Comparative analysis of salt responsive gene regulatory networks in rice and Arabidopsis

Journal Pre-proof Comparative Analysis of Salt Responsive Gene Regulatory Networks in Rice and Arabidopsis Rui Wang, Yanhao Cheng, Xiaojuan Ke, Xiaofan Zhang, Hongsheng Zhang, Ji Huang

PII:

S1476-9271(19)30506-7

DOI:

https://doi.org/10.1016/j.compbiolchem.2019.107188

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CBAC 107188

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Computational Biology and Chemistry

Received Date:

9 June 2019

Revised Date:

25 November 2019

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5 December 2019

Please cite this article as: Wang R, Cheng Y, Ke X, Zhang X, Zhang H, Huang J, Comparative Analysis of Salt Responsive Gene Regulatory Networks in Rice and Arabidopsis, Computational Biology and Chemistry (2019), doi: https://doi.org/10.1016/j.compbiolchem.2019.107188

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Comparative Analysis of Salt Responsive Gene Regulatory Networks in Rice and Arabidopsis Rui Wang, Yanhao Cheng, Xiaojuan Ke, Xiaofan Zhang, Hongsheng Zhang, Ji Huang* State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China

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*E-mail: [email protected]

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Graphical abstract

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Based on public salt –related microArray data, small RNA-seq and degradome sequence, we constructed and compared salt-response networks in rice and Arabidopsis, consisting of miRNAs, genes and transcription factors (TFs).

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In rice, more target genes of networks were enriched in development and growth, while more stress directly-related genes were detected in Arabidopsis networks. Salt response networks of Arabidopsis are typically organized. Salt-response mechanisms of rice are emphasized on avoiding salt stress by

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regulating growth and development according to rice miRNA-TF-genes networks MiRNA171-GRAS and miRNA169-NFYA are possible species-conserved salt-response patterns. Not only are they detected in our results, but also reported in other signaling pathways. For instance, miRNA169-NFYA is found work in ABA signaling pathways.

Abstract

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By using the available expression datasets of mRNAs and small RNAs, we constructed and compared the salt-responsive gene regulatory networks (GRNs) involving both transcriptional and post-transcriptional regulations between model plants rice and Arabidopsis. The salt-responsive GRNs involve the transcription factors (TFs), microRNAs (miRNAs) and their target genes. Here we describe 552 miRNA-target interactions (MTIs), 95 up-regulated TF-target interactions (TTIs) and 56 down-regulated TTIs in rice, while 332 MTIs, 138 up-regulated and 4 down-regulated TTIs in Arabidopsis. Interestingly, we observed the networks in rice are more complicated where target genes were enriched in rice development and growth, while more stress--related genes were detected in Arabidopsis networks. From the construction and comparison of GRNs between rice and Arabidopsis in response to salt stress, we can basically describe the differences of salt responsive mechanisms in two species: rice tends to respond slower and chooses to manipulate its development and growth to avoid salt stress, while Arabidopsis prefers to trigger a serious salt-defending genes to protect itself from stress. Our work provides the foundation for further exploring the molecular basis of plant salt response and the potential breeding practice by engineering the critical components in the networks in improving plant salt tolerance.

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Keywords: salt stress; gene regulatory network; microRNA; rice; Arabidopsis; transcription factor

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1. Introduction

Generally, plants have two different phases in response to salt stress: ion-independent osmotic phase and ion-dependent toxin-accumulation cell injury phase (Munns and Tester, 2008;Roy et al., 2014). Accordingly, plants have evolved sophisticated response mechanisms, which are mainly divided into three types: (1) osmotic stress tolerance, plant’s transient response to rapid osmotic stress to keep their ability of taking water successively; (2) ion exclusion, transportation of ion to reduce accumulation of toxin in plant, relieving cell injury to protect plant growth; (3) toxin tolerance, compartmentalization of Na+ at cellular and intracellular level to avoid a high concentration of Na+ within cytoplasm (Behringer and Schwechheimer,

2015).

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In each phase, different plant species trigger different response mechanisms to cope with salt stress. With the further study about salt signaling pathways, researchers discovered many transcription factors, miRNAs and salt-sensory genes to sense the salt signal and resist salt stress. Salt-sensor genes allow plants respond rapidly to salt change of environment and they are various in different tissues, for example, Salt Overly Sensitive3 (SOS3) and sucrose non-fermenting 1 (SNF1)-related protein kinase 3(SnRK3) were reported to sense and signal in root when the first phase of osmotic stress starts in Arabidopsis (Zhu, 2002); ENHANCED RESPONSE TO ABA 1 (ERA1), protein phosphatase 2C (PP2C), ABA activated protein kinase(AAPK),and PKS3 decrease stomatal closure to survive under osmotic stress and avoid ion toxicity in chloroplasts to delay the toxin accumulation in tissues(Zhang et al, 2014); Na+/H+ antiporter NHX and VACUOLAR H+/- PYROPHOSPHATASE AVP increases sequestration of Na+ into root vacuoles to increase osmotic stress adjustment of plant (Munns and Tester, 2008). These genes directly protect plant cells from salt stress.

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These “direct” genes are usually regulated by up-stream transcription factors. For instance, Members of AP2 /ERF (Apetala2/Ethylene Responsive Factor) family, known as important floral construction factors(Varkonyi-Gasic et al., 2012) but also critical regulators of ABA signaling pathways (Jisha et al., 2015), play crucial roles in abiotic stress, epidermal cell differentiation and cell morphogenesis(Sun et al., 2017). AP2 transcription factor OsEREBP1 was found to confer biotic and abiotic stress tolerance in rice. Jisha’s team over-expressed OsEREBP1 in transgenic rice, which showed increase in endogenous levels of alpha-linolenate, several jasmonate derivatives and abscisic acid and conferred its drought and submergence tolerance (Jisha et al., 2015).

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Besides the transcriptional regulation, miRNAs play non-negligible roles in salt responsive GRNs on the post-transcriptional-level regulation. Obeying base complementary pairing principle, 21-nt microRNAs bind to target regions(seed region) of mRNAs and degrade target mRNAs to repress expression at post-transcriptional level(Kaho M et al., 2018). Additionally, some targets of miRNAs are transcription factors. For examples, miRNA319 represses expression of TCP transcription factor genes to regulate the inflorescence architecture (De Paolo et al., 2015); miRNA164 represses expression of NAC transcription factor genes and induces cell death.(Eva H et al., 2001) Based on different combinations of three regulatory components – transcription factors, salt-response genes and miRNAs (Fig 1), plants may adopt different salt response strategies and exhibit the different performances of salt tolerance. In this research, we constructed and compared the salt responsive GRNs of two model plants, rice and Arabidopsis, and described the conserved and species-specific sub-networks, which aid us to understand and explore the further molecular basis of

salt response mechanisms in plants.

2. Results 2.1 Identification of salt-related TFs, miRNAs and target genes in rice and Arabidopsis

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With analysis on microarray and small RNA sequencing data, we identified a total of 2,216 differential expressed genes (DEGs) (including 2,085 non-TF genes and 131 TF encoded genes) and 444 miRNAs (including 52 upgrade miRNAs and 9 downgrade miRNAs under the salt stimuli) in rice, and 2,357 DEGs (containing 2,201 genes and 136 transcription factor genes) and 346 miRNAs in Arabidopsis. Among 85 salt responsive miRNAs, which differentially expressed in Arabidopsis under salt treatment, including 27.1% up-regulated ones and 72.9% down-regulated ones; in rice, 51.2% DEGs (including 3.6% upgraded TF genes) are up-regulated by salt stress while 48.8% are down-regulated when treated by salt stress; 80 transcription factor genes are up regulated, and 51 are down regulated under salinity stress. In Arabidopsis, 85.0% DEGs, of which 128 are TF genes, are up-regulated under salt treatment and 15.0% are down-regulated, of which 128 are up-regulated transcription factor genes and 18 are down-regulated transcription factor genes. These different distributions of three components represent their different occupation and networks’ complexity in salt signaling pathways in rice and Arabidopsis (Table 1, Fig 2A).

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MiRNAs work more actively in rice salt responsive regulatory networks than in Arabidopsis, while in Arabidopsis, transcription factors account for the more crucial part in response to salt stress. As one of the core regulators of salt response, genes that encode transcription factors, have different expression patterns in two species we investigated. For instance, they are majorly up-regulated in Arabidopsis upon salt while this phenomena was not found in rice (Fig 2).

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In addition, different TF families revealed various performances in rice and Arabidopsis. According to venn diagram (Fig 3), some of them contain both up-regulated and down-regulated members, such as MADS, MYB, LSD in rice; members of NF-YB, Dof, MIKC, and LBD in Arabidopsis, and C2H2, bHLH, MYB, bZIP, C3H, NAC, HD-ZIP and AP2; Some are detected but opposite expression performance in rice and Arabidopsis, for example, ARF genes are up-regulated in rice but down-regulated in Arabidopsis; While some have monopoly expression in two species, like M-type, GATA, VOZ, SBP, CO-like, WOX, DBB, B3, E2F/DP, Trihelix; FAR1, GRAS, GeBP, ARR-B and NF-YC families, which showed up-regulated in both rice and Arabidopsis.

2.2 Construction of salt responsive GRNs in rice and Arabidopsis With all the MTIs and TTIs, we constructed salt responsive GRNs in rice and Arabidopsis, respectively, of which green nodes represent down-regulated component in response to salt stress and red nodes represent up-regulated ones. Rice GRNs contain 597 nodes and 637 different interactions, and there are 508 different interactions with 458 nodes in Arabidopsis GRNs. Thus, since the complexity of networks are similar in two species, it is interesting to find that there are different ratios of up-regulated components and down-regulated ones between two species: more green nodes in rice while more red ones in Arabidopsis (Fig 4), indicating that more regulators tended to down regulate their expression in rice while more up-regulated in Arabidopsis conversely when threatened by salts.

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2.3 Comparison of TF-Target Interaction networks in Arabidopsis and Rice

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By analysis of coefficient expression of TFs and their targets including miRNAs mRNAs , under salt stress and the cis-acting elements in the promoters of miRNAs and mRNAs, we obtained putative TF-target interactions in both rice and Arabidopsis. From these TTIs, we compared TF-miRNA interactions between rice and Arabidopsis and drew Venn diagrams of TF-miRNA interactions in rice and Arabidopsis, with 1044 pairs specifically predicted in rice and 515 in Arabidopsis, and a conserved pattern MYB-miR162, including 18 conserved TF-miRNA pairs in both rice and Arabidopsis, but none of them showed co-expressed relations (Fig 3A).

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We identified 28,862 co-up-regulated TF-Target pairs and 18,644 co-down-regulated TF-Target pairs in rice, and 140,014 co-up-regulated TF-Target pairs and 3,654 co-down-regulated TF-Target pairs in Arabidopsis. Thereafter we analyzed the distribution of transcription factor binding sites on promoters of these targets, and finally screened 95 putative TF-Target pairs in rice and 138 putative up-regulated TF-Target pairs in Arabidopsis (Table 2). 22 bZIP, 11 NAC, 11 Dof, and 11 HD-ZIP genes were detected down-regulated in Arabidopsis, while 35 MYB and 21 AP2 genes specifically down regulated in rice. Besides, 15 bHLH, 54 CPP, and 26 AP2 genes were induced by salinity in rice, and 44 G2-like, 5 WOX, 21 GeBP, 42 ERF, 11 NAC, and 15 MYB type genes up-regulated in Arabidopsis. Transcription factor families varied on gene expression when responding to salt stress. For example, NAC family genes in Arabidopsis have different performances in response to salt stress, of which 11 members are up-regulated and 11 are down-regulated, while 21 down-regulated and 26 up-regulated in rice (Fig 5). Some are only found induced in specific species, for instance, 54 rice CPP genes and 15 rice bHLH up-regulated in salt stress; 44 G2-like genes, 42 ERF genes and 5 WOX genes are specifically detected up-regulated in Arabidopsis, especially G2-like and ERF family, whose number of up-regulated members was much higher compared to down-regulated members. We described and compared TF-Targets binding and

expression relations to construct the networks of Arabidopsis (Fig 5C) and rice (Fig 5D). From these networks, we observed that nodes in Arabidopsis were mainly in red, while more green nodes were in rice networks, suggesting that transcription factors are more active in Arabidopsis.

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In Arabidopsis, TTIs are inclined to play a dominated role in the salt-response networks. We listed conserved transcription factors (Fig 3) regulated Arabidopsis TTIs in Table3 which are centered with MYB and NAC transcription factors. NAC transcription factors are reported to directly join in GA/BR metabolism and signaling, affecting flowering time and plant height (Shahnejat-Bushehri et al., 2016), and enhancing stress tolerance(Lee et al., 2017), while MYB transcription factor triggers NIP1 to transport antimonite and determine antimonite sensitivity. Additionally, MYB regulates ABA signaling genes ALDH3H1, ATAF1 to sense salt and drought signal (Stiti et al., 2014;Liu et al., 2016). MYB also participates in rearrangement of development and growth in different tissues. It adjusts expression of FAB1D to activate Phosphatidylinositol kinases in Arabidopsis (Serrazina et al., 2014).

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In rice, miRNA mediated interactions account for the larger ratio than TTIs in the salt response networks. Moreover, expression of TF-encoded genes are mainly decreased under salt, which are hard to be considered as main regulators in salt responsive GRNs. The information of TTIs of conserved up-regulated TFs, AP2 and bHLH, is listed in Table 4.

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According to the gene ontology(GO) results of targets(miRNAs’ and TFs’), both targets of miRNA and transcription factors are enriched in intraocular organelle parts and metabolic processes(Supplementary Table 1), which indicates that rice salt response mechanisms are relatively active on second phases—ion transport to prevent salt toxin accumulation in tissues, but from view of the whole rice network, most growth and development related genes are repressed, such as Os07g0633200, Os01g0723400 , Os08g0151300 and Os08g0452500, suggesting that rice has a decrease on growth and development when threatened by salt .

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2.4 Comparison of miRNA-Targets interaction networks in Arabidopsis and Rice In order to learn the salt-response difference between rice and Arabidopsis at the post-transcriptional level, we analyzed their miRNA-Target interaction networks. Based on results of degradome verification, we compared the distributions of MTIs with different cleavage levels in rice and Arabidopsis: 37 Cat_1 level miRNA-Target interactions and 276 Cat_2 level MTIs response to salt stress in Arabidopsis, while in rice, 89 Cat_1 cleaved efficient MTIs and 438 Cat_2 MTIs under salt treatment (Fig 6). Similar to the construction of TF-Target networks, we classified miRNAs’ targets, based on their expression response to salinity, into up-regulated ones (red nodes) and down-regulated ones (green nodes), including transcription factor genes and

salt-response genes. From the view of the whole networks, up-regulated targets account for major parts in Arabidopsis while down-regulated targets are the majority in rice MTI networks, which is similar with TF-Target networks.

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In these networks, miRNAs participate in the regulation of transcription factor genes under salt stress in both rice and Arabidopsis. Some of these miRNA-TF patterns are species-conserved, such as miRNA171-GRAS, including 4 rice MTIs of all 552 MTIs and 2 Arabidopsis MTIs of 332 MTIs; and miRNA169-NF-Y-A, including 39 rice MTIs of all 552 MTIs and 10 Arabidopsis MTIs of 332 MTIs. There are some published evidences to support their functions in salt defense. In rice, NF-YA transcription factor has reported concerning with drought stress and is a part of ABA signaling pathway (Lee et al., 2015), and miRNA169 regulated NF-Y-A pattern has been reported as a part of ABA signaling and effected root growth(Zhao et al., 2016). GRAS, the ancient and conserved transcription factors, functionalized in flower development while miRNA171 is a conserved miRNA in regulation of root growth (Tian et al., 2004;Smoczynska and Szweykowska-Kulinska, 2016).But there are few researches about studying the function of miR171-GRAS patterns in salt tolerance.

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2.5 Comparison of the functional enrichment of target genes in rice and Arabidopsis

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After the comparison of TF-Target and miRNA-Target between rice and Arabidopsis, we found their salt-sensory mechanisms emphasize on different regulations (TF-center or miRNA-center), and we were interested in where their targets possibly functionally-enriched in and how to regulate salt-response networks. Combining the Gene ontology (GO) enrichment results (Supplementary table 1) and expression identification, activities of targets in Arabidopsis can be divided into three major types of processes—transport, response to stimulus, metabolic process, and we found 12 targets enriched in transport, 17 in response to stimulus, 23 in metabolic process (Fig 7), while in rice, 35 are enriched in metabolic process, of which Os07g0633200(SCL33), Os01g0723400(NADP-ME2), Os08g0151300(MYB61) and Os08g0452500(SAUR76),which are related genes of growth and development, down-regulated under salt stress, and the rest genes are up-regulated under salt stress. These down-regulated rice genes are related to developments and growth, of which MYB98 has reported to participate in flower tube synthesis and structure architecture, which possibly effect reproductive processes and pollination (Punwani et al., 2007), but the specific function of MYB61 has not been studied. According to the different results of target enrichments (Fig 8) in rice and Arabidopsis, the targets in rice regulated by transcription factors are enriched in cell and organelle cell parts, which probably impede salt toxin on the second phase.

2.6 Sub-network analysis of Arabidopsis and rice

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To dig out more features of Arabidopsis and rice GRNs, we separated some typical functional sub-networks from the global ones. With the target enrichment and other biological functional analysis, we observed three obvious function parts in charge of different phases of salt response in Arabidopsis (Fig 9): process of transportation, direct salt response, and development and growth. We labeled typical interactions in three different frames, where NAC is conserved center-regulator of direct salt response, and MYB is one of the conserved regulators of development and growth. Both of them are probably post-transcriptionally regulated. NACs are probably repressed by miRNA169g, miRNA8182, miRNA883 and miRNA841a, while MYBs are center regulators influencing downstream TFs, bHLHs and Dofs, according to sub-networks. On the other hand, features of networks in rice are not as typical as that in Arabidopsis, so we picked out two relatively complete pathways for further description and discussion containing all types of interactions we studied(Fig 10).

3. Discussion

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Based on all the investigations of miRNAs, transcription factors and target genes, and comparison of networks in rice and Arabidopsis, we made a potential hypothesis of the different salt-responsive mechanisms in rice and Arabidopsis when they are threatened by salt stress: their responsive centers were different. In rice, miRNAs was induced by salt stress and more active while transcription factors and stress-related genes occupied more important statutes in Arabidopsis; The target GO enrichment results in rice and Arabidopsis tell us that targets in Arabidopsis are mainly enriched in directly salt-related signaling pathways to tolerant salt stress by adopting more active response approach, while in rice, salt signaling networks involved more genes related to development and growth to avoid salt stress by coupling with development and growth processes more straightly.

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Arabidopsis evolved more active and organized response mechanisms than that in rice. First, the network can be clearly distributed into three parts—salt stress signal reception and membrane transduction, transient stress-related gene up regulated, and adjustment of biological process and development, while the networks of rice are concentrated on growth and development, not like Arabidopsis, rice tends to rearrange its growth and development to avoid salt stress. 3.1 3.1 Arabidopsis deals with salt threats more actively Salt tolerance, a positive strategy that plants directly up-regulate salt response genes to tolerate salt stimuli, reshape architecture, phytohormone, and dynamic activities to go through salt stress. Arabidopsis shows a typically organized stress response network (Fig 4), with signs of all the three phases of salt stress response-- osmotic stress tolerance, ion exclusion, toxin tolerance, whose salt responsive networks relay

on transcription factors-center mechanisms (Fig 8). The regulatory networks in Arabidopsis show a transient signaling response to salt . When exogenous salt accumulates over normal situation, G2-like protein center signaling transduction pathway can be activated to inform plants of the foreign risk, and transcription factors NACs trigger and activate their downstream salt-resistant genes to respond salt stress, then other transcription factors start to readjust plant development and growth to reduce the damage salt causes(Fig 9). Marino reported the degradation of MYB30 could reduce plant defense in Arabidopsis, proving the regulatory roles of MYB in stress resistance (Marino et al., 2013). Kaho’s team has discovered function of MYB30 to link ROS signaling, root cell elongation and plant immune responses (Kaho Mabuch, 2018)

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NAC and MYB, as two typical representatives of transcription factors, emphasize on regulation of different phases of salt tolerance in Arabidopsis. NAC transcription factors are part of GA/BR metabolism and signaling, affecting flowering time and plant height (Shahnejat-Bushehri et al., 2016), and enhancing stress tolerance (Lee et al., 2017). It suggests NAC is probably a type of transcription factors that directly participate in stress response pathway and prefer to work on the third phase toxin tolerance and development adjustment. Besides, NACs are known to be repressed by miRNA164 in wheat (Feng, 2014), and ATAF1 (AT1G01720), an ABA signaling related genes directly participating in salt response (Stiti et al., 2014;Liu et al., 2016), probably repressed by miRNA8182 in Arabidopsis. ANAC046 (AT3G04060), a positive regulator of senescence (Oda-Yamamizo et al., 2016), is likely regulated by miRNA169 during post transcription. Multiple and complex regulation of NAC gene expression indicates their crucial statute in salt stress response. Different from NACs, MYB transcription factors pay more attentions on salt signal sense, osmotic stress tolerance, and salt and toxin transportation. NIP1, a salt-sensory gene whose expression is was triggered by MYB to transport antimonite and determine antimonite sensitivity. Besides, MYB regulates ABA signaling genes ALDH3H1, ATAF1 to sense salt and drought signals (Stiti et al., 2014;Liu et al., 2016). Besides, MYB also participates in rearrangement of development and growth in different tissues to go through the period of salt stress damage. It adjusts expression of FAB1D to activate phosphatidylinositol kinases in Arabidopsis (Serrazina et al., 2014)(Table 3). 3.2 Rice prefers to rearrange its growth and development to avoid salt stress

Faced with salt stress, rice adopts the approach of avoidance, which is a negative strategy that plants readjust their period of duration, growth and development to avoid salt stress. First evidence in rice is that targets of transcription factors and miRNAs are enriched in reproductive development, metabolic processes (Fig 8C 8D), and most of them are down-regulated, suggesting that rice represses the development and growth when stimulated with salt and results in avoidance of severe damage from high salinity. For instances, MYB reduced its repressed effects

on miRNA162a, and miRNA162a was up-regulated to cleave OsGIGANTEA, a yield related gene (Izawa et al., 2011), and OsGI was also reported to suppress WRKY44 with miRNA172(Han Y, 2013) to avoid drought stress, which indicates the salt response are usually coupled with developmental regulation. Simultaneously, MYBs were reported as downstream genes of bZIP in response to drought and salt (Liu et al., 2014). We found that, MYB reduced expression of miRNA164c to down-regulate NAC, known salt related transcription factors functioning in ABA/GA signaling pathways(Shahnejat-Bushehri et al., 2016) .

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The other example of regulatory pattern in Fig 10 contains two stress-response genes—glyoxalase gene (OsGly I) and a putative cytosolic dihydroorotate dehydrogenase encoded gene (DHODH). OsDHODH1, encoding a putative cytosolic dihydroorotate dehydrogenase, which has been reported as drought response genes in rice (Liu et al., 2009), and OsGly I (Zeng et al., 2016) was markedly up-regulated in response to NaCl, ZnCl2 and mannitol in rice seedlings. Interestingly, in our analysis (DEG from GSE27884, 150mM NaCl treated for 7 days (Zhang et al., 2014)), OsGly I was also repressed by miR1861 and OsDHODH1 was repressed by miR1882, suggesting that OsGly I and OsDHODH1 are regulated by miRNAs (Fig 10).

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Except for the large ratio of miRNA mediated regulatory interactions in rice salt response networks, the conserved transcription factors AP2 and bHLH showed active regulation of salt-response. Both targets of miRNA and transcription factors are concentrated on intraocular organelle parts and metabolic processes, indicating that rice salt response mechanisms are relatively active on second phases—ion transport to prevent salt toxin accumulation in tissues, but from view of the whole rice network, most growth and development related genes are repressed, for instance, SCL33, NADP-ME2 , and SAUR76 ,which suggests that, salt stress probably has caused severe damages on rice growth and development when rice starts its defense mechanisms. Simultaneously, rice re-adjusts its architectures and delays its crucial development phase to avoid the salt stress and reduce the damages to growth and metabolic processes. 3.3 Species-conserved salt-regulatory pattern

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We have detected miRNA171-GRAS and miRNA169-NFYA in both rice and Arabidopsis. In rice, NF-YA transcription factors have been reported to be involved in drought stress (Smoczynska and Szweykowska-Kulinska, 2016), and miRNA169 -NF-YA pattern has been reported in ABA signaling and modulate root growth (Lee et al., 2015;Zhao et al., 2016). GRAS transcription factors are plant specific transcription factors, participating in flower development (Tian et al., 2004;Smoczynska and Szweykowska-Kulinska, 2016). These two conserved miRNA-TF patterns show the coupled relations between salt response and development, and conserved function of miRNA-TF patterns in regulation of salt-stress signaling.

4. Methods and Materials 4.1 Identification of co-expressed TF-DEG pairs

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Since this work aims to compare the salt responsive regulatory networks between Arabidopsis and rice, we try to keep the conditions and materials of small RNA-seq and microarray consistent in two species, and guarantee data quality simultaneously. As a result, we chose microarrays from seedlings treated by 150mM salt for 7 days in rice and 5 days in Arabidopsis, which ensures the accuracy of our conclusion at best. On the other hand, we applied 20 different degradome in Arabidopsis and 12 in rice to verify the authenticity of MTIs. The original microarray data of rice (GSE27884) (Zhang et al., 2014)and Arabidopsis (GSE84221) (Yuichi T, Maki K., 2016) are downloaded from Gene Expression Ominus (GEO), both of them are from seedlings and treated with 150mM NaCl with three biological replicates. P-value of t-test differential expression identification between salt treatment and control are set as 0.05 in rice and 0.001 in Arabidopsis, resulting in 2,193 differential expressed genes (DEG) in rice and 2,606 in Arabidopsis, of which 131 are rice transcription factor (TF) genes and 162 are Arabidopsis transcription factor genes. We calculate Pearson coefficient of TF genes and all the rest DEGs. The TF-DEG pairs whose Pearson correlation coefficient is over 0.8 are considered as co-expressed pairs.

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For each expression of DEG X and DEG Y, we calculated the Pearson correlation coefficient as following formula:

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After calculation, we generated a DEG-correlation coefficient matrix, where we picked out all the co-related TF-DEGs pairs. 4.2 Identification of TF-Target pairs

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Promoter analysis of all DEGs with co-expressed TF genes was undertaken with MAST tool on MEME (Sakai et al., 2013) (http://meme-suite.org/tools/mast). We uploaded transcription factor binding motif files( from Plant Transcription factor Database v4.0, http://planttfdb.cbi.pku.edu.cn/)(Jin et al., 2017) to get predicted distributions of transcription factor binding sites in DEG promoters and TF-DEG promoter bind profiles, and these pairs who are also co-expressed are putative TF-DEG pairs. Simultaneously, we obtained TF-miRNA gene pairs with the same approach. Promoter information of Arabidopsis (upstream 3kb sequence of DEG) are downloaded from The Arabidopsis Information Resource(Eva H et al., 2001) (TAIR, https://www.arabidopsis.org); promoters information of rice DEG came from The Rice Annotation Project Database(Sakai et al., 2013) (RAP-DB, http://rapdb.dna.affrc.go.jp/); and promoter information of miRNAs in rice and Arabidopsis are available at plant microRNA database database (Zhang et al.,

2010)(PMRD, http://bioinformatics.cau.edu.cn/PMRD/). 4.3 Degradome-verified miRNA-Target interactions

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We uploaded all 735 rice miRNAs and 451 Arabidopsis miRNAs from miRBase v.22 (S, 2013)into psRNATarget (Jin et al., 2017) server (http://plantgrn.noble.org/psRNATarget) with default parameters to predict miRNA-Target intereactions. First step of degradome-verification is mapping normalized degradome data to the putative target sequences from prediction results; then with the bowtie program, the predicted miRNA-target interacted pairs were retained if they meet the following criteria :(1)there must be at least one degradome sequence with their 5’ends resided within 9~12 nt regions away from the 5’ends of the target binding sites;(2)read counts of at least one degradome sequence in above region are more than 5RP10M;(3)read counts of one degradome sequence in about region should be the most abundant(Category 1) or higher than median (Category 2), among all the reads mapped on one target. Finally, the t-plot Figures were generated, using our local developed python script. Information of original platforms of degradome and small RNA-seq data are listed in Supplementary Table 2. Processed degradome data can be downloaded from DPMIND(Fei et al., 2018) (http://cbi.njau.edu.cn)

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4.4 Construction and analysis of salt-responsive gene regulatory networks

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Salt-responsive GRNs were constructed and merged with TF-targets putative regulatory pairs and miRNA-Target in both rice and Arabidopsis (Supplementary Table 3, Supplementary Table 4) with Cytoscape. Based on these networks, we classified the rice-specific, Arabidopsis-specific and species-conserved salt-response patterns. Combined with reported salt signaling pathways in plants, more completed salt-response regulatory networks were revealed. 4.5 Gene ontology enrichment

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We picked out gene IDs of targets from MTIs and TF-Target putative pairs, respectively, and uploaded them separately to AgriGO (Zhang et al., 2010) (http://bioinfo.cau.edu.cn/agriGO/analysis.php) and performed gene ontology enrichment analysis. We set the false discovery rate (FDR) of enriched GO processes as 0.05 and then created the heatmaps of significantly enriched targets in rice and Arabidopsis. Funding This work was supported by the Natural Science Foundation of China (31571627), the Fundamental Research Funds for the Central Universities (Y201900029), National Science and Technology Support Program (2015BAD01B01) and the Jiangsu Collaborative Innovation Center for Modern Crop Production.

Author Contribution Statement

HJ and WR conceived this study; WR and ZXF did bioinformatics analysis; WR drafted the manuscript; HJ, WR, CYH, KXJ, ZXF and ZHS edited the draft, and all authors approved the final version of manuscript.

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Acknowledgements We appreciate all authors’ dedications in this work: HJ and WR conceived this study; WR and ZXF did bioinformatics analysis; WR drafted the manuscript; HJ, WR, CYH, KXJ, ZXF and ZHS edited the draft, and all authors approved the final version of manuscript.

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Zhu, J.K. (2002). Salt and drought stress signal transduction in plants. Annu Rev Plant Biol 53, 247-273.

Figure legend

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Fig 1. Analysis patterns of salt-response GRNs.

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Fig 2. Differentially expressed transcriptional factors (DETs) encoded genes under salt stress. A. Distribution of salt-related (up/down regulated ones) miRNAs, genes and transcription factors; B.DETs in Rice (p<0.05) C. DETs in Arabidopsis (p< 0.001). Most TFs in soybean showed down-regulated and up-regulated in Arabidopsis, while no specific features in rice, indicating different response pathways in different species although they had homologous TFs from the same TF family.

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Fig 3. Description of salt-response TF families in rice and Arabidopsis. A. Venn diagram of TF-miRNA gene pairs; B. Venn diagram miRNA-TF patterns; C. Venn diagram of transcription factor family distributions in rice and Arabidopsis; D. Performance of conserved Transcriptional Factor(TF) genes in rice and Arabidopsis (p < 0.05)

Fig 4. Salt-response GRNs in rice and Arabidopsis. A. Salt-response regulatory

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networks in Arabidopsis; B. Salt-response regulatory networks in rice.

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Fig 5. Comparison of TF-Target pairs in Arabidopsis and rice. A. Down-regulated transcription factors in rice and Arabidopsis; B. Up-regulated transcription factors C.DET-center TTIs in Arabidopsis; D.DET-center TTIs in rice.

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Fig 6 Comparison of MTIs between rice and Arabidopsis. A. Comparison of different cleavage-efficient of verified MTIs in rice and Arabidopsis; B. Comparison of different cleavage-efficient verified siRNA-mediated interactions; C. Salt-response MTIs in rice; D. Salt-response MTIs in Arabidopsis. Conserved miR-TF patterns found from intersection of Arabidopsis and rice MTIs are miR169-NF-YA and miR171-GARS

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Fig 7 Functional clusters of targets in Arabidopsis and rice. A. Stress-related miRNA targets in Arabidopsis; B. Stress-related miRNA targets in rice; C. Heatmap of targets of Transcription factors in Arabidopsis (stress response genes gradient bar is different with rest genes). D. Heatmap of miRNAs’ targets in rice.

Fig 8 Biological process enrichment of target genes in salt-response networks. A. Targets of transcription factors function enrichment in Arabidopsis; B. Targets of miRNAs function enrichment in Arabidopsis; C. Targets of transcription factors function enrichment in rice; D. Targets of miRNAs function enrichment in rice; * stars

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point interested processes.

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Fig 9 Arabidopsis salt-response signaling sub-networks. A. Transport functional network;

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B. Direct salt response; C. Development and growth. Sub-networks are up-regulated DET center. The nodes whose borders are in yellow are conserved. The edges in yellow are the one we are interested in.

Fig 10 Two typical salt-response pathways in rice. From complicated rice GRNs, two

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pathways are relevantly complete containing all the components. The upper one contains miRNA164-NAC, MYB-miRNA162/miRNA164 and miRNA162- OsGIGANTEA modules; the nether one includes mIRNA156-SPL, AP2-SPL/miRNA1882, miRNA1882-OsDHODH1, miRNA1861-OsGly I et al. biological modules.

Table 1 Distribution of salt-related genes, transcription factor genes and miRNAs Number of DEMs

Number of DEGs

Number of DETs

Rice up-regulated

52(85.2 %)

1055(47.6%)

80(3.6%)

Rice down-regulated

9(14.8%)

1030(46.5%)

51(2.3%)

Arabidopsis up-regulated

23(27.1 %)

1877(79.6%)

128(5.4%)

Arabidopsis down-regulated

62(72.9 %)

334(14.2%)

18(0.8%)

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DEM, differential expressed miRNAs; DEG, differential expressed gene, which does not include transcription factor encoded genes; DET, differential expressed Transcription factor genes; whose fold change value

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(Salt/Control) is over 2 (up-regulated) or lower than 0.5(down-regulated)

Table 2 Statistics of TF-Targets in rice and Arabidopsis

Rice, up Rice, down Arabidopsis, up Arabidopsis, down

28,862 18,644 140,014 3,654

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Total TF-target pairs based on promoter analysis(MAST)

putative pairs

4,367 3,570 6,380 943

95 56 138 4

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Co-expressed pairs

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Species, Regulation

Table 3 Conserved Transcription factor-Target Interactions (TTIs) in Arabidopsis TF_fam

TF

Target

Pearson

ily

_Accession

_accession

coefficient

NAC

AT1G01720 AT2G18680

MYB

Target

Annotation

0.996412

MSF3.6

Transmembrane protein

AT4G18770 AT5G67470

0.997306

FH6

NAC

AT1G01720 AT5G67470

0.99292

FH6

MYB

AT4G18770 AT1G29030

0.991841

UBQ3

MYB

AT4G18770 AT4G25315

0.995078

UNE12

bHLH DNA-binding

NAC

AT1G01720 AT5G18200

0.987277

MRG7.16

encodes an adenylyltransferase

NAC

AT1G01720 AT3G18820

0.989128

RAB7B

NAC

AT1G01720 AT4G28340

0.983213

F20O9.10

pyrroline-5-carboxylate reductase

MYB

AT4G18770 AT1G01830

0.973761

F4I18.30

ARM repeat superfamily protein

MYB

AT4G18770 AT1G34260

0.994149

FAB1D

Encodes ubiquitin that is attached to proteins

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destined for degradation.[19]

Phosphatidylinositol kinases in Arabidopsis pollen tube growth and fertilization.[13]

Encodes a soluble protein with inorganic NAC

AT1G01720 AT3G53620

0.969309

PPa4

pyrophosphatase activity that is highly specific for Mg-inorganic pyrophosphate

0.997687

NLM1

MYB

AT4G18770 AT1G53340

0.986256

TIE22.120

NAC

AT1G01720 AT3G14070

0.997888

MYB

AT4G18770 AT4G00940

0.990326

NAC

AT1G01720 AT1G78610

MYB

AT4G18770 AT2G22910

CAX9

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Dof

0.987404

MSL6

0.992114

NAGS1

na

AT4G18770 AT4G34240

0.993238

normal condition[33]

DC1 domain-containing protein

Involved in cation (K, Na and Mn) homeostasis

ALDH3H1

and transport [34]

mechanosensitive channel of small conductance-like 6

Encodes an aldehyde dehydrogenase induced by ABA and dehydration that can oxidize saturated aliphatic aldehydes.[12]

MYB

AT4G18770 AT3G56770

0.991216

bHLH

[35]

NAC

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MYB

reduced by ABA and NaCl, low expression level

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AT4G18770 AT3G57280

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MYB

AT1G01720 AT5G11900

0.989942

F14F18

Translation initiation factor SUI1 family protein

AT4G18770 AT1G10540

0.995861

NAT7

nucleobase-ascorbate transporter 7

AT4G18770 AT5G19450

0.9879

CPK7

calmodulin-domain protein kinase CDPK

AT4G18770 AT4G26310

0.99844

LSU1

elongation factor P (EF-P) family protein

NAC

AT1G01720 AT1G71696

0.975673

SOL1

NAC

AT1G01720 AT1G60650

0.991914

ATAF1

cole-inducible[11]

MYB

AT4G18770 AT4G30490

0.987475

CAM1

Induced by various abiotic stimuli[37]

MYB MYB

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MYB

SOL1 isolated as a suppressor of root- specific overexpression of CLE19, a clavata3 like gene[36]

Table 4 conserved Transcription factor-Target Interactions (TTIs) in rice TF family

Target

Pearson

_Accession

coefficient

Target_Annotation

Os03g0770700 Os03g0244600

0.888098

Os03g0770700 Os05g0150500

0.935973

AP2

Os03g0770700 Os02g0258900

0.929545

Similar to Molybdopterin biosynthesis CNX2 protein

bHLH

Os01g0235700 Os06g0617800

0.87575

bHLH13;Ribose-phosphate pyrophosphokinase 2

AP2

Os03g0770700 Os05g0411300

0.959408

bZIP transcription factor 39-like[23]

bHLH

Os01g0235700 Os06g0617800

0.87575

Ribose-phosphate pyrophosphokinase 2

bHLH

Os01g0235700 Os01g0267100

0.965382

MT-A70 family protein.

AP2

Os03g0770700 Os01g0267100

0.903031

MT-A70 family protein.

AP2

Os03g0770700 Os02g0258900

0.929545

Similar to Molybdopterin biosynthesis CNX2 protein

AP2

Os03g0770700 Os02g0811800

0.937134

Similar to Cinnamoyl-CoA reductase

AP2

Os03g0770700 Os01g0952200

0.948644

AP2

Os03g0770700 Os08g0487000

0.978678

Hypothetical conserved gene.

AP2

Os03g0770700 Os01g0965500

0.970467

Similar to SET domain protein.

bHLH

Os01g0235700 Os01g0833800

0.973399

AP2

Os03g0770700 Os03g0737900

0.953034

AP2

Os03g0770700 Os03g0196900

0.979213

AP2

Os03g0770700 Os08g0509600

0.996109

bHLH

Os01g0235700 Os01g0833800

0.973399

AP2

Os03g0770700 Os07g0602200

0.993043

Similar to HDA1.

bHLH

Os01g0235700 Os07g0114500

0.990524

Conserved hypothetical protein.

AP2

Os03g0770700 Os05g0535200

0.841018

Cyclin-like F-box domain containing protein.

bHLH

Os01g0235700 Os06g0617800

0.87575

Os03g0770700 Os07g0227700

0.957976

bHLH

Os01g0235700 Os06g0691000

0.981782

Os03g0770700 Os06g0324000

0.987794

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AP2

AP2

auxin transporter-like protein 3 F-Box auxin receptor protein, Nuclear protein, Flag

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leaf

DNA/RNA helicase, C-terminal domain containing protein.

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IQ calmodulin-binding region domain containing protein.

Similar to predicted protein.

Similar to TFIIB-related protein (Fragment).

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CRL5

lP

AP2/

na

AP2

TF_Accession

squamosa promoter-binding-like protein 14

IQ calmodulin-binding region domain containing protein.

Ribose-phosphate pyrophosphokinase 2 EGF-like region, conserved site domain containing protein. DNA-repair protein, UmuC-like domain containing protein. Conserved hypothetical protein.