Extracellular enzyme kinetics scale with resource ...

Extracellular enzyme kinetics scale with resource availability

Robert L. Sinsabaugh, Jayne Belnap, Stuart G. Findlay, Jennifer J. Follstad Shah, Brian H. Hill, Kevin A. Kuehn, Cheryl R. Kuske, et al. Biogeochemistry An International Journal ISSN 0168-2563 Biogeochemistry DOI 10.1007/s10533-014-0030-y

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Author's personal copy Biogeochemistry DOI 10.1007/s10533-014-0030-y

SYNTHESIS AND EMERGING IDEAS

Extracellular enzyme kinetics scale with resource availability Robert L. Sinsabaugh • Jayne Belnap • Stuart G. Findlay • Jennifer J. Follstad Shah • Brian H. Hill • Kevin A. Kuehn Cheryl R. Kuske • Marcy E. Litvak • Noelle G. Martinez • Daryl L. Moorhead • Daniel D. Warnock

•

Received: 4 April 2014 / Accepted: 20 August 2014 Ó Springer International Publishing Switzerland 2014

Abstract Microbial community metabolism relies on external digestion, mediated by extracellular enzymes that break down complex organic matter into molecules small enough for cells to assimilate. We analyzed the kinetics of 40 extracellular enzymes that mediate the degradation and assimilation of carbon, nitrogen and phosphorus by diverse aquatic and terrestrial microbial communities (1160 cases). Regression analyses were conducted by habitat (aquatic and terrestrial), enzyme class (hydrolases

Responsible Editor: Cory Cleveland.

Electronic supplementary material The online version of this article (doi:10.1007/s10533-014-0030-y) contains supplementary material, which is available to authorized users.

and oxidoreductases) and assay methodology (low affinity and high affinity substrates) to relate potential reaction rates to substrate availability. Across enzyme classes and habitats, the scaling relationships between apparent Vmax and apparent Km followed similar power laws with exponents of 0.44 to 0.67. These exponents, called elasticities, were not statistically distinct from a central value of 0.50, which occurs when the Km of an enzyme equals substrate concentration, a condition optimal for maintenance of steady state. We also conducted an ecosystem scale analysis of ten extracellular hydrolase activities in relation to soil and sediment organic carbon (2,000–5,000 cases/ enzyme) that yielded elasticities near 1.0 (0.9 ± 0.2, n = 36). At the metabolomic scale, the elasticity of extracellular enzymatic reactions is the proportionality

R. L. Sinsabaugh (&) M. E. Litvak N. G. Martinez D. D. Warnock Biology Department, University of New Mexico, Albuquerque, NM 87131, USA e-mail: [email protected]

B. H. Hill National Health and Environmental Effects Laboratory, Mid-Continent Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Duluth, MN 55804, USA

J. Belnap Southwest Biological Science Center, U.S. Geological Survey, Moab, UT 84532, USA

K. A. Kuehn Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA

S. G. Findlay Cary Institute of Ecosystem Studies, Millbrook, NY 12545, USA

C. R. Kuske Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA

J. J. F. Shah Watershed Sciences Department, Utah State University, Logan, UT 84322, USA

D. L. Moorhead Department of Environmental Science, University of Toledo, Toledo, OH 43606, USA

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constant that connects the C:N:P stoichiometries of organic matter and ecoenzymatic activities. At the ecosystem scale, the elasticity of extracellular enzymatic reactions shows that organic matter ultimately limits effective enzyme binding sites. Our findings suggest that one mechanism by which microbial communities maintain homeostasis is regulating extracellular enzyme expression to optimize the short-term responsiveness of substrate acquisition. The analyses also show that, like elemental stoichiometry, the fundamental attributes of enzymatic reactions can be extrapolated from biochemical to community and ecosystem scales. Keywords Ecological stoichiometry Extracellular enzymes Enzyme kinetics Microbial community Microbial metabolism

Introduction The growth and metabolism of microbial communities in soil and water drive global cycles of carbon (C), nitrogen (N) and phosphorus (P). Microbial communities can include hundreds to thousands of populations that interact through signal networks that convey information on population densities and resource availabilities. This communication organizes the spatial distribution and metabolism of individual cells and populations to maximize community growth through mechanisms such as quorum sensing, substrate channeling and syntrophy (Faust and Raes 2012; Huang et al. 2013; Elias and Banin 2012; Strickland et al. 2013; Zhuang et al. 2013). This self-organization has often been described as a precursor to multi-cellular organization. A key difference is that most microorganisms are osmotrophic, i.e. they assimilate soluble carbon and nutrients released through external enzymatic digestion, while metazoans internally digest food following consumption. Consequently, the production and turnover of extracellular degradative enzymes are a fundamental driver of microbial metabolism and the organization of microbial communities (Schimel and Weintraub 2003; Moorhead and Sinsabaugh 2006). Microbes regulate the production and release of extracellular enzymes in response to environmental signals of resource availability, which include the products of extracellular

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enzymatic reactions, as well as chemical signals that convey information on the presence and abundance of nearby organisms (Sinsabaugh and Follstad Shah 2012; Burns et al. 2013; Zhuang et al. 2013; Vega et al. 2014). Coordination of enzyme expression at the population and community level is critical because individual microorganisms typically produce only a few of the myriad enzymes needed to degrade complex organic structures such as plant and microbial cell walls. Thus, the return on investment for a single cell, and the microbial community as a whole, measured as production, depends on the synergistic interaction of extracellular enzymes produced by genetically diverse individuals. The production of extracellular enzymes is thought to be a substantial energy and material cost for individual cells, and the microbial community as a whole, but the aggregate costs and optimal allocation of enzymatic capacity in relation to the multiple resources required for the growth of microbial communities have not been assessed (Allison 2005; Moorhead et al. 2012). Stoichiometric studies have shown that the abundance of C, N and P substrates in the environment are generally correlated with a scaling exponent near 1.0. A meta-analysis by Cleveland and Liptzin (2007) found slopes of 1.0 for log–log plots of soil C versus soil N and soil C versus soil P (R2 = 0.75, 0.31, respectively) with a mean molar C:N:P of 186:13:1. Manzoni et al. (2010) did a meta-analysis using data from woody litter, leaf litter, soil organic matter and microbial biomass. The slope of the log(C:N) versus log(C:P) regression was 0.86. A similar regression for the initial C:N and C:P composition of plant litter had a slope of 1.16 (R2 = 0.77). A meta-analysis by Kirkby et al. (2011) found that the N and P content of soil organic matter scaled linearly with C (R2 = 0.86, 0.44, respectively) with a mean molar C:N:P ratio of 134:10.4:1. Martiny et al. (2013) reported that upper ocean particulate C and N scaled linearly with a mean molar C:N ratio of 6.5 (R2 = 0.86). Paralleling the trend for bulk elemental abundance, the scaling exponents for the most widely assayed C, N and P-acquiring extracellular enzyme activities (EEA) are also near 1.0 (Sinsabaugh et al. 2008, 2009, 2012). Using b-1,4 glucosidase (BG), which is required for the hydrolysis of cellulose and other b-linked glucans, as an indicator for C acquisition and acid (alkaline) phosphatase (AP), which hydrolyzes phosphate from phospholipids and phosphosaccharides, as an indicator for


ecoenzymatic P acquisition, the ln–ln regression slopes of ecoenzymatic C versus P activities were 1.16 ± 0.06 (R2 = 0.40), 0.95 ± 0.03 (R2 = 0.43) and 0.86 ± 0.06 (R2 = 0.72) for terrestrial soils, freshwater sediments and aquatic plankton, respectively (Sinsabaugh and Follstad Shah 2012). Using leucine aminopeptidase (LAP), which hydrolyzes peptide bonds associated with the two most abundant protein amino acids (leucine and alanine), and b-1,4-N-acetylglucosaminidase (NAG), which is required for the hydrolysis of chitin and other blinked aminopolysaccharides, as indictors of ecoenzymatic N acquisition, the ln–ln regression slopes ecoenzymatic C versus N activities were 1.09 ± 0.06 (R2 = 0.16), 1.10 ± 0.03 (R2 = 0.62) and 1.28 ± 0.18 (R2 = 0.50) for terrestrial soils, freshwater sediments and aquatic plankton, respectively (Sinsabaugh and Follstad Shah 2012). The similarity of the scaling exponents for bulk elemental abundance and extracellular enzyme activities is not surprising given that extracellular enzymes mediate carbon and nutrient acquisition by microbial communities, thereby influencing environmental substrate abundances, but the correspondence is not a direct mechanistic link. To make a direct connection, enzyme expression and substrate availability must be linked through analyses of enzyme kinetics. Kinetics of enzymatic reactions are described by the parameters kcat (the number of substrate to product reactions per second per enzyme), Vmax (the maximum rate of substrate to product conversion for a system) and Km (the substrate concentration that halfsaturates catalytic capacity). These parameters are related through the Michaelis–Menten model (Michaelis and Menten 1913), but are subject to different selection pressures. If substrate concentrations are consistently high, maximizing kcat minimizes the number of enzymes needed to sustain a reaction flux. This situation is typical for reactions of central metabolism (Bar-Even et al. 2011). At low substrate concentrations, there is selection pressure for Km values that approximate substrate concentration to optimize the responsiveness of reaction rates (Klipp and Heinrich 1994). This situation is more applicable to the extracellular enzymatic reactions of microbial communities. Enzymatic efficiency, the ratio kcat/Km, can be increased by either increasing kcat or reducing Km, depending on prevailing substrate concentration. Consequently, the two parameters are only weakly correlated (Bar-Even et al. 2011).

As the scale of interest expands from single enzymes to ecological systems, kcat and Km transition from descriptors of individual reactions to apparent measures (appVmax and appKm) that describe a kinetic consensus of reactions catalyzed by multiple enzymes of diverse origin. For an ecological system, catalytic capacity (appVmax) is the product of the effective total concentration of an assemblage of enzymes and their effective kcat (reaction rate per enzyme). appKm is a measure of substrate availability in part because of selective pressure for Km values that approximate substrate concentration, which maximizes responsiveness and efficiency (Williams 1973; Klipp and Heinrich 1994). But more directly, the substrates added to a sample to measure activity compete for binding sites with the native substrates present (Cornish-Bowden 2012). As a result, appKm observed in assays of environmental samples increases as the concentration of ambient substrates increases (Sinsabaugh et al. 1997). To examine relationships among degradative capacity, bulk elemental abundance and environmental substrate availability, we assembled kinetic data, paired measurements of apparent Km and Vmax, from a broad range of terrestrial and aquatic ecosystems. The data span 40 hydrolytic and oxidoreductive enzymes that mediate the extracellular depolymerization, mineralization and assimilation of C, N and P by microbial communities; a total of 1160 observations drawn from 51 studies (Table 1). Regression analyses were conducted by habitat (aquatic and terrestrial), enzyme class (hydrolases and oxidoreductases) and assay methodology (low affinity, i.e. high apparent Km, and high affinity, low apparent Km, substrates) to compare the scaling of potential reaction rates in relation to substrate availability. We also conducted an ecosystem scale analysis of ten extracellular hydrolase activities in soils and sediments in relation to organic carbon concentration. Our findings show that microbial communities maintain homeostasis by regulating extracellular enzyme expression to optimize the short term responsiveness of substrate acquisition, and that fundamental attributes of enzymatic reactions can be extrapolated from biochemical to community and ecosystem scales.

Methods Estimated values of apparent Km (appKm) and Vmax (appVmax) for 40 enzymes were compiled from 51

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123 8.0 8.0

River bacterioplankton Lake water River bacterioplankton River bacterioplankton Lentic biofilm Caribbean sea Coastal marine Estuarine Grassland soil Lake water Seawater North sea Grassland soils Grassalnd soils Grassland soils Grassland soils Grassland soils Grassland soils Grassland soils Grassland soils River bacterioplankton River bacterioplankton Lake plankton River bacterioplankton River bacterioplankton Temperate forest soils Five diverse soils Four soils River bacterioplankton River bacterioplankton Lake water Coastal marine Temperate forest soils Five diverse soils

Siuda and Chro´st (2002)

Foreman et al. (1998)

Sinsabaugh et al. (1997)

Thompson and Sinsabaugh (2000)

Rath et al. (1993)

Chro´st and Riemann (1994)

Saliot et al. (1996)

Marx et al. (2005)


Somville and Billen (1983)

Fontigny et al. (1987)

Acosta-Martinez and Tabatabai (2000)

Tabatabai et al. (2002)









Siuda et al. (2007)



Stone et al. (2012)

German et al. (2012)

Eivazi and Tabatabai (1988)




Chro´st and Riemann (1994)

Grandy et al. (2008)


5.0

5.0

In situ

In situ

8.0

8.0

6.0

5.0

5.0

8.0

8.0

9.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

In situ

7.8

8.0

8.0

8.0

8.0

8.0

8.0

7.2

8.0

River bacterioplankton

8.0

pH


System


Depolymerase reactions

Reference

Table 1 Data sources for enzyme kinetic analyses

4–40

20

20

20

20

20

37

4–40

4–40

20

20

20

20

20

37

37

37

37

37

37

37

37

In situ

20

?

30

In situ

18–21

In situ

20

20

20

?

20

20

Temp

35

12

3

4

23

30

4

35

28

21

30

3

17

25

4

4

4

4

4

4

4

4

1

1

4

8

6

3

2

41

26

30

4

24

30

Obs

MUB-a-D-glucoside MUB-a-D-glucoside MUB-a-D-glucoside pNP-a-glucoside MUB-b-D-glucoside MUB-b-D-glucoside

a-1,4-glucosidase a-1,4-glucosidase a-1,4-glucosidase a-1,4-glucosidase b-1,4-glucosidase b-1,4-glucosidase

MUB-b-D-glucoside MUB-b-D-glucoside MUB-b-D-glucoside

b-1,4-glucosidase b-1,4-glucosidase b-1,4-glucosidase

MUB-b-D-glucoside

MUB-a-D-glucoside

a-1,4-glucosidase

b-1,4-glucosidase

Ambient protein

MUB-p-guanidinobenzoate

MUB-p-guanidinobenzoate

Serine-b-naphthylamide

Histidine-b-naphthylamide

Lysine-b-naphthylamide

Arginine-b-naphthylamide

Glycine-b-naphthylamide

Alanine-b-naphthylamide

Leucine-b-naphthylamide




L-Leucine-AMC

2,863

173

62

183

187

22.9

462

156

329

307

14.9

5.16

2,832

5,634

1,053

137

231

225

228

1,287

621

480

29.2

150

1,666

465

25.9

L-Leucine-AMC

557

550

L-Leucine-AMC

L-Leucine-AMC

757

L-Leucine-AMC

L-Leucine-AMC

1,223

742

L-Leucine-AMC L-Leucine-AMC

269

3,869

2,940

Vmax

app

Fluoroscein diacetate

MUB- acetate

MUB- acetate

Substrate

Protease

Endopeptidase

Endopeptidase

Ser aminopeptidase

His aminopeptidase

Lys aminopeptidase

Arg aminopeptidase

Gly aminopeptidase

Ala aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Leu aminopeptidase

Acetyl esterase

Acetyl esterase

Acetyl esterase

Enzyme

6,206

215

48.0

313

294

21.2

172

293

405

555

9.76

1.28

2,711

4,324

1,338

40.0

139

104

106

1,072

318

235

NA

NA

1,956

419

18.8

369

NA

1,796

1,075

448

328

3,139

1,422

STD

52,051

53,238

33,667

9,650

1,609

2,435

8,275,000

78,034

77,704

1,214

1,878

276

51,336

123,288

1136,667

292,500

217,500

320,000

465,000

940,000

230,000

282,500

6,667

100,000

20,075

7,051

43,017

38,667

24,095

72,263

66,070

41,782

3,850

35,464

16,921

KM

app

35,698

27,600

21,079

4,527

1,694

2,134

4,821,048

58,958

58,952

1,930

1,781

372

30,473

62,479

1,014,906

161,323

84,212

236,784

269,011

345,060

350,000

73,201

NA

NA

9,958

2,579

21,146

11,590

NA

28,541

36,736

19,802

819

36,130

670

STD

29.5

407

588

26.6

2.58

4.61

14,493

658

247

2.87

45.2

17.5

15.5

14.8

1,471

1,613

704

943

1,389

800

575

613

228

667

15.5

14.7

1,567

70.9

1.92

78.7

53.2

46.1

11.9

6.79

5.23

Turnover

25.5

474

2,500

14.4

1.41

0.91

23,810

704

204

1.98

36.8

0.78

10.8

9.89

2,381

2,381

709

1,205

1,639

1,667

8,333

1,587

NA

NA

18.9

18.0

840

185

NA

35.1

64.5

59.5

8.86

11.2

9.39

STD


6.1

Grassland soils

Grassland soils

Marx et al. (2001)


6.0 8.0

Grassland soil

Temperate forest soils

Five diverse soils

Grassland soils

Grassland soils

Marx et al. (2005)

Stone et al. (2012)




5.0

6.0


Five diverse soils

Llake water

Aalpine meadow soil

Stone et al. (2012)



Nannipieri et al. (1991)



N, P, S mineralization reactions

In

Grassland soil

Marx et al. (2005)

8.0

situ

5.0

5.0

6.1

5.0

Estuarine



Grandy et al. (2008)

6.0

5.0

5.0

6.1

5.5

Five diverse soils

Grassland soils


5.0

6.1

6.0

6.1

6.0

6.1

Deng and Tabatabai (1994)

Grassland soil

Agricultural soils

Moscatelli et al. (2012)


Grassland soils

Marx et al. (2001)

Marx et al. (2005)

Agricultural soils

Moscatelli et al. (2012)

Stone et al. (2012)

6.0

Grassland soil

Marx et al. (2005)

8.0


5.0

Estuarine

pH

Stone et al. (2012)

System


Reference

Table 1 continued

20

37

NA

4–40

4–40

30

20

In situ

37

37

4–40

4–40

30

30

4–40

4–40

30

37

30

37

30

30

30

4–40

In situ

Temp

30

1

4

35

28

8

12

3

4

4

35

28

8

4

35

28

8

4

2

2

2

2

8

28

4

Obs

MUB-b-D-glucoside MUB-b-D-glucoside MUB-b-D-glucoside MUB-b-D-glucoside pNP-b-glucoside pNP-b-glucoside pNP-b-glucoside

b-1,4-glucosidase b-1,4-glucosidase b-1,4-glucosidase b-1,4-glucosidase b-1,4-glucosidase b-1,4-glucosidase b-1,4-glucosidase

MUB-N-acetyl-bglucosaminide

b-1,4-Nacetylglucosaminidase

Phosphatase

Phenol oxidase

MUB- phosphate

Guaiacol






pNP-b-galactoside

b-1,4-galactosidase


pNP-a-galactoside

a-1,4-galactoside

pNP-b-galactoside

MUB-b-D-xyloside

b-1,4-xylosidase


MUB-b-D-xyloside

b-1,4-xylosidase

b-1,4-galactosidase

MUB-b-D-xyloside

b-1,4-xylosidase


Carboxymethyl cellulose

MUB-b-D-cellobioside


Cellulase

Cellobiohydrolase

Cellobiohydrolase


MUB-b-D-glucoside

b-1,4-glucosidase

Cellobiohydrolase

Substrate

Enzyme

312

4,000

142

2,759

4,501

1,271

113

17.8

537

895

472

799

555

44,213

676

1,307

471

913

3,247

1,890

2,007

438

2,344

5,434

6.2

Vmax

app

608

NA

253

5,702

4,982

699

82.8

15.5

128

170

925

998

558

1,389

1,543

1,768

414

638

851

330

854

60.0

1,425

7,354

2.50

STD

8,516

50,000,000

10,450

112,311

154,450

48,071

171,942

16,500

6,875,000

10,000,000

78,017

67,389

106,621

85,000,000

25,646

30,930

11,466

1,725,000

1,147,000

6,150,000

19,530

20,000

17,859

51,234

29,200

KM

app

14,851

NA

4,548

98,180

87,166

19,504

48,962

16,830

5,387,872

4,451,966

101,353

67,812

188,005

21,666,666.7

25,667

22,576

10,504

457,347

276,000

250,000

3,430

3,000

9,309

30,854

13,332

STD

18.6

12,500

43.5

61.3

31.9

37.2

1,447

515

8,696

10,101

222

78.7

18.1

1,812

66.7

28.0

11.5

1,678

355

3,268

8.77

45.6

6.00

11.5

3,817

Turnover

16.2

NA

23.5

45.0

28.9

74.1

2,049

454

12,658

34,483

172

73.5

16.5

8,065

44.2

23.5

10.8

2,008

14,286

24,390

15.7

3,333

7.58

11.7

5,714

STD

Author's personal copy

Biogeochemistry

123

123

Tropical soils

Andisols

Waterlogged soils

Grassland soils

Grassland soils

Grassland soils

Grassland soils

Grassland soils

Grassalnd soils

Agricultural soils

Agricultural soils

Nor (1982)

Cartes et al. (2009)

Juan et al. (2009)

Frankenberger and Tabatabai (1991b)

Frankenberger and Tabatabai (1991b)

Frankenberger and Tabatabai (1980)

Frankenberger & Tabatabai (1980)

Frankenberger and Tabatabai (1980)

Senwo and Tabatabai (1996)

Zhang et al. (2010)

Farrell et al. (1994)

Agricultural soils

Cambisols

Landfill soils

Grassland soil

Dubey et al. (2000)

Bender and Conrad (1993)

Abichou et al. (2011)

Horz et al. (2002)

Dissimilatory reactions

Agricultural soils

Grassland soils

Marx et al. (2001)

Zhang et al. (2010)

6.1

Grassland soil

Marx et al. (2005)

Grassland soils

Tropical soils

Stone and Plante (2014)

Agricultural soils

Marine


Marx et al. (2001)

Lake & seawater

Nedoma et al. (2007)

Zhang et al. (2010)

6.1

Humic lakes

Mu¨nster (1994)

6.0

?

5–8

7.8

5.8

5.8

8.5

8.5

8.5

8.5

10.0

10.0

6.5

7.0

In situ

7.0

6.5

6.1

3.7–5.0

8.0

8.0

Situ

8.0

Biofilm

Thompson and Sinsabaugh (2000)

8.0

pH


System


Reference

Table 1 continued

20

?

25

30

37

37

37

37

37

37

37

37

37

37

37

37

37

30

30

30

25

In situ

In situ

25

20

20

Temp

6

3

5

3

10

12

3

8

8

8

9

9

6

12

3

12

12

2

2

8

24

4

11

7

32

25

Obs

Methane monooxygenase




Sulfatase

Sulfatase

Aspartate ammonia lyase

Propionamide amidohydrolase

Acetamide amidohydrolase

Formamide amidohydrolase

Glutamine amidohydrolase

Asparagine amidohydrolase

Urease

Urease

Urease

Urease

Phosphatase

Phosphatase

Phosphatase

Phosphatase

Phosphatase

Phosphatase

Phosphatase

Phosphatase

Phosphatase

Phosphatase

Enzyme

Methane

Methane

Methane

Methane

pNP-sulfate

pNP-sulfate

Aspartate

Proprionamide

Acetamide

Formamide

L-glutamine

L-asparagine

Urea

Urea

Urea

Urea

pNP-phosphate

pNP-phosphate

MUBphosphate

MUBphosphate

MUBphosphate

MUBphosphate

MUBphosphate

MUBphosphate

MUBphosphate

MUBphosphate

Substrate

2.75

899

1.35

15.8

2,401

1,099

1,535

197

915

10,112

17,385

2,179

3,547

13,925

253

2,340

3,200

6,718

6,561

7,157

1,642

9.0

46.4

470

899

653

Vmax

app

1.89

322

1.27

19.9

2,136

930

1,105

64.0

336

3,950

9,041

1,216

203

8,142

133

1,980

2,262

1,173

309

6,406

2,044

12.2

489

373

1,579

1,486

STD

439

464,286

1,513

6,107

4,724,000

15980482.6

187,000

15,212,500

4,725,000

12,937,500

21,300,000

5,833,333

23,371,836.5

13,875,000

1,610,000

61,853,642.1

6,764,468

1,366,000

9,880

10,250

12,196

21,650

670

4,300

6,516

6,181

KM

app

396

64,847

447

8,071

2,488,441

14334076.6

18,520

4,021,172

503,559

4,893,126

10,364,844

1,991,858

1,353,955

6,749,966

520,481

57,846,960.2

4,818,715

282,000

2,140

2,263

10,992

9,257

1,646

5,912

2,848

11,582

STD

87.0

526

885

352

2,160

13,423

121

76,336

5,181

1,274

1,063

2,262

6,579

885

6,579

24,450

2,037

200

1.42

1.52

13.1

2,353

145

3.40

7.05

6.33

Turnover

94.3

2,326

694

3,571

3,846

21,413

159

312,500

15,385

11,364

1,479

3,584

100,000

1,887

27,248

91,241

4,717

5,000

5.44

2.13

7.60

2,160

2.82

4.13

3.83

3.08

STD


Macrophyte roots

Boreal forest soils

Silt loam soil

Forest soil, O horizon

Peat, silt

Soil

Seawater

3 forest soils

Wetland soils

Grassland soil

Seawater

Forest soil

Sorrell et al. (2002)

Whalen and Reeburgh (2001)

Bartholemew and Alexander (1981)

King (1999)

Rich and King (1999)

Stark and Firestone (1996)

Hashimoto et al. (1983)

Holtan-Hartwig et al. (2000)

Akatsuka and Mitamura (2011)

Abdelmagid and Tabatabai (1987)

Olson (1981)

Guo and Conrad (2008)

Lake phytoplankton

Seip and Reynolds (1995)

?

7.1–8.3

?

8.2

7.3

8.2

7.0

In situ

5.4–7.1

8.2

6.2

4.0

4.0

5.8

?

?

?

pH

20

?

In situ

In situ

25

In situ

25

20

20

27

22

22

20

23

?

20

30

Temp

Vmax units are nmol L-1 h-1 for aquatic systems and nmol g-1 h-1 for soils

MUB 4-methylumbelliferyl, AMC 7-amido-4-methylcoumarin, pNP p-nitrophenyl

Turnover time (appKm/appVmax) in hours

app

Km in units of nmol L

Llake plankton

Hwang et al. (1998)

-1

Lake plankton

Toetz et al. (1973)

app

Seawater

McCarthyl et al. (1992)

N & P uptake reactions

Landfill leachate, sludge

System

Dote (2002)

Reference

Table 1 continued

3

12

6

31

2

1

2

4

9

1

8

2

1

1

4

18

6

Obs

P uptake

P uptake

Nitrate uptake

Nitrate uptake

Hydrogenase

Nitrite oxidoreductase

Nitrate reductase

Nitrate reductase

Nitrous oxide reductase

Ammonium oxidase

Ammonium oxidase

Carbon monooxide dehyrogenase






Enzyme

Phosphate

P- phosphate

32

Nitrate

Nitrate

Dihydrogen

N-nitrite

15

2,4-Dinitrophenol

Nitrate

Nitrous oxide

Ammonium

Carbon monoxide

C– carbon monoxide

14

C– carbon monoxide

14

C – carbon monoxide

14

Methane

Methane

Substrate

138.5

87.5

11.47

69

2.92

369

109,464

985

2.5

28.7

1.7

246

6.86

45.0

4.98

595

Vmax

app

206

91.6

10.5

44.0

NA

6.0

105,475

1,056

NA

22.2

0.70

NA

NA

34.9

3.67

72.0

STD

66.7

3,284

306

126,659

900

3,300,000

219,643

1,956

5,000

15,000

25.9

748

1,947

938

5,406

331,833

KM

app

73.3

6,927

671

28,208

NA

800,000

294,587

1,781

NA

NA

15.9

NA

NA

514

1,049

254,763

STD

0.12

3.20

2.87

7.83

1,529

309

8,850

1.56

1.77

2,000

524

7.54

3.04

284

16.4

110

525

Turnover

0.07

2.16

2.71

14.7

2,028

NA

83,333

3.62

2.51

NA

676

1.49

NA

NA

21.1

180

427

STD


Biogeochemistry

123


publications, a total of 1160 observations (Table 1). The data encompass a broad range of aquatic and terrestrial ecosystems. However, the uneven distribution of observations and the varied conditions under which kinetic parameters were estimated limit the resolution of our analyses to broad macroecological trends (supplemental information). The scaling relationships between appVmax and app Km were analyzed using ordinary least squares (OLS) regressions on the assumption that the expression of extracellular enzymes by microbial communities is a function of substrate availability. The OLS ln– ln regressions of appVmax versus appKm yielded intercepts close to the axis origin and slopes (elasticity coefficients) in the range of 0–1. In general, the variance in appVmax was greater than the variance in app Km as a consequence of measuring reaction rates that are highly variable from system to system as a function of discrete substrate concentrations. Because of this variance differential, standardized major axis regressions across systems exaggerated the sensitivity of appVmax to appKm, yielding slopes that were extraneous ([1) in the context of metabolic control analysis, and thus were not used in our analyses. Global regressions were conducted by pooling all enzyme observations, as well as using study means to remove potential effects of uneven sample size among studies. Independent regressions were conducted for aquatic and terrestrial systems, which differ in organic matter composition, nutrient concentrations and other variables; for high affinity (4-methylumbelliferyl substrates, median appKM = 41 lM) and low affinity soil hydrolytic reactions (p-nitrophenyl substrates, median appKM = 4,900 lM), which had distinct kinetic distributions; and for assimilatory and dissimilatory (respiratory and lithotrophic) reactions. In metabolic control analysis, the elasticity of a reaction with respect to substrate (eV S ) is the fractional change in reaction rate (V) in response to a fractional change in substrate (S) (Cornish-Bowden 2012). For irreversible enzymatic reactions, eV S = dV/dS S/ V = d(ln V)/d(ln S) = Km/(Km ? S). Thus, eV S is approximately 1.0 for S Km, 0.5 when S = Km and 0 for S Km. Enzyme elasticity (eV E ), the fractional change in V in response to a fractional change in enzyme concentration (E), is 1.0. Thus, the slope of the ln(appVmax) versus ln(appKm) regression is an apparent elasticity that can be described as appeES = dE/dS S/

123

E = d(ln E)/d(ln S) because changes in appVmax are effected by changes in E. To place these kinetic analyses within a larger scale ecosystem perspective, elasticity coefficients were also calculated, using ln–ln OLS regressions, for ten hydrolytic enzyme activities as functions of bulk organic matter concentration, i.e. substrate in the broadest sense (Table 3). The data for these analyses, approximately 2,000–5,000 cases/enzyme, were taken from the soil ecosystem survey by Sinsabaugh et al. (2008) and recent studies of stream sediments (Hill et al. 2012), estuarine sediments (Hill et al. 2014a), peatlands (Hill et al. 2014b) and aridland soils (unpubl.). Methylumbelliferyl-linked substrates were used in all of these studies. The aridland soil data include 180 cases from a Larrea tridentata-Ambrosia dumosa shrubland located in the Lake Mead National Recreation Area (Nevada, USA), 450 cases from Pinus edulis-Juniperus monosperma forest in central New Mexico (USA), and 300 cases from Bouteloua dominated grassland sites located in the Sevilleta National Wildlife Refuge (New Mexico, USA). Because these ln–ln regressions are hyperbolic functions, similar to the Michaelis-Menton equation, app Vmax values were calculated for each enzyme in each data set as the reaction rate corresponding to a soil or sediment organic matter content of 100 % (0.45 g OC/g DM). The apparent half-saturation constant, appKm was calculated as the organic carbon concentration corresponding to appVmax/2. With this approach, appKm is a function of the regression slope (elasticity). For example, an elasticity of 1.0 will yield an appKm of 0.22 gOC/gDM; a value of 0.5 will yield an appKm of 0.11 gOC/gDM. Expressing the regression results in this manner creates appKm/appVmax distributions that can be compared with the distributions for specific enzyme—substrate reactions.

Results Extracellular reaction kinetics Across enzyme classes and habitats, the relationships between appVmax and appKm followed similar power laws with scaling exponents of 0.44 to 0.67 (Table 2, Fig. 1). Regressions that used all observations (n = 1160) and study means only (n = 100) had

Author's personal copy Biogeochemistry Table 2 Ordinary least squares ln–ln regression statistics for extracellular enzymatic reactions Reaction

Slope

95 % CI

Intercept

95 % CI

R2

Aquatic hydrolase

0.67

0.56–0.78

-0.70

-1.73–0.34

0.30

Soil hydrolase - high affinity

0.54

0.36–0.71

0.35

-1.55–2.25

0.11

416

Soil hydrolase - low affinity Dissimilation/uptake

0.51 0.44

0.37–0.64 0.28–0.60

-0.54 -0.14

-2.62 1.54 -1.42–1.14

0.32 0.26

171 115

GLOBAL - all data

0.46

0.41–0.50

0.93

0.46–1.41

0.33

1,160

GLOBAL - study means

0.44

0.31–0.58

0.97

-0.60–2.54

0.38

100

Enzyme regressions calculated as

app

-1

Vmax (nmol L

-1

-1

(or g ) h ) versus

app

N 459

-1

Km (nmol L )

For hydrolases, high affinity substrates are those linked to 4-methylumbelliferone. Hydrolase substrates linked to p-nitrophenol or bnaphthol are low affinity on the basis of their appKm values

A

B

C

D

Fig. 1 Elasticity regressions for extracellular enzyme reactions: appVmax (nmol L-1 h-1 for aquatic systems, nmol g-1 h-1 for soils) versus appKm (nmol L-1). a aquatic hydrolytic reactions. b soil low (p-nitrophenyl substrates) and high (4-

methylumbelliferyl substrates) affinity hydrolytic reactions. c oxidoreductase and assimilation reactions. d all reactions. Data sources listed in Table 1, regression statistics in Table 2

123


A

B

Fig. 2 Relative frequency distribution of kinetic parameters. a frequency distribution of apparent Km values for ecoenzymes (median = 33 lM). The Km distribution of cellular enzymes is shown for comparison (median = 130 lM, Bar-Even et al. 2011). b frequency distribution of turnover times for ecoenzyme-substrate reactions. Distributions are shown for the global data (median = 93 h) as well as aquatic (35 h), high affinity soil (115 h) and low affinity soil (2,750 h) reactions

identical exponents (0.46 ± 0.04 and 0.44 ± 0.13, respectively) and similar goodness of fit statistics (R2 = 0.33–0.38). Most of the regression slopes were not statistically distinct from a central value of 0.50. In the cases where statistical differences may exist, we cannot evaluate whether the differences are ecologically meaningful or artifacts of a limited data set with uneven coverage of enzymes and ecosystem types. The median appKm for all enzyme reactions was 33 lM with 78 % of observations falling in the range of 1–1,000 lM (Fig. 2a). The median appKm for surface waters and soils, based on hydrolysis of 4-methylumbelliferyl linked substrates, were 9.0 and

123

41 lM, respectively. Soil reactions measured using p-nitrophenyl linked substrates had a median appKm of 4,900 lM. The lower limits on the distribution of app Km values suggest that the minimum substrate concentrations needed to support microbial community metabolism in planktonic and soil ecosystems are approximately 0.4 and 1.5 lM, respectively (Fig. 1). For reference, Bar-Even et al. (2011) analyzed kinetic data for 5000 in vitro enzyme reactions involving natural substrates. The median Km value for these reactions was 130 lM (60 % range 1–1,000 lM, lower limit *0.1 lM, Fig. 2a). Compared to these data for cellular enzymes, the appKm values of extracellular reactions were more narrowly distributed with lower median values, suggesting that environmental substrate concentrations are generally lower than intracellular concentrations. The median (and geometric mean) appVmax was 229 (250) nmol L-1 h-1 in surface waters (n = 460) and 358 (404) nmol g-1 h-1 for soils (n = 417), using high affinity 4-methylumbelliferyl linked substrates, and 1171 (1411) nmol g-1 h-1 for soils using low affinity p-nitrophenyl or b-naphthyl linked substrates (n = 172). For aquatic ecosystems, the median (and geometric mean) activities of the most represented enzymes, leucine aminopeptidase (LAP, n = 114), b-1,4-glucosidase (BG, n = 67), phosphatase (AP, n = 119) and a-1,4glucosidase (n = 51) were 471 (337), 30 (41), 163 (165) and 20 (25) nmol L-1 h-1, respectively. For the high affinity soil data, the median (and geometric mean) activities for leucine aminopeptidase (n = 8), b-glucosidase (n = 87), b-N-acetylglucosaminidase (NAG, n = 83), cellobiohydrolase (n = 71), b-xylosidase (n = 71), a-glucosidase (n = 63), phosphatase (n = 10) and were 477 (410), 4,499 (4,496), 5,166 (4,756), 1,572 (1,767), 1,806 (1,677), 80 (101) and 684 (792) nmol g-1 h-1, respectively. These values are comparable to those previously reported for soils, sediments and plankton (Sinsabaugh and Follstad Shah 2012). For the global data set, the median turnover time for enzyme-substrate reactions (TS = appKm/appVmax) was 93 h (Fig. 2b). The median turnover times for aquatic, high affinity soil and low affinity soil hydrolytic reactions were 35, 115 and 2570 h, respectively. Hydrolase activities and organic matter At the ecosystem scale, organic matter is the substrate for microbial growth. For soils and sediments, the

AP

AP

AP

AP

AP

AS

AS

AS

AAP

AAP

AAP

Peatland soils

Terrestrial soils

Aridland soils

GLOBAL

Stream sediments

Peatland soils

Estuarine sediments

Stream sediments

Peatland soils

Aridland soils

NAG

Stream sediments

AP

NAG

Peatland soils

Stream sediments

CBH

Terrestrial soils

Estuarine sediments

CBH

Peatland soils

BX BX

BG

GLOBAL

NAG

BG

Estuarine sediments

Stream sediments Peatland soils

BG

Aridland soils

GLOBAL

BG

Terrestrial soils

NAG

BG

Peatland soils

Estuarine sediments

BG

Stream sediments

NAG

BGAL BGAL

Stream sediments Peatland soils

NAG

AGAL

Peatland soils

Terrestrial soils

AGAL

Stream sediments

Aridland soils

Enzyme

System

1.08

0.61

0.72

1.29

0.55

0.72

0.97

1.15

0.83

0.87

0.68

1.10

0.81 0.91

0.97

1.01

1.21

1.19

0.77

0.83

1.12

0.97

0.89

1.11

0.82

1.10

0.88

0.81

0.89 1.10

1.07

0.85

Slope

0.961

0.531

0.664

0.975

0.496

0.674

0.938

1.012

0.767

0.804

0.643

0.85

0.774 0.869

0.938

0.639

1.094

1.113

0.732

0.761

1.045

0.891

0.857

0.901

0.725

1.036

0.819

0.766

0.848 1.046

1.006

0.805

CI low

1.2

0.683

0.77

1.598

0.606

0.769

1.003

1.288

0.893

0.94

0.726

1.353

0.856 0.954

1.002

1.39

1.319

1.264

0.811

0.899

1.185

1.045

0.914

1.326

0.921

1.159

0.931

0.847

0.937 1.16

1.127

0.894

CI up

0.12

0.08

0.05

0.31

0.05

0.05

0.03

0.14

0.06

0.07

0.04

0.25

0.04 0.04

0.03

0.38

0.11

0.08

0.04

0.07

0.07

0.08

0.03

0.21

0.1

0.06

0.06

0.04

0.04 0.06

0.06

0.04

95 % CI

-7.8

-5.0

-6.6

-15.5

-4.8

-6.2

-8.0

-9.4

-6.8

-5.9

-4.2

-11.3

-7.9 -8.9

-9.0

-12.0

-12.5

-13.2

-5.7

-6.2

-12.6

-10.0

-6.9

-11.9

-5.5

-11.2

-6.4

-5.4

-8.8 -12.2

-11.3

-8.3

Intercept

-9.52

-6.254

-7.339

-19.77

-5.662

-6.854

-8.5

-11.312

-7.69

-7.005

-4.784

-14.72

-8.442 -9.621

-9.47

-17.127

-14.05

-14.34

-6.202

-7.375

-13.64

-11.25

-7.34

-14.76

-6.85

-12.08

-7.37

-5.973

-9.38 -13.13

-12.32

-8.96

CI

-6.13

-3.73

-5.866

-11.3

-3.852

-5.532

-7.56

-7.41

-5.85

-4.755

-3.632

-7.89

-7.289 -8.208

-8.54

-6.92

-10.87

-12.09

-5.105

-5.1

-11.54

-8.7

-6.5

-8.99

-4.08

-10.27

-5.52

-4.84

-8.13 -11.25

-10.31

-7.71

CI

1.7

1.3

0.7

4.2

0.9

0.7

0.5

2.0

0.9

1.1

0.6

3.4

0.6 0.7

0.5

5.1

1.6

1.1

0.5

1.1

1.3

0.4

2.9

1.4

0.9

0.9

0.6

0.6 0.9

1.0

0.6

95 % CI

0.33

0.33

0.31

0.28

0.43

0.36

0.50

0.31

0.51

0.55

0.40

0.30

0.49 0.78

0.49

0.14

0.42

0.58

0.49

0.53

0.60

0.54

0.50

0.38

0.30

0.62

0.65

0.49

0.50 0.74

0.70

0.47

R2

903

703

2,189

247

724

2,204

4,919

835

906

723

2,204

247

2,202 720

5,026

245

880

973

2,204

720

903

725

5,175

248

918

1,077

725

2,204

2,201 724

720

2,202

N

61,372

268

366

991

128

595

7,245

44,195

2,221

11,165

2,282

2,706

571 1,077

2,735

288

5,308

1,825

2,445

3,783

948

992

5,001

1,890

7,252

2,872

6,727

5,756

903 1,148

1,450

653

Vmax

app

19,750,940

11,966,965

14,265,725

21,880,459

10,659,706

14,349,004

18,356,799

20,528,132

16,271,110

16,932,420

13,627,678

19,988,235

16,021,682 17,531,868

18,355,772

18,935,353

21,113,930

20,929,924

15,273,213

16,271,586

20,142,817

18,324,395

17,143,767

20,123,774

16,154,425

19,942,999

16,985,412

15,878,586

17,246,376 20,006,071

19,579,151

16,586,358

Km

app

Km/appVmax

322

44,653

38,977

22,079

83,279

24,116

2,534

464

7,326

1,517

5,972

7,387

28,059 16,278

6,711

65,748

3,978

11,468

6,247

4,301

21,248

18,472

3,428

10,647

2,228

6,944

2,525

2,759

19,099 17,427

13,503

25,400

app

Table 3 Ordinary least squares ln–ln regression statistics for extracellular hydrolase activities (nmol g-1 h-1) in soils and sediments versus organic carbon (nmol g-1)


Biogeochemistry

123

Author's personal copy AGAL a-galactosidase, BGAL b-galactosidase, CBH cellobiohydrolase, NAG b-N-acetylglucosaminidase, BX b-xylosidase, AP acid (alkaline) phosphatase, AS aryl sulfatase, AAP alanine aminopeptidase, LAP leucine aminopeptidase

GLOBAL

123

Stream sediment data from Hill et al. (2012); estuarine sediment data from Hill et al. (2014a); peatland soil data from Hill et al. (2014b); terrestrial soil data from Sinsabaugh et al. (2008); aridland soil data (unpubl.) from semiarid grassland, shrubland and forest sites in New Mexico and Nevada

30,275 11,050,344 365 4,660 0.13 0.7 -3.25 -4.74 -4.0 0.618 LAP

Peatland soils Terrestrial soils

0.57

0.516

0.05

58,665 53,020 12,671,701 4,029,554 216 76 680 811 0.31 0.07 1.4 1.4 -4.364 0.27 -7.163 -2.433 -5.8 -1.1 0.723 0.401 LAP LAP

Stream sediments

0.64 0.31

0.554 0.221

0.08 0.09

45,612

17,368 22,126,540

15,371,169 337

1,274 96

2,166 0.29

0.17 9.5

0.9 -6.878

-6.27 -25.25

-8.588 -7.7

-15.8 0.71 2.021

0.838

0.606 LAP

LAP

Estuarine sediments

1.31

0.716

0.06

N R2 95 % CI CI CI Intercept 95 % CI CI up CI low Slope Enzyme System

Table 3 continued

0.78

app

Vmax

app

Km

app

Km/appVmax

Biogeochemistry

potential activities of ten hydrolytic enzymes increased with organic carbon concentration with scaling exponents ranging from 0.31 to 1.67 (mean 0.91, n = 36) (Table 3). Across all studies, the four most widely assayed enzymes—b-glucosidase (BG), acid (alkaline) phosphatase (AP), b -N-acetylglucosaminidase (NAG), and (alanine) leucine aminopeptidase (LAP)—had scaling exponents of 0.89, 0.97, 0.97, 0.57, respectively (Fig. 3). The low value for LAP arises from large differences in the magnitude of LAP activity between alkaline and acid soils (Sinsabaugh et al. 2008). In alkaline environments, i.e. desert soils and marine and inland water columns, LAP activity is similar in magnitude to b-glucosidase and phosphatase activities. In acidic soils, LAP is very low and often difficult to detect. For alkaline aridland soils, the exponent for aminopeptidase was 1.08 (R2 = 0.33), compared to 0.31 (R2 = 0.07) for terrestrial soils in general. The frequency distributions of these four activities substantially overlapped with median (and geometric mean) values of 17.6 (15.7), 16.1 (17.6), 8.7 (6.6) and 2.1 (2.9) lmol gOC-1 h-1, respectively (Fig. 4a). The displaced distribution for LAP is a consequence of pooling data from high and low pH soils, as described previously. Because most scaling exponents were near 1.0, the apparent half-saturation constants with respect to organic carbon concentration averaged about 21 % OC (Table 3). The median value of the frequency distribution of appKm/appVmax ratios was roughly 1009 greater than those of specific enzyme substrate reactions, 13,500 h (562 days), compared to 115 h (4.8 days) for the high affinity soil reactions, which also used 4-methylumbelliferyl substrates (Fig. 4b).

Discussion The apparent elasticity coefficients of the extracellular enzymatic reactions that transform C, N and P substrates ranged from 0.44 to 0.67. The value for the global regression was 0.46 ± 0.04 (Table 2). Across enzymes and habitats, appVmax increased with substrate concentration such that appeES maintained a value close to that expected for an enzymatic reaction app V when S & Km: appeES = appeV eE & 0.5. In other S/ words, the elasticity of extracellular catabolism at steady state is such that reaction rates remain


B

β

A

C

D

Fig. 3 Elasticity regressions for hydrolytic enzyme activities (nmol g-1 h-1) and organic carbon (nmol g-1). a b-glucosidase. b acid (alkaline) phosphatase. c b-N-acetylglucosaminidase. d leucine (alanine) aminopeptidase. Regression statistics in Table 3

equivalent to about half the catalytic potential, even as individual substrate concentrations vary over several orders of magnitude. This pattern suggests that one mechanism by which microbial communities maintain homeostasis in a fluctuating resource environment is by regulating extracellular enzyme expression to optimize the responsiveness of substrate acquisition. Within cells, feedback regulation of substrate flux through metabolic pathways is effected by modulating the activity of key allosteric enzymes. At the organismal and community scales, short-term regulatory feedback on the acquisition of environmental resources is exercised at the level of enzyme expression based on signals of resource availability. This regulation is more diffuse and subject to temporal lags, which generates selective pressure for extracellular catabolic and assimilation systems that have some reserve capacity to respond to rapid spatiotemporal fluctuations in substrate availability. As apparent

elasticity diverges from 0.5, it becomes increasingly difficult to maintain a steady state, because enzyme capacity is either too low or too close to saturation to respond effectively to substrate pulses. The latter condition corresponds to exponential growth with overexpression (via constitutive expression) or accumulation of enzyme relative to substrate concentration, which is ultimately self-limiting due to the cost of enzyme production and resulting depletion of substrate. At the other extreme, low elasticities limit the capacity of communities to maintain homeostasis by reducing their responsiveness to environmental changes in resource availability. Hobbie and Hobbie (2012, 2013) and Manzoni et al. (2014) have highlighted this phenomenon in the context of microbial growth in soils. Soil communities maintain high biomass and high elasticity to substrate pulses even though metabolic activity may be low for extended periods of time due to limited water availability.

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A

B

Fig. 4 Relative frequency distribution of hydrolase activities with respect to soil and sediment organic carbon. a frequency distribution of b-glucosidase (BG), acid (alkaline) phosphatase (AP), b-N-acetylglucosaminidase (NAG) and leucine (alanine) aminopeptidase (LAP) activities expressed as nmol gOC-1 h-1. b frequency distribution of appKm/appVmax ratios calculated from ln–ln regressions of enzyme activity versus organic carbon concentration (Table 3)

At the ecosystem scale, microbial growth and the density of effective enzyme binding sites is ultimately limited by the availability of organic matter. As a result, the apparent elasticity of environmental enzymatic reactions with respect to organic matter concentration approaches a value of 1.0. In microbial process models, the generation of substrate for microbial consumption can be represented by either a Michaelis–Menten model that controls enzymatic reaction rates through substrate availability (e.g. Moorhead and Sinsabaugh 2006), or by a ‘‘reverse’’ model in which reactions are controlled by the availability of effective binding sites for enzymes (Schimel and Weintraub 2003). The elasticity analyses for specific enzyme—substrate reactions

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are applicable to the former, while the enzyme— organic matter relationships support the latter, illustrating that both approaches are useful depending on the scale of interest. As described in the introduction, the concentrations of organic C, N and P and the activities of C, N and P-acquiring ecoenzyme activities both show stoichiometric coefficients near 1.0, where stoichiometric coefficients are defined as the slope of ln–ln regressions of mass versus mass and rate versus rate, respectively (Fig. 5). For C:N and C:P relationships, the intercepts of the regressions are directly related to the chemical composition of organic matter. Stoichiometric regressions for ecoenzymatic C:N and C:P activities are more complex in that enzyme expression is related to the difference in the elemental compositions of available (labile) organic matter and biomass, i.e. the intercepts are proportional to the ratio BC:X/ LC:X (Sinsabaugh et al. 2009, Sinsabaugh and Follstad Shah 2012), where BC:X is elemental C:N or C:P ratio of biomass and LC:X is the elemental C:N or C:P ratio of labile organic matter, which may differ from bulk ratios (Fanin et al. 2013; Wagai et al. 2013; Kaiser et al. 2014). In this context, the apparent elasticity of extracellular enzymatic reactions, i.e. the slope of ln– ln rate versus mass regressions, can be interpreted as a proportionality constant that connects the stoichiometries of organic matter composition and ecoenzymatic activities (Fig. 5): BC:X =LC:X ¼ appeES EEAC:X

ð1Þ

where EEAC:X is the ratio of C:N or C:P acquiring ecoenzyme activities. On a global basis, the value of app E eS was estimated to be about 0.46 (Table 2). Equation (1) can be evaluated independently of the analyses presented here by estimating appeES from previously published estimates of BC:X/LC:X and EEAC:X. For terrestrial soils, freshwater sediments and aquatic plankton, the mean ratios of BC:N/LC:N were reported as 8.6/14.3 = 0.60, 8.6/18.2 = 0.47 and 6.6/17.3 = 0.38, respectively; the corresponding EEAC:N ratios were 1.43, 1.83 and 1.12 (Sinsabaugh and Follstad Shah 2012). From these values, the predicted elasticities, using Eq. (1), are 0.42, 0.26 and 0.34, respectively. For C and P, the ratios are wider. The mean BC:P/LC:P ratios for soil, sediments and plankton, respectively, were 60/186 = 0.32, 60/60 = 1.0, 106/656 = 0.16. The corresponding EEAC:P ratios were 0.62, 1.64 and 0.26, yielding


Fig. 5 Environmental stoichiometry of microbial community homeostasis. Stoichiometric coefficients (s) are the slopes of ln– ln regressions for mass versus mass or rate versus rate relationships. Elasticity coefficients (e) are slopes of ln–ln regressions that relate reaction rates to mass concentrations. a the C, N and P contents of organic matter are correlated with s circa 1.0. b the potential rates of ecoenzymatic reactions are often correlated with s circa 1.0. c microbial growth, including

enzyme expression, is ultimately limited by organic matter availability and the availability of effective enzyme binding sites (e = 1.0). d Enzyme activities are correlated with substrate concentrations such that reaction velocity approximates half of capacity (e = 0.5). These elasticities are the proportionalities that link the elemental C:N:P ratios of labile organic matter and biomass to ecoenzymatic activities

elasticities, using Eq. (1), of 0.50, 0.61 and 0.62 (Sinsabaugh and Follstad Shah 2012). The mean of these six predicted elasticity values (0.46) is identical to the slope of the global ln–ln regression for appVmax and appKm (Table 2). Our findings show that heterotrophic microbial communities converge toward a common steady state functional organization in relation to resource availability by regulating extracellular enzyme expression to optimize the short-term responsiveness of carbon and nutrient acquisition. This homeostatic mechanism directly connects the stoichiometries of organic matter composition and ecoenzymatic activities (Fig. 5). More broadly, the analyses show that, like the elemental stoichiometry of biomolecules, the fundamental attributes of enzymatic reactions can be extrapolated from biochemical to community and ecosystem scales.

commercial products does not constitute endorsement or recommendation for use.

Acknowledgments RLS acknowledges support from the NSF Ecosystem Sciences program (DEB-0918718) and the Sevilleta LTER Program. KAK acknowledges support from NSF (DEB0315686, DBI-0420965, DBI-0521018) and the Michigan Sea Grant College Program (NA76RG0133) under NOAA. CRK acknowledges support from a DOE BER Science Focus Area grant. MEL and RLS acknowledge support from DOE BER Grant number DE-SC0008088. Mention of trade names or

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