Letters in Applied Microbiology ISSN 0266-8254

ORIGINAL ARTICLE

Real-time PCR for quantification in soil of glycoside hydrolase family 6 cellulase genes C. Merlin1,2, L. Besaury1, M. Niepceron1, C. Mchergui3, W. Riah4, F. Bureau3, I. Gattin4 and J. Bodilis1 1 2 3 4

University of Rouen, LMSM laboratory, Mont Saint Aignan, France INRA, UMR 1347 Agroecology, Dijon, France University of Rouen, ECODIV laboratory, Mont Saint Aignan, France Esitpa, Agri’Terr Unit, Mont Saint Aignan, France

Significance and impact of the study: Telluric micro-organisms able to use cellulose as carbon and energy sources for growth are widely distributed in the environment, but the factors controlling the rate of cellulose degradation are not well understood. The objective of our study was to develop a qPCR for rapid quantification of GH6 cellulase genes in soil. This qPCR could be applied to study the potential for cellulose degradation in different soils in order to better understand the factors controlling the stability of the soil organic matter.

Keywords cellulase, glycoside hydrolase family 6, organic matter, quantitative real-time PCR, soil. Correspondence Josselin Bodilis, Universite de Rouen, Laboratoire de Microbiologie Signaux et Microenvironnement, B^ atiment IRESE B, UFR des Sciences, 76821 Mont Saint Aignan, France. E-mail: [email protected] 2014/0293: received 12 February 2014, revised 29 March 2014 and accepted 7 April 2014 doi:10.1111/lam.12273

Abstract Cellulose is the main structural component of the cell walls of higher plants, representing c. 35–50% of a plant’s dry weight; after decomposition and transformation, and constituting a large part of soil organic matter. Telluric micro-organisms able to use cellulose as carbon and energy sources for growth are widely distributed in the environment, but the factors controlling the rate of cellulose degradation are not well understood. In this study, we have developed a quantitative real-time PCR (qPCR) primer set to quantify the glycoside hydrolase family 6 (GH6 family) cellulase genes in soil samples. The qPCR assays were linear over 8 orders of magnitude and sensitive down to 10 copies per assay. qPCR analysis of contrasted soil samples showed densities between 247 9 107 and 148 9 1010 copies per gram of soil. Cloning and sequencing of the PCR products from environmental DNA confirmed both specific amplification (more than 96%) and the wide diversity targeted by the primer set, throughout nearly all the GH6 family, including sequences of bacteria and fungi.

Introduction Soil is the biggest reservoir of organic carbon, with 2344 gigatons of carbon at depths up to 3 m, which is more than biomass and atmospheric CO2 combined (Fontaine et al. 2007). Conserving soil organic matter (SOM), and even increasing it, is considered beneficial for agricultural and environmental purposes (Bandick and Dick 1999). Bacteria and fungi make up more than 90% of the soil microbial biomass and are the main agents for the decomposition of organic matter in soil (Rinnan and Baath 2009). Enzymes of microbial origin regulate the ecosystem’s functions and play a key role in nutrient cycling 284

(Bandick and Dick 1999; Burns et al. 2013) by degrading (mineralization) and transforming (humification) SOM. These enzymes represent key factors in controlling the stability of SOM and have been suggested as potential indicators of soil quality because of their relationship to soil biology (Bandick and Dick 1999). Cellulose is the main structural component of higher plant cell walls and represents c. 35–50% of plant dry weight, and through decomposition and transformation makes up much of soil organic matter (Lynd et al. 1999). The ability to degrade cellulose is distributed widely throughout the universal tree of life, both in bacteria and in fungi (Lynd et al. 2002). Cellulose-degrading enzymes

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have been essentially described as members of the superfamily of glycoside hydrolases (GH), in at least ten GH families (GH 1, 3, 5, 6, 8, 9, 12, 44, 45 and 48) (Cantarel et al. 2009; Berlemont and Martiny 2013). These GH families include at least three types of protein active on b-1,4 glycosidic bonds: (i) endocellulases, active on internal b-1,4 glucosidic bonds, (ii) exocellulases degrading the polymer from its extremities and (iii) b-glucosidases producing glucose from cellobiose. While some cellulosedegrading enzyme families catalyse only one specific cellulose-degrading reaction (e.g. GH 8, 44 and 45), two or three different activities are found in other families. Moreover, most of the GH families involved in cellulose degradation also present additional noncellulase activities (Berlemont and Martiny 2013). The aim of this study was to develop a molecular method for the quantification of genes encoding cellulases. We focussed our study on the GH6 family, which contains enzymes involved essentially in the early steps of cellulose degradation, that is, endocellulases and/or exocellulases. In addition, for this GH family, no noncellulase activities have been detected so far. Results and discussion Design of the qPCR primers Cellulose can be degraded by several different enzymes called cellulases. These enzymes belong to the superfamily of glycoside hydrolases (GH) that also includes other enzymes for plant cell wall degradation, such as laccase or xylanase (Cantarel et al. 2009; Gilbert 2010). As each GH family has been defined only from the similarity between its protein sequences (Henrissat 1991), a given GH family could contain proteins with different enzymatic activities. In the same way, a given enzymatic activity is frequently found in several GH families. The cellulases degrade the cellulose polymer by hydrolysing the b-1,4 glycosidic bonds in three different ways and have been described in at least ten different GH families (1, 3, 5, 6, 8, 9, 12, 44, 45, 48). In the early steps, the endoglucanase (EC 3.2.1.4) and exoglucanase (EC 3.2.1.74) activities degrade the polymer into smaller cellobioses, made up of two glucose molecules. Then, b-glucosidases (EC 3.2.1.21) produce glucoses from cellobioses. Microorganisms expressing active endoglucanase and/or exoglucanase are called ‘cellulose degraders’ and could represent up to 24% of the micro-organisms for which the genome is sequenced (Allison 2005; Romani et al. 2006). In the great majority of cases, the cellulose degraders also express b-glucosidases to use the cellulose polymer as carbon and energy sources for their growth. In addition to the cellulose degraders, several micro-organisms (up to 56% of the

Cellulase gene quantification

micro-organisms for which the genome is sequenced) have only a b-glucosidase activity and are called ‘opportunists’ because their use of cellulose depends on the endoglucanase and/or exoglucanase activities of other micro-organisms. To study the potential degradation of cellulose in an environmental sample, we specifically targeted the GH families containing enzymes with endoglucanase and/or exoglucanase, that is, corresponding to the earlier steps of cellulose degradation. Moreover, we excluded from our analysis, the GH families containing enzymes with noncellulolytic activities. Altogether, three GH families (GH6, GH45 and GH48) contain enzymes with only endoglucanases and/or exoglucanase activities. Neither b-glucosidase nor noncellulolytic activities have so far been detected in these GH families (Berlemont and Martiny 2013). The GH6 family was a family often represented in the database (393 entries available in the CAZy database in June 2013) and contained sequences from both bacteria and fungi. Interestingly, an alignment of the protein sequences available in the CAZy database showed two conserved blocks, separated from each other by about 50 amino acids (i.e. about 150 nucleotides in the genes). These blocks are positioned in the N-terminal part of the protein. The first block shows the conserved motif (D/N)LP(G/D)RDC (positions 134–140, in the sequence of Cellulomonas fimi ATCC 484), and the second block shows the conserved motif IEPDSL (positions 185–190, in the sequence of C. fimi ATCC 484). Interestingly, these blocks correspond to the catalytic domain and have already been described as signatures of the GH6 family (Rouvinen et al. 1990). Moreover, BLASTP analysis revealed a high specificity of these sequences, which were present at the same time only in the sequences from the GH6 family. These blocks were used for the design of primers specific to the GH6 genes that would yield an amplicon of about 150 bp. Primer sets were designed using either several codon possibilities (degenerated nucleotides) or the most frequent nucleotide in a given position (Table 2). Then, different primer sets were tested to find the best compromise between the degeneration of the primers (to amplify the broader diversity in the GH6 family) and the accuracy of our qPCR protocol. It is noteworthy that, in one case, we retained the second most frequent nucleotide to minimize the primer dimers (the first position of the reverse primer). So, the best primer set retained in our study (see Materials and methods section; Table 2) was used for the following analyses. Accuracy and sensitivity of our qPCR A pGEMT Easy plasmid containing a GH6 gene cloned was used as positive control, as well as to draw standard

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curves. Standard curves were drawn using 10-fold dilution series, extended from 10 to 108 gene copy ll1. qPCR assays were linear over eight orders of magnitude and sensitive down to 10 copies per assay (Fig. 1), which was better than or similar to results obtained in other studies (e.g. Henry et al. 2004; Betelli et al. 2012). The absence of inhibitory substances was checked using a dilution series of extracted soil samples DNA. Independent qPCR assays, performed from 2 and 02 ng of total extracted DNA, showed similar quantification results (data not shown). Primer specificity The primer specificity as well as the capacity of our qPCR to amplify a large diversity of the GH6 sequences was estimated in silico, by studying the two conserved amino acyl motifs which correspond to the position of primer hybridization in the nucleic sequence (see Table 2). These motifs are exclusively present at the same time in the sequences of the GH6 family (BLASTP analysis, data not shown). Moreover, these motifs are spread throughout the GH6 sequences (Fig. 2), that is, corresponding to 36 genera of the 75 different genera with a GH6 sequence in the CAZy database (10/44 for the bacteria and 26/31 for the fungi). This estimated specificity was checked in several ways. First, separation by electrophoresis of the qPCR products obtained from the positive control or environmental DNA samples resulted in a single band with the expected size, that is, about 150 bp (data not shown). Second, melting curves of qPCR products from positive controls or environmental DNA samples were quite similar, with a melting temperature between 85 and 90°C. Finally, qPCR products obtained from environmental DNA corresponding to the three soil replicates of the grassland soil at the Yvetot site were pooled, cloned and sequenced. BLASTP analysis revealed a high specificity, most of the clones

tested (966%; 57 of 59) showing the best matches with GH6 sequences of the database. Phylogenetic analysis of the 57 partial cellulase sequences obtained in this study as well as of 77 reference sequences was carried out (Fig. 2). The phylogenetic tree confirmed that our 57 sequences belong to the GH6 family. Moreover, this study showed that our primer sets are able to detect most of the GH6 diversity, from both the bacteria and the fungi. A finer analysis of the diversity of the clones obtained showed that our clones (noted Cell6 in Fig. 2) were not easily affiliated to known sequences. Indeed, great incongruence was observed between the GH6 phylogeny and the species phylogeny, the different phyla and even the two taxonomic domains (Bacteria and Eucarya) being mixed in the GH6 phylogeny (Fig. 2). This incongruence could be explained both by lateral transfers, which have been frequently described in all the GH families (Berlemont and Martiny 2013), and by the small (partial) sequences used to build our phylogenetic tree. Nevertheless, it could be observed that some clones were close to sequences affiliated either to bacteria (e.g. Cell6-13 and Catenulispora acidiphila) or to Fungi (e.g. Cell6-7 and Neurospora crassa), confirming the wide diversity of the GH6 sequences targeted by our qPCR primers. Interestingly, among the 57 clones sequenced, 63% (Cell6-12, -13, -18, -19 and -23) were grouped together in the same cluster, which included two reference sequences affiliated to the bacteria Catenulispora acidiphila and Acidothermus cellulolyticus (Actinobacteria phylum). Altogether, it is notable that such diversity was found by sampling only one soil, although it must be said that this soil did present the highest GH6 gene content among our tested soils. As an increase in both the abundance and diversity of the GH enzymes seems to improve the ability to degrade cellulose (Fontes and Gilbert 2010; Wilson 2011), it could be interesting to further investigate the GH6 gene diversity in other soils.

Threshold cycle (Ct)

Quantification of cellulase genes in soil samples 45 40 35 30 25 20 15 10 5 0

y = –4·1545x + 42·506 R 2 = 0·9934

0

1

2 3 4 5 6 7 Log (GH6 cellulase gene copy number)

8

9

Figure 1 Standard curve used for absolute quantification of GH6 cellulase gene in soil samples by qPCR (n = 2). Error bars represent standard deviation.

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To evaluate our quantification method, we analysed seven soils, from five different geographical sites. Soil samples were collected in the Haute-Normandie region (France) from soils with various morphological characteristics and vegetations (see Materials and methods section; Table 1). The sampled soils were classified as ‘REDUCTISOL fluvique’, ‘LUVISOL’, ‘REDUCTISOL/FLUVIOSOL’, or ‘HISTOSOL’ (French Classification; AFES 1998). In the different soils, the types of vegetation observed were a Salix alba L forest, a Populus sp. forest, grassland, peatland, or no vegetation on the mudflat in the intertidal zone. Interestingly, the sampled soils also presented a wide variation in their organic carbon contents, with six

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Table 1 Quantification of GH6 cellulase genes and other parameters from different soils

Soil sample*

Soil texture (% clay,% silt,% sand)

Vegetation

MP SP PP GY STB GTD PY

114, 221, 352, 163, 402, 235, ND†

Mudflat Salix alba L Populus sp. Grassland Salix alba L Grassland Peatland

591, 663, 348, 633, 564, 581,

295 116 70 204 34 184

pH

Organic carbon (g kg1)

C/N ratio

Quantification of the GH6 cellulase gene in copies per gram of dry soil‡

823 821 785 590 794 802 643

148 171 633 332 109 427 251

120 110 110 105 126 102 15

294E+08 186E+08 128E+08 148E+10 363E+08 247E+07 189E+08

(141E+08) (610E+07) (609E+07) (339E+09) (185E+08) (247E+06) (205E+08)

[bc] [bc] [b] [d] [c] [a] [bc]

*The signification of the abbreviations and the sample sites is described in the Materials and methods section. †Not determined. ‡Values correspond to averages of three in situ replicates SD (n = 3). Values with a different letter in brackets (a, b, c and/or d) are significantly different at P < 0025 (Mann–Whitney test).

mineral soils (from 148 to 109 g kg1 of organic carbon) and an organic soil (251 g kg1 of organic carbon). qPCR measurements were performed (in duplicate) from three independent samples for each soil. GH6 cellulases were successfully quantified in all samples. Cellulase contents ranged from 247 9 107 (in a REDUCTISOL/ FLUVIOSOL grassland) to 148 9 1010 (in a LUVISOL grassland) copies per gram of dry soil (Table 1). Thus, our qPCR has been applied successfully in several contrasting soil types, showing that the GH6 gene content was quite high in all the soils tested. In addition, some significant differences have been observed between the levels of abundance of GH6 in our tested soils (Table 1). However, no clear relation was found between the organic carbon content and the GH6 abundance. Indeed, the two extreme values found correspond to the two grassland soils tested (from the Trou Buquet and Yvetot sites, respectively) and show similar organic carbon contents (i.e. 427 and 332 g kg1, respectively). Interestingly also, in these two soils, the pH was the only strongly different physico-chemical parameter measured, this parameter having been described as one of the main factors driving bacterial biomass distribution (Mulder et al. 2005; Dequiedt et al. 2011). This qPCR could now be applied to study the potential for cellulose degradation in different soils in order to better understand the factors controlling the stability of SOM.

by an oceanic and temperate climate characterized by mild temperatures. Seven soils were sampled, from five different geographical sites. (i) Three mineral soils were collected at the site of Petitville. These soils were sampled from mudflats (called ‘MP’ for Mudflats at Petitville), from S. alba L forest (called ‘SP’) and from Populus sp. forest (called ‘PP’). These three soils are typical hydric soils classified as ‘REDUCTISOL fluvique’ (French Classification; AFES 1998). (ii) One mineral soil was collected from long-term grassland at the site of Yvetot (called ‘GY’). This soil is classified as ‘LUVISOL’ (French Classification; AFES 1998). (iii) One mineral soil was collected from S. alba L forest at the Trou Buquet site (called ‘STB’). This soil is a typical hydromorphic soil classified as ‘REDUCTISOL fluvique’ (French Classification; AFES 1998). (iv) One mineral soil was collected from the Trou Deshayes grassland site (called ‘GTD’). This soil is a typical hydromorphic mineral soil classified as ‘REDUCTISOL/FLUVIOSOL’ (French Classification; AFES 1998). (v) One organic soil was collected from peatland (called ‘PY’). This soil is a hydromorphic organic soil classified as ‘HISTOSOL’ (French Classification; AFES 1998). Some morphological and physicochemical properties of the seven soils used in this study are shown in Table 1. For each soil, three composite samples were made up, each one consisting of ten pooled and homogenized subsamples taken from the surface horizon (0–10/15 cm). After sieving to 2-mm particle size, DNA was extracted from 05 g (see the next section).

Materials and methods DNA extraction Environmental samples Samples of soil with various morphological characteristics and vegetations were collected from different sites in the Haute-Normandie region (France) in March 2010 or April 2011. The Haute-Normandie region is dominated

From the 21 environmental samples (three composite samples for each of the seven soils), total DNA was directly extracted from 05 g of moist sieved soil (04 g dry weight equivalent) using the BIO101 Fast DNA spin Kit for Soil (MP Biochemicals, Illkrich, France) according to the manu-

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Emericella nidulans (Q5B2E8) Talaromyces emersonii (Q8NIB5) Chaetomium thermophilum (Q5G2D4) Humicola insolens (Q9C1S9) Aspergillus niger (A2QQ99) 51 97 Acremonium cellulolyticus (O93837) Penicillium funiculosum (B5TMG4) 80 Gibberella zeae (Q6JV41) Fusarium oxysporum (P46236) 98 Trichoderma koningii (Q1HCL5) Hypocrea jecorina (D3YNY1) Cochliobolus heterostrophus (Q8J215) Cell6 -21 64 Cell6 -11 (3) Cell6 -8 Cell6 -20 Shewanella violacea (D4ZGP8) Coprinopsis cinerea (B7X9Z0) Hahellache juensis (Q2SQR7) Cell6 -17 Irpex lacteus (B2ZZ24) Coniophora puteana (C4B8I1) Neolentinus lepideus (E2JAJ2) Trametes versicolor (Q9P8N1) Agaricus bisporus (P49075) Pleurotus sajor (Q96TP4) Volvariella volvacea (Q6E5B1) Phanerochaete chrysosporium (Q02321) 63 Lentinula edodes (Q96VU2) Polyporus arcularius (A8CED8) Cell -10 52 Cell6 -9 Cell6 -14 Streptomyces xylophagus (E2GJC5) Cell6 -6 Podospora anserina (B2AE04) Sequences likely to Leptosphaeria maculans (E5AAV1) 58 be detected by qPCR Cell6 -7 Neurospora crassa (Q7RXI7) Cellulomonas fimi (P50401) Xylanimonas cellulosilytica (D1BZM7) Cell6 -15 Cell6 -22 Magnaporthe oryzae (G4MS86) Cell6 -16 (2) Cellulosimicrobium sp (E5LP78) Jonesia denitrificans (C7R1T1) Stigmatella aurantiaca (E3FP49) 51 Orpinomyces sp (Q96V98) Piromyces equi (Q7Z7X6) Cell6 -13 Catenulispora acidiphila (C7Q036) Cell6 -12 86 Cell6 -18 (29) Cell6 -19 (4) Acidothermus cellulolyticus (A0LSH8) Cell6 -23 Actinosynnema mirum (C6WI93) Nocardiopsis dassonvillei (D7B4C5) Stackebrandtia nassauensis (D3Q9E0) Streptomyces bingchenggensis (D7C1F4) Streptosporangium roseum (D2B768) 91 Thermomonospora fusca (Q9KH72) Thermobifida fusca (Q47SA9) 68 Salinispora arenicola (A8M2G4) Micromonospora aurantiaca (D9TBM7) Paenibacillus polymyxa (E3EEC5) Xylellafa stidiosa (Q9PDW2) Xanthomonas campestris (Q8P622) Ralstonia solanacearum (Q8XS97) 80 Cell6 -4 Cell6 -5 Cellvibrio japonicus (B3PKK5) Saccharophagus degradans (Q21IE7) Teredinibacter turnerae (C5BTS3) Sorangium cellulosum (A9FHT2) Amycolatopsis mediterranei (D8I1M3) 52 Thermobispora bispora (D6Y5G2) Herpetosiphon aurantiacus (A9AWC6) Cell6 -3 61 Cell6 -1 Cell6 -2 Piromyces rhizinflatus (O93860) 93 Anaeromyces sp (Q6A4K7) Neocallimastix frontalis (Q6EH22) Xylanimicrobium pachnodae (Q9RQE6) 59 Myxococcus xanthus (Q1D2X5) Saccharopolyspora erythraea (A4FMF8) Capnocytophaga ochracea (C7M3P9) Gordonia bronchialis (D0L4F5) Sanguibacter keddieii (D1BIS4) Mycobacterium microti (D0Q163) Segniliparus rotundus (D6Z8A2) Brachybacterium faecium (C7MC53) Frankia alni (Q0RFZ3) Neisseria sicca (E1CAJ8) Kineococcus radiotolerans (A6WGZ2) Geodermatophilus obscurus (D2S411) 55 0·1 substitution per site Conexibacter woesei (D3FCY4) Nocardioides sp (A1SG30) 55

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Table 2 Sequences used for primer design based on GH6 sequences from the CAZy database. The cell2F forward primer encompassed positions 401–418 and the cell2R reverse primer encompassed positions 553–570 based on the GH6 gene of the Cellulomonas fimi ATCC 484 strain (Accession number P50401). Numbers in bold print correspond to the nucleotides selected for primer design. Degenerated sites: R = A/G; Y = C/ T; S = G/C. Thirty-six GH6 sequences from the phylogenetic study in Fig. 2, for which the protein sequence presented the conserved motifs, were used to design the primers Primer cell2F (forward), designed from the motif (D/N)LP(G/D)RDC Position

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

%A %G %T %C

8 92 0 0

100 0 0 0 A

3 0 33 64 C

0 0 22 78 C

0 0 100 0 T

14 56 19 11 G

0 0 0 100 C

0 0 0 100 C

22 14 19 44 C

0 100 0 0 G

61 39 0 0 R

8 3 28 61 C

3 0 0 97 C

0 100 0 0 G

6 8 50 36 Y

0 100 0 0 G

100 0 0 0 A

0 0 31 69 C

0 0 100 0 T

0 100 0 0

0 0 36 64

Primer cell2R (reverse), designed from the motif IEPDSL Position

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

%A %G %T %C

22 28 3 47 G

100 0 0 0 A

19 81 0 0 G

25 28 14 33 S

0 81 0 19 G

81 0 19 0 A

33 67 0 0 R

0 0 100 0 T

0 0 0 100 C

28 47 6 19 S

0 100 0 0 G

0 100 0 0 G

0 0 25 75 C

0 0 100 0 T

0 0 0 100 C

53 47 0 0 R

100 0 0 0 A

0 0 100 0 T

The positive control used for the amplification of the cellulase-encoding genes was obtained from the C. fimi ATCC 484 strain (Meinke et al. 1994). This strain was kindly send by R.A.J. Warren. The GH6 cellulase gene was cloned in a pGEMT Easy plasmid (Promega, Fitchburg, WI, USA) and checked by sequencing. This control was used in serial dilution for the quantification of the environmental genes.

using either NCBI or CAZy databases (http://www.ncbi. nlm.nih.gov/ and http://www.cazy.org/). The protein sequences deduced were aligned using the Clustal algorithm implemented in the SEAVIEW software (Thompson et al. 1997; Gouy et al. 2010). Then, the blocks of conserved amino acids were selected manually. These blocks were used to design primers specific to the GH6 genes. The primers were arranged in a set that would yield an amplicon of a size suitable for qPCR amplification (150–250 bp). Primers were designed using either several codon possibilities (degenerated primers) or the most frequent nucleotide (see Table 2). Their sequences were compared in silico with the NCBI nucleotide database (using BLAST) to confirm target specificity. The two primers retained were the forward primer cell2F (50 -ACCTGCCCGRCCGYGACT-30 ) and the reverse primer cell2R (50 -GAGSGARTCSGGCTCRAT-30 ), where R = A or G; Y = C or T; S = G or C.

Primer design

Quantitative real-time PCR assay

To design the primer set, all the nucleic sequences of GH6 genes available from cultivated strains (i.e. not from environmental sequences), and from complete and unfinished microbial (bacteria and fungi) genomes were obtained

qPCR was carried out with the Chromo4 Real-time PCR Detector (Bio-Rad, Marnes-la-Coquette, France) using SYBR Green PCR Master Mix as the detection system in a reaction mixture of 25 ll containing: 08 lg l1 of each

facturer’s recommendations. DNA was resuspended in 50 ll sterile de-ionized water and quantified on agarose gel stained with ethidium bromide (05 lg ml1) with software QUANTUM-CAPT on Quantum ST4 Image Acquisition System (Vilber Lourmat, Fisher Scientific, Illkrich, France). DNA extracts were stored at 20°C. Positive controls for the qPCR assays

Figure 2 Phylogenetic tree constructed from the 57 partial cellulase sequences determined in this study together with 77 reference sequences retrieved from the CAZy database. The unrooted dendrogram was generated from amino acid sequences, using neighbour-joining analysis (with Poisson correction). The degree of statistical support for the branches was determined with 1000 bootstrap replicates. The sequences determined in this study are noted Cell6, following by a number. An additional number into parentheses corresponds to the number of near identical (>99% identity in nucleotides) sequences found.  and denote GH6 sequences presenting the two conserved motifs targeted by our primer set (from bacterial and fungal origin, respectively). s and M denote GH6 sequences without these two conserved motifs (from bacterial and fungal origin, respectively).



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Cellulase gene quantification

primer, 125 ll of SYBR Green 1X (Eurogentec, Angers, France), 05 lg ll1 BSA and 2 ll of DNA diluted template corresponding to 2 ng of DNA. The conditions for qPCR were as follows: 10 min for enzyme activation at 95°C, then a cycle of 40 s for denaturation at 95°C, 45s for primer hybridization at 64°C and 30 s for extension at 72°C was carried out 40 times. A final step, 5 min at 72°C, was carried out. Then, a melting curve was obtained by measuring the intensity of SYBR Green as temperature was increased from 30 to 100°C in steps of 1°C. For each environmental sample, two independent qPCR assays were performed from 2 ng of total DNA. The standard curve was created (in duplicate) using 10-fold dilution series of a PGEM plasmid containing a cellulase gene. Standard curves extend from 10 to 108 copies ll1 (see Fig. 1). Cloning and sequencing of amplified products To check for specificity, the qPCR products from the three environmental DNA samples from the grassland soil at the Yvetot site (the sample called ‘GY’) were pooled and cloned into the pGEM-T Easy Vector System (Promega) according to the manufacturer’s instructions. Fiftynine clones were then randomly chosen for sequencing. Phylogenetic analysis A phylogenetic tree was constructed from the 57 partial cellulase sequences determined in this study together with 77 reference sequences retrieved from the CAZy database (Cantarel et al. 2009). The 77 reference sequences were chosen from the 393 entries available in the CAZy database (GH6 sequences, June 2013), to represent each of the 75 different genera present in the database. Two genera (the Streptomyces and Pyromyces genera) were included twice because their sequences were close to several of the sequences determined in our study. The aligned protein sequences were truncated to the same size as the shortest sequence. Then, all ambiguous positions and positions with gaps were removed (complete deletion). An unrooted dendrogram was generated from the amino acid sequences, using neighbour-joining analysis (with Poisson correction) with the MEGA ver. 5.0 software (Tamura et al. 2011). The degree of statistical support for the branches was determined with 1000 bootstrap replicates. Acknowledgements This work was supported by grants from the Agence de l’Environnement et de la Ma^ıtrise de l’Energie (ADEME) and the Region Haute Normandie (via the BIOINDICA290

TEUR project). The authors are grateful to Dilys Moscato for her critical reading of the manuscript. Conflict of interest No conflict of interest declared. References AFES (1998) A sound reference base for soils: the Referentiel Pedologique. Text in English. Translation by J.M. Hodgson,  N.R. Eskenazi & D. Baize. INRA Editions, Paris. 322 pp. Allison, S.D. (2005) Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments. Ecol Lett 8, 626–635. Bandick, A.K. and Dick, R.P. (1999) Field management effects on soil enzyme activities. Soil Biol Biochem 31, 1471–1479. Berlemont, R. and Martiny, A.C. (2013) Phylogenetic distribution of potential cellulases in bacteria. Appl Environ Microbiol 79, 1545–1554. Betelli, L., Duquenne, P., Grenouillet, F., Simon, X., Scherer, E., Gain, E. and Hartmann, A. (2012) Development and evaluation of a method for the quantification of airborne Thermoactinomyces vulgaris by real-time PCR. J Microbiol Methods 92, 25–32. Burns, R.G., DeForest, J.L., Marxsen, Jr., Sinsabaugh, R.L., Stromberger, M.E., Wallenstein, M.D., Weintraub, M., and Zoppini, A. (2013) Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol Biochem 58, 216–234. Cantarel, B.L., Coutinho, P.M., Rancurel, C., Bernard, T., Lombard, V. and Henrissat, B. (2009) The CarbohydrateActive EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res 37, D233–D238. Dequiedt, S., Saby, N.P.A., Lelievre, M., Jolivet, C., Thioulouse, J., Toutain, B., Arrouays, D., Bispo, A. et al. (2011) Biogeographical patterns of soil molecular microbial biomass as influenced by soil characteristics and management. Glob Ecol Biogeogr 20, 641–652. Fontaine, S., Barot, S., Barre, P., Bdioui, N., Mary, B. and Rumpel, C. (2007) Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature 450, 277–280. Fontes, C. and Gilbert, H. (2010) Cellulosomes: highly efficient nanomachines designed to deconstruct plant cell wall complex carbohydrates. Annu Rev Biochem 65, 5–681. Gilbert, H.J. (2010) The biochemistry and structural biology of plant cell wall deconstruction. Plant Physiol 153, 444–455. Gouy, M., Guindon, S. and Gascuel, O. (2010) SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol 27, 221–224. Henrissat, B. (1991) A classification of glycosyl hydrolases based on amino-acid sequence similarities. Biochem J 280, 309–316.

Letters in Applied Microbiology 59, 284--291 © 2014 The Society for Applied Microbiology

C. Merlin et al.

Henry, S., Baudoin, E., Lopez-Gutierrez, J.C., Martin-Laurent, F., Brauman, A. and Philippot, L. (2004) Quantification of denitrifying bacteria in soils by nirK gene targeted realtime PCR. J Microbiol Methods 59, 327–335. Lynd, L.R., Wyman, C.E. and Gerngross, T.U. (1999) Biocommodity engineering. Biotechnol Prog 15, 777– 793. Lynd, L.R., Weimer, P.J., van Zyl, W.H. and Pretorius, I.S. (2002) Microbial cellulose utilization: fundamentals and biotechnology. Microbiol Mol Biol Rev 66, 506–577. Meinke, A., Gilkes, N.R., Kwan, E., Kilburn, D.G., Warren, R.A.J. and Miller, R.C. (1994) Cellobiohydrolase A (Cbha) from the cellulolytic bacterium cellulomonas fimi is a Beta1,4-exocellobiohydrolase analogous to Trichoderma reesei CBH-II. Mol Microbiol 12, 413–422. Mulder, C., Van Wijnen, H.J. and Van Wezel, A.P. (2005) Numerical abundance and biodiversity of below-ground taxocenes along a pH gradient across the Netherlands. J Biogeogr 32, 1775–1790.

Cellulase gene quantification

Rinnan, R. and Baath, E. (2009) Differential utilization of carbon substrates by bacteria and fungi in tundra soil. Appl Environ Microbiol 75, 3611–3620. Romani, A.M., Fischer, H., Mille-Lindblom, C. and Tranvik, L.J. (2006) Interactions of bacteria and fungi on decomposing litter: differential extracellular enzyme activities. Ecology 87, 2559–2569. Rouvinen, J., Bergfors, T., Teeri, T., Knowles, J.K.C. and Jones, T.A. (1990) 3-Dimensional structure of cellobiohydrolaseII from Trichoderma reesei. Science 249, 380–386. Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M. and Kumar, S. (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28, 2731–2739. Thompson, J.D., Gibson, T.J., Plewniak, F., Jeanmougin, F. and Higgins, D.G. (1997) The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res 25, 4876–4882. Wilson, D.B. (2011) Microbial diversity of cellulose hydrolysis. Curr Opin Microbiol 14, 259–263.

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Real-time PCR for quantification in soil of glycoside hydrolase family 6 cellulase genes.

Cellulose is the main structural component of the cell walls of higher plants, representing c. 35-50% of a plant's dry weight; after decomposition and...
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