Abstract
Fungi of the Trichoderma genus are called "biostimulants" because they promote plant growth and development and induce disease resistance. We used conventional transcriptome and gene co-expression analyses to understand the molecular response of the plant Arabidopsis thaliana to inoculation with Trichoderma atroviride or Trichoderma virens. The transcriptional landscape of the plant during the interaction with these fungi showed a reduction in functions such as reactive oxygen species production, defense mechanisms against pathogens, and hormone signaling. T. virens, as opposed to T. atroviride, was more effective at downregulating genes related to terpenoid metabolism, root development, and chemical homeostasis. Through gene co-expression analysis, we found functional gene modules that closely link plant defense with hypoxia. Notably, we found a transcription factor (locus AT2G47520) with two functional domains of interest: a DNA-binding domain and an N-terminal cysteine needed for protein stability under hypoxia. We hypothesize that the transcription factor can bind to the promoter sequence of the GCC-box that is connected to pathogenesis by positioned weight matrix analysis.
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Introduction
Plants are naturally exposed to abiotic and biotic stresses that affect their growth and reproduction. Abiotic stress is triggered by physical factors such as temperature, drought, and salinity, among others. In contrast, biotic stress is caused by living organisms (or biological entities), including insects, bacteria, fungi, oomycetes, viruses, or other plants1.
Stress triggers different responses in plants, such as reprogramming of gene expression, changes in cell metabolism, and alterations in the vegetative and reproductive phases1. Biotic stress directly deprives the plant of its nutrients, provokes tissue damage, and could lead to death1. Biotic interactions, however, are not always unfavorable to plants since they can also be established with beneficial organisms. Plant-beneficial microorganisms comprise specific groups of bacteria and fungi that have positive effects, such as plant growth promotion, increases in nutrient uptake, and might even activate defense mechanisms. Conversely, pathogenic organisms provoke plant disease and may cause tissue damage or death2. In recent decades, research has focused on studying plant–microbe beneficial mutualistic interactions due to their implications in agriculture3.
Plants have evolved various defense mechanisms against attack by pathogens. The first layers of plant defense against pathogens comprise the physical and chemical mechanisms. Subsequently, an innate immune response may be established. Basal resistance is a type of local resistance that involves the detection of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRR), leading to PAMP-triggered immunity (PTI)4. In response to plant hostility, pathogens counteract with effector molecules to block the plant defense signal transduction, triggering effector susceptibility (ETS). To hinder pathogen effectors activity, plants evolved resistance proteins (R) of the NLR (nucleotide-binding/leucine-leucine-rich-repeat) family, commonly found in the cytoplasm. NLRs directly or indirectly recognize their cognate effectors, leading to effector-triggered immunity (ETI)5,6,7.
Furthermore, PAMPs and effectors can trigger the systemic acquired resistance (SAR) at distant parts of the original point of infection, mediated by the phytohormone salicylic acid (SA). Arabidopsis mutants defective in SA biosynthesis or responsiveness are compromised in basal defense and SAR8. SAR is effective against biotrophic and hemibiotrophic microorganisms9. In addition, PAMPs or MAMPs (microbe-associated molecular patterns) of necrotrophic or beneficial microorganisms can trigger induced systemic resistance (ISR) through the phytohormones jasmonate/ethylene (JA/ET) pathways10. Intriguingly, the interaction of plants with mutualistic microorganisms result in plant growth stimulation, biomass gaining, and changes in root architecture that lead to a more efficient nutrient uptake11.
Species of the Trichoderma genus are cosmopolitan filamentous fungi of agricultural interest, since several isolates have been used as biological control agents of phytopathogens because they feed on other fungi, produce antibiotics, and are rhizosphere competent11. Furthermore, Trichoderma spp. colonize the plant root surface to establish mutualistic relationships with plants, modifying root architecture12, increasing nutrient uptake capacity and growth13. In addition, Trichoderma simultaneously induces SAR and ISR to render plants resistant to a wide range of pathogens with different lifestyle12,14,15.
In addition, after their interaction with Trichoderma, plants become more tolerant to abiotic stress. Mona et al.16 reported increased drought resistance of tomato plants inoculated with Trichoderma harzianum. According to these authors, Trichoderma mitigates the harmful effects of drought in the host, which correlates with an increase in secondary metabolites such as phenols, flavonoids, indole acetic acid (IAA), indole butyric acid and gibberellic acid. Similarly, Zhang et al.17 reported that cucumber plants (Cucumis sativus) treated with T. atroviride HN082102.1 show increased tolerance to high concentration of NaCl (sodium chloride).
It has been proposed that low oxygen concentrations (hypoxia) could trigger resistance in plants against pathogens, thus suggesting a cross-talk between hypoxia and plant immunity18. Like animals, higher plants are aerobic organisms because oxygen is essential for their metabolism and growth. In nature, plants experience hypoxia under excessive rainfall or flooded soils, resulting in low oxygen concentration in the rhizosphere19. This hypoxia leads plants to fine-tune growth and metabolism to adapt under such conditions. Ethylene response transcription factors (ERFs) are known to play an important role in hypoxia. Some rice varieties have adapted to hypoxia thanks to transcription factors known as "snorkels" that respond to ethylene, allowing them to adapt their growth 20. In A. thaliana, oxygen regulates the stability of the ERF-VII family of transcription factors. These proteins have an N-terminal cysteine targets them for degradation by the proteasome in the presence of oxygen. On the contrary, in the absence of oxygen, the ERF-VII is stabilized and translocated to the nucleus, where they induce the transcription of genes to alleviate hypoxia 2.
Although the Trichoderma-plant interaction is a well-studied phenomenon, we need more information to elucidate the molecular mechanisms that govern the functional associations between biological processes in plants during their interaction with the fungus. Conventional approaches that usually characterize one or two genes do not represent the complex adaptation mechanism of the plant against biotic and abiotic stresses. However, with the advent of “omic sciences" in combination with bioinformatics, various mathematical methodologies have been proposed to analyze massive amounts of data that allow us to understand these biological interactions. In this regard, in transcriptomic analysis of tomato interacting with T. harzianum (T22) and aphids, the induction of early and late defense genes clarified the dynamics in the behavior of genes in time21. They identified genes with an indirect role in tomato defense against insects, such as sesquiterpene synthase and geranylgeranyl phosphate synthase encoding genes. de Palma et al.22, based on transcriptomic analysis, proposed that the promotion of growth in tomato plants by T. harzianum was due to the differential expression of nutrient transporter genes and the modifications in root architecture through the ethylene/indole-3-acetic acid signaling pathways.
The analysis of co-expression networks allows us to simultaneously identify and explore thousands of genes with similar expression patterns under certain conditions that are grouped to respond to a stimulus. This methodology aims to identify causal variables and regulatory mechanisms that control these processes23.
Gene co-expression network analysis is a mathematical methodology that helps to explore and visualize how genes are associated in a biological system to fulfill a specific function. Co-expression network analysis has its theory behind it, known as the graph theory. This theory is a mathematical methodology whose origin dates to 1736 and has been successfully applied in biological sciences to analyze phenomena such as protein interactions, protein structural analysis, gene expression, and microbiome phylogeny23,24,25,26.
We can generically define a network as a set of actors (nodes) maintaining certain connections between them (edges)27. Network analysis makes it easier for us to identify central nodes. These central nodes represent genes that are master regulators in a network of genes and typically play essential roles in a biological system27.
Here, the transcriptional response of the model plant Arabidopsis thaliana during its interaction with Trichoderma virens or Trichoderma atroviride was analyzed. We performed a gene co-expression network analysis. These analyses were assessed from a mathematical perspective based on their centrality measures to identify functional modules and relevant genes during the plant-fungus interaction.
Results
Differential expression analysis
To determine the transcriptional response of Arabidopsis to T. atroviride and T. virens, we inoculated the roots of A. thaliana seedlings with the fungi and performed RNA-seq analyses using 27 libraries. Quality reports of the sequenced reads showed that all libraries were high quality, exhibiting a Phred > 30 (Fig. S1A). Therefore, we mapped all reads to the reference concatenated genome. On average, more than 95% of the sequencing reads mapped to the reference genome for each library (Fig. S1B). The differential expression analysis showed that 256 and 263 genes were upregulated in A. thaliana, whereas 49 and 126 were downregulated by T. atroviride and T. virens respectively (Figs. 1 and S2; Table S1). Intriguingly, no significant statistical differences (α = 0.05) were found in in the upregulation of genes by T. atroviride or T. virens. However, T. virens was more effective in repressing genes in A. thaliana (p-val = 0.02, significance 95%) (Fig. 1C,D; Table S1; Fig. S2).
Differential expression analysis. (A) MA-plot of the differential expression analysis A.thaliana(T.atroviride). On y-axis is show the log2 fold changes versus x-axis represents log10 the average expression signal. Red dots represent up-regulated genes; blue dots represent down-regulated genes. (B) MA-plot of the differential expression analysis A.thaliana(T.virens). On y-axis is show the log2 fold changes versus x-axis represents log10 the average expression signal Red dots represent up-regulated genes; blue dots represent down-regulated genes. (C) Comparison of genes up-regulated in A. thaliana by T. atroviride vs T. virens. Left Venn diagram showing up regulated genes by T. atroviride, T. virens or both. Middle. Comparison of frequency distribution of up-regulated genes by T. atroviride and T. virens Right. Results T-test of upregulated genes. The yellow dots represent the genes induced by T. atroviride while the pink dots were induced by T. virens. (D) Comparison of genes down-regulated in A. thaliana by T. atroviride vs T. virens. Left. Venn diagram showing down regulated gens by T. atroviride, T. virens or both. Middle. Comparison of frequency distribution of down regulated genes by T. atroviride and T. virens Right. Results T-test of downregulated genes. The yellow dots represent the genes repressed by T. atroviride while the pink dots were repressed by T. virens.
We observed that 138 genes increased their expression in response to both Trichoderma species. In addition, 118 and 125 genes were upregulated only in response to T. atroviride or T. virens, respectively (Figs. 1C,D; S2; Table S1). Using the logFC of the group of 138 upregulated genes in both interactions, applied a t-test to determine if any of the two Trichoderma species was more effective in modulating differential expression in the plant (Fig. 1C,D; Fig. S2a). The t-student test suggested that both fungi similarly modulate differential gene expression (p-val = 0.055, significance 95%). Similarly, we observed 37 genes that decreased their expression in response to both Trichoderma species. In addition, whereas 12 genes decreased their expression in response to T. atroviride and 89 in response to T. virens (Fig. 1B,D; Table S1; Fig. S2). The t-student test for downregulated genes suggested a difference in gene repression, with T. virens being more effective than T. atroviride (p-val = 0.02, significance 95%).
The Gene Ontology (GO) enrichment analysis showed the induction of phytohormone pathways such as ethylene, jasmonate, and salicylic acid (Fig. 2; Table S2). Additionally, the induction of mechanisms related to plant defense, such as acquired systemic resistance (GO:0009627), immune system process (GO:0002376), and response to molecule of bacterial origin (GO:0002237), among others, was observed (Fig. 2, Table S2). Mechanisms involved in the regulation of reactive oxygen species scavenging mechanisms, such as the glutathione metabolic process (GO:0006749) and response to hydrogen peroxide (GO:0042542), were also enriched. Finally, among the repressed GO terms: root development (GO:0048364), root morphogenesis (GO:0010015) and chemical homeostasis (GO:0048878) (Fig. 2, Table S2).
Gene ontology enrichment analysis. Biological process that were differentially regulated in Arabidopsis thaliana during its interaction with Trichoderma spp. Each column represents a contrast of differential expression, and each row represents the gene ontologies in terms of the biological processes involved. The color gradient goes from blue to red for downregulated and upregulated biological processes, respectively.
Differential network analysis
As expected, the differential network analysis showed changes in the topology of the interaction networks (network(T. atroviride) and network(T. virens)) against their controls (Fig. 3A,B), probably because of the association of genes under different conditions. When the connectivity of the nodes using the degree as a metric was analyzed, the control networks exhibited a right asymmetry in both cases that extended to 150 and 300 degrees for the T. atroviride and the T. virens network, respectively. Although the control and the stimulus networks were built with the same nodes, we observed that the control networks had a much greater number of edges in both cases. The T. atroviride network contained 9826 edges, whereas its control presented 33,222. On the other hand, the T. virens network showed 43,784 edges, while its control had 116,682.
Differential network analysis. (A) Co-expression networks under control conditions (green edges), and in interaction with T.atroviride (yellow edges) and bottom histogram of degree. (B) Co-expression networks under control conditions (green edges), and in interaction with T. virens (pink edges) and bottom histogram of degree. (C) Correlation distribution analysis for the control, T. atroviride and T. virens networks. The color gradient goes from black to light yellow for low values and high values of correlation respectively. The graph presents the absolute value of correlation.
The evaluation of the weights of edges for each network and their respective controls showed that the edges of the interaction networks have greater weight than the edges of the control networks (Fig. 3C). The edges of the T. virens network have a correlation coefficient (r) greater than rs≥0.95. Similarly, the edges of the T. atroviride network have an rs≥0.9; finally, the edges of the control networks have an rs⩾0.85.
Gene co-expression network analysis
We generated two gene co-expression networks of A. thaliana when stimulated with T. atroviride or T. virens. The resulting networks consisted of 1094 and 2011 nodes for Arabidopsis(T. atroviride) and Arabidopsis(T. virens), respectively (Fig. S4a,b). The networks were simplified, as explained in Fig. S4.
The community identification analysis found 104 communities for the A. thaliana(T. atroviride) network and 256 for the A.thaliana(T.virens) network (Fig. S5). We focused only on those communities formed by at least 100 nodes and generated a functional enrichment analysis for communities Ca9, Ca16, and Ca18 for A.thaliana(T. atroviride) and Cv5, Cv14, Cv22 and Cv41 for A.thaliana(T. virens) (Figs. 4B & 5B; Table S2).
(A) Communities of co-expression genes network A.thaliana(T.atroviride). Communities were identified using the cluster_walktrap(x) algorithm of the igraph package. Each community is represented by a color. (B) Functional enrichment analysis. The dark-brown color represents the community 9 (Ca9), the brown color represents the community 16 (Ca16) and the color light-brown represents the community 18 (Ca18).On the y-axis the enriched gene ontologies are shown on the x axis the fold enrichment is observed.
(A) Communities of co-expression genes network A.thaliana(T.virens). Communities were identified using the cluster_walktrap(x) algorithm of the igraph package. (B) Functional enrichment analysis. The dark-brown and light-brown bars represent the Cv5 and Cv14 respectively. The dark-pink and light-pink bars represent the Cv22 and Cv41 respectively. On the y-axis the enriched gene ontologies are shown on the x-axis the fold enrichment is observed.
The processes with the most significant enrichment for each community were in the chorismate biosynthetic process (GO:0009423, Fold enrichment = 84.84) for community Ca9, the stress response (GO:0006950, Fold enrichment = 2.0) for community Ca16, and defense response to fungus (GO: 0050832, Fold enrichment = 3.27) for A.thaliana(T. atroviride). Besides, in the A.thaliana(T. virens) network, the processes with the greatest enrichment were carboxylic acid metabolic process (GO:0019752, Fold enrichment = 3.4), mitochondrial electron transport, ubiquinol to cytochrome (GO:0006122, Fold enrichment = 45), cellular response to hypoxia (GO:0071456, Fold enrichment = 14.1) and aerobic electron transport chain (GO:0019646, Fold enrichment = 12) for Cv5, Cv14, Cv22 y Cv41 respectively.
Finally, we selected the HUB nodes using a score based on degree, authority score, and closeness to refine the network (Fig. 4A) and selected the best 50 nodes according to this score from each of the communities (Ca9, Ca16, Ca18 (150 total nodes) and Cv5, Cv14, Cv22 y Cv41(200 total nodes). When we removed the HUB nodes from the network (Fig. S6a), we observed that the network becomes less cohesive and an increase in the geodesic path between nodes. On the other hand, when the nodes considered non-relevant are eliminated, their general cohesion measures increased, indicating that our methodology to identify relevant nodes was adequate.
In the HUB nodes, we identified the AT2G47520 locus in both networks (Arabidopsis(T. atroviride) and Arabidopsis(T. virens)), which encodes a transcription factor necessary to cope with hypoxia in A. thaliana28 (Fig. S7). This transcription factor has two functional domains (AP2/ERF and Cys2) that suggest a dual role: mitigating hypoxia and coping with pathogen stress. The AT2G47520 locus interacted with 22 genes in the Arabidopsis(T. atroviride) network, while in the Arabidopsis(T. virens) network, it directly connected with 35 genes. Genes related to hypoxia, jasmonate, and ethylene hormonal pathways, defense mechanisms, and uncharacterized proteins were identified within these groups.
Experimental validation
We focused on validating our transcriptome and hypotheses using the A.thaliana-T.atroviride system. However, considering that both networks have the same statistical parameters, we expected them to behave similarly between the high throughput sequencing and the experimental validation.
To validate our transcriptomic analysis, we selected 12 genes and compared the similarities between our differential expression results obtained by transcriptome and RT-qPCR (Fig. 6). During this analysis, we observed a similar behavior between the expression profiles obtained in the transcriptome and those obtained by RT-qPCR. As expected, in some cases, the same trend was observed for the transcriptomic data and the data obtained by RT-qPCR, as in the cases of the AT5G44420 (ρs = 0.98), AT2G47520 (ρs = 0.95), AT3G45140 (ρs = 0.98) loci, while in other cases the trend was not exact. Inherently, minimal, and imperceptible changes between the experimental biological replicas explain this phenomenon29.
Transcriptome validation. Experimental validation of the transcriptome was performed by RT-qPCR at 48, 72 and 96 hpi. The validation experiment was performed by duplicate. Blue color values obtained of differential expression from the transcriptome; red color values obtained by RT-qPCR. The bars represent the standard deviation (sd) of the average of a triplicate. The correlation coefficient of Sperman (ρs).
Even though the magnitudes differed since the quantification methods have different principles30, we observed similar trends.
Discussion
Differential expression analysis
To obtain a general overview of the biological processes involved in A. thaliana when in interaction with Trichoderma spp., we generated a heat map using differential expression contrasts and GO enrichment analysis for each time point (48, 72, and 96 hpi) (Fig. 2; Table S2). Figure 2 shows the biological processes that were differentially expressed in A. thaliana during its interaction with T. atroviride or T. virens, including mechanisms related to hormonal pathways such as camalexin metabolic process (GO:0052317), response to salicylic acid (GO:0009751), camalexin biosynthetic process (GO:0010120), and response to jasmonic acid (GO:0009753). In addition, defense mechanisms to other organisms including systemic acquired resistance (GO:0009627), response to molecule of bacterial origin (GO:0002237), response to oomycetes (GO:0002239), defense response to fungus (GO:0050832), immune system process (GO:0002376), defense response to nematode (GO:0002215), innate immune response (GO:0045087), and jasmonic acid and ethylene-dependent systemic resistance (GO:0009861). Finally, mechanisms that help alleviate reactive oxygen species were enriched (glutathione metabolic process (GO:0006749), response to hydrogen peroxide (GO:0042542). These results explain well the phenotype observed in the plants after their interaction with Trichoderma previously published by different groups12,31,32,33.
It has been reported that different species of Trichoderma affect the mechanisms of induced systemic resistance (ISR) in plants, which is related to the jasmonic acid/ethylene signaling pathways12. To contrast the induction of ISR by T. virens or T. atroviride, we visualized (Fig. S3) the differential expression of some relevant genes for this biological process. The LOX2 and PDF1.2 genes are ISR marker genes. Despite having no statistical significance (p-val = 0.1 & p-val = 0.2, respectively), when we performed a Wilcoxon test, we observed that T. virens was more efficient for the induction of marker genes (Fig. 3S). Besides, the WRKY51 (AT5G64810), a JA-regulated gene, showed a lower induction by T. virens than that by T. atroviride (Fig. S3). The transcription factor WRKY51 was reported by Yan et al.32 as a negative regulator of the jasmonate pathway, which forms a protein complex with the JAV1 and JAZ8 proteins. This protein complex (JAV1-JAZ8-WRKY51) controls the biosynthesis of JA so that the plant can defend itself from insect attack32. Our results show that T. virens is more effective in inducing ISR than T. atroviride. Our results agree with those reported by Salas-Marina et al15, where they observed that T. virens had an enhanced effect on the induction of resistance against the necrotrophic fungus Botrytis cinerea in tomato plants (Solanum lycopersicum).
On the contrary, the ontological term development of the root (GO:0048364) was repressed in both cases after 96 hpi; these data agree with what was reported by Garnica‐Vergara et al.33, where they observed an inhibition of primary root growth and induction of lateral root formation in A. thaliana after its treatment with T. atroviride. Garnica‐Vergara mentions that the plant modifies the architecture of the root due to a blockage in auxin signaling, highlighting the participation of indole-3-acetic acid (IAA). Among the genes found in this group was AT5G56320, which codes for a plant expansin that causes the expansion of cell walls in plants and is known to promote the growth of lateral roots. Additionally, we observed repression of the transcription factor LRL3 (AT5G58010) involved in root hair development. These facts show the complex interaction between plant hormones and their repercussions on the phenotype33.
Differential network analysis
The plant stress response is characterized by an intricate network of signaling cascades that trigger transcriptional reprogramming34. To analyze the behavior of genes in A. thaliana under control conditions and under stimulus conditions, we performed differential network analysis (Fig. 3A,B). The differential network analysis showed a change in the topology of the interaction networks (network(T. atroviride) and network(T.virens)) against their controls. This fact represents the association behavior of genes under different conditions. Besides, the connections between the nodes were reconfigured, as seen in the histograms in Fig. 3A,B, where the control networks had more edges (33,222 and 116,682 for control-network(T. atroviride) and control-network(T. virens) respectively) than the interaction networks which had 9826 and 43,784 edges for network(T. atroviride) and network(T.virens) respectively. This fact suggests that the association between A. thaliana genes changes when Trichoderma colonizes the plant.
The control network is denser and more connected with edge density = 0.06. In contrast, the T. atroviride and T. virens networks had edge densities of 0.008 and 0.007, respectively, when we filtered the correlation matrix with a p-val = 0.001. Even though the interaction networks have fewer edges than the control network they showed the highest correlation values higher than 0.9 and 0.95 for T. atroviride and T. virens, respectively. In contrast, the control network has correlation values higher than 0.8 (Fig. 3C). Taken results show that although interaction networks have fewer connections than the control, they are more robust and have higher correlations than their control networks. Thus suggesting a reconfiguration of the genetic transcriptional landscape of the plant when subjected to an external stimulus.
Gene co-expression network analysis
Here, we generated two gene co-expression networks of A. thaliana when stimulated with T. atroviride or T. virens, and these were simplified as explained below (Fig. 4S). From the transcriptome data, we generated the correlation matrices. The resulting networks consisted of 1094 and 2011 nodes for Arabidopsis(T. atroviride) and Arabidopsis(T. virens), respectively (Fig. S4a,b).
An important step in network analysis is to know how the nodes group in communities. Newman and Girvan35 defined a community as natural divisions of network nodes into densely connected subgroups. For this purpose, we used the algorithm developed by Pons & Latapi36. Our analysis identified 104 communities for the A. thaliana(T. atroviride) network and 265 for the A.thaliana(T. virens) network (Fig. S5). However, only communities Ca9, Ca16, and Ca18 for Arabidopsis(T. atroviride) and Cv5, Cv14, Cv22, and Cv41 for Arabidopsis(T. virens) were formed with a large number of nodes (Figs. 4, 5).
Due to the nature of the gene co-expression phenomenon, it is important that the genes form a compact and densely interconnected structure37. An adequate synergy between the functioning of the genes or their products (proteins) allows the cell to respond and adapt better to stress. Therefore, we focused only on those communities formed by at least 100 nodes (Figs. 4, 5).
Starting from the premise that communities within gene co-expression networks represent biologically functional modules, we generated a functional enrichment analysis for communities Ca9, Ca16, and Ca18 for A.thaliana(T. atroviride) and Cv5, Cv14, Cv22, and Cv41 for A.thaliana(T. virens) (Figs. 4B, 5B; Table S2). According to graph theory, the most relevant nodes for a network are those that give the network the most stability38. To verify if our methodology to select relevant nodes was adequate, we used the networks formed by 1094 and 2011 nodes (Fig. S6). When we eliminated the relevant nodes, their general cohesion measures decreased with a diameter from 19.14 to 15.85 and 17.29 to 10.85, resulting in a much more compact and stable network. These results showed that our methodology to identify relevant nodes was adequate.
The GO results by community analysis (Figs. 4, 5), such as chorismate biosynthetic process (GO:0009423), defense response to fungus (GO:0050832) or aerobic electron transport chain (GO:0019646), agree with what has been reported. However, studies on the role of hypoxia during the plant-Trichoderma interaction are limited. In this sense, plants induce hypoxia-related genes during immersion, flooding, or when growing at high altitudes as the partial pressure of molecular oxygen decreases; however, little is known about the significance of hypoxia triggered by biotic stress. Thus, we focused on this biological process and its possible role during the Arabidopsis-Trichoderma interaction.
Despite the scarce studies on hypoxia during plant-microorganism interactions, there is experimental evidence indicating that hypoxia is a common phenomenon during the process. Establishing a symbiotic relationship with nitrogen-fixing rhizobia in legumes requires hypoxic conditions since nitrogenase activity, which can fix N2, is inactivated by free O2. In other plant species, hypoxia-sensitive genes are induced in stems infected by Agrobacterium tumefaciens, suggesting that hypoxic conditions are generated at sites of infection by these microorganisms39,40 Although the plant-microorganism interaction largely depends on the lifestyle of the microbe, these studies show that hypoxia exists during the plant colonization event. However, to our knowledge, Trichoderma has not been reported to generate hypoxia in its hosts. Based on our results, we hypothesize that Trichoderma triggers hypoxia in the host and enhances defense mechanisms against pathogens by stabilizing a family VII transcription factor (ERF-VII) that responds to ethylene.
The association between communities Ca9 and Ca18 makes biological sense since it is known that plants obtain their energy during hypoxia from ATP alcoholic fermentation pathway41. On the other hand, the association between communities Cv14 and Cv22 in the A.thaliana(T. virens) network supports what was reported by28, who observed a functional relationship between the mitochondrial electron transport chain and hypoxia after evaluating the functioning of mitochondrial oxidases in different oxygen concentrations. Besides, the network and functional enrichment analyses suggest that hypoxia may play a relevant role in the defense response against fungi (Figs. 4, 5).
To retrieve information, we searched for all the genes with an ontological term related to hypoxia within the general network (1094 and 2011 nodes; Fig. S4). We identified the AT2G47520 locus in both networks (Arabidopsis(T. atroviride) and Arabidopsis(T. virens)), which codes for a transcription factor necessary to cope with hypoxia in A. thaliana28. Interestingly, this transcription factor has an Ethylene-inducible DNA binding domain (AP2/ERF position 49-106 a.a) (Fig. S7a). In addition, it has a cysteine (Cys2) prone to oxidation in its N-terminal (Fig. S7a,c). Therefore, this transcription factor is more stable during hypoxia than under normoxic conditions. It is known that the AP2/ERF domain can interact with the GCC-box promoter regions upstream of genes encoding pathogenesis-related proteins42. Therefore, the presence of these two domains (AP2/ERF and Cys2) in AT2G47520 suggests a dual role, first being more stable in hypoxic conditions to activate defense genes further. We analyzed positional weight matrices (PWM) and identified that the transcription factor can bind to the GCC-box (Fig. S7b,d). Brown et al.43, observed that deletion or point mutations in the core sequence of the GCC box substantially reduced the plant's ability to respond to JA. On the other hand, Epple & Bohlmann44 observed that plants that overexpress the Thi2.1 gene, a gene with the GCC-box sequence in its promoter, improve resistance against the phytopathogen Fusarium oxysporum.
The AT2G47520 locus interacted with 22 genes in the Arabidopsis(T.atroviride) network. AT3G03270, AT4G24110, and AT3G27220 respond to hypoxia. AT1G43800 encodes a protease that participates in ISR, among others. In the Arabidopsis(T.virens) network, it was directly connected with 35 genes. Within this set of genes is AT2G26020, which encodes the plant defensin PDF1.2b, which is involved in defense mechanisms against fungi; AT5G07580, which codes for an ethylene response transcription factor; and AT1G66350, a protein with a role in jasmonic acid signaling. This fact suggests a functional relationship between hypoxia and the plant's defense mechanisms, as well as the participation of hormones in this genetic association.
The idea that plants induce their hypoxic genes and defense mechanisms against pathogens is consistent if we consider that plants suffer constant biotic stress after floods. On the one hand, there is dispersion of pathogens by rain, in addition to the fact that plants exhibit morphological changes after floods that facilitate infection by pathogens, such as change in intracellular pH, change in stomata behavior, and a decrease in hydraulic conductance45. These observations suggest that despite being a stressful stage for the plant, hypoxia promotes resistance against certain pathogens. For these reasons, we hypothesize that hypoxia and plant defense mechanisms are interconnected through signaling pathways that have not yet been explored and could involve signaling pathways related to the synthesis of JA. Yuan et al.46 reported that after hypoxia, there is a rapid accumulation of (JA) and an induction of genes involved in the synthesis of JA. In addition, applying exogenous methyl jasmonate (MeJA) improved tolerance to posthypoxic stress in A. thaliana plants. A recent work47 mentions that these two stresses occur sequentially or simultaneously in plants. During evolution, plants developed efficient strategies to respond to stimuli with the minimum energy expenditure through multiple functions of a single gene.
Experimental validation
To validate our transcriptomic analysis, we selected 12 genes, and we collected samples at times 48, 72, and 96 hpi. In this sense,10 genes were chosen from the main network, selecting those that will help us to solve future investigations and hypotheses and that represent different communities of the general network (1094 nodes). Additionally, 2 extra genes PR-1a (AT2G14610) and LOX2 (AT3G45140) were selected, which were not in the network but are commonly used as marker genes to validate the induction of systemic responses against pathogens. We compared the similarities between the differential expression results obtained by transcriptome and RT-qPCR (Fig. 6). During this analysis we observed a similar trend between the expression profiles obtained in the transcriptome, RNA-seq, and those obtained by RT-qPCR. In some cases, the exact same trend is observed for the transcriptomic data and the data obtained by RT-qPCR, as in the cases of the AT5G44420 (ρs = 0.98), AT2G47520 (ρs = 0.95), AT3G45140 (ρs = 0.98) loci, while in other cases the trend is not exact (for example, AT2G16060 ρs = 0.58, AT4G34200 with ρs = 0.48 and AT1G05680 ρs = 0.77). This fact is completely natural and expected, inherently there are minimal and imperceptible changes between the experimental biological replicas that explain this phenomenon29.
Conclusions
Our current study shows gene co-expression network analysis as a methodology that enables us to observe how genes associate into biologically functional modules in Arabidopsis that respond to an interaction with mutualistic fungi.
During plant-fungus interaction, there is a functional interconnection between hypoxic mechanisms and the plant defense mechanisms. However, we have carefully mentioned that we have only generated hypotheses requiring further experimental evidence. In the future, we will experimentally study the possible interconnection between hypoxia and defense mechanisms during the plant-fungus interaction. These interconnections will be identified in mutant plants in selected genes of our analysis. These experiments will allow us to clarify this complex mechanism that has led plants to respond appropriately to different types of stress by expending the minimum energy and anticipating future challenges that could end in plant death.
The AT2G47520 locus encodes a transcription factor with protein domains (AP2/ERF position 49-106 a.a. and Cys2) with dual function to counteract hypoxia and pathogens.
Materials and methods
Plant and fungal growth conditions
Columbia ecotype (Col-0) of Arabidopsis thaliana plants, T. atroviride (IMI206040), and T. virens (Gv29-8)48 fungus as colonizing were used in this study. Trichoderma spp. was grown in potato dextrose agar (PDA) (Becton, Dickinson & Co) and incubated at 28 °C under 12/12 light/dark cycles. Seeds of A. thaliana Col-0 were sterilized by two consecutive washings using a 75% ethanol solution for 3 min under constant shaking at a speed of 1800 rpm using a MixMate (Eppendorf SE, 5353, Germany) and with 20% chlorine solution for 7 min. Subsequently, the seeds were rinsed with sterile distilled water. A. thaliana seeds were germinated in Murashige and Skoog medium [1x] containing 10% sucrose (PhytoTechnology Laboratories, USA). Subsequently, the seeds were incubated for ≈12 days at 22 ± 1 °C with a regime of 16 h of light and 8 h of darkness49.
Arabidopsis thaliana–Trichoderma spp. Interaction assay
The 12-day-old Arabidopsis plants were inoculated with T. atroviride or T. virens mycelium plugs (d = 5 mm). Uninoculated plants were used as controls. Subsequently, biological leaf and root samples were collected at 48, 72, and 96 h after inoculation (hpi) for both the control groups and the different interactions. The collected samples were frozen in liquid nitrogen until their use. A new Arabidopsis-Trichoderma interaction was carried out to validate the transcriptome under the same conditions.
Total RNA extraction, library preparation, and sequencing
Total RNA was extracted using the method reported by Ferdous et al.50 with slight modifications. Briefly, frozen samples were macerated in extraction buffer. The cell lysate was carried out using the cationic detergent hexadecyltrimethylammonium bromide (CTAB). The recovery of the organic phase was carried out with 150 μL of phenol pH 8, and chloroform-isoamyl alcohol (24:1). Finally, the RNA was precipitated with isopropanol at − 20 °C overnight. The samples were treated with DNAse to remove contaminating DNA, using the commercial kit TURBO DNA-free™ KIT (Ambion, Life Technologies). RNA was quantified in an Epoch Microplate Spectrophotometer (BioTek®) at an OD of 260 and 280 nm (nanometers). RNA integrity was visualized by denaturing agarose gel electrophoresis [0.8%], and the samples were inspected using Agilent 2100 Bioanalyzer technology (Agilent Technologies, Inc., USA). Finally, 27 libraries were generated and sequenced in a paired manner with the Illumina Hiseq 2500 technology (Illumina Inc., Hiseq 2500, USA) by the National Laboratory of Genomics for Biodiversity, LANGEBIO, Irapuato Mexico.
Data collection and processing
A transcriptome analysis of the 27 libraries of A. thaliana interaction with T. atroviride or T. virens at 48, 72, and 94hpi and their respective controls without the fungi was performed. Each interaction (Arabidopsis(T. atroviride) and Arabidopsis(T. virens)) and controls included three independent biological replicates. Gene expression datasets were processed using the R software version 4.2.1 (2022–06-23 ucrt) (https://www.R-project.org/) and Rstudio Team (2022). Rstudio: Integrated Development for R. Rstudio, PBC, Boston, MA URL (http://www.rstudio.com/) version 2022.7.1.554.
Quality analysis and mapping of the reads to the reference genome
Langebio provided a quality analysis of the FASTQ files. All reads presented a Phred quality score higher than 30, which was considered as high quality and used for mapping. Reads were mapped to a concatenated reference genome (A. thaliana-T. atroviride-T. virens). The concatenated genome was constructed taking the reference genomes reported by TAIR (The Arabidopsis Information Resource) version TAIR10 and for T. atroviride and T. virens reported by Ensembl Fungi (https://fungi.ensembl.org) version IMI_206040_v2.0 and ASM17099_v1 respectively. Mapping of the fragments was carried out using the HISAT2 software with default parameters for paired-end reads and a bash script51. Mapped fragments were quantified with the featureCounts tool from the SubRead package52. With these quantifications, a counts matrix was generated with an in-house R script.
Differential expression analysis
The differential gene expression analysis was performed with the edgeR package53 using the count matrix. Normalization factors and variability were calculated with the calcNormFactors(x) and estimateDisp(x) functions, respectively. Subsequently, the data were fitted with the function glmFit(x). Contrasts were performed with a likelihood ratio test, glmQLFTest(x) function, to identify changes in gene expression under control and plant-microorganism interaction conditions for each time (48, 72, and 96 hpi), with which a differential expression matrix was generated for each contrast. We keep differential expressed genes with a cutoff of FDR < 0.001 and a logFC > 1.5 or logFC < -1.5.
Differential network analysis
Two gene co-expression networks were generated (Arabidopsis(T. virens) and Arabidopsis(T. atroviride)). All differential expression contrasts (Arabidopsis(T.virens) and Arabidopsis(T.atroviride)) results were used to keep differentially expressed genes, using a false discovery rate (FDR) of FDR = 0.001. The control networks were built using the same genes that were present in the interaction networks (Arabidopsis(T.atroviride)) 1094 and Arabidopsis(T.virens), 2011 nodes) and the control libraries were used (A. thaliana without interaction). The control networks were filtered with an FDR = 0.001. A correlation test was performed with the cor.test(x) functions from the R base package. The correlations were filtered using a threshold of FDR = 0.001, and the correlation significance threshold was ρs = 0.8.
Construction and analysis of co-expression networks
Two gene co-expression networks were generated (Arabidopsis(T. virens) and Arabidopsis(T. atroviride)). Each of the differential expression contrasts (Arabidopsis(T. virens) and Arabidopsis(T. atroviride)) results were used to keep differentially expressed genes, using a false discovery rate (FDR) of FDR = 0.001. The resulting genes were used to generate the correlation matrix using Spearman's method defined in the cor(x) function from the R base package. A correlation test was performed for each correlation with the cor.test(x) functions from the R base package. For each correlation test, we obtained a Spearman correlation coefficient \({\rho }_{s}\) and a p-value. For all the correlation tests we obtained the FDR by using the p.adjust(x) frunction from R stats package. The correlations were filtered using a threshold of FDR = 0.001. The resulting correlations were used to build gene co-expression networks. The analysis and visualization of the network were carried out using the igraph package with the R programming language54. The correlations were filtered using a threshold of FDR = 0.001, and the correlation significance threshold was ρs = 0.8.
The two co-expression networks Arabidopsis(T. virens) and Arabidopsis(T. atroviride) formed by 1094 and 2011 nodes, respectively. Communities were identified for each of the networks (Arabidopsis(T. virens) and Arabidopsis(T. atroviride)) using the algorithm developed by Pons and Latapy36 with the function cluster_walktrap(x) from the igraph package, with default parameters. Subsequently, the communities with more than 100 nodes were compared with each other based on the degree, authority score, and closeness metrics. Finally, an analysis of functional enrichment was carried out in the identified communities using the Protein analysis through evolutionary relationships (PANTHER, https://www.panther.db.org) server version 17.0 (released 2022-02-22)55 which implements an additional statistical test method (Fisher's exact test) for the PANTHER overrepresentation test, as well as the Benjamini–Hochberg False Discovery Rate for multiple testing correction.
For selecting HUB nodes, we use a score defined as the product of centrality, closeness, and authority measures. Using the score in decreasing order, we used the best 50 nodes for each community and generated two sub-networks Arabidopsis(T. virens) and Arabidopsis(T. atroviride).
A new interaction assay was carried out to validate the transcriptome. Two biological replicates were carried out with three technical replicates in each. For the experimental validation, ten genes were chosen from the main network, selecting those that will help us solve future investigations and hypotheses and represent different communities of the general network (1094 nodes). Additionally, two extra genes PR1a (AT2G14610) and Lox2 (AT3G45140), were selected, which were not in the network but are commonly used as marker genes to validate the induction of systemic responses against pathogens.
Finally, we calculated the Spearman correlation coefficient between the transcriptomic data (logFC) and the data obtained by RT-qPCR.
Plants guidelines
The authors confirm that the use of plants in the present study complies with international, national, and institutional guidelines.
Data availability
The raw RNA-seq data can be found in the Gene Expression Omnibus (GEO) database under the series record GSE235893.
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Acknowledgements
This work was supported by grants IFC-2016-1538 to SC-F. J-D, V-A is grateful to CONAHCYT for his doctoral fellowship 763150. Authors acknowledge IPICYT Supercomputing National Center (CNS) Thubat Kaal 2.0 computing resources provided to process de RNA-seq data under the project TKII-R2018-COV1. C.O-V acknowledges the Investigadores por México CONAHCYT program. The authors wish to thank Nicolás Gómez-Hernández and Saul Jijon-Moreno for their technical support.
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S.C.-F. and A.H.-E. contributed funds to support this research. They also developed and planned the experimental design for the collection and processing of biological samples. C.O.-V. processed the raw data and contributed to the creation and revision of the work code. S.C.-F., C.O.-V. and J.-D. V.-A. planned the methodology for the data analysis. J.-D. V.-A. performed the validation experiment, prepared the figures, and performed the statistical analysis. All authors contributed to the structuring and writing of the manuscript. All authors reviewed the manuscript.
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Vega-Arroy, JD., Herrera-Estrella, A., Ovando-Vázquez, C. et al. Inferring co-expression networks of Arabidopsis thaliana genes during their interaction with Trichoderma spp.. Sci Rep 14, 2466 (2024). https://doi.org/10.1038/s41598-023-48332-w
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DOI: https://doi.org/10.1038/s41598-023-48332-w
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