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Rapid geographical differentiation of the European spread brown macroalga Sargassum muticum using HRMAS NMR and Fourier-Transform Infrared spectroscopy
Talanta 132 (2015) 451–456
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Short communication
Rapid geographical differentiation of the European spread brown macroalga Sargassum muticum using HRMAS NMR and Fourier-Transform Infrared spectroscopy Tanniou Anaëllea, Vandanjon Laurentb,d, Gonçalves Olivierb, Kervarec Nellyc, Stiger-Pouvreau Valériea,n a LEMAR UMR CNRS UBO IRD IFREMER 6539, Université de Bretagne Occidentale (UBO), Institut Universitaire Européen de la Mer (IUEM), Technopôle BrestIroise, Rue Dumont d'Urville, Plouzané 29280, France b GEPEA UMR CNRS 6144, Université de Nantes, Laboratoire GEPEA, CRTT, 44602 Saint-Nazaire, France c Service RMN-RPE, UFR Sciences et Techniques, Université de Bretagne Occidentale (UBO), Avenue Le Gorgeu, 29200 Brest, France d LBCM EA 3884, Université de Bretagne Sud (UBS), IUEM, Campus de Tohannic, 56000 Vannes, France
art ic l e i nf o
a b s t r a c t
Article history: Received 23 October 2013 Received in revised form 21 August 2014 Accepted 2 September 2014 Available online 22 September 2014
Two recent techniques based on chemical footprinting analysis, HRMAS NMR and FTIR spectroscopy, were tested on a brown macroalgal model. These powerful and easily-to-use techniques allowed us to discriminate Sargassum muticum specimens collected in five different countries along Atlantic coasts, from Portugal to Norway. HRMAS NMR and FTIR permitted the obtaining of an overview of metabolites produced by the alga. Based on spectra analysis, results allowed us to successfully group the samples according to their geographical origin. HRMAS NMR and FTIR spectroscopy respectively point out the relation between the geographical localization and the chemical composition and demonstrated macromolecules variations regarding to environmental stress. Then, our results are discussed in regard of the powerful of these techniques together with the variability of the main molecules produced by Sargassum muticum along the Atlantic coasts. & 2014 Elsevier B.V. All rights reserved.
Keywords: HRMAS NMR FTIR spectroscopy Sargassum muticum Geographical origin discrimination Latitudinal gradient
1. Introduction Sargassum muticum is a brown macroalga native from Japan and widely spread along the European Atlantic coasts [1–6]. Distributed from Northward to Southward of Europe, it can be found nowadays from Norway to Portugal, demonstrating its large capability to adapt to different climate conditions (temperature, irradiance, hydrodynamism). This large sustainable biomass could represent a viable biotechnological asset in the European resource development programs. For this reason, S. muticum was collected in three sites of five countries (Portugal, Spain, France, Ireland and Norway). The present study is integrated in a global approach to find applications of the huge biomass of S. muticum observed along the Atlantic coasts. Previous works carried out on French populations highlighted interesting properties such as anti Reactive Oxygen Species (ROS), i.e. antioxidative [7–11] or antibacterial, antibiotic or antifouling, properties [12–15] of polar and apolar extracts and semi-purified fractions from S. muticum. The aim of our present study was then to obtain an overview of the global
n
Corresponding author. Tel.: þ 33 2 98 49 88 06; fax: þ33 2 98 49 87 72. E-mail address: [email protected] (V. Stiger-Pouvreau).
http://dx.doi.org/10.1016/j.talanta.2014.09.002 0039-9140/& 2014 Elsevier B.V. All rights reserved.
biochemical composition of specimens from different countries using new techniques for this purpose, and for the end, to know if S. muticum populations present or not a chemical adaptation along Atlantic coasts. Results on the lipid, protein or glucid macromolecules quantification are very used as indicators in biotechnology, especially in having information about the physiological state of the studied organism, reflecting for example the metabolic changes in answer to a given environmental stress. In this context, we were interested in studying the biochemical composition of S. muticum populations collected along a latitudinal gradient, by using two non invasive techniques: the in vivo 1 H HRMAS NMR (High Resolution Magic Angle Spinning Nuclear Magnetic Resonance) and the infrared (IR) spectroscopy in order to pointing out or not the sources of chemical profile differences. These two techniques are used for about twenty years in various domains; the HRMAS NMR was already used in chemotaxonomy on various marine models to point out the differences between bacterial strains or algal species for example [16–20]. But this technique had only rarely used to follow physiological processes, in particular on seaweeds. Fourier Transform IR spectroscopy (FTIR) was still never used on macroalgae but was previously used to differentiate species of microorganisms or even strains [21–24]. These two rapid techniques are convenient as they use small
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amount of sample thus permitting repeated manipulations (allowing a better reproductibility of the results). Finally, these two techniques allowed studying a wide range of molecules, and thus, pointing out very fine differences between samples. We selected then these two interesting techniques as a source of complementary information, FTIR allowing a global chemical variations analysis and the possibility to access to the content in the main macromolecules while HRMAS NMR allows to point out complementary chemical fingerprinting for a species or a given sample [17,19–20].
2. Materials and methods 2.1. Sampling sites and sample preparation Thalli of Sargassum muticum were collected between March and May 2011 in three sites of five countries along a latitudinal gradient in Europe. Samples were collected, from South (March) to North (May), in respectively Portugal, Spain, France, Ireland and finally Norway (Table 1). Sampling period was chosen according to the physiological state of the alga in all the countries. During these periods, Sargassum muticum was still immature [1,6]. During collection, only the apical and median parts of the thalli were retained and the holdfast was left in place to allow regrowth and thus minimize collecting impact. Immediately after collection and epiphytes removal, specimens were firstly washed with filtered seawater then with distilled water in order to get rid of residual sediments and salts. The cleaned algal materials were then surface dried with blotting paper towel, chopped into fragments, freezedried, powder reduced with a Waring Blender and finally sieved at 250 mm. 2.2. HRMAS-NMR spectroscopy All HRMAS NMR spectra were obtained using a Bruker DRX 500 spectrometer (Bruker BioSpin, Wissembourg, France) equipped with an indirect HRMAS 1H/31P probehead with gradient Z at 25 1C. A typical proton 1H HRMAS spectrum consisted of 64 scans and it was performed with presaturation of the water peak. Each spectrum was phased and baseline-corrected using a polynomial function. Spectra data processing was performed using the software MestReNova 6.0.2 for Windows (Mestrelab Research S.L., Spain). Afterwards the data were subjected to statistical analysis using the Statistica 8 (StatSoft s) software for PC. For HRMAS NMR, three independent samples per site were analyzed, which
represent then a pool of 45 samples analyzed. About 5 mg of each sample were put in a zirconium oxide vial and set into a 4 mm MAS rotor. Around 30 mL of D2O were added into the rotor with the sample for 2 H field locking (depending on the quantity of the freeze-dried powder). The sample was placed in a rotor spinning around an axis, which is oriented at the so-called magic angle of 5417 with respect to the magnetic field B0. Good homogenization and synchronization were obtained at a spinning rate of 5000 Hz. In this study, three HRMAS NMR spectra, corresponding to three independent records for each sample, were obtained by sample. The statistical procedure used in this study was based on a cluster analysis to illustrate S. muticum geographical origin discrimination using HRMAS NMR analysis. Variables used to perform this analysis were the form of the peak (single, double, triple or massive peaks) together with the peak position (δ). These variables were determined for each HRMAS NMR spectrum (three for each site) between 0 and 7 ppm, since some chemical shifts appeared to be different in the samples depending on sites and countries all along this spectral region. We took into account only positive peaks with a noise factor of 4.00. The cluster analysis was then made using a matrix of absence/presence of the observed signals in order to summarize the in vivo chemical diversity of the samples. 2.3. FTIR spectroscopy Infrared spectra were recorded in reflection mode directly on the single reflexion diamond crystal of the ATR (Attenuated Total Reflexion) accessory loaded with few milligrams of freeze-dried algal powder. The Bruker tensor 27 FTIR spectrometer equipped with the ATR platinum module, with a deuterated triglycine sulfate detector RT-DLaTGS and the OPUS v7.0.122 software (Bruker Optics, Germany) was set up with the following parameters. The spectral resolution was fixed to 1 cm 1, the number of scans to 32, the selected spectral range between 4000 and 400 cm 1. Background spectra were collected using the same instrument settings as those employed for the samples and was performed against air. Spectra were recorded for 3 technical replicates, i.e. 3 biological replicates, per site, representing a total of 45 samples analyzed for all studied countries. The recorded spectra were treated in group to compensate the CO2/H2O and the base-line effects and with the correction tools integrated into the OPUS software. The spectra were then smoothed using the Savitzky–Golay algorithm [25] but for presentation purpose only. For the multivariate analysis, the spectra pre-processing sequence was different and excluded the smoothing step, since it could
Table 1 List of sampling data (sites localization) for each country and hydrodynamism conditions. Country
Region
Sampling date
Locality
Site
Latitude
Longitude
Exposition
Norway
Øygarden Øygarden Øygarden
May 17, 2011 May 17, 2011 May 17, 2011
Solberg Krækjebærholmen Sauøyna
1 2 3
60135' N 60135' N 60135' N
4147' W 4150' W 4151' W
Sheltered Semi-exposed Semi-exposed
Ireland
Connaught Munster Munster
April 17, 2011 April 18, 2011 April 18, 2011
Clifden Spanish Point Miltown Malbay
1 2 3
53124' N 52151' N 52150' N
1017' W 9126' W 9126' W
Exposed Semi-exposed Semi-exposed
France
Bretagne Bretagne Bretagne
April 18, 2011 April 18, 2011 April 18, 2011
Fort du Dellec Le Minou Pointe du Diable
1 2 3
48121' N 48120' N 48121' N
4134' W 4136' W 4133' W
Semi-exposed Semi-exposed Exposed
Spain
Galicia Galicia Galicia
March 18, 2011 March 18, 2011 March 21, 2011
Museo del Mar Cabo Estay Cabo Udra
1 2 3
42113' N 42111' N 42120' N
8146' W 8148' W 8149' W
Semi-exposed Exposed Exposed
Portugal
Algarve Algarve Algarve
April 1, 2011 April 1, 2011 April 1, 2011
Queimado Almograve Zambujeira
1 2 3
37149' N 37139' N 37133' N
8147' W 8148' W 8147' W
Semi-exposed Semi-exposed Semi-exposed
A. Tanniou et al. / Talanta 132 (2015) 451–456
impact the resolution of the data, and therefore decrease the detection of putative important vibrators and their contribution to the FTIR signature. The CO2/H2O and base-line effects corrected replicate spectra were averaged to generate a new spectrum from the three original spectra of the same type, i.e. the new spectrum intensities were calculated by averaging original spectra intensities. Then, mean spectra were vector normalized first to account for differences in samples thickness commonly found when working on powders, and second derivative transformed to detect a maximum of vibrators (peak position) and enhance their contribution to the FTIR signature on the whole spectral range [26–29]. Multivariate analysis was performed on the mean, vector normalized, second derived spectra using the Metaboanalyst 2.0 pipeline [30]. Principal component analysis (PCA) and Cluster analysis were performed on those pre-processed spectra. Then the replicate spectra were averaged to generate a new spectrum from the three original spectra of the same type, i.e. the new spectrum intensities were calculated by averaging original spectra intensities. Afterwards the data were subjected to cluster analysis employing the Ward's algorithms and using the second derivative and vector normalization preprocessing method to calculate interspectral distances (Euclidian distance).
3. Results and discussion The 1H HRMAS NMR spectra comparison shown on Fig. 1A (only one spectrum was selected for each country) revealed some differences between the five different sampled countries. As visible on the Fig. 1B, countries presenting the least homogeneous grouping are Spain and Ireland, where the site 1 is different from the two others. This corresponds to the measured geographical distances between the different sites (Table 1). In every case we can notice a clear
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distinction between the five countries. Some areas present a larger variability, as for example: the aromatic domain towards 5.5 and 6.5 ppm, the edge of peaks between 3.0–5.5 ppm, which corresponded to polyols described as mannitol in S. muticum [19], the proportions of the amino acid glutamine/glutamates (carboxylate anions and salts of glutamic acid), against the malic acid/malates (esters and salts of malic acid) (peaks around 2.0–2.5 ppm As N. Kervarec is a co-author of this paper) and some unsaturated fatty acids towards 1.0–1.5 ppm (Fig. 1A). Some countries are recognizable rather easily thanks to certain characteristic peaks, as for example the peak towards 2.6 ppm very marked in Spain and the peak at 2.91 ppm very marked in Ireland (Fig. 1A). We can also observe in a general way that sugars (3.0 to 5.5 ppm) are more plentiful and diversified in Norway, France and Portugal while more lipidic compounds (0.8 to 3.0 ppm) are found in Ireland, France and Spain. In view of spectrum it would be rather saturated lipids; even if some unsaturated ones are visible in France and Spain for example towards 4.8 ppm. Differences of composition in fatty acids were already observed in other Sargassum species collected from two different geographical zones but never within the same genus [31]. Fig. 1 shows that chemically closed populations resulted from places geographically closed, which is not the case of FTIR dendogram presented on Fig. 3, where the grouping is different. Indeed, populations in Spain and France are the most similar while populations in Norway present the “most original” profile compared with those of the other countries. Samples providing from Norway contained a great amount of malate and sugars (3.5 and 4.5 ppm). The geographical position of populations belonging to the same species has a real effect on chemical composition and this effect is adequately important for allowing their differentiation according to their origin. However, some authors affirm that, in brown algae, geographical variations of marine algae metabolites composition were nonsignificant [32–33] and could be further due to seasonal variations in S. muticum metabolites content [34]. This bias was avoided by
Fig. 1. (A) Typical 1H HRMAS NMR spectra of Sargassum muticum sampled in five different countries (only one spectrum is represented by country as the three spectra by site were all identical). Spectral regions are indicated in gray at the bottom and specific differences between countries by arrows at the top. (B) Cluster analysis based on 45 spectral observations issued from Norway (N), Ireland (I), France (F), Spain (S) and Portugal (P) with the numbers 1,2,3 representing the three sites sampled by country, regrouping the triplicate per site.
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Fig. 2. (A) Spectra from analysis in FTIR of samples of Sargassum muticum (averaged spectra calculated by averaging the 3 sites spectra intensities for each country). (B) Cluster analysis performed using the second derivative and vector normalization of the spectra from 400 to 4000 cm 1. The Ward's algorithm was used to build the dendrogam. Letters and numbers as on Fig. 1.
harvesting seaweeds in the various countries at the same physiological state during spring 2011 (Table 1). Nevertheless, the same experiment should be carried out during the other seasons to test the seasonality effect on the chemical contents. In addition, it would be also interesting to pursue this experiment on seaweeds collected at the same period the following years to see if the tendencies are confirmed from one year to the next. This technique showed that seaweeds coming from different geographical places could be differentiated by their biochemical profile. Blind assays were realized on spectra of unknown origin allowing to recognize their geographical origin (country and site) with 100% success except for samples resulting from Ireland where sites can be confused; the success rate was then 60% (data not shown). Those scores are upper than those obtained for similar studies [35–36] for food applications on aliment origin. Fig. 2 permitted to obtain an overview of the intensity of peaks in specific areas of FTIR spectra. The highlighted areas corresponded to the major macromolecules spectral region [37]. Thus, few peaks are characteristic of a single sample. On the other hand, we can quote for example: a peak at 2965 cm 1 only visible in France and Spain; at 688 and 3594 cm 1 present for samples originated from Norway, Ireland and Spain. A great variability between countries was observed between 800 and 950 cm 1 and 1930–2560 cm 1. The intensity of the vibrators bands composing an IR spectrum is proportional to the abundance of the chemical groupings in the analyzed sample (Beer–Lambert law). It is thus possible to obtain, besides the usually exploited qualitative data, quantitative or rather semi-quantitative information. FTIR spectroscopy permitted then to distinguish samples by their geographical origins (Fig. 2). The vector
normalized second derivative spectral data were qualitatively analyzed using principal component analysis (PCA) for each algal sample, taking also into account the location of the collection in the considered country. In Fig. 3, results indicated that the five algal samples were clustered into five distinct groups, clearly associated to each geographical origin (axis 2, with a contribution superior to 17% of the total variability). Inside each cluster, it was moreover possible to distinguish subgroups according to the specific location of collection in a considered country (axis 1, with a contribution superior to 36% of the total variability). The clustering results clearly revealed that each algal sample could be distinguished according to its FTIR spectrum, exhibiting moreover differences for the three tested sampling sites for a considered country. Those observations suggested that each algal sample presented its own FTIR signature, depending on its particular chemical composition. Indeed, according to the work of [27–29] this spectral signature could be directly related to the relative amount of the matrix main constitutive molecules (at least lipids, proteins and carbohydrates), which were moreover in our case geographical/climate dependent. Actually, numerous approaches are developed for the estimation of the relative or absolute intracellular metabolites abundance on the whole cell [38–39]. IR spectroscopy expresses here all its analytical potential compared with classical dosage techniques, because it is not invasive, fast and requires only some milligrams or even micrograms of biomass [40]. The recent works of [41] demonstrated in particular the possibility to estimate the effect of the light intensity variations or the nitrogen and sulfur concentrations on the lipids accumulation for the microalga Chlamydomonas reinhardtii. These spectral data immediately inform on the
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Fig. 3. PCA score plot of the vector normalized second derivative FTIR signatures of the various sample of Sargassum muticum collected in Norway, Ireland, France, Spain and Portugal. The legend in the upright corner details the name of the algal samples together with their site of collection and the geographical clusters are highlighted on the score plot.
presence or the absence (of detection) of a family of macromolecules with regard to another one for a given sample. It is also possible to compare the relative abundances of these molecules (since the concentration of one of them does not vary during the studied process) [38]. On the other hand, the exploitation of the bands intensity in the quantification applications absolved from these macromolecules requires taking precautions because the sources of IR spectra variations are numerous and they may modify the recorded intensities. Here, we can see on Fig. 3 that the proteins compartment (1400–1800 cm 1) did not vary between samples while polysaccharides (1000–1200 cm 1) and lipids (2800–3000 cm 1) area were very variable [37]. Thus, polysaccharides were more present in Norway and France and lipids more present in France, Norway and Portugal. Except for the lipids compartment where the Spanish samples seemed to contain a lot of lipids in HRMAS NMR and not in FTIR, the results were close between the HRMAS NMR and the FTIR method. The fact is that for present Spanish samples, lipids were saturated for the greater part and thus very visible in HRMAS NMR while the IR gives us an image of total lipids content. But numerous factors influence the appearance of the recorded IR spectra. The relevant works of [42] underlined clearly the importance of these effects, which were widely ignored in numerous studies previous to their investigations. According to these authors, the spectral variations for a succession of samples can be attributed to four different factors: (i) the dispersal effect of a radiation produces wide fluctuations which overlap on the spectra [43] (ii) the presence of a nucleus is also going to modify and to amplify this distortion effect [42] (iii) the variations of intensity observed on the whole IR spectra can be attributed to the heterogeneousness of thickness of the sample and finally, (iv) the significant contribution of the
signal of the water on the amide I (primary amide) area (3300– 3400 cm 1) [44] which can make difficult the interpretation of the obtained results. To get away from this type of problem it seems thus essential to choose bands with the least possible covering or when it is not possible, because of the complexity of the sample for example, to use more global processing methods which will thus exploit the influence of all the spectral contributions for a given sample. We found recently approaches of relative estimations of macromolecular microalgal contents using IR spectrometry as a routine technique. Stehfest et al. [45] were among the first ones to study the effect of nitrogenous and phosphorous stresses on phytoplanktonic species with IR spectrometry. Recently, Di Giambattista et al. [46] used FTIR as an efficient method to diagnostic the presence of tumor cell death, showing the great potential of this technique also in medical studies. We can thus admit that whatever the used technique and the observed variations, no specific major molecule seems to be really produced by the seaweeds from the different studied countries, along our chosen latitudinal gradient (Portugal to Norway). Only the relative proportions of each molecule seem to evolve along the latitudinal gradient, as it was already shown in the particular phenolic compounds studied in a previous study [47]. Complementary analyses carried out at the laboratory allowed, thanks to chemical analysis techniques, to validate the variations and the tendencies observed in HRMAS NMR and FTIR. Indeed the colorimetric tests used for the determination of protein and carbohydrates contents for example, show a weak variability of the protein compartment following countries while, on the other hand, lipids and polysaccharides are subjected to big variations in S. muticum populations collected along Atlantic coasts [48].
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4. Conclusion In our study both HRMAS NMR and FTIR appeared as two quick and reliable methods for geographical origin discrimination studies together with the obtaining of an overview of biochemical composition of samples. These two relevant techniques provided results analogous to those obtained by standard chemical tests. Their main advantages are that they are quick and easy to use methods, which need low biomass consumption (some milligrams). These techniques are then promising for this type of investigation because they would allow, without preliminary extraction, the simultaneous and fast qualitative overview of macromolecules for a given species with the possibility of connection with the environmental variations. These results are exciting as no DNA polymorphism could be highlighted in populations of Sargassum muticum collected in Europe (Viard, pers. comm.). Our chemical procedure could then represent a real tool of traceability for further studies of S. muticum in Europe. Acknowledgments This study is part of the Ph.D. thesis work carried out by the first author within the Laboratoire des Sciences de l'Environnement Marin (LEMAR UMR 6539) set at the IUEM (UBO-UEB). It was supported by the Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche (UBO allocation for the first author). This study is part of the BIOTECMAR project contract no. 2008-1/032 for the collection of samples in Europe, and was co-financed with the support of the European Union ERDF – Atlantic Area Program. This study is also related to the research projects INVASIVES (Era-net Seas-era, 2012–2016) and COSELMAR for the analysis of samples. References [1] E. Plouguerné, K. Le Lann, S. Connan, G. Jechoux, E. Deslandes, V. Stiger-Pouvreau, Aquat. Bot. 85 (2006) 337–344. [2] A. Engelen, C. Espirito-Santo, T. Simoes, C. Monteiro, E.A. Serrao, G.A. Pearson, R.O.P. Santos, Eur. J. Phycol. 43 (2008) 275–282. [3] S. Kraan, J. Appl. Phycol. 20 (2008) 825–832. [4] M. Incera, C. Olabarria, J.S. Troncoso, J. Lopez, Mar. Ecol. Prog. Ser. 377 (2009) 91–101. [5] C. Olabarria, I.F. Rodil, M. Incera, J.S. Troncoso, Mar. Env. Res 67 (2009) 153–158. [6] K. Le Lann, S. Connan, V. Stiger-Pouvreau, Mar. Env. Res 80 (2012) 1–11. [7] M. Nakai, N. Kagayama, K. Nakahara, W. Miki, Mar. Biotechnol. 8 (2006) 409–414. [8] T. Kuda, T. Kunii, H. Goto, Jpn. Food Chem. 103 (2007) 900–905. [9] S. Kumar Chandini, P. Ganesan, N. Bhaskar, Food Chem. 107 (2008) 707–713. [10] K. Le Lann, C. Jegou, V. Stiger-Pouvreau, Phycol. Res. 56 (2008) 238–245. [11] A Tanniou, E. Serrano Leon, L. Vandanjon, E. Ibanez, J.A. Mendiola, S. Cerantola, N. Kervarec, S. La Barre, L. Marchal, V. Stiger-Pouvreau, Talanta 104 (2012) 44–52.
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Rapid geographical differentiation of the European spread brown macroalga Sargassum muticum using HRMAS NMR and Fourier-Transform Infrared spectroscopy Tanniou Anaëllea, Vandanjon Laurentb,d, Gonçalves Olivierb, Kervarec Nellyc, Stiger-Pouvreau Valériea,n a LEMAR UMR CNRS UBO IRD IFREMER 6539, Université de Bretagne Occidentale (UBO), Institut Universitaire Européen de la Mer (IUEM), Technopôle BrestIroise, Rue Dumont d'Urville, Plouzané 29280, France b GEPEA UMR CNRS 6144, Université de Nantes, Laboratoire GEPEA, CRTT, 44602 Saint-Nazaire, France c Service RMN-RPE, UFR Sciences et Techniques, Université de Bretagne Occidentale (UBO), Avenue Le Gorgeu, 29200 Brest, France d LBCM EA 3884, Université de Bretagne Sud (UBS), IUEM, Campus de Tohannic, 56000 Vannes, France
art ic l e i nf o
a b s t r a c t
Article history: Received 23 October 2013 Received in revised form 21 August 2014 Accepted 2 September 2014 Available online 22 September 2014
Two recent techniques based on chemical footprinting analysis, HRMAS NMR and FTIR spectroscopy, were tested on a brown macroalgal model. These powerful and easily-to-use techniques allowed us to discriminate Sargassum muticum specimens collected in five different countries along Atlantic coasts, from Portugal to Norway. HRMAS NMR and FTIR permitted the obtaining of an overview of metabolites produced by the alga. Based on spectra analysis, results allowed us to successfully group the samples according to their geographical origin. HRMAS NMR and FTIR spectroscopy respectively point out the relation between the geographical localization and the chemical composition and demonstrated macromolecules variations regarding to environmental stress. Then, our results are discussed in regard of the powerful of these techniques together with the variability of the main molecules produced by Sargassum muticum along the Atlantic coasts. & 2014 Elsevier B.V. All rights reserved.
Keywords: HRMAS NMR FTIR spectroscopy Sargassum muticum Geographical origin discrimination Latitudinal gradient
1. Introduction Sargassum muticum is a brown macroalga native from Japan and widely spread along the European Atlantic coasts [1–6]. Distributed from Northward to Southward of Europe, it can be found nowadays from Norway to Portugal, demonstrating its large capability to adapt to different climate conditions (temperature, irradiance, hydrodynamism). This large sustainable biomass could represent a viable biotechnological asset in the European resource development programs. For this reason, S. muticum was collected in three sites of five countries (Portugal, Spain, France, Ireland and Norway). The present study is integrated in a global approach to find applications of the huge biomass of S. muticum observed along the Atlantic coasts. Previous works carried out on French populations highlighted interesting properties such as anti Reactive Oxygen Species (ROS), i.e. antioxidative [7–11] or antibacterial, antibiotic or antifouling, properties [12–15] of polar and apolar extracts and semi-purified fractions from S. muticum. The aim of our present study was then to obtain an overview of the global
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Corresponding author. Tel.: þ 33 2 98 49 88 06; fax: þ33 2 98 49 87 72. E-mail address: [email protected] (V. Stiger-Pouvreau).
http://dx.doi.org/10.1016/j.talanta.2014.09.002 0039-9140/& 2014 Elsevier B.V. All rights reserved.
biochemical composition of specimens from different countries using new techniques for this purpose, and for the end, to know if S. muticum populations present or not a chemical adaptation along Atlantic coasts. Results on the lipid, protein or glucid macromolecules quantification are very used as indicators in biotechnology, especially in having information about the physiological state of the studied organism, reflecting for example the metabolic changes in answer to a given environmental stress. In this context, we were interested in studying the biochemical composition of S. muticum populations collected along a latitudinal gradient, by using two non invasive techniques: the in vivo 1 H HRMAS NMR (High Resolution Magic Angle Spinning Nuclear Magnetic Resonance) and the infrared (IR) spectroscopy in order to pointing out or not the sources of chemical profile differences. These two techniques are used for about twenty years in various domains; the HRMAS NMR was already used in chemotaxonomy on various marine models to point out the differences between bacterial strains or algal species for example [16–20]. But this technique had only rarely used to follow physiological processes, in particular on seaweeds. Fourier Transform IR spectroscopy (FTIR) was still never used on macroalgae but was previously used to differentiate species of microorganisms or even strains [21–24]. These two rapid techniques are convenient as they use small
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amount of sample thus permitting repeated manipulations (allowing a better reproductibility of the results). Finally, these two techniques allowed studying a wide range of molecules, and thus, pointing out very fine differences between samples. We selected then these two interesting techniques as a source of complementary information, FTIR allowing a global chemical variations analysis and the possibility to access to the content in the main macromolecules while HRMAS NMR allows to point out complementary chemical fingerprinting for a species or a given sample [17,19–20].
2. Materials and methods 2.1. Sampling sites and sample preparation Thalli of Sargassum muticum were collected between March and May 2011 in three sites of five countries along a latitudinal gradient in Europe. Samples were collected, from South (March) to North (May), in respectively Portugal, Spain, France, Ireland and finally Norway (Table 1). Sampling period was chosen according to the physiological state of the alga in all the countries. During these periods, Sargassum muticum was still immature [1,6]. During collection, only the apical and median parts of the thalli were retained and the holdfast was left in place to allow regrowth and thus minimize collecting impact. Immediately after collection and epiphytes removal, specimens were firstly washed with filtered seawater then with distilled water in order to get rid of residual sediments and salts. The cleaned algal materials were then surface dried with blotting paper towel, chopped into fragments, freezedried, powder reduced with a Waring Blender and finally sieved at 250 mm. 2.2. HRMAS-NMR spectroscopy All HRMAS NMR spectra were obtained using a Bruker DRX 500 spectrometer (Bruker BioSpin, Wissembourg, France) equipped with an indirect HRMAS 1H/31P probehead with gradient Z at 25 1C. A typical proton 1H HRMAS spectrum consisted of 64 scans and it was performed with presaturation of the water peak. Each spectrum was phased and baseline-corrected using a polynomial function. Spectra data processing was performed using the software MestReNova 6.0.2 for Windows (Mestrelab Research S.L., Spain). Afterwards the data were subjected to statistical analysis using the Statistica 8 (StatSoft s) software for PC. For HRMAS NMR, three independent samples per site were analyzed, which
represent then a pool of 45 samples analyzed. About 5 mg of each sample were put in a zirconium oxide vial and set into a 4 mm MAS rotor. Around 30 mL of D2O were added into the rotor with the sample for 2 H field locking (depending on the quantity of the freeze-dried powder). The sample was placed in a rotor spinning around an axis, which is oriented at the so-called magic angle of 5417 with respect to the magnetic field B0. Good homogenization and synchronization were obtained at a spinning rate of 5000 Hz. In this study, three HRMAS NMR spectra, corresponding to three independent records for each sample, were obtained by sample. The statistical procedure used in this study was based on a cluster analysis to illustrate S. muticum geographical origin discrimination using HRMAS NMR analysis. Variables used to perform this analysis were the form of the peak (single, double, triple or massive peaks) together with the peak position (δ). These variables were determined for each HRMAS NMR spectrum (three for each site) between 0 and 7 ppm, since some chemical shifts appeared to be different in the samples depending on sites and countries all along this spectral region. We took into account only positive peaks with a noise factor of 4.00. The cluster analysis was then made using a matrix of absence/presence of the observed signals in order to summarize the in vivo chemical diversity of the samples. 2.3. FTIR spectroscopy Infrared spectra were recorded in reflection mode directly on the single reflexion diamond crystal of the ATR (Attenuated Total Reflexion) accessory loaded with few milligrams of freeze-dried algal powder. The Bruker tensor 27 FTIR spectrometer equipped with the ATR platinum module, with a deuterated triglycine sulfate detector RT-DLaTGS and the OPUS v7.0.122 software (Bruker Optics, Germany) was set up with the following parameters. The spectral resolution was fixed to 1 cm 1, the number of scans to 32, the selected spectral range between 4000 and 400 cm 1. Background spectra were collected using the same instrument settings as those employed for the samples and was performed against air. Spectra were recorded for 3 technical replicates, i.e. 3 biological replicates, per site, representing a total of 45 samples analyzed for all studied countries. The recorded spectra were treated in group to compensate the CO2/H2O and the base-line effects and with the correction tools integrated into the OPUS software. The spectra were then smoothed using the Savitzky–Golay algorithm [25] but for presentation purpose only. For the multivariate analysis, the spectra pre-processing sequence was different and excluded the smoothing step, since it could
Table 1 List of sampling data (sites localization) for each country and hydrodynamism conditions. Country
Region
Sampling date
Locality
Site
Latitude
Longitude
Exposition
Norway
Øygarden Øygarden Øygarden
May 17, 2011 May 17, 2011 May 17, 2011
Solberg Krækjebærholmen Sauøyna
1 2 3
60135' N 60135' N 60135' N
4147' W 4150' W 4151' W
Sheltered Semi-exposed Semi-exposed
Ireland
Connaught Munster Munster
April 17, 2011 April 18, 2011 April 18, 2011
Clifden Spanish Point Miltown Malbay
1 2 3
53124' N 52151' N 52150' N
1017' W 9126' W 9126' W
Exposed Semi-exposed Semi-exposed
France
Bretagne Bretagne Bretagne
April 18, 2011 April 18, 2011 April 18, 2011
Fort du Dellec Le Minou Pointe du Diable
1 2 3
48121' N 48120' N 48121' N
4134' W 4136' W 4133' W
Semi-exposed Semi-exposed Exposed
Spain
Galicia Galicia Galicia
March 18, 2011 March 18, 2011 March 21, 2011
Museo del Mar Cabo Estay Cabo Udra
1 2 3
42113' N 42111' N 42120' N
8146' W 8148' W 8149' W
Semi-exposed Exposed Exposed
Portugal
Algarve Algarve Algarve
April 1, 2011 April 1, 2011 April 1, 2011
Queimado Almograve Zambujeira
1 2 3
37149' N 37139' N 37133' N
8147' W 8148' W 8147' W
Semi-exposed Semi-exposed Semi-exposed
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impact the resolution of the data, and therefore decrease the detection of putative important vibrators and their contribution to the FTIR signature. The CO2/H2O and base-line effects corrected replicate spectra were averaged to generate a new spectrum from the three original spectra of the same type, i.e. the new spectrum intensities were calculated by averaging original spectra intensities. Then, mean spectra were vector normalized first to account for differences in samples thickness commonly found when working on powders, and second derivative transformed to detect a maximum of vibrators (peak position) and enhance their contribution to the FTIR signature on the whole spectral range [26–29]. Multivariate analysis was performed on the mean, vector normalized, second derived spectra using the Metaboanalyst 2.0 pipeline [30]. Principal component analysis (PCA) and Cluster analysis were performed on those pre-processed spectra. Then the replicate spectra were averaged to generate a new spectrum from the three original spectra of the same type, i.e. the new spectrum intensities were calculated by averaging original spectra intensities. Afterwards the data were subjected to cluster analysis employing the Ward's algorithms and using the second derivative and vector normalization preprocessing method to calculate interspectral distances (Euclidian distance).
3. Results and discussion The 1H HRMAS NMR spectra comparison shown on Fig. 1A (only one spectrum was selected for each country) revealed some differences between the five different sampled countries. As visible on the Fig. 1B, countries presenting the least homogeneous grouping are Spain and Ireland, where the site 1 is different from the two others. This corresponds to the measured geographical distances between the different sites (Table 1). In every case we can notice a clear
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distinction between the five countries. Some areas present a larger variability, as for example: the aromatic domain towards 5.5 and 6.5 ppm, the edge of peaks between 3.0–5.5 ppm, which corresponded to polyols described as mannitol in S. muticum [19], the proportions of the amino acid glutamine/glutamates (carboxylate anions and salts of glutamic acid), against the malic acid/malates (esters and salts of malic acid) (peaks around 2.0–2.5 ppm As N. Kervarec is a co-author of this paper) and some unsaturated fatty acids towards 1.0–1.5 ppm (Fig. 1A). Some countries are recognizable rather easily thanks to certain characteristic peaks, as for example the peak towards 2.6 ppm very marked in Spain and the peak at 2.91 ppm very marked in Ireland (Fig. 1A). We can also observe in a general way that sugars (3.0 to 5.5 ppm) are more plentiful and diversified in Norway, France and Portugal while more lipidic compounds (0.8 to 3.0 ppm) are found in Ireland, France and Spain. In view of spectrum it would be rather saturated lipids; even if some unsaturated ones are visible in France and Spain for example towards 4.8 ppm. Differences of composition in fatty acids were already observed in other Sargassum species collected from two different geographical zones but never within the same genus [31]. Fig. 1 shows that chemically closed populations resulted from places geographically closed, which is not the case of FTIR dendogram presented on Fig. 3, where the grouping is different. Indeed, populations in Spain and France are the most similar while populations in Norway present the “most original” profile compared with those of the other countries. Samples providing from Norway contained a great amount of malate and sugars (3.5 and 4.5 ppm). The geographical position of populations belonging to the same species has a real effect on chemical composition and this effect is adequately important for allowing their differentiation according to their origin. However, some authors affirm that, in brown algae, geographical variations of marine algae metabolites composition were nonsignificant [32–33] and could be further due to seasonal variations in S. muticum metabolites content [34]. This bias was avoided by
Fig. 1. (A) Typical 1H HRMAS NMR spectra of Sargassum muticum sampled in five different countries (only one spectrum is represented by country as the three spectra by site were all identical). Spectral regions are indicated in gray at the bottom and specific differences between countries by arrows at the top. (B) Cluster analysis based on 45 spectral observations issued from Norway (N), Ireland (I), France (F), Spain (S) and Portugal (P) with the numbers 1,2,3 representing the three sites sampled by country, regrouping the triplicate per site.
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Fig. 2. (A) Spectra from analysis in FTIR of samples of Sargassum muticum (averaged spectra calculated by averaging the 3 sites spectra intensities for each country). (B) Cluster analysis performed using the second derivative and vector normalization of the spectra from 400 to 4000 cm 1. The Ward's algorithm was used to build the dendrogam. Letters and numbers as on Fig. 1.
harvesting seaweeds in the various countries at the same physiological state during spring 2011 (Table 1). Nevertheless, the same experiment should be carried out during the other seasons to test the seasonality effect on the chemical contents. In addition, it would be also interesting to pursue this experiment on seaweeds collected at the same period the following years to see if the tendencies are confirmed from one year to the next. This technique showed that seaweeds coming from different geographical places could be differentiated by their biochemical profile. Blind assays were realized on spectra of unknown origin allowing to recognize their geographical origin (country and site) with 100% success except for samples resulting from Ireland where sites can be confused; the success rate was then 60% (data not shown). Those scores are upper than those obtained for similar studies [35–36] for food applications on aliment origin. Fig. 2 permitted to obtain an overview of the intensity of peaks in specific areas of FTIR spectra. The highlighted areas corresponded to the major macromolecules spectral region [37]. Thus, few peaks are characteristic of a single sample. On the other hand, we can quote for example: a peak at 2965 cm 1 only visible in France and Spain; at 688 and 3594 cm 1 present for samples originated from Norway, Ireland and Spain. A great variability between countries was observed between 800 and 950 cm 1 and 1930–2560 cm 1. The intensity of the vibrators bands composing an IR spectrum is proportional to the abundance of the chemical groupings in the analyzed sample (Beer–Lambert law). It is thus possible to obtain, besides the usually exploited qualitative data, quantitative or rather semi-quantitative information. FTIR spectroscopy permitted then to distinguish samples by their geographical origins (Fig. 2). The vector
normalized second derivative spectral data were qualitatively analyzed using principal component analysis (PCA) for each algal sample, taking also into account the location of the collection in the considered country. In Fig. 3, results indicated that the five algal samples were clustered into five distinct groups, clearly associated to each geographical origin (axis 2, with a contribution superior to 17% of the total variability). Inside each cluster, it was moreover possible to distinguish subgroups according to the specific location of collection in a considered country (axis 1, with a contribution superior to 36% of the total variability). The clustering results clearly revealed that each algal sample could be distinguished according to its FTIR spectrum, exhibiting moreover differences for the three tested sampling sites for a considered country. Those observations suggested that each algal sample presented its own FTIR signature, depending on its particular chemical composition. Indeed, according to the work of [27–29] this spectral signature could be directly related to the relative amount of the matrix main constitutive molecules (at least lipids, proteins and carbohydrates), which were moreover in our case geographical/climate dependent. Actually, numerous approaches are developed for the estimation of the relative or absolute intracellular metabolites abundance on the whole cell [38–39]. IR spectroscopy expresses here all its analytical potential compared with classical dosage techniques, because it is not invasive, fast and requires only some milligrams or even micrograms of biomass [40]. The recent works of [41] demonstrated in particular the possibility to estimate the effect of the light intensity variations or the nitrogen and sulfur concentrations on the lipids accumulation for the microalga Chlamydomonas reinhardtii. These spectral data immediately inform on the
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Fig. 3. PCA score plot of the vector normalized second derivative FTIR signatures of the various sample of Sargassum muticum collected in Norway, Ireland, France, Spain and Portugal. The legend in the upright corner details the name of the algal samples together with their site of collection and the geographical clusters are highlighted on the score plot.
presence or the absence (of detection) of a family of macromolecules with regard to another one for a given sample. It is also possible to compare the relative abundances of these molecules (since the concentration of one of them does not vary during the studied process) [38]. On the other hand, the exploitation of the bands intensity in the quantification applications absolved from these macromolecules requires taking precautions because the sources of IR spectra variations are numerous and they may modify the recorded intensities. Here, we can see on Fig. 3 that the proteins compartment (1400–1800 cm 1) did not vary between samples while polysaccharides (1000–1200 cm 1) and lipids (2800–3000 cm 1) area were very variable [37]. Thus, polysaccharides were more present in Norway and France and lipids more present in France, Norway and Portugal. Except for the lipids compartment where the Spanish samples seemed to contain a lot of lipids in HRMAS NMR and not in FTIR, the results were close between the HRMAS NMR and the FTIR method. The fact is that for present Spanish samples, lipids were saturated for the greater part and thus very visible in HRMAS NMR while the IR gives us an image of total lipids content. But numerous factors influence the appearance of the recorded IR spectra. The relevant works of [42] underlined clearly the importance of these effects, which were widely ignored in numerous studies previous to their investigations. According to these authors, the spectral variations for a succession of samples can be attributed to four different factors: (i) the dispersal effect of a radiation produces wide fluctuations which overlap on the spectra [43] (ii) the presence of a nucleus is also going to modify and to amplify this distortion effect [42] (iii) the variations of intensity observed on the whole IR spectra can be attributed to the heterogeneousness of thickness of the sample and finally, (iv) the significant contribution of the
signal of the water on the amide I (primary amide) area (3300– 3400 cm 1) [44] which can make difficult the interpretation of the obtained results. To get away from this type of problem it seems thus essential to choose bands with the least possible covering or when it is not possible, because of the complexity of the sample for example, to use more global processing methods which will thus exploit the influence of all the spectral contributions for a given sample. We found recently approaches of relative estimations of macromolecular microalgal contents using IR spectrometry as a routine technique. Stehfest et al. [45] were among the first ones to study the effect of nitrogenous and phosphorous stresses on phytoplanktonic species with IR spectrometry. Recently, Di Giambattista et al. [46] used FTIR as an efficient method to diagnostic the presence of tumor cell death, showing the great potential of this technique also in medical studies. We can thus admit that whatever the used technique and the observed variations, no specific major molecule seems to be really produced by the seaweeds from the different studied countries, along our chosen latitudinal gradient (Portugal to Norway). Only the relative proportions of each molecule seem to evolve along the latitudinal gradient, as it was already shown in the particular phenolic compounds studied in a previous study [47]. Complementary analyses carried out at the laboratory allowed, thanks to chemical analysis techniques, to validate the variations and the tendencies observed in HRMAS NMR and FTIR. Indeed the colorimetric tests used for the determination of protein and carbohydrates contents for example, show a weak variability of the protein compartment following countries while, on the other hand, lipids and polysaccharides are subjected to big variations in S. muticum populations collected along Atlantic coasts [48].
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4. Conclusion In our study both HRMAS NMR and FTIR appeared as two quick and reliable methods for geographical origin discrimination studies together with the obtaining of an overview of biochemical composition of samples. These two relevant techniques provided results analogous to those obtained by standard chemical tests. Their main advantages are that they are quick and easy to use methods, which need low biomass consumption (some milligrams). These techniques are then promising for this type of investigation because they would allow, without preliminary extraction, the simultaneous and fast qualitative overview of macromolecules for a given species with the possibility of connection with the environmental variations. These results are exciting as no DNA polymorphism could be highlighted in populations of Sargassum muticum collected in Europe (Viard, pers. comm.). Our chemical procedure could then represent a real tool of traceability for further studies of S. muticum in Europe. Acknowledgments This study is part of the Ph.D. thesis work carried out by the first author within the Laboratoire des Sciences de l'Environnement Marin (LEMAR UMR 6539) set at the IUEM (UBO-UEB). It was supported by the Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche (UBO allocation for the first author). This study is part of the BIOTECMAR project contract no. 2008-1/032 for the collection of samples in Europe, and was co-financed with the support of the European Union ERDF – Atlantic Area Program. This study is also related to the research projects INVASIVES (Era-net Seas-era, 2012–2016) and COSELMAR for the analysis of samples. References [1] E. Plouguerné, K. Le Lann, S. Connan, G. Jechoux, E. Deslandes, V. Stiger-Pouvreau, Aquat. Bot. 85 (2006) 337–344. [2] A. Engelen, C. Espirito-Santo, T. Simoes, C. Monteiro, E.A. Serrao, G.A. Pearson, R.O.P. Santos, Eur. J. Phycol. 43 (2008) 275–282. [3] S. Kraan, J. Appl. Phycol. 20 (2008) 825–832. [4] M. Incera, C. Olabarria, J.S. Troncoso, J. Lopez, Mar. Ecol. Prog. Ser. 377 (2009) 91–101. [5] C. Olabarria, I.F. Rodil, M. Incera, J.S. Troncoso, Mar. Env. Res 67 (2009) 153–158. [6] K. Le Lann, S. Connan, V. Stiger-Pouvreau, Mar. Env. Res 80 (2012) 1–11. [7] M. Nakai, N. Kagayama, K. Nakahara, W. Miki, Mar. Biotechnol. 8 (2006) 409–414. [8] T. Kuda, T. Kunii, H. Goto, Jpn. Food Chem. 103 (2007) 900–905. [9] S. Kumar Chandini, P. Ganesan, N. Bhaskar, Food Chem. 107 (2008) 707–713. [10] K. Le Lann, C. Jegou, V. Stiger-Pouvreau, Phycol. Res. 56 (2008) 238–245. [11] A Tanniou, E. Serrano Leon, L. Vandanjon, E. Ibanez, J.A. Mendiola, S. Cerantola, N. Kervarec, S. La Barre, L. Marchal, V. Stiger-Pouvreau, Talanta 104 (2012) 44–52.
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