Prot, GO, COG, KEGG, KOG, eggNOG, Pfam, and TrEMBL databases (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results show that a total of 1,961 DEGs were annotated for all treatment groups, with each treatment group obtaining 258, 996, and 707 DEGs in NoP_vs._Pi, NoP_vs._Po and Pi_vs._Po, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The DEGs annotation statistics reveal that both the Nr and TrEMBL databases recorded the highest (1,916) and the same total number of DEGs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eSummary of differentially expressed genes (DEGs) in response to different phosphorus\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEG Set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll DEGs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp regulated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDown regulated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoP_vs._Pi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoP_vs._Po\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePi_vs._Po\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal DEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e651\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\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\u003eSummary of DEGs annotation from databases comparisons between treatments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEG Set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnotated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKOG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePfam\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSwissprot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTrEMBL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eeggNOG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoP vs. Pi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoP vs. Po\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePi vs. Po\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1,916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,916\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\u003eThe volcano plots reveal more off-centre point distributions, indicating a larger fold difference between DEGs. The closer the DEGs are to the top, the more significant variations are observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Subsequently, a cluster heatmap analysis was employed to evaluate the expression pattern of the DEGs in the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F), and the data shows contrasting expression patterns among treatments.\u003c/p\u003e \u003cp\u003eTo identify DEGs uniquely expressed by all treatment groups and those expressed within and between treatment groups, a Venn diagram was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). The result clearly shows that a total of 23 genes were uniquely expressed by all treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). The Venn diagram analysis indicates that 175, 656, and 385 genes were unique to NoP_vs._Pi, NoP_vs._Po and Pi_vs._Po, respectively. A total of 86 genes were expressed between the NoP_vs._Pi, and NoP_vs._Po groups, 24 between NoP_vs._Pi, and Pi_vs._Po, 379 between NoP_vs._Po, and Pi_vs._Po, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Raw sequence data have been deposited at NCBI SRA with accession number PRJNA1030752 and website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra/PRJNA1030752\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra/PRJNA1030752\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe annotations from the COG, eggNOG and KOG databases reveal 98, 214, and 145 DEGs, respectively, and were grouped into 25 functional categories (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-C\u003c/b\u003e) under the NoP_vs._Pi treatment group. Under the NoP_vs._Po treatment group, the COG, eggNOG and KOG databases generated 421, 855, and 855 DEGs, respectively, and were categorized into 25 functional categories (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e D-F\u003c/b\u003e). For Pi_vs._Po, similar classifications were obtained, with the COG, eggNOG and KOG databases recording 310, 615, and 408 DEGs, respectively (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e G-I\u003c/b\u003e). The functional classifications under KOG and eggNOG generally reveal that most of the DEGs were classified under the function \u0026ldquo;unknown category\u0026rdquo;. However, the COG classification generally observed a higher number of genes under the carbohydrate transport and metabolism category (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e A, D, G\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 \u003cb\u003eGene Ontology (GO) Annotation and KEGG Pathway Analyses of the DEGs\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe performed GO and KEGG enrichment analyses to explore the relevant pathways and biological functions, which enlightened us to understand the functional differences between the P treatment groups in \u003cem\u003eVicia faba\u003c/em\u003e nodules (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe evaluated the potential functions of the DEGs between the treatments, and DEGs with \u0026gt;\u0026thinsp;2-fold expression change were assigned to different GO categories, viz., biological process, molecular function, and cellular location, with 57 functional GO terms analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the categories, a total of 830 DEGs were involved in the treatment group of NoP_vs._Po for all three GO categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), whereas 215 DEGs were involved in the GO term of the NoP_vs._Pi treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). For treatment group Pi_vs._Po, a total of 597 DEGs were involved in the GO analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). For all three P treatment groups, the biological processes mainly associated with the DEGs were involved in metabolic processes, cellular processes, response to stimulus and single-organism processes, biological regulation, localization, multi-organism process and cellular component organization or biogenesis. The cellular components mainly included the cell, cell part and organelle, membrane part, macromolecular complex, and organelle. The main molecular functions of the DEGs were binding, catalytic activity, structural molecule activity, transporter activity, nucleic acid binding transcription factor, signal transducer activity, molecular transducer activity, molecular function regulator, and antioxidant activity. It was generally realized that most of the DEGs involved in these 57 functional GO terms were identified in treatment comparisons involving the Po (phytic acid) treatment relative to the comparison between the two inorganic P treatments (KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eBased on the KEGG pathway enrichment analysis, our results consistently reveal that the majority of the DEGs were significantly enriched in most pathways in all treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). DEGs were found to be significantly involved in starch and sucrose metabolism (ko00500), MAPK signaling pathway-plant (ko04016), plant hormone signal transduction (ko04075) and plant-pathogen interaction (ko04626) in the treatment group of NoP_vs._Po, with a total of 367 DEGs involved in 114 pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). For the NoP_vs._Pi treatment group, a total of 91 DEGs were involved in the 61 KEGG pathways, with most of the DEGs significantly enriched and involved in pathways such as glycolysis/gluconeogenesis (ko00010), starch and sucrose metabolism (ko00500), plant hormone signal transduction (ko04075), and oxidative phosphorylation (ko00190) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Likewise, alpha-linolenic acid metabolism (ko00592), cysteine and methionine metabolism (ko00270), MAPK signaling pathway-plant (ko04016), and plant hormone signal transduction (ko04075) were the most significant pathways, with 265 genes involved in the 107 pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The DEGs involved in most of the pathways were up- and down-regulated.\u003c/p\u003e \u003cp\u003eWe went further to classify the DEGs in the KEGG according to the secondary metabolism classification, which includes cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F). Intriguingly, the data consistently show that most of the DEGs were involved in metabolism and genetic information processing. In the treatment comparison group of NoP_vs._Po, under the metabolism classification pathway, starch and sucrose metabolism was the most dominant and significant pathway, with a total of 28 genes, representing 7.63% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), consistent with the KEGG enrichment pathway in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. In the same treatment group, plant hormone signal transduction (34 genes, representing 9.26%), ribosome (37, 10.08%), and plant-pathogen interactions (38, 10.35%) were the significant pathways in environmental information processing, genetic information processing, and organismal systems classifications, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). However, when the two inorganic P treatments were compared (NoP_vs._Pi), glycolysis/gluconeogenesis (11, 12.09%), starch and sucrose metabolism (9, 9.89%) and carbon metabolism (8, 8.79%) were the significant pathways involved in the metabolism classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Similarly, plant hormone signal transduction (12 genes, 13.19%) and plant-pathogen interactions (8, 8.79%) were the most enriched pathways in the environmental information processing and organismal systems classifications, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Consistent with the primary classification, starch and sucrose metabolism was the dominant and significantly enriched pathway, with 15 DEGs representing 5.66% of the metabolism classification in the Pi_vs._Po treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Remarkably, plant hormone signal transduction (24 genes, 9.06%), ribosome (25, 9.43%), and plant-pathogen interactions (26, 9.81%) were the significant pathways in the classification of environmental information processing, genetic information processing, and organismal systems, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eThe P treatment groups underwent five main (top 5 pathways) pathways in the KEGG. The specific genes that were significantly involved in pathways and their interactions are elaborated using gene and pathway network analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). From the network analysis, it was revealed that treatment group NoP_vs._Po was specifically involved in five main pathways which include cyanoamino acid metabolism, MAPK signaling pathway-plant, plant hormone signal transduction, plant-pathogen interaction, and starch and sucrose metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The results show that 7, 32, 34, 38, and 28 DEGs were involved in cyanoamino acid metabolism, the MAPK signaling pathway-plant, plant hormone signal transduction, plant-pathogen interaction, and starch and sucrose metabolism, respectively, with details of the specific genes attached as supplementary data (\u003cb\u003esuppl. data 2; Table S5\u003c/b\u003e). It should be noted that the genes have been attached to avoid crowding and redundancy, since so many genes were generated in response to this treatment comparison. Under NoP_vs._Pi, the pathways and DEGs were significantly involved in glycolysis/gluconeogenesis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), oxidative phosphorylation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), phenylpropanoid biosynthesis (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), plant hormone signal transduction (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and starch and sucrose metabolism (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) pathways, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). However, Pi_vs._Po group observed three unique pathways distinguishable from the other treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Cysteine and methionine metabolism, linoleic acid metabolism, and alpha-linolenic acid metabolism were identified as unique, and 11, 11, and 4 DEGs, respectively, were observed to be significantly involved in these pathways. Interestingly, the MAPK signaling pathway-plant and plant hormone signal transduction were least observed to be common to all treatment groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 \u003cb\u003eIdentification of DEGs Involved in Transport in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eSeveral DEGs present in the study were revealed to be implicated in transport within the nodule compartment, as depicted in the barplots and clustering heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A total of 61 DEGs participated in the transport of various minerals and solutes, with the majority of these DEGs expressing when phytic acid was applied (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Under NoP_vs._Pi treatment, the results reveal that \u003cem\u003eBMK_Unigene_029164\u003c/em\u003e and \u003cem\u003eBMK_Unigene_032564\u003c/em\u003e genes were specific to this group and observed to be coding for bidirectional sugar transporter \u003cem\u003eSWEET4\u003c/em\u003e and bidirectional \u003cem\u003esugar transporter N3\u003c/em\u003e, respectively, with the former being down-regulated and the latter up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand D\u003c/b\u003e). \u003cem\u003eThe BMK_Unigene_032815\u003c/em\u003e coding for a metal transporter Nramp3 was up-regulated, whereas the \u003cem\u003eBMK_Unigene_036844\u003c/em\u003e involved in sodium/hydrogen exchanger 6 was observed to be down-regulated in NoP_vs._Pi (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand D\u003c/b\u003e). In comparison, NoP_vs._Po group had the majority of the DEGs involved in ABC transporters, zinc, magnesium and molybdate transporters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, C). We found \u003cem\u003eBMK_Unigene_058922\u003c/em\u003e, \u003cem\u003eBMK_Unigene_054678\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_060918\u003c/em\u003e genes to be putatively coding for the ATP binding cassette (ABC) transporter, and \u003cem\u003eBMK_Unigene_060469\u003c/em\u003e, \u003cem\u003eBMK_Unigene_129480\u003c/em\u003e putatively coding for the ABC-2 type transporter in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C). Additionally, \u003cem\u003eBMK_Unigene_136703\u003c/em\u003e, \u003cem\u003eBMK_Unigene_131646\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_061734\u003c/em\u003e were observed to encode putative for \u003cem\u003eZIP\u003c/em\u003e Zinc transporter, CorA-like Mg\u003csup\u003e2+\u003c/sup\u003e transporter protein, and Molybdate transporter of the MFS superfamily, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C). Similarly, these transporter genes (\u003cem\u003eBMK_Unigene_058922, BMK_Unigene_060469, BMK_Unigene_131646\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_061734\u003c/em\u003e) were also found in Pi_vs._Po treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Interestingly, Pi_vs._Po treatment group observed copper, magnesium, calcium, iron, potassium, sodium and phosphorus related transporter genes and other genes, as depicted in the cluster heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE) being significantly upregulated. For examples, the \u003cem\u003eBMK_Unigene_081823, BMK_Unigene_062257, BMK_Unigene_063447, BMK_Unigene_064567, BMK_Unigene_054555\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_034906\u003c/em\u003e genes are putatively coded for copper transporter 5.1, inorganic phosphate transporter 1\u0026ndash;4, calcium-transporting ATPase, potassium transporter 2, vacuolar iron transporter-like protein, and sodium-coupled neutral amino acid transporter 1-like protein, respectively, in Pi_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B, E) and were all found up-regulated. Interesting genes, viz., \u003cem\u003eamino acid transporter AVT1A isoform X2\u003c/em\u003e (\u003cem\u003eBMK_Unigene_133019\u003c/em\u003e), sodium-coupled neutral amino acid transporter 1-like protein (\u003cem\u003eBMK_Unigene_034906\u003c/em\u003e), probable polyamine transporter \u003cem\u003eAt1g31830\u003c/em\u003e (\u003cem\u003eBMK_Unigene_059198\u003c/em\u003e), and nodulin 26-like intrinsic protein 1;1 (\u003cem\u003eBMK_Unigene_059591\u003c/em\u003e), were uniquely and significantly expressed exclusively in Pi_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 \u003cb\u003eIdentification of Transcription Factors in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe data reveal transcription factors (TFs) involved in gene expression in P-induced nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The clustering heatmap analysis indicates the expression levels with respect to each treatment and is calculated based on FPKM. A total of 86 TFs genes were generated in this study. A total of 16 TFs genes were exclusively expressed in NoP_vs._Pi, with 12 and 4 being up- and down-regulated, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B, D). Under NoP_vs._Po, 48 TF genes were expressed in total, and 40 were up-regulated, with 8 observed to be down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C). Furthermore, a total of 22 TF genes were expressed, with 17 and 5 being up- and down-regulated, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B, E) in Pi_vs._Po. The most abundant families were \u003cem\u003ebHLH\u003c/em\u003e, \u003cem\u003eWRKY\u003c/em\u003e, \u003cem\u003eM-type\u003c/em\u003e, \u003cem\u003eARF\u003c/em\u003e and \u003cem\u003eERF\u003c/em\u003e families, followed by \u003cem\u003eNAC\u003c/em\u003e, \u003cem\u003eC2H2\u003c/em\u003e, \u003cem\u003eRAX2\u003c/em\u003e, \u003cem\u003eRAV2\u003c/em\u003e, \u003cem\u003eGATA\u003c/em\u003e and others as identified. Among the treatment comparisons, we observed higher TFs in NoP_vs._Po treatment group, with \u003cem\u003eERF\u003c/em\u003e (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), \u003cem\u003eWRKY\u003c/em\u003e (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), \u003cem\u003eMYB\u003c/em\u003e (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), \u003cem\u003ebHLH\u003c/em\u003e (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), \u003cem\u003eGATA\u003c/em\u003e (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), \u003cem\u003eNAC\u003c/em\u003e (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), \u003cem\u003eRAX2\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and \u003cem\u003eRAV\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) TF genes being the most expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). For instance, \u003cem\u003eBMK_Unigene_131668\u003c/em\u003e encodes putative \u003cem\u003eNAC\u003c/em\u003e transcription factor 47, \u003cem\u003eBMK_Unigene_020831\u003c/em\u003e coding for transcription factor \u003cem\u003ebHLH51\u003c/em\u003e, \u003cem\u003eBMK_Unigene_029658\u003c/em\u003e coding for a probable \u003cem\u003eWRKY transcription factor 69\u003c/em\u003e, \u003cem\u003eBMK_Unigene_029647\u003c/em\u003e coding for \u003cem\u003eMYB\u003c/em\u003e transcription factor \u003cem\u003eMYB70\u003c/em\u003e, \u003cem\u003eBMK_Unigene_061074\u003c/em\u003e coding for \u003cem\u003eGATA\u003c/em\u003e transcription factor 1, \u003cem\u003eBMK_Unigene_025025\u003c/em\u003e coding for a putative transcription factor \u003cem\u003eC2H2\u003c/em\u003e family, \u003cem\u003eBMK_Unigene_134678\u003c/em\u003e coding for AP2-like ethylene-responsive transcription factor ANT, \u003cem\u003eBMK_Unigene_132837\u003c/em\u003e coding for \u003cem\u003eMADS-box transcription factor\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_132927\u003c/em\u003e coding for bZIP transcription factor 11, with most of these TFs expressed upon Po treatment. Meanwhile, some of these TFs were also expressed in the other treatment groups in different isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C). However, \u003cem\u003eBMK_Unigene_128791\u003c/em\u003e coding for transcription factor \u003cem\u003eKUA1\u003c/em\u003e and \u003cem\u003eBMK_Unigene_103216\u003c/em\u003e coding for transcription factor \u003cem\u003eRAX2\u003c/em\u003e were uniquely expressed in NoP_vs._Pi, and Pi_vs._Po, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B, D, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e3.7\u003c/em\u003e \u003cb\u003eDEGs Involved in Plant Hormone and Signal Transduction in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe possible impacts of P supply on \u003cem\u003eVicia faba\u003c/em\u003e resulted in the identification of different groups of genes implicated in signaling and signaling-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). A total of 102 genes were identified, of which 80 and 22 were identified to be up- and down-regulated, respectively (\u003cb\u003eFigure. 9A, B\u003c/b\u003e). The results indicate a total of 26 DEGs, with 14 up-regulated and 12 down-regulated in the NoP_vs._Pi group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B, D), whereas the NoP_vs._Po group altered 48 DEGs in total, of which 41 were up-regulated and 7 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-C). Conversely, the two sufficient treatments (Pi_vs._Po) identified 28 DEGs in total, 25 of which were found to be up-regulated, with only 3 found to be down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B, E). The results, however, indicated that the \u003cem\u003eBMK_Unigene_062196\u003c/em\u003e, \u003cem\u003eBMK_Unigene_061185\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_131117\u003c/em\u003e genes were found to be putatively coding for calmodulin-binding transcription activator 4 isoform X1, calcium-dependent protein kinase 1, and calmodulin-binding receptor cytoplasmic kinase 1, respectively, and were exclusively found to be expressed in the NoP_vs._Po group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, C). We identified \u003cem\u003eBMK_Unigene_063802\u003c/em\u003e and \u003cem\u003eBMK_Unigene_064212\u003c/em\u003e coding, respectively, for lipopolysaccharide kinase and Galactoside-binding lectin in NoP_vs._Pi (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B, D). Additionally, genes such as \u003cem\u003eBMK_Unigene_021451\u003c/em\u003e, \u003cem\u003eBMK_Unigene_090095\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_077658\u003c/em\u003e were uniquely identified only under Pi_vs._Po and putatively found to be coding for receptor-like cytoplasmic kinase 176, auxin-induced in root cultures protein 12-like and nudix hydrolase 8, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B, E). Interestingly, genes such as \u003cem\u003eBMK_Unigene_131481\u003c/em\u003e, \u003cem\u003eBMK_Unigene_095015\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_053704\u003c/em\u003e were expressed in all treatments and identified to be coding for \u003cem\u003eprotein phosphatase 2C\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 \u003cb\u003eNodulation-Related DEGs in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eNodule formation in response to different P levels and sources altered several genes involved in nodulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In general, a total of 24 genes were identified, of which only 8 were up-regulated and 16 were found to be down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-D). It was realized that most of these nodulation related genes were found to be coding for late nodulin proteins (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Under the NoP_vs._Po group, most of these late nodulin proteins were down-regulated, with only one being up-regulated (\u003cem\u003eBMK_Unigene_114973\u003c/em\u003e). Similarly, BMK_Unigene_061444, \u003cem\u003eBMK_Unigene_059591\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_130549\u003c/em\u003e are putatively encoding \u003cem\u003elegume\u003c/em\u003e lectin domain and protein tyrosine and serine/threonine kinase, nodulin 26-like intrinsic protein 1;1, and early nodulin-16, respectively, in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, B). However, \u003cem\u003eBMK_Unigene_000106\u003c/em\u003e putatively coding for Early nodulin 93 \u003cem\u003eENOD93\u003c/em\u003e protein was found to be identified only in NoP_vs._Pi and was up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, C). The other late nodulin proteins were downregulated. Meanwhile, Pi_vs._Po uniquely induced \u003cem\u003eBMK_Unigene_029218\u003c/em\u003e, which codes for Nodule Cysteine-Rich (NCR) secreted peptide and was identified to be significantly up-regulated in nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, D). These genes (\u003cem\u003eBMK_Unigene_059591\u003c/em\u003e and \u003cem\u003eBMK_Unigene_065419\u003c/em\u003e) were significantly implicated and up-regulated in nodules and were putatively coding for nodulin 26-like intrinsic protein 1;1 and putative late nodulin (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.9 \u003cb\u003eAmino Acid and Nitrogen Metabolism-Related DEGs in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eDifferent amino acid and nitrogen metabolism related DEGs were induced in response to varied P levels and sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE-H). We identified a total of 50 DEGs, with 40 and 10 up- and down-regulated, respectively, and clustered differently as depicted (\u003cb\u003eFigure F, G\u003c/b\u003e). Out of these genes, 6 genes, of which 3 each were found to be up- and down-regulated, respectively, in NoP_vs._Pi (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, G). For NoP_vs._Po, 23 DEGs were identified and 21 were up-regulated, with only 2 found down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, F). Meanwhile, Pi_vs._Po treatment group identified 21 genes, of which 16 were up-regulated and 5 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, H). The individual treatment analysis shows that the \u003cem\u003eBMK_Unigene_060395\u003c/em\u003e gene was identified to code for nitrate transporter (protein \u003cem\u003eNRT1/PTR FAMILY 1.1\u003c/em\u003e) and up-regulated only in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, F). Genes such as \u003cem\u003eBMK_Unigene_033352\u003c/em\u003e, \u003cem\u003eBMK_Unigene_001084\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_061333\u003c/em\u003e were identified as putatively coding for cationic amino acid transporter 4, gamma-glutamyl peptidase 5, and gamma-glutamylputrescine oxidoreductase, respectively, in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, F). The NoP_vs._Pi treatment group identified \u003cem\u003eBMK_Unigene_035727\u003c/em\u003e and \u003cem\u003eBMK_Unigene_068511\u003c/em\u003e as being observed to encode serine carboxypeptidase-like isoform X1 and probable isoaspartyl peptidase/L-asparaginase 2, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, G). Genes involved in nitrogen assimilation were only implicated when sufficient P was applied. The comparison between Pi and Po (Pi_vs._Po) identified genes involved in amino acid transport and nitrogen metabolism and included \u003cem\u003eBMK_Unigene_063924\u003c/em\u003e (nitrate reductase), \u003cem\u003eBMK_Unigene_134721\u003c/em\u003e (nitrate regulatory gene 2 protein), \u003cem\u003eBMK_Unigene_026586\u003c/em\u003e (2-oxoglutarate-dependent dioxygenase DAO), \u003cem\u003eBMK_Unigene_062144\u003c/em\u003e (probable 2-oxoglutarate-dependent dioxygenase \u003cem\u003eAt5g05600\u003c/em\u003e), and \u003cem\u003eBMK_Unigene_133019\u003c/em\u003e (transmembrane amino acid transporter protein, tryptophan/tyrosine permease family, transmembrane amino acid transporter protein).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.10 \u003cb\u003eIdentification of DEGs Involved in P and Pi-Remobilization in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo identify P and Pi remobilization genes preferentially expressed during nodule development, we performed cluster analysis to compare the different P levels and sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). We identified 96 DEGs in total, and 75 and 21 were up- and down-regulated, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Most of the DEGs were largely expressed in the NoP_vs._Po treatment group relative to the others. In the NoP_vs._Po, a gene involved in phytic acid biosynthesis (\u003cem\u003eBMK_Unigene_059958\u003c/em\u003e) was identified to code putatively for inositol 1,3,4-trisphosphate 5/6-kinase ATP-grasp domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB, C). The results identified genes implicated in Pi metabolism and remobilization and include \u003cem\u003eBMK_Unigene_030805\u003c/em\u003e (\u003cem\u003ePAP2 superfamily\u003c/em\u003e), \u003cem\u003eBMK_Unigene_022815\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_033339\u003c/em\u003e (endonuclease/exonuclease/phosphatase family), whereas genes such as \u003cem\u003eBMK_Unigene_061323\u003c/em\u003e, \u003cem\u003eBMK_Unigene_057098\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_130692\u003c/em\u003e were observed to code for purple acid phosphatase in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA-C). Additionally, \u003cem\u003eBMK_Unigene_048749\u003c/em\u003e was putatively coding for the 14-3-3 protein and was uniquely expressed in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA, C). We observed that \u003cem\u003eBMK_Unigene_035177\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_025675\u003c/em\u003e were found to encode phosphatidic acid phosphatase (PAP2) family protein and acid phosphatase 1 and were exclusively expressed in NoP_vs._Pi (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB, D). The Pi_vs._Po treatment identified genes exclusively expressed under high P supply. \u003cem\u003eBMK_Unigene_052701\u003c/em\u003e was found to code for a putative glycerol-3-phosphate transporter 1-like protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB, E). However, genes such as \u003cem\u003eBMK_Unigene_057098\u003c/em\u003e (putative extracellular phytase\u003cem\u003e) and BMK_Unigene_061323\u003c/em\u003e (nucleotide pyrophosphatase/phosphodiesterase) were found to be commonly expressed in both Pi_vs._Po and NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA, B, E). Meanwhile, the inorganic phosphate transporter 1\u0026ndash;4 (\u003cem\u003eBMK_Unigene_062257\u003c/em\u003e) gene was upregulated and exclusively expressed in Pi_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.11 \u003cb\u003eP-Responsive Lipids and Secondary Metabolisms-related Genes in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eA total of 62 genes related to lipids and secondary metabolites were identified in response to P supply, as depicted in barplots and cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Out of this, 51 genes were significantly up-regulated in all treatment groups, and 11 genes were identified as down-regulated. The results show up-regulation of genes such as \u003cem\u003eBMK_Unigene_063961\u003c/em\u003e (coding for biotin-lipoyl like and biotin carboxylase C-terminal domain), \u003cem\u003eBMK_Unigene_060394\u003c/em\u003e (coding for alpha/beta hydrolase), and \u003cem\u003eBMK_Unigene_056057\u003c/em\u003e (coding for 2OG-Fe (II) oxygenase superfamily and non-haem dioxygenase in morphine synthesis N-\u003cem\u003eterminal\u003c/em\u003e) in only NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA-C). However, genes (\u003cem\u003eBMK_Unigene_063566\u003c/em\u003e and \u003cem\u003eBMK_Unigene_128525\u003c/em\u003e) putatively coding for squalene epoxidase 1 and cytochrome \u003cem\u003eP450 CYP73A100\u003c/em\u003e, respectively, were found to be solely expressed under NoP_vs._Pi (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA, B, D). Under Pi_vs._Po, \u003cem\u003eBMK_Unigene_029385\u003c/em\u003e, \u003cem\u003eBMK_Unigene_065309\u003c/em\u003e, \u003cem\u003eBMK_Unigene_132493\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_032427\u003c/em\u003e genes were uniquely identified and putatively coding for cytochrome P450 71A23, geraniol 8-hydroxylase, 4-coumarate-CoA ligase 7-like protein, and sphingolipid delta (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)-desaturase DES1-like, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.12 \u003cb\u003eCarbon and Carbohydrate Metabolisms in Nodules of Vicia faba\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eBased on KEGG pathway analysis, DEGs identified in the present study were implicated in carbon and carbohydrate metabolism (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). These DEGs mainly participated in starch and sucrose metabolism, glycolysis/gluconeogenesis, galactose metabolism, and pyruvate metabolism. We identified that these genes (\u003cem\u003eBMK_Unigene_062409, BMK_Unigene_033731, BMK_Unigene_048556, and BMK_Unigene_063126\u003c/em\u003e) were all up-regulated and putatively encoding different isoforms of \u003cem\u003eglycosyl hydrolases\u003c/em\u003e such as glycosyl hydrolases family 1, 14, and 18 and glycosyl hydrolase family 3 N terminal domain and fibronectin type III-like domain in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA-C). Apart from these, genes such as \u003cem\u003eBMK_Unigene_082109\u003c/em\u003e, \u003cem\u003eBMK_Unigene_030779\u003c/em\u003e, \u003cem\u003eBMK_Unigene_048610\u003c/em\u003e, \u003cem\u003eBMK_Unigene_110457, BMK_Unigene_062430\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_027824\u003c/em\u003e were found to putatively encode for aldose 1-epimerase, pfkB family carbohydrate kinase, glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate kinase, glucokinase and transaldolase/fructose-6-phosphate aldolase, respectively, in NoP_vs._Po (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA-C and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). Comparatively, the NoP_vs._Pi identified genes implicated in pyruvate biosynthesis and include \u003cem\u003eBMK_Unigene_065186\u003c/em\u003e (encoding \u003cem\u003epyruvate decarboxylase 1\u003c/em\u003e), \u003cem\u003eBMK_Unigene_134540\u003c/em\u003e (encoding pyruvate decarboxylase 2), \u003cem\u003eBMK_Unigene_129620\u003c/em\u003e (encoding pyruvate kinase 1, cytosolic) and \u003cem\u003eBMK_Unigene_048128\u003c/em\u003e (encoding pyruvate kinase, cytosolic isozyme) and were all found to be up-regulated (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA, B, D and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). Additionally, carbon metabolites (\u003cem\u003eBMK_Unigene_060394\u003c/em\u003e coding for probable \u003cem\u003ecarboxylesterase 15\u003c/em\u003e, BMK_Unigene_059780 coding for probable alpha-trehalose-phosphate synthase [UDP-forming] 9, and \u003cem\u003eBMK_Unigene_033669\u003c/em\u003e coding for probable trehalose-phosphate phosphatase J) and sugar transporter genes such as \u003cem\u003eBMK_Unigene_029164\u003c/em\u003e (bidirectional sugar transporter \u003cem\u003eSWEET4\u003c/em\u003e) and \u003cem\u003eBMK_Unigene_032564\u003c/em\u003e (bidirectional sugar transporter N3) in NoP_vs._Pi (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA, B, D). Interestingly, \u003cem\u003eBMK_Unigene_110457\u003c/em\u003e and \u003cem\u003eBMK_Unigene_031571\u003c/em\u003e were putatively coding for hypothetical protein CDD82_7894 and hypothetical protein TSUD_01980, respectively, in Pi_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA, B, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.13 \u003cb\u003eValidation of Selected Genes using RT-qPCR\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe performed a regression analysis to evaluate and validate 12 selected genes from RNA-seq data using RT-qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e). The results generally indicate a significant correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.86; y\u0026thinsp;=\u0026thinsp;1.29\u0026thinsp;+\u0026thinsp;1.02x) between RNA-seq and RT-qPCR data as quantified in log\u003csub\u003e2\u003c/sub\u003eFC (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e). The RT-qPCR results validate the expression of the various genes randomly chosen from the RNA-seq pathways. For example, gene isoforms such as \u003cem\u003eBMK_Unigene_021141\u003c/em\u003e and \u003cem\u003eBMK_Unigene_021141\u003c/em\u003e were observed to up- and down-regulated both in RNA-seq and RT-qPCR, with \u003cem\u003eBMK_Unigene_065447\u003c/em\u003e being the most expressed gene. The relative expression of the 12 unigenes was consistent with the RNA-Seq data, signifying the reliability of our RNA-Seq results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eAlthough the genome of \u003cem\u003eVicia faba\u003c/em\u003e is not fully annotated, through de novo RNA-seq analysis, we have comprehensively identified DEGs involved in cellular, biological, and molecular processes of nodule organogenesis and SNF. To the best of our knowledge, this is the first exhaustive report that has identified Po-induced-responsive genes and pathways involved in the possible molecular and metabolic reprogramming mechanisms in response to phytic acid (Po), which is proposed as an alternative P source to the inorganic P (Pi) as previously asserted (Amoako et al. 2023a, b; 2024).\u003c/p\u003e \u003cp\u003eIn this current study, nine nodule samples from \u003cem\u003eVicia faba\u003c/em\u003e plants exposed to negative control (low KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, tagged as NoP), positive control (high KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, tagged as Pi), and phytic acid (Po) were subjected to de novo RNA-seq analysis to comparatively identify DEGs and related metabolic pathways in nodules. Our results revealed that a total of 64.06 Gb of clean data was generated, and the Q30 was found to be greater than 93%, indicating highly referential and uncompromized analysis and highlighting the quality of the sequencing results (\u003cb\u003esuppl. data1; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Interestingly, a total of 1,961 DEGs were annotated for all treatment groups, where 258, 996, and 707 DEGs were identified in NoP_vs._Pi, NoP_vs._Po and Pi_vs._Po, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, it was revealed that the supply of phytic acid to \u003cem\u003eVicia faba\u003c/em\u003e nodules altered significantly higher DEGs under NoP_vs._Po/Pi_vs._Po relative to inorganic P (NoP_vs._Pi), highlighting that the metabolic reprogramming of \u003cem\u003eVicia faba\u003c/em\u003e nodules was significantly altered and markedly induced higher gene expression in nodules. As stated earlier, the \u003cem\u003eVicia faba\u003c/em\u003e genome and transcriptome are not fully annotated; however, with the help of bioinformatics tools and databases, we identified a total of 1,916 DEGs that were annotated by both Nr and TrEMBL (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In this study, several P-triggered-related DEGs, pathways and mechanisms have been altered in nodule during SNF, however, DEGs and mechanisms crucial and implicated in nodule organogenesis, transport/fluxes across the symbionts and carbon metabolism and trade-offs have been elaborated in this discussion.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1 \u003cb\u003ePo Supply Employs Different Nodulation and Nodule Organogenesis Mechanisms during SNF\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe modifications and terminal differentiation of bacteroids during legume symbiosis are associated with signals and transcriptional expression of nodule-specific peptides that are specific to bacteroids in the nodule symbiosome. For example, \u003cem\u003enodule-specific cysteine-rich (NCR) peptides\u003c/em\u003e and related genes have been reported to specifically express exclusively in infected cells of \u003cem\u003eM. truncatula\u003c/em\u003e nodules and are known to be triggered during nodule organogenesis and bacteroid differentiation (Guefrachi et al. 2014; Horv\u0026aacute;th et al. 2023; Nallu et al. 2013). Our de novo RNA-seq analysis revealed differential expression of the \u003cem\u003eBMK_Unigene_029218\u003c/em\u003e gene, encoding the \u003cem\u003eNodule Cysteine-Rich (NCR) secreted peptide\u003c/em\u003e, which was exclusively found to be induced and significantly up-regulated in Pi_vs._Po treatment comparisons in nodules compared to the other treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, D). The significant up-regulation of the \u003cem\u003eNCR\u003c/em\u003e gene being exclusively expressed in Pi_vs._Po signifies that the transcriptional expression of \u003cem\u003eNCR\u003c/em\u003e was markedly higher in concomitant with higher P application and that terminal bacteroid differentiation (TBD) was highly enhanced under Po supply. This hypothesis means that the supply of phytic acid as a P source enhances swelling of bacteroids and endoreduplication of the genome for better N\u003csub\u003e2\u003c/sub\u003e-fixation and nodule biomass as well as biomass ratio (Roy et al. 2020), which is consistent with nodule number and biomass in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C) and our previous studies (Amoako et al. 2023a, b; Amoako et al. 2024). A recent comparative RNA-seq analysis of two alfalfa cultivars (G3 and G9) inoculated with \u003cem\u003eEnsifer meliloti strain LL2\u003c/em\u003e identified 87 genes encoding \u003cem\u003eNCR\u003c/em\u003e, and their upregulation ensured effective initiation of specific rhizobia\u0026ndash;alfalfa symbioses, which led to higher and more efficient nitrogen fixation (Kang et al. 2023). Several plant-associated proteins have been identified to be involved in nodule symbiosis and are collectively called nodulins. They are categorized as early or late nodulins depending on the period of expression during nodule organogenesis (Olivares et al. 2011). It\u0026rsquo;s been widely revealed that the early nodulin genes are associated with earlier signaling, infection development, and nodule formation, with the \u003cem\u003elate nodulin\u003c/em\u003e genes primarily involved in nodule metabolism and function (Olivares et al. 2011). Intriguingly, the results of this study revealed downregulation of genes putatively encoding late nodulin among the treatment comparisons, especially in NoP_vs._Po group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-D), implicating that \u003cem\u003eVicia faba\u003c/em\u003e plants under NoP_vs._Po predominantly employ downregulation mechanisms in their metabolic pathway during nodule symbiosis (Olivares et al. 2011). However, the \u003cem\u003eBMK_Unigene_114973\u003c/em\u003e gene, encoding late nodulin in NoP_vs._Po, was identified to be significantly upregulated and was involved in the metabolism and functioning of nodules. Indeed, it is quite tempting to suggest that a sufficient supply of P in the form of phytic acid enhances nodulation, confirming an earlier report that the supply of Po stimulates higher nodulation and N\u003csub\u003e2\u003c/sub\u003e-fixation efficiency in \u003cem\u003eVicia faba\u003c/em\u003e plants (Amoako et al. 2023a, b; Amoako et al. 2024). Additionally, an upregulation of a gene putatively coding for \u003cem\u003eEarly nodulin 93, ENOD93\u003c/em\u003e (\u003cem\u003eBMK_Unigene_000106\u003c/em\u003e), was identified and exclusively expressed in NoP_vs._Pi, suggesting that the supply of inorganic P (KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e) could trigger initial signaling events, infection development, and nodule organogenesis better than Po (Olivares et al. 2011). This clearly indicates that both the inorganic and organic P sources employed different mechanisms in their nodulation processes. In agreement with our study, \u003cem\u003eMedicago truncatula\u003c/em\u003e plants inoculated with purified nod factor (NF) for 4 and 24 h identified early nodulin genes such as \u003cem\u003eMtENOD12\u003c/em\u003e (\u003cem\u003eMedtr3g415650\u003c/em\u003e), \u003cem\u003eMtENOD11\u003c/em\u003e (\u003cem\u003eMedtr3g415670\u003c/em\u003e), \u003cem\u003eMtENOD40-1\u003c/em\u003e, and were involved in the formation of symbiotic structures in nodules, viz., nodule parenchyma, infection thread (IT) walls, and peribacteroid membrane (PBM) (Jardinaud et al. 2016). Interestingly, nodulin 26-like intrinsic protein 1;1, aquaporin genes (\u003cem\u003eBMK_Unigene_059591, and BMK_Unigene_065419\u003c/em\u003e) were upregulated and exclusively expressed in phytic acid-treated comparisons (NoP_vs._Po/Pi_vs._Po; Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, D) and are reported to trigger root hair swelling during early nodule formation (Jardinaud et al. 2016). Strikingly, NoP_vs._Po altered genes such as BMK_Unigene_061444, \u003cem\u003eBMK_Unigene_059591\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_130549\u003c/em\u003e that encoded the legume lectin domain and tyrosine and serine/threonine kinases. The legume lectin domain, for instance, has been found to be involved in the binding of host-produced lectins to rhizobium cells during symbiosis (Hirsch 1999). The lectins bind to the rhizobium via surface polysaccharides such lipopolysaccharides (Hirsch 1999). It is quite plausible to highlight that the supply of Po induces other proteins and receptors that stimulate recognition, attachment, infection, differentiation, and nodule organogenesis in comparison to Pi during SNF. It could be concluded that the supply of phytic acid as a P source perturbs several nodulation genes and uses, in part, different metabolic and molecular mechanisms during nodulation, N\u003csub\u003e2\u003c/sub\u003e-fixation, and nodule reprogramming, supporting the hypothesis of this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2 \u003cb\u003eP-induced Nodules Trigger Diverse DEGs Involved in Transports and Exchanges (Fluxes) during Nodulation and SNF\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTransport across the symbiosome membrane (SM) is mediated by plasma membrane H\u003csup\u003e+\u003c/sup\u003e-ATPases, which energize the SM to enhance influx and efflux of minerals and solutes. We reported in our previous study (Amoako et al. 2024) that the supply of Po causes significant upregulation of H\u003csup\u003e+\u003c/sup\u003e-ATPases gene isoforms in nodules relative to Pi. This signifies that many genes related to transport across the SM would be triggered to enhance effective transport between the plant and nodule cells when Po is exogenously applied. The SM is the physical barrier that engulfs the differentiated bacteroids and separates them from the cytoplasmic compartment of the plant (Clarke et al. 2014; Clarke et al. 2015). It is a selective permeable membrane that regulates, controls fluxes, and facilitates the movement of solutes and minerals between symbionts (Clarke et al. 2014; Clarke et al. 2015). Several transporters and channels have been identified on the SM that facilitate and coordinate the fluxes of various metabolites. Through non-gel proteomic techniques, Clarke et al. (2015) identified 197 proteins as constituents of the SM, and these proteins were associated with cascades of cellular processes such as metabolism, protein folding and degradation, membrane trafficking, and transport of solutes. Most of these proteins were found to be localized to the SM and peribacteroid space (PBS).\u003c/p\u003e \u003cp\u003eThe nodule is considered a preferential organ and serves as a P-sink. Its organogenesis has been reported to be very sensitive to P, since 16 ATP molecules are required for the nitrogenase enzyme to convert atmospheric N into ammonia. Our de novo RNA-seq analysis revealed upregulation of inorganic phosphate transporter 1\u0026ndash;4 \u003cem\u003e(BMK_Unigene_062257\u003c/em\u003e) exclusively expressed in Pi_vs._Po comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), suggesting a unique role in transporting the needed P from cytoplasmic compartment of the plant tto the nodule for nodule homeostasis and N\u003csub\u003e2\u003c/sub\u003e-fixation upon Po supply. Recently, Nasr et al. (2017) observed upregulation of the \u003cem\u003eXM_004502090.2\u003c/em\u003e gene encoding phosphate transporter 1\u0026ndash;4 (\u003cem\u003ePHT1;4\u003c/em\u003e) in MmSWR19-Pd/MmSWR19-Ps and found to be associated with P acclimation during P starvation. However, in our study, the \u003cem\u003einorganic phosphate transporter 1\u0026ndash;4\u003c/em\u003e was uniquely expressed under sufficient P supply (Pi_vs._Po), signifying a dual role under both sufficient and deficient P conditions in nodules. The upregulation of this gene under Po is consistent with the accumulation of high Pi contents observed in nodules under phytic acid supply, as reported (Amoako et al. 2023a, b; Amoako et al. 2024).\u003c/p\u003e \u003cp\u003eIron and molybdenum are essential components of the nitrogenase complex and are required in larger amounts in the nodules to synthesize the nitrogenase complex (Clarke et al. 2014). Studies have identified several metal transporters, including iron and molybdenum transporters, that regulate the fluxes of metals between symbionts during SNF (Clarke et al. 2014; Clarke et al. 2015). For example, zinc, copper, iron, potassium, calcium, molybdenum, magnesium transporters, and metal transporters (Nramp3) have been identified on the SM to ensure metal homeostasis in the nodule. Our analysis revealed both up- and down-regulation of the above-mentioned metal transporters when \u003cem\u003eVicia faba\u003c/em\u003e nodules were treated with different P sources. The upregulation of the \u003cem\u003eBMK_Unigene_032815\u003c/em\u003e gene encoding the \u003cem\u003emetal transporter Nramp3\u003c/em\u003e in NoP_vs._Pi (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand D\u003c/b\u003e) indicates that a lot of metal transporters were altered and expressed in this study. It has previously been reported that the transport of ferrous and ferric iron in nodules has been demonstrated on the SM and \u003cem\u003eGmDMT1\u003c/em\u003e (Divalent Metal Transporter 1), which is a member of the \u003cem\u003eNRAMP\u003c/em\u003e (Natural resistance-associated macrophage protein) family of transporters, was also identified to transport ferrous transporters and other metals such as zinc, magnesium, and copper in nodules (Clarke et al. 2014; Clarke et al. 2015; Jardinaud et al. 2016). Consistent with our results, the upregulation of genes (such as \u003cem\u003eBMK_Unigene_081823, BMK_Unigene_063447, BMK_Unigene_064567, BMK_Unigene_054555\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_034906)\u003c/em\u003e putatively encoding copper transporter 5.1, calcium-transporting ATPase, potassium transporter 2, vacuolar iron transporter-like protein, and sodium-coupled neutral amino acid transporter 1-like protein, respectively, were observed in Pi_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B, E), suggesting that transport are significantly improved when sufficient P is supplied to the nodules. Additionally, the treatment of Po altered CorA-like and Mg\u003csup\u003e2+\u003c/sup\u003e transporter proteins (\u003cem\u003eBMK_Unigene_061734\u003c/em\u003e), molybdate transporter 1-like protein and molybdate transporter of the MFS superfamily (\u003cem\u003eBMK_Unigene_131646\u003c/em\u003e), and zinc transporter 2 (\u003cem\u003eBMK_Unigene_136703\u003c/em\u003e and \u003cem\u003eBMK_Unigene_069675\u003c/em\u003e). The upregulation of these metals in only Po (Pi_vs._Po/NoP_vs._Po) comparisons suggests that Po-treated nodules observed improved transportation and fluxes of the above-mentioned metals, which subsequently ensured increased metal homeostasis relative to NoP_vs._Pi (Jardinaud et al. 2016) for efficient N\u003csub\u003e2\u003c/sub\u003e-fixation in this study. In accordance with our results, a Vacuolar Iron transporter \u003cem\u003e(VIT1)\u003c/em\u003e family was found to be exclusively expressed in nodules of infected cells (Hakoyama et al. 2012). Kim et al. (2006) demonstrated in \u003cem\u003eArabidopsis\u003c/em\u003e that the \u003cem\u003eVIT1\u003c/em\u003e transporter gene transports and translocate ferrous iron into the vacuole, which was symmetrical to the transport across the SM (Kim et al. 2006). These metals are necessary for the synthesis of the nitrogenase enzyme, and the bacteroids only acquire it from the host plant, hence, observing significant upregulation of these metal transporters are not surprising in this study.\u003c/p\u003e \u003cp\u003eThe energization of the SM via the extrusion of protons (H\u003csup\u003e+\u003c/sup\u003e ions) by an H\u003csup\u003e+\u003c/sup\u003e-ATPase not only creates an electrochemical gradient across the SM to mediate the transport and exchange processes between symbionts, but also protonates the conversion of fixed ammonia (NH\u003csub\u003e3\u003c/sub\u003e) to ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) for assimilation by plants after N\u003csub\u003e2\u003c/sub\u003e-fixation (Amoako et al. 2024). For instance, a transcriptome study has recently identified 13 H\u003csup\u003e+\u003c/sup\u003e-ATPase genes expressed in nodules (Severin et al. 2010), which are found in the family of ATP-binding cassette transporter families. This study identified several ABC (ATP-binding cassette) family transporters that play significant roles in the uptake of minerals and other solutes in the nodules. For example, our analysis identified four ABC family transporter gene isoforms, which were mostly upregulated in NoP_vs._Po, and included those encoding the ABC-2 type transporter (\u003cem\u003eBMK_Unigene_060918, BMK_Unigene_129480, and BMK_Unigene_060469\u003c/em\u003e), E1-E2 ATPase (\u003cem\u003eBMK_Unigene_132193\u003c/em\u003e), and \u003cem\u003eABC type transporter\u003c/em\u003e (\u003cem\u003eBMK_Unigene_058922\u003c/em\u003e), with \u003cem\u003ethe\u003c/em\u003e ABC transporter G family member 17 (\u003cem\u003eBMK_Unigene_060469\u003c/em\u003e) being expressed only in Pi_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-E). The detection of these transporter types and families in Po-treatment comparisons (NoP_vs._Po/Pi_vs._Po) relative to Pi-treatment comparison (NoP_vs._Pi) indicates that the energization of the SM and transport of solutes in nodules were greatly improved, and, in part, significantly resulted in higher expression of H\u003csup\u003e+\u003c/sup\u003e-ATPase genes and activities of N assimilation enzymes observed in our previous study (Amoako et al. 2024). Clarke et al. (2015) identified five peptides with homology to the ABC superfamily transporters in the SM proteome, and \u003cem\u003eGmABCA2\u003c/em\u003e, \u003cem\u003eGmABCA7\u003c/em\u003e, and \u003cem\u003eGmABCA11\u003c/em\u003e had higher expression patterns exclusively in soybean nodules. These were found to perform bidirectional functions, with their activities driven by ATP hydrolysis.\u003c/p\u003e \u003cp\u003eNodulin 26 is a major intrinsic membrane protein that acts as a multifunctional aquaglyceroporin, and is known to facilitate the movement of glycerol and formamide (Sakamoto et al. 2019). Intriguingly, genes associated with nodulin 26, such as \u003cem\u003eBMK_Unigene_059591\u003c/em\u003e, encoding nodulin 26-like intrinsic protein 1;1, were significantly upregulated exclusively in Pi_vs._Po comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, E), suggesting that Po treatment altered the transport of water and glycerol during SNF. The nodulin 26 gene, \u003cem\u003eGlyma08g12650\u003c/em\u003e, was detected on the SM in soybean (Clarke et al. 2015) and in arbuscular mycorrhiza (AM)-induced \u003cem\u003enodulin 26 and nodulin 21\u003c/em\u003e transcripts in soybean roots (Sakamoto et al. 2019). These genes were suggested to be localized to the peribacteroid and periarbuscular membranes and were implicated in osmoregulation in AM symbiosis and rhizobia symbiosis.\u003c/p\u003e \u003cp\u003eThe Po-induced genes were associated with membrane transports, including those encoding the nitrate and peptide transporter family \u003cem\u003e(NRT1/PTR)\u003c/em\u003e, amino acid, and bidirectional sugar transporters (sugar will eventually enter transporter\u003cem\u003e-SWEET\u003c/em\u003e). The identification of nitrate transporters on the SM of soybean nodules has been reported (Vincill et al. 2005). It is postulated that these transporter genes ensure the regulation of ions and membrane potential via the transport of nitrate, which facilitates the regulation of symbiosis in legumes (Udvardi \u0026amp; Day 1989). This study identified the upregulation of the \u003cem\u003eBMK_Unigene_060395\u003c/em\u003e gene putatively encoding nitrate/peptide transporter proteins (\u003cem\u003eNRT1/PTR FAMILY 1.1\u003c/em\u003e) only in NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, F), suggesting that the supply of Po as a P source enhanced the fluxes of a wide range of nitrogen-based compounds between the symbionts. This is consistent with the upregulation of amino acid transporters, viz., amino acid transporter \u003cem\u003eAVT1A isoform X2\u003c/em\u003e (\u003cem\u003eBMK_Unigene_133019\u003c/em\u003e), sodium-coupled neutral amino acid transporter 1-like protein (\u003cem\u003eBMK_Unigene_034906\u003c/em\u003e), and probable polyamine transporter \u003cem\u003eAt1g31830\u003c/em\u003e (\u003cem\u003eBMK_Unigene_059198\u003c/em\u003e) in Pi_vs._Po/NoP_vs._Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B, E) observed in this study. To support the claim or hypothesis that Po-induced nodules altered higher and upregulated amino acid transporters than Pi-induced nodules, two amino acid permease (\u003cem\u003eAap\u003c/em\u003e) genes (\u003cem\u003eBMK_Unigene_059198 and BMK_Unigene_081174\u003c/em\u003e) were uniquely expressed and significantly upregulated in only NoP_vs._Po/Pi_vs._Po comparisons (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE), highlighting the crucial role of Po supplementation in amino acid metabolism and nitrogen distribution to the cytosol of the host plant during symbiosis for assimilation. This upregulation is consistent with the higher activities of N assimilation enzymes (GS/GOGAT, AAT) previously reported (Amoako et al. 2024). It\u0026rsquo;s been reported that \u003cem\u003eDCAT1\u003c/em\u003e belongs to the nitrate/peptide transporter \u003cem\u003e(NRT/PTR)\u003c/em\u003e family \u003cem\u003e(NPF)\u003c/em\u003e (L\u0026eacute;ran et al. 2014), and this family is associated with the transport of dicarboxylate in legumes. A transcriptome analysis has revealed that \u003cem\u003eNPF\u003c/em\u003e-encoding genes are strongly altered in nodules (Severin et al. 2010). This means that the transportation of dicarboxylate (such as malate) that provides the carbon skeleton (energy) for N\u003csub\u003e2\u003c/sub\u003e-fixation in this study cannot be ruled out. This is because the \u003cem\u003eNPF\u003c/em\u003e gene was significantly upregulated in Pi_vs._Po, affirming the hypothesis that the supply of phytic acid stimulated and provided the required carbon in the form of dicarboxylate (malate) that can be utilized by the bacteroids to enhance N\u003csub\u003e2\u003c/sub\u003e-fixation (Amoako et al. 2023b). It\u0026rsquo;s been widely known that bacteroids do not directly utilize sucrose and other hexoses as carbon sources during N\u003csub\u003e2\u003c/sub\u003e-fixation, but a class of sucrose transporters known as SWEET have been discovered in infected nodules, the meristem, invasion zone, and vasculature of nodules (Kryvoruchko et al. 2016). It was reported that two independent \u003cem\u003eTnt1-insertion sweet11\u003c/em\u003e mutants did not compromize the SNF process, indicating that the \u003cem\u003eMtSWEET11\u003c/em\u003e observed in nodules of \u003cem\u003eM. truncatula\u003c/em\u003e distributed sucrose within the nodules, but was not crucial during SNF (Kryvoruchko et al. 2016). Interestingly, our analysis revealed \u003cem\u003eSWEET transporter\u003c/em\u003e genes been specifically expressed in Pi treatment comparison (NoP_vs._Pi) relative to Po (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand D\u003c/b\u003e). The downregulation of the \u003cem\u003eBMK_Unigene_029164\u003c/em\u003e gene encoding bidirectional sugar transporter \u003cem\u003eSWEET4\u003c/em\u003e in this study confirms that \u003cem\u003eSWEET4\u003c/em\u003e is identified in nodules, and even though identified to distribute sucrose within nodules, it is found not to be a critical component in efficient SNF process (Kryvoruchko et al. 2016). However, the nodule-specific gene (\u003cem\u003eBMK_Unigene_032564\u003c/em\u003e) encoding the bidirectional sugar transporter N3 in nodules was up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand D\u003c/b\u003e), suggesting a dual functional role during SNF. It could be concluded that the observation of no \u003cem\u003eSWEET\u003c/em\u003e transporter genes in the Po-treatment comparisons (NoP_vs._Po/Pi_vs._Po) indicates that the supply of phytic acid directs dicarboxylates to the nodule for SNF and instead of hexose or sucrose as previously reported (Amoako et al. 2023b). We can confidently assert and confirm that diverse P sources recruit different genes and mechanisms in the SM during nodule organogenesis and SNF, confirming the hypothesis of this study. Our previous study concluded that Po-treated plants stimulated higher organic acids (malate) relative to Pi-treated plants, so it is not surprising we did not observe any sucrose transporters in this transcriptome analysis (Amoako et al. 2023b). Even though the entire genome of \u003cem\u003eVicia faba\u003c/em\u003e is yet to be fully annotated, the findings of this study are not surprising because transporter genes analogous to metal and ion transporters, sugars, amino acids and peptide transporters have been reported to be significantly increased in nodules and are presumed to be localized on SM in infected cells of \u003cem\u003eLotus japonicus\u003c/em\u003e (Kouchi et al. 2004).\u003c/p\u003e \u003cp\u003e4.3 \u003cb\u003eOrganic and Inorganic P Sources Adopt Varied Carbon and Sucrose Metabolism Mechanism\u003c/b\u003e\u003cb\u003es\u003c/b\u003e \u003cb\u003ein Nodules Organogenesis During SNF\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe general principle for SNF is the supply of reduced carbon (dicarboxylates) to the bacteroids by the host plant in exchange for reduced nitrogen (ammonia). The amount of carbon supplied by plants is equivalent to the nitrogen provided for assimilation, making carbon a very important and crucial resource for SNF in legume-rhizobia symbiosis. The source of carbon for SNF is sucrose supplied from the shoot via photosynthesis by the host plant (Amoako et al. 2023b; Liu et al. 2018). However, bacteroids cannot directly use sucrose as a substrate by the bacteroids to enhance SNF (Liu et al. 2018). Therefore, sucrose is split into glucose/fructose and fructose/UDP-glucose by alkaline invertase (INV) and sucrose synthase (SUS) (Liu et al. 2018), which subsequently enters the TCA cycle to provide the required dicarboxylates. Malate and succinate, preferably malate, are the main sources of dicarboxylates (organic acids) that can enter the SM to provide the energy and carbon skeleton required for efficient N\u003csub\u003e2\u003c/sub\u003e-fixation (Amoako et al. 2023b). Our analysis identified key genes that participated and directly involved in starch and sucrose metabolism to produce the precursors for the activation and synthesis of dicarboxylates needed for SNF (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). Starch obtained from photosynthesis is degraded into sucrose for translocation into the cytosol of the host plant for further degradation. After the splitting of sucrose by INV and SUS, glucose or fructose enter glycolysis by the enzymes glucokinase and fructose-6-phosphatase aldolase. In this study, we found genes such as \u003cem\u003eBMK_Unigene_027824\u003c/em\u003e (encoding fructose-6-phosphate aldolase) and \u003cem\u003eBMK_Unigene_062430\u003c/em\u003e (putatively encoding glucokinase) to be downregulated and upregulated, respectively, only in NoP_vs._Po (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA-C and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). This indicates that \u003cem\u003eVicia faba\u003c/em\u003e plants treated with phytic acid employed different pathways (i.e., \u003cem\u003eglucokinase\u003c/em\u003e pathway) instead of the fructose-6-phosphate aldolase pathway in their glycolytic pathway and carbon metabolism mechanism during SNF. Interestingly, aldolase-1 epimerase (\u003cem\u003eBMK_Unigene_082109\u003c/em\u003e), which catalyzes the conversion of alpha-D-glucose to beta-D-glucose (galactose metabolism, Hucho \u0026amp; Wallenfels, 1971), was found to be exclusively upregulated and enriched in NoP_vs._Po (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA-C and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e), confirming that Po-treated plants used an alternative pathway in their sucrose metabolism. Meanwhile, two isoforms of glyceraldehyde-3-phosphate dehydrogenase genes (\u003cem\u003eBMK_Unigene_021856\u003c/em\u003e and \u003cem\u003eBMK_Unigene_048610)\u003c/em\u003e, which catalyzes the conversion of glyceraldehyde 3-phosphate to glycerate-1, 3-biphosphate, a process associated with the construction of NADH (Wang et al. 2020), were significantly upregulated in NoP_vs._Po/NoP_vs._Pi (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA-C and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). However, upregulation of the phosphoglycerate kinase gene (\u003cem\u003eBMK_Unigene_110457\u003c/em\u003e), which reversibly transforms 1,3-bisphosphoglycerate (1,3-bPG) to generate 3-phosphoglycerate (3PG) and ATP (Yagi et al., 2021), was also found to be keenly involved in the starch/sucrose metabolism pathway strictly in Po-treatment comparisons (NoP_vs._Po/Pi_vs._Po). Indeed, this result suggests that Po treatments significantly enhanced sucrose metabolism and glycolysis via upregulation of this gene in \u003cem\u003eVicia faba\u003c/em\u003e nodules. The higher upregulation of glycolytic enzymes in this present study is consistent with the higher net photosynthesis observed in our previous study (Amoako et al. 2024). A report by Nasr et al. (2017) identified two pyruvate kinase genes (\u003cem\u003eXM_004496111.2\u003c/em\u003e and \u003cem\u003eXM_004489126.2\u003c/em\u003e) in \u003cem\u003eMmSWR19-Pd/MmSWR19-Ps\u003c/em\u003e- and \u003cem\u003eMcCP-31-Pd/McCP-31-Ps\u003c/em\u003e-induced nodules in chickpea plants exposed to different P levels. Similarly, our results identified gene isoforms such as \u003cem\u003eBMK_Unigene_065186\u003c/em\u003e and \u003cem\u003eBMK_Unigene_134540, (\u003c/em\u003eputatively encoding pyruvate decarboxylase 1, and pyruvate decarboxylase 2\u003cem\u003e)\u003c/em\u003e, and \u003cem\u003eBMK_Unigene_129620 and BMK_Unigene_048128 (\u003c/em\u003eencoding pyruvate kinase 1-cytosolic and pyruvate kinase, cytosolic isozyme\u003cem\u003e)\u003c/em\u003e, respectively, which were significantly upregulated in NoP_vs._Pi (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). These genes were found to be keenly implicated in the sucrose metabolism pathway in Pi treatment (NoP_vs._Pi), but not in Po treatment (NoP_vs._Po/Pi_vs._Po) comparisons, supporting what was observed previously (Nasr et al., 2017). This phenomenon suggests that Po treatment recruits pathways different from its corresponding Pi treatments in producing the precursors (such as PEP and pyruvate) for the synthesis of the dicarboxylates (activation of the TCA cycle enzymes) required for SNF. Furthermore, the results of this study have consistently revealed that Po-induced plants adopt different molecular and metabolic pathways in their glycolytic mechanisms and processes, as reported earlier. An obvious example was enolase, which is an enzyme involved in the reversible and catalytic transformation of D-2-phosphoglycerate (2-PGA) to phosphoenolpyruvate (PEP) during glycolysis and gluconeogenesis in plants (Avil\u0026aacute;n et al., 2011). Intriguingly, we observed upregulation of the \u003cem\u003eBMK_Unigene_063190\u003c/em\u003e gene encoding Enolase (C-terminal TIM barrel domain and N-terminal domain), which was solely enriched in NoP_vs._Po and participated significantly in the glycolytic pathway in this study. The significant upregulation of this gene specifically in NoP_vs._Po confirms that Po-induced nodules employ different pathway relative to Pi-induced nodules (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA-C and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e) in metabolizing sucrose for SNF. It is quite plausible to suggest that Po- and Pi-induced nodules adopt diverse carbon metabolic mechanisms and pathways in their starch/sucrose metabolism processes when plants are exclusively dependent on nodule symbiosis for nitrogen.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eTaken together, our findings reveal that Po-induced nodules triggered and altered a higher number of DEGs relative to Pi-induced nodules, highlighting its crucial role in nodule programming, SNF, and the possibility of Po serving as a strong alternative to Pi. It was demonstrated that both treatments (organic and inorganic P) employ diverse nodulation, transport, and carbon metabolism mechanisms in nodules when plants are exclusively dependent on nodule symbiosis for nitrogen, confirming our hypothesis that phytic acid-induced nodulation in \u003cem\u003eVicia faba\u003c/em\u003e plants employs different mechanistic and metabolic pathways relative to Pi. Thus, both host and bacteroid ensure fluxes of solutes, metabolites, and ions to enhance effective mutualistic relationships during SNF. We adopted both positive and negative controls to ensure comprehensive comparisons of DEGs via de novo RNA-seq transcriptome data analysis and thereby chronicling, for the first time, the perturbations occurring in \u003cem\u003eVicia faba\u003c/em\u003e nodules induced with different P sources under symbiotic conditions. This work is a very crucial step towards the ongoing genome annotation project of \u003cem\u003eVicia faba\u003c/em\u003e plants and the creation of its database.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;\u003cem\u003eAuthors and Affiliations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitute of Plant Nutrition and Soil Science, Kiel University, Hermann-Rodewald-Stra\u0026szlig;e 2, 24118 Kiel, Germany.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrank Kwarteng Amoako, Amit Sagervanshi\u0026nbsp;\u0026amp; Karl H. M\u0026uuml;hling\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJiangsu Key Laboratory of Sericulture Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang 212100, China.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMichael Ackah \u0026amp; Weiguo Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitut f\u0026uuml;r Pflanzenwissenschaften und Mikrobiologie Pflanzenbiochemie und Infektionsbiologie Ohnhorststr.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e18, 22609, University of Hamburg, Hamburg, Germany.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEbenezer Kweku Ntiriakwa\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Molecular Biology and Biotechnology, School of Biological Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, PMB Ghana.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrank Kwarteng Amoako, Michael Ackah \u0026amp; Aaron Tettey Asare\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors Contributions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrank Kwarteng Amoako\u003c/strong\u003e: Conceptualization, Methodology, Investigation, Supervision, Project administration, Funding, Formal analysis, Data curation, Software, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eEbenezer Ntiriakwa\u003c/strong\u003e: Methodology, Investigation, Resources. \u003cstrong\u003eMichael Ackah\u003c/strong\u003e: Formal analysis, Data curation, Software, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eKarl H. M\u0026uuml;hling and Amit Sagervanshi:\u003c/strong\u003e Conceptualization, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eWeiguo Zhao:\u003c/strong\u003e Supervision, Funding, Writing \u0026ndash; review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;Aaron Tettey Asare:\u0026nbsp;\u003c/strong\u003eSupervision, Funding, Writing \u0026ndash; review \u0026amp; editing. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDeclaration of competing interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that could have influenced the findings chronicled in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequence data are available on NCBI with accession number PRJNA1030752 and website https://www.ncbi.nlm.nih.gov/sra/PRJNA1030752\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Biomarker Tech. Co. Ltd (Beijing, China) for assisting in the RNA sequencing and bioinformatics analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAppendix A. Supplementary data\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmoako, F. K., Jillani, G., Sulieman, S., \u0026amp; M\u0026uuml;hling, K. H. (2023a). Faba bean (Vicia faba L.) varieties reveal substantial and contrasting organic phosphorus use efficiencies (PoUE) under symbiotic conditions. \u003cem\u003eJournal of Plant Nutrition and Soil Science\u003c/em\u003e, \u003cem\u003e186\u003c/em\u003e(6), 673-692. https://doi.org/10.1002/jpln.202300198 \u003c/li\u003e\n\u003cli\u003eAmoako, F. K., Sagervanshi, A., Hussain, M. A., Pitann, B., \u0026amp; M\u0026uuml;hling, K. H. (2024). 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Molecular mechanism of glycolytic flux control intrinsic to human phosphoglycerate kinase. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(50), e2112986118. https://doi.org/10.1073/pnas.2112986118\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vicia faba, nodule, phytic acid, symbiotic nitrogen fixation, De novo RNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-6417689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6417689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFaba bean (\u003cem\u003eVicia faba\u003c/em\u003e L.) is an essential crop that contributes enormously to nitrogen (N) input due to its ability to fix atmospheric nitrogen via symbiotic N fixation (SNF). During this process, the symbionts undergo cascades of gene expression perturbations and reprogramming in the organogenesis of nodules under nutritional stresses. Inorganic phosphorus (Pi) has been the ultimate source for nodulation, to the neglect of organic P (Po) in many SNF studies. To elucidate the role of Po in SNF, we used de novo RNA-seq tools and the results identified a total of 2,263 differentially expressed genes (DEGs) in nodules when \u003cem\u003eVicia faba\u003c/em\u003e plants were exposed to different P sources, viz., low Pi, high Pi, and Po when inoculated with \u003cem\u003eRhizobium leguminosarum\u003c/em\u003e bv. \u003cem\u003eviciae\u003c/em\u003e 3841 in hydrponics experiment. The results consistently reveal that Po-induced nodules comparisons altered 1,144 and 811 DEGs, respectively, relative to the Pi-induced nodules comparison (308 DEGs), highlighting higher DEGs triggered by phytic acid supply. The results further reveal differential nodulation, transport and carbon metabolism mechanisms employed by the different P sources during N-fixation. The expression of these genes in \u003cem\u003eVicia faba\u003c/em\u003e will provide more insights into the functional characterization of these DEGs for breeding purposes, and also contribute enomously towards the ongoing genome annotation project and database of \u003cem\u003eVicia faba\u003c/em\u003e plants.\u003c/p\u003e","manuscriptTitle":"Transcriptome Analysis Unravels Diverse Response Mechanisms of Nodules to Phytic Acid Supply in Vicia faba","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-18 16:44:42","doi":"10.21203/rs.3.rs-6417689/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43803b99-8d51-4d2b-bb07-8990f02cce10","owner":[],"postedDate":"May 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-15T15:34:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-18 16:44:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6417689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6417689","identity":"rs-6417689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}