Characterization of leaf transcriptome in grafted tomato seedlings inoculated with beneficial rhizobacteria Azospirillum brasiliensis and Paraburkholderia graminis

doi:10.21203/rs.3.rs-1520948/v1

Aims: Characterization of the transcriptomic responses of grafted tomato seedlings inoculated with two beneficial microorganisms.

Methods: mRNA sequencing analyses were performed from leaves of grafted tomato seedlings at flowering stage, 35 days after inoculations. The differential expression analysis results were validated by carrying out a quantitative reverse transcription PCR on nine selected genes.

Results and Conclusions: Root inoculations with the two beneficial bacteria can influence the genes activity also in organs distant from roots. Interactions between grafted tomato seedlings and microorganisms suggest that the interactions between tomato and microorganisms are species-specific, and affect the activity of specific sets of genes, that in turn activate and/or involve similar pathways and mechanisms. This study provides novel knowledge on the effects of inoculation of Azospirillum brasiliensis (AB) and Paraburkholderia graminis (PG)on grafted tomato seedling metabolism, and on the molecular mechanisms underlying the improved performance of tomato plants after these treatments as far as yield and stress responses are regarded. AB induced genes coding for different MYB trascrption factors known to be involved in response to biotic and abiotic stresses, PG upregulated 5 genes coding for putative late blight resistance protein homolog. Auxine responsive molecules and giberellins involved in the fruit-set and early fruit growth in tomato were mainly induced by AB.

Azospirillum brasiliensis

grafting

Paraburkholderia graminis

PGPR

Tomato crop

transcritomics.

Tomato (Solanum lycoprsicum L.) is one of the major horticultural crops worldwide, with an average cropping area of 5 million hectares producing ~ 181 million tons of fruits (FAO, 2022). Tomato fruits are a source of bioactive phenolic compounds, carotenoids (such as lycopene and β-carotene) (Storniolo et al. 2020), vitamins (A and B) and minerals (Foolad 2007) that make them an important part of human diet.

In order to guarantee a higher yield and a higher fruit quality, requested to satisfy the increasing demands of a growing population and to enhance the beneficial features of tomatoes, sustainable agriculture practices should be taken in consideration. Practices with low enviromental impact can reduce the use of external inputs, such as plant protection products and fertilisers, improve the nutrient and water use efficency, and mitigate the negative impact of climate change (Cammarano et al. 2020).

The exploitation of the great variety of bacterial species dwelling in the soil is a promising and sustainable approach to increase tomato yield and reduce the use of external inputs (Raklami et al. 2019). Indeed, bacteria can colonize plant root and rhizosphere creating commensalistic, parasitic and mutualistic relationships with host plants (Wang et al. 2019). Mutualistic microorganisms can provide a series of beneficial effects on crop growth, health and production quality of pea, maize, tomato, lattuce, wheat, etc. (Dimkpa et al. 2009). Some rhizobacteria, such as Azospirillum spp. and Paraburkholderia spp. are able to improve the macro- and micro-nutrients uptakes by the plants. The root growth stimulation, the improvement of nitrogen fixation and the conversion of insoluble mineral to bioavailable forms are among the main ways in which the nutrients uptake is enhanced by plant growth promoting rhizobacteria (Vessey 2003). Beneficial microorganisms also produce or induce plants to produce compounds influencing germination, flowering and ripening, such as phytormones (Rothballer et al. 2005), and also improve crop tolerance to stresses. In a recent study, Azospirillum lipoferum FK1 was demonstrated to increase chickpea tolerance to high salinity by modulating the osmolytes concentration (El-Esawi et al. 2019). During chilling stress, Paraburkholderia graminis improved tomato seedling re-growth and reduced cell membrane injuries in terms of electrolytic leakage and efficiency of photosystem II (Caradonia et al. 2019). Mendes et al. (2011) also reported a costant association between suppression activity against Rhizoctonia solani on sugar beet plants and the presence in soil of several bacteria pyla, such as Proteobacteria, Firmicutes and Actinobacteria.

As outlined above, the use of plant growth promoting rhizobacteria (PGPR) can result in many positive effects on crop performance. However, the underlying molecular mechanisms are not well elucidated, since each beneficial microorganism provides cross-protection against several stresses and, at same time, stimulates plant growth by activating many genes in different plant organs. Understanding the mechanisms of action of beneficial microorganisms is a key requirement for optimizing their use on different crops and environmental conditions, as well as the formulation of bio-based commercial products (Brown and Saa 2015; Yakhin et al. 2017). Transcriptomic studies, based on RNA sequencing, are a good way of defining the molecular mechanisms involved in the plant-beneficial microorganisms interactions, as already used for plant-pathogen interplays characterization (Naidoo et al. 2018; Zanardo et al. 2019). Several studies examined the tomato interactions with root pathogens (Milc et al. 2019; Tan et al. 2015) and, as reported by Zouari et al. (2014), tomato plants could be considered a good model for evaluating the plant-PGPR interactions also in organs distant from the inoculation points.

In a previous experiment, Azospirillum brasiliensis sp 245 was reported to induce early flowering in grafted tomato seedlings in greenhouse, whereas in field experimenent using the same tomato plants inoculated with P. graminis or a bacterial consortium (P. graminis and A. brasiliensis), an increase of the total yield was observed (Caradonia et al. 2020). In this context, the present study aims to perform an RNA sequencing analysis to highlight the differences in the leaf transcriptome of grafted tomato seedlings in responses to P. graminis C4D1M and A. brasiliensis sp.245 root inoculation.

Plant materials, growth conditions and microbial inoculation

The commercial processing tomato genotype ‘H3402’ (HEINZ, Pittsburgh, Pennsylvania, USA), grafted onto the commercial genotype ‘Tomito’ (ISI Sementi SpA, Fidenza, Italy), resistant to Fusarium oxysporum f.sp. lycopersici race 0, Verticillium albo-atrum and Verticillium dahliae race 0. was used for this experiment based on our previous experiments. Grafted seedlings, provided by Coop Habitat (San Vito, Ferrara, Italy), were grown as reported in Caradonia et al. (2020). Two weeks after grafting, at four true leaves stage, seedlings were transplanted in pots (6.5 cm x 8.0 cm x 5.5 cm) filled with neutral peat (23% organic carbon, 0.5% nitrogen, pH 6, electrical conductivity 0.25 dS m^− 1, and dry apparent density 214 kg m^− 3; Dueemme S.r.l., Reggio Emilia, Italy). Immediately after transplanting, bacterial treatments with A. brasiliensis sp.245 (AB) and P. graminis C4D1M (PG), belonging to collection of the CREA Research Center for Genomics and Bioinformatics, were performed by adding 1 mL of inoculum (10⁷ colony forming unit (CFU) mL^− 1) close to the seedlings’s root collars as described in Caradonia et al. (2019). Briefly, inocula were carried out cultivating a single bacterial colony in 60 mL of Tryptone Soya Yeast Extract Broth (Liofilchem srl, Teramo, Italy). Inocula were incubated at 28°C at 150 rpm for 24 hours. Then, suspensions were centrifuged for 4 minutes at 8,000 g, and pellets were washed and suspended in sterilized distilled water. A V-550 UV-VIS spectrophotometer (600 nm) (Jasco Europe, Cremella (LC), Italy) was used to estimate the bacterial concentrations, and sterilized distilled water was added until the concentration of 10⁷ CFU mL^− 1 was reached. Seedlings grown in pot without inoculation were used as control.

After inoculations, seedlings were grown in a greenhouse located at the Department of Life Sciences, University of Modena and Reggio Emilia (Reggio Emilia, Italy), with a photoperiod of 16 h light and 8 h dark and the day/night temperatures of 25/19°C. Seedlings were watered every two days with 50 mL of water per pot. Compo BIO fluid stillage (organic nitrogen 3%, potassium oxide 6% and organic carbon 10%, COMPO ITALIA S.R.L (Cesano Maderno, Italy) was added to the irrigation water (10 mL L^− 1) once a week for two weeks. A fully randomized experimental design with ten biological replicates per treatment were adopted.

Rna Extraction And Illumina Sequencing

Leaf samples were harvested at flowering stage, 35 days after inoculations, from three plants for each treatment (control, inoculated with AB, inoculated with PG), frozen in liquid nitrogen and stored at − 80°C.

Frozen samples were pulverized in a sterile mortar using liquid nitrogen, and the total RNAs were extracted from ground tissues using Nucleospin® RNA plant kit (Macherey–Nagel, Duren, Germany) according to manufacturer instructions, and eluted in 60 µL of RNase-free water. The concentration and the integrity of RNA samples were assessed using Nanodrop 1000 spectrophotometer (Thermo Scientific, Wilmington, USA) and Bioanalyzer 2100 (Agilent Technologies, USA), respectively. The high-quality RNA samples were sent in dry ice to Novogene company (UK sequencing centre, Cambridge) for libraries construction and paired-ends sequencing using NovaSeq 6000s Sequencing System (Illumina, San Diego, California).

Rna-seq Data Handling And Differential Gene Expression Analysis

After assessing the RNA-Seq raw reads quality with FastQC v0.11.9 (Andrews 2010), Trimmomatic v0.36 (Bolger et al. 2014) was used to trim the adapters sequences and remove the low-quality reads. The filtered reads were mapped to tomato reference assembly version SL4.0 (Hosmani et al. 2019; Sato et al. 2012) using HiSat2 v2.2.1 (Kim et al. 2019). Reads counts were generated from alignment files using featureCounts v1.6.0 software (Liao et al. 2014) with default parameters, basing on ‘exon’ feature and ‘transcript_id’ meta-feature of ITAG4.0 tomato annotation file retrieved from Sol Genomics website (https://solgenomics.net/).

Differential expression (DE) analyses were carried out using Bioconductor EdgeR v3.28.1 (Robinson et al. 2010). EdgeR was used to filter out the not expressed or poorly expressed transcripts (a transcript was considered ‘active’ if reads per million mapping to that transcript was > 1 in at least two libraries), normalize the RNA libraries, and do the differential expression analysis with the likelihood ratio test. EdgeR, whose computing approach fits a negative binomial generalized linear model (GLM) to the read counts for each transcript, was used to calculate the Log₂ Fold Change (LFC) of each transcript expression between treated (both AB and PG) and control samples. The transcripts with resulting false discovery rate (FDR) lower than 0.05 and LFC lower than − 1 or higher than 1 were considered as differentially expressed.

Go Annotation And Enrichment

The gene ontology (GO) annotation of the whole SL4.0/ITAG4.0 proteome was obtained through InterProScan mapping using Blast2GO v5.2.5 software (Conesa and Götz 2008). GO enrichment analyses were conducted with GOseq Bioconductor package v1.38.0 (Young et al. 2010). GOseq software is designed to bias the RNA-Seq data by transcripts length, so the median length of the transcripts were calculated with GenomicFeatures Bioconductor package v1.38.2 (Lawrence et al. 2013) using the ITAG4.0 annotation file as input. The GO terms with a p-value lower than 0.05 were considered as enriched.

Quantitative Rt-real Time Pcr Validation

The differential expression analysis results were validated by carrying out a quantitative reverse transcription PCR (RT-qPCR) of a selected gene set. Nine genes coding for proteins involved in defence, reponse to abiotic stress, water transport, hormone signalling or signal transduction were chosen among differentially expressed genes (DEGs) for RNA-Seq data validation.

RNA samples (500 ng per sample), extracted as decribed in the previous paragraph, were reverse-transcribed using SuperScript™ II Reverse Transcriptase (Invitrogen™, Carlsbad, California, USA) following the instruction manual provided by manufacturer, and the resulting cDNAs were diluited 10 times in nuclease-free distilled water. Oligo Explorer v1.1.2 tool was used to design primers for each gene. Then the primers sequences specificity (no multiple allignments) were verified using the BLAST function against tomato genome v2.4 at Sol Genomics Network website (https://solgenomics.net/). The primers IDs and sequences are listed in Supplementary Table 1.

Quantitative reverse transcription PCR assays were performed using a 7300 real-time PCR system (Applied Biosystems, Foster City, CA, USA). The elongation factor 1-alpha, commonly used as a housekeeping gene in tomato (Løvdal and Lillo 2009; Milc et al. 2019) was used. Reactions were carried out in 20 µl containing 10 µL SYBR Green PCR, 2X GoTaq qPCR Master Mix (Promega) and 100X Reference Dye (Promega), 0.3 µl of each primers, 2µl of cDNA (1ng µL^− 1) and 7.4µl of water, and performed in triplicate following this cycling protocol: 95°C for 10 min, 40 cycles of 95°C for 15 s, and 60°C for 1 min. A melting curve analyis was carried out for each run. The comparative threshold cycle method 2^{− ΔΔCt} (Salvioli et al. 2012) was used to analyze the data. The fold change was calculated as the log₂ (2^{− ΔΔCt}).

RNA sequencing and differential expression analyses

Total RNAs were extracted from leaves and sequenced with Illumina NovaSeq 6000s sequencing system. Overall, 221,891,789 paired 150 bp-long reads were generated, ranging from 23 to 26 million across the nine RNA samples (Table 1). After assessing their quality, RNA-Seq raw reads have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-11398

Adapters sequences and low-quality nucleotides were filtered out, resulting in 95–97% of ‘surviving’ reads (Table 1). For each RNA library, 91–95% of filtered reads were aligned to the tomato SL4.0 reference assembly, and the number of reads mapping to each predicted transcript was estimated according to ITAG4.0 annotation.

Table 1

RNA-seq data elaboration statistics. For each RNA library we reported the sample ID, the treatment (‘Ctrl’ for non-inoculated control plants; ‘AB’ for plants treated with *Azospirillum brasiliensis*; ‘PG’ for plants treated with *Paraburkholderia graminis*), the replicate number, the amount of paired-end raw reads, the number and the percentage of paired reads survived to trimming and filtering, the number and the percentage of paired reads aligned to tomato SL4.0 reference genome.
Sample properties				Trimming and filtering		Aligning to genome
Sample ID	Treatment	Replicate	Paired-end reads	N° pairs survived	% pairs survived	N° aligned pairs	% aligned pairs
T25l_CON	Ctrl	1	26,783,825	25,548,670	95.39%	24,277,920	95.03%
T26l_CON	Ctrl	2	24,651,592	23,369,549	94.80%	21,413,685	91.63%
T27l_CON	Ctrl	3	23,637,298	22,874,761	96.77%	20,835,340	91.08%
T22l_ABA	AB	1	22,012,255	21,065,854	95.70%	19,617,111	93.12%
T23l_ABB	AB	2	27,225,211	26,424,621	97.06%	24,341,237	92.12%
T24l_ABC	AB	3	23,976,755	22,934,673	95.65%	21,820,168	95.14%
T19l_PGA	PG	1	23,263,479	22,361,982	96.12%	21,024,789	94.02%
T20l_PGB	PG	2	26,445,081	25,487,216	96.38%	24,121,624	94.64%
T21l_PGC	PG	3	23,896,293	23,217,583	97.16%	22,004,594	94.78%

Normalization factors were calculated according to library sizes, and normalized reads counts were used as input data for the differential expression analyses of two comparisons: plants treated with Azospirillum brasiliensis (AB) Vs. non-inoculated control plants, and plants treated with Paraburkholderia graminis (PG) Vs. non-inoculated control plants. Detailed results of DE analyses were reported in Supplementary Table 2 and graphically represented in Supplementary Fig. 1. According to the above mentioned thresholds (FDR < 0.05; |LFC|>1), the DE transcripts for the two analyses carried out for this study were reported in Supplemental Table 3, while the resulting numbers of up- and down-regulated transcripts were summarised in Table 2. PG treatment led to a higher number of DE transcripts than AB, moreover more transcripts were up- than down-regulated in both treatments.

Table 2

Differential expression analysis results statistics: numbers of resulting down-regulated, up-regulated and total DE transcripts for plants treated with *Azospirillum brasiliensis* (AB) and *Paraburkholderia graminis* (PG) *Vs.* control plants (Ctrl).
DE analysis	Down-regulated transcripts	Up-regulated transcripts	Total DE transcripts
AB Vs. Ctrl	184	243	427
PG Vs. Ctrl	123	389	512

As reported in Fig. 1, 47 up- and 24 down-regulated transcripts were shared among treatments and no DE transcripts were found to have opposite modulation after AB and PG treatments.

Functional Annotation And Go-enrichment Analyses

Functional annotation and identification of over-represented Gene Ontology (GO) categories among DE transcripts allowed us to identify the GO terms affected by A. brasiliensis (AB) and P. graminis (PG) treatments in leaves of grafted tomato.

A total of 65 and 60 GO terms were identified as significantly enriched for AB and PG treatments, respectively (Table 3). The complete list of enriched GO terms is reported in Supplementary Table 4.

Table 3

Number of enriched GO terms in AB and PG treatments for Molecular Function (MF), Cellular Component (CC) and Biological Process (BP) categories.
Treatment	Molecular Function	Cellular Component	Biological Process	TOTAL
AB (Azospirillum brasiliensis)	33	7	25	65
PG (Paraburkholderia graminis)	24	6	30	60

Common (15 GO), AB-specific (50 GO) and PG-specific (45 GO) enriched terms were identified, highlighting a substantial lower number of terms associated to both responses than to specific terms for AB and PG.

As evident in Figs. 2 and 3, eight enriched biological process-related GO terms were shared among the two treatments (GO:0019464 ‘glycine decarboxylation via glycine cleavage system’; GO:0009156 ‘ribonucleoside monophosphate biosynthetic process’; GO:0050482 ‘arachidonic acid secretion’; GO:0006355 ‘regulation of transcription, DNA-templated’; GO:0046274 ‘lignin catabolic process’; GO:0009765 ‘photosynthesis, light harvesting’; GO:0006529 ‘asparagine biosynthetic process’; GO:0016567 protein ubiquitination). Moreover, apart from GO:0006355 ‘regulation of transcription, DNA-templated’ where 26 and 20 DEGs were identified for PG and AB, respectively, all the other terms were represented by single or few DEGs in both treatments. GO:0006355 term included several transcription factor genes. BHLH, MYB and WRKY were the most rappresented transcription factor families found for AB treatment. On the other hand, in PG treaments, BHLH and MYB, ERF and GRASS were observed to be the most represented gene families (Supplementary Table 3). Another group of highly enriched GO terms in both treatments were those related to photosynthesis such as GO:0009765 ‘photosynthesis, light harvesting’, GO:0015979 ‘photosynthesis’, and GO:0009773 ‘photosynthetic electron transport in photosystem’.

GO terms related to ROS homeostatis of the cell, such as GO:0055114 ‘oxidation-reduction process’, GO:0042744 ‘hydrogen peroxide catabolic process’ and GO:000697 ‘response to oxidative stress’ were among the most highly enriched categories in the AB treatment. These GO categories included several DEGs coding for Reactive Oxygen Species (ROS)-scavenging enzymes, such as peroxidase and polyphenol oxidase (Supplementary File 3). Also GO terms related to response to abiotic stress factors such as water stress were positively modulated in AB treatment. On the other hand, in case of PG treatment, terms related to cell wall biosynthesis and modifications, especially those that occur during fruit ripening and organ abscission (GO:0006073 ‘cellular glucan metabolic process’, GO:0010411 ‘xyloglucan metabolic process’ and GO:0042546 ‘cell wall biogenes’ and GO:0046274 ‘lignin catabolic procss’) were the most highly enriched. GO categories related to response to abiotic and biotic factors, GO:0009733 ‘response to auxine’ and GO:0006952 ‘defense response’, were enriched after PG treatment as well.

DEGs were also used for a functional analysis using MapMan v3.6.0RC1 (Thimm et al. 2004), results of which showed that, while genes related to signalling were sligthly modulated (both down and up) after AB treatment, genes related to calcium signalling and receptor like kinase were induced after PG treatment (Supplementary Table 5). A similar pattern was observed for hormone-related genes: while five genes related to auxine and ethylene signalling were modulated in AB, several genes – in particular those involved in ethylene signalling pathways – resulted strongly induced in PG. An induction of genes coding for PR proteins was observed after AB treatment, that could suggest involvement of salicylic acid pathways in tomato seedlings molecular response. Genes involved in the regulation of transcription, protein degradation and transport resulted induced in both the treatments, even if the number of involed genes was often higher in PG. Finally genes involved in essential functions of the plants such as nutrition and water transport were modulated in both the treatments (Supplementary Table 4).

Validation Of 9 Degs Using Qrt-pcr

A RT-qPCR analysis of nine selected genes was carried out to validate the RNA-Seq data and elaboration results. Expression levels of the nine genes were reported in Table 4. The comparison between RNA-Seq and RT-qPCR fold change results showed a similar trend for all the assessed genes, validating the RNA-Seq analysis.

Table 4

Validation of RNA-seq results by qRT-PCR
Gene ID tomato SL 4.0	Chromosome	Gene description	AB		PG
Gene ID tomato SL 4.0	Chromosome	Gene description	RNA-Seq	RT q-PCR	RNA -Seq	RT q-PCR
Solyc10g076240.3.1	10	Cationic peroxidase 1-like	6.06	3.55	-0.33	-0.22
Solyc02g078650.4.1	2	Polyphenol oxidase, chloroplastic-like	5.33	2.78	1.16	0.74
Solyc01g106605.1.1	1	Basic form of pathogenesis-related protein 1-like	8.24	2.11		0.15
Solyc02g062390.3.1	2	Dehydrin DHN2	5.05	4.27	2.47	1.65
Solyc06g075650.3.1	6	Aquaporin TIP1-3-like	3.18	2.48	2.29	1.52
Solyc03g026280.3.1	3	AP2 domain CBF protein	0.55	-0.58	4.41	3.13
Solyc07g056000.2.1	7	Xyloglucan Endotransglucosylase/hydrolase 2	2.19	1.81	2.61	2
Solyc05g052040.1.1	5	Ethylene-responsive transcription factor 5	0.43	0.03	5.56	1.44
Solyc12g009240.1.1	12	Ethylene-responsive transcription factor ERF017	1.95	-0.22	4.72	2.07

Plant biostimulants, such as PGPR (plant growth-promoting rhizobacteria), are important tools of an integrated crop management system, that may help agriculture become more sustainable and resilient. The mechanisms of their beneficial effect may include enhanced nutrient uptake, improvement of biotic/abiotic stress resilience by activation of growth regulators and stress-responsive hormones, oxidative stress reduction and induction of plant defence mechanisms (for review see Hamid et al. 2021).

Two PGPR used in the present study were already demonstrated to have beneficial effect on crop production; Azospirillum brasilense (AB) Ab-V5 increased nitrogen use efficiency, improved maize tolerance to stress (Zeffa et al. 2019), while Paraburkholderia graminis (PG) increased the leaf chlorophyll content in three different processing tomato genotypes (Caradonia et al. 2019).

In a previous study in greenhouse and open field experiments using grafted tomato plants and different biostimolants, the treatment with A. brasiliensis induced flowering stage and increased the number of flowers in greenhouse experiment, while for both AB and PG treatments increased marketable and total yields with higher fruit dry weight, leaf dry weight, plant total dry weight were observed in open field (Caradonia et al. 2020).

In order to understand the molecular mechanisms underlying these interactions, the aim of this study was to profile, for the first time, to the author knowledge, the transcriptomic changes induced in leaves of grafted tomato seedlings by root inoculations of beneficial microorganisms P. graminis C4D1M and A. brasiliensis sp.245. Interactions between root beneficial microorganisms and plants can improve crop performances inducing molecular and physiologic changes also in organs distant from the point of inoculation such as shoots, leaves and fruits (Zouari et al. 2014; Cervantes-Gámez et al. 2016; Fiorilli et al. 2018). Leaves were chosen for this experiment as they play an important role in growth and development of crops, are involved in many processes such as photosynthesis and response to drought stress and represent the first barrier of defence that foliar pathogens meet, during infection of plants. In the present study, grafted tomato was used as a host, since currently, the commercial tomato grafting is widely adopted in the main cropping areas. Data on interactions between A. brasiliensis sp. 245 or P. graminis C4D1M and not grafted plants would not be informative for interactions between these beneficial microorganisms and widely used grafted tomato plants since a recent study has revealed that grafting modifies tomato transcriptome (Spanò et al. 2020) and studies on maize and rice demonstrated that the effects of Azospirillum spp. inoculations differed on the strain/cultivar combinations (Chamam et al. 2013; Walker et al. 2011). Moreover, RNA trascriptomic profiling studies available in literature cooncetrated mainly on effects of microorganisms assessed under a biotic (Hu et al. 2018; Zhou et al. 2021) or under an abiotic stress factor (Balestrini et al. 2019). Conversely, our stategy was to compare the influence of two beneficial microorganisms on tomato seedlings grown in optimal conditions that may allow to better undestand the molecular mechanisms influenced by AB and PG treatments regardless of whether the plants are under stress or not.

The number of DEGs detected after PG and AB treatments was 512 and 427, respectively, and is consistent with those obtained in other studies evaluating the effects of microorganism-plant interactions on gene expression in leaves (Cervantes-Gámez et al. 2016; Liu et al. 2007).

Only few DEGs were modulated in both treatments, suggesting that the interactions between tomato and microorganisms are species-specific, and affect the activity of specific sets of genes, that however activate and/or involve similar pathways and mechanisms. As it could be expected for PGRP-plant interaction, the shared DEGs included genes involved in nutrients metabolism or activated in response to nutrient starvation, such as Solyc06g062540.3.1, encoding Inorganic pyrophosphatase 1-like, and Solyc06g007180.31 encoding asparagine synthetase [glutamine -hydrolizing]. Asparagine synthetase is responsible for the biosynthesis of asparagine, an ammino acid used for protein production and nitrogen assimilation, and a key molecule involved in recycling, transport and storage of nitrogen in all plant organs (Gaufichon et al. 2010). An up-regulation of some inorganic pyrophosphatase genes in both roots and leaves of chickpea plants under nutritional stress has been recently reported (Nasr Esfahani et al. 2021).

Functional annotation of the DEGs revelead that they coded mainly for proteins involved in water transport, regulation of transcription and hormones synthesis and singnalling pathways and those activated in response to oxidative or biotic and abiotic stresses.

In the field experiment of our previous study (Caradonia et al. 2020), all the investigated treatments reduced the number of fruits affected by blossom-end rot (BER), a physiological disorder that causes important economic losses Although BER is mainly associated to the soil concentration of calcium available, it may be influenced also by reduced nutrient and water uptake (Hagassou et al. 2019; Caradonia et al. 2020). In the present study, the transcription profiling enabled identification of three acquaporins as differentially expressed: Solyc06g075650.3.1 in both treatments, and Solyc06g060760.3.1 and Solyc06g011350.3.1 only in AB treatment. Aquaporins, known as water channel proteins, help plants in the transport of water and other solutes such as glycerol and urea. Aquaporins also regulate the opening and closure of stomata, that are crucial processes for the temperature regulation of leaves and the evaporation of water (Chaumont et al. 2005). Therefore, the induction of aquaporin-coding genes can improve plant performance, especially under drought stress (Reuscher et al. 2013). Other proteins important for drought stress response are dehydrins, in this work Solyc02g062390.3.1 gene that codes for Dehydrin DHN2 was observed to be induced in both treatments. Dehydrins with an hydrophilic nature can improve hydratation and reduce water loss in plants (Roychoudhury and Nayek 2014). A study on pepper plants inoculated with Bacillus licheniformis K11 showed an induced expression of dehydrin-like protein gene and an higher number of survived plants, compared with the not inoculated control under drought stress (Lim and Kim 2013).

Many studies documented that plant growth promoting rhizobacteria can trigger a wide variety of defence mechanisms in plants (e.g. oxidative burst, production of antimicrobial compounds and expression of defence-related genes) (Bari and Jones 2009; Robert-Seilaniantz et al. 2011). In this study, particularly AB treatment modulated the expression of many genes involved in responses to oxidative stress and defense response such as ascorbate oxidase, peroxydase, polyphenol oxidase, PRs proteins, etc. Oxidative stress occurs when the balance between ROS production and degradation is broken, leading to an increase of ROS concentration that damages nucleic acids, proteins, and lipids (Lushchak 2011). To respond to the harmful effects of ROS, plants have developed systems involving enzymes such as superoxide dismutase, catalase, ascorbate oxidase, the peroxidases, etc. (Hasanuzzaman et al. 2012). Ascorbate oxidase (AO) catalyses the oxidation of ascorbic acid to monodehydroascorbate, influencing the content of ascorbate and oxigen, and affecting the redox state. Furthermore, this enzyme has a role in the perception of environmental factors and stress responses (Pignocchi et al. 2006). In addition, AO was also proposed as relevant in the establishment of mutualistic plant-microbe interactions as its induction was during nodulation in Lotus japonicus and during the colonization by an arbuscular mycorrhizal (AM) fungus (De Tullio et al. 2013). Studies on trasgenic tomato plants reported that peroxidase (POX) and polyphenol oxidase (PPO) are induced by wounding and pathogen attacks (Li and Steffens 2002; Thipyapong et al. 2004). Moreover, induced defense responses of PGPR-treated tomato plants to Alternaria solani were shown to be associated with enhanced POX and PPO biosynthesis (Babu et al. 2015). In the present study, a significant activation of antioxidant peroxidase and polyphenol oxidase ezymes was observed in plants treated with plant growth promoting rhizobacteria (PGPR) without pathogen infections. This suggests that these enzyme may be involved also in a sort of defense priming.

Interestingly, the PG inoculation induced the expression of five genes (Solyc05g007630.3.1, Solyc07g049700.1.1, Solyc05g013260.3.1, Solyc09g098100.4.1, Solyc05g005130.3.1) coding for putative late blight resistance protein homolog, while Solyc10g008700.1 coding for MYB49 trascription factor, whose expression was reported to correlate with an increase of resistance of tomato plants to Phytophtora infestans, (Cui et al. 2018) was induced by AB treatment. These results may suggest that treatment with beneficial microorganisms AB and PG might induce reponses that share mechanisms with those involved in resistance response to P. infestans. In a previous study (Caradonia et al. 2020), the field high moisture conditions allowed the spread of the oomicete Phytophthora infestans that was only partially controlled by foliar spray application using copper treatments in the field. However, at harvest time, better results were obtained for PGPR-treated than for control plants for both morphological parameters and fruit quality traits, suggesting the treatment contrasted the effects of infection. Since only few plant protection products are authorized in Europe against this pathogen for both potato and tomato crops, the control of late blight is a challenge (Park et al. 2013) especially in organic farming, where only copper compounds can be applied. It would be thus very intestesting to investigate in futher field and greenhouse trials the use of these beneficial bacteria as alternative or to reduce the use of copper compounds in organic farming against P.infestans for development of sustainable tomato management with low external inputs.

The expression of many transcription factor genes beloging to MYB and WRKY families, known to be modulated in response to different abiotic and biotic stresses (Huang et al. 2012; Wang et al. 2020), was modulated by AB and PG treatments in the present study as well. The unique nomenclature for tomato MYB and WRKY transcription factors family adopted and reviewed by Zhao et al. 2014 and Huang et al. 2012 respectively, was followed. AB treatment induced SlMYB71 (Solyc05g053150.2.1) together with SlMYB49 (Solyc10g008700.3.1) (already mentioned above), known to be also up-regulated along with the fruit development (Zhao et al. 2014), SlMYB41 (Solyc07g054840.4.1), that was reported to affects root architecture and improves tolerance to salinity in tomato plants (Campobenedetto et al. 2021), and SlMYB63 (Solyc10g005550.3.1), a root-specific transcription factor functioning as a node of convergence in the induced systemic resistance and Fe starvation signalling pathways (Buoso et al. 2019). Among WRKY transcription factors that were reported in literature to show significant induction under stresses of drought, salt and invasion of pathogen implying that these family members might be putative regulators in response to various biotic and abiotic stresses (Huang et al. 2012), we found SlWRKY43 (Solyc12g042590.2.1) and SlWRKY73 (Solyc03g113120.4.1) induced by AB treatments, SlWRKY41 (Solyc01g095630.3.1) and SlWRKY46 (Solyc08g067340.4.1) upregolated by PG, and SlWRKY6 (Solyc02g080890.3.1) induced by both treatments.

Many signalling molecules are involved in the cross-talks beetween crops and soil microorganisms. Phytohormones, such as auxins, gibberellins, ethylene, etc, are considered as the main signal molecules in plants (Puga-Freitas and Blouin 2015). In this study, genes involved in auxins, giberrellin, ethylene and abscisic acid metabolism and signaling were found to be induced in at least one of the two treatments. Auxins and gibberellins are involved in many aspects of plant growth and development. Mariotti et al. (2011) associated the positive modulation of indoleacetic acids (IAA), auxine responsive moelcules, and gibberellins with the fruit-set and early fruit growth in tomato. Recently, auxine responsive GH3.1 was proposed as a major player in balancing the auxin synthesis and metabolism, ensuring fruit set in pathenocarpic tomato in any conditions (Zhang et al. 2021). In the present study, Solyc01g107390 coding for GH3.1 was induced by AB treatment; it is worth noting that in the field experiment (Caradonia et al. 2020), higher number of fruits was reported for AB than for PG treatment, the latter anyway being higher than the control not inoculated.

Flower development, fruit ripening, organ senescence, abscission and responses to abiotic and biotic stresses are also modulated by ethylene (Iqbal et al. 2017; Liu et al. 2016). Ethylene signaling and response, in turn, are regulated by Ethylene Response Factors (ERFs) (Khan et al. 2017). The unique nomenclature for tomato ERF transcription factors family, adopted and reviewed by Liu et al. (2016), was followed for the genes identified in the present work. Numerous ERFs related to fruit ripening were modulated by PG treatments: SlERF.B4 (Solyc03g093540.1.1), SlERF.B5 (Solyc03g093550.1.1) and SlERF.D4 (Solyc10g050970.1.1) with preferential expression in young unripe fruits that declines at the onset of ripening and, SlERF.B1(Solyc05g052040.1.1) and SlERF.B13 (Solyc08g078190.2.1), known to be upregualated during ripening (Liu et al. 2016). Two other ERFs, SlERF.B2 (Solyc03g093560.1.1) and SlERF.F1 (Solyc10g006130.1.1), reported to be involved in salt and drought tolerance (Pan et al. 2012) and photosynthesis and growth regulation (Upadhyay et al. 2013), respectively, were induced by PG treatment as well.

AB induced expresion of a gene coding for SlERF.C4 (Solyc09g089930) reported in leterature to be involved in pathogen resistance and exhibiting high expression in roots, leaves, flowers, and immature fruits (Zhang et al. 2004).

Sequencing of RNA extracted from leaves of grafted tomato seedlings, inoculated with two beneficial plant promoting rhizobacteria and under optimal conditions, helped to decipher the mechanisms underlying the plant-beneficial microorganisms interactions in organ distant from roots. Functional analysis of DEGs enabled identification of common mechanisms modulated by the two treatments that, however, shared only few genes. The common pathways activated by PGPR in the present study involved genes coding for proteins related to water and nuntrients uptake, defence responses to biotic and abiotic stresses and hormonal regulation of fruitset and ripening. As far as differences identified in genes modulated by each treatments are regarded, while AB induced genes coding for different MYB trascrption factors known to be involved in response to biotic and abiotic stresses, PG upregulated 5 genes coding for putative late blight resistance protein homolog. Auxine responsive molecules and giberellins involved in the fruit-set and early fruit growth in tomato were mainly induced by AB correlating to higher fruit number obtained in a previous field study. On the other hand ERF transcription factors involved in ripening were induced mainly by PG treatment. All these results suggest that, as good performance was observed in field for plants incoluated with those PGPR applied individually and as consortium, and as the mechanisms they activate seem at least partially complementary, it would be interesting to study their synergic effect on tomato cultivation.

Acknowledgements: We thank Dr. Alessandro Infantino (Plant Protection and Certification Research Center, CREA) for the critical comments on the manuscript. we wish to thank R. Guidetti (Furia Seed, Monticelli Terme, Italy) and M. Beretta (ISI Sementi SpA, Fidenza, Italy) for providing the seeds of the genotypes used in this work. We wish to thank Dr. Valentino Landini from Coop. Habitat (S.Vito, Ferrara, Italy) for providing the grafted plant. The study has been partially funded by the Italian Ministry of Agriculture and Forestry in the DiBio–BIOPRIME project (Prot. 76381, MiPAAF PQAI I).

Funding: The study has been partially funded by the Italian Ministry of Agriculture and Forestry in the DiBio–BIOPRIME project (Prot. 76381, MiPAAF PQAI I).

Competing interest: All authors declare they have no financial interests.

Availability of data and material: RNA-Seq raw reads have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-11398

Code availability: Not aplicable

Author contribution: Federica Caradonia, Milc Justyna Anna, Enrico Francia, Valeria Terzi contributed to the study conception and design. Material preparation, data collection and analysis were performed by Federica Caradonia, Alessia Flore, Morcia Caterina, Domenico Ronga, Roberto Gatti and Lionel Moulin. Bioinformatic analalysis were performed by Matteo Buti. The first draft of the manuscript was written by Federica Caradonia, Milc Justyna Anna and Matteo Buti. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethics approval: Not applicable

Consent to participate: Not applicable

Consent for publication: Not applicable

Andrews S (2010) Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Balestrini R, Rosso LC, Veronico P, et al (2019) Transcriptomic Responses to Water Deficit and Nematode Infection in Mycorrhizal Tomato Roots. Front Microbiol 10:1807. https://doi.org/10.3389/fmicb.2019.01807
Bari R, Jones JDG (2009) Role of plant hormones in plant defence responses. Plant Mol Biol 69:473–488. https://doi.org/10.1007/s11103-008-9435-0
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics (Oxford, England) 30:2114–20. https://doi.org/10.1093/bioinformatics/btu170
Brown P, Saa S (2015) Biostimulants in agriculture. Front Plant Sci 6:. https://doi.org/10.3389/fpls.2015.00671
Buoso S, Pagliari L, Musetti R, et al (2019) ‘Candidatus Phytoplasma solani’ interferes with the distribution and uptake of iron in tomato. BMC Genomics 20:703. https://doi.org/10.1186/s12864-019-6062-x
Cammarano D, Ronga D, Di Mola I, et al (2020) Impact of climate change on water and nitrogen use efficiencies of processing tomato cultivated in Italy. Agricultural Water Management 241:106336. https://doi.org/10.1016/j.agwat.2020.106336
Campobenedetto C, Mannino G, Beekwilder J, et al (2021) The application of a biostimulant based on tannins affects root architecture and improves tolerance to salinity in tomato plants. Sci Rep 11:354. https://doi.org/10.1038/s41598-020-79770-5
Caradonia F, Francia E, Morcia C, et al (2019) Arbuscular Mycorrhizal Fungi and Plant Growth Promoting Rhizobacteria Avoid Processing Tomato Leaf Damage during Chilling Stress. Agronomy 9:299. https://doi.org/10.3390/agronomy9060299
Caradonia F, Ronga D, Flore A, et al (2020) Biostimulants and cherry rootstock increased tomato fruit yield and quality in sustainable farming systems. Ital J Agronomy. https://doi.org/10.4081/ija.2020.1553
Cervantes-Gámez RG, Bueno-Ibarra MA, Cruz-Mendívil A, et al (2016) Arbuscular Mycorrhizal Symbiosis-Induced Expression Changes in Solanum lycopersicum Leaves Revealed by RNA-seq Analysis. Plant Mol Biol Rep 34:89–102. https://doi.org/10.1007/s11105-015-0903-9
Chamam A, Sanguin H, Bellvert F, et al (2013) Plant secondary metabolite profiling evidences strain-dependent effect in the Azospirillum–Oryza sativa association. Phytochemistry 87:65–77. https://doi.org/10.1016/j.phytochem.2012.11.009
Chaumont F, Moshelion M, Daniels MJ (2005) Regulation of plant aquaporin activity. Biology of the Cell 97:749–764. https://doi.org/10.1042/BC20040133
Conesa A, Götz S (2008) Blast2GO: A comprehensive suite for functional analysis in plant genomics. International journal of plant genomics 2008:619832. https://doi.org/10.1155/2008/619832
Cui J, Jiang N, Zhou X, et al (2018) Tomato MYB49 enhances resistance to Phytophthora infestans and tolerance to water deficit and salt stress. Planta 248:1487–1503. https://doi.org/10.1007/s00425-018-2987-6
De Tullio M, Guether M, Balestrini R (2013) Ascorbate oxidase is the potential conductor of a symphony of signaling pathways. Plant Signaling & Behavior 8:e23213. https://doi.org/10.4161/psb.23213
Dimkpa C, Weinand T, Asch F (2009) Plant-rhizobacteria interactions alleviate abiotic stress conditions: Plant-rhizobacteria interactions. Plant, Cell & Environment 32:1682–1694. https://doi.org/10.1111/j.1365-3040.2009.02028.x
El-Esawi MA, Al-Ghamdi AA, Ali HM, Alayafi AA (2019) Azospirillum lipoferum FK1 confers improved salt tolerance in chickpea (Cicer arietinum L.) by modulating osmolytes, antioxidant machinery and stress-related genes expression. Environmental and Experimental Botany 159:55–65. https://doi.org/10.1016/j.envexpbot.2018.12.001
Fiorilli V, Vannini C, Ortolani F, et al (2018) Omics approaches revealed how arbuscular mycorrhizal symbiosis enhances yield and resistance to leaf pathogen in wheat. Sci Rep 8:9625. https://doi.org/10.1038/s41598-018-27622-8
Foolad MR (2007) Genome Mapping and Molecular Breeding of Tomato. International Journal of Plant Genomics 2007:1–52. https://doi.org/10.1155/2007/64358
Gaufichon L, Reisdorf-Cren M, Rothstein SJ, et al (2010) Biological functions of asparagine synthetase in plants. Plant Science 179:141–153. https://doi.org/10.1016/j.plantsci.2010.04.010
Hagassou D, Francia E, Ronga D, Buti M (2019) Blossom end-rot in tomato (Solanum lycopersicum L.): A multi-disciplinary overview of inducing factors and control strategies. Scientia Horticulturae 249:49–58. https://doi.org/10.1016/j.scienta.2019.01.042
Hamid B, Zaman M, Farooq S, et al (2021) Bacterial Plant Biostimulants: A Sustainable Way towards Improving Growth, Productivity, and Health of Crops. Sustainability 13:2856. https://doi.org/10.3390/su13052856
Hasanuzzaman M, Hossain MA, da Silva JAT, Fujita M (2012) Plant Response and Tolerance to Abiotic Oxidative Stress: Antioxidant Defense Is a Key Factor. In: Venkateswarlu B, Shanker AK, Shanker C, Maheswari M (eds) Crop Stress and its Management: Perspectives and Strategies. Springer Netherlands, Dordrecht, pp 261–315
Hosmani PS, Flores-Gonzalez M, van de Geest H, et al (2019) An improved de novo assembly and annotation of the tomato reference genome using single-molecule sequencing, Hi-C proximity ligation and optical maps. bioRxiv 767764
Hu H, Wang C, Li X, et al (2018) RNA-Seq identification of candidate defense genes targeted by endophytic Bacillus cereus -mediated induced systemic resistance against Meloidogyne incognita in tomato: RNA-Seq identification of candidate defense genes targeted by endophytic Bacillus cereus. Pest Manag Sci 74:2793–2805. https://doi.org/10.1002/ps.5066
Huang S, Gao Y, Liu J, et al (2012) Genome-wide analysis of WRKY transcription factors in Solanum lycopersicum. Mol Genet Genomics 287:495–513. https://doi.org/10.1007/s00438-012-0696-6
Iqbal N, Khan NA, Ferrante A, et al (2017) Ethylene Role in Plant Growth, Development and Senescence: Interaction with Other Phytohormones. Front Plant Sci 08: https://doi.org/10.3389/fpls.2017.00475
Khan NA, Khan MIR, Ferrante A, Poor P (2017) Editorial: Ethylene: A Key Regulatory Molecule in Plants. Front Plant Sci 8:1782. https://doi.org/10.3389/fpls.2017.01782
Kim D, Paggi JM, Park C, et al (2019) Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology 37:907–915. https://doi.org/10.1038/s41587-019-0201-4
Lawrence M, Huber W, Pagès H, et al (2013) Software for Computing and Annotating Genomic Ranges. PLoS Computational Biology 9:e1003118. https://doi.org/10.1371/journal.pcbi.1003118
Li L, Steffens J (2002) Overexpression of polyphenol oxidase in transgenic tomato plants results in enhanced bacterial disease resistance. Planta 215:239–247. https://doi.org/10.1007/s00425-002-0750-4
Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930. https://doi.org/10.1093/bioinformatics/btt656
Lim J-H, Kim S-D (2013) Induction of Drought Stress Resistance by Multi-Functional PGPR Bacillus licheniformis K11 in Pepper. The Plant Pathology Journal 29:201–208. https://doi.org/10.5423/PPJ.SI.02.2013.0021
Liu J, Maldonado-Mendoza I, Lopez-Meyer M, et al (2007) Arbuscular mycorrhizal symbiosis is accompanied by local and systemic alterations in gene expression and an increase in disease resistance in the shoots: Local and systemic alterations in transcript profiles in AM symbiosis. The Plant Journal 50:529–544. https://doi.org/10.1111/j.1365-313X.2007.03069.x
Liu M, Gomes BL, Mila I, et al (2016) Comprehensive Profiling of Ethylene Response Factor Expression Identifies Ripening-Associated ERF Genes and Their Link to Key Regulators of Fruit Ripening in Tomato. Plant Physiol 170:1732–1744. https://doi.org/10.1104/pp.15.01859
Løvdal T, Lillo C (2009) Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress. Analytical Biochemistry 387:238–242. https://doi.org/10.1016/j.ab.2009.01.024
Lushchak VI (2011) Adaptive response to oxidative stress: Bacteria, fungi, plants and animals. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology 153:175–190. https://doi.org/10.1016/j.cbpc.2010.10.004
Mariotti L, Picciarelli P, Lombardi L, Ceccarelli N (2011) Fruit-set and Early Fruit Growth in Tomato are Associated with Increases in Indoleacetic Acid, Cytokinin, and Bioactive Gibberellin Contents. J Plant Growth Regul 30:405–415. https://doi.org/10.1007/s00344-011-9204-1
Mendes R, Kruijt M, de Bruijn I, et al (2011) Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria. Science 332:1097–1100. https://doi.org/10.1126/science.1203980
Milc J, Bagnaresi P, Aragona M, et al (2019) Comparative transcriptome profiling of the response to Pyrenochaeta lycopersici in resistant tomato cultivar Mogeor and its background genotype—susceptible Moneymaker. Funct Integr Genomics 19:811–826. https://doi.org/10.1007/s10142-019-00685-0
Naidoo S, Visser EA, Zwart L, et al (2018) Dual RNA-Sequencing to Elucidate the Plant-Pathogen Duel. Current Issues in Molecular Biology 127–142. https://doi.org/10.21775/cimb.027.127
Narendra Babu A, Jogaiah S, Ito S, et al (2015) Improvement of growth, fruit weight and early blight disease protection of tomato plants by rhizosphere bacteria is correlated with their beneficial traits and induced biosynthesis of antioxidant peroxidase and polyphenol oxidase. Plant Science 231:62–73. https://doi.org/10.1016/j.plantsci.2014.11.006
Nasr Esfahani M, Inoue K, Nguyen KH, et al (2021) Phosphate or nitrate imbalance induces stronger molecular responses than combined nutrient deprivation in roots and leaves of chickpea plants. Plant Cell Environ 44:574–597. https://doi.org/10.1111/pce.13935
Pan Y, Seymour GB, Lu C, et al (2012) An ethylene response factor (ERF5) promoting adaptation to drought and salt tolerance in tomato. Plant Cell Rep 31:349–360. https://doi.org/10.1007/s00299-011-1170-3
Park Y, Hwang J, Kim K, et al (2013) Development of the gene-based SCARs for the Ph-3 locus, which confers late blight resistance in tomato. Scientia Horticulturae 164:9–16. https://doi.org/10.1016/j.scienta.2013.08.013
Pignocchi C, Kiddle G, Hernández I, et al (2006) Ascorbate Oxidase-Dependent Changes in the Redox State of the Apoplast Modulate Gene Transcript Accumulation Leading to Modified Hormone Signaling and Orchestration of Defense Processes in Tobacco. Plant Physiology 141:423–435. https://doi.org/10.1104/pp.106.078469
Puga-Freitas R, Blouin M (2015) A review of the effects of soil organisms on plant hormone signalling pathways. Environmental and Experimental Botany 114:104–116. https://doi.org/10.1016/j.envexpbot.2014.07.006
Raklami A, Bechtaoui N, Tahiri A, et al (2019) Use of Rhizobacteria and Mycorrhizae Consortium in the Open Field as a Strategy for Improving Crop Nutrition, Productivity and Soil Fertility. Front Microbiol 10:1106. https://doi.org/10.3389/fmicb.2019.01106
Reuscher S, Akiyama M, Mori C, et al (2013) Genome-Wide Identification and Expression Analysis of Aquaporins in Tomato. PLoS ONE 8:e79052. https://doi.org/10.1371/journal.pone.0079052
Robert-Seilaniantz A, Grant M, Jones JDG (2011) Hormone Crosstalk in Plant Disease and Defense: More Than Just JASMONATE-SALICYLATE Antagonism. Annu Rev Phytopathol 49:317–343. https://doi.org/10.1146/annurev-phyto-073009-114447
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616
Rothballer M, Schmid M, Fekete A, Hartmann A (2005) Comparative in situ analysis of ipdC-gfpmut3 promoter fusions of Azospirillum brasilense strains Sp7 and Sp245. Environ Microbiol 7:1839–1846. https://doi.org/10.1111/j.1462-2920.2005.00848.x
Salvioli A, Zouari I, Chalot M, Bonfante P (2012) The arbuscular mycorrhizal status has an impact on the transcriptome profile and amino acid composition of tomato fruit. BMC Plant Biol 12:44. https://doi.org/10.1186/1471-2229-12-44
Sato S, Tabata S, Hirakawa H, et al (2012) The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485:635–641. https://doi.org/10.1038/nature11119
Spanò R, Ferrara M, Montemurro C, et al (2020) Grafting alters tomato transcriptome and enhances tolerance to an airborne virus infection. Sci Rep 10:2538. https://doi.org/10.1038/s41598-020-59421-5
Storniolo CE, Sacanella I, Lamuela-Raventos RM, Moreno JJ (2020) Bioactive Compounds of Mediterranean Cooked Tomato Sauce (Sofrito) Modulate Intestinal Epithelial Cancer Cell Growth Through Oxidative Stress/Arachidonic Acid Cascade Regulation. ACS Omega 5:17071–17077. https://doi.org/10.1021/acsomega.9b04329
Tan G, Liu K, Kang J, et al (2015) Transcriptome analysis of the compatible interaction of tomato with Verticillium dahliae using RNA-sequencing. Front Plant Sci 6:. https://doi.org/10.3389/fpls.2015.00428
Thimm O, Bläsing O, Gibon Y, et al (2004) mapman: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. The Plant Journal 37:914–939. https://doi.org/10.1111/j.1365-313X.2004.02016.x
Thipyapong P, Hunt MD, Steffens JC (2004) Antisense downregulation of polyphenol oxidase results in enhanced disease susceptibility. Planta 220:105–117. https://doi.org/10.1007/s00425-004-1330-6
Upadhyay RK, Soni DK, Singh R, et al (2013) SlERF36, an EAR-motif-containing ERF gene from tomato, alters stomatal density and modulates photosynthesis and growth. Journal of Experimental Botany 64:3237–3247. https://doi.org/10.1093/jxb/ert162
Vessey JK Plant growth promoting rhizobacteria as biofertilizers. 16
Walker V, Bertrand C, Bellvert F, et al (2011) Host plant secondary metabolite profiling shows a complex, strain-dependent response of maize to plant growth‐promoting rhizobacteria of the genus Azospirillum. New Phytologist 189:494–506. https://doi.org/10.1111/j.1469-8137.2010.03484.x
Wang M, De Deyn GB, Bezemer TM (2019) Separating effects of soil microorganisms and nematodes on plant community dynamics. Plant Soil 441:455–467. https://doi.org/10.1007/s11104-019-04137-3
Wang Y, Zhuang K, Meng Q, Meng C (2020) Characterization and analysis of some chilling-response WRKY transcription factors in tomato. In Review
Yakhin OI, Lubyanov AA, Yakhin IA, Brown PH (2017) Biostimulants in Plant Science: A Global Perspective. Front Plant Sci 7:. https://doi.org/10.3389/fpls.2016.02049
Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11:R14. https://doi.org/10.1186/gb-2010-11-2-r14
Zanardo LG, de Souza GB, Alves MS (2019) Transcriptomics of plant–virus interactions: a review. Theor Exp Plant Physiol 31:103–125. https://doi.org/10.1007/s40626-019-00143-z
Zeffa DM, Perini LJ, Silva MB, et al (2019) Azospirillum brasilense promotes increases in growth and nitrogen use efficiency of maize genotypes. PLoS ONE 14:e0215332. https://doi.org/10.1371/journal.pone.0215332
Zhang H, Zhang D, Chen J, et al Tomato stress-responsive factor TSRF1 interacts with ethylene responsive element GCC box and regulates pathogen resistance to Ralstonia solanacearum. 10
Zhang S, Gu X, Shao J, et al (2021) Auxin Metabolism Is Involved in Fruit Set and Early Fruit Development in the Parthenocarpic Tomato “R35-P.” Front Plant Sci 12:671713. https://doi.org/10.3389/fpls.2021.671713
Zhao P, Li Q, Li J, et al (2014) Genome-wide identification and characterization of R2R3MYB family in Solanum lycopersicum. Mol Genet Genomics 289:1183–1207. https://doi.org/10.1007/s00438-014-0879-4
Zhou C, Zhu J, Qian N, et al (2021) Bacillus subtilis SL18r Induces Tomato Resistance Against Botrytis cinerea, Involving Activation of Long Non-coding RNA, MSTRG18363, to Decoy miR1918. Front Plant Sci 11:634819. https://doi.org/10.3389/fpls.2020.634819
Zouari I, Salvioli A, Chialva M, et al (2014) From root to fruit: RNA-Seq analysis shows that arbuscular mycorrhizal symbiosis may affect tomato fruit metabolism. BMC Genomics 15:221. https://doi.org/10.1186/1471-2164-15-221

SupplementalFigure1.tif
Supplementary Figure 1. Scatter plots of mean log₂ counts-per-million versus log₂ fold change plots. Transcriptional changes for AB and PG treatments Vs. non treated control are represented. AB and PG treatments significant DEGs (FDR<0.05) are indicated in blue and red, respectively.
SupplementaryTable1.xlsx
Supplementary Table 1. Primers used for quantitative reverse transcription PCR assays: IDs and sequences.
SupplementaryTable2.xlsx
Supplementary Table 2. Statistics for RNA-seq data elaboration. For each of the nine RNA-seq libraries, we reported the number of raw reads pairs and the statistics of filtering (number of survived paired reads, survival rate), aligning to reference genome (aligned concordantly 1 time, aligned concordantly >1 times, aligned discordantly 1 time, aligned singularly 1 time, aligned singularly >1 time, overall alignment rate) and assigning to annotated transcripts (total fragments, fragments assigned to transcripts, assigning rate).
SupplementaryTable3.xlsx
Supplementary Table 3. EdgeR differential expression analysis results for all the active transcripts.
SupplementaryTable4.xlsx
Supplementary Table 4. GO-enrichment analysis results for AB and PG treatments DE transcripts. For each GO term, over- and under-represented p-value, number of DE genes, total genes, ontology and term description were reported.
SupplementaryTable5.xlsx
Supplementary Table 5. Results of MapMan ‘Biotic stress’ response for AB and PG treatments DE transcipts.

Characterization of leaf transcriptome in grafted tomato seedlings inoculated with beneficial rhizobacteria Azospirillum brasiliensis and Paraburkholderia graminis

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Plant materials, growth conditions and microbial inoculation

Rna Extraction And Illumina Sequencing

Rna-seq Data Handling And Differential Gene Expression Analysis

Go Annotation And Enrichment

Quantitative Rt-real Time Pcr Validation

Results

RNA sequencing and differential expression analyses

Functional Annotation And Go-enrichment Analyses

Validation Of 9 Degs Using Qrt-pcr

Discussion

Conclusions

Statement And Declarations

References

Supplementary Files

Status:

Version 1