As described in Chapter 2, section 2.2.4, raw data was processed using the Profiling Solution software (Shimadzu, Kyoto, Japan). The chromatographic alignment was performed using the time base ‘Warping’ method. The default settings were used as processing parameters except for the following: 2–60 min retention time (Rt) range of the chromatogram, ion Rt tolerance of 0.5 min, ion m/z tolerance of 20 mDa, and ion intensity threshold of 2000 counts. The Shimadzu bundled software provided the most probable empirical formula, where each empirical formula obtained from the accurate mass values were subsequently compared to literature and online metabolites databases (Dictionary of Natural Products (http://www.dnp.chemnetbase.com), KNApSAck (http://kanaya.nais.jp/KNApSAcK) and Chemspider (http://www.chemspider.com) to identify the known compounds.
3.2.5. Multivariate data analysis
Multivariate data analysis was carried out by obtaining the data matrices from Profiling Solution. The use of SIMCA 15.1 (Umetrics, Umea, Sweden) software facilitated the generation of statistical models such as principal component analyses (PCA) derived score plots and hierarchical clustering analysis (HCA). The significant biomarkers of each plant were then extracted and identified from the orthogonal projection to latent
structures-60 discriminant analysis (OPLS-DA) derived score plots and S-plots (Tugizimana et al, 2012). The variances were normalized by centering and pareto-scaling pre-processed data before PCA modelling. Only thoroughly validated and closely fitted models were taken for consideration to warrant reliability of the results (Camacho et al, 2005; van den Berg et al, 2006; Tugizimana et al, 2015). The quality of the obtained PCA and OPLS-DA models was determined by, the statistical values such as (i) cumulative modelled variation in the X matrix, R2X (cum) also known as the goodness-of-fit parameter and (ii) the predictive ability parameter Q2 (cum) were evaluated, where values close to 1 are indicative of a robust model (Tugizimana et al, 2012).
3.3. Results and Discussion
Liquid chromatography hyphenated to mass spectrometry (LC-MS) based metabolomics approaches are in the forefront of providing sufficient information on plant metabolites. An untargeted metabolomic profiling facilitates the evaluation of a wide array of metabolites, giving a global view of the metabolites present in a particular plant species (Martucci et al, 2014). Visual inspection of the total ion chromatograms showed variations among the three species owing to the presence and absence of some chromatographic peaks (Figure. 3.1). As shown elsewhere, the visual comparison of the chromatograms does not allow clear cut differentiation of the metabolite features across different samples. Therefore various multivariate statistical models are used to unearth such differences (Madala et al, 2014).
61 Figure 3.1: HPLC-ITTOF-MS chromatograms obtained from ESI negative mode of dried leaves extracts from A) S. oleraceus B) B. pilosa and C) V. fastigiata. The TIC chromatograms show evident differences in metabolite profiling of the three-plant species.
In the current study, a more holistic way of using multivariate data analysis tools to explore the relative differences in metabolite distribution across the three plant species. Principal component analysis (PCA) is an unsupervised MVDA tool used to demonstrate clustering and variation within and/or between sample groupings (Trygg et al, 2007; Tugizimana et al, 2013).
In the current study, PCA was able to differentiate among the different plant species, with the different species clustering at the different regions of the PCA score plots (Fig. 3.2A), an indication that the three plants contain different metabolite content. It is worth mentioning that the PCA score plot was generated using the first two (of five) principal components (PC’s) namely PC 1 (explaining 43.9 %) and PC 2 (explaining 42.62 %) of the total variation. The loadings plot (Fig. 3.2B) was generated to reveal the metabolite features that significantly
62 contribute to the different clustering of the three species as seen on the PCA score plots. From the PCA loadings plot it was clearer that the three species also share many metabolites as seen by those metabolites features, which are projected on the centre of the plot (Fig. 3.2B).
Hence metabolites which are unique for each species are visually projected towards the direction of clustering as seen on the PCA score plot. Furthermore, hierarchical cluster analysis (HCA) dendogram (Fig. 3.3) was also used as an additional tool to provide a global view of the inter-relationship amongst the three plant species (Madala et al, 2014). From the HCA dendogram, it can be seen that B. pilosa and V. fastigiata are closely related to one-another, as they were observed to cluster closer to each other. The two afore-mentioned species, as a group, are distantly related to S. oleraceus, which is shown to cluster away from the other species (Fig. 3.3). Interestingly, the different technical and biological replicates of each plant species clustered tightly together in both PCA and HCA models, an indication that the observed differences between the species is strictly because of biological variation.
Figure 3.2: Principal component analysis (PCA) plots generated from negative ionization data showing A. scatter plot of various clustering patterns of the Asteraceae species and B. loadings plots showing the dimensional orientation of the different plant species samples. These plots were computed from PC1 and PC2, explaining 43.9 % of the total variation.
63 Figure 3.3: Hierarchical clustering analysis (HCA) of three Asteraceae spp: HCA plot compliments the PCA scores plot and shows the metabolic profiles and inter-relationship between the different three plant species. The different plant species could be separated in two major groups and three subgroups, with V. fastigiata related more to S. oleraceus.
OPLS-DA, a predictive supervised linear regression method that aids in the identification of the metabolite ions responsible for the discrimination between groups (Yamamonto et al, 2009; Tugizimana et al, 2013), was also used complimentarily to PCA and HCA. A good separation of samples can be seen on the OPLS-DA score plot (Fig. 3.4 A). To further highlight the possible metabolites responsible for the differences seen on the OPLS-DA score plots, an S-plot (Fig. 3.4 B) was used. S-plot allowed for the extraction of statistically significant or discriminatory mass ions associated with chemotaxonomic markers or ‘stamps’. The selected mass ions (m/z) from the S-plot were putatively annotated according to the Metabolomic Standards Initiative (MSI)-level 2 identification category (Sumner et al, 2007) and these are presented in Table 3.1. The rest of the metabolites annotated from S. oleraceus leaf extracts are presented in Table 3.2.
64 Figure 3.4: Typical OPLS-DA modelling. A 1+1+0 component OPLS-DA model, with R2X= 92.1%, R2Y=
99.3% and Q2= 98.9% to classify S. oleraceus and B. pilosa data. A. The score plot infographically shows sample classification and B. the corresponding loadings S-plot showing discriminatory features (in the outer ends of the S) that explain the classification (clustering) of the two groups shown in the scores space (A).
65 Table 3.1: Putatively annotated metabolite from leaf extracts of three selected Asteraceae species; S. oleraceus, B. pilosa and V. fastigiata that contributed to the species clusters by OPLS-DA. Other metabolites which were significant but not statistivally discriminating are indicated in Table S1.
Metabolite Rt (min) Elemental
composition
Precursor (m/z)
Fragmentation(m/z) S. oleraceus B. pilosa V. fastigiata
Hydroxycinnamic acids
1 Caftaric acid I 5.1 C13H12O9 311.0437 311, 179, 149 √
2 4’ Cafffeoylquinic acid 6.3 C16H18O9 353.0905 353, 191, 179, 173, 135 √ √
3 Caftaric acid II 9.5 C13H12O9 311.0441 311, 179, 149, 135 √
4 Caffeic acid glycoside I 9.5 C15H18O9 341.0896 341, 179, 135 √
5 Fertaric acid I 10.0 C14H14O9 325.0731 325, 193, 149 √
6 4’ Cafffeoylquinic acid 11.2 C16H18O9 353.0912 353, 191, 179, 173, 135 √ √
7 Clovamide 12.3 C18H17NO7 358.0945 358, 222, 178, 161, 135 √
8 Caftaric acid III 13.3 C13H12O9 311.0811 311, 179, 149, 135 √
9 Coutaric acid 13.7 C13H12O8 295, 0474 295, 179, 133 √
10 Fertaric acid II 15.5 C14H14O9 325.0589 325, 193, 149 √
11 4’Coumaroylquinic acid I 16.2 C16H18O8 337.0936 337, 191, 173, 163 √
12 Clovamide 16.9 C18H17NO7 358.0953 358, 222, 178, 161, 135 √
66
13 Caftaric acid 18.7 C13H12O9 311.0483 311, 179, 149, 135 √
14 Chicoric acid 18.7 C22H18O12 473.0763 473, 311, 293, 179, 149, 135 √
15 Feruloylquinic acid 19.2 C17H20O9 367.1052 367, 191 √
16 5’ Caffeoylquinic acid II 22.5 C16H18O9 353.0929 353, 191 √
17 3.5 Dicaffeoylquinic acid 23.7 C25H24O12 515.1229 515, 353, 225, 191, 179 √
18 3.4 Dicaffeoylquinic acid I 24.7 C25H24O12 515.1263 515, 353,335, 191, 179, 173, 135 √
19 3.5 Dicaffeoylquinic acid 26.8 C25H24O12 515.1246 515, 353, 191, 179 √ √
20 4.5 Dicaffeoylquinic acid 26.9 C25H24O12 515.1220 515, 353, 191, 179, 173 √
21 5’ Caffeoylquinic acid 27.0 C16H18O9 353.0923 353, 191 √
22 Caftaric acid IV 27.3 C13H12O9 311.0435 311, 179, 149, 135 √
23 Chicoric acid 27.3 C22H18O12 473.0763 473, 311, 293, 179, 149, 135 √
24 4’ Cafffeoylquinic acid 29.2 C16H18O9 353.0906 353, 191, 179, 173, 135 √
25 3.4 Dicaffeoylquinic acid 29.5 C25H24O12 515. 1246 515, 353, 335, 317, 299, 191, 179, 173, 135
√ √
26 4’Caffeoyl-p-coumaroylquinic 30.4 C25H24O11 499.1320 499, 337, 173, 163 √
27 4’Caffeoyl-5’p-coumaroylquinic 32.1 C25H24O11 499.1311 499, 353, 337, 299, 191, 179, 173 √
67
28 3’Caffeoyl-5’p-coumaroylquinic 33.1 C25H24O11 499.1311 499, 353, 191, 179, 163 √
29 Tri-caffeoylquinic acid 36.9 C34H30O15 677.1646 677, 515, 353, 173 √
Coumarin
30 Chicoriin 9.0 C15H16O9 339.0752 339, 177 √
Flavonoids
31 Quercetin glucoronide glucoside 17.5 C27H28O18 639.1307 639, 463, 301, 300, 271 √
32 Okanin glucuronide 19.0 C21H20O12 463.0934 463, 287, 175, 151, 135 √
33 Okanin acetylglucoside I 20.2 C23H24O12 491.1237 491, 287, 246, 151 √
34 Luteolin glucuronide glucoside 23.0 C27H28O17 623.1392 623, 461, 285 √
35 Luteolin dihexose 24.1 C27H30O16 609.1558 609, 285, 217, 199, 175, 151, 133 √
36 Quercetin hexoside pentoside 24.5 C26H28O16 595.1361 595, 300, 271, 255, 227, 178 √
37 Quercetin acetylglycoside I 25.4 C23H22O13 505.1425 505, 301, 283, 268 √
38 Quercetin glucuronide 26.3 C21H18O13 477.0754 477, 301, 179, 151 √
39 Luteolin glucuronide 26.6 C21H18O11 461.0786 461, 285, 217, 199, 175, 151, 133 √
40 Quercetin glycoside I 26.7 C21H20O11 463.0950 463, 301, 271, 151 √ √
68
41 Quercetin malonylglucoside 26.7 C24H22O15 549.0931 549, 505, 301, 271, 179, 151 √
42 Luteolin glycoside 26.8 C21H20O11 447.1001 447, 285, 255, 217, 199, 175, 151 √
43 Kaempferol pentosly glucoside 26.8 C26H28O15 579.1431 579, 285, 299, 255, 135 √
44 Quercetin rutinoside I 27.0 C27H30O16 609.1582 609, 301, 178, 151 √
45 Kaempferol pentosly glucoside 27.6 C26H28O15 579.1424 579, 285, 255, 229, 162 √
46 Luteolin rutinoside 27.7 C27H30O16 593.1606 593, 285, 217, 199, 175, 151 √
47 Quercetin acetylglycoside I 27.8 C23H22O13 505.1042 505, 300, 271, 179, 151 √
48 Okanin glycoside 28.3 C21H22O11 449.1141 449, 287, 151, 135 √
49 Okanin diacetylglycoside I 28.5 C25H26O13 533.1376 533, 287, 151, 135 √
50 Quercetin acetylglycoside II 28.9 C23H22O13 505.1377 505, 301, 271, 179, 151 √
51 Apigenin glucuronide 29.3 C21H18O11 445.0839 445, 269, 186, 175 √
52 Kaempferol 29.9 C15H10O6 285.1396 285, 225, 181 √
53 Kaempferol rutinoside 30.3 C27H30O16 593.1615 593, 285, 255 √
54 Kaempferol acetylglucoside 30.7 C23H22O12 489.1073 489. 284, 255, 229, 227 √
55 Isorhamnetin glucoside 31.0 C22H22O12 477.1125 477, 314, 300, 285, 271, 243 √
69
56 Kaempferol malonyl glucoside 31.8 C24H22O14 533.0928 533, 489, 284, 252, 201 √
57 Luteolin malonyl glucoside 31.8 C24H22O14 533.1009 533, 489, 285, 217, 151 √
58 Quercetin galactosyl glucoside 32.6 C27H30O17 625.1542 625, 463, 301 √
59 Kaempferol rhamnoside 33.4 C21H20O10 431.1027 431, 285, 255, 227 √
60 Kaempferol acetylglucoside 33.6 C23H22O12 489.1115 489. 285, 255 √
61 Quercetin glycoside II 34.3 C21H20O11 463.1312 463, 301, 271, 151 √
62 Luteolin 35.4 C15H10O6 285.0427 285, 217, 199, 175, 151, 133 √
63 Quercetin rhamnosyl glucoside 35.7 C27H30O16 609.1433 609, 463, 301, 108 √
64 Quercetin acetylglycoside II 35.8 C23H22O13 505.1404 505, 301, 283, 268 √
65 Kaempferol diacetylglucoside 36.7 C25H24O13 531.1205 531, 285, 159, 145 √
66 Quercetin acetylglycoside III 38.0 C23H22O13 505.1404 505, 301, 283, 249 √
Sesquiterpenes
67 Roseoside 14.0 C19H30O8 431.1959 431, 385, 223, 205, 153 √ √
70 A total of sixty seven (67) metabolites were putatively annotated and these belonged to various metabolites classes such as hydroxycinnamic acids, flavonoids and sesquiterpenes, with flavonoids representing the most abundant class in the three plants. Here, twenty eight (28) hydroxycinnamic acids were annotated. These included chlorogenic acids, tartaric acid esters (Fig. 3.5) and clovamide (Fig. 3.6A). All three plant species produced chlorogenic acids (mono- and di- caffeoylquinic acids). According to Clifford (2000), plants of the Asteraceae family are rich sources of chlorogenic acid. Therefore, chlorogenic acids are of little taxonomic value, since they are found in most plants. However, the presence of chlorogenic acid has been linked to various biological properties (Giner et al, 1993; Stalikas, 2007; Healy et al, 2009;
Makola et al, 2016). Contrary, both S. oleraceus and B. pilosa produce tartaric acid esters including caftaric acid and chicoric acid. However, these two compounds from S. oleraceus and B. pilosa leaf extracts were further shown to be structurally different as fully shown in Chapter 4. Briefly, the difference in stereochemistry of the chicoric acid found in these two plants is an indication that though these two molecules produce similar MS signals, they are structurally different from one another. Therefore, they can be used as chemotaxonomic markers between S. oleraceus and B. pilosa. Like, chlorogenic acids, chicoric acid has also been reported to have anti-HIV activity (Healy et al, 2009; Makola et al, 2016), an indication that both S. oleraceus and B. Pilosa aqueous methanolic leaf extracts can be used for management of this devastating disease .
71 Figure 3.5: Chemical structures of selected hydroxycinnamic acids from S. oleraceus, B. pilosa and V. fastigiata leaf extracts. The replacement of Rgroups indicated in the structures, with substituents shown in the table gives rise to various derivatives of the phenolic acids (http://www.chemspider.com).
Clovamide (N-caffeoyl-L-DOPA) (Fig. 3.6A), tri-caffeoylquinic acids and caffeoyl-p-coumaroylquinic acids were found to be produced exclusively by V. fastigiata. Therefore, they can be regarded as the chemotaxomic markers for this plant. Clovamide in particular can be used as a distinguishing characteristic for V. fastigiata, as it is shown to be produced solely by this plant. This compound has been reported to have radical scavenging properties and ability to inhibit lipid peroxidation (Arlorio et al, 2008; Locatelli et al, 2013). The coumarin glycoside, chicoriin was found to be produced solely by S. oleraceus and can therefore be used as a chemotaxonomic marker of this plant as compared to the other two plants. Chicoriin (Fig.
3.6B) has been used in the treatment of diarrhoea and fever (Kar, 2003), suggesting that S.
oleraceus can also be used for management of these diseases.
72 Figure 3.6: Chemical structure of clovamide (N-caffeoyl-L-DOPA) A. and coumarin glycoside, chicoriin B. Clovamide and chicoriin are regarded as chemotaxomic markers for V. fastigiata and S.
oleraceus, respectively as they are exclusive from the plants.
In this study, Chapter 3, 36 flavonoid were putatively annotated. These varied from flavonols (quercetin, kaempferol and isorhamnetin), flavones (apigenin and luteolin) and chalcone compounds (okanin) (Fig. 3.7). Quercetin derivatives were found in all three species. However quercetin acetylglycosides were not observed in S. oleraceus (Table 3.1). This might be an indication that these derivatives are uniquely found in this plant and can therefore be regarded as a taxonomic marker. The acylation of flavonoids including quercetin compounds improve their structural and functional modification, which in turn improves their physiochemical and biological properties (Viskupičová et al, 2009). The distribution of isobaric flavonoids, namely, luteolin and kaempferol, was also found to be different across the three plant species, with B. pilosa and V. fastigiata producing kaempferol and S. oleraceus producing luteolin. On the other hand, apigenin glucoronide was also observed in S. oleraceus only. Elsewhere B. pilosa leaf extracts was reported to contain insignificant amounts of luteolin and apigenin (Geissberger & Séquin, 1991).
The presence of the chalcone compounds known as okanin (Fig. 3.7) is a characteristic that differentiate B. pilosa from the other two species. According to Hofffmann & Holzl (1988), okanin is one of the most abundant chalcone compounds found in the Bidens genus. Okanin has been reported to have anticancer and antibacterial activity (Cushnie & Lamb, 2005). This shows that indeed flavonoids can be used as chemotaxonomic markers for the identification of species, genera and tribes as reported elsewhere (Harborne, 2000; Joshi et al 2004). Rees
73 and Harborne (1984) suggested that different species of Asteraceae could be assigned according to their flavonoids profile. This is also observed in this study, where B. pilosa produces chalcones (okanin), S. oleraceus have luteolin and apigenin, while V. fastigiata has similar flavonoid pattern with B. pilosa with an exception of the chalcone molecules.
Figure 3.7: Basic chemical structure of selected flavonoids from S. oleraceus, B. pilosa and V.
fastigiata. The skeleton structure of A and B consists of three phenolic rings, yet C the chalcone skeleton consists of two. The Rgroups indicated in the structures represent the substituents on the table. The attachment on A/B or C results in the different flavonoids and okanin, respectively.
3.4. Conclusion
Chemotaxonomic metabolites profiling of three Asteraceae plant species was successfully carried out using the LC-IT-TOF-MS in combination with the multivariate data analysis. The use of multivariate data analysis such as PCA and OPLS-DA score plots and the HCA dendogram showed a clear clustering of the plant species. Furthermore, this clustering according to plant species shows that secondary metabolites are species-specific, with each producing the unique metabolite fingerprints. The presence of some flavonoids molecules such as luteolin, kaempferol and okanin in specific specie is an indication that they can be used as better discriminatory markers. Furthermore, other non-flavonoid molecules such clovamide, which is only found in V. fastigiated and chicoriin found in S. oleraceus can also be used as chemotaxonomic markers. Overall, the use of metabolomics in this study has not only facilitated metabolite fingerprinting, but has also allowed for the identification of marker metabolites which is useful in chemotaxonomic studies. Furthermore, the presence of highly
74 sought-after metabolites such as derivatives of hydroxycinnamic acids in these three plants is an indication that they can all be used for management of various diseases.
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