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BMC Research Notes

Open Access

Genome-wide analysis of overlapping genes regulated by iron deficiency and phosphate starvation reveals new interactions in Arabidopsis roots

BMC Research Notes20158:555

https://doi.org/10.1186/s13104-015-1524-y

Received: 9 September 2015

Accepted: 23 September 2015

Published: 12 October 2015

Abstract

Background

Iron (Fe) and phosphorus (P) are essential mineral nutrients in plants. Knowledge regarding global changes in the abundance of Fe-responsive genes under Pi deficiency as well as the processes these genes are involved in remains largely unavailable at the genome level. In the current study, we comparatively analyzed RNA sequencing data sets relative to Fe deficiency (NCBI: SRP044814) and Pi starvation (NCBI: SRA050356.1).

Results

Analysis showed a total of 579 overlapping genes that are responsible for both Fe deficiency and Pi starvation in Arabidopsis roots. A subset of 137 genes had greater than twofold changes in transcript abundant as a result of the treatments. Gene ontology (GO) analysis showed that the stress-related processes ‘response to salt stress’, ‘response to oxidative stress’, and ‘response to zinc ion’ were enriched in the 579 genes, while Fe response-related processes, including ‘cellular response to nitric oxide’, ‘cellular response to iron ion’, and ‘cellular iron ion homeostasis’, were also enriched in the subset of 137 genes. Co-expression analysis of the 579 genes using the MACCU toolbox yielded a network consisting of 292 nodes (genes). Further analysis revealed that a subset of 90 genes were up-regulated under Fe shortage, but down-regulated under Pi starvation. GO analysis in this group of genes revealed an increased cellular response to iron ion/nitric oxide/ethylene stimuli. Promoter analysis was performed in 35 of the 90 genes with a 1.5-fold or greater change in abundance, showing that 12 genes contained the PHOSPHATE STARVATION RESPONSE1-binding GNATATNC cis-element within their promoter regions. Quantitative real-time PCR showed that the decreased abundance of Fe acquisition genes under Pi deficiency exclusively relied on Fe concentration in Pi-deficient media.

Conclusions

Comprehensive analysis of the overlapping genes derived from Fe deficiency and Pi starvation provides more information to understand the link between Pi and Fe homeostasis. Gene clustering and root-specific co-expression analysis revealed several potentially important genes which likely function as putative novel players in response to Fe and Pi deficiency or in cross-talk between Fe-deficient responses and Pi-deficient signaling.

Keywords

Iron deficiencyPhosphate deficiencyRNA-seqCo-expressionInteraction

Background

The evolutionary ability of iron (Fe) to change oxidation states between Fe(III) and Fe(II) renders it irreplaceably important in many essential processes associated with basic redox reactions, such as in photosynthesis, respiration and many vital enzymatic reactions [15]. Although Fe is abundant in the earth’s crust, it is one of the least available elements for plants in aerobic soils with neutral to basic pH [15]. Approximately 30 % of the land worldwide consists of alkaline soils, leading to a demand in bioavailable Fe for plant fitness [1, 5, 6]. As a consequence, Fe deficiency is a major constraint in crop yield and quality [7]. In contrast, in acidic and anaerobic conditions, accumulation of excess Fe is toxic to plant growth and development due to formation of potentially harmful reactive oxygen species (ROS). Plants therefore must tightly regulate cellular Fe homeostasis to allow for effective acquisition, distribution and utilization of Fe [1, 8, 9].

Under Fe-deficient conditions, Arabidopsis (Arabidopsis thaliana) and other dicotyledonous and non-graminaceous monocotyledonous plants use a reduction strategy, referred to as strategy I [10], to increase Fe bioavailability. In this strategy, acidification of the rhizosphere mediated by the H+-translocating P-type ATPase AHA2 [6, 11] occurs as the first step, which leads to an increase in the concentration of chelated Fe(III). Fe(III) is subsequently reduced to soluble Fe(II) by the root surface-localized ferric chelate reductase FERRIC-REDUCTION OXIDASE2 (FRO2) [12]. Soluble Fe(II) is then transported into epidermal cells by the Fe-REGULATED TRANSPORTER1 (IRT1) [13]. Being the major components of the Fe acquisition system, FRO2 and IRT1 are the major players controlling entry of Fe from the soil into cells. At the transcriptional level, expression of both genes is coordinately regulated by the basic helix-loop-helix (bHLH) transcription factor FER-LIKE Fe DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT), but not the transcription factor POPEYE, which is also involved in Fe homeostasis [1417]. FIT forms heterodimers with bHLH38 and bHLH39 and positively regulates a subset of Fe-responsive genes, including three key genes required for Fe acquisition [1214, 18, 19]. Recent studies have shown that the transcription factors bHLH100 and bHLH101, which belong to the Ib sub-group of bHLH proteins, are also involved in Arabidopsis Fe deficiency responses by interacting with FIT [20] or via a FIT-independent manner [21].

Studies have shown that expression of FRO2 and IRT1 is tightly controlled both locally and systemically [22, 23]. However, in some cases disrupted Fe signaling in several mutants, such as frd3 [24, 25], opt3 [26] and the quadruple nicotianamine synthase mutant nas4x-1 [27] in Arabidopsis, dgl and brz mutants [2830] in pea (Pisum sativum) and the chloronerva mutant chln [31] in tomato (Solanum lycopersicum), constitutively activates expression of Fe acquisition genes even under sufficient Fe conditions. By contrast, FRO2 and IRT1 expression has been documented to be decreased under phosphate (Pi)-deficient conditions [3236]. Currently, the predominate explanation for decreased expression of Fe acquisition genes under Pi-deficient conditions is that Pi deficiency results in enhanced Fe accessibility to plants in the media, which leads to an over accumulation of Fe in plants, subsequently causing down-regulated expression of Fe-responsive genes. However, if the Pi-deficient media without available Fe or with low concentrations of Fe, does the down-regulated expression of Fe-responsive genes occur? A recent report showed that PHOSPHATE STARVATION RESPONSE1 (PHR1), a major regulator of the Pi deficiency response, could bind the promoter of the Fe storage gene Ferritin1 through the imperfect palindromic sequence motif P1BS (PHR1 binding sequences, GNATATNC), strongly supporting the link between Fe and Pi homeostasis [37]. However, it remains an open question whether this link exists or not in phr1 mutant plants.

Moreover, although down-regulation of Fe deficient-induced Fe acquisition genes under Pi deficient conditions has been documented [33, 38, 39], knowledge regarding genome-wide transcriptional changes of Fe-responsive genes under Pi deficiency remains unavailable, and the processes of the genes involved are largely unknown. To provide systemic information about transcriptional changes in Fe-responsive genes under Pi deficiency and to further extend knowledge of the relationship between Fe and Pi at the transcript level, we mined and re-analyzed previous RNA sequencing (RNA-seq) data sets relative to transcriptome profiling in Fe-deficient [40] and Pi-deficient Arabidopsis roots [36], with an emphasis on 579 overlapping genes that respond to both Fe and Pi deficiency. We revealed that a subset of 137 genes had a twofold or greater change in abundance under either of the treatments. A subset of 90 genes with an increased abundance under Fe deficiency, but a decrease under Pi deficiency, may be critical for Fe responses under Pi-deficient conditions. By gene clustering and root-specific co-expression analysis, we revealed several potentially important genes that likely function as putative novel players in response to Fe and Pi deficiency or in the cross talk between Fe deficient responses and phosphate-deficient signaling, which may be determined in follow-up experiments. Finally, we found that FIT-regulated genes were down-regulated by Pi deficiency, and an extent of Fe in the Pi deficient media is required for this down-regulation, suggesting that, besides FIT, PHR1, Fe itself might be a critical factor involved in the transcriptional regulation under both Pi- and Fe-deficiency.

Results

Genes responsible for Fe and Pi deficiency in Arabidopsis roots

Previously published RNA-seq data sets [36, 40] relative to Fe and Pi deficiency in Arabidopsis roots were re-analyzed, and differentially expressed genes (P < 0.05) upon Fe deficiency were compared with those (P < 0.05) exposed to Pi deficiency. Subsequent analyses focused on the 579 overlapping genes (Additional file l) as shown in Fig. 1. Of the 579 genes, 137 showed an increase or decrease in transcript abundance, with fold changes greater than twofold under either of the treatments (Table 1; Additional file 2). Fe acquisition genes FRO2 and IRT1, copper transporter COPT2, Fe(II)-dependent oxygenase gene AT3G12900, cytochrome P450 CYP82C4 (AT4G31940), mannose-binding lectin protein gene AT1G52120, glutathione transferase lambda 1 GSTL1 and amino acid transporter gene AT5G38820 showed the strongest induction under Fe deficiency and were up-regulated by more than 50-fold (Table 1; Fig. 2a). Excluding AT1G52120, these genes were among the most repressed under Pi deficiency and were down-regulated by two to more than tenfold (Table 1; Fig. 2a). Genes encoding transcriptional factor bHLH039, ZIP9, zinc binding protein (AT1G74770), ATROPGEF10, receptor like protein 24 RLP24, phloem protein 2-B6 and other functionally unknown proteins were among the second group of highly induced genes following Fe deficiency and were up-regulated by more than fivefold (Table 1; Fig. 2a, b). The most induced genes following Pi deficiency were AtOCT1, an unknown protein gene AT5G20790 and a major facilitator protein gene AT1G30560, which were induced by more than 50-fold (Table 1; Fig. 2c). Highly induced genes under Pi deficiency were ATPS3 (phosphate starvation-induced gene 3), SQD2 (sulfoquinovosyl diacylglycerol 2) and U-box domain-containing protein kinase gene AT5G65500, with changes more than fivefold (Table 1; Fig. 2d, e). Interestingly, a subset of genes involved in lignin biosynthesis was induced by both Fe and Pi deficiency (Table 1; Fig. 2f).
Fig. 1

Bioinformatic analysis scheme of the 579 differentially expressed overlapping genes regulated by both iron and phosphate deficiency in Arabidopsis roots

Table 1

Subset of 137 of the 579 overlapping genes with more than twofold changes in transcript abundance due to either of the treatments

AGI

Annotation

Mean (−Fe/+Fe)

SD

Mean (−Pi/+Pi)

SD

At3G12900

2-Oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein

612.44

199.47

0.06

0.10

At4G31940

CYP82C4, cytochrome P450, family 82, subfamily C, polypeptide 4

184.70

3.63

0.08

0.01

At1G52120

Mannose-binding lectin superfamily protein

157.65

59.83

6.13

2.70

At3G46900

COPT2, copper transporter 2

71.15

33.39

0.40

0.36

At1G01580

ATFRO2, FRD1, FRO2, ferric reduction oxidase 2

59.73

10.56

0.35

0.05

At5G02780

GSTL1, glutathione transferase lambda 1

57.98

10.87

0.69

0.06

At4G19690

ATIRT1, IRT1, iron-regulated transporter 1

54.72

7.88

0.26

0.04

At5G38820

Transmembrane amino acid transporter family protein

54.35

11.69

0.43

0.09

At3G56980

BHLH039, ORG3, basic helix-loop-helix (bHLH) DNA-binding superfamily protein

34.23

13.48

0.73

0.18

At3G13610

2-Oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein

10.04

0.65

1.63

0.25

At1G73120

Unknown protein

8.98

2.09

0.28

0.11

At1G73220

1-Oct, AtOCT1, organic cation/carnitine transporter1

7.38

6.18

196.69

139.63

At4G33020

ATZIP9, ZIP9, ZIP metal ion transporter family

7.09

2.01

2.21

0.97

At5G05250

Unknown protein

6.62

0.95

0.57

0.10

At3G61410

BEST Arabidopsis thaliana protein match is: U-box domain-containing protein kinase family protein (TAIR:AT2G45910.1)

6.53

1.32

3.79

0.43

At1G74770

Zinc ion binding

6.43

0.50

0.66

0.07

At5G19560

ATROPGEF10, ROPGEF10, ROP uanine nucleotide exchange factor 10

5.33

0.51

2.12

0.20

At2G02310

AtPP2-B6, PP2-B6, phloem protein 2-B6

5.30

2.01

0.49

0.12

At2G33020

AtRLP24, RLP24, receptor like protein 24

5.05

1.74

0.48

0.33

At3G59880

Unknown protein

4.58

2.42

2.51

0.54

At5G01060

Protein kinase protein with tetratricopeptide repeat domain

4.44

0.17

1.70

0.10

At5G04950

ATNAS1, NAS1, nicotianamine synthase 1

4.39

0.50

0.49

0.01

At3G57157

Other RNA

4.28

0.37

9.81

1.46

At3G60330

AHA7, HA7, H(+)-ATPase 7

4.27

0.69

1.79

0.21

At3G21500

DXPS1, 1-deoxy-d-xylulose 5-phosphate synthase 1

3.76

1.94

0.40

0.26

At3G21240

4CL2, AT4CL2, 4-coumarate:CoA ligase 2

3.73

0.16

1.25

0.09

At3G50710

F-box/RNI-like/FBD-like domains-containing protein

3.57

1.09

2.71

1.32

At1G18910

Zinc ion binding

3.33

0.13

0.78

0.04

At5G48657

Defense protein-related

3.33

0.71

1.36

0.23

At1G01380

ETC1, Homeodomain-like superfamily protein

3.22

1.09

3.73

1.22

At1G51680

4CL.1, 4CL1, AT4CL1, 4-coumarate:CoA ligase 1

3.22

0.06

1.27

0.19

At5G54790

Unknown protein

3.20

0.75

2.02

0.19

At2G01880

ATPAP7, PAP7, purple acid phosphatase 7

3.14

0.21

3.31

0.81

At5G19970

Unknown protein

2.81

0.43

0.57

0.07

At5G65500

U-box domain-containing protein kinase family protein

2.73

0.60

6.26

0.42

At4G12735

Unknown protein

2.67

1.20

1.87

0.47

At3G57160

Unknown protein

2.57

0.55

1.46

0.23

At5G26820

ATIREG3, IREG3, IREG3, MAR1, RTS3, iron-regulated protein 3

2.54

0.28

0.77

0.19

At4G30490

AFG1-like ATPase family protein

2.52

0.32

1.40

0.08

At1G78230

Outer arm dynein light chain 1 protein

2.51

0.57

0.59

0.22

At3G18290

BTS, EMB2454, zinc finger protein-related

2.51

0.14

0.76

0.09

At1G51870

Protein kinase family protein

2.42

0.42

2.25

0.83

At5G22555

Unknown protein

2.37

0.58

4.13

2.07

At1G53310

ATPEPC1, ATPPC1, PEPC1, PPC1, phosphoenolpyruvate carboxylase 1

2.34

0.19

2.21

0.25

At3G51570

Disease resistance protein (TIR-NBS-LRR class) family

2.32

0.58

2.70

1.18

At5G22890

C2H2 and C2HC zinc fingers superfamily protein

2.32

0.53

1.91

0.25

At4G22980

Pyridoxal phosphate (PLP)-dependent transferases superfamily protein (TAIR:AT5G51920.1)

2.30

0.28

0.56

0.14

At1G14190

Glucose-methanol-choline (GMC) oxidoreductase family protein

2.29

0.41

0.78

0.08

At1G48300

Unknown protein

2.27

0.25

0.85

0.07

At1G24320

Six-hairpin glycosidases superfamily protein

2.26

0.27

0.78

0.09

At1G62422

Unknown protein

2.25

0.43

1.37

0.10

At4G38950

ATP binding microtubule motor family protein

2.25

0.45

1.50

0.15

At3G47420

ATPS3, PS3, phosphate starvation-induced gene 3

2.24

0.27

12.82

5.53

At5G26320

TRAF-like family protein

2.24

0.26

2.95

0.37

At2G43570

CHI, chitinase, putative

2.22

0.87

2.26

0.45

At4G26890

MAPKKK16, mitogen-activated protein kinase kinase kinase 16

2.16

0.42

3.00

0.85

At5G13910

LEP, Integrase-type DNA-binding superfamily protein

2.14

0.15

1.25

0.16

At5G27920

F-box family protein

2.14

0.15

1.29

0.15

At3G15510

ANAC056, ATNAC2, NAC2, NARS1, NAC domain containing protein 2

2.13

0.62

0.71

0.15

At5G53850

Haloacid dehalogenase-like hydrolase family protein

2.09

0.06

0.96

0.01

At2G18193

P-loop containing nucleoside triphosphate hydrolases superfamily protein

2.07

0.26

1.29

0.01

At2G32960

Phosphotyrosine protein phosphatases superfamily protein

2.07

0.23

4.79

1.23

At1G64590

NAD(P)-binding Rossmann-fold superfamily protein

2.06

0.13

2.39

0.70

At5G48930

HCT, hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase

2.04

0.17

1.16

0.07

At2G14210

AGL44, ANR1, AGAMOUS-like 44

2.01

0.37

0.82

0.09

At5G20790

Unknown protein

0.32

0.13

90.27

24.26

At1G30560

Major facilitator superfamily protein

55.93

32.45

At5G01220

SQD2, sulfoquinovosyldiacylglycerol 2

0.86

0.06

10.49

3.02

At1G72070

Chaperone DnaJ-domain superfamily protein

1.72

0.62

6.60

3.34

At3G52720

ACA1, ATACA1, CAH1, alpha carbonic anhydrase 1

0.40

0.20

5.56

3.11

At1G23140

Calcium-dependent lipid-binding (CaLB domain) family protein

1.58

0.43

5.33

1.19

At3G52190

PHF1, phosphate transporter traffic facilitator1

1.22

0.12

5.25

0.61

At3G56040

UGP3, UDP-glucose pyrophosphorylase 3

0.74

0.18

4.98

0.71

At3G02870

VTC4, Inositol monophosphatase family protein

0.79

0.06

4.64

0.29

At3G16390

NSP3, nitrile specifier protein 3

0.67

0.08

4.55

0.59

At3G07350

Protein of unknown function (DUF506)

0.54

0.07

4.40

0.09

At3G19970

Alpha/beta-hydrolases superfamily protein

1.58

0.08

4.37

0.76

At1G15040

Class I glutamine amidotransferase-like superfamily protein

0.60

0.04

3.73

1.12

At3G12500

ATHCHIB, B-CHI, CHI-B, HCHIB, PR-3, PR3, basic chitinase

0.52

0.05

3.05

0.75

At1G18970

GLP4, germin-like protein 4

0.71

0.12

3.04

0.41

At2G29000

Leucine-rich repeat protein kinase family protein

1.85

0.60

3.03

0.79

At3G53620

AtPPa4, PPa4, pyrophosphorylase 4

0.82

0.03

2.96

0.19

At1G14220

Ribonuclease T2 family protein

0.74

0.18

2.89

0.27

At4G32480

Protein of unknown function (DUF506)

1.35

0.19

2.85

1.36

At3G06962

Other RNA

1.82

0.49

2.74

0.44

At1G11920

Pectin lyase-like superfamily protein

2.68

0.51

At1G08650

ATPPCK1, PPCK1, phosphoenolpyruvate carboxylase kinase 1

1.77

0.16

2.58

0.21

At4G04040

MEE51, Phosphofructokinase family protein

0.83

0.04

2.50

0.22

At5G57540

AtXTH13, XTH13, xyloglucan endotransglucosylase/hydrolase 13

1.53

0.11

2.33

0.36

At5G40860

Unknown protein

1.51

0.11

2.32

0.81

At1G68740

PHO1;H1, EXS (ERD1/XPR1/SYG1) family protein

0.39

0.08

2.31

0.44

At3G32040

Terpenoid synthases superfamily protein

1.32

0.21

2.30

0.11

At2G25240

Serine protease inhibitor (SERPIN) family protein

1.53

0.15

2.27

0.08

At2G16430

ATPAP10, PAP10, purple acid phosphatase 10

0.68

0.08

2.26

0.33

At4G30670

Putative membrane lipoprotein

1.61

0.16

2.22

0.17

At3G10420

P-loop containing nucleoside triphosphate hydrolases superfamily protein

1.24

0.08

2.20

0.35

At2G42600

ATPPC2, PPC2, phosphoenolpyruvate carboxylase 2

0.77

0.05

2.18

0.09

At4G11650

ATOSM34, OSM34, osmotin 34

0.67

0.10

2.16

0.38

At2G23960

Class I glutamine amidotransferase-like superfamily protein

1.55

0.27

2.13

0.21

At1G05300

ZIP5, zinc transporter 5 precursor

0.49

0.05

2.13

0.11

At3G13110

ATSERAT2;2, SAT-1, SAT-A, SAT-M, SAT3, SERAT2;2, serine acetyltransferase 2;2

0.83

0.08

2.12

0.22

At2G22290

ATRAB-H1D, ATRAB6, ATRABH1D, RAB-H1D, RABH1d, RAB GTPase homolog H1D

1.91

0.66

2.10

0.59

At4G20160

RING/U-box superfamily protein (TAIR:AT1G30860.1)

1.71

0.10

2.07

0.17

At1G20390

Transposable element gene

0.56

0.11

2.07

0.04

At5G20280

ATSPS1F, SPS1F, sucrose phosphate synthase 1F

0.77

0.06

2.05

0.17

At5G01870

Bifunctional inhibitor/lipid-transfer protein/seed storage 2S albumin superfamily protein

0.71

0.10

2.05

0.51

At3G05858

Unknown protein

1.64

0.09

2.01

0.47

At2G17280

Phosphoglycerate mutase family protein

1.16

0.02

2.01

0.47

At1G26250

Proline-rich extensin-like family protein

0.55

0.16

2.00

0.82

At4G09110

RING/U-box superfamily protein

0.77

0.04

Change in gene expression is shown as the mean and standard deviation (SD)

No change is indicated as “−” in cases when a gene transcript was not determined (read number = zero) in any of the biological replicates under control conditions

Fig. 2

Hierarchical cluster analysis of 137 overlapping genes with greater than twofold changes in transcript abundance in Arabidopsis roots grown under Fe or Pi deficiency. af Indicate six representative sub-clusters. Complete clustering results of the 137 overlapping genes can be found in Additional file 2. Results shown are parts of the representative clusters from Additional file 2. Fold change in transcript abundance was defined as transcript level (reads per kilobase per million mapped reads) in Fe-deficient (Fe−) conditions divided by the level in normal conditions (Fe+), with three biological repeats. The same strategy was applied to Pi treatment. The color key indicates log2 transformed intensity; grey indicates that the number is missing

Gene ontology (GO) analysis of the 579 overlapping genes revealed that stress-related processes, including ‘response to salt stress’, ‘response to oxidative stress’ and ‘response to zinc ion’, were enriched (Additional file 3), while analysis of the subset of 137 genes showed that Fe response-related processes, including ‘cellular response to nitric oxide’, ‘cellular response to Fe ion’ and ‘cellular Fe ion homeostasis’, were also enriched (Additional file 4).

Gene expression patterns of overlapping genes

Expression patterns of the 579 overlapping genes were divided into four types according to changes at the transcript level under two stress conditions (Fig. 3a). Type one was composed of 223 genes (of which 24 genes were down-regulated by more than twofold) with decreased transcription under both Pi and Fe deficiency. GO analysis of this group of genes revealed that the processes of ‘embryo development ending in seed dormancy’, ‘microtubule-based process’ and ‘chloroplast organization’, were most enriched (Fig. 3b). In contrast, transcript abundance of 169 genes in the type two category were shown to be increased following both Pi and Fe deficiency, with processes of ‘glucosinolate and leucine biosynthesis’ and ‘UV response’ being enriched (Fig. 3b). Type three was composed of 97 genes with increased transcript abundance under Pi deficiency, but decreased under Fe deficiency. In contrast, transcript abundance of the 90 genes in type four were decreased under Pi deficiency and increased under Fe deficiency. GO enrichment analysis showed that the zinc-related processes ‘response to zinc ion’, ‘zinc ion transport’ and ‘galactose metabolic process’ were enriched in type three (Fig. 3b). Iron-related processes ‘cellular response to Fe ion’, ‘cellular response to nitric oxide’, ‘cellular response to ethylene stimulus’, ‘cellular Fe ion homeostasis’ and ‘protein import into nucleus’ were enriched in type four (Fig. 3b).
Fig. 3

Expression patterns (a) and differential gene ontology (GO) enrichment analysis of the four types of 579 overlapping genes (b). Bracketed numbers in a indicate the number of genes with greater than a twofold change in transcript level under either of the stresses

Identification of overlapping gene modules by co-expression analysis

Stress-specific variability in gene expression may occur at the individual gene level, but can also occur in a coordinated manner. To determine functional modules, co-expression networks (i.e., groups of genes that show similar expression patterns under diverse conditions) of the 579 overlapping genes were generated using MACCU software [41]. Pairwise co-expressed genes were selected with a Pearson correlation coefficient cutoff of 0.7 [36, 41]. The 300 publicly available microarrays that were mined for co-expression analysis discriminated between root-related experiments. As such, the co-expression relationships reported herein are restricted to roots [4244]. Co-expression relationships between these genes were visualized using Cytoscape (http://www.cytoscape.org). This analysis yielded a network composed of 292 nodes (genes) and 1595 edges (correlations between genes; Additional file 5). The network can be further divided into two large and eight small clusters (modules). The largest module was composed of 210 genes, most of which are associated with stress (Additional file 6). GO enrichment analysis revealed that the biological processes ‘glucosinolate biosynthetic process’, ‘response to cadmium ion’, ‘response to salt stress’ and ‘leucine biosynthetic process’ were most enriched in this module (Additional file 7). Via connection to the zinc binding protein gene AT1G74770, two marker genes strongly induced by Fe deficiency, IRT1 (AT4G19690) and CYP82C4 (AT4G31940), were associated with this module.

Co-expression analysis of the subset of 137 overlapping genes with changes greater than twofold yielded a network consisting of 48 nodes and 56 edges. The Fe deficiency-regulated marker genes IRT1 and CYP82C4 (AT4G31940) remained in the network (Fig. 4a). This network can be divided into one large (26 genes) and six small clusters (Fig. 4a). Detailed expression information of these genes upon Fe or Pi deficiency is shown in Fig. 4b. GO enrichment analysis of the genes involved in the co-expression network revealed that the biological processes ‘cellular response to nitric oxide’, ‘cellular response to Fe ion’ and ‘cellular response to ethylene stimulus’ were enriched (Table 2).
Fig. 4

Co-expression relationships of the 137 differentially expressed genes with changes greater than twofold (a), and heat map of genes involved in network construction (b)

Table 2

Gene ontology enrichment was assessed using GOBUin the 48 genes in the large sub-network shown in Fig. 4a (elim, P < 0.01)

GOID

P value (elim)

GO name

GO: 0071732

1.51E−06

Cellular response to nitric oxide

GO: 0071281

9.74E−06

Cellular response to iron ion

GO: 0009620

1.11E−04

Response to fungus

GO: 0071369

1.52E−04

Cellular response to ethylene stimulus

GO: 0006829

1.54E−04

Zinc ion transport

GO: 0015794

0.001428

Glycerol-3-phosphate transport

GO: 0015678

0.001428

High-affinity copper ion transport

GO: 0010421

0.002855

Hydrogen peroxide-mediated programmed cell death

GO: 0009805

0.004279

Coumarin biosynthetic process

GO: 0009871

0.004279

Jasmonic acid and ethylene-dependent systemic resistance, ethylene mediated signaling pathway

GO: 0009411

0.004536

Response to UV

GO: 0006873

0.005599

Cellular ion homeostasis

GO: 0009963

0.007122

Positive regulation of flavonoid biosynthetic process

GO: 0009311

0.007707

Oligosaccharide metabolic process

GO: 0006828

0.009958

Manganese ion transport

To search for potentially functional novel modules, co-expression analysis was applied to the subset of 90 genes that were induced by Fe deficiency, but down-regulated by Pi deficiency (Additional file l). A network containing 26 nodes and 17 edges was created using the same criteria (Fig. 5). The network can be divided into 10 small clusters (none with more than ten nodes), with the largest one containing several Fe-responsive marker genes and one transcriptional factor WRKY 17 (Fig. 5). The second largest cluster was composed of four genes, including the Pi homeostasis regulator SIZ1 (Fig. 5). For 97 genes induced by Pi deficiency but repressed by Fe deficiency, co-expression analysis resulted in a network containing 26 nodes and 29 edges that were divided into one large and two small clusters (Additional file 8).
Fig. 5

Co-expression relationships of the 90 differentially expressed overlapping genes induced by Fe deficiency but repressed by Pi starvation

Analysis of P1BS motif in 35 genes induced by Fe deficiency but down regulated by Pi deficiency

A subset of 35 genes in the type four category had an increase in transcript abundance under Fe deficiency but a decrease under Pi deficiency, with changes more than 1.5-fold (Additional file 9). A 3000 bp sequence upstream of the translation start (named −3000 bp) of these 35 genes was retrieved from TAIR10 and used to search the PHR1 recognition sequence 5′-GNATATNC-3′ (P1BS motif). Results showed that 12 of the 35 genes contained at least one P1BS motif, and seven of the 12 genes contained the P1BS motif within −1000 bp of their promoter regions (Table 3). In total, 11 P1BS patterns were hit in the 12 genes, with 5′-GTATATGC-3′ and 5′-GTATATTC-3′ being the most frequent (5 and 3 hits out of 18 total hits, respectively).
Table 3

Distribution of the P1BS motif in promoter regions of 12 genes

AGI

Matching positions

Hit pattern (5′–3′)

Start

End

AT1G01580

2660

2667

GTATATTC

AT1G01580

2701

2708

GTATATTC

AT1G18910

134

141

GGATATCC

AT1G18910

359

366

GTATATAC

AT1G18910

1311

1318

GTATATGC

AT1G24320

2052

2059

GCATATCC

AT1G48300

1167

1174

GCATATTC

AT2G02310

645

652

GCATATAC

AT3G12900

445

452

GTATATTC

AT3G18290

129

136

GTATATAC

AT3G18290

703

710

GCATATGC

AT3G56980

153

160

GTATATGC

AT3G56980

2484

2491

GTATATGC

AT3G56980

2484

2491

GTATATGC

AT4G00910

2514

2521

GTATATGC

AT4G19690

838

845

GAATATCC

AT4G22980

1641

1648

GAATATAC

AT5G02780

369

376

GAATATGC

Down-regulation of Fe-acquisition genes upon Pi deficiency is dependent on Fe concentration in the media

To determine how Fe acquisition genes are down-regulated by Pi deficiency and whether this down-regulation is dependent on PHR1, we investigated changes in genes that were most induced by Fe deficiency (including the two Fe acquisition genes FRO2 and IRT1) at the transcript level in wild type and the phr1 mutant under varied growth conditions as follows: Pi deficiency (−Pi, in which the concentration of Fe was 40 µM), Fe deficiency (−Fe), both Pi- and Fe-deficient (−Pi−Fe), Pi deficiency with low Fe concentration (−Pi + 5 µM Fe) and control conditions (+Pi+Fe). Null expression of PHR1 in the phr1 mutant was first verified by quantitative real-time PCR (qPCR) (the ct value of the reference is around 20 cycles while the ct value of the PHR1 is around 34 cycles in the phr1 mutant plants). As shown in Fig. 6a and in agreement with previously reported results [45], transcriptional expression of PHR1 was not significantly regulated by Pi deficiency in wild type plants and could not be detected in phr1 mutant plants under both Pi sufficient and deficient conditions. As a control, the expression of SPX1 [46, 47], a Pi-responsive marker gene, was significantly induced by Pi deficiency. Consistent with our transcriptomic data, transcriptional expression of the Fe acquisition genes IRT1 and FRO2 as well as the Fe deficiency-induced marker gene CYP82C4 was significantly down-regulated under Pi deficiency in both Col-0 and phr1 roots (Fig. 6a). Because all these Fe-responsive genes tested were mainly regulated by transcription factor FIT [14], we thus tested whether the expression of FIT itself was affected or not by Pi deficiency. As shown in Fig. 6a, the expression level of FIT was significantly lower in Pi-deficient roots than in Pi-sufficient roots in wild type plants. In addition to FIT, another transcription factor PYE [34], regulating the expression of another subset of Fe-responsive genes, has been reported to be required for plant Fe homeostasis. However, both PYE and its target AT1G74790 were not affected by Pi deficiency (Fig. 6a). To determine whether this down-regulation is dependent on Fe concentrations in the media, we compared transcript abundance under Pi deficiency with different Fe concentrations in both wild type and mutant plants. In wild type plant roots, all genes evaluated were dramatically induced under Fe deficiency (−Fe) but repressed under Pi deficiency (−Pi) compared to expression under control conditions (+Fe+Pi). Fe deficient-induced up-regulation was not blocked but attenuated by the absence of Pi in Fe-deficient media (−Fe−Pi), while Pi deficient-induced down-regulation was dramatically attenuated by 5 µM Fe (low Fe concentration) in the media (Fig. 6b). Similar to results in wild type plants, these genes were significantly induced under Fe deficiency and Fe and Pi deficiency (−Fe−Pi) in the phr1 mutant roots (Fig. 6b). However, down-regulation of gene expression under Pi deficiency was not all significantly attenuated by 5 µM Fe in Pi-deficient media in the phr1 mutant roots (Fig. 6b).
Fig. 6

Reverse transcription–quantitative PCR (RT–qPCR) detected expression of Pi- and Fe-responsive marker genes under Pi deficiency (a) or Fe deficiency or Pi deficiency without or with low Fe concentrations (b). Total RNA was isolated from roots in wild type or phr1 mutant plants and qPCR was performed. Expression levels are relative to normal controls. Error bars represent SD of biological replicates from three independent experiments. Data significantly different from the corresponding controls are indicated (‘−Pi’ versus ‘+Pi’, *P < 0.05, **P < 0.01; ‘−Fe’ versus ‘−Fe−Pi’, +P < 0.05, ++P < 0.01; ‘−Pi’ versus ‘5 µMFe−Pi’, ′P < 0.05, ″P < 0.01; Student’s t test)

Discussion

As an essential element for all living organisms, particularly as a major constraint in crop yield and quality, Fe deficiency responses in plants have been extensively studied in the last decade [1, 6, 9]. With the emergence of high throughput research platforms, many genes and proteins have been revealed to be regulated by Fe deficiency [40, 4855]. Evidence has shown that transcriptional expression of some Fe-responsive genes can be altered due to deficiencies or excesses of mineral elements, including cross-talk between Fe and other mineral elements. For example, the Fe transporter LeIRT1 is reported to be up-regulated by potassium (K) deficiency, as revealed by microarray analysis [56], and expression of the K transporter gene LeKC1 was induced not only by K starvation but also by Fe deficiency [56]. Via comprehensive analysis of Fe-responsive protein kinase (PK) and protein phosphatase (PP) genes, we found that strong over-representation of PK and PP genes that encode proteins is involved in K homeostasis, which supports the link between potassium uptake and Fe deficiency [44]. The ameliorative effect of K supply on Fe-deficient responses was previously reported [57]. Although several lines of evidence have suggested a link between Pi and Fe homeostasis [3235, 3739, 58], little genome-wide information on transcriptional expression changes in Fe-responsive genes under Pi deficiency is available, and the biological processes that these genes are involved in remain elusive in Arabidopsis.

By mining previous RNA-seq data sets, we present comprehensive information on transcriptional expression of overlapping genes regulated by Fe and Pi deficiency in Arabidopsis roots. In total, 579 overlapping genes, or less than 20 % of all differentially expressed genes evaluated in each treatment, were responsive to both Fe and Pi deficiency. Only 137 of the 579 genes had greater than twofold changes in transcript abundance (Additional file l; Table 1). Many of the most strongly induced genes under Fe deficiency, such as AT3G12900, IRT1, FRO2, CYP82C4 and AT5G38820 [18, 41, 54], are among the 579 overlapping genes, while most of the strongest induced Pi deficiency-induced marker genes, such as pyridoxal phosphate phosphatase-related protein gene AT1G17710, transposable element gene AT2G04460, ATISP1, SPX3, APT1 and AT4, are not overlapping [33, 35]. GO enrichment analysis of the 137 genes with changes greater than twofold (Additional file 4) showed that Fe response-related processes such as ‘cellular response to Fe ion’ and ‘cellular Fe ion homeostasis’ were enriched, but none of the Pi response-related processes were pronounced, suggesting that plant responses to Fe deficiency might be more specific than responses to Pi deficiency under the conditions presented herein. The most strongly induced Fe-responsive genes were clustered together and down-regulated under Pi deficiency, except for AT1G52120 in which transcript abundance was increased under both stress conditions (Fig. 2a). In this cluster, IRT1, FRO2, and BHLH039 are known to be involved in Fe acquisition and transcriptional regulation, and COPT2 was confirmed to participate in cross talk between Fe deficiency responses and low phosphate signaling in a recent study [59]. Other genes in the group (Fig. 2a), such as AT3G12900, CYP82C4, AT5G38820 and AT1G52120, do not have defined functions currently, but may be involved in responses to Fe deficiency or Pi deficiency or cross-talk between Fe deficiency responses and phosphate-deficient signaling. Another group of interesting genes are AT5G20790, AtOCT1 and AT1G30560 (Fig. 2c) given that their transcriptional expression was among the most highly induced under Pi deficiency. In particular, both AT1G30560 and AtOCT1 were significantly up-regulated upon Fe deficiency, suggesting that these two genes might play important roles in responses to both stresses. In animals, organic cation/carnitine transporters (OCTs) are associated with homeostasis and distribution of various small endogenous amines (e.g. carnitine, choline) and detoxification of xenobiotics like nicotine. AtOCT1 has been reported to be involved in Arabidopsis root development. Knockout of AtOCT1 expression results in a higher degree of root branching compared to the wild type in vitro. This disordered development may be due to an inability to transport carnitine [60]. It has been well established that the number and length of lateral roots are increased under Pi deficiency in Arabidopsis and other plants. Therefore, whether AtOCT1-mediated transport of carnitine or related chemicals is involved in lateral root development under Pi deficiency remains elusive.

GO enrichment analysis of the 579 overlapping genes revealed that these Fe- and Pi-responsive genes were associated with diverse biological processes (Additional file 3), particularly with the GO categories ‘response to salt stress’, ‘response to oxidative stress’ and ‘response to zinc ion’ (Additional file 3). These results imply that acclimation of plants to Fe and Pi deficiency and possibly other nutritional stresses is associated with profound changes in the transcriptome, including stress-specific responses such as alteration of ribosome composition [43] and other general responses. Only four (AT1G27760, AT3G04720, AT4G11650 and AT5G24090) of the 21 genes associated with ‘response to salt stress’ had an increase in transcript abundance greater than 1.5-fold, suggesting that this common response to Fe and Pi deficiency might be less important than Fe response-related processes. GO enrichment analysis of the most responsive genes (i.e., those with greater than 1.5-fold change) revealed that Fe response-related processes, were enriched, but none of the Pi response-related processes were (Additional file 4),suggesting that Pi deficiency has more pronounced effects on Fe homeostasis than Fe deficiency has on Pi homeostasis.

Functional annotation of a given gene is the most important goal in modern molecular biology and is essential for understanding how the cell works. All omics studies are discovery tools and are not capable of defining gene function. The actual functions of differentially expressed genes under certain conditions discovered by high throughput platforms require further experimental evidence. However, current research platforms can discover hundreds to thousands of differentially expressed genes in a single run, and most of them are annotated as function unknown. Functional exploration of every differentially expressed gene without selection would be extremely laborious and impossible. Fortunately, co-expression analysis provides the option to choose genes of interest for further study. The basic idea of co-expression analysis is that genes that show transcriptionally coordinated expression patterns under diverse conditions are often functionally related [61], thus allowing functional predictions regarding genes with unknown functions inferred from their co-expression relationships with genes with known functions [62, 63]. Using co-expression analysis, we discovered ten, six and ten potentially critical regulatory modules with diverse nodes from inputs of the 579 (total overlapping genes), 137 (genes with changes greater than twofold) and 90 Fe deficiency-induced, Pi deficiency-repressed genes (Additional file 5; Figs. 4a, 5). Unexpectedly, only 50, 35 and 29 %, respectively, of the input genes were associated with formation of co-expression networks, suggesting that the majority of overlapping genes are functionally diverse and involved in a variety of biological processes. The network obtained from the group of 90 Fe deficiency-induced, Pi deficiency-repressed genes (Fig. 3a) is of particular interest. In this network, several genes may play important roles in responses to Fe and Pi deficiency. For instance, the gene AT1G74770 annotated with zinc ion binding protein showed a strong relationship with the Fe transporter IRT1, implying that this gene may be required for a Fe response. Another putative zinc ion binding protein encoding gene, AT1G18910, was shown to be connected to AT1G74770 and the transcription factor gene WRKY17, suggesting that these genes may also be involved in plant adaptation to Fe deficiency or zinc toxicity elicited by excess zinc under Fe deficiency.

It is generally accepted that a group of genes with similar expression patterns might be positively and/or negatively regulated by the same regulator(s). In Arabidopsis, the PHR1 transcription factor (TF) and its homolog PHL1 consist of the central regulatory system controlling transcriptional expression of a subset of Pi deficiency response genes by binding to the P1BS motif in promoter regions; while FIT and PYE are two major TFs regulating transcriptional expression of two subsets of Fe deficiency response genes. 35 out of the 579 genes, including Fe acquisition genes IRT1 and FRO2 and Fe responsive marker gene CYP82C4, were induced under Fe deficiency but down-regulated under Pi deficiency with changes greater than 1.5-fold. qPCR examination (Fig. 6a, b) confirmed that both IRT1 and FRO2 as well as CYP82C4, mainly regulated by FIT in response to Fe deficiency, were down-regulated by Pi deficiency in wild type plants, probably due to the decreased abundance of FIT (Fig. 6a). By contrast, the transcriptional expression of both PYE and its target AT1G74790 was not altered in response to Pi deficiency in both wild type and phr1 mutant plants. Taken together, these results suggest that FIT-regulated but not PYE-regulated Fe-response genes are affected by Pi deficiency and the down-regulation by Pi deficiency might be partially due to the down-regulation of FIT (since although the transcript abundance of FIT was not significantly different between Pi-sufficient and -deficient conditions in phr1 mutant plants, the transcriptional expression of IRT1 and FRO2 as well as CYP82C4 was still significantly down-regulated by Pi deficiency). In addition, under Pi sufficiency, although the transcript level of FIT was not different between wild type and phr1 mutant plants, the transcript abundance of IRT1 and FRO2 as well as CYP82C4 was still significantly down-regulated in the mutant plants (Fig. 6a, b). These results indicate that, besides FIT and P1BS motif (CYP82C4 doesn’t contain a P1BS motif in the −3000 bp sequence of its promoter region), some other factors may be involved in the down-regulation of gene expression under Pi deficiency. Indeed, only 34 % of the genes (12 of 35) contained at least one P1BS motif in promoter regions (−3000 bp sequence upstream of the translation start) and only 20 % (7 of 35) had a P1BS motif within −1000 bp of their promoter regions, further suggesting that other positive or negative regulators might be involved in down-regulation of these Pi-responsive genes. One of these regulators may be Fe itself. It has been reported that Pi deficiency results in enhanced Fe accessibility to plants in the media, which leads to an over accumulation of Fe in plants, subsequently causing down-regulated expression of Fe-responsive genes. This point of view was confirmed by supply of different Fe concentrations in the Pi deficient media (Fig. 6b). If no additional Fe was supplied to the Pi deficient media (−Fe−Pi), the transcriptional expression of all tested genes was induced both in wild type and phr1 mutant plants, an expression pattern similar to the one of Fe deficiency (Fig. 6b). This result suggests that an extent of Fe in the Pi deficient media is required for the down-regulation of Fe-responsive genes under Pi deficiency. Indeed, Pi-deficiency caused down-regulation was much enhanced by supply of 5 µM Fe in the Pi deficient media both in wild type and phr1 mutant plants (Fig. 6b). In the future, the dose effects of Fe in the Pi-deficient media on the transcriptional expression of Fe-responsive genes need further validation.

Conclusions

In summary, we provide genome-wide information on the transcriptional expression of 579 overlapping genes that responded to both Fe and Pi deficiency in Arabidopsis roots and the biological processes that the genes are involved in. Gene clustering and root-specific co-expression analysis revealed several potentially important genes, including CYP82C4 and AT5G38820, which likely function as putative novel players in response to Fe and Pi deficiency or in cross-talk between Fe-deficient responses and Pi-deficient signaling. These results imply that Pi deficiency has more pronounced effects on Fe homeostasis than Fe deficiency has on Pi homeostasis.

Materials and methods

Plant growth and treatments

Arabidopsis (Arabidopsis thaliana) seeds from the Columbia ecotype obtained from the Arabidopsis Biological Resource Center (ABRC) were used in this study. Phr1 mutant seeds (SALK_067629C) were a gift from Professor Tzyy-Jen Chiou as previously described [64]. Seeds were surface sterilized by immersion in 5 % (v/v) NaOCl for 5 min and 70 % ethanol for 7 min, followed by four rinses in sterile water. Seeds were placed into Petri dishes and stored for 1 day at 4 °C in the dark. Plates were then transferred to a growth chamber and grown at 21 °C under continuous illumination (50 µmol m−2 s−1; Philips TL lamps). The agar-based medium [65] was composed of (mM): KNO3 (5), MgSO4 (2), Ca(NO3)2 (2), KH2PO4 (2.5); (µM): H3BO3 (70), MnCl2 (14), ZnSO4 (1), CuSO4 (0.5), NaCl (10), Na2MoO4 (0.2); and 40 µM Fe-EDTA solidified with 0.8 % agar (Sigma-Aldrich). Sucrose (43 mM) and 4.7 mM MES were included, and the pH was adjusted to 5.8. After 10 d of precultivation, plants were transferred either to fresh agar medium with 100 µM 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine sulfonate without Fe, medium without Pi, medium without both Fe and Pi, medium without Pi containing 5 µm Fe or fresh control medium and grown for another 3 d. Lower potassium concentrations due to the absence of KH2PO4 in the Pi-free medium was corrected by addition of KCl.

Quantitative reverse transcription-PCR

Total RNA was isolated using the RNeasy Plant Mini Kit (Qiagen) and treated with DNase using the TURBO DNA-free Kit (Ambion) as suggested by the manufacturer. cDNA was synthesized and qPCR was performed according to a previous report [40] using the SYBR Green PCR Master Mix (Applied Biosystems) with programs recommended by the manufacturer in the ABI Prism 7500 Sequence Detection System (Applied Biosystems). The melting temperature of the primers ranged from 58 to 62 °C. Primer pairs were selected using Primer3 (http://primer3.sourceforge.net/). Elongation factor1-β2 (At5g19510) and Tubulin3 (At5g19770) were used as internal controls (transcript abundance of both genes did not change under Fe and Pi deficiency) for transcript normalization. The primers used in this study are listed in Additional file 10. Three independent replicates were performed for each sample. The delta threshold cycle (∆ct = the ct of a gene−the ct of the reference) was used to determine the relative amount of gene expression. Student’s t test (P < 0.05) was used to compare differences between samples grown under treatment and control conditions.

Data collection and processing

Transcriptomic data sets of roots from 13-day-old Arabidopsis seedlings grown in the presence or absence of Fe or Pi by RNA-seq were downloaded from a public database (NCBI: SRP044814, SRA050356.1). The 3106 and 3296 differentially expressed genes (P < 0.05) upon Pi and Fe deficiency were compared, and the resulting 579 overlapping genes were subsequently analyzed as shown in Fig. 1. Microarray data of 2671 ATH1 arrays from the NASCarray database (http://affymetrix.arabidopsis.info/) were downloaded and normalized using the RMA function in the Bioconductor Affy package software. Three hundred root-related arrays were manually identified as described [41] and were used as a database for co-expression analysis.

Gene ontology analysis

The gene ontology browsing utility (GOBU) [66] was adopted for gene ontology (GO) enrichment analysis using the TopGo ‘elim’ method [67]. The elim algorithm iteratively removes the genes mapped to significant terms from higher level GO terms, thus avoiding enrichment of unimportant functional categories.

Generation of co-expression networks using the MACCU toolbox

Gene functional networks were constructed based on 300 publicly available root-related microarrays using the MACCU toolbox [41], with a Pearson correlation threshold of 0.7. The generated co-expression networks were visualized by Cytoscape (http://www.cytoscape.org). If one cluster of genes did not have any connection (edges) to any other cluster in the co-expression network, it was referred to as a module.

Declarations

Authors’ contributions

WL and PL performed the data analysis and drafted the manuscript. PL conceived the study. Both authors read and approved the final manuscript.

Acknowledgements

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB15030103), the National Key Basic Research Program of China (No. 2015CB150501), the Natural Science Foundation of China (31470346, 31370280) and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, College of Biology and the Environment, Nanjing Forestry University
(2)
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences

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