Nonlinear responses of the Vmax and Km of hydrolytic and polyphenol oxidative enzymes to nitrogen enrichment

Journal Pre-proof Nonlinear responses of the Vmax and Km of hydrolytic and polyphenol oxidative enzymes to nitrogen enrichment Weixing Liu, Rui Tian, Ziyang Peng, Sen Yang, Xiao xiao Liu, Yashu Yang, Wenhao Zhang, Lingli Liu PII:

S0038-0717(19)30320-7

DOI:

https://doi.org/10.1016/j.soilbio.2019.107656

Reference:

SBB 107656

To appear in:

Soil Biology and Biochemistry

Received Date: 27 June 2019 Revised Date:

25 October 2019

Accepted Date: 3 November 2019

Please cite this article as: Liu, W., Tian, R., Peng, Z., Yang, S., Liu, X.x., Yang, Y., Zhang, W., Liu, L., Nonlinear responses of the Vmax and Km of hydrolytic and polyphenol oxidative enzymes to nitrogen enrichment, Soil Biology and Biochemistry (2019), doi: https://doi.org/10.1016/j.soilbio.2019.107656. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

1

Nonlinear responses of the Vmax and Km of hydrolytic and polyphenol

2

oxidative enzymes to nitrogen enrichment

3

Weixing Liu a, Rui Tian a,b, Ziyang Peng a,c, Sen Yang a,c, Xiao xiao Liu a,b, Yashu Yangd,

4

Wenhao Zhang a, Lingli Liu a, c*

5

a

6

Academy of Sciences, Xiangshan, Beijing 100093, China.

7

b

8

Henan University, Kaifeng, Henan 475004, China.

9

c

10

d

11

Type of Paper: Regular paper

12

Preparing date: June 27, 2019

13

Number of text pages: 36

14

Number of figures, tables: 6 figures and 1 table

15

Corresponding author’s telephone and email:

16

*Correspondence: Lingli Liu, Phone: (86) 10-62836160, Fax: (86) 10-82596134, Email:

17

[email protected]

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese

International Joint Research Laboratory for Global Change Ecology, College of Life Sciences,

University of Chinese Academy of Sciences, Yuquan Road, Beijing 100049, China. College of Plant Protection, Shandong Agricultural University, Taian, Shandong 271018, China.

18

1

19 20

ABSTRACT The kinetics of soil microbial extracellular enzymes are important in regulating soil

21

organic matter decomposition and ecosystem function. However, it is still unclear how

22

the kinetic parameters (Vmax and Km) of hydrolytic and polyphenol oxidative enzymes

23

respond to increased nitrogen (N) deposition and to what extent they regulate microbial

24

respiration under N enrichment. We measured the Vmax and Km of seven soil hydrolytic

25

enzymes and polyphenol oxidase (PPO) in a temperate steppe after 15 years of multi-

26

level N addition treatments. Soil microbial respiration and physicochemical properties in

27

the steppe were also monitored. The results showed that soil microbial respiration

28

decreased exponentially with increasing N addition. The Vmax of carbon (C)-degrading

29

and N-degrading hydrolytic enzymes decreased and the Vmax of acid phosphatase (AP)

30

increased with increasing N addition. The reduction in the Vmax of C- and N-degrading

31

hydrolytic enzymes was primarily caused by the decrease in soil pH under N enrichment.

32

The Km of most hydrolytic enzymes decreased, expect for the Km of AP and β-xylosidase

33

(BX), which increased with increasing N addition. As N addition increased, Vmax and Km

34

of PPO first increased, maximized at 8 g N m-2 y-1, and then decreased. We conducted

35

model averaging to assess the influence of the kinetic parameters on soil microbial

36

respiration across candidate models. The results indicated that the Vmax and Km of BG

37

were the best predictors for soil microbial respiration. The structural equation modeling

38

result further indicated that the response of microbial respiration to N deposition was

39

directly mediated by the response of BG kinetics: N-induced acidification had a negative

40

impact on Vmax and Km for BG, which led to a decrease in microbial respiration. Our

41

empirical data on enzyme Vmax, Km and their relationship to microbial respiration should

2

42

be useful for modelling how microbes and substrates interact to regulate soil carbon

43

cycling under N enrichment.

44 45

Keywords: Acidification; Extracellular enzyme kinetics; Microbial biomass; Microbial

46

decomposition; Nitrogen fertilization.

47

3

48 49

1. Introduction The increased availability of reactive nitrogen (N) stimulates plant production and

50

has widespread effects on carbon (C) cycling in terrestrial ecosystems (Cusack et al.,

51

2011). A growing body of literature found that high N deposition inhibited microbial

52

respiration, resulting in slower decomposition of soil organic matter (SOM) (Janssens et

53

al., 2010; Frey et al., 2014; Riggs et al., 2015). Microbial communities secrete

54

extracellular enzymes to degrade complex polymers into soluble substrates for

55

assimilation and respire CO2 during the decomposition of SOM (Sinsabaugh et al., 2008;

56

Burns et al., 2013; Bödeker et al., 2014). It is generally accepted that the catalysis by

57

extracellular enzymes is the rate-limiting step for the degradation of organic matter in

58

terrestrial ecosystems (Sinsabaugh et al., 2008; Cenini et al., 2016). The reduction in

59

decomposition under anthropogenic N deposition should be achieved by altering enzyme

60

activities in some ways. Therefore, exploring the microbial enzyme catalytic process will

61

facilitate the understanding of biological mechanisms by which soil organic matter

62

decomposition responds to anthropogenic N deposition.

63

Extracellular enzymes in the soil catalyze the degradation of organic matter

64

primarily through hydrolysis (for the breakdown of celluloses, hemicelluloses, chitins,

65

and proteins) and oxidation (for the breakdown of more recalcitrant lignin or humified

66

organic matter) (Sinsabaugh, 2010). Based on cost-efficiency, soil microbes adjust their

67

allocation of resources in the synthesis of various extracellular C-, N-, and phosphorus

68

(P)-acquiring enzymes (Sinsabaugh and Moorhead, 1994; Sinsabaugh and Shah, 2012;

69

Burns et al., 2013). Thus, the synthesis of soil enzymes, as a form of foraging strategy,

70

should vary under different soil nutrient conditions (Allison, 2005; Sinsabaugh et al.,

71

2014). Under high N deposition, microbes allocate more resources to synthesize C4

72

acquiring rather than N-acquiring enzymes due to alleviated N limitation (Craine et al.,

73

2007; Stone et al., 2012). Therefore, N addition may stimulate the activities of hydrolytic

74

C-degrading enzymes, which has been confirmed by recent meta-analyses (Jian et al.,

75

2016; Chen et al., 2018). Most N in soil is locked up in organic matter. Increasing the

76

supply of reactive N thus should reduce microbial mining N from recalcitrant soil organic

77

matter. Indeed, N addition has been found to inhibit the activity of polyphenol oxidase,

78

which breaks aromatic rings and drives N mining (Craine et al. 2007). Furthermore,

79

excessive N input stimulates plant growth and consequently often leads to P limitation

80

(Vitousek et al., 2010; Hedwall et al., 2017). Soil microbes thus need to allocate more

81

resources to synthesize P-acquiring enzymes (Keeler et al., 2009). In line with this

82

expectation, several N addition experiments showed that phosphatase activity enhanced

83

under N enrichment (Xiao et al., 2018; Dong et al., 2019).

84

Among all the potential changes in C, N and P acquiring enzyme activities under N

85

enrichment, the inhibition of soil oxidative enzyme activity has been considered as an

86

explanation for the reduced microbial respiration in forest ecosystems (Carreiro et al.,

87

2000; Entwistle et al., 2018). However, in grassland ecosystems, N addition either

88

stimulates (Riggs and Hobbie, 2016) or has no effect on (Zeglin et al., 2007) oxidative

89

enzyme activities. Hence, the divergent results indicate that oxidative enzyme activity

90

alone could not fully explain how N deposition affects microbial respiration (Keeler et al.

91

2009). The suppression of microbial respiration under N addition has also been attributed

92

to decreased soil pH (Chen et al., 2016) or reduced microbial biomass carbon (Riggs and

93

Hobbie 2016). Changes in soil pH and microbial biomass could alter the production and

94

activity of both oxidative and hydrolytic enzymes (Datta et al., 2017). Therefore, to better

5

95

explore the mechanism by which N enrichment inhibits microbial respiration, we should

96

evaluate the responses of not only oxidative but also hydrolytic enzymes.

97

Extracellular enzyme activity (V) can be described by the Michaelis-Menten model:

98

V = Vmax[S] / (Km +[S])

99

Where Vmax is the maximum reaction rate of the enzyme, [S] is substrate concentration,

100

and Km is the half-saturation constant—an experimentally derived substrate concentration

101

at which the reaction rate is half of the Vmax. These two kinetic parameters can be used to

102

assess the affinity of an enzyme for its substrate and the turnover of substrates. Because

103

the in situ soil enzyme activity is controlled by both microbial activity and substrate

104

availability, Vmax and Km are important indicators of enzyme activity (Allison et al., 2010).

105

Vmax could represent the concentration of active enzymes in soils. High Vmax is often

106

accompanied by high Km because enzymatic reactions could be stimulated by high

107

substrate availability (Wallenstein et al., 2011). Km is also associated with interactions

108

between soil physical properties, microbial cell characteristics (or traits), substrate

109

physical properties and soil moisture status (Tang and Riley, 2019). N addition could

110

alter substrate availability or soil physical properties (Zak et al., 2017; Chang et al., 2019),

111

which could eventually alter Vmax and Km. However, slight attention has been paid to

112

these kinetic parameters of enzymes. Currently, only a few studies have reported that N

113

addition could increase the Vmaxs of hydrolytic enzymes but has no consistent effect on

114

Km (Grandy et al., 2008; Stone et al., 2012). To advance our understanding of the

115

underlying catalytic mechanisms and to better model microbial respiration under

116

increasing N deposition, it is necessary to move beyond examining apparent enzyme

117

activities. More efforts are needed to explore how excessive N input will affect Vmax and

118

Km for both oxidative and hydrolytic enzymes. 6

119

Here we examined soil hydrolytic and oxidative enzyme kinetic parameters after 15

120

years of multi-level N addition (0, 1, 2, 4, 8, 16, 32, 64 g N m-2 y-1) experiment in a

121

temperate grassland in Inner Mongolia, China. We also measured microbial respiration,

122

microbial composition, plant productivity, and soil biochemical properties. We

123

hypothesized that upon N addition, 1) increased N availability alleviates N limitation for

124

microbial growth and metabolism, and consequently stimulates the synthesis of C-

125

degrading enzymes but inhibits the synthesis of N-degrading hydrolytic enzymes; 2) the

126

Vmax of phosphatase is stimulated due to the increasing P limitation; 3) the Vmax of

127

polyphenol oxidative enzyme is suppressed due to decreased microbial N mining; and 4)

128

microbial respiration is reduced due to the decreased activity of oxidative enzyme.

129

2. Materials and methods

130

2.1. Study site, experimental design, and soil and plant sampling

131

The experimental site is in a semiarid steppe (42.01´N, 116.16´E and 1324 m a.s.l) in

132

Duolun County, Inner Mongolia, northern China. The mean annual temperature (MAT)

133

and precipitation (MAP) of the study site are 2.1°C and 382.3 mm, respectively. Ambient

134

N deposition is about 14.7 kg N hm-2 (Zhang et al., 2017). The soil type is classified as

135

Haplic Calcisols (FAO classification) with 69.21 ± 0.06% sand, 15.60 ± 0.02% silt and

136

15.19 ± 0.02% clay. The plant community is dominated by Stipa krylovii Roshev.,

137

Agropyron cristatum (L.), Artemisia frigida Willd, and Cleistogenes squarrosa (Trin.).

138

Sixty-four plots were arranged in eight rows and eight columns. Four rows (one in

139

every two rows) were clipped each year since 2005. The plot size was 10×15 m with 5-m

140

buffer zones between adjacent plots. Starting from 2003, each of the eight plots in each

141

row was randomly subjected to one of the eight levels of N fertilization (0, 1, 2, 4, 8, 16,

142

32 and 64 g m-2 y-1) in the form of solid urea in July each year. 7

143

Soil samples were collected from all non-clipped plots on August 20, 2017. Six soil

144

cores (15 cm in height and 5 cm in diameter) were taken randomly from each plot and

145

combined as one composite sample. After removing the roots and stones by sieving

146

through a 2-mm mesh, the soil samples were put on ice and transported to the lab. Some

147

subsamples were stored at 4°C for analyzing soil physicochemical properties and some

148

subsamples were stored at -80°C for enzyme-related analysis.

149

The aboveground biomass (AGB) of plants was estimated by clipping living biomass

150

from a 1×1 m quadrat in each plot (the same plot where soil sample was taken) during

151

August 15-18, 2017. Plant species richness was estimated by counting the number of

152

plant species in the 1×1 m quadrat. All plant samples were oven-dried at 70°C for 48 h

153

and weighed to determine the biomass.

154

2.2. Soil microbial respiration, microclimate and chemical properties

155

Root exclusion with trenched plot techniques was used to separate auto- (Ra) and

156

microbial (Rh) parts of soil respiration (Rs). PVC collars with 20 cm diameter were

157

inserted 30 cm into the soil to isolate plant roots and exclude Ra since September 2013.

158

Two PVC collars with 11 cm diameters were subsequently inserted 2–3 cm into the soil

159

inside the large PVC collars (Rh without roots as root exclusion collars) and outside (Rs

160

with roots as control collars) to measure respiration. An EGM-4 infrared gas analyzer

161

equipped with an SRC-2 soil respiration chamber (PP systems, Hitchin, UK) was used to

162

measure in situ soil CO2 flux once a week from May 15 to September 30, 2017. All soil

163

respiration measurements were made between 09:00 and 11:00. Soil temperature and

164

moisture inside the PVC collars were higher than outside because of root exclusion. Soil

165

microbial respiration was thus calibrated for the temperature and moisture differences

166

induced by collar trenching as described in our earlier study (Liu et al., 2018b). 8

167

Soil moisture (SM) at 10 cm depth was measured four times per month using the

168

time-domain reflectometer (TDR200, Spectrum Technologies Inc.) from May 15 to

169

September 30, 2017. Soil temperature (ST) was determined concurrently using a

170

thermocouple probe (EGM-4, PP Systems, Hitchin, UK). Soil dissolved inorganic N

171

(DIN) was extracted with 2 M KCl solution, and the concentrations of NH4+-N and NO3--

172

N in the extracts were measured using a flow injection analyzer (SAN-System,

173

Netherlands). Soil microbial biomass C (MBC) was estimated using the chloroform

174

fumigation-extraction method (Vance et al., 1987). Briefly, fresh soil samples (15 g dry

175

weight equivalent) were fumigated for 24 h with ethanol-free CHCl3. The fumigated and

176

unfumigated samples were then extracted with 60 ml of 0.5M K2SO4 for 30 min on a

177

shaker. K2SO4 extracts were filtered through 0.45 µm filters for extractable C by an

178

elemental analyzer (liquid TOC, Analysensystem, Germany). MBC was calculated as the

179

difference between extractable C in the fumigated and the unfumigated samples using a

180

conversion factor of 0.45. Soil pH was determined with a combination glass-electrode

181

[soil:water=1:2.5 (W/V)].

182

2.3. Determination of extracellular enzyme kinetic parameters

183

The Vmax and Km of seven soil hydrolytic enzymes and one oxidative enzyme were

184

were obtained with Michaelis-Menten model fitting after measuring enzyme activities.

185

The enzymes we studied include α-glucosidase (AG), β-glucosidase (BG), β-xylosidase

186

(BX), cellobiohydrolase (CBH), leucine aminopeptidase (LAP), N-acetyl-

187

glucosaminidase (NAG), acid phosphatase (AP), and polyphenol oxidase (PPO). Among

188

the seven hydrolytic enzymes, AG, BX, BG, and CBH are C-degrading hydrolytic

189

enzymes, LAP and NAG are N-degrading hydrolytic enzymes, and AP is a P-degrading

190

hydrolytic enzyme. When the Michaelis-Menten model is applied to enzyme assays in the 9

191

ecological systems, Vmax and Km are measured as apparent Vmax and apparent Km,

192

respectively. The difference in apparent Vmax reflects the difference in the concentration

193

of rate-limiting enzymes (Wallenstein et al. 2011), while the difference in apparent Km

194

reflects not only the affinity of an enzyme for its substrate but also the difference in

195

substrate concentration (Wallenstein et al., 2011; Baker and Allison, 2017).

196

The enzyme activities of seven soil hydrolytic enzymes were measured by a

197

colorimetric method described previously using fluorescently-labeled substrates (German

198

et al., 2011; Allison et al., 2018). Soil oxidative enzyme was measured by the

199

colorimetric method (German et al, 2011). Each enzyme was assayed at a range of eight

200

dissolved substrate concentrations with the maximum concentration to be diluted twofold

201

serially (Table S1). We used 7-amino-4-methylcoumarin (AMC) and 4-

202

methyumbelliferone (MUB) as standards for LAP and the other six hydrolytic enzymes,

203

respectively. Pyrogallol was used as the substrate for PPO. All assays included

204

homogenate blanks and substrate controls. It is notable that the observed reaction rate

205

would be lower than its actual value because there still be microbial activity and some of

206

the natural non-fluorescently labeled substrates will also be cleaved (Baker and Allison

207

2017).

208

In brief, fresh soil sample (equivalent to 0.2 g dry weight) was homogenized in 100

209

mL 25 mM maleate buffer (pH =6.0). One hundred and twenty-five microliters of

210

fluorometric substrate solution in 25 mM maleate buffer was mixed with 125 µL soil

211

homogenate in each well of a 96-well microplate. The 96-well microplate was incubated

212

for four hours for analyzing the seven hydrolytic enzymes or for 24 hours for the PPO

213

assay. Fluorescent signals for the seven hydrolytic enzymes were obtained at 365 nm

214

excitation and 450 nm emission (BioTek Synergy H1 microplate reader, Winooski, VT, 10

215

USA), For the PPO assay, the optical absorption of each well was read at 410 nm.

216

Enzyme activity is expressed as nmol hr-1 g-1 dry soil according to the method described

217

by German et al. (2011). For each enzyme, the activity was assayed at 8 substrate

218

concentrations, respectively. Vmax and Km for each enzyme were thus fitted by the 8

219

values of enzyme activities and 8 corresponding substrate concentrations.

220

2.4. Statistical analyses

221

The kinetic parameters of extracellular enzymes were calculated by fitting observed

222

extracellular enzyme activities at each substrate concentration to the Michaelis-Menten

223

equation. Nonlinear regression was performed using the “nls” function in R. We fit

224

saturating functions of enzyme activity and extracted half-saturation (Km) and maximum

225

reaction rate (Vmax) using 8 substrate concentrations and corresponding 8 enzyme

226

activities. Simple linear regression was used to test the relationship between the kinetic

227

parameters and the levels of N addition. P < 0.05 is considered statistically significant.

228

We used multi-model averaging based on second-order Akaike’s Information

229

Criterion (AICc) to assess the relative contributions of biotic and abiotic factors to the

230

changes in enzyme kinetic parameters (Vmax and Km) of each enzyme. ST and SM were

231

selected as indicators of soil microclimate. DIN was selected as N availability indicator.

232

pH was chosen as an indicator of soil biogeochemistry. AGB, forb/grass ratio (F: G) and

233

plant community C: N ratio (PC: N) were selected as indicators of plant substrate quality

234

and quantity. MBC was selected as an indicator of microbial biomass pool. Theoretically,

235

all these factors could affect enzyme activities. The multicollinearity was assessed by

236

their variance inflation factors and all the predictors were retained (Grueber et al., 2011).

237 238

Compared to the single best AIC model which may miss some important parameters, the model averaging method could: i) treat all candidate models as ‘true’ models. This 11

239

reduces the uncertainty in modeling; ii) consider the relative contribution of each model

240

based on the information of model fitting, which further reduces the variance inflation of

241

estimated coefficients of predictors. A set of top models were obtained using a cut-off of

242

∆AICc < 4, and model parameters were estimated based on the top models (Grueber et al.,

243

2011). The Shapiro-Wilk test indicated that the residual distribution of the mixed models

244

was normal. Before analyses, all predictors were standardized using the Z-score to

245

interpret parameter estimates at a comparable scale. This procedure was performed using

246

the “dredge” function in the R package MuMin (Barton, 2013). The variables were log-

247

transformed when necessary before analysis to meet the assumptions of the tests (Grueber

248

et al. 2011).

249

Structural equation modeling (SEM) was used to gain a mechanistic understanding

250

of how soil properties and enzyme kinetics mediate alterations in microbial respiration

251

under N enrichment conditions. Following current knowledge of the response of

252

microbial respiration to N enrichment, we developed a conceptual full model of

253

hypothesized relationships within a path diagram (Fig. S1), assuming N addition alters

254

soil properties and microbial biomass, which in turn affects enzyme kinetics, and thus

255

affecting microbial respiration. To simplify enzyme kinetics, we firstly conducted the

256

model selection and model averaging analysis to determine which Vmax and Km were the

257

best enzyme kinetic predictors for microbial respiration, respectively. Although latent

258

variable model generally assumes a large sample size, the conservative nature of the

259

latent variable SEM model is less likely to provide false significant coefficients

260

(Ledgerwood and Shrout, 2011). We thus used the selected Vmax and Km as indicators of

261

the latent variable representing enzyme kinetics, and conducted the SEM analysis to

262

explore how enzyme kinetics, soil acidification, and microbial biomass mediate the 12

263

response of microbial respiration to N enrichment (Byrne, 2006). The SEM was

264

performed using AMOS software (IBM SPSS AMOS 20.0.0), and the rest statistical

265

analyses were performed in R 3.5.2 (R Development Core Team, 2015).

266

3. Results

267

3.1. Soil properties, aboveground biomass, soil microbial respiration, and microbial

268

biomass

269

With increasing N addition, average soil temperature during the growing season

270

decreased exponentially (R2 =0.93, P <0.001), but soil moisture did not change (Table 1).

271

N addition also increased soil inorganic N concentration (R2 =0.76, P <0.01) and

272

aboveground biomass (R2 =0.76, P <0.01), by contrast, soil pH was decreased (R2 =0.96,

273

P <0.001) (Table 1). N addition resulted in an exponential reduction in soil microbial

274

respiration and microbial biomass C (Fig. 1). Compared to the control treatment, soil

275

microbial respiration and microbial biomass C decreased by 46.2 and 73.3% at the

276

highest N addition rate, respectively.

277

3.2. The maximum reaction rates of the extracellular enzymes (Vmax)

278

The Vmax of all C-degrading hydrolytic enzymes (AG, BX, BG, and CBH) and N-

279

degrading hydrolytic enzymes (LAP and NAG) decreased with increased N addition. By

280

contrast, the Vmax of AP increased as N addition increased. The Vmax of PPO exhibited a

281

nonlinear response to N addition—the Vmax of PPO first increased, maximized at 8 g m-2

282

y-1 N, and then decreased (Fig. 2). The model averaging analysis showed that soil pH was

283

the best predictor and had a negative impact on Vmax of AG, BX, BG, CBH, LAP, and

284

NAG (Fig. 3). The plant community C: N ratio was the best predictor and had a negative

13

285

impact on the Vmax of AP. DIN was the best predictor and had a negative impact on the

286

Vmax of PPO.

287

3.3. The half-saturation constants of extracellular enzymes (Km)

288

The Km of BX and AP increased with increasing N addition (Fig. 2), whereas the Km

289

of BG, CBH, and LAP decreased. The Km of AG first increased and then decreased when

290

N level reached 32 g N m-2 y-1. The Km of NAG did no.t change along with N addition.

291

The Km of PPO first increased, and then decreased when N level exceeded 8 g N m-2 y-1.

292

The predictors for Km were enzyme-specific (Fig. 4). Soil pH and inorganic N

293

concentration were better Km predictors for most of enzymes, whereas the Km of LAP and

294

AP positively and negatively correlated with MBC and plant community C: N ratio,

295

respectively.

296

3.4. The explanations to microbial respiration

297

The results of variable selection showed that soil microbial respiration was best

298

explained by the Vmax and Km of BG (Fig. 5). We thus use Vmax and Km of BG to define

299

the latent variable for BG kinetics in the SEM. The SEM results showed that N addition

300

had a direct negative effect on soil pH, and soil pH had a direct negative effect on

301

microbial biomass and BG kinetics. Soil pH and MBC had no significant direct influence

302

on microbial respiration, but BG kinetics had a direct and strong negative effect on

303

microbial respiration (Fig. 6), which explained 55% of the total variance of microbial

304

respiration. Soil acidification directly associated with decreased microbial biomass. N

305

addition indirectly associated with decreased microbial respiration through reducing Vmax

306

and Km of BG.

307

4. Discussion 14

308 309

4.1. The responses of hydrolytic enzyme kinetic parameters to high N deposition We hypothesized that soil microbes could allocate more resources to the synthesis of

310

C-degrading hydrolytic enzymes because increased N availability alleviates N limitation

311

(Waldrop and Firestone, 2004; Stone et al., 2012; Chen et al., 2017). However, our

312

results did not support this hypothesis. We found reduced Vmax of AG, BX, BG, and CBH,

313

indicating rapid enzyme turnover or reduced production of those enzymes (Baker and

314

Allison 2017). Our model averaging results further suggested that the decreased Vmax was

315

mainly regulated by soil acidification (Fig. 3). Soil acidification could lead to base cation

316

depletion and produce more aluminum in soil solutions (Tian and Niu, 2015). The

317

toxicity of aluminum suppresses the growth of soil microorganisms and their investment

318

in enzyme synthesis (Treseder, 2008). In addition, N addition alleviates N-limitation for

319

plant growth and stimulates plant and root biomass (Table 1; Fig. S2). Therefore, the

320

increased plant productivity could enhance labile C inputs, thereby reducing the need for

321

microbes to produce C-targeting hydrolytic enzymes. Furthermore, changes in soil

322

microbial composition may also contribute to a decrease in Vmax. We found that the

323

abundance of two bacterial phyla, Actinobacteria and Acidobacteria, decreased under soil

324

acidification at our site (unpublished data). Actinobacteria and Acidobacteria possess

325

well-equipped genetic machinery for the production of enzymes involved in the

326

degradation of plant cell wall materials (Trivedi et al., 2013). Consequently, the

327

decreased abundance of these bacterial phyla results in fewer enzymes to hydrolyze

328

polysaccharides, which may have contributed to the decrease in the Vmax of the C-

329

degrading hydrolytic enzymes.

330

The decreased Vmax of the N-degrading enzymes (LAP and NAG) and the increased

331

Vmax of the P-degrading enzyme (AP) reflected changes in soil nutrient status under high 15

332

N deposition. When the availability of N resource meets the requirements of microbes

333

and plants, the synthesis of N-degrading enzymes has little ecological advantage

334

(Wallenstein and Burns, 2011). The decreased Vmax of NAG and LAP supported this

335

microbial foraging strategy under N enrichment (Fig. 2). In addition, previous studies

336

found that LAP contributed less to overall N-degrading enzymes (NAG+LAP) as soil pH

337

decreased (Sinsabaugh et al., 2008; Moorhead et al., 2016). Similarly, we also found that

338

LAP/NAG decreased with soil acidification (Fig. S3), suggesting that LAP would be

339

more sensitive to a decrease in soil pH than NAG. Furthermore, increased N availability

340

can result in imbalanced N: P supply and often exacerbates P limitation (Tao and Hunter

341

2012). Microbes have to produce more AP enzymes to meet their needs from organic P

342

(Harder and Dijkhuizen, 1983; Meng and Field, 2007), which can explain the increased

343

Vmax of AP. Also, the production of AP required a significant investment of N. AP

344

activity was thus generally increased with N supply (Houlton et al., 2008). Consistently,

345

our model averaging found that the decrease in plant C: N ratio under N enrichment

346

promoted the Vmax of AP (Fig. 3).

347

Together, Vmax and Km reflect how enzyme production and enzyme-substrate

348

interaction regulate its activity. For example, we found that the Vmax of AG and BX

349

decreased but their Km increased. N addition has been found to increase humic acid

350

concentration and particulate organic matter content in soils (Zak et al., 2017; Liu et al.,

351

2018a), which could induce enzyme immobilization (Burns et al., 2013). The

352

immobilized enzymes often have lower Vmax and higher Km, compared to their free

353

counterparts (Datta et al., 2017). Therefore, these changes in kinetic parameters indicate

354

that AG and BX might be immobilized, showing an increase in stability and a decrease in

355

enzyme activity (Sarkar and Burns, 1984; Datta et al., 2017). An alternative explanation 16

356

for the low Vmax and high Km of AG and BX is that the substrates for these enzymes

357

became more available under increasing N addition, and thus decreased the need to

358

produce enzymes and also decreased the need for the enzymes to have high affinity for

359

their substrates. For BG, CBH, LAP, and NAG, the concurrent decreases in Vmax and Km

360

suggest less enzyme production and fewer natural substrates binding with enzymes

361

(Wallenstein et al. 2011). As a result, the realized affinity of these enzymes for their

362

substrates could be stimulated.

363

4.2. The responses of polyphenol oxidase kinetic parameters to high N deposition

364

Contrary to our third hypothesis that N addition decreases polyphenol oxidase

365

activity, the Vmax of PPO increased when the level of N addition was less than 8 g m-2 y-1,

366

but decreased when the level of N addition exceeded 8 g m-2 y-1. The increase in the Vmax

367

of PPO at low N addition levels is consistent with the previous results with grassland

368

ecosystems (Riggs and Hobbie 2016) and could be associated with the changes in

369

substrate concentration. In our earlier study, we found that N addition enhanced lignin

370

concentrations in the dominant plant species in our study site (Yang et al., 2019), which

371

is consistent with the findings of a recent meta-analysis (Liu et al., 2016). Given that PPO

372

targets lignin compounds, the increased lignin concentration in plant tissues may partially

373

contribute to the increase in PPO synthesis (Sinsabaugh 2010).

374

Contrary to our findings, studies conducted in forest ecosystems have revealed that

375

PPO activity was inhibited by increased N deposition (Carreiro et al., 2000; Frey et al.,

376

2014). This contradiction could be caused by the difference in microbial community

377

between grassland and forest ecosystems. In forest ecosystems, Basidiomycetes are the

378

dominant fungal phylum that contributed to the production of PPO. N addition decreased

379

the abundance of Basidiomycetes, which could lead to a decline in PPO production 17

380

(Sinsabaugh 2010). However, in grassland ecosystems, Ascomycetes are the dominant

381

fungal phylum (Amend et al., 2016). According to Amend et al (2016), ascomycetes are

382

not sensitive to increased N deposition. This could partially explain why the Vmax of PPO

383

was not inhibited by low levels of N addition in grasslands. Furthermore, N addition

384

could decrease mycorrhizal biomass, and shift the species of mycorrhizal fungi toward to

385

low production of oxidative enzymes in forest ecosystem (Bödeker et al., 2014).

386

However, it is not clear whether this mechanism is applicable in grassland ecosystems.

387

Further research is needed to investigate whether N enrichment affects PPO activity by

388

altering mycorrhizal fungi community.

389

Our results were congruent with previous studies when the level of N addition

390

exceeded 8 g m-2 y-1. PPO usually functions as a microbial N miner. It can degrade

391

complex compounds to acquire N (Moorhead and Sinsabaugh 2006, Craine et al. 2007).

392

Thus, the excessive N addition often inhibits the secretion of PPO (Carreiro et al. 2000).

393

Consistently, the result of model averaging found that the Vmax of PPO was primarily

394

negatively associated with soil inorganic N concentration (Fig. 3). Further research is

395

needed to elucidate the mechanism of the hump-shaped response of kinetics of PPO

396

enzyme to N addition in grassland ecosystems.

397

4.3. The association between enzyme kinetic parameters and microbial respiration under

398

N enrichment

399

Although it is generally agreed that the rate-limiting steps of microbial respiration

400

are catalyzed by enzymes and different enzymes have different responses to high N

401

deposition, few studies have investigated the association between kinetic parameters of

402

hydrolytic and polyphenol oxidative enzymes and microbial respiration under high N

403

deposition. Thus, previous studies generally attributed the decrease in microbial 18

404

respiration under high N input to soil acidification and microbial biomass reduction,

405

ignoring the intermediate process of soil enzymes (Treseder, 2008; Chen et al., 2016).

406

Indeed, we found that soil microbial biomass and acidification strongly correlated with

407

microbial respiration (Fig. S4). However, when controlling for Vmax of BG, the partial

408

correlations of MBC with microbial respiration, and pH with microbial respiration were

409

no longer significant (Fig. S5).

410

SEM also reached the similar finding that the reduction in microbial respiration

411

under high N deposition was directly attributable to the decrease in the Vmax and Km of

412

BG (Fig. 6), which was induced by soil acidification. Previous studies proposed that the

413

reduced microbial respiration under high N deposition was caused by the inhibition of

414

polyphenol oxidase activity in forest ecosystems (Carreiro et al., 2000; Frey et al., 2014).

415

However, according to our results, β-glucosidase, rather than polyphenol oxidase, is the

416

main factor that contributes to the response of microbial respiration to high N deposition

417

in grassland ecosystems. This is probably because the content of lignin in grassland

418

ecosystems is much lower than that in forest ecosystems (Sinsabaugh 2010, Riggs and

419

Hobbie 2016). Moreover, since forest soils have lower soil pH than grassland soils

420

(Jobbágy and Jackson, 2003), enzyme kinetic parameters could be expected to respond

421

differently to N addition due to different soil pH conditions. Overall, in addition to the

422

generally accepted soil acidification and microbial biomass reduction hypotheses, our

423

results demonstrated that the decreased Vmax and Km associated with decreased BG

424

activity directly led to the reduction in microbial respiration under high N deposition.

425

In conclusion, our study highlights the importance and necessity of obtaining kinetic

426

parameters of hydrolytic and polyphenol oxidative enzymes. Most previous studies only

427

reported the apparent enzyme activities. We assessed Vmax in tandem with Km, which 19

428

enables us to better understand how substrate availability and enzyme specificity together

429

affect the apparent enzyme activities and helps us to explore the potential mechanism by

430

which microbial decomposition responds to environmental changes. Besides, Vmax and Km

431

for hydrolytic and polyphenol oxidative enzymes will provide critical information to

432

improve microbial model to evaluate soil C feedback on global changes (Allison et al.,

433

2010; Wieder et al., 2013). However, we also should be aware that enzymes under

434

realistic conditions catalyze biogeochemical reactions in highly structured soils under

435

varying soil physical properties conditions (Tang and Riley, 2019). Therefore, the

436

enzyme assays like the current study should be combined with specific soil physical

437

properties to be applicable to enzyme dynamics modeling. The collaboration between

438

modelers and experimental scientists is needed to better understand and simulate soil

439

enzyme dynamics under global changes.

440 441 442

Acknowledgments We thank Prof. Steven. D. Allison for providing us with the opportunity to learn

443

enzyme measurement processes at his lab in UC, Irvine. This study was financially

444

supported by the National Key Research and Development Program of China

445

(2016YFC0500701), the Strategic Priority Research, Chinese Academy of Sciences

446

(XDA23080301), the National Natural Science Foundation of China (31770530,

447

31370488).

448 449

Conflict of interests

450

The authors declare no conflict of interests.

20

451 452

Author contributions

453

L.L.L. and W.X.L. designed the study. W.X.L. and L.L. L. analysed the data and wrote

454

the manuscript. R.T., Y.S. Y, and Z.Y. P. and S.Y. conducted the field and lab work.

455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472

References Allison, S.D., 2005. Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments. Ecology Letters 8, 626-635. Allison, S.D., Romero-Olivares, A.L., Lu, Y., Taylor, J.W., Treseder, K.K., 2018. Temperature sensitivities of extracellular enzyme V-max and K-m across thermal environments. Global Change Biology 24, 2884-2897. Allison, S.D., Wallenstein, M.D., Bradford, M.A., 2010. Soil-carbon response to warming dependent on microbial physiology. Nature Geoscience 3, 336-340. Amend, A.S., Martiny, A.C., Allison, S.D., Berlemont, R., Goulden, M.L., Lu, Y., Treseder, K.K., Weihe, C., Martiny, J.B.H., 2016. Microbial response to simulated global change is phylogenetically conserved and linked with functional potential. The ISME Journal 10, 109-118. Baker, N.R., Allison, S.D., 2017. Extracellular enzyme kinetics and thermodynamics along a climate gradient in southern California. Soil Biology & Biochemistry 114, 82-92. Barton, K., 2013. MuMIn: Multi-Model Inference. R package version 1.9.5.(CRAN rProject, online). Available at http://cran.r-project.org/web/packages/

473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492

MuMIn/MuMIn.pdf (accessed 7 January 2016). Bödeker, I., T. M., E., K., Karina, E.C., Wietse de, B., Francis, M., Olson, Å., Lindahl, B.D., 2014. Ectomycorrhizal Cortinarius species participate in enzymatic oxidation of humus in northern forest ecosystems. The New phytologist 203, 245-256. Burns, R.G., DeForest, J.L., Marxsen, J., Sinsabaugh, R.L., Stromberger, M.E., Wallenstein, M.D., Weintraub, M.N., Zoppini, A., 2013. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biology & Biochemistry 58, 216-234. Byrne, B.M., 2006. Structural equation modeling with EQS: Basic concepts, applications, and programming, 2nd ed. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, US. Carreiro, M., Sinsabaugh, R., Repert, D., Parkhurst, D., 2000. Microbial enzyme shifts explain litter decay responses to simulated nitrogen deposition. Ecology 81, 23592365. Cenini, V.L., Fornara, D.A., McMullan, G., Ternan, N., Carolan, R., Crawley, M.J., Clement, J.-C., Lavorel, S., 2016. Linkages between extracellular enzyme activities and the carbon and nitrogen content of grassland soils. Soil Biology & Biochemistry 96, 198-206. Chang, R., Zhou, W., Fang, Y., Bing, H., Sun, X., Wang, G., 2019. Anthropogenic Nitrogen Deposition Increases Soil Carbon by Enhancing New Carbon of the Soil 21

493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540

Aggregate Formation. Journal of Geophysical Research: Biogeosciences 124, 572584. Chen, D., Li, J., Lan, Z., Hu, S., Bai, Y., 2016. Soil acidification exerts a greater control on soil respiration than soil nitrogen availability in grasslands subjected to longterm nitrogen enrichment. Functional Ecology 30, 658-669. Chen, J., Luo, Y., Li, J., Zhou, X., Cao, J., Wang, R.-W., Wang, Y., Shelton, S., Jin, Z., Walker, L.M., Feng, Z., Niu, S., Feng, W., Jian, S., Zhou, L., 2017. Costimulation of soil glycosidase activity and soil respiration by nitrogen addition. Global Change Biology 23, 1328-1337. Chen, J., Luo, Y., van Groenigen, K.J., Hungate, B.A., Cao, J., Zhou, X., Wang, R.-w., 2018. A keystone microbial enzyme for nitrogen control of soil carbon storage. Science Advances 4. Craine, J.M., Morrow, C., Fierer, N., 2007. Microbial nitrogen limitation increases decomposition. Ecology 88, 2105-2113. Cusack, D.F., Silver, W.L., Torn, M.S., Burton, S.D., Firestone, M.K., 2011. Changes in microbial community characteristics and soil organic matter with nitrogen additions in two tropical forests. Ecology 92, 621-632. Datta, R., Anand, S., Moulick, A., Baraniya, D., Pathan, S., Rejšek, K., Vranová, V., Sharma, M., Sharma, D., Kelkar, A., Formánek, P., 2017. How enzymes are adsorbed on soil solid phase and factors limiting its activity: A Review. International Agrophysics 31, 287-302. Dong, C., Wang, W., Liu, H., Xu, X., Zeng, H., 2019. Temperate grassland shifted from nitrogen to phosphorus limitation induced by degradation and nitrogen deposition: Evidence from soil extracellular enzyme stoichiometry. Ecological Indicators 101, 453-464. Entwistle, E.M., Zak, D.R., Argiroff, W.A., 2018. Anthropogenic N deposition increases soil C storage by reducing the relative abundance of lignolytic fungi. Ecological Monographs 88, 225-244. Frey, S., Ollinger, S., Nadelhoffer, K., Bowden, R., Brzostek, E., Burton, A., Caldwell, B., Crow, S., Goodale, C., Grandy, A., 2014. Chronic nitrogen additions suppress decomposition and sequester soil carbon in temperate forests. Biogeochemistry 121, 305-316. German, D.P., Weintraub, M.N., Grandy, A.S., Lauber, C.L., Rinkes, Z.L., Allison, S.D., 2011. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biology & Biochemistry 43, 1387-1397. Grandy, A.S., Sinsabaugh, R.L., Neff, J.C., Stursova, M., Zak, D.R., 2008. Nitrogen deposition effects on soil organic matter chemistry are linked to variation in enzymes, ecosystems and size fractions. Biogeochemistry 91, 37-49. Grueber, C.E., Nakagawa, S., Laws, R.J., Jamieson, I.G., 2011. Multimodel inference in ecology and evolution: challenges and solutions. Journal of Evolutionary Biology 24, 699-711. Harder, W., Dijkhuizen, L., 1983. PHYSIOLOGICAL RESPONSES TO NUTRIENT LIMITATION. Annual Review of Microbiology 37, 1-23. Hedwall, P.-O., Bergh, J., Brunet, J., 2017. Phosphorus and nitrogen co-limitation of forest ground vegetation under elevated anthropogenic nitrogen deposition. Oecologia 185, 317-326. Houlton, B.Z., Wang, Y.-P., Vitousek, P.M., Field, C.B., 2008. A unifying framework for dinitrogen fixation in the terrestrial biosphere. Nature 454, 327-330. 22

541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588

Janssens, I., Dieleman, W., Luyssaert, S., Subke, J.A., Reichstein, M., Ceulemans, R., Ciais, P., Dolman, A., Grace, J., Matteucci, G., 2010. Reduction of forest soil respiration in response to nitrogen deposition. Nature Geoscience 3, 315-322. Jian, S., Li, J., Chen, J., Wang, G., Mayes, M.A., Dzantor, K.E., Hui, D., Luo, Y., 2016. Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: A meta-analysis. Soil Biology & Biochemistry 101, 32-43. Jobbágy, E.G., Jackson, R.B., 2003. Patterns and mechanisms of soil acidification in the conversion of grasslands to forests. Biogeochemistry 64, 205–229. Keeler, B.L., Hobbie, S.E., Kellogg, L.E., 2009. Effects of Long-Term Nitrogen Addition on Microbial Enzyme Activity in Eight Forested and Grassland Sites: Implications for Litter and Soil Organic Matter Decomposition. Ecosystems 12, 1-15. Ledgerwood, A., Shrout, P.E., 2011. The trade-off between accuracy and precision in latent variable models of mediation processes. Journal of Personality and Social Psychology 101, 1174-1188. Liu, J., Wu, N., Wang, H., Sun, J., Peng, B., Jiang, P., Bai, E., 2016. Nitrogen addition affects chemical compositions of plant tissues, litter and soil organic matter. Ecology 97, 1796-1806. Liu, Q., Zhuang, L., Ni, X., You, C., Yang, W., Wu, F., Tan, B., Yue, K., Liu, Y., Zhang, L., Xu, Z., 2018a. Nitrogen additions stimulate litter humification in a subtropical forest, southwestern China. Scientific Reports 8. Liu, W., Qiao, C., Yang, S., Bai, W., Liu, L., 2018b. Microbial carbon use efficiency and priming effect regulate soil carbon storage under nitrogen deposition by slowing soil organic matter decomposition. Geoderma 332, 37-44. Meng, D.N.L., Field, C.B., 2007. Simulated global changes alter phosphorus demand in annual grassland. Global Change Biology 13, 2582-2591. Moorhead, D.L., Sinsabaugh, R.L., Hill, B.H., Weintraub, M.N., 2016. Vector analysis of ecoenzyme activities reveal constraints on coupled C, N and P dynamics. Soil Biology & Biochemistry 93, 1-7. Riggs, C.E., Hobbie, S.E., 2016. Mechanisms driving the soil organic matter decomposition response to nitrogen enrichment in grassland soils. Soil Biology & Biochemistry 99, 54-65. Riggs, C.E., Hobbie, S.E., Bach, E.M., Hofmockel, K.S., Kazanski, C.E., 2015. Nitrogen addition changes grassland soil organic matter decomposition. Biogeochemistry 125, 203-219. Sarkar, J.M., Burns, R.G., 1984. Synthesis and properties of β-d-glucosidasephenolic copolymers as analogues of soil humic-enzyme complexes. Soil Biology & Biochemistry 16, 619-625. Sinsabaugh, R.L., 2010. Phenol oxidase, peroxidase and organic matter dynamics of soil. Soil Biology & Biochemistry 42, 391-404. Sinsabaugh, R.L., Belnap, J., Findlay, S.G., Shah, J.J.F., Hill, B.H., Kuehn, K.A., Kuske, C.R., Litvak, M.E., Martinez, N.G., Moorhead, D.L., Warnock, D.D., 2014. Extracellular enzyme kinetics scale with resource availability. Biogeochemistry 121, 287-304. Sinsabaugh, R.L., Lauber, C.L., Weintraub, M.N., Ahmed, B., Allison, S.D., Crenshaw, C., Contosta, A.R., Cusack, D., Frey, S., Gallo, M.E., Gartner, T.B., Hobbie, S.E., Holland, K., Keeler, B.L., Powers, J.S., Stursova, M., Takacs-Vesbach, C., Waldrop, M.P., Wallenstein, M.D., Zak, D.R., Zeglin, L.H., 2008. Stoichiometry of soil enzyme activity at global scale. Ecology Letters 11, 1252-1264. 23

589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637

Sinsabaugh, R.L., Moorhead, D.L., 1994. Resource allocation to extracellular enzyme production: a model for nitrogen and phosphorous control of litter decomposition. Soil Biology & Biochemistry 26, 1305-1311. Sinsabaugh, R.L., Shah, J.J.F., 2012. Ecoenzymatic Stoichiometry and Ecological Theory. Annual Review of Ecology, Evolution, and Systematics, Vol 43 43, 313-343. Stone, M.M., Weiss, M.S., Goodale, C.L., Adams, M.B., Fernandez, I.J., German, D.P., Allison, S.D., 2012. Temperature sensitivity of soil enzyme kinetics under Nfertilization in two temperate forests. Global Change Biology 18, 1173-1184. Tang, J., Riley, W.J., 2019. A Theory of Effective Microbial Substrate Affinity Parameters in Variably Saturated Soils and an Example Application to Aerobic Soil Heterotrophic Respiration. Journal of Geophysical Research: Biogeosciences 124, 918-940. Tian, D., Niu, S., 2015. A global analysis of soil acidification caused by nitrogen addition. Environmental Research Letters 10, 024019. Treseder, K.K., 2008. Nitrogen additions and microbial biomass: a meta‐analysis of ecosystem studies. Ecology Letters 11, 1111-1120. Trivedi, P., Anderson, I.C., Singh, B.K., 2013. Microbial modulators of soil carbon storage: integrating genomic and metabolic knowledge for global prediction. Trends in Microbiology 21, 641-651. Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring soil microbial biomass C. Soil Biology & Biochemistry 19, 703-707. Vitousek, P.M., Porder, S., Houlton, B.Z., Chadwick, O.A., 2010. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecological Applications 20, 5-15. Waldrop, M., Firestone, M., 2004. Altered utilization patterns of young and old soil C by microorganisms caused by temperature shifts and N additions. Biogeochemistry 67, 235-248. Wallenstein, M.D., Burns, R.G., 2011. Ecology of extracellular enzyme activities and organic matter degradation in soil: a complex community-driven process. 1-21. Wallenstein, M.D., Steven D. Allison, Jessica Ernakovich, J. Megan Steinweg, Sinsabaugh, R., 2011. Controls on the temperature sensitivity of soil enzymes: A key driver of In Situ enzyme activity rates. 1-14. Wieder, W.R., Bonan, G.B., Allison, S.D., 2013. Global soil carbon projections are improved by modelling microbial processes. Nature Climate Change 3, 909-912. Xiao, W., Chen, X., Jing, X., Zhu, B., 2018. A meta-analysis of soil extracellular enzyme activities in response to global change. Soil Biology & Biochemistry 123, 21-32. Yang, S., Liu, W., Qiao, C., Wang, J., Deng, M., Zhang, B., Liu, L., 2019. The decline in plant biodiversity slows down soil carbon turnover under increasing nitrogen deposition in a temperate steppe. Functional Ecology http://doi.org/10.1111/13652435.13338. Zak, D.R., Freedman, Z.B., Upchurch, R.A., Steffens, M., Kögel‐Knabner, I., 2017. Anthropogenic N deposition increases soil organic matter accumulation without altering its biochemical composition. Global Change Biology 23, 933-944. Zeglin, L.H., Stursova, M., Sinsabaugh, R.L., Collins, S.L., 2007. Microbial responses to nitrogen addition in three contrasting grassland ecosystems. Oecologia 154, 349359. Zhang, Y., Xu, W., Wen, Z., Wang, D., Hao, T., Tang, A., Liu, X., 2017. Atmospheric deposition of inorganic nitrogen in a semi-arid grassland of Inner Mongolia, China. Journal of Arid Land 9, 810-822. 24

638

Table 1. Effects of N addition on soil microclimate, chemical properties and plant

639

aboveground biomass production (mean ± standard error, n = 4). Different letters indicate

640

significant differences among treatments (P < 0.05). N input

ST (OC)

SM (%)

(g N m-2)

DIN

pH

(mg kg-2)

AGB (g m-2)

0

18.67±0.5

15.50±0.7 a

21.47±3.19d

6.81±0.06a

153.5±12.72b

1

7a 18.01±0.5

15.13±0.5 a

27.53±6.62cd

6.52±0.06ab

254.1±31.33ab

2

6ab 17.91±0.4

14.09±0.84a

33.95±3.82cd

6.63±0.13ab

268.9±25.42ab

4

9ab 17.12±0.3

15.83±0.75a

39.12±6.89cd

6.30±0.17ab

295.8±19.56ab

8

7ab 16.94±0.4

15.13±1.07a

46.72±1.32bcd

5.82±0.07ab

290.4±27.82ab

16

0ab 16.63±0.4

16.46±2.03a

116.2±20.95ab

c5.52±0.12bc

332.2±20.98a

32

5ab 16.35±0.3

18.82±0.84a

102.7±21.06abc

4.71±0.13c

335.2±21.03a

64

4ab 15.92±0.3

15.83±1.87a

125.1±14.42a

4.63±0.18c

392.7±34.50a

7b

641

ST: soil temperature; SM: soil moisture; DIN: dissolved inorganic nitrogen; AGB:

642

aboveground biomass.

643

25

644

Figure captions

645

Figure 1. Effects of N addition on soil microbial respiration (Rh, a) and microbial biomass

646

carbon (MBC, b). Circles represent the mean ± standard error (n=4) at each N addition

647

level. Solid lines represent the regression between enzymatic parameters and N levels.

648 649

Figure 2. Vmax and Km of extracellular α-glucosidase (AG), β-xylosidase (BX), β-

650

glucosidase (BG), cellobiohydrolase (CBH), Leucine aminopeptidase (LAP), N-acetyl-

651

glucosaminidase (NAG), acid phosphatase (AP), and polyphenol oxidase (PPO) upon

652

different levels of N addition. Circles represent mean ± standard error (n=4) at each N

653

addition level. Solid lines represent the regression between Vmax and Km value and N

654

levels.

655 656

Figure 3. Effects of environmental, microbial community, and plant community variables

657

on Vmax. The average parameter estimate (standardized regression coefficient) of the

658

model predictors and their associated 95% confidence intervals is shown. ST, soil

659

temperature; SM, soil moisture; DIN, dissolved inorganic nitrogen; AGB, aboveground

660

biomass; F: G, forb to grass ratio; PC: N, plant community C to N ratio; MBC, microbial

661

biomass C.

662 663

Figure 4. Effects of environmental, microbial community, and plant community variables

664

on the Km of the extracellular enzymes. The average parameter estimate (standardized

665

regression coefficients) of the model predictors and their associated 95% confidence

666

intervals is shown. Abbreviations are defined in the legend of Fig. 3. 26

667 668

Figure 5. Effects of Vmax and Km of different enzymes on microbial respiration (Rh). The

669

average parameter estimate (standardized regression coefficients) of the model predictors

670

and their associated 95% confidence intervals is shown. Abbreviations are defined in the

671

legend of Fig. 2.

672 673

Figure 6. The structural equation modeling (SEM) analysis of the effect of N enrichment

674

on soil microbial respiration via the pathways of soil N addition, soil acidification, soil

675

microbial biomass, and kinetics of BG (the latent variate indicated by Vmax and Km of BG)

676

(b). Square boxes represent the variables included in the model. “↓” indicated significant

677

decrease upon N addition. Results of the final model fitting were λ2 = 0.060, P = 0.806,

678

d.f. = 1, TLI =0.951, and n = 32 (a high P-value associated with a λ2 test indicates that the

679

model fits the data well). Red and blue solid arrows connecting the boxes represent

680

significant positive and negative effects (P < 0.05), respectively. Pathways without a

681

significant effect are indicated by broken lines (P > 0.05). Percentages close to variables

682

refer to the variance accounted for by the model (R2). Values associated with the arrows

683

represent standardized path coefficients.

684

27

685

Figure 1.

686

28

687

Figure 2.

688

29

689

Figure 3. AG Vmax

BG Vmax

BX Vmax

CBH Vmax

ST SM

Parameters

DIN pH AGB F:G PC:N MBC

-1.0 -0.5

0.0

0.5

1.0 -1.0 -0.5

Estimates

0.0

1.0 -1.0 -0.5

0.5

LAP Vmax

0.0

0.5

1.0

Estimates

Estimates

-0.5 0.0 0.5 Estimates

1.0

PPO Vmax

AP Vmax

NAG Vmax

-1.0

ST SM

Parameters

DIN pH AGB F:G PC:N MBC

-1.0 -0.5

690

0.0

0.5

Estimates

1.0

-1.0 -0.5

0.0

0.5

1.0

-1.0 -0.5 0.0

0.5

Estimates

Estimates

691

30

1.0

-1.0 -0.5 0.0

0.5

Estimates

1.0

692

Figure 4. AG Km

BX Km

BG Km

CBH Km

-1.0 -0.5 0.0 0.5 1.0

-1.0 -0.5 0.0 0.5 1.0

ST SM

Parameters

DIN pH AGB F:G PC:N MBC

-1.5 -1.0 -0.5 0.0 0.5

-1.0 -0.5 0.0 0.5 1.0

Estimates

Estimates

LAP Km

Estimates

NAG Km

AP Km

Estimates PPO Km

ST SM

Parameters

DIN pH AGB F:G PC:N MBC

693

-1.0 -0.5 0.0 0.5 1.0

-1.0 -0.5 0.0 0.5 1.0

Estimates

Estimates

-1.0 -0.5 0.0 0.5 1.0

Estimates

694

31

-1.0 -0.5 0.0 0.5 1.0

Estimates

695

Figure 5

AG Vmax

AG Km

BX Vmax

BX Km

BG Vmax CBH Vmax LAP Vmax

BG Km CBH Km LAP Km

NAG Vmax

NAG Km

AP Vmax

AP Km

PPO Vmax

PPO Km -0.2

696

Rh

Parameters

Parameters

Rh

-0.1

0.0

0.1

0.2

-0.2

-0.1

0.0

0.1

Estimates

Estimates

697

32

0.2

698

Figure 6.

699 700

33

701

Supporting Material

702

Table S1. Enzymes and substrates analyzed in the current study.

703 704

Table S2. The apparent enzyme activities (unit as nmol hr-1 g-1) under different levels of

705

N addition. Enzyme abbreviations can be found in Table S1.

706 707

Table S3. Vmax of enzymes (unit as nmol hr-1 g-1) under different levels of N addition.

708

Enzyme abbreviations can be found in Table S1.

709 710

Table S4. Km of enzymes (unit as µmol) under different levels of N addition. Enzyme

711

abbreviations can be found in Table S1.

712 713

Figure S1. The full structural equation modeling (SEM) depicting pathways by which N

714

addition, soil pH, microbial biomass and enzyme kinetics may influence microbial

715

respiration. Enzyme abbreviations can be found in Table S1.

716 717

Figure S2. Root biomass upon different levels of N addition. Circles represent mean ±

718

standard error (n=4) at each N addition level. Solid lines represent the regression between

719

root biomass and N levels.

720

34

721

Figure S3. Vmax of LAP, Vmax of NAG (a) and their ratio (b) upon different levels of soil

722

pH. Enzyme abbreviations can be found in Table S1.

723 724

Figure S4. The regressions between soil microbial biomass (MBC) and microbial

725

respiration (Rh) (a), between soil pH and Rh (b).

726 727

Figure S5. The partial regressions between soil microbial biomass (MBC) and microbial

728

respiration (Rh) (a), between soil pH and Rh (b) after controlling for Vmax of β-glucosidase

729

(BG), respectively.

35

Highlights Vmax for C- and N-degrading hydrolytic enzymes decreases with increasing N addition levels. Vmax and Km for P-degrading enzyme increase with N addition levels. The Vmax and Km for PPO respond nonlinearly to N addition levels. Soil acidification drives the decreased Vmax for C-degrading enzymes. Decreased microbial respiration is due to decreased Vmax and Km for BG under N deposition.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: