Transcriptomic responses of arbuscular mycorrhizal symbioses with negative growth phenotypes

doi:10.21203/rs.3.rs-5277187/v1

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Transcriptomic responses of arbuscular mycorrhizal symbioses with negative growth phenotypes

https://doi.org/10.21203/rs.3.rs-5277187/v1

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Arbuscular mycorrhizal (AM) fungi can play important roles in sustainable agriculture, given that they provide multiple benefits for numerous plant species. Conversely, negative plant growth effects induced by AM fungi are also commonly observed. At present, however, comparatively little information is available regarding the effects of AM fungi at the molecular level. In this study, compared with an absence of AM fungus inoculation, tomato seedlings grown in soil inoculated with Funneliformis mosseae or Rhizophagus intraradices were characterized by reduced shoot and root growth. To gain further insights into the underlying mechanisms at the molecular level, we performed transcriptomic analyses. We accordingly identified 190 and 870 differentially expressed genes (DEGs) in the F. mosseae vs. control and R. intraradices vs. control comparisons, respectively. KEGG enrichment analysis of the former 190 DEGs revealed significant enrichment of the “Protein processing in endoplasmic reticulum,” “Flavonoid biosynthesis,” “Flavone and flavonol biosynthesis,” and “Stilbenoid, diarylheptanoid, and gingerol biosynthesis” pathways, whereas “DNA replication,” “Photosynthesis - antenna proteins,” “Cutin, suberine, and wax biosynthesis,” “Protein processing in endoplasmic reticulum,” and “Glycerophospholipid metabolism” were identified as pathways significantly enriched with the latter 870 DEGs. GO functional analysis revealed that among both groups of DEGs, many genes were assigned the “Response to stimulus” term. Moreover, we established that many of the enriched terms were associated with stimulus and stress response processes, including response to salt stress, heat, and reactive oxygen species. Collectively, our findings in this study indicate that under the experimental conditions assessed, AM fungi may trigger defense-related responses in hosts, even though the symbioses were characterized by negative growth phenotypes. These findings will contribute to advancing our current understanding of AM fungi and highlight the fact that AM fungi should not be unthinkingly applied in agricultural production without due consideration of the prevailing conditions.

arbuscular mycorrhizal fungus

tomato

negative plant growth

response to stimulus

defense-related response

Among the most abundant organisms on this planet, arbuscular mycorrhizal (AM) fungi are found in nearly all terrestrial ecosystems, including agricultural systems, in which they establish symbiotic associations with a majority of vascular plant species (Wang et al., 2023; Rosendahl, 2008). These associations are characterized by the reciprocal exchange of mineral and organic resources between fungal symbionts and host plants (Martin et al., 2017), and given that AM fungi often assist hosts in enhancing plant nutrition, stress resistance and tolerance, and improving plant productivity, they have considerable potential in contributing to sustainable agriculture (Melini et al., 2023; Tang et al., 2023). However, these fungal–plant associations are not entirely beneficial, and indeed, when the net costs of the symbiosis exceed the net benefits, AM fungi might have parasitic effects on their host plants (Johnson et al., 1997). Consequently, gaining a comprehensive understanding of the activities of AM fungi is necessary.

Plants show diverse growth-responses to mycorrhizal colonization, ranging from positive through neutral to negative, and it has been found that the establishment and maintenance of AM symbioses may account for up to 20% of host photosynthetic products (Kameoka and Gutjahr, 2022; Ryan and Graham, 2002). Consequently, these fungi may have no beneficial effects on their hosts or even constitute a net drain on plant resources. For example, Rooney et al. (2011). who investigated the effects of four different species of AM fungi on the growth of Populus euramericana, found that these fungi generally reduced plant biomass, particularly root biomass. Furthermore, Changey et al. (2019) found that whereas AM fungi had no significant effect on the biomass of clover and tall fescue, they caused a reduction in that of ryegrass, and Malcova et al. (2003) showed that an association with the AM fungus Glomus intraradices resulted in a significant reduction in the dry weight in Agrostis capillaris under both lead stress and non-stress conditions. Similarly, Abdelhalim et al. (2019) demonstrated that association with the AM fungus Rhizophagus irregularis contributed to a significant reduction in the root dry weight of sorghum, although it had no apparent effect on shoot dry weight. Negative responses inclined to occur at high phosphorus (P) soil conditions, because fungus may continue to exact carbohydrates from the plant such that plant growth may be restrained, though the host can directly obtain P (Ryan and Graham, 2002). However, little information illuminate genes changes, when the symbiosis play negative responses in plant growth.

Tomato (Solanum lycopersicum) is one of the most widely cultivated and consumed vegetables worldwide, including China (Zhang et al., 2023). Tomato plants can be cultivated in both open fields and greenhouses, thereby ensuring that tomatoes are produced year-round in China. However, a common phenomenon associated with greenhouse cultivation is the long-term and excessive application of chemical fertilizers, which can contribute to an excess accumulation of nutrients (e.g., P) within the soil. Although symbiotic associations can be established between tomatoes and AM fungi (Melini et al., 2023; Chandrasekaran et al., 2022), high levels of soil nutrients can readily lead to situations in which the C-costs to host plants outweigh any benefits gained from AM colonization (Ryan and Graham, 2002).

Transcriptomic analyses have frequently been conducted to examine the effects of AM fungi on tomatoes under different conditions (Tominaga et al., 2022; Balestrini et al., 2019; Cervantes-Gamez et al., 2016; Salvioli et al., 2012). For example, Balestrini et al. (2019) assessed the transcriptomic responses of tomatoes to drought and nematode infection in mycorrhizal roots and highlighted the role of AM colonization in triggering defense responses against root-knot nematodes. Cervantes-Gamez et al. (2016) performed RNA-seq analysis to examine the effects of AM symbiosis on gene expression in tomato leaves and thereby revealed that AM fungi induce important modifications in transcription regulation, nutrient transport, and defense responses.

In this study, tomatoes were grown in a glasshouse during winter under natural light conditions and subjected to two AM fungi inoculation treatments, and we accordingly recorded significant reductions in the fresh and dry weights of the inoculated plants. Subsequently, we conducted transcriptomic analyses to investigate the effects of AM fungi at the molecular level. Our primary objective in this study was gain a further understanding of the molecular mechanisms of AM fungi in mycorrhizal associations, and on the basis of our findings, we caution against unthinkingly employing AM fungi in agricultural production without initially considering the prevailing environmental conditions.

2.1. Experimental conditions and design

Pot experiments were conducted from November 20, 2022, to February 12, 2023, in a glasshouse at Shanxi Agricultural University, Taiyuan, China, under natural light conditions at temperatures ranging from 10°C to 30°C. During this period, the average daytime length was 9.86 h (Fig. S1). The soil used in the experiment was collected from a greenhouse in which tomatoes had previously been cultivated. During the experiment, the moisture of the soil was maintained at 75% of the water-holding capacity based on daily weighing and adding tap water, as necessary. As experimental plants, we used tomato (Solanum lycopersicum L.), which were inoculated with one of two species of AM fungi [Funneliformis mosseae (FM) and Rhizophagus intraradices (RI)], with non-inoculated plants serving as control (CK). Each treatment was conducted using four replicates of one seedling per pot.

2.2 Experimental materials and culture

The F. mosseae (Bank of Glomales in China, No. BGC HEB07B) and R. intraradices (No. BGC BJ09) fungi used for inoculation were provided by the Beijing Academy of Agriculture and Forestry Sciences, China. The variety chose Jinfanqie No. 4 that was infinite-growth-typed tomato, which was purchased by Shanxi Kemeng Zhongye Company.

The tomato seeds were sterilized by immersing in 10% NaClO for 10 min, followed by three washes with sterile water. The plastic pots with upper and lower diameters of 17.0 and 14.0 cm, respectively, and a height of 18.0 cm were washed and sterilized using 10% NaClO, each of which was filled with 2.5 kg of sterilized soil that had been autoclaved for 2 h at 121°C and 0.24 MPa pressure. The soil properties were as follows: pH, 8.33; electrical conductivity, 0.47 mS·cm^− 1; organic matter content, 5.7%; available nitrogen, 8.7 mg·kg^− 1; available phosphorus, 390.0 mg·kg^− 1; and available potassium, 56.8 mg·kg^− 1. For the FM treatments, soils were treated with a 50-g inoculum of F. mosseae (a mixture of soil substrate, mycorrhizal root fragments, spores, and mycelia), and similarly, for the RI treatments, soils were treated with a 50-g inoculum of R. intraradices. For the control treatment, we added 50 g of a sterilized inoculum of F. mosseae and R. intraradices (an autoclaved mixture of soil substrate, mycorrhizal root fragments, spores, and mycelia of F. mosseae and R. intraradices). In addition to each pot, we added 10 mL of an aqueous filtrate of inoculant (passed through a 0.25-µm filter membrane to maintain the same bacterial biota but free from mycorrhizal propagules). Inocula were placed 2 cm below the soil surface, and seeds were sown at a depth of 1 cm. On day 15, after sowing, the seedlings were thinned out to leave a single seedling in each pot, and then each pot was added 50 mL Hogland solution. At 84 days after sowing, the plants between treatments and control showed significant different on phenotype under naked eyes, and then all plants were sampled and harvested.

2.3. Biomass and mycorrhizal colonization measurements

The fresh leaves were firstly washed with phosphate buffer saline (PBS) and distilled water, and gathered at 1 g fresh weight and then frozen in liquid nitrogen and transferred to ‒80°C refrigerator. The fresh roots were gathered at 1 g fresh weight, and then immersed in 50% ethanol to measure mycorrhizal colonization. The remainder shoots and roots were washed and weighed as fresh weight, and then dried at 70°C for 2 days and finally measured the shoot dry weight and root dry weight.

The preparative roots were washed and then cut 1 cm root segments. The fractions were decolorized with 10% KOH and stained with 0.05% trypan blue in lactophenol (Phillips and Hayman, 1970). The stained roots were magnified 200 times and observed using microscope. Mycorrhizal colonization was measured by the gridline intersect method (Giovannetti and Mosse 1980).

2.4. RNA isolation, cDNA library construction and RNA-seq

Each treatment randomly selected 3 biological repetitions. Total RNA of leaf tissues were extracted by RNA prep pure plant kit (Invitrogen, Carlsbard, CA, USA), including the removal of genomic DNA by DNase I. The library construction and RNA-seq assay entrusted to the Majorbio Technology Company (Shanghai, China).

The transcriptome libraries were constructed using Illumina TruSeqTM RNA sample preparation Kit (San Diego, CA, USA). Briefly, the mRNA was firstly extracted from total RNA using oligo-dT-attached magnetic beads, and fragmented using a fragmentation buffer. The cDNA was synthesized with random hexamer primers using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, CA, USA) and followed to end-repair, phosphorylation and “A” base addition. The libraries were amplify using Phusion DNA polymerase (Boston, MA, USA), and the these were selected from 200–300 bp cDNA target fragments on 2% Ultra Agarose, and then quantified using TBS380, and finally sequenced in single lane on an Illumina HiSeq X Ten (Illumina, San Diego, CA, USA). The data were finally submitted to the Sequence Read Archive (SRA) module of the National Center for Biotechnology Information (NCBI) database (Accession number: PRJNA1096969).

2.5. Clean reads evaluation and gene functional annotation

The raw data were tidied, based on the removal of reads containing adapters, ambiguous “N” bases and low quality. A total of 454,956,762 clean reads were got in 9 samples. The GC contents and the Q30 of clean reads were estimated. These clean reads were mapped into the reference genome of tomato (Solanum lycopersicum) from RefSeq assembly edition GCF_000188115.5 in NCBI (https://www.ncbi.nlm.nih.gov/genome/?term=txid4081) with software Hisat2. These mapped reads were assembled and merged to get transcript and genes with software StringTie. The length distribution of transcripts was showed in Fig. S2. Principal component analysis (PCA) was performed in the expression levels of genes, which was plotted in Fig. S3. Next, the genes and transcript were aligned to the Non-Redundant Protein Sequence Database (NR), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and Pfam databases.

2.6. Calculation of gene expression, identification and enrichment analysis of differentially expressed genes.

The gene and transcript expression levels were calculated with software RSEM using a method of FPKM normalization. Differentially expressed genes (DEGs) were determined with R package edger using a cutoff of adjusted p-value (FDR) < 0.05 and |log₂^{(fold change)}| > 1, as previously described (Chen et al., 2016).

These DEGs were hit and classified in the KEGG and GO database, and further the significances of KEGG pathways and GO terms were analyzed with adjusted p-value (FDR: Benjamini-Hochberg method) < 0.05, respectively.

2.7. qRT-PCR

A total of 6 candidate DEGs were randomly chosen from all DEGs, whose expression levels were significant regulation in treatments, compared with that in control. The 6 DEGs were performed qRT-PCR analysis to validate the reliability of RNA-seq data. The materials and methods of qRT-PCR analysis were described in Supplementary text.

2.8. Statistical analysis

“Fresh weight” and “Dry weight” in shoot and root, and “Mycorrhizal colonization” were analyzed with One-Way Anova and Duncan's multiple range test by software SPSS 16.0 (SPSS Inc., IL, USA), and data were indicated with means ± standard deviation (n = 4). All figures were visualized with R package ggplot2 or pheatmap.

3.1. Growth parameters

There were no colonization in CK (Fig. 1c). AM fungi F. mosseae and R. intraradices successfully founded symbiosis with tomatos, and all roots of plants in FM and RI were observed architectures of AM fungus, including arbuscules, hyphae and vesicles. The colonization rates were 49.2% and 52.3% in FM and RI, respectively.

The shoot fresh weights were significantly decreased by 37.7% and 33.3% in FM and RI, respectively, compared with that in CK (Fig. 1a). The shoot dry weights were significantly decreased by 37.8% and 32.3% in FM and RI, respectively (Fig. 1b). Similarly, the fresh and dry weights of root were significantly decreased by 39.4% and 42.6% in FM, respectively, and by 41.5% and 52.3% in RI, respectively.

3.2. Quality assessment of transcriptome

To further understand changes of genes induced by AM fungi in tomatoes, leaf tissues were collected to perform transcriptomic analysis. A total of 9 cDNA libraries were structured in experiment, and 457 million raw reads were got (Table S1). From 47 to 55 million clean reads were retained in per library, after trimming low-quality bases and adaptor sequences. The percent Q30 were computed and the lowest value was 94.4%. The ranges of GC content ranged from 43.1–43.6% in 9 libraries. All clean reads were mapped into the reference genome of tomato, and the mapped average percent was 97.1%. These parameters indicated the accuracy of RNA-seq data.

3.3. Transcriptomic annotation and identification of DEGs

After assembling with reference-based genome of tomato, transcripts and genes were hit the NR, GO and KEGG database. A total of 24,355 (97.4%) genes were hit in NR database, and 19,872 (79.5%) and 10,710 (42.8%) genes were hit in GO and KEGG database, respectively. After computing the expression levels and filtering out genes with extremely low expression, a total of 190 DEGs and 870 DEGs were screened in the FM vs. CK and RI vs. CK comparisons, respectively (Fig. 2). Volcano plots were drawn to visualize the regulations.

3.4. KEGG functional category and enrichment analysis of DEGs

To examine the effects of AM fungi on plant molecular pathways, the 190 and 870 DEGs were categorized functionally by performing enrichment analysis using the KEGG database (Fig. 3). Among the former 190 DEGs, we were able to assign 58 genes to a KEGG pathway, with the “Folding, sorting and degradation” pathway accounting for the largest number of DEGs (18) (Fig. 3a), followed by the “Biosynthesis of other secondary metabolites” and “Carbohydrate metabolism” pathways. Enrichment analysis revealed four significantly enriched pathways in the third category of the KEGG database (FDR < 0.05), namely, “Protein processing in endoplasmic reticulum,” “Flavonoid biosynthesis,” “Flavone and flavonol biosynthesis” and “Stilbenoid, diarylheptanoid, and gingerol biosynthesis” (Fig. 3c), the first of which contained the largest number of DEGs (17).

Among the 870 DEGs identified in the RI vs CK comparison, 227 genes were assigned to KEGG pathways, the top three categories of which were “Carbohydrate metabolism,” “Lipid metabolism,” and “Biosynthesis of other secondary metabolites,” which accounted for 90 (39.6%) of the DEGs (Fig. 3b). A total of five pathways were significantly enriched (FDR < 0.05), which, in descending order, were “DNA replication,” “Photosynthesis - antenna proteins,” “Cutin, suberine, and wax biosynthesis,” “Protein processing in endoplasmic reticulum,” and “Glycerophospholipid metabolism” (Fig. 3d). Again, the “Protein processing in endoplasmic reticulum” pathway contained the largest number of DEGs (18).

3.5. GO functional category and enrichment analysis of DEGs

To examine the influence of AM fungi on the biological processes of plants, we performed functional category and enrichment analysis of the DEGs based on reference to the GO database (Fig. 4). Among the 190 DEGs identified in the FM vs. CK comparison, we were able to assign 116 to at least one biological process term (Fig. 4a), the top three of which were “Cellular process,” “Metabolic process,” and “Response to stimulus,” the latter of which accounted for 41 (35.3%) DEGs. The results of enrichment analysis revealed a significant enrichment of 20 biological process terms (Fig. 4c) (FDR < 0.05), which were mostly associated with the responses to stress or stimulus. Furthermore, we found that the GO term “Response to abiotic stimulus” accounted for the highest number (22) of DEGs.

Among the 870 DEGs identified in the RI vs. CK comparison, 473 were assigned to a biological process term, the top three of which were again “Cellular process,” “Metabolic process,” and “Response to stimulus” (Fig. 4b), the latter of which accounted for 179 (37.8%) of the DEGs. Enrichment analysis revealed a significant enrichment of 27 biological process terms (Fig. 4d), which were mainly associated with the responses to stress and stimulus, with the exception of some terms relating to DNA replication. The term “Response to stimulus” accounted for the largest number (179) of DEGs, followed by “Response to stress,” which was enriched with 127 DEGs.

3.6. Common DEGs

Among the 190 and 870 genes that were found to be differentially expressed in the FM vs. CK and RI vs. CK comparisons, respectively, we identified 58 genes that were differentially expressed in plants treated with both of the assessed fungi (Fig. 5, Table S2). Figure 5a presents a heatmap showing the differences in the expression of the 58 genes. These common DEGs were subsequently subjected to KEGG (Fig. 5b) and GO (Fig. 5c) enrichment analyses, on the basis of which we identified one significantly enriched KEGG pathway (FDR < 0.05), namely “Protein processing in endoplasmic reticulum” (12 DEGs), whereas “Protein complex oligomerization” was identified as the most significant GO term among biological process, and “Response to stress” was the term enriched with the most DEGs (17).

3.7. DEGs responding to stimuli

The DEGs, which were enriched in GO term “Response to stimulus” (GO0050896), were gained an insight into detailed expression patterns (Fig. 6, Table S3). In the FM vs. CK comparison, we identified a total of 41 DEGs that were enriched this term, of which two were up-regulated, and 39 were down-regulated (Fig. 6a, Table S3). The up-regulated genes were STP11 (a sugar transport protein) and MSTRG.19800. The gene NDPS1 (neryl diphosphate synthase 1), with a 0.059 fold change, was identified as the most down-regulated gene.

Among the 179 DEGs enriched in the “Response to stimulus” term in the RI vs. CK comparison, 107 genes were up-regulated (Fig. 6c, Table S3), and 72 genes were down-regulated (Fig. 6b, Table S3). The gene LOC101247255, associated with sugar transport, was identified as the most up-regulated, with a 14.57-fold change, whereas with a fold change of 0.051, LOC101248236, described as peroxidase 44, was the most down-regulated.

3.8. qRT-PCR validation

A qRT-PCR technique was employed to verify the data of RNA sequencing in the experiment. A total of 6 DEGs were randomly chose to accomplish qRT-PCR analysis. The expression levels of the 6 genes from qRT-PCR analysis strongly tallied with those from RNA sequencing (Fig. S4). Therefore, this result indicated the credibility of the RNA-Seq data.

AM symbioses, which are among the oldest interactions on earth, are believed to have emerged during the Early Devonian period (~ 393 to 419 Ma) (Martin et al., 2017), and are considered among the most universal of ecological relationships (Sportes et al., 2021). These associations have been established to confer multiple benefits to host plants, including enhanced nutrient uptake and improved tolerance to salinity, drought, and heavy metals, and biotic stresses, and may also contribute to strengthening the plasticity of plants and promoting their growth (Wang et al., 2023; Sportes et al., 2021). However, these beneficial effects are not necessarily ubiquitous. Indeed, we have often found evidence to indicate that AM fungi have little or no influence on the accumulation of plant dry matter, and can even inhibit the growth of hosts, particularly during winter. Consistent with these observations, in this study, we found that the presence of both the AM fungi F. mosseae and R. intraradices was associated with a significant reduction in the shoot and root dry weights of inoculated tomato seedlings (Fig. 1). Similar findings have been reported by Liu et al. (2005) and Xu et al. (2018), who found that AM fungi suppressed the growth of tomatoes. Similar negative responses have also been observed in other plants, including ryegrass, P. euramericana, and A. capillaris (Changey et al., 2019; Rooney et al., 2011; Malcova et al., 2003). These finding are perhaps unsurprising given the necessity for AM fungi obtain photo-assimilates from hosts to subsist and release certain exudates into the soil to promote their functional activity (Wang et al., 2023). In this regard, recent discoveries have provided important insights with respect to the transfer of lipids and sugars from host plants to AM fungi (Fossalunga and Novero, 2019; Wang et al., 2017). Among the DEGs detected in the present study, we identified a number of genes classified into Sugar_tr (described as sugar transporters) and LTP_2 (described as lipid transporters) families (Table S4) using the Pfam database. All eight DEGs classified as Sugar_tr family genes were found to be up-regulated under the experimental conditions, whereas of the six DEGs classified as LTP_2 family genes, three were up-regulated and three down-regulated. These findings are consistent with the fact that AM fungi can induce the expression of sugar transporters and influence that of lipid transporters. Moreover, our functional categorization results revealed that a high proportion of the identified DEGs are associated with carbohydrate metabolism. Hence, for the purposes of the present study, we performed a greenhouse experiment during the winter months under natural light conditions. The weak light intensity and limited periods of sunlight during this time of year contribute to reduced levels the carbon fixation in plants, although maintenance of AM symbionts continues to exact a cost in terms synthesized carbohydrates.

To identify the tomato genes induced by AM fungi when these symbionts play a parasitic role, we further analyzed the transcriptomic data to characterize the modulatory effects of AM fungi in tomato leaves. We accordingly identified totals of 190 and 870 genes that were differentially expressed in the FM vs. CK and RI vs. CK comparisons, respectively (Fig. 2). In both cases, GO enrichment analysis revealed many significant functional terms associated with responses to stimulus or stress, including responses to salt stress, heat, and reactive oxygen species (Fig. 4). Moreover, numerous DEGs were enriched in the GO term “Response to stimulus” (Figs. 4 and 6, Table S3). In this regard, the findings of a number of previous studies have revealed that AM fungi can trigger genes associated with the response to stress or plant immunity. For example, Sportes et al. (2023), who performed the transcriptome profiling of grapevine rootstocks responding to mycorrhizal symbiosis, found that the AM fungus R. irregularis induced stress-relevant genes, and Zouari et al. (2014) found that the AM fungus F. mosseae also regulates these genes in tomato fruits at the turning ripening stage. Furthermore, Cervantes-Gamez et al. (2016) showed that the AM fungus R. irregularis may be able to activate resistance mechanisms prior to the imposition of stress, based on inducing stress response-related genes in tomatoes. Similarly, Cope et al. (2022) found that the AM fungus G. aggregatum induced the expression of stress defense response genes in Medicago truncatula, although its presence did not contribute to increasing the dry weights of plants, which accordingly provided evidence to indicate that the host may prime defense responses. Accordingly, AM fungi might activate host mechanisms associated with the responses to stimuli and stresses and promote resistance mechanisms in tomatoes, even though these plants may be characterized by the negative growth phenotype.

Our enrichment analysis analyses in this study, based on an examination of two groups of DEGs, revealed that in mycorrhizal associations between tomato plants and two different AM fungi, the “Protein processing in endoplasmic reticulum” pathway was the pathway enriched with the largest number of DEGs (Fig. 3). Zhang et al. (2020) have similarly reported the enrichment of this pathway in their study of the effects of F. mosseae on gene expression in soybean plants infected with the fungal pathogen Fusarium oxysporum. The endoplasmic reticulum plays an important role in numerous cellular processes, including the synthesis of proteins and lipid and the folding and post-translational processing of newly synthesized proteins (Griffing et al., 2017; Kumar et al., 2017). These membranes are also the site of entry for extracellular proteins into the secretory network (Kumar et al., 2017). Thus, these findings would tend to indicate that AM fungi can influence the synthesis and secretion of certain proteins or lipids. Further investigations are accordingly warranted to determine the underlying mechanisms.

Finally, our enrichment analysis conducted for those genes that were commonly differentially expressed in both of the two treatment groups revealed that the enriched terms were primarily associated with response to stimuli or stresses, with “Protein processing in endoplasmic reticulum” being identified as the pathway enriched with the largest number of DEGs (Fig. 5, Table S2), which is consistent with the aforementioned findings.

In this study, we found that tomato seedlings cultivated in soil inoculated with two different species of AM fungi (F. mosseae and R. intraradices) were characterized by reductions in the fresh and dry weights of shoots and roots. To examine the mechanisms underlying these responses at the molecular level, we adopted a transcriptomic approach, on the basis of which we identified 190 and 870 DEGs in F. mosseae vs. control and R. intraradices vs. control comparisons, respectively. Among these DEGs, we identified eight associated with sugar transport that were all up-regulated under the experimental conditions, as well as numerous DEGs associated with carbohydrate metabolism. This accordingly provides evidence to indicate that AM fungi may regulate the allocation of photo-assimilates. Enrichment analysis of the two DEG groups based on reference to the KEGG database revealed that the largest number of DEGs were enriched in the “Protein processing in endoplasmic reticulum” pathway. The results of GO functional enrichment analysis revealed many DEGs to be assigned to the “Response to stimulate” term. Moreover, we identified numerous significantly enriched terms associated with response to stimulus or stress. These findings would thus tend to indicate that AM fungi might activate the resistance mechanisms of hosts, even though plants were characterized by a negative growth phenotype. Future studies should focus comprehensively on the transition from negative to positive effects in order to further elucidate the roles of AM fungi in plant growth.

Author contributions

Fengwei Diao: Writing-original draft, visualization, software, methodology, investigation, funding acquisition, data curation. Ke Liu: Writing-editing, investigation, software, data curation. Wenjing Wu: Writing-editing, methodology, validation. Xiangyuan Shi: Resources, conceptualization, supervision. Xiuhong Wang: Writing-review, project administration, funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding information

This work was supported by the Program of Doctoral research Foundation of Shanxi Agricultural University [2021BQ132]; the Shanxi Province Science Foundation for Youths [202103021223152]; the Shanxi Provincial Key Research and Development Project [202102140601012]; the National Key Research and Development Program of China [2021YFD1901105]; the Science and Technology Major Project of Shanxi Province, China [202101140601026].

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No competing interests reported.

FigureS1.tif
Fig. S1. Daytime length of each day in the experiment duration.
FigureS2.tif
Fig. S2. Length distribution of transcripts of 9 libraries.
FigureS3.tif
Fig. S3. Principal component analysis (PCA) based on the expression levels of genes for inoculated tomatoes.
FigureS4.tif
Fig. S4. Validation of 6 randomly selected differentially expressed genes using the qRT-PCR.
SupplementarytextSupplementarymaterial.docx
TablesSupplementarymaterial.docx
Table S1 Quality assessment of 9 libraries of inoculated tomatoes Table S2 Regulation details of 58 commonly differentially expressed genes Table S3 Regulation details of differentially expressed genes enriched in the GO term “Response to stimulus” Table S4 Differentially expressed genes classified into Sugar_tr or LTP_2 family in the Pfam database

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Transcriptomic responses of arbuscular mycorrhizal symbioses with negative growth phenotypes

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1. Experimental conditions and design

2.2 Experimental materials and culture

2.3. Biomass and mycorrhizal colonization measurements

2.4. RNA isolation, cDNA library construction and RNA-seq

2.5. Clean reads evaluation and gene functional annotation

2.6. Calculation of gene expression, identification and enrichment analysis of differentially expressed genes.

2.7. qRT-PCR

2.8. Statistical analysis

3. Results

3.1. Growth parameters

3.2. Quality assessment of transcriptome

3.3. Transcriptomic annotation and identification of DEGs

3.4. KEGG functional category and enrichment analysis of DEGs

3.5. GO functional category and enrichment analysis of DEGs

3.6. Common DEGs

3.7. DEGs responding to stimuli

3.8. qRT-PCR validation

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1