hexachlorbenzol conversion ball mill böttcher

the geochemistry of the stable carbon isotopes - sciencedirect

Several hundred samples of carbon from various geologic sources have been analyzed in a new survey of the variation of the ratio C13/C12 in nature. Mass spectrometric determinations were made on the instruments developed by H. C. Urey and his co-workers utilizing two complete feed systems with magnetic switching to determine small differences in isotope ratios between samples and a standard gas. With this procedure variations of the ratio C13/C12 can be determined with an accuracy of 0.01% of the ratio.

The results confirm previous work with a few exceptions. The range of variation in the ratio is 4.5%. Terrestrial organic carbon and carbonate rocks constitute two well defined groups, the carbonates being richer in C13 by some 2%. Marine organic carbon lies in a range intermediate between these groups. Atmospheric CO2 is richer in C13 than was formerly believed. Fossil wood, coal and limestones show no correlation of C13/C12 ratio with age. If petroleum is of marine organic origin a considerable change in isotopic composition has probably occurred. Such a change seems to have occurred in carbon from black shales and carbonaceous schists. Samples of graphites, diamonds, igneous rocks and gases from Yellowstone Park have been analyzed. The origin of graphite cannot be determined from C13/C12 ratios. The terrestrial distribution of carbon isotopes between igneous rocks and sediments is discussed with reference to the available meteoritic determinations. Isotopic fractionation between iron carbide and graphite in meteorites may indicate the mechanism by which early fractionation between deep seated and surface terrestrial carbon may have occurred.

The work described in this paper forms part of a dissertation submitted to the Faculty of the Division of the Physical Sciences of the University of Chicago in partial fulfillment of the requirements for the degree of Doctor of Philosophy.

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total, hot water extractable, and oxidation-resistant carbon in sandy hydromorphic soils-analysis of a 220-year chronosequence | springerlink

Soil organic carbon stocks decrease after conversion of soils from pasture to cropland. It has been assumed that this applies especially to mineral hydromorphic soils. In this paper we evaluate hot-water extractable carbon (Chwe) as a measure for detecting long-term changes in the SOM following land use change. Furthermore, we assess whether a treatment of the soils with NaOCl leads to the isolation of long-term stable C fractions. For these purposes, we established a chronosequence of sandy hydromorphic soils that have been converted from pasture to cropland at different periods of history. To gain further insight into the impacts of different types of land use on carbon sequestration, soils under forest, either afforested or permanent, were studied. Bulk density, total organic carbon (TOC), Chwe, and NaOCl-resistant C were quantified in the surface soils of 72 Gleyic Podzols and Haplic Gleysols. The bulk density increased from 0.9 (0.2) gcm3 to 1.4 (0.1) gcm3 during the first 25years after the conversion of the soils from permanent pasture to cropland. In the permanent pasture sites, the TOC concentration amounted to 35.4 (12.1) gkg1. It decreased to 12.88 (5.9) gkg1 during the first 46years of cultivation (R2=0.71). In the permanent forest soils the TOC concentrations were significantly higher than in the soils that have been afforested. Chwe concentrations of the chronosequence sites were linearly correlated to the TOC concentrations (R2=0.84), while permanent forest sites exhibited significantly higher Chwe/TOC ratios. This shows that the determination of the Chwe is a very promising measure for detecting changes in SOM dynamics following afforestation. In the permanent pasture sites, 14.3 (5.38) gkg1 NaOCl-resistant C was measured, while 46years after conversion, only 2.8 (1.2) gkg1remained. No enrichment of NaOCl-resistant C was observed in the chronosequence, as NaOCl-resistant C decreased faster in the course of cultivation than the TOC. Therefore, we conclude that that the C fraction that resists the oxidation with NaOCl is not long-term stable in soils, and most probably, there is no such long-term stable C fraction in the soils under study.

Breuer L, Huisman JA, Keller T, Frede HG (2006) Impact of a conversion from cropland to grassland on C and N storage and related soil properties: analysis of a 60-year chronosequence. Geoderma 133:618

Flessa H, Amelung W, Helfrich M, Wiesenberg GL, Gleixner G, Brodowski S, Rethmeyer J, Kramer C, Grootes PM (2008) Storage and stability of organic matter and fossil carbon in a Luvisol and Phaeozem with continuous maize cropping: a synthesis. J Plant Nutr Soil Sci 171:136151

Jungkunst HF, Flessa H, Scherber C, Fiedler S (2008) Groundwater level controls CO2, N2O and CH4 fluxes of three different hydromorphic soil types of a temperate forest ecosystem. Soil Biol Biochem 40:20472054

Ltzow M, Kgel-Knabner I, Ekschmitt K, Matzner E, Guggenberger G, Marschner B, Flessa H (2006) Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions. Eur J Soil Sci 57:426445

Marschner B, Brodowski S, Dreves A, Gleixner G, Gude A, Grootes PM, Hamer U, Heim A, Jandl G, Ji R, Kaiser K, Kalbitz K, Kramer C, Leinweber P, Rethemeyer J, Schffer A, Schmidt MWI, Schwark L, Wiesenberg GLB (2008) How relevant is recalcitrance for the stabilization of organic matter in soils? J Plant Nutr Soil Sci 171(1):91110

Overesch M (2007) Kohlenstoff- und Stickstoffumsatz in Sandbden Niedersachsens. Indikatoren umsetzbarer organischer Substanz, Bilanzierung und Bodenprozessmodellierung auf Bodendauerbeobachtungs- und Kompostversuchsflchen, Shaker, Aachen

Schulz E (1990) Die heiwasserextrahierbare C-Fraktion als Kenngre zur Einschtzung des Versorgungszustandes der Bden mit organischer Substanz (OS). Tagungsberichte der Akademie der Landwirtschaftswissenschaften. Berlin 295:269275

Sleutel S, Leinweber P, Begum SA, Kader MA, Van Oostveldt P, De Neve S (2008) Composition of organic matter in sandy relict and cultivated heathlands as examined by pyrolysis-field ionization MS. Biochemistry 89:253271

Solomon D, Kehmann J, Kinyangi J, Amelung W, Lobe I, Pell A et al (2007) Long-term impacts of anthropogenic perturbations on dynamics and speciation of organic carbon in tropical forest and subtropical grassland ecosystems. Glob Chang Biol 13:511530

Springob G, Brinckmann S, Engel N, Kirchmann H, Bttcher J (2001) Organic C levels of Ap horizons in North German Pleistocene sands as influenced by climate, texture, and history of land-use. J Plant Nutr Soil Sci 164:681690

Strebel S, Bttcher G, Eberle M, Aldag R (1988) Quantitative und qualitative Vernderungen im A-Horizont von Sandbden nach Umwandlung von Dauergrnland in Ackerland. Z Pflanzenernhr Bodenk 151:341347

Spohn, M., Giani, L. Total, hot water extractable, and oxidation-resistant carbon in sandy hydromorphic soils-analysis of a 220-year chronosequence. Plant Soil 338, 183192 (2011). https://doi.org/10.1007/s11104-010-0322-5

transcription activator-like effector nuclease-mediated generation and metabolic analysis of camalexin-deficient cyp71a12 cyp71a13 double knockout lines | plant physiology | oxford academic

This work was supported by the Deutsche Forschungsgemeinschaft (grant nos. GL346/5 [Heisenberg Fellowship to E.G.] and LA1338/5 [to T.L.]), the Hans-Fischer-Gesellschaft, and the TUM Junior Fellow Fund.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Erich Glawischnig ([email protected]).

T.M.M. designed and conducted the majority of the experiments and supported drafting the article; C.B. performed the metabolomics analysis; R.M. performed the TALEN construction; C.C.G. and J.L. performed the enzymatic analysis; T.L. conceptualized and supervised the TALEN construction; E.G. supervised the project, supported the experiment design and analysis, and wrote the article with contributions of all authors.

Teresa M. Mller, Christoph Bttcher, Robert Morbitzer, Cornelia C. Gtz, Johannes Lehmann, Thomas Lahaye, Erich Glawischnig, TRANSCRIPTION ACTIVATOR-LIKE EFFECTOR NUCLEASE-Mediated Generation and Metabolic Analysis of Camalexin-Deficient cyp71a12 cyp71a13 Double Knockout Lines, Plant Physiology, Volume 168, Issue 3, July 2015, Pages 849858, https://doi.org/10.1104/pp.15.00481

In Arabidopsis (Arabidopsis thaliana), a number of defense-related metabolites are synthesized via indole-3-acetonitrile (IAN), including camalexin and indole-3-carboxylic acid (ICOOH) derivatives. Cytochrome P450 71A13 (CYP71A13) is a key enzyme for camalexin biosynthesis and catalyzes the conversion of indole-3-acetaldoxime (IAOx) to IAN. The CYP71A13 gene is located in tandem with its close homolog CYP71A12, also encoding an IAOx dehydratase. However, for CYP71A12, indole-3-carbaldehyde and cyanide were identified as major reaction products. To clarify CYP71A12 function in vivo and to better understand IAN metabolism, we generated two cyp71a12 cyp71a13 double knockout mutant lines. CYP71A12-specific transcription activator-like effector nucleases were introduced into the cyp71a13 background, and very efficient somatic mutagenesis was achieved. We observed stable transmission of the cyp71a12 mutation to the following generations, which is a major challenge for targeted mutagenesis in Arabidopsis. In contrast to cyp71a13 plants, in which camalexin accumulation is partially reduced, double mutants synthesized only traces of camalexin, demonstrating that CYP71A12 contributes to camalexin biosynthesis in leaf tissue. A major role of CYP71A12 was identified for the inducible biosynthesis of ICOOH. Specifically, the ICOOH methyl ester was reduced to 12% of the wild-type level in AgNO3-challenged cyp71a12 leaves. In contrast, indole-3-carbaldehyde derivatives apparently are synthesized via alternative pathways, such as the degradation of indole glucosinolates. Based on these results, we present a model for this surprisingly complex metabolic network with multiple IAN sources and channeling of IAOx-derived IAN into camalexin biosynthesis. In conclusion, transcription activator-like effector nuclease-mediated mutation is a powerful tool for functional analysis of tandem genes in secondary metabolism.

In response to pathogens, cruciferous plants synthesize a large variety of Trp-derived phytoalexins, which are metabolically related to indole glucosinolates (Rauhut and Glawischnig, 2009; Pedras et al., 2011). Both classes of metabolites are important for defense against fungal pathogens (Bednarek et al., 2009; Clay et al., 2009; Pedras et al., 2011). In the biosynthesis of camalexin, the characteristic phytoalexin in Arabidopsis (Arabidopsis thaliana), Trp is converted to indole-3-acetaldoxime (IAOx) by CYP79B2 and CYP79B3 (Glawischnig et al., 2004). IAOx is dehydrated to indole-3-acetonitrile (IAN), oxidized, and conjugated with glutathione (Nafisi et al., 2007; Parisy et al., 2007; Bttcher et al., 2009; Su et al., 2011). From this glutathione conjugate (GS-IAN), a Cys conjugate [Cys(IAN)] is formed, involving -GLUTAMYL PEPTIDASE1 (GGP1) and GGP3 (Geu-Flores et al., 2011). Cys(IAN) is then converted to camalexin by the unique bifunctional cytochrome P450 CYP71B15/PAD3 (Zhou et al., 1999; Schuhegger et al., 2006; Bttcher et al., 2009). The cyp71b15/pad3 mutant synthesizes only traces (typically 1% of the wild-type level) of camalexin (Glazebrook and Ausubel, 1994), largely independent of the applied stimulus triggering its biosynthesis.

CYP71A13 is highly transcriptionally coregulated with CYP71B15/PAD3 and, therefore, was a clear candidate for involvement in camalexin biosynthesis. A cyp71a13 mutant showed strong reduction in camalexin formation in response to AgNO3, Alternaria brassicicola, and Pseudomonas syringae in leaves, but in all cases, a significant amount of camalexin was still synthesized (Nafisi et al., 2007). CYP71A13 shows 89% identity on the amino acid level compared with CYP71A12, and the corresponding genes are located as tandem copies on chromosome 2. CYP71A13, expressed in Escherichia coli, converted IAOx to IAN in vitro (Nafisi et al., 2007). CYP71A12 could partially functionally replace CYP71A13 in a Nicotiana benthamiana expression system (Mldrup et al., 2013) and also catalyzed the formation of IAN and indole-3-carbaldehyde (ICHO) from IAOx in vitro (Klein et al., 2013). It is involved in camalexin biosynthesis in roots in response to Flg22 treatment (Millet et al., 2010). The extent to which CYP71A12 also plays a role in camalexin biosynthesis in leaves remained unclear. A third homolog, CYP71A18, shares 87% and 85% identity on the amino acid level to CYP71A12 and CYP71A13, respectively. It is expressed very weakly in leaf tissue also in response to pathogen infection (http://bbc.botany.utoronto.ca/efp). Its biological function is unclear.

In addition to camalexin, derivatives of ICHO and indole-3-carboxylic acid (ICOOH) are synthesized in Arabidopsis in substantial quantities (Hagemeier et al., 2001; Bednarek et al., 2005; Bttcher et al., 2009, 2014; Iven et al., 2012). In response to AgNO3 treatment, the total molar amount of these derivatives was similar to that of camalexin. Analysis of cyp79b2 cyp79b3 mutants demonstrated that, similar to camalexin, ICHO/ICOOH derivatives are synthesized from IAOx (Bttcher et al., 2009, 2014). In addition, incorporation studies suggest IAN as a putative biosynthetic precursor. Consequently, here, we address the extent to which CYP71A12 and/or CYP71A13 are important for the biosynthesis of these metabolites in leaves.

Reverse genetics in Arabidopsis typically relies on transfer DNA- or transposon-induced mutant alleles, and mutational events from distinct plants can be merged into plant lines carrying multiple mutations following Mendelian genetics (Bolle et al., 2011). The genetic versatility of plant secondary metabolism has been accelerated by gene duplication events, resulting in functionally redundant tandemly arranged gene copies (Hofberger et al., 2013). However, due to the lack of recombination, it is very unlikely that distinct mutational events of tandemly arranged genes are combined into a higher order mutant plant. Recently developed approaches for targeted genome editing offer a solution to this problem but have thus far not been applied to unravel plant secondary metabolic pathways. We used TRANSCRIPTION ACTIVATOR-LIKE EFFECTOR NUCLEASEs (TALENs; Christian et al., 2010; Joung and Sander, 2013) to create stable cyp71a12 cyp71a13 double knockout lines. These double mutants synthesized only traces of camalexin, demonstrating that, in addition to CYP71A13, CYP71A12 is involved in camalexin biosynthesis in leaves. Based on a detailed metabolite analysis and on the characteristics of the corresponding enzymes, a differential function for CYP71A12 and CYP71A13 in the biosynthesis of other IAN-derived metabolites was established. This work demonstrates that targeted genome editing eliminates the limitations of classical genetic approaches and breaks ground for the elucidation of plant secondary metabolic pathways by reverse genetic approaches.

Previously, it was shown that recombinant CYP71A13 and CYP71A12 convert IAOx to IAN (Nafisi et al., 2007; Klein et al., 2013). In the presence of thiol donors such as Cys or glutathione, CYP71A13 produces Cys(IAN) or GS-IAN in vitro as a side product, while CYP71A12 generates ICHO as a side product (Klein et al., 2013). Here, we studied the enzymatic characteristics of CYP71A12 and CYP71A13, expressed in yeast (Saccharomyces cerevisiae), to understand their functional differences in more detail (Fig. 1). When CYP71A13-containing microsomes were incubated with IAOx and NADPH, IAN was synthesized (Supplemental Fig. S1). In the vector control, no IAN formation was observed. When glutathione was added, formation of GS-IAN and traces of ICHO were observed, as reported previously by Klein et al. (2013). The amount of GS-IAN synthesized varied strongly between enzyme preparations (2.7%21.5% [molar] GS-IAN of total product; n = 3), indicating the influence of yeast proteins on the product spectrum. For CYP71A12, after 30 min of enzymatic reaction, ICHO was the major product of the IAOx turnover independent of reduced glutathione addition (60% 10% [molar] ICHO of total product; n = 4). In addition, NADPH-dependent turnover of IAN to ICHO accompanied by a release of cyanide was detected for CYP71A12 (Supplemental Fig. S1), as observed for CYP71B6 (Bttcher et al., 2014). In conclusion, CYP71A12 consecutively catalyzes a CYP71A13-type and a CYP71B6-type reaction.

Enzymatic parameters of CYP71A12 with IAOx and IAN as substrates and of CYP71A13 with IAOx as substrate. All turnover rates (s1) were calculated based on P450 quantification (Supplemental Fig. S1). Data points represent turnover rates, calculated based on product quantification after individual enzymatic conversions.

Enzymatic parameters of CYP71A12 with IAOx and IAN as substrates and of CYP71A13 with IAOx as substrate. All turnover rates (s1) were calculated based on P450 quantification (Supplemental Fig. S1). Data points represent turnover rates, calculated based on product quantification after individual enzymatic conversions.

Concentration of active cytochrome P450 was determined by carbon monoxide differential spectroscopy (Supplemental Fig. S1), and the kinetic parameters of CYP71A12 and CYP71A13 were determined (Fig. 1). For the reaction with IAOx, catalytic efficiency was approximately 0.066 m 1 s1 for CYP71A12 and approximately 0.029 m 1 s1 for CYP71A13. Besides the limitations regarding comparisons between catalytic efficiencies of different enzymes (Eisenthal et al., 2007), it is clear that both enzymes are efficiently dehydrating IAOx. The catalytic efficiency for the turnover of IAN by CYP71A12 was approximately 0.001 m 1 s1, indicating that IAOx is preferred over IAN as a substrate of CYP71A12.

In TALEN-mediated genome editing, two distinct TALEN proteins, each containing a DNA-binding domain and a FokI cleavage domain, bind on opposite sites of a given cleavage site. Upon TALEN-induced cleavage, the strands are rejoined by nonhomologous end joining repair, which typically results in small deletions (Joung and Sander, 2013). To simplify the identification of plants with mutations, the cleavage sites are positioned in such a way that the TALEN-induced deletions cause the loss of endonuclease recognition sites (Hoshaw et al., 2010). We aimed to induce mutations at the CYP71A12 gene and generated two TALENs that bind on opposite sites of a BsaI endonuclease recognition site that is present within the CYP71A12 coding sequence. Loss of this site was monitored by BsaI-based cleaved-amplified polymorphic sequence (CAPS) marker analysis (Fig. 2, A and B).

Somatic mutagenesis and inheritance of TALEN-mediated mutations in CYP71A12. A, Schematic representation of the analysis of targeted mutagenesis; the TALEN-binding site targeting one of two BsaI sites is indicated. NHEJ, Nonhomologous end joining. B, Representative CAPS analysis for a wild-type plant and a homozygous mutant plant. C and D, Analysis of TALEN-induced mutations in CYP71A12. The sequence of the CYP71A12 wild-type allele with TALEN-binding sites is in italic letters, and the targeted BsaI restriction site is in boldface; insertions are in lowercase letters, and deletions are indicated as dashes. C, Somatic events detected. D, Stable lines generated. prim. transf., Primary transformant; *, of the 82-bp deletion event, only 56 deleted bp are indicated.

Somatic mutagenesis and inheritance of TALEN-mediated mutations in CYP71A12. A, Schematic representation of the analysis of targeted mutagenesis; the TALEN-binding site targeting one of two BsaI sites is indicated. NHEJ, Nonhomologous end joining. B, Representative CAPS analysis for a wild-type plant and a homozygous mutant plant. C and D, Analysis of TALEN-induced mutations in CYP71A12. The sequence of the CYP71A12 wild-type allele with TALEN-binding sites is in italic letters, and the targeted BsaI restriction site is in boldface; insertions are in lowercase letters, and deletions are indicated as dashes. C, Somatic events detected. D, Stable lines generated. prim. transf., Primary transformant; *, of the 82-bp deletion event, only 56 deleted bp are indicated.

These CYP71A12-specific TALENs were transformed into cyp71a13-1 knockout mutants with the aim to generate cyp71a12 cyp71a13-1 double mutants. Using primers specific for the FokI domain, PCR was carried out on seven primary transformants and confirmed the presence of the TALEN coding sequence. A CYP71A12-specific CAPS marker assay indicated that three of the seven tested plants indeed contain the desired mutation at the CYP71A12 locus. For each of these three primary transformants, the PCR product was cloned, and random clones were analyzed via BsaI digestion. A variety of mutant alleles in CYP71A12 were detected by sequence analysis (Fig. 2C). Notably, among 10 random clones for primary transformant 3, no wild-type sequence was detected, whereas for primary transformants 2 and 1, 20% and 50% of the clones, respectively, contained a wild-type sequence. This demonstrates efficient TALEN activity especially in plant 3.

T1, T2, and T3 plants of primary transformant 3 were analyzed for the presence of TALEN constructs using FokI-specific primers as indicators for a transgenic line. The presence of TALEN sequence segregated 44:4 for T2 and 9:1 for T3 (transgenic to wild-type plants).

A total of 150 T2 plants of each line were screened for TALEN-induced mutation in the targeted BsaI restriction site in the CYP71A12 coding sequence. For plants 1 and 2, none of the progeny showed evidence for mutation of this BsaI site in CYP71A12, indicating that all progeny plants are homozygous for the CYP71A12 wild-type allele. In contrast, for line 3, 19% of the T2 plants showed amplicons that lack the targeted BsaI site, indicating that they have homozygous or transheterozygous cyp71a12 mutant alleles. In 21% of the T2 plants, the amplicons were only partially cleaved by BsaI, indicating that the plants are probably heterozygous. In 60% of the T2 plants, the informative BsaI restriction site was cleaved, indicating the presence of a CYP71A12 wild-type allele.

Sequence analysis determined two distinct CYP71A12 mutant alleles, carrying a 5-bp and a 3-bp deletion, respectively. The latter one causes a loss of Asp-488. Interestingly, only this 3-bp deletion allele was among the 16 characterized somatic events (Fig. 2). One T2 plant lacking the TALEN gene and homozygous for the 5-bp deletion allele was identified (cyp71a12 cyp71a13-1). In the third generation, also a nontransgenic homozygous plant carrying the 3-bp deletion was obtained (cyp71a12 cyp71a13-2). The genotype was confirmed in the T3 and T4 generations, which were analyzed for metabolic phenotypes.

With respect to potential off targets of the TALEN pair and resulting unwanted mutations, the genomic sequences of CYP71A18 (with coding sequence 91% identical to CYP71A12) and CYP71B15/PAD3 (related with respect to the metabolic phenotype) were checked for their integrity, and no change in sequence was observed.

We analyzed camalexin levels in rosette leaves of 6-week-old plants of cyp71a12 and cyp71a13 single knockout and cyp71a12 cyp71a13 double knockout mutants in response to AgNO3 and UV irradiation (Fig. 3). For cyp71a12, no significant differences in comparison with the wild type were detected. In cyp71a13, we determined approximately 2.2% of wild-type camalexin level in response to UV irradiation and approximately 12% in response to AgNO3, similar to previous observations (Nafisi et al., 2007). Camalexin concentration in the cyp71a12 cyp71a13 double mutant was less than approximately 0.15% and 0.5% of the wild-type level for UV irradiation and AgNO3 treatment, respectively. In comparison with cyp71a13, camalexin concentrations were significantly reduced in cyp71a12 cyp71a13 double mutants for both treatments. In summary, the strongly reduced camalexin level in the cyp71a12 cyp71a13 double mutant as compared with the cyp71a13 single mutant suggests that CYP71A12 contributes to camalexin biosynthesis in leaf tissue.

Camalexin quantification. Camalexin concentrations in response to AgNO3 and UV light were determined in single and double mutants of cyp71a12 and cyp71a13 (n = 10). Different letters indicate significant differences according to ANOVA (Scheffs test; P < 0.05). FW, Fresh weight.

Camalexin quantification. Camalexin concentrations in response to AgNO3 and UV light were determined in single and double mutants of cyp71a12 and cyp71a13 (n = 10). Different letters indicate significant differences according to ANOVA (Scheffs test; P < 0.05). FW, Fresh weight.

Previously, we identified the spectrum of compounds that derive from IAOx provided by CYP79B2 and CYP79B3 (Bttcher et al., 2009). Here, we performed targeted metabolite profiling for these compounds in a cyp79b2 cyp79b3 double mutant and in single and double mutants of cyp71a12 and cyp71a13 (Fig. 4; Supplemental Table S1). In accordance with previous findings (Glawischnig et al., 2004; Bttcher et al., 2009), cyp79b2 cyp79b3 leaves were deficient in camalexin and ICHO/ICOOH derivatives. Interestingly, irrespective of the treatment, we observed strong accumulation of Trp in cyp79b2 cyp79b3 in comparison with the wild type, consistent with the lack of major Trp sinks in this line (Fig. 4E).

Metabolomics analysis. A to D, Metabolites in rosette leaves of wild-type (Columbia-0 [Col-0]; black), cyp71a12 (green), cyp71a13 (red), and cyp71a12 cyp71a13 (blue) plants, 24 h after detachment and spraying with AgNO3 (A), 22 h after detachment and 2 h of UV irradiation (B), untreated (C), or 24 h after detachment and spraying with water (D). Characterized metabolites for which significant differences in cyp71a12, cyp71a13, or cyp71a12 cyp71a13 in comparison with the wild type were observed. The full data set is presented in Supplemental Table S1. Error bars indicate se (n = 911, except n = 20 for cyp71a12 cyp71a13 in A and D). DHCA, Dihydrocamalexic acid; HC MalonylHex, hydroxycamalexin malonylhexoside; ICOOGlc, Glc ester of ICOOH; 6-OH-ICOOH, 6-hydroxyindole-3-carboxylic acid; 6-GlcO-ICOOH, 6-hydroxyindole-3-carboxylic acid 6-O--d-glucoside. E, Relative quantification of Trp in Col-0 (black) and cyp79b2 cyp79b3 (violet) in the data sets denoted. Significance analysis of differences between the wild type and mutant was performed by two-tailed Students t test (*, P 0.05; **, P 0.01; and ***, P 0.001).

Metabolomics analysis. A to D, Metabolites in rosette leaves of wild-type (Columbia-0 [Col-0]; black), cyp71a12 (green), cyp71a13 (red), and cyp71a12 cyp71a13 (blue) plants, 24 h after detachment and spraying with AgNO3 (A), 22 h after detachment and 2 h of UV irradiation (B), untreated (C), or 24 h after detachment and spraying with water (D). Characterized metabolites for which significant differences in cyp71a12, cyp71a13, or cyp71a12 cyp71a13 in comparison with the wild type were observed. The full data set is presented in Supplemental Table S1. Error bars indicate se (n = 911, except n = 20 for cyp71a12 cyp71a13 in A and D). DHCA, Dihydrocamalexic acid; HC MalonylHex, hydroxycamalexin malonylhexoside; ICOOGlc, Glc ester of ICOOH; 6-OH-ICOOH, 6-hydroxyindole-3-carboxylic acid; 6-GlcO-ICOOH, 6-hydroxyindole-3-carboxylic acid 6-O--d-glucoside. E, Relative quantification of Trp in Col-0 (black) and cyp79b2 cyp79b3 (violet) in the data sets denoted. Significance analysis of differences between the wild type and mutant was performed by two-tailed Students t test (*, P 0.05; **, P 0.01; and ***, P 0.001).

In order to elucidate the role of CYP71A12 and CYP71A13 in the biosynthesis of camalexin precursors and of soluble ICHO/ICOOH derivatives, their accumulation levels were determined relative to the wild type under four different conditions: detached leaves sprayed with AgNO3 or water (mock) and incubated for 24 h, detached leaves irradiated with UV light for 2 h and incubated for 22 h, and untreated leaves (control). Irrespective of the treatment, the camalexin level was strongly reduced in cyp71a13 and was nearly absent in cyp71a12 cyp71a13 (Fig. 4, A and B), consistent with our targeted absolute quantification (Fig. 3). Similarly, significantly reduced levels were detected for the biosynthetic intermediates GS-IAN and dihydrocamalexic acid as well as for a hydroxycamalexin malonylhexoside representing the major camalexin metabolite (Fig. 4). This demonstrates that the synthesis of GS-IAN as a biosynthetic precursor of camalexin is dependent on CYP71A12/A13.

Among the analyzed ICOOH derivatives, the methyl ester of ICOOH (ICOOMe) was strongly and significantly reduced in cyp71a12 in response to UV irradiation (21% of the wild-type level; P = 3.6E4) and AgNO3 (12% of the wild-type level; P = 2.3E5; Fig. 4, A and B). For cyp71a12 cyp71a13, we detected 4.3% (P = 5.2E5) and 11% (P = 1.8E9) of the wild-type level for UV and AgNO3 treatment, respectively. In conclusion, CYP71A12 is important for the biosynthesis of this ICOOH derivative, which is de novo formed in response to both stress applications but below the detection limit in mock-treated and control leaves. For a number of other ICOOH derivatives, which in contrast to ICOOMe constitutively accumulate already in nontreated leaf tissue, we observed significantly reduced levels (Fig. 4), although to a much lower degree and not necessarily in all data sets. For example, the level of the Glc ester of ICOOH, which is a major compound, was reduced to 64% (P = 5.4E3) and 68% (P = 1.1E4) of the wild-type level in cyp71a12 cyp71a13, respectively, in UV- and AgNO3-challenged leaves.

In contrast, we did not observe any significant reduction of ICHO derivatives in cyp71a12 cyp71a13, although exogenously applied IAN is converted to ICHO derivatives in vivo (Bttcher et al., 2009, 2014). IAN synthesized by CYP71A12/A13 is not a major precursor of these metabolites.

For the applied TALEN pair, introduction of a mutation into CYP71A12 was very efficient in leaf tissue. For several primary transformants, we have observed somatic apparently transheterozygous mutations, with the wild-type allele being underrepresented. We detected 12 different mutated alleles: nine of them were deletions and three were insertion/deletion combinations.

In leaves of the primary transformant from which stable mutant alleles were transmitted, we did not detect a wild-type allele, indicating that very efficient somatic TALEN activity is a prerequisite for generating heritable lines, as observed by Christian et al. (2013). Most of the somatic events were not inherited, in accordance with other attempts to generate stable mutant lines via TALEN technology (Christian et al., 2013). This indicated that TALENs are mostly inactive in germline tissue. Whether this is due to them being expressed under the control of the 35S promoter remains to be investigated. Once a novel cyp71a12 allele was transmitted to the T2 generation, it was inherited in a Mendelian fashion, freely segregating from the TALEN transgene. Consequently, homozygous cyp71a12 cyp71a13 T2 plants were identified that did not carry the TALEN construct.

CYP71A12 catalyzed two consecutive reactions, the dehydration of IAOx to IAN, which is then further converted to ICHO and cyanide (Klein et al., 2013; this study). These two activities were shown for CYP71A13 and CYP71B6, respectively (Nafisi et al., 2007; Bttcher et al., 2014), suggesting some genetic redundancy for these steps. Based on the comparison of cyp71a13 and cyp71a12 cyp71a13 phenotypes, the contribution of CYP71A12 to camalexin formation in leaves is significant but minor in relation to CYP71A13. In contrast, we observed a strong reduction of ICOOMe in cyp71a12, which was not further enhanced in the double knockout, demonstrating that CYP71A12 is important for the biosynthesis of this compound (Fig. 4, A and B). To a lesser extent, we also detected a contribution of CYP71A12 to the biosynthesis of ICOOH, Glc ester of ICOOH, 6-hydroxyindole-3-carboxylic acid, and 6-hydroxyindole-3-carboxylic acid 6-O--d-glucoside (Fig. 4, A, B, and D). We conclude that the biosynthesis of inducible ICOOH derivatives is the major biological function of CYP71A12 in leaves.

In cyp71a12 cyp71a13 double mutants, only traces of camalexin are synthesized, showing that dehydration of IAOx by CYP71A12 and CYP71A13 is essential for IAN synthesis as a camalexin precursor. This phenotype was observed when a 5-bp deletion allele of cyp71a12 was present, but also for a 3-bp deletion allele, resulting in the deletion of Asp-488. This amino acid, conserved in the CYP71A12/A13/A18 branch, might be essential for enzymatic function, or its deletion might destroy protein structure.

For ICOOH derivatives, smaller changes than for camalexin have been observed in comparison with the wild type, and the level of ICHO derivatives was essentially unaffected. The known ICHO/ICOOH biosynthetic genes CYP71B6 and Aldehyde Oxidase1 (AAO1) are to some extent coregulated with the camalexin biosynthetic genes CYP71A13 and CYP71B15 (Bttcher et al., 2014), indicating that different timing of protein expression and different leaf cell types for the two biosynthetic routes are unlikely to explain this observation. Moreover, the known enzymes of camalexin and ICHO/ICOOH derivative biosynthesis are cytosolic or endoplasmic reticulum bound with catalytic activity on the cytosolic side, so both processes occur in the same subcellular compartment. Therefore, for camalexin, ICOOMe, and other ICOOH derivative biosynthesis, we propose a different degree of exchange of IAN synthesized as product/intermediate of the CYP71A12/A13 reaction, with an IAN pool derived from glucosinolate degradation (Fig. 5). Possibly, the glucosinolate degradation product indole-3-carbinol (Agerbirk et al., 2008) is the major source for ICHO derivatives.

Model for the biosynthetic pathways of Trp-derived secondary metabolites in Arabidopsis. We propose pools of free IAN and ICHO derived from glucosinolate degradation and, in addition, IAN and ICHO as channeled intermediates of camalexin and ICOOH biosynthesis. Enzyme functions are denoted. Square brackets indicate proposed channeling of intermediates, and double arrows indicate multiple steps. ICOOGlc, Glc ester of ICOOH; I3M GLS, indole-3-methyl glucosinolate; SP, specifier protein; 6-OH-ICOOH, 6-hydroxyindole-3-carboxylic acid; 6-GlcO-ICOOH, 6-hydroxyindole-3-carboxylic acid 6-O--d-glucoside.

Model for the biosynthetic pathways of Trp-derived secondary metabolites in Arabidopsis. We propose pools of free IAN and ICHO derived from glucosinolate degradation and, in addition, IAN and ICHO as channeled intermediates of camalexin and ICOOH biosynthesis. Enzyme functions are denoted. Square brackets indicate proposed channeling of intermediates, and double arrows indicate multiple steps. ICOOGlc, Glc ester of ICOOH; I3M GLS, indole-3-methyl glucosinolate; SP, specifier protein; 6-OH-ICOOH, 6-hydroxyindole-3-carboxylic acid; 6-GlcO-ICOOH, 6-hydroxyindole-3-carboxylic acid 6-O--d-glucoside.

Most likely, IAN synthesized by CYP71A12 and CYP71A13 during the course of camalexin biosynthesis is directly channeled. In the presence of glutathione, GS-IAN is a minor product of IAN turnover by CYP71A13 in vitro and was not detected after turnover by CYP71A12. However, in planta, specific interaction with glutathione S-transferases, GGP1 (Geu-Flores et al., 2011), and CYP71B15 (Glazebrook and Ausubel, 1994; Zhou et al., 1999; Schuhegger et al., 2006; Bttcher et al., 2009) might drive efficient IAN conversion. Also, other cytochrome P450 enzymes could play a role in activating IAN in the camalexin biosynthetic pathway, functionally overlapping with CYP71A13, as indicated by the complementation of camalexin deficiency in cyp71a13 by the addition of IAN (Nafisi et al., 2007).

In addition, we propose a free cellular IAN pool, which could be fed by the degradation of indole glucosinolates (de Vos et al., 2008), from which ICHO derivatives and subsequently ICOOH derivatives are synthesized, involving CYP71B6. The binding constant of IAN and CYP71B6 is very low (Bttcher et al., 2014), consistent with the fact that IAN did not accumulate in leaves in response to AgNO3, UV irradiation, or Phytophthora spp. infection (Bttcher et al., 2009, 2014; this study). As IAN can act as an auxin (indole-3-acetic acid) precursor (Bartling et al., 1992; Normanly et al., 1997; Kriechbaumer et al., 2007), avoiding IAN accumulation ensures that the production of IAN-derived defense compounds can be induced without effects on the indole-3-acetic acid pool, which could counteract defense responses.

IAOx was synthesized according to Ahmad et al. (1960) with the following modifications: 5 mg of indole-3-acetaldehyde (Sigma-Aldrich) was suspended in 400 L of 1 m sodium carbonate. The suspension was extracted three times with 500 L of ethyl acetate. The organic phases were combined, 500 L of 0.1 m hydroxylamine was added, and after 1 h of shaking, the organic phase was removed, dried over Na2SO4, and evaporated to dryness.

The CYP71A12 coding sequence was amplified from the complementary DNA clone R21987 (Arabidopsis Biological Resource Center) using the primers 5-GGATTAAUAATGATGTCTAATATTCAAGAAATGGAAATGGATATTG-3 and 5-GGGTTAAUTTAAATAACGGAAGATGGAAATG-3. CYP71A13 was amplified from total Arabidopsis (Arabidopsis thaliana) Col-0 leaf complementary DNA using the primers 5-GGATTAAUAATGATGTCTAATATTCAAGAAATGGAAATGGATATTG-3 and 5-GGGTTAAUTTACACAACCGAAGATGGAAATG-3. The PCR fragments were cloned into pYEDP60u via USER technology (Nour-Eldin et al., 2006) and transformed into yeast (Saccharomyces cerevisiae) WAT11 (Pompon et al., 1996). Yeast microsomes were prepared as described by Schuhegger et al. (2006). Cyanide derivatization and HPLC analysis were performed as described previously (Bttcher et al., 2009). The concentration of active cytochrome P450 was determined by carbon monoxide differential spectroscopy (Omura and Sato, 1964). For analysis of the enzymatic parameters, reactions were performed with 18 g of microsomal protein (representing approximately 27 ng of CYP71A12 or 59 ng of CYP71A13) in 100 L of potassium phosphate buffer (20 mm, pH 7.5) for 30 min and then stopped by adding 200 L of methanol. IAOx, IAN, and ICHO were analyzed by reverse-phase HPLC (MultoHigh 100 RP18, 5-m particle size; Ghler Analytik) as follows: flow rate of 1 mL min1; solvents, 0.3% (v/v) formic acid in water (A) and acetonitrile (B); and gradient: 0 to 2 min, isocratic, 23% B; 2 to 16 min, linear from 23% to 48% B; 16 to 16.5 min, linear from 48% to 100% B; 16.5 to 18.5 min, isocratic, 100% B. The HPLC device was equipped with a photodiode array detector (Dionex). Retention time values were as follows: IAOx, 11.8/12.6 min; IAN, 15.6 min; ICHO, 9.5 min; and GS-IAN, 8.2 min. Quantification was based on calibration curves with authentic standards. Turnover rates were calculated for individual enzymatic conversions, and the data were fitted to Michaelis-Menten kinetics using GraphPad Prism 4 software.

TALEN effector-binding elements proceeded by a T, 18 bp long, separated by a 12-bp spacer sequence, were identified manually. Potential TALEN-binding sites on CYP71A12 exon sequences flanking restriction sites were screened manually. Low off-target probability was estimated using The Arabidopsis Information Resource Patmatch with the single effector-binding elements (mismatches, three; mismatch type, substitutions). TALEN-encoding modules lacking the repeats were assembled with BsaI site-flanked modules containing short 35S promoter (plCH51277; Weber et al., 2011), HA-Nuclear Localization Signal (de Lange et al., 2014), truncated TALEN N- and C-terminal sequences, FokI (Mussolino et al., 2011), and octopine synthase terminator (plCH41432; Weber et al., 2011) into pICH47732 (Weber et al., 2011) and pICH47742 (Weber et al., 2011). The repeat domains of TALEN211 and TALEN212 were created using a previously described method (Morbitzer et al., 2011) and cloned via BpiI into pICH47732 TALENRep and pICH47742 TALENRep. BpiI-flanked TALEN modules with repeats were assembled together with pICH47751 kanamycin, conferring in planta resistance against kanamycin, and pICH47766 (Weber et al., 2011) into pICH50505 (Weber et al., 2011).

The transfer DNA expression vector was transformed into Agrobacterium tumefaciens strain GV3101 MP90. cyp71a13-1 (SALK_105136) plants were transformed by the floral dip method (Clough and Bent, 1998). The progeny were selected on solid one-half-strength Murashige and Skoog medium (Duchefa) containing 50 g mL1 kanamycin (Duchefa). Seven plants were singled out, and the presence of the TALEN construct was confirmed by PCR on the FokI gene with the primer pair 5-GTGAAATCTGAATTGGAAGAG-3 and 5-TATCTCACCGTTATTAAATTTCC-3.

From leaves of primary transformants, genomic DNA was isolated and a sequence of 665 bp in the target region of the TALEN pair was amplified with the primer pair 5-AAGCCGTGATTAAAGAGGTG-3 and 5-AAATTGTAGGATATGCTTATTTTCT-3. A total of 5 L of the PCR product was used directly for digestion with BsaI (New England Biolabs). The amplicon sequence contains two cleavage sites for BsaI, one targeted by the TALEN pair, resulting in different digestion patterns of the wild-type sequence and mutated or partially mutated CYP71A12 (the wild type, 255, 253, and 157 bp; TALEN mutated, 255 and 410 bp). PCR products representing plants carrying somatic mutations were cloned into pGEM T-Easy (Promega). Corresponding Escherichia coli XL1 Blue clones were randomly picked, and the plasmids harbored were sequenced.

For screening of T2 and T3 plants, PCR was conducted with the CYP71A12 TALEN primer set using small leaf discs as template (annealing, 54C; Phire Plant Direct PCR Kit; Thermo Scientific). Of each PCR product, 5 L was used directly for digestion with BsaI.

Plants were grown on a 3:1 mixture of soil (Einheitserde) and sand in a growth chamber in a 12-h photoperiod at a light intensity of 80 to 100 mol m2 s1 at 21C. AgNO3 challenge was conferred by spraying 5 mm AgNO3 on detached leaves and incubating for 24 h in the growth chamber. For UV treatment, leaves were detached and placed under a UV lamp (Desaga; = 254 nm, 8 W) at a distance of 20 cm, irradiated for 2 h, and incubated for an additional 22 h in the growth chamber. cyp79b2 cyp79b3 and cyp71a13-1 (SALK_105136) knockout lines have been described previously (Zhao et al., 2002; Nafisi et al., 2007). cyp71a12 (Millet et al., 2010; GABI-Kat 127 H03) was provided by the European Arabidopsis Stock Centre.

Rosette leaves of 6-week-old cyp71a12 cyp71a13-1, cyp71a12 cyp71a13-2, cyp71a12, cyp71a13-1, and Col-0 plants were analyzed untreated or after 5 mm AgNO3 or UV exposure. Leaves were weighed, and camalexin was extracted using 400 L of methanol:water (80:20, v/v) for 1 h in a thermoshaker at 65C. Extracts were cleaned twice by centrifugation and analyzed by reverse-phase HPLC (MultoHigh 100 RP18, 5-m particle size; Ghler Analytik) as follows: flow rate of 1 mL min1; solvents, 0.3% (v/v) formic acid in water (A) and acetonitrile (B); and gradient, 0 to 1 min, isocratic, 20% B; 1 to 7 min, linear from 20% to 80% B; 7 to 7.5 min, linear from 80% to 100% B; 7.5 to 9 min, isocratic, 100% B. Camalexin (retention time of 8.4 min) was quantified using a fluorescence detector (318-nm excitation and 370-nm emission) based on calibration with an authentic standard (Schuhegger et al., 2006).

Rosette leaves (50150 mg) of 6-week-old cyp71a12 cyp71a13-1, cyp79b2 cyp79b3, cyp71a12, cyp71a13-1, and Col-0 (AgNO3/UV treated and untreated/mock control) plants were analyzed. Individual leaves were weighed, transferred into 2-mL tubes, and frozen in liquid nitrogen. Samples were homogenized in a ball mill using steel balls (3 mm) and placed in a precooled (70C) rack. After 400 L of precooled (70C) methanol:water (80:20, v/v) was added, samples were immediately vortexed and slowly thawed under periodic vortexing. At room temperature, 400 pmol of biochanin A (Sigma-Aldrich) dissolved in methanol:water (1:1, v/v), per 100 mg fresh weight, was added, and the samples were extracted for 1 h in a thermoshaker at room temperature. Samples were centrifuged for 10 min at 16,000g, and after the supernatant was collected, the pellet was extracted for a second time by adding 400 L of methanol:water (80:20, v/v) and shaking for another 1 h at room temperature. Samples were centrifuged, and supernatants were combined and evaporated to dryness in a vacuum centrifuge (less than 10 mbar, 30C).

The residue was reconstituted in 50% methanol (400 L per 100 mg fresh weight), sonicated for 10 min at 20C, and centrifuged for 10 min at 12,000g. One microliter of the supernatant was separated on an Agilent Infinity 1290 UHPLC System (1290 binary pump, 1290 autosampler with 20-L loop, 1290 thermostatted column compartment, and 1260 diode array detector) equipped with a Zorbax RRHD Eclipse Plus C18 column (100 2.1 mm, 1.8-m particle size; Agilent). The following binary gradient was applied at a flow rate of 400 L min1: 0 to 12 min, linear from 95% A (0.1% [v/v] formic acid in water) and 5% B (0.1% [v/v] formic acid in acetonitrile) to 65% B; 12 to 15 min, isocratic, 95% B; and 15 to 17 min, isocratic, 5% B. The column temperature was maintained at 40C. Eluting compounds were detected from a mass-to-charge ratio (m/z) of 100 to 1,100 using an Agilent 6550 iFunnel Q-TOF LC/MS-System equipped with a Dual Agilent Jet Stream electrospray ion source in positive and negative ion modes. The following instrument settings were applied for positive (negative) ion mode: nebulizer gas, nitrogen, 35 pounds-force per square inch gauge; dry gas, nitrogen, 200C, 18 L min1; sheath gas, nitrogen, 300C, 12 L min1; capillary voltage, 3,000 V; nozzle voltage, 0 V; fragmentor voltage, 300 V; high-pressure funnel, voltage drop 150 V, radio frequency (RF) voltage 100 V; low-pressure funnel, voltage drop 100 V, RF voltage 60 V; funnel exit direct current 40 V; octopole RF voltage, 750 V; collision gas, nitrogen; collision energy, 0 V; and acquisition rate, 3 Hz. The mass spectrometer was operated in extended dynamic range (2 GHz) mode, and the slicer mode was set to high sensitivity. The mass resolution (Resolution [full width at half maximum]) within the analyzed m/z range was 12,000 to 27,000. For reference mass correction, a solution of purine (5 m) and hexakis-(2,2,3,3-tetrafluoropropoxy)phosphazine (0.5 m) in acetonitrile:water (95:5, v/v) was continuously introduced through the second sprayer of the dual ion source at a flow rate of 15 L min1 using an external HPLC pump equipped with a 1:100 splitting device. Collision-induced dissociation mass spectra were acquired in targeted tandem mass spectrometry mode using a medium isolation width of 4 m/z and applying collision energies in the range of 5 to 25 V. MassHunter software packages were used for data acquisition (version B.05.01) as well as qualitative (version B.06.00) and quantitative (version B.06.00) analyses.

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This work was supported by the Deutsche Forschungsgemeinschaft (grant nos. GL346/5 [Heisenberg Fellowship to E.G.] and LA1338/5 [to T.L.]), the Hans-Fischer-Gesellschaft, and the TUM Junior Fellow Fund.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Erich Glawischnig ([email protected]).

T.M.M. designed and conducted the majority of the experiments and supported drafting the article; C.B. performed the metabolomics analysis; R.M. performed the TALEN construction; C.C.G. and J.L. performed the enzymatic analysis; T.L. conceptualized and supervised the TALEN construction; E.G. supervised the project, supported the experiment design and analysis, and wrote the article with contributions of all authors.

ftir-atr-based prediction and modelling of lignin and energy contents reveals independent intra-specific variation of these traits in bioenergy poplars | plant methods | full text

There is an increasing demand for renewable resources to replace fossil fuels. However, different applications such as the production of secondary biofuels or combustion for energy production require different wood properties. Therefore, high-throughput methods are needed for rapid screening of wood in large scale samples, e.g., to evaluate the outcome of tree breeding or genetic engineering. In this study, we investigated the intra-specific variability of lignin and energy contents in extractive-free wood of hybrid poplar progenies (Populus trichocarpa deltoides) and tested if the range was sufficient for the development of quantitative prediction models based on Fourier transform infrared spectroscopy (FTIR). Since lignin is a major energy-bearing compound, we expected that the energy content of wood would be positively correlated with the lignin content.

Lignin contents of extractive-free poplar wood samples determined by the acetyl bromide method ranged from 23.4% to 32.1%, and the calorific values measured with a combustion calorimeter varied from 17260 to 19767 J g-1. For the development of calibration models partial least square regression and cross validation was applied to correlate FTIR spectra determined with an attenuated total reflectance (ATR) unit to measured values of lignin or energy contents. The best models with high coefficients of determination (R2 (calibration) = 0.91 and 0.90; R2 (cross-validation) = 0.81 and 0.79) and low root mean square errors of cross validation (RMSECV = 0.77% and 62 J g-1) for lignin and energy determination, respectively, were obtained after data pre-processing and automatic wavenumber restriction. The calibration models were validated by analyses of independent sets of wood samples yielding R2 = 0.88 and 0.86 for lignin and energy contents, respectively.

These results show that FTIR-ATR spectroscopy is suitable as a high-throughput method for lignin and energy estimations in large data sets. Our study revealed that the intra-specific variations in lignin and energy contents were unrelated to each other and that the lignin content, therefore, was no predictor of the energy content. Employing principle component analyses we showed that factor loadings for the energy content were mainly associated with carbohydrate ring vibrations, whereas those for lignin were mainly related to aromatic compounds. Therefore, our analysis suggests that it may be possible to optimize the energy content of trees without concomitant increase in lignin.

There is an increasing demand for the production of fast-growing woody plants such as poplars as a sustainable resource for the production of biofuels, heat or electricity as well as for pulp and paper production. However, efficient wood utilization is strongly affected by the lignification of the cell wall. Lignin is an intensely cross-linked heteropolymer that renders plant cell walls rigid, hydrophobic and chemically stable; in angiosperms lignin is mainly composed of guaiacyl and syringyl units [1]. Wood processing requiring delignification is often an expensive bottleneck for its utilization. The lignin content is, therefore, a key target for breeding or genetic engineering to enhance wood properties [2]. The energy content of wood is another important breeding target because renewable biomass feedstock is increasingly used to replace fossil fuels in modern heating systems operated with wood pellets.

Attempts to improve wood properties have mainly focused on poplars (Populus spp.) because these tree species display fast growth rates, can be grown in coppicing systems and are amenable to genetic modification [3, 4]. The direct determination of lignin content by wet chemical methods and the measurement of energy contents are laborious and time-consuming. Hence reliable, high-throughput methods for the determination of lignin and the energy content are in great demand to identify valuable germplasm for specific applications such as bioenergy poplars for the conversion into bioethanol or for the production of heat and electricity.

Several spectroscopic techniques have been employed to analyze wood. For example, the chemical composition [58], basic density [9], and physical properties [10] of wood samples have been predicted by near infrared reflectance spectroscopy (NIRS) and multivariate statistical analyses. Furthermore, Fourier transform infrared (FTIR) spectroscopy has been applied for determination of wood density [11], chemical composition [1214], lignin distribution [15], discrimination of wood from various tree species [16, 17], and changes in wood properties during wood composites manufacture [18, 19]. NIRS uses infrared light to detect overtones and combinational vibrations, whereas FTIR employs mid infrared regions of the radiation to detect primarily functional and fundamental vibrations of the molecular constituents of the irradiated matter. The FTIR absorption bands are often overlapping and make direct assignment of peaks to chemical constituents difficult [20]. Nevertheless, selection of wavenumbers related to lignin was successfully applied to predict lignin in eucalypt and spruce wood [21, 22].

In the past decade, FTIR spectroscopy was improved by the development of an attenuated total reflectance (ATR) unit. Earlier measurements required preparation of samples in transparent KBr pellets whose absorbance was measured by the FTIR spectrometer [20]. In modern FTIR instruments an ATR crystal, which is pressed onto the untreated sample, enables direct interaction of the measuring beam with the sample and reflection of the attenuated radiation to the spectrometer. This technological advance increases the sensitivity of FTIR-based analyses and has, e.g., been used to determine the S/G ratio of poplar wood [7]. Calibrations to determine the lignin content of wood without preparation of KBr pellets have not yet been published. Furthermore, it may be possible to use the same spectra for the prediction of other wood properties. Here, we tested if FTIR-based models can be developed as tools for rapid prediction of the energy content.

The goal of the present study was to investigate the natural variability of lignin and energy contents in wood of field-grown poplar progenies of Populus trichocarpa P. deltoides and to develop FTIR-based calibration models for high-throughput measurements of these traits. For this purpose the lignin and energy contents were determined in coppiced wood of the hybrid poplars with a modified acetyl bromide lignin assay and a calorimeter combustion test, respectively. FTIR-ATR spectra were recorded for extractive-free wood powder. Multivariate statistical analyses, in particular partial least square regression (PLSR) modelling, were applied to calibrating the FTIR spectra against the primary wet laboratory chemical data and the measured energy contents. Data pre-processing methods and automated selection of wavenumber ranges resulted in a high predictability and precise estimation of lignin contents and the calorific values of poplar wood from short-rotation forestry.

The lignin contents of extractive-free wood samples of P. trichocarpa deltoides ranged from 23.4% to 32.1% (w/w) with a mean of 27.0% (Figure 1A). The natural variation in lignin contents within this poplar plantation was comparable to that found for Eucalyptus globulus wood (23.4% - 34.5%, [21]) or for juvenile wood of Sitka spruce [22]. The different wood samples analysed here also broadly cover the variability observed for the lignin contents of different poplar species [23, 24]. The natural variability in lignin in our sample set was an important precondition for the development of calibration models (see below), because if the range was too narrow, i.e., within one order of magnitude of the measurement error, it would be impossible to determine correlations between the FTIR-ATR measurements and the lignin contents.

The energy content in the extractive-free poplar wood samples ranged from 17260 to 19767 J g-1 with a mean of 18514 J g-1 (Figure 1B). Similar calorific values have previously been found in P. euramericana wood from a short rotation plantation (average calorific value: 19.3 MJ kg-1; [15]), whereas straw of agricultural crops generally contains lower energy contents (Glycine max (L.) Merr.: 17.0 MJ kg-1, Sorghum bicolor (L.) Moench: 17.2 MJ kg-1, [25]).

Since lignin has about twice the energy content of cellulose [26], we wondered if the lignin content of the samples would correlate with their energy content. However, we obtained no significant linear correlation between the calorific values and lignin contents (R2 = 0.0973, P-value = 0.1344). This contrasts previous reports in which strong correlations were obtained for these traits [2729]. In contrast to our study, the previous analyses were conducted across different plant species, thus, encompassing a broader range of lignin and calorific values. Furthermore, untreated samples were used and therefore, additional constituents such as wood extractives may have affected the calorific values of biomass. Our study shows that the intra-specific variations in lignin and energy contents were unrelated in poplar.

Figure 2 illustrates FTIR-ATR spectra of hybrid poplar coppice wood for the fingerprint region between 1800 cm-1 and 800 cm-1. The absorption peaks were assigned tentatively to chemical components according to literature data [3039]. The positions of the most characteristic bands for lignin in the fingerprint region are 1593 and 1506 cm-1 for aromatic skeletal vibrations, 1458 and 1420 cm-1 for C-H deformation, 1328 cm-1 for syringyl ring plus guaiacyl ring, 1234 cm-1 for syringyl ring and C = O stretch, and 1120 cm-1 for aromatic skeletal vibrations (Figure 2). As shown in the inset a closer examination of the region from 1650 cm-1 to 1380 cm-1 indicated a clear association between absorbance changes in this region and differences in chemically determined lignin contents (Figure 2 inset). Therefore, this region was used for manual wavenumber selection during the cross validation phase (see below).

FTIR-ATR spectra of wood samples with low (L), medium (M) and high (H) lignin contents in the finger-print region. Each spectrum is the mean of three replicate samples. ATR-FTIR spectra were converted to transmission spectra by automatic correction for the wavenumber-dependent influence on the penetration depth on the radiation, then base-line corrected (Rubberband method) and pre-processed with the method of vector normalization. The lignin contents determined with the acetyl bromide method of the wood samples were L: 23.4%, M: 27.5% and H: 31.5%, respectively.

In contrast to lignin, nothing is known about wavenumber regions related to differences in wood energy contents. A direct comparison of FTIR spectra of wood samples with low, medium and high calorific values did not reveal any conspicuous absorption bands (not shown). To obtain evidence for the wood constituents that might be responsible for differences in energy content and those important for differences in lignin contents, we conducted principle component analyses (PCA) on two sets of selected samples: one consisting of spectra of samples with the lowest (10 samples), medium (10 samples) and highest energy contents (10 samples) according to their calorific values and the other of spectra of samples with lowest (10 samples), medium (10 samples) and highest (10 samples) lignin contents as determined by wet chemical analyses. Factor loadings of the two spectral sample sets were calculated in the wavenumber region from 1800 cm-1 to 900 cm-1 to identify the most divergent wavenumbers (Additional file 1, Figure S1, Additional file 2 Table S1). PC1, PC2, PC3 and PC4 of the "energy set" explained 73.3%, 20.1%, 5.5%, and 1.1% of the variation, respectively. In the "lignin sample set", the factor loadings of the first, second, third, and fourth PC explained 68.7%, 22.2%, 6.8%, and 2.3% of the variation respectively. The first three peaks in PC1 in the two data sets for energy and lignin samples were overlapping encompassing the carbohydrate region and a wavenumber for guaiacyl lignin (Additional file 1, Figure S1, Additional file 2, Table S1 and Additional file 3, Table S2). All other major peaks of the first four PCs diverged between the energy and lignin sets, respectively. Influential wavenumbers for wood energy content were identified mainly as peaks in the factor loadings for ring vibrations of carbohydrates (Additional file 2, Table S1). As expected, wavenumbers typical for aromatic compounds were prevalent in PCs for lignin (Additional file 3, Table S2: 14 out of 32 most divergent wavenumbers) but not in those for energy content (Additional file 2, Table S1: 7 out of 32). In conclusion, this analysis shows that the lack of correlation between lignin and energy content was the result of different constituents contributing either to energy content (mainly certain properties of the carbohydrates) or lignin (aromatic compounds), respectively.

Quality spectra with high peak resolution and smoothness of baseline are a prerequisite for further quantitative analysis. As demonstrated by Faix and Bttcher [40], the traditional KBr pellet method suffers from poor spectral reproducibility caused by various factors including, among others, moisture content in the pellets, room humidity, sample inhomogeneity in the pellet, and variable pellet thickness. In contrast, wood powder can directly be used for FTIR-ATR spectroscopy. For our wood samples the reproducibility of the FTIR-ATR spectra was high (SD = 0.23%), which is a precondition for PLS prediction model building. Furthermore, analyses of the score plots of the PCA up to four factors did not reveal obvious patterns between the wavenumber range (2000 - 800 cm-1) of the calibration FTIR spectra (not shown). Outliers identified by the Mahalanobis distance test (8%) were removed prior to calibration and cross validation.

The FTIR spectra were, thus, suitable to construct predictive models for lignin and the energy contents, respectively. To optimize the model, several data pre-processing methods were examined for the wavenumber range from 2000 - 700 cm-1 and for selected wavenumbers, respectively. With respect to lignin, vector normalization improved the calibration model [R2 = 0.782 and a low number of PLS factors (4)] in comparison with the utilization of raw spectra when the spectral range between 2000 and 700 cm-1 was included (Table 1). Application of this spectral range implies that the exclusion of wavenumbers unrelated to functional groups within the lignin molecules would not reduce the predictive ability of the PLS models. To test this assumption, the wavenumber range between 1650 cm-1 and 1380 cm-1, which exhibited the largest differences for samples differing in lignin (Figure 2), was selected for model construction. The resultant predictive calibration was significantly improved because the R2 values for the model statistics increased from 0.782 to 0.823 for calibration, and from 0.666 to 0.734 for cross validation (Table 1). Correspondingly, root mean square errors also decreased for both calibration and cross validation (Table 1). This supports that the observed differences in the FTIR-ATR spectra of different trees were associated with changes in the lignin contents and indicates that inclusion of unrelated wavenumbers in the model construction decreases the predictive power. We, therefore, also tested automatic wavenumber selection for the prediction of lignin content applying a set of pre-defined frequency regions and combinations of subregions. This method achieved the best test statistics based on high R2 and low values for RMSEC and RMSEP; automatic wavenumber selection yielded the following reduced wavenumber ranges: 1802 - 1690 cm-1, 1362 - 1250 cm-1, and 1140 - 1028 cm-1. The predictive model for lignin content constructed on this basis including the first 12 of the total of 16 calculated PLS factors and accounted for 90.6% of the variance in the predicted lignin content values. The inclusion of the subsequent 4 PLS factors caused merely marginal increases (92.4%) and was therefore, not taken into account. The FTIR-ATR prediction model resulting from internal cross validation for lignin was highly acceptable as shown by the plot of the measured versus the predicted lignin contents (Figure 3A). This prediction model did not include wavenumber ranges typical for lignin, and the number of PLS factors used in the model is relatively high compared to the other models.

Partial least square regression (PLSR) models for the prediction of lignin (A) and energy (B) content in extractive-free poplar wood exhibiting a natural range of variability. Lignin was measured with the acetyl bromide method. The energy content was determined with a combustion calorimeter. Plots of measured versus predicted values for lignin (A) and energy (B) content were calculated with the best models with cross-validation results after data preprocessing and automatic wavenumber selection (Table 1 and Table 2). Solid lines represent regression line of best fit between measured and predicted values.

Concerning the estimation of energy content, data pre-processing (i.e., first derivative and baseline correction with Rubberband method) also led to an improved calibration model with a high R2 (0.865) but a relatively high number of PLS factors (10) (Table 2). The model was slightly improved by automatic wavenumber selection with pre-defined frequency regions and combinations of subregions, which reduced the range from 2000 to 700 cm-1 to the finger print region of 1770 - 990 cm-1. The predictive model for energy content with automatic wavenumber selection including the first 11 of 16 total calculated PLS factors was considered most appropriate, as this model accounted for 87.6% of variance in the predicted calorific values (Table 2, Figure 3B).

The performances of the FTIR-based predictive models for the lignin content were comparable to other studies employing NIRS or FTIR spectroscopy [5, 8, 11, 21, 22, 41, 42]. In those previous studies R2 values for the lignin models ranged from 0.74 to 0.98 and for the independent validation from 0.57 to 0.97, respectively and the corresponding errors RMSEC and RMSECV were 0.58 to 1.0% and 0.36 to 1.6%, respectively. However, caution must be exercised when comparing the regression coefficients because many variables such as the spectral range [5, 8], utilization of raw spectra [22] or smoothing, offset and normalization [21], and tree species [41, 42] used for the predictive models may all have significant effects on this parameter. Although some previous studies obtained slightly stronger correlations and lower errors our predictive models are still sufficiently strong to cope with the relatively limited ranges of lignin and energy contents arising from natural intra-specific variability.

External validation was performed using the best predictive model obtained after internal cross validation. For this purpose an additional set of independent wood samples was scanned and the FTIR-ATR spectra were used to predict the amount of lignin and energy contents, respectively. Samples whose predicted values exceeded the calibration range were counted as outliers and excluded from the evaluation procedure. The samples were also used for the determination of lignin and energy contents in the evaluation step. The predicted values were plotted as dependent and the measured values as independent variables. The regression models for the validation of lignin and energy content gave high R2 values and low root mean square errors of prediction (RMSEP = 0.75% and 69 J g-1 respectively, Figures 4A and 4B). In general, the predicted values for independent validation samples were in good agreement with experimental data, even though for the validation of energy content wood material from different growth conditions was used.

External validation of the PLSR models for lignin (A) and energy contents (B) prediction. FTIR-ATR spectra were produced for an independent set of wood samples and used to predict the lignin or energy contents using the best models from table 1 and 2, respectively. Lignin and energy contents were determined by the acetyl bromide method and a combustion calorimeter, respectively. The predicted values were plotted against the measured values. Solid line represents regression line of best fit between measured and predicted values.

In this study we have shown that the natural variation of wood components in extractive-free samples of hybrid poplar wood was sufficient for the construction of calibration models for lignin and the energy contents. This required the determination of lignin via wet chemical methods and the measurement of calorific values through calorimetry, acquisition of high quality FTIR spectra achieved by the application of an ATR unit and the building PLS model by means of a chemometric software. Once established, data acquisition time for the analysis of extracted wood materials is reduced to minutes and permits large numbers of samples to be processed. The optimized and externally validated calibrations are of sufficient quality to efficiently assess lignin and energy content of poplar wood in large-scale breeding or genetic engineering programmes. FTIR-ATR spectroscopy in combination with partial least squares regression modelling may also be useful for the optimization of wood utilization in the pulping industry or for biofuel or heat production purposes. Furthermore, our study documents for the first time that the intra-specific variability of lignin and energy contents are unrelated to each other. Using principle component analyses we identified influential wavenumbers for lignin and energy contents. While the factor loadings for lignin identified as expected aromatic compounds, carbohydrate ring vibrations were prevalent for the energy content. As both traits are apparently associated with different chemical constituents, we suggest that it will be possible to improve the energy content of wood without a concomitant increase in lignin.

Single stem hybrid poplars (Populus trichocarpa deltoides) were coppiced and plant material was harvested in the fourth year of the second coppice cycle (n = 95 individuals) on the field site at Headley (U.K.). Further details of the plantation have been described elsewhere [4]. The stems were stripped of bark and pith. Wood blocks were oven-dried (60 C) for 2 days. For some validation experiments one-year-old poplar wood (P. canescens), grown in Gttingen (Germany), was used (n = 15 individuals). Dry wood was cut into small pieces with secateurs and ground to a flour in a ball mill (MM2000, Retsch, Haan, Germany) at an amplitude of 90 min-1 for approximately 4 min in liquid nitrogen to prevent heating and to accelerate the milling process. A fine powder with a particle size less than 20 m was achieved to avoid disturbance originating from the influence of particle size on FTIR spectra [40].

Interfering extraneous substances (e.g., soluble fats, waxes, simple sugars, and low-molecular soluble phenolics) were removed by extraction with acetone. For this purpose the wood mill was successively extracted 4-times for 2-days in 100% acetone at room temperature [43]. The resulting extractive-free wood, also known as structural biomass or plant cell walls, was used for all further analyses.

The lignin content of wood powder was determined using a modified acetyl bromide method [44]. One mL of freshly prepared 25% (w/w) acetyl bromide/glacial acetic acid solution was added to 1 mg air-dry, extractive-free wood powder in a 2-mL polypropylene safe-seal micro-tube (Sarstedt, Nmbrecht, Germany). The micro-tube was sealed, placed in a water bath and maintained for 30 min with repeated mixing at 70 C. Subsequently, the reaction was stopped by cooling the micro-tube in an ice-water bath. The reaction mixture was mixed and 100 L of the mixture was transferred into a 2 mL safe-seal micro-tube containing 200 L of 2.0 M sodium hydroxide. The volume was made up to 2 mL with 1.7 mL of glacial acetic acid. The UV absorbance of the solution was determined at 280 nm against a blank solution which was run in conjunction with the sample. The extinction coefficient of lignin extracted by acetyl bromide of = 20.09 Lg-1cm-1 was used to calculate the lignin contents of the samples [44]. All analyses were conducted in triplicate and means were calculated for each of the 95 wood samples from Headley. The pooled standard deviation obtained by the assay was 0.042%. The lignin content was expressed as percentage of oven-dry extractive-free wood. Moisture content of the wood powder was determined before and after drying at 60 C.

The calorific value of the extractive-free wood was analyzed with a bomb calorimeter (IKA C200 Calorimeter System; IKA Werke GmbH & Co. KG, Staufen, Germany). About 100 mg extractive-free wood was weighed and pressed into a pellet using a press attached to the calorimeter. The resultant pellet placed inside a combustible crucible was then combusted with O2 (ca. 30 mbar) in a decomposition bomb. The calorific value was determined as the increase in the temperature of the water as a direct measure for the internal energy of the burning reaction in the decomposition vessel via an isoperibolic automatic procedure. Benzoic acid tablets were used as the standard (net calorific value: 26457 20 J g-1) to calculate the calorific values of the samples. All tests were performed in duplicate and means were calculated for 61 samples from Headley and 15 samples from Gttingen. It was not possible to use all 95 Headley samples because we did not have sufficient material.

The FTIR-ATR spectra of extractive-free wood powder were measured with the FTIR spectrometer Equinox 55 (Bruker Optics, Ettlingen, Germany), equipped with a deuterium triglycine sulfate detector and an attached ATR unit (DuraSamplIR, SensIR Europe, Warrington, UK). The scanning range was from 600 to 4000 cm-1 with a resolution of 4 cm-1.

The wood powder was pressed against the diamond crystal of the ATR device. A pressure applicator with a torque knob ensured that the same pressure was applied for all measurements. For each wood sample, 32 scans were acquired and averaged. Background scanning and correction was carried out regularly at 15-20 min intervals. For each sample, three different subsamples were measured and the resultant mean spectra were used for further analyses. The standard deviation of spectra of the subsamples was obtained by the OPUS 5.5 software (Bruker Optics, http://www.brukeroptics.com/). The standard deviations of the different biological samples were used to create an overall standard deviation using the multi-evaluation tool in the OPUS software. All samples were included.

For PCA we identified two sample sets, each consisting of 30 spectra. One contained 10 wood samples with lowest, 10 with medium and 10 with the highest lignin content measured with the acetyl bromide method in 95 Headley samples. The second set consisted of 10 wood samples with the lowest, 10 with medium and 10 with the highest calorific values determined with the bomb calorimeter in 61 Headley samples. For data analysis, the region of 1800-900 cm-1 of the FTIR spectra was baseline-corrected via the Rubberband method, vector-normalized, and mean-centred. Then the data were used for PCA. PCA removes the redundancy of having many data points varying in a correlated way by transforming the original data into a set of new and uncorrelated PCs. The first four factor loadings were plotted to gather information about the major components responsible for variability in the fingerprint region of the IR spectrum. All mathematical operations were carried out with OPUS version 5.5 software (Bruker Optics, http://www.brukeroptics.com/).

The calibration models were developed using the QUANT 2 chemometric software package provided in the OPUS 5.5 software (Bruker Optics, http://www.brukeroptics.com/). For calibration and internal validation of lignin contents, the 95 Headley samples were split into two groups, a first group of 61 samples for internal validation (calibration and cross validation) and a second group of 34 samples for external validation of lignin.

For the calibration and validation of calorific values, aliquots of the same group of 61 samples from Headley employed for lignin analyses and additionally 15 samples from Gttingen were used. Of this set 15 samples (some from Gttingen and some from Headley) were removed as independent validation set. The remaining samples were used for calibration and cross validation. Subsequently, the model was validated by testing the validation.

For all calibrations, the following data pre-processing algorithms were tested prior to model construction: first derivative, vector normalization, baseline correction (Rubberband method), and first derivative + vector normalization. Subsequent to pre-processing, wavenumber selection was executed either by iteratively combining and restricting wavenumber ranges or by automatically choosing wavenumber ranges via a set of pre-defined frequency regions and combinations of subregions.

The QUANT software package can be used for principal component analysis (PCA) and for developing partial square models (PLS modelling). The FTIR-ATR spectra for all wood samples were combined into a single data matrix (X-matrix) and the values obtained by chemical lignin analyses or by bomb calorimetry were combined into a response matrix (Y-matrix). The calibration spectra were mean centred by subtracting the mean spectrum from each sample spectrum prior to PLS modelling. Wood component concentrations and energy contents were also mean centred. The PLS algorithm available in the QUANT software package simultaneously decomposes both absorbance spectra and constituent (or calorific value) matrices. The number of principal components (or factors) used for PLS prediction model was determined by observing the response of the residual Y-variance with added factors. When additional factors did not substantially reduce the residual Y-variance, the model constructing process was completed.

All PLS models were constructed with cross validation. The cross validation process was performed as follows: One sample was removed systematically from the data set, then a PLS mode was constructed with the remaining samples to predict the value of the Y-variable for the removed sample. This process continued until each sample had been excluded from the data set and used for validation.

External validation of the PLS model for the estimation of lignin content was performed with an independent sample set consisting of 34 hybrid poplar coppice wood samples that were not included in the development of the calibration model. External validation of the PLS model for the prediction of calorific energy value was carried out with another sample set consisting of 15 samples of stem wood from poplars that had been grown in Gttingen (Germany) or Headley (U.K.) not included in model development or cross-calibration.

Spectral outliers during multivariate calibration and validation phase were detected trough Mahalanobis distance calculations. The Mahalanobis distance is a measure of the similarity of the analyzed spectrum and the mean value of all others [45]. A spectrum with a Mahalanobis distance larger than the limit [Limit = (Factor Rank)/M; M is the number of samples in the calibration dataset] can be recognized as an outlier and removed from the list of standards.

The factor ranged between 2 and 10. To calculate the limit of the Mahalanobis distance, a factor of two was too restrictive for the prediction of unknown natural samples. As a consequence, too many samples were marked as outliers. A factor of five was used in this study. 8% of the calibration samples were detected as outliers in calibration and cross validation stages.

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The research was funded by the European Union project - EnergyPoplar (FP7-211917). We thank Dr A Naumann for the introduction of FTIR spectrophotometer, and G Lehmann (Laboratory for Radioisotopes) for the introduction to acetyl bromide lignin assay. G Zhou would also like to acknowledge the DAAD-CSC Joint PhD scholarship (German Academic Exchange Service and China Scholarship Council) held between 2008 and 2010. We are grateful to an anonymous reviewer for thoughtful comments on this paper.

GZ performed the experiments and wrote the manuscript. GT supplied material and participated in the preparation of the manuscript. AP conceived the project, supervised the experiments and preparation of the manuscript. All authors read and approved the final manuscript.

Additional file 1:. (a) first, (b) second, (c) third, and (d) fourth factor loadings for lignin (black line) and energy content (red line), respectively. The different numbers in the figures refer to absorption peaks described in Additional file 2, Table S1 and Additional file 3, Table S2. According to the approach of Tillmann [46], Rana et al. [47] and Nuopponen et al. [41] the first eight highest peaks were assigned in the factor loading for PC1 (a), PC2 (b), PC3 (c), and PC4 (d), respectively. PC1, PC2, PC3, and PC4 of the "lignin set" (black line) explained 68.7%, 22.2%, 6.8%, and 2.3% of the variation respectively. PC1, PC2, PC3, and PC4 in the "energy sample set" (red line) explained 73.3%, 20.1%, 5.5%, and 1.1% of the variation, respectively. (TIFF 146 KB)

Additional file 2:. The eight highest peaks are indicated for each factor loading. The numbers in parentheses indicate the position according to peak height (see Additional file 1, Figure S1). (DOC 122 KB)

Additional file 3:. The eight highest peaks are indicated for each factor loading (Additional file 1, Figure S1). The numbers in parentheses indicate the position according to peak height. (DOC 108 KB)

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Zhou, G., Taylor, G. & Polle, A. FTIR-ATR-based prediction and modelling of lignin and energy contents reveals independent intra-specific variation of these traits in bioenergy poplars. Plant Methods 7, 9 (2011). https://doi.org/10.1186/1746-4811-7-9

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nitrate uptake and carbon exudation do plant roots stimulate or inhibit denitrification? | springerlink

Plant growth affects soil moisture, mineral N and organic C availability in soil, all of which influence denitrification. With increasing plant growth, root exudation may stimulate denitrification, while N uptake restricts nitrate availability.

We conducted a double labeling pot experiment with either maize (Zea mays L.) or cup plant (Silphium perfoliatum L.) of the same age but differing in size of their shoot and root systems. The 15N gas flux method was applied to directly quantify N2O and N2 fluxes in situ. To link denitrification with available C in the rhizosphere, 13CO2 pulse labeling was used to trace C translocation from shoots to roots and its release by roots into the soil.

Plant water and N uptake were the main factors controlling daily N2O+N2 fluxes, cumulative N emissions, and N2O production pathways. Accordingly, pool-derived N2O+N2 emissions were 3040 times higher in the treatment with highest soil NO3 content and highest soil moisture. CO2 efflux from soil was positively correlated with root dry matter, but we could not detect any relationship between root-derived C and N2O+N2 emissions.

Soil conditions for denitrification have frequently been studied with the main prerequisites being availability of nitrate (NO3) and easily decomposable organic substances, and oxygen deficiency (Burford and Bremner 1975; Firestone et al. 1979). Growing plants modify all these parameters, particularly the availability of the main substrates (NO3 and Corg) and soil moisture, and may thus play an important role in regulating denitrification in situ (von Rheinbaben and Trolldenier 1984).

Plant N uptake largely controls concentration and distribution of mineral N in soils. Amounts and rates of plant N uptake depend on plant species, age, physiological status, root size, and nutritional status. N uptake rates of maize and cereals remain low during the first two months of growth, then increase linearly with increasing biomass reaching a maximum around the time of flowering (Novk and Vidovi 2003, Malhi et al. 2011).

Plant roots contribute to organic C input to the soil through rhizodeposition and decaying roots and root hairs. Thus, total and available concentration of Corg is higher in the rhizosphere compared to bulk soil (Cheng et al. 1993). The amount of rhizodeposited C and its quality depend on plant species, age, and development (Gransee and Wittenmayer 2000; Vancura 1964; Vancura and Hovadik 1965), and plant nutrient status (Carvalhais et al. 2011). In general, younger plants translocate a higher share of assimilated C belowground than mature plants (Kuzyakov and Domanski 2000; Nguyen 2003), and perennial plants translocate a higher share of assimilated C belowground than annual plants (Huskov et al. 2018; Pausch and Kuzyakov 2018).

C and N availability are closely interrelated in the rhizosphere: Under low mineral N concentrations, root morphology is altered, and exudation related to root mass is increased (Paterson and Sim 1999). In addition, the composition of maize root exudates is altered under N deficiency (Carvalhais et al. 2011). On the other side, N fertilization decreases the portion of below-ground translocated C (Kuzyakov and Domanski 2000).

Several studies have tried to disentangle the effects of N and C availability on denitrification with contradictive results. Higher denitrification rates were measured from planted compared to bare soil (Senbayram et al. 2020; Vinther 1984). Some studies showed a strong influence of roots (Philippot et al. 2009), increasing denitrification rates with increasing root biomass (Klemedtsson et al. 1987), and higher potential denitrification activity in rhizosphere soil compared to bulk soil (Hamonts et al. 2013; Malique et al. 2019). Higher denitrification rates in planted soils have been associated with higher Corg availability in the rhizosphere (Bakken 1988; Philippot et al. 2009). In addition, denitrification rates correlated with soil NO3 content (Philippot et al. 2009; von Rheinbaben and Trolldenier 1984). In contrast, other studies found no differences between planted and unplanted soil (Haider et al. 1985). Denitrification was increased only with poorly growing plants (von Rheinbaben and Trolldenier 1984) or when root biomass started to decrease (Haider et al. 1987), and NO3 availability did not affect denitrification (Haider et al. 1987; Hamonts et al. 2013). The majority of these studies measured potential denitrification applying the acetylene inhibition method (Yoshinari and Knowles 1976), which is considered outdated due to a number of drawbacks such as inhibiting nitrification (Groffman et al. 2006).

Accordingly, it is still unclear whether growing plants stimulate denitrification through root exudation or restrict it through NO3 uptake. Reliable measurements of N2 fluxes and N2O/(N2O+N2) ratios in the presence of plants are scarce. Direct measurement of N2 fluxes is only possible in either artificial N2-free atmosphere (Scholefield et al. 1997, Senbayram et al. 2020) or by applying highly enriched 15N labeled NO3 (Hauck and Melsted 1956). The latter is used in the 15N gas flux technique which enables direct quantification of N2O and N2 produced from the labelled NO3 pool and estimation of processes contributing to N2O and N2 formation including denitrification, co-denitrification, or nitrification and nitrifier denitrification (Buchen et al. 2016, Laughlin and Stevens 2002).

This study aimed to directly quantify N2O and N2 fluxes from soil with plants of the same age but different size of shoot and root systems and to relate denitrification to C availability from root exudation. As plant water uptake may also affect denitrification (von Rheinbaben and Trolldenier 1984), we aimed to keep soil moisture constant by continuous irrigation. We hypothesized that (I) plant N uptake governs NO3 availability for denitrification. When plant N uptake is low due to smaller root system or root senescence, N2O and N2 emissions are increased. (II) Denitrification is stimulated by higher Corg availability from root exudation or decaying roots increasing total gaseous N emissions and decreasing their N2O/(N2O+N2) ratios.

The experiment consisted of a pre-cultivation phase followed by the experimental phase. A schematic overview of both phases is presented in Fig.1. In the pre-cultivation phase, plants were raised under controlled conditions. Maize plants (Zea mays L. cv. Ronaldinio) were grown under different N fertilization to obtain plants with different root and shoot biomass. As a second species, cup plant (Silphium perfoliatum L.) was included, a bioenergy plant that can produce similar aboveground biomass as maize (Gansberger et al. 2015) but has a higher root:shoot ratio. As cup plant is a perennial plant, it likely transfers more C belowground and exudes more organic substances than maize (Huskov et al. 2018; Pausch and Kuzyakov 2018). In all treatments, the N supply was scheduled to assure that at the end of the pre-cultivation phase, the soils were equally depleted in plant available N. With respect to background N supply, this permitted nearly equal starting conditions for the subsequent stable isotope labeling experiment.

To account for all necessary measurements, each treatment was replicated 19 times (Table 1). At the end of the pre-cultivation phase, the first set of replicates (16) was harvested to determine shoot and root biomass, N and C content, and 15N and 13C background concentrations. The second set of replicates (712) was labeled with 15NO3 and 13CO2, and gases evolving from soil were measured for the following 10 days. At the end of the experiment, replicates 712 were harvested. Replicate 13 was used to determine 13C uptake during 13CO2 pulse labeling and replicates 1419 were used to determine 13C background values in soil-emitted CO2.

The soil for the experiment was collected from a long-term experimental field site of the Hhere Landbauschule Rotthalmnster, Germany (latitude N4821, longitude E1311, elevation 360m above sea level) in summer 2016. It was sieved to 10mm, air dried, and stored at 4C until setup of the experiment. The soil was classified as a Haplic Luvisol with a silty loam texture (19% clay, 71% silt, 10% sand). Soil properties were: total C 1.23%, total N 0.14%, C:N 8.76, pH (CaCl2) 6.74.

Seven kg dry soil was mixed with fertilizers (0.14g P kg1 as Ca(H2PO4)2, 0.2g K kg1 as K2SO4 and 0.04g Mg kg1 as MgSO4 * 7 H2O including 0.135g S kg1) and filled in pots of 15cm diameter and 35cm height to a bulk density of 1.3g cm3. Soil moisture was adjusted to 60% water holding capacity (WHC, 21.9% gravimetric water content) and watered regularly. TDR soil moisture sensors (Decagon Devices, Pullman, USA) were used to monitor soil water content during plant growth.

Maize seeds (Zea mays L. cv. Ronaldinio) were germinated on wet paper for 4days. Cup plant (Silphium perfoliatum L.) had been pre-cultivated for 2years in 5cm-pots and had 24 leaves. Per pot, either one germinated maize seed or one cup plant seedling was transplanted. Plants were cultivated in a climate chamber (Weiss, Loughborough, UK) with a diurnal cycle of 16h day (light intensity 300mol m2 s1, air temperature 25C, relative humidity 50%) and 8h night (18C, 60%). Daytime included 4h sunrise and 4h sunset when light intensity, temperature, and relative humidity were gradually adjusted. All pots were fully randomized weekly to avoid microclimatic effects.

Maize N fertilization differed between treatments to achieve plants of different size: Maize S (no N fertilization, small plants), Maize M (0.05g N kg1 (0.35g N pot1, as NH4NO3 split in 7 doses), medium sized plants), and Maize L (0.086g N kg1 (0.6g N pot1, as NH4NO3 split in 7 doses), large plants). Cup plants were fertilized like Maize M (0.05g N kg1 (0.35g N pot1, as NH4NO3 split in 7 doses)).

Replicates 712 of each treatment received 0.1g N kg1 15N-labeled Ca(NO3)2 (~60 at% 15N2, Campro Scientific GmbH, Berlin, Germany) 45 days after transplanting. The tracer was dissolved in H2Odest and applied by injection with stainless steel needles as described by Buchen et al. (2016). Briefly, 15N fertilizer solution was injected via 12 needles to 6 depths (2.5, 7.5, 12.5, 17.5, 22.5 and 27.5cm) aiming for optimal three-dimensional homogenous label distribution (Wu et al. 2011). Per injection point, 10ml tracer solution were injected via a peristaltic pump (Ismatec, Wertheim, Germany) to simultaneously increase soil water content to 75% water-filled pore space (WFPS, equivalent to 80% WHC).

After injection of 15N tracer solution, all pots were closed with acrylic glass lids with a hole for the plant shoot, leaving a small headspace (23cm) between soil surface and lid. Pots were then sealed with silicone paste (Tacosil 171, Thauer & Co. KG, Dresden, Germany). Plants were labeled with 13C in four separate chambers made from translucent greenhouse film (one for each treatment Maize S, Maize M, Maize L, and Cup plant). In each chamber, 15N labeled replicates 712 and the non-labeled replicate 13 were labeled with 13C. To enrich the chamber atmosphere with 13CO2, 60ml of 5M H2SO4 were added to 5g Na213CO3 (99 at%) dissolved in H2Odest in each chamber. For internal chamber ventilation, two fans were installed. The plants were pulse labeled in the 13CO2 enriched atmosphere for 5h. Before opening each chamber, an air sample was analyzed for CO2 concentration to ensure that maximum amounts of CO2 had been taken up by plants. Chambers were opened and CO2 evolving from the soil was trapped. Replicate 13 was harvested directly after chamber opening to estimate the amount of 13C assimilated during labeling.

To determine natural abundance background of 13C in CO2, replicates 1419 were used. 0.1g N kg1 was injected using Ca(NO3)2*6 H2O dissolved in H2Obidest, and pots were sealed using the same methods as described above.

After pots had been sealed with silicon paste, plants were irrigated by injecting water through a valve on the bottom of the pots. To irrigate pots without applying too much pressure, peristaltic pumps (Watson-Marlow, Zollikon, Switzerland) with a pumping rate of 1.5ml min1 were used. Target soil moisture was 75% water-filled pore space (WFPS, equivalent to 80% WHC) during the gas sampling phase. To estimate irrigation demand, TDR soil moisture sensors (Decagon Devices, Pullman, USA) were used to monitor soil water content during the experiment in one replicate for each treatment. In addition, all pots were weighed one, three, and five days after 13C labeling to compare whether irrigation demand differed between pots. As pot weights were comparable within treatments, sensor data were used to compare soil moisture.

One additional pot was filled with dry quartz sand, sealed with silicone paste as described before, and used as a reference to determine background gas concentrations. To flush the headspace of all pots with CO2-free air, pressurized air was first run through a glass column filled with soda lime (pellets made of NaOH and Ca(OH)2 mixture) to remove CO2. For trapping CO2 emitted from soils, the outlet tubes of the pots headspaces were connected to glass tubes containing 15ml of 1M NaOH solution. Starting one day after labeling, NaOH solution was changed in intervals of 6, 12, or 24 hours. To determine 13C background in CO2, replicates 1419 were treated similarly: the headspaces were flushed for 6 hours and CO2 was trapped in glass tubes containing 15ml of 1M NaOH solution. To estimate the total CO2 efflux, the C concentration of the NaOH solutions was determined with a TIC-analyzer (multi N/C 2100S, Analytik Jena, Jena, Germany). For 13C measurements, CO2 trapped in NaOH was precipitated as SrCO3 with an excess of 1M SrCl2 solution. The precipitants were centrifuged, washed with deionized water until the pH was neutral, the precipitate was frozen, and then freeze-dried with a rotation vacuum concentrator (RVC 225 CDplus, Martin Christ, Osterode am Harz, Germany) and a cooling trap (CT 0250, Martin Christ, Osterode am Harz, Germany), both connected to a vacuum pump.

13C enrichment in precipitated SrCO3 was analyzed: Natural abundance samples were measured on an elemental analyzer NA 11,100 (CE Instruments, Milano, Italy) linked to a Delta Plus gas-isotope ratio mass spectrometer (Finnigan MAT, Bremen, Germany) via a ConFlo III interface (Finnigan MAT, Bremen, Germany). For enriched samples, depending on capacity, one of the following combinations was used: (i) elemental analyzer Flash 2000 (Thermo Fisher Scientific, Cambridge, UK) linked to a Delta V Advantage gas-isotope ratio mass spectrometer (Thermo Electron, Bremen, Germany) via a ConFlo III interface (Thermo Electron, Bremen, Germany), or (ii) elemental analyzer NA1108 (Fisons-Instruments, Milan, Italy) linked to a Delta C gas-isotope ratio mass spectrometer (Finnigan MAT, Bremen, Germany) via a ConFlo III interface (Thermo Electron Cooperation, Bremen, Germany).

For N2O and N2 sampling, the airflow through the pots headspace was interrupted to accumulate gases in the headspace. After 1h, duplicate samples were taken using a syringe and filled in pre-evacuated 12-ml Exetainer glass vials (Labco, High Wycombe, UK). Samples were analyzed for N2O concentration using a gas chromatograph (GC 7890A, Agilent, Santa Clara, USA). The analytical precision of the GC was determined by repeated measurements of standard gases (300ppb N2O) and was consistently<3%. The second duplicate was analyzed for m/z 28 (14N14N), 29 (14N15N) and 30 (15N15N) of N2 using a modified GasBench II preparation system coupled to an isotope ratio mass spectrometer (MAT 253, Thermo Fisher Scientific, Bremen, Germany) according to Lewicka-Szczebak et al. (2013). This system allows a simultaneous determination of mass ratios 29R (29/28) and 30R (30/28) of three separated gas species (N2, N2+N2O, and N2O), all measured as N2 gas after N2O reduction in a Cu oven. Typical repeatability of 29R and 30R (1 of 3 replicate measurements) was 5107 for both values. For each of the analyzed gas species, the fraction originating from the 15N-labeled pool with respect to total N in the gas sample (Fp) as well as the 15N enrichment of the active 15N-labeled N pool (ap) producing N2O (ap_N2O) or N2+N2O (ap_N2+N2O) were calculated after Spott et al. (2006) as described in Lewicka-Szczebak et al. (2017).

Before labeling (44 days after transplanting), replicates 16 were harvested. Eleven days after 13C labeling (57 days after transplanting), all labeled plants (replicates 712) were harvested. At both harvests, plants were separated into shoots and roots including maize crown roots. As all pots were densely rooted, a separation of rhizosphere and bulk soil was not possible. Roots were shaken gently to separate them from soil and washed. From replicates 712, a subsample of root washing water was analyzed for water-extractable organic C (WEOC) and the amount of 13C lost during root washing was determined. To estimate their amount in soil, fine roots were picked by hand from a subsample of soil (~400g soil) for a defined time. All plant material and a soil subsample were dried at 60C, milled in a ball mill and analyzed for total C, 13C, total N, and 15N content using an elemental analyzer coupled to a gas-isotope ratio mass spectrometer as described earlier. For determination of water-extractable organic C (WEOC) content, a subsample of fresh soil was analyzed according to Chantigny et al. (2007). Briefly, fresh soil was homogenized with deionized water (1:2 w/v). Samples were centrifuged and filtered with 0.45m polyether sulfone filters (Labsolute, Renningen, Germany), split in two subsamples and stored at -20C. The extracts were analyzed for total C, organic C, and total N content using a multi N/C Analyzer (Analytik Jena, Jena, Germany).

For determination of soil mineral N content, a subsample of 50g was frozen at -20C. Frozen samples were extracted with a 2M KCl solution (1:5 w/v) for 60min on an overhead shaker (85rpm). The extracts were filtered with 615 filter paper (Macherey Nagel GmbH & Co. KG, Dren, Germany), split in two subsamples, and stored at -20C. The extracts were analyzed colorimetrically for the concentrations of NO3 and NH4+ using a San++ continuous flow Analyzer (Skalar Analytical B.V., Breda, The Netherlands). 15N concentration in NH4+ and NO3 was analyzed using an automated sample preparation unit for inorganic nitrogen coupled to a membrane inlet quadrupole mass spectrometer (QMS, GAM 200, InProcess, Bremen, Germany) as described in detail by Eschenbach et al. (2017, 2018). In parallel subsamples, soil water content was determined by oven drying at 105C.

Cumulative CO2 emissions were calculated from CO2 trapped in NaOH, CO2 fluxes were calculated by dividing cumulative CO2 through the respective trapping time (6, 12, or 24h). 13C recovery in CO2 (13Crecovery; CO2, mg) was calculated as the excess (above background) 13C concentration multiplied with the total CO2 trapped (CO2, mg CO2-C):

where 13CCO2 is the 13C enrichment of CO2 (at%) trapped after labeling and 13CNA; CO2 is the natural 13C background (at%) from unlabeled plants (replicates 1419). 13C recovery in soil (13Crecovery; soil, mg) was calculated as follows:

where 13Csoil is the enrichment of 13C (at%) of the soil C pool after labeling, 13CNA; soil is the natural abundance of 13C in soil before labeling (at%), Csoil is the total content of C in soil (mg g1), and masssoil is the mass of soil per pot (g). Relative 13Crecovery (% of recovered 13C) of a particular pool (CO2, soil) was calculated by dividing the amount of 13C recovered in that pool (13Crecovery; pool) by the sum of the amount of 13C recovered in all pools (CO2, shoot, root, soil, root washing water).

where CH is the mass concentration in the headspace and CB is the background concentration in the reference pot (g N m3) corrected by the chamber temperature according to the ideal gas law, t is the accumulation time (h), V is the volume of the headspace (m3), and m is the dry mass of soil per pot (kg).

We calculated the 15N enrichment of the active NO3 pool undergoing denitrification (ap_N2O, ap_N2O+N2) from the non-random distribution of N2 and/or N2O isotopologues using calculations by Spott et al. (2006) as described by Buchen et al. (2016) and Lewicka-Szczebak et al. (2017):

The fraction of N derived from the active NO3 pool (Fp) was calculated using Eq.(8) if 30R was significant and otherwise Eq.(9) was used. In the latter case, ap_N2O was assumed to be identical with ap_N2 and ap_N2O+N2 and was thus used when calculating Fp_N2 and Fp_N2O+N2 from Eq.9. If ap_N2O of a sampling date was not available, the mean value from the other replicates from the same sampling date was used as best estimate. Fp calculated from Eq.(9) with a given 29R is relatively insensitive to changes in ap between 0.4 and 0.6 since the nominator yields values between 0.48 and 0.5. Hence, uncertainty in the estimation of ap within that range causes minor uncertainty in calculated Fp (Well and Myrold 1999). Because ap values in our study were typically between 0.4 and 0.6, we assume that uncertainty in Fp calculation from the missing of individual ap values was small.

Fp values were multiplied with respective total sample N concentration (N2O, N2) to obtain pool-derived gas concentrations (in ppm). Then, pool-derived fluxes (fp) were calculated from concentrations similar to Eq.(4). The same calculations were used for N2O, N2, and N2O+N2, resulting in respective values for fractions of pool-derived N and for the respective 15N abundances of the active N pools (ap_N2O, ap_N2, ap_N2O+N2). Non-pool derived N2O fluxes were calculated by subtracting pool-derived N2O fluxes from total N2O fluxes.

Cumulative N2O, N2, and N2O+N2 emissions were calculated by linear interpolation of fluxes. The % of N2+N2O emitted with respect to added N was estimated by dividing cumulative pool-derived N2O+N2 emission by the amount of N added with 15NO3 labeling.

All statistical analyses were performed using the statistical software R version 3.6.0 (R Core Team 2019). Means and standard deviations were calculated over all replicates. For harvest data, cumulative CO2, and 13C recovery a one-way ANOVA was calculated followed by Tukeys HSD post-hoc test at p0.05 to separate treatment effects. As cumulative N emissions were not normally distributed, the Kruskal-Wallis rank sum test was used followed by LSD post-hoc test at p0.05 to separate treatment effects. To compare soil and plant samples between harvests, and to test whether soil 15a_NO3 contents at final harvest and ap_N2O or ap_ N2O+N2 at last sampling date differed, a t-test was used at p0.05. Simple linear regression models were tested to analyze the effects of soil and plant parameters on CO2 and N fluxes and cumulative emissions.

Shoot dry matter increased significantly in all treatments between the first and the second harvest, but differences between treatments did not change (Table 2, results of 1st harvest are displayed in supplementary table S1). Root dry matter significantly decreased in Maize S and M until the end of the experiment. Increases in root dry matter in Maize L and Cup plant were not significant. Root:shoot ratio decreased in all treatments but remained twice as high in cup plant compared to maize, which is typical for perennial plants compared to annual plants (Huskov et al. 2018). Nitrogen content increased in previously unfertilized Maize S plants and was similar in all maize treatments at the final harvest. Nitrogen content in cup plant shoots and roots was significantly greater than in all maize treatments and total N uptake corresponded with N fertilization Maize L>Maize M=Cup plant>Maize S. Soil NO3 content analyzed at the end of experiment was on average still twice as high in Maize S compared to all other treatments.

Total and pool-derived N fluxes were highest in Maize S but followed a similar pattern in all treatments (Fig.2a+c). Total N2O fluxes strongly increased in Maize S reaching highest values on day 3 (11.3g N2O-N kg1 h1, Fig.2a) and a second smaller peak on day 6 (5.9g N2O-N kg1 h1). Pool-derived N2O+N2 fluxes followed a similar general pattern as total N2O fluxes (Fig.2c) and peaks were detected at similar times as N2O peaks, in Maize S on day 3 (48g N2-N kg1 h1) and larger peaks on day 6.5 (67g N2-N kg1 h1) and day 9.5 (61g N2-N kg1 h1). Total N2O and pool-derived N2O+N2 fluxes in all other treatments followed a similar pattern but on a lower scale. The product ratio (N2O/(N2O+N2), Fig.2d) of pool-derived fluxes followed a similar pattern in all treatments. The product ratio decreased for the first days after onset of incubation reaching values between 0.2 and 0.5 as N2 became the dominant end product of denitrification. It shortly increased and peaked on day 3.5, then decreased again until day 6 to values between 0 and 0.2. After day 6.5, the product ratio ranged between 0 and 0.5 until the end.

Total and pool-derived cumulative N emissions were 2043 times higher in Maize S compared to all other treatments (Table 3). No significant differences were detected between the other treatments. Similarly, recovery of added NO3 in N2O+N2 was highest in Maize S and not significantly different in the other treatments. The mean N2O/(N2O+N2) ratio ranged from 0.14 to 0.16 in maize treatments and was 0.24 in cup plant.

Treatments did not exhibit continuous patterns of ap and Fp values throughout the experiment. The fraction of N2O derived from the active labeled NO3 pool (Fp_N2O) decreased during the experiment showing that the contribution of N2O from sources other than the labeled NO3 pool increased with time (Fig.2b). Fp_N2O was close to 1.0 in Maize S for three days after labeling, then decreased to 0.4 on day 5, and ranged between 0.5 and 0.8 until the end of the experiment. For the other treatments, Fp_N2O continuously decreased until day 5/6. After day 6.5, Fp_N2O increased in Maize M and L, fluctuating between 0.1 and 0.6. At the last day, Fp_N2O was <0.07 in all treatments.

The time course of 15N enrichment of the active NO3 pool producing N2O and N2 (ap_N2O, ap_N2O+N2) was different in Maize S than in the other treatments. During the first days after labeling, 15N enrichment of the active NO3 pool producing N2O and N2 (ap_N2O, ap_N2O+N2) was close to 60 at% in all treatments (Fig.3). In Maize S, ap_N2O and ap_N2O+N2 were higher than 50 at% during the whole experiment and only decreased on the last day. In all other treatments, ap_N2O and ap_N2O+N2 continuously decreased until day 6.5. On day 7, ap-values were higher than 50 at% and decreased again until the end of the experiment. 15N enrichment of the total soil NO3 pool (15a_NO3) was measured at final harvest and was mostly significantly lower (p<0.05) than 15N enrichment of the active NO3 pool producing N2O and N2 (ap_N2O and ap_N2O+N2) from the last gas measurement (Fig.3, Supplementary table S2).

15N enrichment of the activeN pool undergoing denitrification (ap_N2O, ap_N2O+N2)and 15N enrichmentof total soilNO3pool (15a_NO3). Means and standard deviation for n=6. When not visible, error bars are smaller than the symbols

Data from soil moisture sensors showed that soil moisture content was higher in Maize S than all other treatments for the first days after labeling (Fig.4). However, it was lower than the targeted value of 75%WFPS. As all plants respired large amounts of water, it took a few days to adjust irrigation amounts to plant water demand, and soil moisture could not be kept constant throughout the experiment.

In Maize S and Cup plant, soil moisture increased with irrigation three days after labeling reaching values around 70%WFPS. In Maize M and L, soil moisture increased five days after labeling reaching values around 55%WFPS. In Cup plant, soil moisture stayed on a similar level around 70%WFPS with fluctuations due to water uptake and irrigation. Although soil moisture was in a similar range in Maize S and Cup plant from day 4 to 6 and in all maize treatments after day 7, total N2O and pool-derived N2O+N2 fluxes were always highest in Maize S. Thus, we did not find significant relationships between N fluxes and soil moisture during the experiment indicating that soil moisture was not the only factor controlling gaseous N losses (Table 5, supplementary table S3, supplementary figure S1). However, the N2O/(N2O+N2) ratio of pool-derived fluxes decreased with increasing soil moisture (%WFPS, adj. R=0.14, p<0.05) indicating that increasing soil moisture stimulated N2O reduction.

The time course of cumulative CO2 efflux and 13C enrichment in CO2 was similar in all treatments (Fig.5a+b). CO2 efflux was similar in Maize M and Maize L where it increased almost linearly during the whole experiment, and total cumulative CO2 was only slightly higher in Maize L than in Maize M (Table 4). Lowest cumulative CO2 efflux was measured under Maize S plants where efflux decreased considerably after 1.5 days. Cumulative CO2 efflux under cup plants was significantly lower than from Maize L and not statistically different from the other two maize treatments (Table 4). Overall, cumulative CO2 efflux was positively correlated with root dry matter at final harvest (adj. R=0.36,p<0.01, Table 5).

13C enrichment in CO2 strongly decreased in all treatments two days after labeling until the end of CO2 sampling (Fig.5b). Highest 13C enrichment of soil emitted CO2 was measured under Cup plant and lowest in Maize L. It strongly decreased two days after labeling until the end of CO2 sampling. No statistically significant differences (p<0.05) were found in relative 13C recovery in CO2, soil, or soil+CO2 (Table 4), but overall, mean relative 13C recovery in soil increased with root dry matter, indicating that root-derived C recovered in soil increased with root biomass.

Simple linear regression models were tested to identify effects of plant growth, N uptake, and Corg availability on cumulative N2O and N2 emissions (Table 5, supplementary table S3). Total cumulative N2O and pool-derived cumulative N2O+N2 emissions were significantly (p<0.01) negatively correlated with root dry matter (adj. R=0.41 and adj.R2=0.32, respectively) and plant N uptake (adj.R=0.49 and adj.R2=0.33, respectively) indicating that gaseous N losses were highest under plants with small root system and low N uptake. In addition, total cumulative N2O emissions and soil NO3 content at final harvest were positively correlated (adj.R=0.10, p<0.05). As cumulative CO2 emissions were positively correlated with root dry matter and cumulative N emissions negatively, total cumulative N2O and pool-derived cumulative N2O+N2 emissions were negatively correlated with cumulative CO2 efflux (adj.R=0.36, p<0.01). No correlations were found between total or pool-derived cumulative N emissions or N2O/(N2O+N2) ratio and 13C recovery in soil and/or CO2. However, we identified a weak but significant positive relationship between N2O/(N2O+N2) of pool-derived fluxes and CO2 efflux (adj.R=0.11, p<0.01).

Cumulative N emissions were 2040 times higher in Maize S compared to all other treatments. Plant transpiration strongly affected soil moisture which was highest in Maize S during the first 4 days after 13C labeling. Soil moisture is an important control of denitrification as it regulates O2 concentration and diffusion in soil (Schlter et al. 2018, Rohe et al. 2020). Plant roots constantly alter soil moisture and its distribution in soil by root water uptake. Accordingly, previous studies reported that plant growth controlled soil moisture and denitrification rates (Bakken 1988; von Rheinbaben and Trolldenier 1984). In our study, when soil moisture increased with irrigation, N2O and, especially, N2 fluxes increased shortly thereafter. Furthermore, the N2O/(N2O+N2) ratio of pool-derived fluxes decreased with increasing soil moisture which is consistent with N2 being the dominant end product of denitrification under high WFPS (Davidson 1991). Although soil moisture was highest in Cup plant from day 4 to 8, and similar in Maize treatments from day 6 to 9, N fluxes were highest in Maize S throughout the experiment. Thus, differences in soil moisture alone cannot explain the vast differences in N fluxes between Maize S and the other treatments in our study.

Maize S plants were characterized by lowest root dry matter of all treatments and lower shoot dry matter compared to the other maize treatments. Furthermore, soil NO3 content at final harvest was more than twice as high in Maize S compared to all other treatments. The relationship between soil NO3 content at the end of the experiment and cumulative N2O emissions was weak (0.10, p<0.05). However, N uptake was negatively correlated with total cumulative N2O emissions and pool-derived cumulative N2O+N2 emissions (adj.R=0.49, p<0.001 and adj.R2=0.33, p<0.01, respectively) indicating that NO3 availability played an important role in regulating denitrification. Our results show clearly that an increase in soil moisture led to increasing N2O+N2 fluxes, but N fluxes remained on a low level when NO3 availability was low due to rapid plant N uptake. Only when both N and water uptake were low, high NO3 availability and high soil moisture led to strongly increased N losses.

Different NO3 pools and N turnover processes contributed to N2O formation throughout the experiment. The 15N enrichment of the total soil NO3 pool (15a_NO3) decreased from 60 at% after labeling to 1030 at% until the end of the experiment as unlabeled organic N was mineralized and diluted the labeled NO3 pool (Buchen et al. 2016; Deppe et al. 2017). Thus, denitrification of unlabeled NO3, as well as nitrification, nitrifier denitrification, and coupled nitrification-denitrification may have contributed to N2O formation (van Groenigen et al. 2015; Wrage-Mnnig et al. 2018; Wrage et al. 2001).

In Maize S, 15a_NO3 at final harvest was significantly higher than in all other treatments, indicating that nitrification was less relevant in this treatment. Accordingly, the fraction of N2O derived from the active labeled NO3 pool (Fp_N2O) was >0.5 throughout the experiment indicating that most N2O was lost through denitrification from labeled NO3. The 15N enrichment of the active NO3 pool producing N2O and N2O+N2 (ap_N2O and ap_N2O+N2) stayed close to its initial value of 60 at% highlighting that N2O and N2 were mainly lost from anoxic microsites where labeled NO3 had not been diluted by nitrification (Buchen et al. 2016).

In the other treatments, ap_N2O, ap_N2, and Fp_N2O did not exhibit continuous trends. While values first decreased due to dilution with NO3 from nitrification, values increased after day 6.5 (Fig.3), presumably due to the slight increase in WFPS after irrigation on day 6.5 (Fig.4). Increasing soil moisture increased denitrification rates in anoxic hotspots, which corresponds with increasing N2O and especially N2 fluxes in Maize M and L. At the same time, it restricted nitrification and thus decreased the share of nitrification-dependent processes contributing to N2O formation (reflected in increasing Fp_N2O). Simultaneously increasing ap_N2O and ap_N2O+N2 in Maize M and L on Day 7 indicate that 15N enrichment of the active labeled NO3 pool was still close to 60 at% in microsites where denitrification took place. The rise of actual ap-values back towards initial values is in line with a change in the anaerobic volume where denitrification occurs (Bergstermann et al. 2011). A recent study conducted with the same soil but without plants showed that O2 concentration in repacked soil cores was highly variable and average O2 saturation decreased with increasing soil moisture while the anaerobic soil volume fraction increased with increasing soil moisture and soil depth (Rohe et al. 2020). After day 6.5, N2O+N2 were predominately lost from domains that had been continuously anoxic or were most distant from oxic domains and thus were less diluted with unlabeled NO3. We anticipate that in these microsites O2 concentrations had been low enough to prevent nitrification during the first days so that the labeled pool was not diluted by unlabeled NO3 from nitrification.

While fungal co-denitrification has been reported as the dominant N2O source in a planted soil with high NO3 content (Laughlin and Stevens 2002), our data provide no indications for co-denitrification, because ap_N2O and ap_N2O+N2 were always higher than 15a_NO3, but co-denitrification would lead to ap lower than 15a_NO3 due to hybrid formation of N2O or N2 (Spott and Stange 2007).

Thus, in our study, N2O and N2 fluxes mainly derived from denitrification of labeled 15NO3 in anoxic microsites, while nitrification simultaneously occurred in more oxic parts of the soil, potentially contributing to formation of unlabeled N2O.

One of the core hypotheses of this study was that availability of root-derived C is a key driver of denitrification in planted soils. It was based on a number of studies reporting higher denitrification activity in rhizosphere compared to bulk soil which was explained by higher soil C (Hamonts et al. 2013; Klemedtsson et al. 1987; Malique et al. 2019 ; Smith and Tiedje 1979). Detectable rhizodeposition and C flow into belowground respiration result from C translocation from shoots to roots (Remus and Augustin 2016). Thus, we used 13CO2 pulse labeling to trace C translocation from shoots to roots, its release by roots into the soil, and 13CO2 emitted from soil.

We found a positive correlation between root dry matter at final harvest and cumulative CO2 efflux (adj.R=0.36, p<0.01) and, on average, relative 13C recovery in soil increased with increasing root dry matter indicating that root exudation increased with root dry matter. However, we could not detect any relationship between total or pool-derived N fluxes and total CO2 efflux or root-derived C and cumulative N emissions or the ratio of gaseous products.

Most previous studies investigating plant effects on denitrification did not measure N2O and N2 emissions under growing plants, but either potential denitrification (Klemedtsson et al. 1987; Malique et al. 2019; Smith and Tiedje 1979) or denitrification capacity (Hamonts et al. 2013) from soil samples taken from rhizosphere and/or bulk soil. In those studies, conducted under anoxic conditions with unlimited NO3 supply, higher C availability in rhizosphere soil samples led to higher emissions of N2O and N2. However, when separation of bulk soil and rhizosphere was not well-defined due to densely rooted soil in pots, no differences in potential denitrification were found (Malique et al. 2019). In the few studies with growing plants, higher denitrification rates were measured during the first weeks after emergence (Senbayram et al. 2020), with poorly growing plants (von Rheinbaben and Trolldenier 1984), or when root biomass was decreasing (Haider et al. 1987). No stimulation of denitrification was found when actively growing maize plants were compared to unplanted soil (Haider et al. 1985). Accordingly, root-derived C may stimulate denitrification when soil NO3 is not limited. We were not able to estimate the effect of C availability on denitrification as NO3 limitation and O2 inhibition were the factors controlling denitrification in our study.

The activity of denitrifying microorganisms in soil is primarily controlled by availability of O2, NO3, and Corg (proximal factors, Groffman et al. 1988). Plant roots affect these through rhizodeposition, root respiration, N and water uptake (distal factors, Groffman et al. 1988). Figure 6 shows how proximal and distal factors change during plant and root growth and how these affect denitrification in planted soil. The presented conceptual drawing is based on two assumptions: (i) NO3-based fertilizer is only added before plant growth and (ii) root water uptake is the main regulator of soil moisture.

Conceptual drawing of plant root effects (distal factors, green/grey) on drivers of denitrification (proximal factors, light grey) and potential N2O+N2 losses (orange/dark grey). Based on two assumptions: (i) NO3-based fertilizer is only added before plant growth and (ii) root water uptake is the main regulator of soil moisture

In most agricultural soils, available Corg is low. With increasing root growth, rhizodeposition and root turnover increase Corg availability. At the same time, root respiration and microbial activity increase, decreasing O2 concentrations in the rhizosphere. This offers favorable conditions for denitrifying microorganisms as long as sufficient NO3 is available (Klemedtsson et al., 1987; Senbayram et al., 2020; Stefanson, 1972). As N uptake increases with plant and root growth, NO3 becomes limited for denitrifiers (Haider et al., 1985). Furthermore, increasing water uptake decreases soil moisture and restricts formation of anoxic microsites for denitrification (Bakken 1988, von Rheinbaben and Trolldenier, 1984). Accordingly, our study showed that with increasing plant and root growth, plant water and N uptake became the most important controls of denitrification. Similarly, soil moisture can vary strongly under field conditions depending on precipitation and plant water uptake. When NO3 is available (i.e. after fertilization), increasing soil moisture after rainfall can lead to strongly increased N2O+N2 emissions (Buchen et al. 2017, Ruser et al. 2017).

Overall, plants continuously change boundary conditions and substrate availability for denitrification in soil, and it requires high technical input and equipment to keep experimental conditions stable and controlled. However, as plants do control these conditions so strongly, it is very exciting and very important to further investigate these processes to understand and predict N cycling, denitrification, and gaseous N losses on the field scale. Further research is thus indispensable.

We aimed to investigate how plants control the main substrates for denitrification (NO3 and Corg) through N uptake and root exudation. To our knowledge, this is the first study combining in situ measurements of N2O+N2 fluxes with estimations of root-derived C availability.

Plant water uptake was a main factor controlling soil moisture and, thus, daily N2O+N2 fluxes, cumulative N emissions, and N2O production pathways. However, N fluxes remained on a low level when NO3 availability was low due to rapid plant N uptake. Only when both N and water uptake were low, high NO3 availability and high soil moisture led to strongly increased N losses. Our study provides evidence that most N losses originated from denitrification in small anoxic hotspots where NO3 was not diluted by nitrification. Simultaneously occurring nitrification in oxic parts of the soil potentially contributed to formation of unlabeled N2O.

Total CO2 efflux was positively correlated with root dry matter, but there was no indication of any relationship between recovered 13C from root exudation and cumulative N emissions. We anticipate that higher Corg availability in pots with large root systems did not lead to higher denitrification rates, as NO3 was limiting denitrification due to plant N uptake. Overall, we conclude that root-derived C stimulates denitrification only when soil NO3 is not limited and low O2 concentrations enable denitrification.

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The authors thank Simone Urstadt, Finn Malinowski, Bridith Angulo Schipper, Ilse Thaufelder, and Jakob Streuber for help during experimental and laboratory work. We are thankful to Tomor Krasniqi for weighing endless numbers of C:N samples and to the Centre for Stable Isotope Research and Analysis of the University of Gttingen for analysis of C and N isotopes of soil and plant samples. Further, we thank Dr. Daniel Ziehe and Kerstin Gilke for GC analyses, Dr. Anette Giesemann and Martina Heuer for IRMS analyses of gas samples, and Dr. Wolfram Eschenbach for analysis of 15N in NO3 and NH4+. We thank Prof. Dr. Jrgen Bttcher for soil classification and Prof. Kenneth Albrecht and Dr. Pedro Gerstberger for supplying cup plant seedlings. We thank Dr. Bernd Steingrobe for his feedback on the manuscript. Furthermore, we acknowledge two anonymous reviewers for their advice to improve the quality of the manuscript.

Open Access funding enabled and organized by Projekt DEAL. This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the research unit DFG-FOR 2337 (DI 546/41, We 1904/102): Denitrification in Agricultural Soils: Integrated Control and Modelling at Various Scales (DASIM).

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Rummel, P.S., Well, R., Pfeiffer, B. et al. Nitrate uptake and carbon exudation do plant roots stimulate or inhibit denitrification?. Plant Soil 459, 217233 (2021). https://doi.org/10.1007/s11104-020-04750-7

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How we use cookies A cookie is a small file which asks permission to be placed on your computers hard drive. Once you agree, the file is added and the cookie helps analyse web traffic or lets you know when you visit a particular site. Cookies allow web applications to respond to you as an individual. We use traffic log cookies to identify which pages are being used. This helps us analyse data about web page traffic and improve our website in order to tailor it to customer needs. We only use this information for statistical analysis purposes and then the data is removed from the system. Overall, cookies help us provide you with a better website, by enabling us to monitor which pages you find useful and which you do not. A cookie in no way gives us access to your computer or any information about you, other than the data you choose to share with us. You can choose to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. This may prevent you from taking full advantage of the website. Links to other websites Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question. How to delete cookies or control them This site will not use cookies to collect personally identifiable information about you. However, if you wish to restrict or block cookies set by this or any other website, you can do this through your browser settings. The Help function within your browser should tell you how. Alternatively, you may wish to visit www.aboutcookies.org which contains comprehensive information on how to do this for a wide variety of browsers. You will also find details on how to clear cookies from your computer as well as more general information about cookies. For information on how to do this on your mobile phones browser, you will need to refer to your handset manual. Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

We use traffic log cookies to identify which pages are being used. This helps us analyse data about web page traffic and improve our website in order to tailor it to customer needs. We only use this information for statistical analysis purposes and then the data is removed from the system. Overall, cookies help us provide you with a better website, by enabling us to monitor which pages you find useful and which you do not. A cookie in no way gives us access to your computer or any information about you, other than the data you choose to share with us. You can choose to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. This may prevent you from taking full advantage of the website. Links to other websites Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question. How to delete cookies or control them This site will not use cookies to collect personally identifiable information about you. However, if you wish to restrict or block cookies set by this or any other website, you can do this through your browser settings. The Help function within your browser should tell you how. Alternatively, you may wish to visit www.aboutcookies.org which contains comprehensive information on how to do this for a wide variety of browsers. You will also find details on how to clear cookies from your computer as well as more general information about cookies. For information on how to do this on your mobile phones browser, you will need to refer to your handset manual. Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Overall, cookies help us provide you with a better website, by enabling us to monitor which pages you find useful and which you do not. A cookie in no way gives us access to your computer or any information about you, other than the data you choose to share with us. You can choose to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. This may prevent you from taking full advantage of the website. Links to other websites Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question. How to delete cookies or control them This site will not use cookies to collect personally identifiable information about you. However, if you wish to restrict or block cookies set by this or any other website, you can do this through your browser settings. The Help function within your browser should tell you how. Alternatively, you may wish to visit www.aboutcookies.org which contains comprehensive information on how to do this for a wide variety of browsers. You will also find details on how to clear cookies from your computer as well as more general information about cookies. For information on how to do this on your mobile phones browser, you will need to refer to your handset manual. Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

You can choose to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. This may prevent you from taking full advantage of the website. Links to other websites Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question. How to delete cookies or control them This site will not use cookies to collect personally identifiable information about you. However, if you wish to restrict or block cookies set by this or any other website, you can do this through your browser settings. The Help function within your browser should tell you how. Alternatively, you may wish to visit www.aboutcookies.org which contains comprehensive information on how to do this for a wide variety of browsers. You will also find details on how to clear cookies from your computer as well as more general information about cookies. For information on how to do this on your mobile phones browser, you will need to refer to your handset manual. Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Links to other websites Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question. How to delete cookies or control them This site will not use cookies to collect personally identifiable information about you. However, if you wish to restrict or block cookies set by this or any other website, you can do this through your browser settings. The Help function within your browser should tell you how. Alternatively, you may wish to visit www.aboutcookies.org which contains comprehensive information on how to do this for a wide variety of browsers. You will also find details on how to clear cookies from your computer as well as more general information about cookies. For information on how to do this on your mobile phones browser, you will need to refer to your handset manual. Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

How to delete cookies or control them This site will not use cookies to collect personally identifiable information about you. However, if you wish to restrict or block cookies set by this or any other website, you can do this through your browser settings. The Help function within your browser should tell you how. Alternatively, you may wish to visit www.aboutcookies.org which contains comprehensive information on how to do this for a wide variety of browsers. You will also find details on how to clear cookies from your computer as well as more general information about cookies. For information on how to do this on your mobile phones browser, you will need to refer to your handset manual. Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Alternatively, you may wish to visit www.aboutcookies.org which contains comprehensive information on how to do this for a wide variety of browsers. You will also find details on how to clear cookies from your computer as well as more general information about cookies. For information on how to do this on your mobile phones browser, you will need to refer to your handset manual. Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Privacy Policy This privacy policy sets out how Sayers Publishing Group Ltd uses and protects any information that you give Sayers Publishing Group Ltd when you use this website. Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Sayers Publishing Group Ltd is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement. Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Sayers Publishing Group Ltd may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Data Collected to Manage Your Membership At checkout, we will collect your name, email address, username, and password. This information is used to setup your account for our site. If you are redirected to an offsite payment gateway to complete your payment, we may store this information in a temporary session variable to setup your account when you return to our site. At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

At checkout, we may also collect your billing address and phone number. This information is used to confirm your credit card. The billing address and phone number are saved by our site to pre populate the checkout form for future purchases and so we can get in touch with you if needed to discuss your order. At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

At checkout, we may also collect your credit card number, expiration date, and security code. This information is passed to our payment gateway PayPal to process your purchase. You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

You can read the PayPal privacy policy here, We will not sell, distribute or lease your personal information to third parties unless required by law to do so. Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

Right to Access You have certain rights in respect of your Personal Data. In particular, you have a right of access, rectification, restriction, opposition, erasure and data portability. Please contact [email protected] if you wish to exercise these rights or if you wish to complete an access request to all off the personal data that Sayer Publishing Group Ltd holds on you. How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.Close

How Do We Protect Your Personal Data We take appropriate organisational and technical security measures to protect your data against loss, misuse, unauthorised access, disclosure or processing. The security measures include firewalls, data encryption, physical access controls to our data centres, and information access authorisation controls. While we are dedicated to securing our systems and Services, you are responsible for securing and maintaining the privacy of your password(s) and Account/profile registration information and verifying that the Personal Data we maintain about you is accurate and current.