Application of gas chromatography-mass spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms

Luís Miguel da Fonseca Carvalho Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms Dissertação do 2º Ciclo de Estudos Conducente ao Grau de Mestre em Toxicologia Analítica, Clínica e Forense Trabalho realizado sob a orientação da Doutora Paula Guedes de Pinho – REQUIMTE, Laboratório de Toxicologia, Departamento de Ciências Biológicas da Faculdade de Farmácia da Universidade do Porto; e co-orientação do Professor Doutor Félix Carvalho – Laboratório de Toxicologia, Departamento de Ciências Biológicas da Faculdade de Farmácia da Universidade do Porto; e da Professora Doutora Paula Baptista – CIMO/Escola Superior Agrária do Instituto Politécnico de Bragança. Outubro de 2012 Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms ii É autorizada a reprodução integral deste trabalho apenas para efeitos de investigação, mediante declaração escrita do interessado, que a tal se compromete. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms iii ACKNOWLEDGMENTS Agradecimentos Em primeiro lugar quero agradecer à Doutora Paula Guedes de Pinho, orientadora deste trabalho, pelos conhecimentos que me transmitiu ao longo deste ano, os quais foram fundamentais para a realização desta investigação, bem como pelo seu constante apoio, preocupação e incentivo. De igual modo tenho a agradecer aos meus co-orientadores. Ao Professor Doutor Félix Carvalho pelos conselhos e saber transmitidos e pelo seu constante entusiasmo e otimismo. O meu agradecimento também à Professora Doutora Paula Baptista pela disponibilidade, simpatia e grande ajuda na vertente “micológica” deste trabalho. Tenho também de agradecer à Professora Doutora Maria de Lourdes Bastos pelo seu empenho e esforço para o desenvolvimento, primando sempre pela excelência e qualidade, do Laboratório de Toxicologia da Faculdade de Farmácia da Universidade do Porto. É devido ao seu trabalho ao longo de vários anos que é, foi e será possível que os estudantes aprendam Toxicologia. Quero também agradecer a todos os membros do Laboratório de Toxicologia por tornarem a integração mais fácil, a jornada mais descontraída e os momentos mais divertidos. O meu obrigado especial ao Professor Doutor António César Ferreira e à Rita Monforte pela imensa ajuda no tratamento “non-targeted” dos dados, que em muito enriqueceu este trabalho. Também agradeço à Nathalie Moreira pela ajuda no tratamento estatístico dos dados e respetiva interpretação. Aos meus colegas do MTACF, tanto aos “resistentes” como àqueles que seguiram por outros rumos, obrigado pela amizade, companheirismo e bons momentos passados. Desejo que todos encontrem aquilo que procuram! Aos meus colegas da Residência Universitária da Bandeirinha que ao longo dos últimos dois anos foram os “lá de casa” e fizeram parte integrante da minha vivência nesta Cidade. A todos um obrigado do “Zé”! Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms iv Aos meus pais, ao meu irmão, ao meu tio e à minha madrinha, aos meus avós e, em particular, à minha Avó Natércia, pelo apoio, amor e incentivo. Sei que estão orgulhosos! Aos meus amigos de sempre, a “família que se escolhe”, o Dário e a Paula, o Tiago, o Rui e a Nicole e todos os outros que não me lembro neste momento, o meu enorme obrigado pela amizade e apoio de há tanto tempo! É recíproco… E porque os últimos são sempre os primeiros: À minha Márcia!... Obrigado! Luís Carvalho Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms v ABSTRACT Macrofungi is an artificial group of fungi based on the size of reproductive structures, the carpophores or mushrooms, which are visible to the naked eye. Nowadays the mushrooms are important in many contexts, particularly, in the areas of gastronomy, economy, medicine and biotechnology. Mushrooms are, in general, best known for their culinary value, given the diversity and richness of flavours, textures and odours that characterize them. They are also nutritionally relevant, as they have high levels of proteins, amino acids, dietary fibers, vitamins and minerals and low calorie and fat levels. Besides all those nutrients, mushrooms are also rich in volatile compounds that are responsible for their smell. However, many species of mushrooms are toxic and therefore not edible, leading, in several cases, to death by accidental poisoning. The high morphological similarity between certain species and the fact that accurate expertise is needed, makes identification through morphological, macroand microscopic characters difficult and therefore other alternatives are required. Currently, chemotaxonomy is used in these situations, resorting to molecular analysis, essentially, DNA sequencing, but also to chemical analysis, including the study of AA and FA profiles, as well as secondary metabolites such as volatile compounds. Furthermore, non-targeted approaches can be used, and it seems promising to identify chemotaxomical markers. In the present work, two GC-MS methodologies were applied in the chemical analysis of 22 mushrooms species (12 edible, 3 toxic and 7 possibly toxic). The first one was a multi-target procedure to extract and derivatize AA, FA and sterols. The result was the identification of 25 compounds, of which 21 were quantified. Moreover, the resulting GC-MS data was also submitted to non-targeted analysis through PCA and PLS-DA, allowing the identification of a compound (5carbon sugar alcohol) which is candidate to be a species-marker as it was present in a much higher amount in one edible species (Suillus bovinus). The second methodology applied was a HSSPME/GC-MS procedure to volatile profiling of the species. Targeted analysis of data resulted in the identification of the main volatiles in mushrooms, i.e.8-carbon skeleton molecules, in almost all species. On the other hand, non-targeted data analysis (PCA and PLS-DA) allowed the identification of 6 molecules that can be speciesor genus-specific: an ester of hexanoic acid, which was only identified in one edible species - Lycoperdon perlatum; and five sesquiterpene-like molecules that have not been formally identified, these molecules were only present in Lactarius aurantiacus, a mushroom species whose edibility/toxic remains unknown. Keywords: AA, FA, sterols and volatile compounds; GC-MS; mushroom; targeted and nontargeted analysis Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms vi RESUMO Os macrofungos constituem um grupo artificial de fungos baseado no tamanho das estruturas reprodutoras, os carpóforos ou cogumelos, que são visíveis a olho nu. Atualmente os cogumelos são importantes em diversos contextos, nomeadamente, nas áreas da gastronomia, economia, medicina e biotecnologia. Os cogumelos são mais reconhecidos pelo seu valor culinário, dada a diversidade e riqueza de sabores, texturas e odores que os caracterizam. São também alimentos nutricionalmente relevantes, uma vez que apresentam elevados níveis de proteínas, aminoácidos, fibras, vitaminas e minerais e baixos níveis calóricos e lipídicos. Além de todos esses nutrientes, os cogumelos são também ricos em compostos voláteis que são responsáveis pelo seu aroma. Contudo, existem muitas espécies de cogumelos que são tóxicas e por isso não edíveis, provocando intoxicações, em certos casos, fatais. A elevada semelhança morfológica entre determinadas espécies e o facto de serem precisos conhecimentos especializados, torna difícil a identificação através de características morfológicas, macroe microscópicas sendo, por isso, necessário encontrar outras alternativas. Atualmente, a quimiotaxonomia é utilizada nestas situações, recorrendo-se à análise molecular, essencialmente, a sequenciação de DNA, mas também à análise química, nomeadamente o estudo do perfil de aminoácidos (AA), ácidos gordos (AG), bem como de metabolitos secundários como os compostos voláteis. Além disto, os estudos metabólicos, principalmente quando são utilizadas estratégias “non-targeted”, constituem metodologias promissoras para a identificação de marcadores quimiotaxonómicos. No presente trabalho, dois métodos de análise por GC-MS foram aplicados na análise química de 22 espécies de cogumelos (12 comestíveis, 3 tóxicas e 7 possivelmente tóxicas). O primeiro consistiu num procedimento analítico para extração e derivatização de AA, AG e esteróis. O resultado foi a identificação de 25 compostos, dos quais 21 foram quantificados. Além disso, os dados de GC-MS foram também submetidos a um tratamento “non-targeted”, cujo principal resultado foi a identificação de um composto (poliálcool com 5 átomos de carbono) que é um candidato a marcador químico de espécie, uma vez que estava presente em quantidades muito superiores numa espécie comestível (Suillus bovinus). O segundo método aplicado foi um procedimento de HS-SPME/GC-MS para obter os perfis de compostos voláteis das espécies. A análise de compostos-alvo resultou na identificação dos principais compostos voláteis dos cogumelos, i.e. moléculas com 8 átomos de carbono, em quase todas as espécies. Por outro lado, a análise “non-targeted” dos dados permitiu a identificação de 6 moléculas que poderão ser específicas da espécie ou do género: um éster do ácido hexanóico, que apenas foi identificado numa espécie comestível - Lycoperdon perlatum; e cinco compostos da família química dos sesquiterpenos, que não foi possível identificar formalmente, mas que apenas estavam presentes nas amostras de Lactarius aurantiacus, uma espécie de cogumelos cuja comestibilidade/toxicidade ainda é desconhecida. Palavras-Chave: AA, AG, esteróis e compostos voláteis; análise “targeted” e “non-targeted”; cogumelo; GC-MS Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms vii INDEX ACKNOWLEDGMENTS ............................................................................................. iii ABSTRACT ............................................................................................................. v RESUMO .............................................................................................................. vi INDEX .................................................................................................................. vii FIGURES INDEX ..................................................................................................... ix TABLES INDEX........................................................................................................ x ABBREVIATIONS LIST ............................................................................................. xi PART I - INTRODUCTION ............................................................................................. 1 1. MUSHROOMS .................................................................................................. 3 1.1 Definition and taxonomical classification ...................................................... 3 1.2 The importance of mushrooms ................................................................... 5 1.3 Mushroom identification ............................................................................. 6 1.4 Misidentification problems .......................................................................... 8 2. MUSHROOM AUTHENTICITY ............................................................................ 12 2.1 DNA analysis .......................................................................................... 12 2.2 Analysis of other molecules ...................................................................... 13 Chemical markers ............................................................................. 13 2.2.1 Chemical analysis techniques ............................................................ 15 2.2.2 3. METABOLOMICS ............................................................................................ 18 3.1 Terminology ............................................................................................ 18 3.2 Role of metabolomics in chemotaxonomy .................................................. 19 3.3 Metabolomics workflow ............................................................................ 20 PART II – OBJECTIVES ............................................................................................. 23 4. OBJECTIVES AND EXPERIMENTAL APPROACHES .............................................. 25 PART III – EXPERIMENTAL PART ............................................................................... 27 5. MATERIAL AND METHODS .............................................................................. 29 Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms viii 5.1 Mushroom samples ................................................................................. 29 5.2 Standards .............................................................................................. 31 5.3 Preparation of standard solutions ............................................................. 31 5.4 Methodology ........................................................................................... 32 Multi-target experiment ..................................................................... 32 5.4.1 5.4.1.1 Metabolites extraction .................................................................. 32 5.4.1.2 Gas Chromatography-Ion Trap-Mass Spectrometry Analysis ........... 32 Volatile profiling experiment............................................................... 33 5.4.2 5.4.2.1 SPME fibers ................................................................................ 33 5.4.2.2 HS-SPME extraction and Gas Chromatography-Ion Trap-Mass Spectrometry Analysis ............................................................................... 33 5.5 Data analysis .......................................................................................... 34 Multi-target experiment ..................................................................... 34 5.5.1 Volatile profiling experiment............................................................... 35 5.5.2 6. RESULTS AND DISCUSSION ............................................................................ 36 6.1 Multi-target experiment ............................................................................ 36 Targeted approach: AA, FA and Sterols profiling ................................. 36 6.1.1 Non-targeted approach: multivariate analysis ...................................... 42 6.1.2 6.2 Volatile profiling experiment ..................................................................... 48 Targeted approach: Main volatiles qualitative analysis ......................... 48 6.2.1 Non-targeted approach: multivariate analysis ...................................... 50 6.2.2 CONCLUSIONS ..................................................................................................... 58 REFERENCES ...................................................................................................... 60 PART IV – ANNEX ................................................................................................... 71 Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms ix FIGURES INDEX Figure 1.1. Basidiocarp structure ............................................................................ 4 Figure 1.2. Spore print ............................................................................................ 7 Figure 1.3. Result of reaction with 5% KOH ............................................................ 8 Figure 1.4. Examples of some mushroom species that may be confounded because their morphological similarities ................................................................ 10 Figure 5.1. Some of the mushrooms collected and analysed in this work ............. 30 Figure 5.2. Schematic representation of the extraction and derivatization procedure used in multi-target experiment. ........................................................... 32 Figure 5.3. Some differences in retention times of alanine in some mushroom species .................................................................................................................. 35 Figure 6.1. Scores plot resulting from PCA of mushrooms‘ AA, FA and sterols quantification data ................................................................................................. 41 Figure 6.2. GC-MS chromatogram resulting from the AA, FA, and sterols extraction procedure in Clitocybe dealbata (Sowerby) P. Kumm.species ............. 42 Figure 6.3. Scores plot resulting from PCA of mushrooms‘ data resulting from the multi-target experiment ............................................ Erro! Marcador não definido. Figure 6.4. Loadings plots corresponding to PC1 (A) and PC2 (B) resulting from PCA of multi-target experiment data ..................................................................... 45 Figure 6.5. Main loadings of PC1 and PC2 distribution among mushroom species subjected to multi-target experiment ..................................................................... 47 Figure 6.6. GC-MS chromatogram resulting from the volatile profiling procedure in Agaricus sylvicola (Vittad.) Perck. species. ........................................................... 50 Figure 6.7. GC-MS chromatograms resulting from the volatile profiling procedure in CB,CD and SI species ....................................................................................... 51 Figure 6.8. Scores plot resulting from PCA of mushrooms’ volatile composition data .......................................................................... Erro! Marcador não definido. Figure 6.9. Loadings plots corresponding to PC1 (A) and PC2 (B) resulting from PCA of volatile profiling experiment data .............................................................. 53 Figure 6.10. Main loadings of PC1 and PC2 distribution among mushroom species subjected to volatile profiling ................................................................................. 54 Figure 6.11. Scores plot resulting from PLS-DA of mushrooms’ volatile composition data ...................................................... Erro! Marcador não definido. Figure 6.12. Loadings plots corresponding to Factor 1resulting from PLS-DA of volatile profiling experiment data ........................................................................... 57 Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 4 Figure 1.1. Basidiocarp structure: A – cap (or pileus); B – scales; C – hymenium; D – annulus (or ring); E – stipe (or stalk) ; F – volva (or cup) (Adapted from [1]) The main taxonomical groups where the macrofungal species belong have been hierarchically organized as showed in Table 1.2. Table 1.2. Main taxonomical groups of macrofungal species.[2] Phylum Ascomycota Caval.-Sm. Basidiomycota R.T. Moore Class Ascomycetes G. Winter Basidiomycetes G. Winter Subclass Agaricomycetidae Parmasto (non-septated basidia) Tremellomycetidae Locq. (septated basidia) Order Pezizales J. Schrӧt Agaricales Underw. Boletales E.-J. Gilbert Cantharellales Gӓum. Phallales E. Fisch. Polyporales Gӓum. Russulales Kreisel ex P.M. Kirk, P.F. Cannon & J.C. David Auriculariales J. Schrӧt. (transversal septum) Tremellales Fr. (longitudinal septum) Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 5 1.2 The importance of mushrooms Fungi are extremely important organisms, despite the fact that sometimes they are undervalued. Ecologically these species are essential, as they are crucial in the regulation of the ecosystems processes, but they also show a high anthropic value in gastronomy, medicine and biotechnology. Fungi, being chemoheterotrophic organisms, need to obtain their nutrients from the environment.[2] Depending on the strategy used to obtain these compounds, the fungi may be classified in three groups[2, 4]: decomposer, which are important in the decomposition of the dead organic and in nutrient recycling matter through the release of enzymes to the soil[2, 4]; mycorrhizal, which, with the establishment of a symbiotic association with plant roots (mycorrhizal association), improve the plant intake of water and nutrients and bestow resistance/tolerance to the biotic and abiotic stresses[4, 5]; and finally the parasite, which establish an harmful association with hosts (usually plants), being able to create habitat to other organisms in case of hosts’ death.[6] Beyond participating in all these processes, macrofungi, specifically their fruiting bodies, integrate the food chains of some animals and are part of the human diet (micophagy)[2]. Furthermore, mushrooms of some species are highly appreciated by humans due to their organoleptic properties (taste, smell and texture)[7, 8]. Furthermore, they are high nutritional value food, as they are rich in proteins, amino acids (AA), vitamins and minerals and have low lipid and caloric contents.[8-10] Hence, these macrofungal species have high gastronomic and economic values. Among these species, the mycorrhizal ones are more valorised because they are difficult to produce in culture. In this context, truffles (fruiting bodies of the species belonging to genus Tuber P. Micheli ex F.H. Wigg.) are the most appreciated and, consequently, they have expensive prices. The most expensive price of the “white truffle”, also known as “Alba Madonna” (Tuber magnatum Pico & Vitt.), may reach 17.500€/kg. In Portugal, Boletus edulis Bull., Cantharellus cibarius Fr. and Amanita caesarea (Scop.) are, economically, the most important species, with prices in the range of 15-30€/kg.[2, 11] Besides their nutritional value, mushrooms are also important in medicine, since they have bioactive compounds that confer anti-atherosclerotic, anti-microbial, anti-neoplastic, anti-oxidant, immunomodulatory and hypoglycaemic properties.[12] Some of the species with recognized medicinal value are: Armillaria mellea (Vahl) P. Kumm., Auricularia auricula (L.) Underw, B. edulis, Flammulina velutipes (Curtis) Singer, Lactarius deliciosus Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 6 (L.) Gray, Lepista nuda (Bull.) Cooke, Marasmius oreades (Bolton) Fr., Pleurotus ostreatus (Jacq.) P. Kumm. and Trametes versicolor (L.) Lloyd.[2, 10] 1.3 Mushroom identification Mushroom identification is always a challenge! Even if a species was previously identified, doubts about the identity of a mushroom can always arise when it is seen for the first time. Nevertheless, any mushroom is, probably, identifiable, if the time and resources (bibliographic and experimental) are available.[13] Mushroom identification is performed through the analysis of macro and microscopic features. The macroscopic characteristics are essential to the identification of species in the field, and, in many cases, are enough to recognize the genus which the mushroom belongs.[14] At macroscopic level, the identification is based on ecological, morphological and organoleptic features and also by using chemical reactions with specific reagents. The most relevant ecological information is the habitat, involving the type of soil and vegetation, the colonized substrate (soil, wood, etc.) and the growth patterns (alone, gregarious, etc.). Then, the ephemeral features must be registered, like the remains of veil and viscosity and also the organoleptic properties (colour, smell and taste). After the ecological identification, the next step is the morphological analysis which should be thorough and organized. To achieve a complete morphological analysis of the mushroom, it is important to note, from the cap to the stipe, all the important details, named a “downward observation”. For each part of the mushroom, some specific details should be noticed, for example: the colour and shape of the cap; the presence/absence of gills or tubes in hymenium; and the presence/absence of annulus and/or volva in the stipe. Despite the importance of the external analysis of the mushroom, it is also important to observe the internal structure of the specimen and some of its microscopic details. Concerning the microscopic features, the technique used by mycologists is called “spore print” – it is a procedure that results in the deposition of the spores on a paper sheet (Figure 1.2). The colour of the “spore print” must be registered once this is important to the mushroom identification. Furthermore, the obtained spores are usually tested with the Melzer’s reagent and observed in a microscope, to analyse their shape and size. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 7 The morphological analysis is accomplished by observing the mushroom interior. Normally the specimen is longitudinally sliced, allowing the observation of the flesh’s anatomy and its colour – it is also important to pay attention to colour changes due to contact with air.[13] This procedure is also important to evidence some organoleptic properties, concerning taste and aroma, since they are not perceptible when the mushroom is intact. The analysis of these properties can be imperative to the identification of certain mushroom species. Despite the fact that the majority of the mushrooms have similar odours[15], certain species/genus have very specific smells which can help to the specimen identification – e.g. some species belonging to Mariasmus Fr., Agaricus Murrill and Clitocybe (Fr.) Staude genera smell like garlic, almonds and anise, respectively.[16-18] The taste, being one of the more specific features of mushrooms, could also be important in the species/genus identification. However, this practice can be dangerous, even deadly, and should only be done when there is already some certainty in the prior identification of the mushroom.[16] Moreover, when tasting the specimen some “safety measures” should also be taken – chew a small piece of mushroom for one or two seconds and then spit it, never swallow it. Figure 1.2. Spore print: A – hymenium placed on the paper sheet; B – spore deposition (Adapted from the original available at: http://www.anbg.gov.au/fungi/images-captions/spore-print-0018.html [05/06/12]) Some chemical reactions can be performed by mycologists to improve mushroom identification. The goal of these reactions is search if there are specific colour changes in fruiting bodies (Figure 1.3). The reagents most commonly used are: potassium hydroxide Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 8 (30-40% KOH), iron sulfate (10% FeSO4)[19], ammonium hydroxide (25% NaOH) and Melzer’s reagent, aforementioned.[13] Figure 1.3. Result of reaction with 5% KOH in the cap (A) and flesh region (B) of a mushroom belonging to the species Cortinarius callisteus (Fr.) Fr. (Adapted from the original available at: http://www.anbg.gov.au/fungi/images-captions/spore-print-0018.html [06/09/12]) Finally, when there is the need to preserve a mushroom to further studies, the specimen is submitted to a dehydration process, and kept dry, which is called exsiccation (from Latin exsiccatus – to dry). 1.4 Misidentification problems The correct identification of a wild mushroom, especially when the specimen is intended to human consumption, is extremely important, even if it is difficult and time consuming. If identification is careless and undervalued, the wild mushroom consumption is always risky, once the ingestion could result in a severe intoxication, whose worst consequence may be death. Despite the fact that most cases of mushroom intoxications result from misidentification, there are also many cases where these intoxications results from Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 9 suicide/homicide attempts and as consequence of “intentional magic mushrooms” ingestion – those with hallucinogenic properties [Amanita muscaria (L.) Lam. and species from Psilocybe (Fr.) P. Kumm., Panaeolus (Fr.) Quél. and Copelandia Bres. genera].[20, 21] Concerning the misidentification cases, most of them are due to the morphological similarity between some edible and toxic species (Figure 1.4 Table 1.3).[20, 22] Epidemiologic studies reveal that mushrooms poisonings have been increasing.[21, 22] The most severe cases, those resulting from ingestion of mushrooms containing the toxins amanitine [species belonging to Amanita Pers., Galerina Earle and Lepiota (Pers.) Gray genera] and orellanine [species belonging to Cortinarius (Pers.) Gray genus], although follow the trend, remain uncommon.[22] Nowadays, several reports of wild mushroom poisoning are described in the literature.[23, 24] Finally, it should be noted that the increasing number of poisonings is a consequence of the popularity of wild mushroom harvesting and consumption, and also the growing number of individuals who consume “magic mushrooms” in order to experience its hallucinogenic effects.[21, 22] Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 10 Figure 1.4. Examples of some mushroom species that may be confounded because their morphological similarities: (A) Volvariella gloiocephala (DC.) Boekhout & Enderle (edible) confounded with (B) Amanita phalloides (Vaill. ex Fr.) Link (deathly), (C) Cantharellus cibarius Fr. (edible) confounded with (D) Omphalotus olearius (DC.) Singer (toxic) and (E) Amanita caesarea (Scop.) Pers. (edible) confounded with (F) Amanita muscaria (L.) Lam. (toxic and hallucinogenic) when this, because of rain, loses is characteristic white scales. (Adapted from the originals available at: (A) http://www.koleopterologie.de/arbeitsgemeinschaft/beitraege/esser/volvariella-gloiocephala-foto-keller-640x480.jpg [06/09/12], (B) http://upload.wikimedia.org/wikipedia/commons/9/99/Amanita_phalloides_1.JPG [06/09/12], (C) http://www.naturephoto-cz.com/photos/others/cantherellus-31880.jpg [06/09/12], (D) http://www.naturamediterraneo.com/Public/data/polypo/20041017181445_Omphalotus%20olearius.JPG [06/09/12], (E) http://t3.gstatic.com/images?q=tbn:ANd9GcRePlo5HgXbpVUBTMiUlAzu1QeKylifLI5D0XCE_X6XYMiccz4lWw&t=1 [06/09/12] e (F) http://farm1.static.flickr.com/124/319800093_b55e2cbba9.jpg [06/09/12]) Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 11 Table 1.3. Some examples of edible and toxic mushrooms usually confounded. (Adapted from [22]) Edible mushroom species Toxic mushroom species Agaricus arvenis Schaeff Agaricus xanthodermus Genev. Amanita ovoidae (Bull.) Link Amanita proxima Dumée Armillaria mellea (Vahl) P. Kumm. Clitocybe acromelalga Singer Galerina unicolor (Vahl) Singer Calocybe gambosa (Fr.) Donk Inocybe erubescens A. Blytt Clitocybe gibba (Pers.) P. Kumm. Clitocybe amoenolens Malençon Coprinus comatus (O.F. Müll.) Pers. Coprinus atramentarius (Bull.) Fr. Marasmius oreades (Bolton) Fr. Clitocybe dealbata (Sowerby) Gillet Lepista inversa (Scop.) Pat. Clitocybe amoenolens Malençon Agaricus xanthodermus Genev. Morchella esculenta (L.) Pers. Gyromitra esculenta (Pers.) Fr. Tricholoma magnivelare (Peck) Redhead Amanita smithiana Bas Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 12 2. MUSHROOM AUTHENTICITY The unequivocal identification of a mushroom species through its phenotypical features (morphology and physiology) is extremely difficult, and in certain cases even impossible, due to subjectivity of these kind of analysis and also as a consequence of scarcity and ambiguity of these characteristics.[25, 26] Thus, it is essential to find and use other properties that allow a correct identification of the specimens. Therefore, the chemical/molecular composition of species is a very important taxonomical tool, being complementary to the phenotypical features in identification and classification of such organisms. Chemotaxonomy, also designated molecular taxonomy, uses the chemical composition and/or molecular analysis of species to its classification and identification. The molecules which are the most used in this context are deoxyribonucleic acid (DNA), fatty acids (FA), proteins, carbohydrates and secondary metabolites (SM).[27] 2.1 DNA analysis Interest and investigation in molecular biology lead to development of new technics that allows species identification[28], namely, through the study of the DNA molecule. Thus, the DNA analysis, also called molecular analysis, is, nowadays, the most common technique in fungal chemotaxonomy. The importance of DNA in this context is due to the fact that the genetic composition of each species is unique and specific, not being affected by age, physiological conditions or environmental factors.[29] Molecular analysis techniques require, in first place, the extraction of genetic material. In the case of mushrooms, there are described in the literature different “basic” methods to extract DNA, both for fresh and dried mushrooms (exsiccated), ranging from simple commercial extraction kits to laboratory protocols[29, 30] a little more complex. After the extraction of DNA, the genetic material is amplified through the polymerase chain reaction (PCR). Then, there are two methodologies that are the most utilized: DNA sequencing[31] or the analysis of DNA fragments length[28]. To obtain and analyse DNA fragments, two techniques can be used: RAPD – randomly amplified polymorphic DNA[32]; and ARDRA – amplified ribossomal DNA restriction analysis[33]. Concerning, DNA sequencing and ARDRA technique, as its name indicates, the recommended and most commonly used DNA region in these studies is rDNA[28, 33], i.e. ribossomal DNA – the DNA region that contains the group of genes that codifies the ribossomal ribonucleic acid (rRNA). The Fungal Work Group (FWG) of the Consortium for Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 13 the Barcode of Life (CBOL), after several studies, concluded that the ITS1-5.8S-ITS2 region of rDNA (also known simply by ITS region – Internal Transcribed Spacer region), should be chosen as the molecular marker for the identification of fungal species.[34] The ITS region was chosen because it has several advantages, e.g. ease of amplification and the fact that it’s the most universal option[31, 34]. 2.2 Analysis of other molecules Chemotaxonomy, as mentioned above, is based on the study of different molecules of organisms in order to identify them. Thus, besides the aforementioned DNA molecule study, the analysis of other molecules, using, obviously, other kind of techniques and methodologies, is important and useful in the chemotaxonomical context. Chemical markers 2.2.1 Chemotaxonomy, in its most global definition, can use any molecule (or molecule groups) of an organism to the species identification. However, depending on the organism to identify, it is common that certain groups of compounds are preferred. Concerning macrofungi, SM are the most used molecules for chemotaxonomical purposes.[35] Apart from the study of these compounds, in literature, there are also chemotaxonomical studies that are based on the analysis of FA, proteins, AA and carbohydrates, despite the fact that the use of these molecules is much smaller compared to SM. SM have been extremely used in macrofungal species identification, although, they have been interpreted as “morphological/organoleptic markers”, once they are responsible for characteristics like colour, odour and taste.[35] When compared to primary metabolites (PM), SM are preferable in these kind of studies once most of them show higher ability to species differentiation.[35] PM, being vital to the maintenance and survival processes of organisms, such as growth and reproduction, are the “base molecules of life” and so they are common to the majority of species, even if these are phylogenetically distinct. Otherwise, the SM, not being essential to survival, are more differentiated among species as consequence of mutations and evolutionary processes. Yet, not all SM, when used individually, are potential chemotaxonomical markers, once phylogenetically different species can, through synthesis processes that are evolutionarily distinct, produce the same SM.[27] Thus, the “choice” of a molecule as chemotaxonomical marker should be Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 20 ultimate tool to find those compounds/metabolite profiles that are species-specific or, at least, species-indicative. As previously referred, molecules resulting from secondary metabolism are very important in this context, since a “fungal secondary metabolite” is a chemical compound produced by a limited number of species in a genus, an order, or even phylum, and has a high differentiation power”[27]. Nowadays there are some reviews on the use of metabolite profiling in fungal species (e.g. Frisvad et al. (2008)[27], Nielsen et al. (2004)[45] and Smedsgaard & Nielsen (2005)[74]), but in this context, Basidiomycota phylum is still widely unexplored[75]. 3.3 Metabolomics workflow Metabolomics investigations results from the combination of analytical methodologies, the tool to ‘reach’ metabolome, and bioinformatics (multivariate statistical analysis or chemometrics), which allows data processing and interpretation.[63, 65] Thus, a metabolomic experiment is a sequential multi-step procedure that can be divided into four parts: sample collection and preparation; metabolites separation and identification; data mining and extraction; and data analysis and interpretation.[76] Sample collection refers to the samples harvesting and storage. Herein, it is important to collect an appropriate number of samples to match the study requirements and reduce the influence of biological variance.[77] This is not always possible, namely when wild species are under analysis, such as wild mushrooms. Sample sampling and storage should ensure that there is no formation or degradation of metabolites due to enzymatic activity, oxidation processes or bacterial growth. So, after sample collection, metabolism should be stopped (sample quenching) as quickly as possible, which can be done by different processes (e.g. freezing in liquid nitrogen).[76] Sample preparation is a procedure step that serves several purposes: metabolite extraction from matrix (Section 2.2.2) – i.e. removal of matrix components that can interfere in the analysis (e.g. macromolecules); transference of metabolites to a medium compatible to the analytical technique – e.g. extract metabolites from biological tissues to an appropriate solvent to inject it in a gas chromatograph; metabolite enrichment – i.e. a pre-concentration step to increase the concentration of low-abundance metabolites; and, specifically in GC, derivatization to improve metabolites volatility.[76, 77] Ideally, a metabolomics analysis Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 21 would be performed without sample preparation, as this procedure necessary leads to metabolites loss and perhaps to contaminations.[76, 77] Metabolite separation is achieved using chromatographic techniques such as liquid and gas chromatographies (Section 2.2.2) and capillary electrophoresis. There are a large collection of separation methodologies that can be used in metabolomic experiments, and so the advantages and limitations, sensibility and costs of those techniques but also the nature of samples and metabolites under analysis should be taken in account to choose the most suitable technique to be applied.[70, 78] Nowadays the two analytical techniques most applied to metabolite identification in metabolomics’ studies are NMR spectroscopy and MS (Section 2.2.2).[63, 76] Both are high-throughput techniques, which allow the analysis of a large spectrum of metabolites in a fast and reproducible way. The main advantages of NMR are that it provides structural information on molecular structure, which facilitates unknown metabolite identification, and requires minimal sample preparation, but otherwise its low sensitivity is a considerable drawback.[63, 70, 76] In contrast to NMR, MS is more sensitive, but in the other hand it is destructive to samples and usually requires sample preparation.[63, 76] Besides these, other techniques can be used such as FTIR and Raman spectroscopies.[70] Once the goal of metabolomics is the study a wide group of compounds, the application of separation/identification techniques to samples generates a large amount of data.[67, 76] Thus, data pre-processing, i.e. transformation of raw data into a standard and uniform format, is an essential part of data mining and extraction, once it allows the correction of data differences caused by experimental variables.[67, 77] Due to the high volume of data, this is done through specific software and this step includes: peak alignment (matching peaks across multiple samples), peak normalization (adjust peak intensities and reduce analytical drift), peak deconvolution (separate overlapped peaks to distinguish co-eluted metabolites) and baseline correction (remove background noise).[67, 76, 77] Nowadays there are several commercial and free software programs to perform these operations[67, 77], each one with its own advantages and limitations, but still none of those is universal.[76] After pre-processing, data analysis is performed through several multivariate statistical analysis methods, which allows to “see” spectral patterns, i.e. metabolic signature of samples.[76] Herein there are two main approaches: supervised analysis and unsupervised analysis. Unsupervised methods (e.g. principal component analysis – PCA, probably the most commonly multivariate statistical analysis method used in metabolomics[79]; or Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 22 hierarchical cluster analysis – HCA) discriminate samples through their metabolite composition in an unbiased way, i.e. without any prior knowledge about samples.[78, 79] Thus, these methods enable the identification of differences between samples and also the presence of outliers in data.[79] In contrast, supervised methods, such as partial leastsquare discriminant analysis (PLS-DA) – one of the most currently used[79], requires the supply of information about samples (e.g. diseased and healthy subject, toxic and nontoxic specimen, etc.) to create cluster of patterns, i.e. reveal the metabolic features which are better to differentiate those groups .[77, 78] Once found the compounds responsible for differences, metabolite identification must be done through the techniques previously referred – i.e. comparison of NMR and/or MS spectrum(a) with those present in spectral databases or with the standard compound spectrum or, when it is a compound identified for the first time and its standard is not available in suppliers, through molecular structural information provided by those techniques.[78] In order to establish that a metabolite (or a group of them) is indeed a marker, either a biomarker or a chemotaxonomical marker, it is obviously necessary to perform more extensive studies to validate that conclusion. Despite the improvements in separation, analytical and statistical methods, there are still some limitations in metabolomics, which can be a result of the complexity of metabolome, the large amount of data generated and the intraand inter-individuality of metabolites composition among samples. PART II – OBJECTIVES Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 24 This page was intentionally left in blank Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 25 4. OBJECTIVES AND EXPERIMENTAL APPROACHES The main purpose of this research work was to analyse wild edible and toxic mushrooms through GC-MS technique, aiming samples discrimination through metabolic profiles. Thus, to achieve this goal two approaches were chosen: A. The application of a multi-target method for the identification and quantification of three classes of metabolites – amino acids, fatty acids and sterols. Although these compounds were previously studied in different mushroom species and found not to be the most suitable to identification purposes, as they are primary metabolites, there were several reasons to perform this study:  Test the applicability of this multi-target method in mushrooms, once this methodology was never been applied in macrofungal species.  Identify and quantify metabolites of the mentioned classes (AA, FA and sterols) in several mushrooms species, some of which have been analysed in the present work for the first time.  Apply the “non-targeted analysis”, through multivariate statistical methods, to explore the metabolic pattern achieved with this technique, as it contains more compounds than those belonging to the referred classes and some of which can have chemotaxonomical potential. B. The application of a HS-SPME/GC-MS methodology to study volatile profiles of mushrooms. According to the purpose of this investigation, volatiles are important candidate compounds to distinguish edible from toxic mushrooms as they are fulcral SM participating to the specific aroma of each mushroom species. In agreement with that, there must be some volatiles, even present in low contents that may allow discrimination between edible and toxic species. Animals, whose olfaction is usually more developed than that of the humans, “use” this characteristic of volatiles (aroma) to choose which mushrooms may or may not be eaten. Although the volatile composition of mushrooms is unquestionably widely studied, its analysis through a non-targeted approach, i.e. metabolic fingerprinting, has never been Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 26 used and this seems to be a good strategy to achieve the goal of this research. Herein, the two objectives were:  To analyse data through an unsupervised statistical methodology in order to identify the most suitable metabolites to differentiate mushroom species.  To perform a supervised data analysis (edible mushrooms vs. toxic mushrooms), aiming to understand if there are significant similarities within each group and, if so, to identify the volatile molecules which are the most appropriated to differentiate those groups. PART III – EXPERIMENTAL PART Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 28 This page was intentionally left in blank Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 29 5. MATERIAL AND METHODS 5.1 Mushroom samples Samples of 22 different wild mushroom species were collected in Trás-os-Montes and Douro Litoral regions – Table 5.1 and Figure 5.1. After being harvested, species were taxonomically identified according to several authors[80-83] and a representative voucher of each specimen was deposited at the herbarium of Escola Superior Agrária of Instituto Politécnico de Bragança. Table 5.1. Characterization of mushroom species. Code Species Edibility Origin Habitat Date of Collection AA Agrocybe aegerita (V. Brig.) Singer Edible Porto Platanus x acerifolia Nov. 2011 AC Amanita caesarea (Scop.) Pers. Edible Bragança Castanea sativa Sept. 2011 AM Amanita muscaria (L.) Lam. Toxic Bragança Castanea sativa Dec. 2011 AS Agaricus sylvicola (Vittad.) Perck Edible Bragança Meadow Nov. 2011 AV Amanita vaginata (Bull.) Lam. Toxica,c,d Bragança Castanea sativa Oct. 2010 BE Boletus edulis Bull. Edible Bragança Castanea sativa Sept. 2011 CB Collybia butyracea (Bull.) P. Kumm. Edible Bragança Castanea sativa + Pinus pinaster Nov. 2011 CD Clitocybe dealbata (Sowerby) P. Kumm. Toxic Bragança Castanea sativa + Pinus pinaster Nov. 2011 HS Hebeloma sinapizans (Paulet) Gillet Toxica,c,e Bragança Castanea sativa + Pinus pinaster Nov. 2011 LA Lactarius aurantiacus (Pers.) Gray Toxicb Bragança Castanea sativa + Pinus pinaster Dec. 2011 LC Lactarius controversus (Pers.) Pers. Toxica,c Bragança Castanea sativa + Pinus pinaster Oct. 2010 LP Lycoperdon perlatum Pers. Edible Bragança Castanea sativa Nov. 2011 MP Macrolepiota procera (Scop.) Singer Edible Bragança Castanea sativa Nov. 2011 MR Mycena rosea Gramberg Toxica,f Bragança Castanea sativa + Pinus pinaster Nov. 2011 RC Russula cyanoxantha (Schaeff.) Fr. Edible Bragança Castanea sativa Oct. 2010 RD Russula delica Fr. Edible Bragança Castanea sativa Oct. 2010 RL Rhizopogon luteolus Fr. Toxicb Bragança Castanea sativa + Pinus pinaster Dec. 2011 SB Suillus bovinus (Pers.) Roussel Edible Bragança Pinus pinaster Dec. 2011 SI Sarcodon imbricatus (L.) P. Karst. Edible Bragança Pinus pinaster Nov. 2011 TA Tricholoma acerbum (Bull.) Vent. Toxica, c Bragança Castanea sativa Nov. 2011 TE Tricholoma equestre (L.) P. Kumm. Toxic Bragança Pinus pinaster Dec. 2011 TP Tricholoma portentosum (L.) P. Kumm. Edible Bragança Castanea sativa + Pinus pinaster Dec. 2011 a Suspected to be toxic – Considered toxic to comparison purposes; b Edibility/Toxicity unknown – Considered toxic to comparison purposes; c It is suspected that causes gastrointestinal disorders; d It is suspected that causes haemolytic disorders; e May have cytotoxic cucurbitacins: f It is suspected to contain the toxin muscarine Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 36 6. RESULTS AND DISCUSSION 6.1 Multi-target experiment The previous established multi-target method for the identification and quantification of AA, FA an sterols[84] was successfully applied to all 22 mushrooms species. Despite [85]AA, FA and sterols compositions were already studied for several mushroom species[8, 50, 86-88], no reference has been found in literature in which these compounds were studied thru a single multi-target methodology. Furthermore, to our best knowledge this is the first time that these compounds were studied in some toxic species. Targeted approach: AA, FA and Sterols profiling 6.1.1 The applied extraction and derivatization procedure allowed the quantification of 21 compounds in mushroom samples– 9 AA, 11 FA and 1 sterol. It was possible to identify 13 AA, all previously reported in mushrooms[9, 39, 86, 89], and among them 9 were quantified (Table 6.1) and 4 other were identified but not quantified - glutamic acid, methionine, L-tyrosine and tryptophan. The average total of free AA content in mushroom species was 318.72 mg/100g of fresh weight (fw). MP and BE were the species with higher free AA content (819.13 and 788.94 mg/100g fw, respectively), in the other hand, RL was the specie with the lowest contents (26.92 mg/100g fw). In previous works, BE was also described as the species with the highest AA content among groups of 3[90], 10[91] and 11[8] mushrooms species. Regarding AA contents, alanine, being reported as one of the main AA in mushrooms[8, 86, 92], was, as expected, present in high amounts (213.67 mg/100g fw). On the other hand, proline was the AA present in small amounts (6.53 mg/100g fw). Although there were statistically significant differences among all compounds, through Tukey’s HSD it was possible to observe that the mean amount of glycine in BE was significantly different from the quantities in all other species. The same occurs with the concentration of valine in LC and the quantity of serine and threonine in MP species. It was found in literature that BE was also the species with high glycine content in two other studies concerning 3[90] and 11[8] mushrooms specimens, and in the first one the higher amount of glycine in BE were statistically different too. FA are among the most widely studied compounds in mushrooms with several publications available in literature.[47, 50, 93, 94]. In the presented work, 11 FA were identified (Table 6.2), and the total average among all the samples was 325.55 mg/100g fw. AM and Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 37 LC were the species that presented higher levels of FA (1479.69 and 1064.43 mg/100g fw, respectively) and BE, an edible specie, was the mushroom specie with the lowest FA content – 43.23 mg/100g fw. The high FA content in AM is due to the large quantity of Oleic acid (cis-9-octadecenoic acid [C18:1,n-9]) – 1172.44 mg/100g fw. This is in agreement with other studies reported in the literature.[43, 95] Indeed, oleic and palmitic (hexadecanoic acid [C16:0]) acids were the main FA, with an average content of 151.05 and 112.06 mg/100g fw, which is also in agreement with literature reports.[50, 89, 92]. On the other hand, margaric acid (heptadecanoic acid [C17:0]) was only present in small amounts (2.19 mg/100g fw). Moreover, linoleic acid (cis,cis-9,12-octadecadienoic acid [C18:2,n-6]), which in other studies were reported as the FA present in higher amounts[50, 92, 96], was one of the compounds with lower level (8.31 mg/100g fw). Statistical analysis showed, as previously found for AA, that there were statistically significant differences among all compounds. Moreover, Tukey’s HSD test revealed that the amount of myristic acid (tetradecanoic acid [C14:0]) in AC was distinct from the quantities found in all the other macrofungal species. The same occurred with the higher values of pentadecylic acid (pentadecanoic acid [C15:0]) in TE and the higher concentrations of stearic acid (octadecanoic acid [C18:0]) and, as mentioned before, oleic acid in AM. Furthermore, AM higher FA content is also statistically different from the values obtained in all other species. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 38 Table 6.1. Quantification of amino acids in mushroom samples (mg/100g, wet basis). Mushroom species Amino acids1 [Retention time] Ala [3,21 min] Gly [3,34 min] Val [3,97 min] Leu [4,35 min] Ile [4,50 min] Pro [4,58 min] Ser [4,92 min] Thr [5,09 min] Phe [6,69 min] Total2 AA 109.33 (27.43) nd 15.11 (7.39) 4.16 (0.71) 14.73 (3.99) 3.63 (0.72) 2.82 (1.83) 2.63 (0.48) nd 166.09 (13.02) AC 169.21 (0.57) 13.31 (3.01) 12.08 (1.73) 34.11 (8.49) 17.19 (1.45) 9.41 (1.62) 12.80 (2.54) 9.80 (1.60) nd 292.70 (22.00) AM 118.14 (12.11) 19.62 (9.63) 7.79 (1.85) 28.15 (6.41) 5.80 (0.92) 3.35 (1.26) 5.14 (0.32) 4.95 (1.24) 34.00 (10.11) 225.21 (22.27) AS 346.78 (7.18) 1.75 (1.45) 22.21 (4.27) 23.16 (2.93) 35.02 (4.89) 20.24 (14.29) 21.23 (8.80) 11.05 (0.63) 33.28 (4.14) 514.12 (35.31) AV 124.39 (0.52) 2.20 (1.11) 19.28 (2.16) 34.31 (2.54) 20.09 (2.09) 2.55 (0.63) 3.36 (1.73) 3.82 (0.91) 26.07 (5.48) 248.50 (32.15) BE 503.09 (5.67) 56.27 (6.93) 25.67 (4.96) 55.16 (14.55) 26.55 (7.85) 12.22 (4.88) 53.79 (27.84) 30.10 (12.47) 45.34 (6.74) 788.94 (90.30) CB 209.38 (13.56) 1.09 (0.28) 16.25 (1.86) 8.35 (0.87) 20.02 (2.20) 5.61 (0.70) 21.67 (0.84) 12.56 (0.42) 15.98 (1.56) 312.39 (21.32) CD 152.61 (31.85) nd 2.42 (0.20) 3.10 (0.38) 4.66 (2.65) 2.65 (0.43) 14.93 (3.88) 8.13 (1.55) 11.19 (0.40) 202.23 (3705) HS 117.3 (27.43) 2.36 (0.67) 3.78 (0.52) 4.55 (0.33) 6.43 (0.81) 2.93 (0.17) 7.10 (1.40) 2.71 (0.08) 19.8 (0.93) 166.98 (29.95) LA 283.01 (13.46) nd 4.95 (1.12) 3.53 (0.06) 3.09 (0.23) 3.74 (0.19) 24.73 (6.20) 7.09 (0.56) 9.89 (2.04) 339.73 (11.13) LC 180.96 (1.80) 35.39 (2.52) 53.63 (13.12) 63.11 (6.37) 35.57 (4.80) 11.04 (6.64) 18.99 (4.84) nd 61.28 (6.67) 488.10 (99.84) LP 176.99 (15.43) 4.58 (1.84) 8.77 (1.63) 6.57 (1.26) 14.96 (1.55) 12.60 (0.98) 50.38 (6.74) 18.44 (0.84) 24.40 (5.01) 315.98 (33.25) MP 677.4 (66.92) 17.16 (5.91) 15.97 (3.25) 8.47 (2.29) 14.70 (3.57) 11.74 (5.21) 118.92 (17.65) 43.64 (5.74) 11.92 (1.67) 819.13 (40.20) MR 76.45 (8.75) 2.16 (1.34) 3.89 (0.43) 7.32 (0.59) 6.43 (0.25) 3.53 (0.52) 6.48 (0.65) 6.02 (0.85) 13.85 (1.58) 123.99 (15.10) RC 190.16 (15.01) 6.38 (2.77) 33.22 (2.84) 53.70 (5.00) 39.93 (5.10) 7.71 (2.74) 7.34 (4.00) 10.04 (3.85) 48.10 (5.02) 381.44 (5.52) RD 89.96 (8.33) 2.80 (2.08) 6.28 (0.27) 25.58 (5.40) 9.12 (0.25) 4.45 (1.47) 12.93 (1.35) 7.73 (0.64) 28.89 (3.91) 183.44 (13.99) RL 15.95 (2.96) nd nd 0.99 (0.07) nd nd 2.75 (0.23) 1.39 (0.21) 5.84 (0.26) 26.92 (3.57) SB 579.95 (40.86) nd 13.71 (2.62) 5.80 (1.19) 3.53 (2.62) nd nd 5.53 (1.05) 48.71 (3.18) 656.07 (43.10) SI 114.36 (7.13) nd 1.27 (0.68) 2.66 (0.28) 4.68 (0.85) 2.18 (0.22) 13.81 (4.35) 3.08 (0.89) 10.58 (0.10) 138.45 (11.17) TA 85.85 (7.83) nd 3.21 (0.24) 12.88 (1.61) 6.59 (0.30) 2.42 (0.04) nd 8.69 (0.72) 6.60 (1.56) 124.04 (8.93) TE 289.60 (12.92) 1.82 (0.01) 8.10 (0.12) 9.01 (0.46) 13.95 (0.90) 6.32 (1.32) 15.12 (1.62) 5.80 (0.76) 12.37 (0.21) 363.47 (11.72) TP 89.9 (34.07) nd 2.02 (1.48) 13.53 (1.30) 2.84 (1.48) 2.19 (0.18) nd 2.48 (0.45) 7.28 (0.54) 133.76 (10.01) Average Total 213.67 (16.00) 11.92 (2.78) 13.31 (3.11) 18.55 (3.58) 14.57 (2.08) 6.53 (3.40) 21.80 (6.87) 9.79 (2.79) 23.77 (2.75) 318.72 (215.898)3 1 Results are expressed as mean (standard deviation) of three determinations ; 2 Mean of total AA of three determinations (data not showed); 3 Mean of Totals (values in the same column) nd – not detected; Ala – alanine; Gly – glycine; Val – valine; Leu – leucine; Ile – isoleucine; Pro – proline; Ser – serine; Thr – threonine; Phe – phenylalanine Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 39 Table 6.2. Quantification of fatty acids in mushroom samples (mg/100g, wet basis). Mushroom species Fatty acids1 [Retention time] Pel (C9:0) [5,00 min] Cap (C10:0) [5,64 min] Lau (C12:0) [6,81 min] Myr (C14:0) [7,89 min] Pen (C15:0) [8,39 min] Pal (C16:0) [8,91 min] Mar (C17:0) [9,49 min] Lin (C18:2,n-6) [9,95 min] Ole (C18:1,n-9) [10,01 min] Ste (C18:0) [10,14 min] Ara (C20:0) [11,80 min] Total2 AA nd nd 6.57 (1.83) 54.98 (0.00) nd 88.72 (13.98) 2.13 (0.71) 6.58 (0.86) 24.37 (6.55) 14.25 (7.59) nd 154.41 (23.40) AC 4.48 (0.56) 0.52 (0.00) 30.71 (12.50) 93.17 (7.86) nd 237.56 (56.11) 3.52 (2.69) 5.42 (0.12) 41.24 (5.90) 132.41 (23.41) 2.73 (1.91) 516.66 (20.65) AM nd 0.50 (0.01) nd nd nd 178.08 (14.24) nd 5.82 (0.37) 1172.44 (51.05) 131.44 (6.24) 11.53 (0.69) 1479.69 (10.46) AS nd nd nd nd 7.22 (0.12) 93.48 (8.06) 2.65 (0.28) 12.08 (2.62) nd 22.53 (3.38) nd 135.55 (10.13) AV 4.12 (0.14) 0.61 (0.17) 14.49 (1.05) nd nd 191.20 (2.35) 1.57 (0.11) 5.88 (0.17) nd 67.75 (2.46) nd 283.94 (4.83) BE 4.52 (0.52) 0.48 (0.00) nd nd nd 28.26 (0.85) 1.74 (0.21) 5.03 (0.09) nd 4.86 (0.43) nd 43.23 (2.68) CB nd nd nd nd nd 63.84 (9.62) 2.11 (0.04) 10.82 (0.10) 26.89 (1.75) 39.50 (3.15) nd 140.07 (11.08) CD nd nd 5.12 (0.01) nd nd 48.21 (14.53) 1.78 (0.36) 6.39 (0.12) 19.15 (9.52) 17.66 (7.27) nd 106.77 (15.47) HS 5.62 (0.59) 4.18 (0.78) 5.47 (0.45) nd nd 88.84 (24.72) 1.88 (0.20) 23.90 (9.00) 83.62 (21.17) 33.68 (5.59) nd 176.56 (12.56) LA nd nd 6.40 (1.64) nd nd 50.71 (0.60) nd 8.04 (0.29) 50.94 (5.96) 64.01 (2.92) nd 182.16 (5.37) LC nd 0.69 (0.14) 29.62 (1.63) nd nd 285.75 (27.63) nd 8.44 (1.52) 270.44 (31.52) 457.36 (15.79) 12.12 (1.17) 1064.43 (56.92) LP nd nd 5.28 (0.48) nd nd 52.55 (9.85) 2.26 (0.39) 7.19 (0.03) 10.13 (4.49) 14.69 (8.53) nd 84.07 (25.61) MP 4.54 (0.52) nd 6.12 (0.57) nd 9.32 (3.32) 148.19 (35.46) 2.90 (0.49) 12.78 (2.66) 45.77 (6.44) 28.91 (4.75) nd 242.48 (19.72) MR 7.68 (2.05) 4.16 (0.45) 5.73 (0.51) nd 2.54 (0.22) 112.74 (26.90) 1.62 (0.11) 10.91 (3.12) 68.06 (29.43) 58.65 (8.15) nd 218.93 (12.31) RC 4.09 (0.08) 0.55 (0.02) 14.45 (1.21) nd nd 201.83 (36.22) nd 5.61 (0.46) 320.46 (50.21) 91.77 (8.30) nd 684.65 (3.78) RD 4.16 (0.21) 0.56 (0.03) 11.48 (0.34) nd nd 188.43 (46.12) 1.87 (0.00) 5.21 (0.12) 335.32 (100.75) 199.67 (23.41) nd 678.90 (35.75) RL 5.32 (1.25) 3.81 (0.20) 5.17 (0.15) nd 5.06 (0.50) 51.78 (7.83) 1.56 (0.05) 6.85 (0.91) 25.72 (10.06) 11.54 (1.09) 5.28 (0.28) 93.92 (6.01) SB nd nd nd nd nd 44.70 (10.11) 1.81 (0.14) 5.57 (0.00) 29.00 (7.22) 16.97 (4.91) nd 97.43 (21.77) SI nd nd nd nd nd 68.62 (6.32) 1.92 (0.14) 6.40 (0.38) 32.79 (7.74) 22.91 (0.83) nd 123.56 (19.92) TA nd nd 5.14 (0.19) nd nd 103.56 (13.00) 4.05 (0.30) 5.58 (0.38) 190.42 (2.82) 70.86 (4.81) nd 281.62 (87.77) TE nd nd 5.32 (0.07) nd 26.57 (1.06) 72.36 (5.93) 2.41 (0.21) 12.78 (0.91) 84.15 (9.26) 43.06 (1.62) nd 246.66 (11.03) TP nd nd 5.47 (0.23) nd nd 65.89 (4.59) 1.62 (0.09) 5.56 (0.11) 39.08 (1.95) 24.78 (2.59) nd 126.35 (28.06) Average Total 4.95 (0.63) 3.22 (7.17) 10.16 (3.01) 74.07 (5.56) 10.14 (1.32) 112.06 (15.20) 2.19 (0.63) 8.31 (1.99) 151.05 (24.94) 71.33 (6.42) 7.92 (0.70) 325.55 (359.30) 3 1 Results are expressed as mean (standard deviation) of three determinations ; 2 Mean of total FA of three determinations (data not showed); 3 Mean of Totals (values in the same column) nd – not detected; Pel – pelargic ac.; Cap – capric ac.; Lau – lauric ac.; Myr – myristic ac.; Pen – pentadecylic ac.; Pal – palmitic ac.; Mar – Margaric ac.; Lin – linoleic ac.; Ole – Oleic ac.; Ste – stearic ac.; Ara – arachidonic ac. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 40 Concerning sterols, only ergosterol (ergosta-5,7,22-trien-3β-ol), the main sterol in several mushrooms[85], was identified and quantified – Table 6.3. Esgosterol, the vitamin D2 precursor, was identified in all samples, except in RC, with an average content of 213.67 mg/100g fw and was present in higher amount in AV species (240.47 mg/100g fw). Other sterol-like compounds were also present in almost all species and by comparison with literature database one of them was tentatively identified as fungisterol, as it showed intense ions at m/z 255 and 472.[85] Table 6.3. Quantification of sterols in mushroom samples (mg/100g, wet basis). Mushroom species Sterols1 [Retention time] Ergosterol [18,58 min] AA 50.13 (6.43) AC 137.23 (6.69) AM 78.61 (5.95) AS 145.40 (15.05) AV 240.47 (12.18) BE 122.51 (6.09) CB 81.76 (6.60) CD 95.81 (8.21) HS 107.46 (6.98) LA 86.60 (1.04) LC 58.57 (11.41) LP 79.39 (14.80) MP 191.26 (21.35) MR 32.76 (2.60) RC nd RD 16.88 (0.72) RL 92.87 (3.53) SB 122.53 (9.50) SI 159.73 (34.22) TA 87.65 (4.76) TE 22.08 (0.74) TP 125.26 (4.00) Average Total 213.67 (16.00) 1 Results are expressed as mean (standard deviation) of three determinations nd – not detected A PCA of quantitative results of AA, FA and sterols was performed (Figure 6.1) in order to understand the relevant compounds in each mushroom species. In this PCA some compounds were not utilized in order to improve the variance explained – Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 41 approximately 60%. The main conclusions obtained from the integrated analysis of quantifications (Table 6.1 and 6.2) and PCA are: 1) BE and MP species was characterized by the higher amounts of Ala, Ser, Pro and Thr; 2) AC, AM, AV and RD species was characterized by their high levels of FA and presented lower levels of AA; 3) RC species was characterized by its high amounts of Leu, Phe and Val as well as palmitic acid; 4) LC presented higher amounts of both AA and FA; 5) CB, LP, SE and TE species are characterized by their low contents in FA (mainly positive Factor 1 and negative Factor 2) as they are in the opposite position of such compounds (negative Factor 1 and positive Factor 2); 6) The species projected in negative axis of both Factors are characterized by their low amounts of AA which are in the opposite position, i.e. positive axis of the two Factors. Figure 6.1. Scores plot resulting from PCA of mushrooms‘ AA, FA and sterols quantification data. The species are represented by blue dots and their codes and metabolites are represented by red dots and it their abbreviations. Despite the fact that in certain species the levels of some AA and FA differ significantly from all other specimens (e.g. serine in BE and oleic acid in AM), the use Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 42 of those compounds as biomarkers is not recommended, as they are primary metabolites which exist in almost species and their amounts are dependent on different variables (environmental conditions, growth stage, etc.)[43]. Nevertheless they are important molecules in biosynthetic process, which can be indicators of synthetic pathways that can occur in some species and not in other. Non-targeted approach: multivariate analysis 6.1.2 In addition to the three compound classes referred, this multi-target procedure also results in the extraction and derivatization of several other metabolites, such as sugarlike compounds (Figure 6.2), since the derivatization reagent used (MSTFA) reacts with other different metabolites from different classes[84]. Thus, it seems important to analyse those results through a non-targeted approach, in order to explore if there were relevant differences among metabolite composition which were not detected in targeted analysis. Figure 6.2. GC-MS chromatogram resulting from the AA, FA, and sterols extraction procedure in Clitocybe dealbata (Sowerby) P. Kumm.species. The boxes correspond to amino acids (red), sugar-like compounds (green), fatty acids (blue) and sterol-like compounds (purple) regions. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 43 After chromatograms pre-processing (peak alignment and normalization), the resulting data were submitted to two different statistical tools: PCA and PLS-DA. PCA analysis was used to verify if there are discriminating metabolites among mushroom species. In samples scores plot presented in Figure 6.3, principal component 1 (PC1) and principal component 2 (PC2) have a discriminant power of 64 and 8%, respectively, explaining more than 70% of variance. Sample distribution in the PC1 vs. PC2 space (Figure 6.3), shows a cluster which includes almost all species. SB and AM species were those which were out from cluster, and showed distinct projections in PC1 and PC2 axis, respectively. The recognition of peaks responsible for distribution among axes can be done analysing the loadings of the singular value decomposition analysis of those components (Figure 6.4). Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 44 Figure 6.3. Scores plot resulting from PCA of mushrooms‘ data resulting from the multi-target experiment. AM and SB species, which are apart from the main cluster, are flagged with the green and red boxes, respectively. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 45 Figure 6.4. Loadings plots corresponding to PC1 (A) and PC2 (B) resulting from PCA of multi-target experiment data. The annotations correspond to peaks selected to further analysis and identification. Peaks labelled in Figure 6.4 were found in almost species and were tentatively identified through NIST05 MS Library Database – Table 6.4. Thus, (PC1-1) and (PC12) presented 5and 6-carbon sugar alcohol structures, respectively. According to NIST05 MS Library Database, (PC1-1) and (PC1-2) were identified as being one of two isomers: xylitol or adonitol and sorbitol or mannitol, respectively. Indeed, xylitol and sorbitol were previously identified in a mushroom species from northern Thailand[97] and mannitol is the main representative of monosaccharides derivatives[92] in A Loadings @ PC1 Retention time (PC1-1) (PC1-2) B Loadings @ PC2 Retention time (PC2-1) (PC2-2) Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 52 Figure 6.8. Scores plot resulting from PCA of mushrooms’ volatile composition data. The species more isolated, i.e. BE, LP and SB, are flagged with the green, red and purple boxes, respectively. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 53 Figure 6.9. Loadings plots corresponding to PC1 (A) and PC2 (B) resulting from PCA of volatile profiling experiment data. The peaks marked with a (PC1/1), (PC2/1) and (PC2/2) labels correspond to those selected to further analysis and identification. Besides 1-octen-3-ol and 3-octanone, there are 1 and 2 other volatiles marked in PC1 and PC2 loadings plot (Figure 6.9), respectively. Those were the molecules selected to be studied in order to understand their potential for species discrimination. They were tentatively identified through NIST05 MS Library Database (Table 6.6) and semi-quantified (Figure 6.10), i.e. a ratio “peak area”/”sum of scan intensities” was calculated in the species in which they exist. Loadings @ PC1 Retention time A 1-Octen-3-ol (PC1/1) Loadings @ PC2 Retention time 3-Octanone (PC2/2) (PC2/1) B Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 54 Table 6.6. Tentative identification of the main loadings of PC1 and PC2 resulting from the principal component analysis of volatile profiling experiment data. Label Retention time (min) Presence in mushrooms Ions (m/z) Tentative identification Structure Compound [KI]* PC1/1 23,37 AA, AM, CD, HS, LP, MR, SI and TA 108A (1); 81 (0.63); 79 (0.37); 107 (0.32); 109 (0.31) Sesquiterpene δ-Guaiene [1505] or (+)-sativene [1405] PC2/1 14,55 LP 43 (1); 39 (0,38); 99 (0.38); 41 (0,37); 55 (0,28); 71 (0.21) Ester of hexanoic acid Allyl caproate [1080] or vinyl caproate [974] PC2/2 25,94 AA, CB and HS 69 (1); 107 (0,96); 41 (0.88); 81 (0.79); 121 (0.49); 161B (0.44) Sesquiterpene alchohol trans-Nerolidol [1568] * Kovats Index presented by NIST05 MS Library Database for a VF-5MS column, or similar; A Ions used in semi-quantification Figure 6.10. Main loadings of PC1 and PC2 distribution among mushroom species subjected to volatile profiling. Values represent mean ± SE of three replicates. According to NIST05 MS Library Database, (PC1/1) and (PC2/2) correspond to a sesquiterpene and a sesquiterpene alcohol, respectively. (PC1/1) was identified as being one of two compounds: δ-guaiene and (+)-sativene. Despite there were not found any reports of the existence of δ-guaiene in mushrooms, its isomers α-guaiene and β-guaiene were already described in mushrooms[15] and other fungal species[103], respectively. Thus, it is possible that some mushrooms, sharing a common ancestral metabolite biosynthetic pathway with those species, as a result of evolution specialized in the synthesis of that specific isomer. On the other hand, (+)-sativen was also already Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 55 described in mushrooms species, such as Fomitopsis pinicola (Swartz ex Fr.) Karst.[104] and Coprinus cinereus (Schaeff.) Gray[105]. Concerning (PC2/2), it was identified as trans-nerolidol, a sesquiterpene alcohol previously reported in mushrooms[7, 55, 106, 107]. (PC2/1) was identified as an ester of hexanoic acid (C6:0). Despite some esters of hexanoic acid were already described in mushrooms species, namely methyl[108] and ethyl hexanoate[7], the compounds identified as being the more probable by NIST05 MS Library Database, i.e. allyl or vinyl caproate, were not hitherto found in mushrooms. Besides the unequivocal identification of (PC2/1) was not possible, such compound seems to be a promising metabolite to species identification/distinguish, since it was only present in LP samples. This is in agreement with the scores plot (Figure 6.8), in which LP is the species more detached in PC2. The other loadings studied were present in few species (Table 6.4 and Figure 6.10) and among those an ANOVA revealed that there were statistical significant differences among those species. (PC1/1) showed better discrimination potential than (PC2/2) – Figure 6.10, since it presented a higher difference between the two most concentrated species (i.e. CD and TA, respectively) - approximated ratio of 27:1. The results of PLS-DA did not show clustering of edible neither toxic species (Figure 6.11), i.e. there were not sufficient similarities among edible or toxic species to group them. Nevertheless, some of the loadings resulting from PLS-DA were studied (Figure 6.12). The semi-quantification of those compounds revealed that they are only present in the toxic LA species and so they can be particular for the Lactarius Pers. genus or even more specific, existing only in the referred species. However, the identification of such compounds was not possible, since the identification’s probability presented by NIST05 MS Library Database for the suggested compounds were not significant. On the other hand, the comparison of mass spectra of the different compounds revealed the fragment ions at m/z 105, 199, 214, 215 and 217 which are common to all the molecules and several other fragments are present in almost all of them (m/z 189 and 232). These similarities indicate that those compounds can probably share a common structure and so they must came from the same biosynthetic pathway. Thus, it is possible that this pathway was a result from evolution and it is specific to certain mushrooms taxa, being an important tool for chemotaxonomy of such taxonomic groups. However, several other studies are required to confirm such hypothesis. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 56 Figure 6.11. Scores plot resulting from PLS-DA of mushrooms’ volatile composition data. Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 57 Figure 6.12. Loadings plots corresponding to Factor 1resulting from PLS-DA of volatile profiling experiment data. The peaks labelled correspond to those selected to further analysis and identification. Loadings @ Factor1 Retention time F1/1 F1/2 F1/3 F1/4 F1/5 Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 58 CONCLUSIONS In the present work a multi-target GC-MS methodology to identify and quantify several amino acids (AA), fatty acids (FA) and sterols was, to our best knowledge, applied for the first time in mushrooms species. It was the first time that the composition of such metabolites was studied in several toxic mushroom species. This method allowed the quantification of a total of 21 compounds – 9 AA, 11 FA and 1 sterol. Moreover, some other metabolites as sugar-like compounds were also analysed by this methodology. This indicates that, if pure standards are available and its calibration curves were obtained, this extraction and derivatization procedure allows the identification of a much large group of primary metabolites (PM). A non-targeted analysis was also carried through two different approaches: nonsupervised (PCA) and supervised (PLS-DA). The PCA results revealed that these metabolites do not have a high discriminant power. This result is not unexpected, as compounds extracted by this methodology are essentially PM, which are not the most suitable compounds for species discrimination. Nevertheless, it was possible to identify a compound that can be promising in species differentiation, once it was present in much higher amounts in SB samples. The supervised analysis (PLS-DA) did not separate edible from toxic mushrooms. Once again, the fact that the analysis focuses on PM is the main reason of such results, i.e. PM are common to all samples and therefore they are not suitable to discriminate species, even if the amounts of such compounds are different among species. It was also studied the volatile profile of mushroom species by using HS-SPME/GC-MS analysis. In targeted data analysis, the identification of main volatiles in mushroom species, i.e. 8-carbon skeleton compounds, was achieved and revealed that, as expected, 1-octen-3-ol, 3-octanone and 3-octanol were the volatiles present in higher amounts in mushroom samples. The non-targeted analysis of volatiles composition data was done through two statistical tools: PCA (non-supervised) and PLS-DA (supervised). The analysis of some PCA loadings allowed the identification of three compounds, one of which was only present in LP species. On the other hand, PLS-DA showed that the volatile composition did not allow a clear separation between edible and toxic mushrooms. Despite that the species presented different volatile profiles, which are responsible for one of the most Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 59 distinctive features of mushrooms – smell, there were not many similarities among edible species neither between the toxic specimens. This means that there are not specific volatiles that allow predicting if a mushroom is edible or toxic. That conclusion does not contradict the hypothesis that animals “use” this characteristic of volatiles (smell) to choose which mushrooms may or may not be eaten. What is possible to conclude is that animals choose the mushrooms through the specific smell of each species instead of “using” an “universal smell”, once, as the results indicates, there are not one compound or even group of compounds which can reliably characterize the edibility of species. Despite the inefficiency to distinguish edible species from the toxic ones, the study of some PLSDA loadings was still performed and it allowed the detection of 5 related compounds that were specific to LA species. Finally, the main conclusion of this work is that metabolomics is an important strategy to identify speciesgenus-specific metabolites. In the present work it was possible to identify 7 candidates to chemotaxonomical markers: one PM present in higher amounts in SB samples, one SM specific to LP species and five other SM which were only detected in LA specimens. 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Porto (IJUP’12)”, held in “Reitoria da Universidade do Porto” on February 22nd, 23rd and 24th. Volatile and semi-volatile profiling of wild mushrooms by GC-MS and their importance in species identification Luís Carvalho, Félix Carvalho, Maria de Lourdes Bastos, Paula Baptista e Paula Guedes de Pinho Application of Gas Chromatography-Mass Spectrometry for targeted and non-targeted analysis in toxic and edible mushrooms 74 This page was intentionally left in blank