Volatile compounds in cryptic species of the Aneura pinguis complex and Aneura maxima (Marchantiophyta, Metzgeriidae)

Phytochemistry 105 (2014) 115–122

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Volatile compounds in cryptic species of the Aneura pinguis complex and Aneura maxima (Marchantiophyta, Metzgeriidae) Rafał Wawrzyniak a,⇑, Wiesław Wasiak a, Alina Ba˛czkiewicz b, Katarzyna Buczkowska b a b

´ , Poland Faculty of Chemistry, Adam Mickiewicz University, Umultowska 89b, 61-614 Poznan ´ , Poland Faculty of Biology, Institute of Experimental Biology, Adam Mickiewicz University, Umultowska 89, 61-614 Poznan

a r t i c l e

i n f o

Article history: Received 22 February 2014 Received in revised form 16 June 2014 Available online 14 July 2014 Keywords: Aneura pinguis Aneura maxima Liverworts Cryptic species HS-SPME GC/MS DNA sequences Terpenoids PCA CA

a b s t r a c t Aneura pinguis is one of the liverwort species complexes that consist of several cryptic species. Ten samples collected from different regions in Poland are in the focus of our research. Eight of the A. pinguis complex belonging to four cryptic species (A, B, C, E) and two samples of closely related species Aneura maxima were tested for the composition of volatile compounds. The HS-SPME technique coupled to GC/FID and GC/MS analysis has been applied. The fiber coated with DVB/CAR/PDMS has been used. The results of the present study, revealed the qualitative and quantitative differences in the composition of the volatile compounds between the studied species. Mainly they are from the group of sesquiterpenoids, oxygenated sesquiterpenoids and aliphatic hydrocarbons. The statistical methods (CA and PCA) showed that detected volatile compounds allow to distinguish cryptic species of A. pinguis. All examined cryptic species of the A. pinguis complex differ from A. maxima. Species A and E of A. pinguis, in CA and PCA, form separate clusters remote from two remaining cryptic species of A. pinguis (B and C) and A. maxima. Relationship between the cryptic species appeared from the chemical studies are in accordance with that revealed on the basis of DNA sequences. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Aneura pinguis (L.) Dumort., the species belonging to the subclass Metzgeriidae is a thallose liverwort with simple morphological structure. A. pinguis is a cosmopolitan species occurring from the Arctic to the tropics. In Poland, the species occurs both in the mountains and in the lowlands, it can be found in the valleys of streams, marshy meadows, forests, and at the lakes (Szweykowski, 2006). The species is characterized by a high variation of the thallus size (Schuster, 1992; Paton, 1999; Damsholt, 2002). Until the 1990s, A. pinguis was regarded as taxonomically uniform within its entire distribution range. However, genetic studies revealed that this species was a complex of cryptic species i.e. species completely isolated reproductively and unambiguously distinct genetically, but characterized by a complete, or almost complete, absence of morphological differences (Mayr, 1996). Up to now, four cryptic species A, B, C and D, were distinguished within the A. pinguis complex (Ba˛czkiewicz and Buczkowska, 2005; Wachowiak et al., 2007). The last genetic research revealed ⇑ Corresponding author. Tel.: +48 61 8291569; fax: +48 61 8291555. E-mail address: [email protected] (R. Wawrzyniak). http://dx.doi.org/10.1016/j.phytochem.2014.06.010 0031-9422/Ó 2014 Elsevier Ltd. All rights reserved.

the presence of the next cryptic species – E (Buczkowska and Ba˛czkiewicz, unpubl.). Four of the species (A, B, C, E) occur in Poland, species D was discovered in the British Isles (Ba˛czkiewicz and Buczkowska, 2005). Genetic studies proved that all cryptic species of the A. pinguis complex differ from Aneura maxima (Schiffn.) Steph. – another species of Aneura genus (Ba˛czkiewicz et al., 2008). Morphometric examination of three cryptic species A, B and C of the A. pinguis complex demonstrated slight differences between them at the morphological level (Buczkowska et al., 2006). However, due to the slight morphological differences, mainly related to the continuous quantitative traits of gametophyte, the correct identification of each species without the use of genetic methods is extremely difficult. Liverworts, including A. pinguis, are characterized by the presence of oil bodies – organelles surrounded by a membrane which accumulate the compounds from the group of terpenoids (Flegel and Becker, 2000; Asakawa, 2004). It was decided to check whether the differences between the various cryptic species of the liverwort A. pinguis are so large that they will have an impact on the composition of volatile compounds present in them. Small size of A. pinguis makes it difficult to obtain large amount of biological material for testing. For this reason it was decided to use solid phase microextraction (SPME) (Pawliszyn, 1997). In case of volatile

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compounds, very useful is the version in which the subject of the study is a headspace phase of the sample (headspace – solid phase microextraction, HS-SPME). This technique combines together the process of extraction and concentration. SPME does not require any sample preparation, what reduces the possibility of the loss of analytes. The study requires a small amount of material. Moreover, this technique has already been used in the study of volatile compounds in food products, giving satisfactory results (Adams, 2007). An analysis and comparison of the composition of secondary metabolites falling within chemotaxonomy may be one of the methods helpful for identification of taxonomically difficult species (Stace, 1991). Chemotaxonomic studies of different species of liverworts revealed that there are differences in the composition of these compounds even in between closely related species belonging to the same genus e.g. Pellia, Riccardia, Pallavicinia, Mylia, Porella (Asakawa, 2004; Ludwiczuk et al., 2011). Many sesquiterpenoids and diterpenoids belonging to labdane-, clerodane- and fusicoccane-types can be used as a chemosystematic markers (Ludwiczuk and Asakawa, 2010). The presence of pinguisane-type sesquiterpenoids, for the first time detected in A. pinguis, is also characteristic for some leafy liverworts of the subclass Jungermanniidae e.g. Porella, Lejeunea and Ptilidium (Asakawa, 1995; Asakawa et al., 2013a). In the family of Aneuraceae two genera, Aneura, and Cryptothallus produce pinguisone-type sesquiterpenoids, whereas Riccardia do not (Asakawa et al., 2013a). Up to now, more than 40 pinguisanes have been described (Asakawa et al., 2013a). In recent years, the content of chemical compounds has been determined for about 6–10% of liverwort species (Asakawa et al., 2013b), but there are no studies available, with the exception of the Conocephalum conicum complex (Ludwiczuk et al., 2013), that take into account the fact that some liverwort species are complexes composed of several cryptic species (Shaw, 2001), as considered in the present study A. pinguis. A. pinguis have been studied in terms of its chemical composition by Benešová et al. (1969) and Tazaki et al. (1995, 1996) and the pinguisone and its derivatives were isolated from this liverwort and described by they as characteristic compounds for this species. However, in the previous studies, the genetic diversity and presence of cryptic species within the A. pinguis complex have not been taken into account. To the best of our knowledge, chemical composition of A. maxima is not analyzed so far. Also the climate conditions can influence the composition of studied species, however this problem is not considered in the present paper. The aim of the research is the identification and determination of differences in the composition of volatile compounds present in cryptic species of the A. pinguis complex. Building a map of typical compounds, on the basis of detected differences of chemical composition, that will allow identification of individual cryptic species. Also the comparison of the composition of the volatile compounds of A. pinguis with A. maxima – another species of the genus Aneura has been completed.

2. Results and discussion The biological material used for the study has been collected in different parts of Poland. The details concerning time and place of collection of the individual species are shown in the Table 1. The volatile compounds were determined in samples of four cryptic species of A. pinguis (A, B, C, E) and A. maxima determined genetically on the basis of isozyme pattern (Ba˛czkiewicz and Buczkowska, 2005) and DNA sequences (see Table 1). The volatile components detected by headspace analysis in study of the A. pinguis samples are presented in Table 2 in order of their elution from VF-5ms column. The total number of detected compounds is 66, of which 35 were identified. The analysis showed that the richest composition takes place for A. pinguis A and E. These cryptic species demonstrated the value of 56 and 57 compounds, respectively. The most characteristic compounds in analyzed samples of Aneura were sesquiterpenoids, oxygenated sesquiterpenoids and aliphatic hydrocarbons (Fig. 1). In the case of cryptic species A. pinguis B, C and A. maxima of identified compounds are more than 90% of the composition. The identified compounds of the sample A2 takes only 43% of the composition. The dominant component in the study of Aneura proved to be pinguisone (52), which accounted for 83.5% of the composition in the case of A. maxima. Second priority takes deoxopinguisone (40), the content of which reached 28.2% in the case of the sample E2. Comparison of the data available in a literature on the fragmentation of the pinguisone molecule with the spectra of molecules (53, 55, 56, 62, 63, 65 and 66) lead to conclusion that they are derivatives of this compound undoubtedly. These derivatives of pinguisone were found in the cryptic species A, C and E. In the first cryptic species these compounds took as much as 19% of composition. Further study, using other analytical techniques, such as 1Hand 13C-NMR, might help to elucidate the structure of these unidentified compounds. The presence of the a-pinene (7), camphene (8), a-copaene (14) and longicamphenylone (39) are characteristic for samples belonging to species E of the A. pinguis complex. The presence of benzaldehyde (9) is observed only in the case of species B. Among the compounds identified, the pentanal (2), d-elemene (11) and d-cadinol (48) are also found what is characteristic for A and B species. In the case of cryptic species C, the analyzed samples were chemically quite different. In the sample C1 costunolide (60) has been found in an amount of 12.1%, while in the sample C2 this compound was not present at all. This indicate the presence of polymorphism in chemical composition caused by environmental conditions and geographic distribution, since C1 was collected in the lowlands (Pomerania) whereas C2 in the mountains (Tatra Mts.). In order to check whether the composition of volatile compounds can be used as chemosystematic markers of the A. pinguis cryptic species, the compounds detected in the studied samples

Table 1 Sampling data of Aneura species used for studies and GenBank accession numbers. Host plant

A. A. A. A. A. A. A. A. A. A.

pinguis A pinguis A pinguis B pinguis B pinguis C pinguis C pinguis E pinguis E maxima maxima

Symbol

A1 A2 B1 B2 C1 C2 E1 E2 M1 M2

POZW No.

42811 42799 42711 42813 42764 42814 42812 42815 42810 42796

Collection place

S Poland, Beskidy Mts. SE Poland, Bieszczady Mts. SE Poland, Bieszczady Mts. S Poland, Tatra Mts. NW Poland, Pomerania S Poland, Tatra Mts. S Poland, Tatra Mts. S Poland, Tatra Mts. NW Poland, Pomerania SE Poland, Bieszczady Mts.

Date

01-09-2012 10-08-2012 03-08-2012 21-09-2012 28-06-2012 22-09-2012 21-09-2012 21-09-2012 27-06-2012 02-08-2012

Accession no. trnL-trnF

ITS1-5.8S-ITS2

KJ400407 KJ400408 KJ400409 KJ400410 KJ400411 KJ400412 KJ400413 KJ400414 KJ400415 KJ400416

KJ400397 KJ400398 KJ400399 KJ400400 KJ400401 KJ400402 KJ400403 KJ400404 KJ400405 KJ400406

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R. Wawrzyniak et al. / Phytochemistry 105 (2014) 115–122 Table 2 Volatile compounds detected in the analyzed samples of the A. pinguis complex and A. maxima.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

Compounds

RIa

1-Pentene-3-ol Pentanal 2-Penten-1-ol Hexanal 3-Hexen-1-ol 1-Hexanol a-Pinene Camphene Benzaldehyde b-Pinene d-Elemene a-Longipinene a-Ylangene a-Copaene Sativene b-Caryophyllene Longifolene b-Ylangene Cedrene 204[M]+, 161(100), 105(85) Cis-Thujopsene a-Guaiene 204[M]+, 93(100), 81(82) 204[M]+, 93(100), 79(85) 204[M]+, 119(100), 93(75) a-Himachalene 204[M]+, 91(100), 105(93) Alloaromadendrene c-Gurjunene b-Himachalene 204[M]+, 119(100), 91(70) Cuparene d-Cadinene b-Sesquiphellandrene 204[M]+, 119(100), 91(66) 204[M]+, 69(100), 91(38) 216[M]+, 109(100), 145(75) a-Copaene-11-ol Longicamphenylone Deoxopinguisone 220[M]+,91(100), 110(90) 204[M]+, 137(100), 81(60) 218[M]+, 91(100), 119(57) 220[M]+, 135(100), 91(60) 220[M]+, 110(100), 91(97) 220[M]+, 91(100), 79(82) 218[M]+, 133(100), 147(84) d-Cadinol Aromadendrene oxide-(2) 222[M]+, 95(100), 81(75) 220[M]+, 81(100), 67(90) Pinguisone 232[M]+, 108(100), 79(40) 260[M]+, 185(100), 201(68) 262[M]+, 108(100), 79(25) 262[M]+, 108(100), 203(60) 236[M]+, 95(100), 109(85) 244[M]+, 189(100), 105(15) 246[M]+, 192(100), 91(43) Costunolide 190[M]+, 147(100), 119(75) 276[M]+, 108(100), 79(26) 276[M]+, 108(100), 79(35) 272[M]+, 81(100), 161(75) 217[M]+, 108(100), 79(50) 230[M]+, 108(100), 79(35) % Identification Aliphatic Aromatics Monoterpene hydrocarbons Sesquiterpene hydrocarbons Oxygen-containing sesquiterpenes

673 677 766 773 858 867 936 953 970 978 1324 1350 1373 1376 1396 1415 1417 1423 1427 1431 1435 1439 1441 1443 1445 1447 1453 1457 1470 1500 1503 1505 1517 1520 1524 1530 1533 1540 1559 1563 1585 1593 1603 1620 1626 1633 1641 1646 1678 1685 1687 1705 1712 1735 1755 1791 1846 1851 1882 1914 1934 1938 1951 1955 1989 2051

tr – traces < 0.10%. a Retention index obtained on VF-5ms column. b Abbreviations of samples see Table 1.

Speciesb A1

A2

B1

B2

C1

C2

E1

E2

M1

M2

0.2 0.3 0.2 2.0 1.7 7.6 – – – 0.1 0.2 tr 0.6 – 0.3 0.7 0.2 0.1 – – 0.2 1.1 0.7 0.1 – 0.2 – 0.1 2.0 0.1 0.2 0.1 tr – – 0.2 1.0 0.1 – 23.1 – tr 0.2 1.7 – 2.5 0.4 0.2 tr tr 0.2 34.0 5.4 tr 0.2 2.6 0.3 0.7 0.1 – 5.5 – tr tr tr – 75.4 12.0 – 0.1 5.9 57.4

0.7 0.3 0.8 2.3 1.9 7.9 – – – 0.3 0.5 tr 0.4 – 0.3 0.5 0.3 0.3 0.2 0.1 1.3 1.1 0.8 tr tr 0.3 – 0.8 3.6 0.1 0.2 0.4 tr 0.2 0.1 – 1.4 0.3 – 8.5 0.1 tr 0.3 7.0 – 9.4 0.5 0.3 0.5 0.2 0.8 9.0 – 0.1 7.9 8.2 0.1 0.2 0.2 – 9.6 – 1.5 0.1 1.4 – 43.1 13.9 – 0.3 10.3 18.6

0.2 0.3 0.3 1.1 0.2 2.6 – – 0.5 0.2 0.2 – 0.1 – 1.0 0.8 0.3 0.5 1.9 – – – – – 0.6 0.2 0.4 0.1 0.9 0.1 – 0.1 – – – – – – – 2.6 – – 0.7 0.4 – 1.8 0.1 0.2 0.3 0.2 0.9 76.3 – – – – – – tr – – – – – – – 91.0 4.7 0.5 0.2 6.2 79.4

0.4 0.1 0.2 3.7 0.5 2.1 – – 1.2 0.1 – – 0.2 – 0.4 0.9 0.3 0.3 1.1 0.1 0.2 0.5 0.3 0.2 – 0.3 0.1 0.9 0.1 – 0.1 0.1 – – – – – – – 2.1 – – 0.1 – – 1.1 0.1 tr tr tr 0.3 79.0 – 0.2 – – – – – – – – – – – – 94.7 7.0 1.2 0.1 5.3 81.1

0.9 – 0.4 1.1 1.8 2.4 – – – 0.1 – tr tr – 0.1 0.5 0.6 0.2 2.4 – 0.8 – – – 0.3 tr tr 0.2 tr tr tr – – tr – – 1.5 – – 17.3 – – 0.2 – – 0.4 – – – tr 0.2 53.9 – – 0.2 – – – – 12.1 – – – – – – 94.8 6.6 – 0.1 4.8 83.3

0.3 – 0.2 1.2 0.8 1.0 – – – 0.2 – – tr – tr 0.4 0.4 0.2 2.7 – 0.4 – – – 0.3 tr – – tr tr – tr – – – – 0.2 – – 5.6 – – 0.1 – – 0.9 – – – 0.1 0.3 82.0 – – 0.1 – – – 0.2 – – – – – – – 95.4 3.5 – 0.2 4.1 87.6

0.2 – 0.2 0.2 0.7 0.7 0.1 tr – tr – 0.3 0.1 0.2 1.0 0.3 3.1 3.0 0.8 0.2 2.4 0.5 7.1 tr 1.3 2.8 – 10.0 – 5.9 0.8 0.7 0.1 0.4 0.1 0.9 0.3 0.4 1.0 6.7 0.3 1.1 1.8 5.1 tr 1.9 1.7 – 2.6 2.5 0.9 6.1 3.2 0.1 4.6 1.3 – – – – – 0.1 0.9 tr 0.4 0.2 50.5 2.0 – 0.1 31.6 16.8

0.7 – 0.5 0.7 1.4 2.1 0.8 0.2 – tr – 0.1 tr 0.1 0.9 0.3 1.2 0.5 0.1 0.7 1.0 1.0 1.6 0.2 0.2 0.4 0.9 0.2 0.5 0.1 0.1 0.2 0.1 0.1 – 0.2 0.4 0.2 0.2 28.2 – 0.2 tr 0.5 0.4 0.2 0.4 – 0.1 0.3 0.2 32.9 5.1 5.1 5.8 0.2 – – – – – 0.2 0.6 tr 0.6 0.3 74.8 5.4 – 1.0 6.8 61.6

0.2 – 0.2 0.4 0.6 1.2 – – – 0.5 – tr 0.1 – – 0.5 0.1 0.2 0.2 – – 0.2 1.4 – tr 0.1 tr – tr tr tr 0.1 – 1.0 – – 0.2 – – 5.8 – – tr – – 0.5 0.1 – – – 0.4 83.5 – – – – – – – – – – – – – – 94.9 2.6 – 0.5 2.5 89.3

0.3 – 0.3 0.7 0.7 1.3 – 0.1 – 0.2 – 0.1 0.1 – 0.2 0.5 0.1 0.1 0.2 – 0.1 1.0 0.5 – – 1.0 – 0.3 0.1 0.1 0.2 0.4 0.1 1.7 tr – 0.4 – – 2.3 – – 0.1 – – 0.7 – – 0.1 – 0.4 81.3 – – – – – – – – – – – – – – 93.4 3.3 – 0.3 6.1 83.7

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Fig. 1. Structure of the most characteristic volatile components detected in the A. maxima and the A. pinguis complex. The identities of compound are presented in Table 2.

were subjected to multivariate statistical analysis. The statistical classification methods applied cluster analysis (CA) and principal component analysis (PCA) allow to uncover the hidden structure from analyzed data set and isolate the most diagnostic variables. Principal component analysis initially was performed for all volatile compounds detected in the examined samples and was based on the correlation matrix of these traits (Sneath and Sokal, 1973). This method was used to determine whether the volatile compounds can be useful in discrimination of the studied species and to reduce the large number of initial set of variables. On the basis of PCA, these volatile compounds, which are highly correlated (r P 0.67) with the first three principal components were selected as the best to differentiate the studied species. A varimax rotation of the normalized factor loadings was applied to obtain the simplest and clear structure of factors. The results of PCA allowed us to extract the most important volatile compounds from the entire set of data, enabling the distinction of 5 groups of the samples, which are in accordance with species identified by genetic markers as well as to define relationship between them (Fig. 2). Samples of species E and A are clearly separated along the PC1 and the PC2 axis, respectively, from species B, C and A. maxima, which in turn are distinguished by the PC3 axis (Fig. 2). The first three principal components explain 72.6% of the total variance included in the analyzed compounds. The factor loadings indicates that the compounds no. 2, 6, 11, 13, 22, 29, 46, 56, 57, 61 are most strongly correlated with the PC1 axis whereas the compounds no. 7, 10, 14, 15, 17, 33, 38, 52, 62 and 66 with PC2 axis (Fig. 3A). Thus they play the most important role in separating the species A and E from B, C of A. pinguis and A. maxima. The high correlation with PC3 axis is found for compounds no. 4, 9, 16 and 34 (Fig. 3B), thus they are crucial to distinguish species B and C of A. pinguis and A. maxima. The cluster analysis grouping examined units into clusters, so that the units within a cluster are as similar to each other as possible and the units between clusters dissimilar as much as possible (Crossa and Franco, 2004). Results of hierarchical clustering of studied samples based on Euclidean distance and Ward’s linkage

Fig. 2. Three-dimensional scatter plot of PCA based on the characteristic compounds detected in samples of A. pinguis cryptic species and A. maxima. The percentage explained variability: PC1 = 33.9%, PC2 = 29.0%, PC3 = 9.7%.

method are shown on dendrogram (Fig. 4). Four clusters can be recognized based on the Mojena index. The classification of samples based on the volatile compounds correspond well with the results of molecular studies. Samples identified as A. pinguis A were allocated to cluster I, while cluster II was formed by samples of A. pinguis E, and cluster III consisted of samples belonging to A. pinguis B, and finally cluster IV comprised samples of A. pinguis C and A. maxima. Cryptic species A and E of A. pinguis are the most distinct in terms of chemical composition, they are grouped in another branch than the two remaining cryptic species (B and C)

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Fig. 3. Factor loadings plots: (A) PC1 vs. PC2 and (B) PC2 vs. PC3. Volatile compounds with factor loading P 0.67 were marked with rectangle, compounds no. are consistent with Table 2.

of A. pinguis. The most similar are A. pinguis C and A. maxima, which form one cluster. At the same time, species A and E show the highest within-specific polymorphism (Fig. 4). The present studies confirmed the distinctiveness of the newly detected cryptic species E of the A. pinguis, which in respect of its chemical content form clearly distinct group (Figs. 2 and 4). In the examined species of Aneura genus besides the volatile compounds, which presented polymorphism within-species, the compounds characteristic for particular species were detected in samples originating from different geographic regions (see Table 1). Similar results were reported for cryptic species of the C. conicum complex. The volatile compounds were determined in five species (four cryptic species of C. conicum complex A, F, J, L and recently described Conocephalum salebrosum). Three groups were distinguished, the most distinct was cryptic species L, whereas species F and J as well as A and C. salebrosum showed similarity and were grouped together. Despite their similarities, chemical markers,

which can describe each species were found (Ludwiczuk et al., 2013). Results obtained from chemical analysis of Aneura are consistent with the grouping on the basis of DNA sequences (Fig. 5). The neighbour-joining tree based on combined data of the studied sequences revealed the presence of five distinct clades supported by a high value of the bootstrap test (BST 98–100%). The all cryptic species distinguished within the A. pinguis complex also differ, in respect of the DNA sequences, from A. maxima. According to the DNA sequences the studied samples form two main groups: the first one includes A. pinguis B and C and A. maxima, the second comprise A. pinguis A and E. The results indicated that the cryptic species B and C of A. pinguis are closely related with A. maxima rather than with two other (A and E) cryptic species of A. pinguis (Fig. 5). It is noteworthy to know that both samples of a particular species are identical regarding the examined DNA sequences. Analysis of the DNA sequences confirmed differences between previously

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Fig. 4. A dendrogram of studied samples of A. pinguis cryptic species and A. maxima constructed on the basis of the Euclidean distance according to the Ward’s linkage method, based on the characteristic compounds detected in studied samples. The transverse line indicates the intersection of dendrogram according to the Mojena index.

Fig. 5. Phylogram of A. pinguis cryptic species and A. maxima resulting from neighbour-joining (NJ) analysis based on combined data of all studied sequences (trnL-trnF, ITS15.8S-ITS2). Bootstrap values are given at branches.

detected cryptic species (A, B, C) of the A. pinguis complex (Ba˛czkiewicz and Buczkowska, 2005) and recently detected new cryptic species E. 3. Conclusions Cluster analysis and principal component analysis showed that cryptic species A. pinguis detected on the basis of genetic studies differ also in terms of volatile compounds. All examined cryptic species of the A. pinguis complex differ also from A. maxima. Our results confirmed the presence of another cryptic species E within A. pinguis complex. Relationships between examined species, which emerged from the chemical studies are in accordance with that revealed by analyzed DNA sequences. Species A and E of A. pinguis in CA and PCA form separate clusters remote from two remaining cryptic species of A. pinguis (B and C) and A. maxima. Our study proved that the dominant compounds in all cryptic species of the A. pinguis complex and in closely related A. maxima are pinguisone (52) and deoxopinguisone (40). The results are consistent with

the previous reports (Benešová et al., 1969; Tazaki et al., 1995; Tazaki et al., 1996) on the chemical composition of A. pinguis. The presence of compound no. 9 is characteristic only for cryptic species B, whereas compound no. 7, 8, 14, 39 for species E. Species A and B shared three compounds (2, 11, 48), which were not detected in remaining studied material. Unluckily species-specific compounds for A. pingius C and A. maxima were not discovered. Unfortunately the study revealed that the identification of the individual components cannot be carried out unequivocally on the basis of GC/MS and it is necessary to collect more biological material to perform it on the basis of NMR spectra. 4. Experimental 4.1. Plant material From different regions of Poland, 8 samples of the A. pinguis complex (2 samples of species A, B, C and E) and 2 samples of A. maxima were studied (Table 1). Each sample was divided into 2

R. Wawrzyniak et al. / Phytochemistry 105 (2014) 115–122

parts: one was deposited as a voucher in the POZW Herbarium, while the other was used for analyses. Samples for DNA and chemical analyses were stored at – 30 °C until used. Each cryptic species of A. pinguis was identified on the basis of isozyme pattern according to Ba˛czkiewicz and Buczkowska (2005). A. maxima was identified morphologically according to Furuki (1991), Schuster (1992), and Buczkowska and Ba˛czkiewicz (2006). Moreover, DNA sequences of internal transcribed spacer region (ITS1-5.8S-ITS2) of nuclear ribosomal DNA and the trnL-trnF region (intron of trnL gene and the trnL-F intergenic spacer) of the chloroplast genome were applied in order to provide additional and precise identification of particular cryptic species of A. pinguis and A. maxima. GenBank accession numbers of the sequences are given in Table 1. As an outgroup in DNA analysis Lobatiriccardia lobata (Schiffn.) Furuki was used, DNA sequences were obtained from the GenBank, Accession Numbers: AY507553.1 and DQ986148.1.

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Total genomic DNA was extracted from fresh material, from a single thallus per one sample. A standard CTAB procedure (Murray and Thompson, 1980) was used for DNA extraction. Primers used for the amplification and sequencing of of trnL-trnF region were according to Taberlet et al. (1991) and for the ITS1-5.8S-ITS2 the primers described by Sun et al. (1994). PCR amplification was carried out according to Krawczyk et al. (2013). Purified PCR products of the studied DNA regions were sequenced in both directions using the ABI BigDye 3.1 Terminator Cycle Kit (Applied Biosystems) and were then visualized using an ABI Prism 3130 Automated DNA Sequencer (Applied Biosystems). Chromatograms of DNA sequences were edited and assembled using Sequencher 4.5 (GeneCodes, Ann Arbor, MI, USA). Contigs were aligned manually and neighbour-joining tree were generated with MEGA 5.2 (Tamura et al., 2011) to show relationship among examined samples.

not significantly affect the efficiency of extraction. Hence it was decided that the measurement will be carried out with the sample of liverwort of 50 mg. Sorption process was executed in a room and higher temperatures. However, it has been noticed that the increase of temperature do not improve of extraction efficiency of less volatile compounds. On contrary the higher temperature brought deterioration of extraction of high volatile compounds. Hence, it was decided to use the optimum temperature for that process which appeared to be 50 °C. The analysis was performed in the most appropriate time 60 min of sorption. Since the longer time negatively affects the estimation of amount of high volatile compounds content in the sample. In order to optimize the conditions of the process, careful desorption was performed. Raising the temperature of the injector gave positive effect to the desorption process of determined compounds. Due to manufacturer’s recommendations and to keep fiber in a good condition the all processes were performed in 250 °C. Also a time of fiber exposure has been tested. It turned out that the extension of desorption time is beneficial for the determination of less volatile compounds, but at the same time it caused some problems with desorption of high volatile compounds. For these reasons 10 min of desorption has been applied. The desorption procedure was repeated one more time after the analysis. The GC analysis did not determine any peaks. Therefore we concluded that with the selected conditions the efficiency of desorption process reached 100%. Each sample of cryptic species was analyzed three times. The constituents were identified by comparing their Kovats retention indices with those from the literature, reference compounds, computer matching against the NIST 11 (NIST11/2011/EPA/NIH), data obtained from NIST Chemistry WebBook databases (Stein, 2011) and Pherobase databases (ElSayed, 2012). The Kovats retention indices were determined in relation to a homologous series of n-alkanes (C8–C26) under the same operating conditions. The relative concentrations of the components, in percentage (Table 2) were obtained by peak areas normalization without applying correction factors.

4.3. GC analysis of volatile compounds

4.4. Statistical analysis

Qualitative gas chromatography analyses of liverwort were carried out with the use of Varian 4000 GC/MS gas chromatograph (Palo Alto, USA). Mass spectrometry detector was operated at 70 eV in the EI mode over the m/z range 30–550 and temperature transfer line 250 °C. Quantitative gas chromatography analyses were performed with Hewlett–Packard 5890 series II (Santa Clara, USA), equipped with flame-ionization detector – FID (temp. 250 °C). Both GC instruments were equipped with VF-5ms capillary column (30 m  0.25 mm i.d., film thickness 0.25 lm; Agilent, Santa Clara, CA, USA). Helium was applied as the carrier gas at a flow rate of 1 ml min 1. The oven temperature was programmed from 60 °C to 230 °C at 4 °C min 1 and then held isothermal at 230 °C for 40 min. The liverwort sample was manually injected by means of SPME technique. The SPME holder and the fused silica fibers coated with stationary phases were obtained from Sigma– Aldrich (St. Louis, USA). Due to the applied SPME technique splitless injection mode and liner 1 mm i.d. was used. All available fibers were tested regarding their usefulness in the determination of compounds contained in the studied biological material. It has been found that the best extraction and repeatability of measurements provide fiber coated with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). Conditions of sorption and desorption process were optimized by choosing the amount of biological material, time and temperature. For this purpose, frozen samples of liverworts were placed in screw-capped vial with a silicone/Teflon membrane of 4 cm3 volume. It was found that larger amount of biological material does

Volatile compounds detected in the examined samples were subjected to multivariate statistical analysis in order to check if the composition of volatile compounds differ the studied cryptic species of the A. pinguis and A. maxima. Two classification methods were applied: cluster analysis based on Euclidean distances and principal component analysis. In cluster analysis hierarchical clustering were done according to the Ward’s linkage method (Ward, 1963). Clusters number was determined based on the Mojena (Mojena, 1977) index with critical value 1.25, that according to Milligan and Cooper (1985) gives the best results. In the first step, all 66 compounds detected in the examined samples were subjected in analyses. Next, the best diagnostic features that facilitate discriminating between the examined groups were chosen. For cluster analysis, standardized data were used. Statistical analyses were performed using STATISTICA 10 (StatSoft, Poland).

4.2. DNA extraction, PCR amplification and sequencing

Acknowledgements This work was partly supported by a grant NCN no. 2011/01/B/ NZ8/00364 and 2013/09/B/NZ8/03274. The authors thank A. Jarczewski for helpful discussion and suggestions. References Adams, R.P., 2007. Identification of Essential Oil Components by Gas Chromatography/Mass Spectrometry, 4th ed. Allured, Carol Stream, Illinois, pp. 1–804.

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