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Fig. 4 in Phytochemical profiling of several Hypericum species identified using genetic markers

Fig. 4. PCA (A) and HCA (B) analyses of 13 metabolites quantified in the extracts of shoots of ex vitro grown Hypericum plants. The PCA biplot (A) shows grouping of the species (points), and the contribution (loadings) of the variables (metabolites, indicated by the arrows) to the grouping. Each species is represented by three biological replicates (three points per species). The points are colored according to their quality of representation on the factor map, expressed by squared cosine (cos2). The direction and length of the loading vectors show how each metabolite contributes to clustering. An ellipse is drawn around each cluster. The species are clustered according to the branches as depicted at HCA dendrogram (B). Each square in the heatmap dendrogram represents the relative content of a metabolite. The red and blue color represent an increase and a decrease of relative metabolite content, respectively. Different color of metabolites (PCA) or lines under the heatmap (HCA) indicate the metabolite contents in leaves (green) and stems (brown). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 3 in Phytochemical profiling of several Hypericum species identified using genetic markers

Fig. 3. PCA (A) and HCA (B) analyses of 13 metabolites quantified in the extracts of shoots of in vitro-grown Hypericum plants. The PCA biplot (A) shows grouping of the species (points), and the contribution (loadings) of the variables (metabolites, indicated by the arrows) to the grouping. Each species is represented by three biological replicates (three points per species). The points are colored according to their quality of representation on the factor map, expressed by squared cosine (cos2). The direction and length of the loading vectors show how each metabolite contributes to clustering. An ellipse is drawn around each cluster. The species are clustered according to the branches as depicted at HCA dendrogram (B). Each square in the heatmap dendrogram represents the relative content of a metabolite. The red and blue color represent an increase and a decrease of relative metabolite content, respectively. Different color of metabolites (PCA) or lines under the heatmap (HCA) indicate the metabolite contents in leaves (green) and stems (brown). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 2 in Phytochemical profiling of several Hypericum species identified using genetic markers

Fig. 2. Metaphase chromosomes in root tip squashes of several representatives of the genus Hypericum arranged in ascending order from the lowest to the highest value of the DNA content per chromosome: H. xylosteifolium, 2n =40 (A), H. androsaemum, 2n =40 (B), H. hircinum, 2n =40 (C), H. athoum, 2n =16 (D), H. empetrifolium, 2n =18 (E), H. atomarium, 2n =16 (F), H. monogynum, 2n =42 (G), H. stellatum, 2n =18, 20 (H), H. patulum, 2n =36 (I). Photo: S. Hovancov´a.

Fig. 5 in Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots

Fig. 5. Cytotoxic activities of transformed E. lathyris root extract in human carcinoma and embryonic cell lines. a) DU-145 (prostate) b) HeLa (cervix) c) MCF-7 (breast) d) MDA-MB-231 (breast) e) and H2347 (lung) were treated with DMSO (carrier), or increasing concentrations of transformed root MeOH extracts (31.3 μg/ml, 52.5 μg/ml, 125 μg/ml, 250 μg/ml). Titer-Glo® (Promega) was used to count cell lines 48 h post-treatment. Each dose and timepoint was performed in triplicate. Asterisks indicate statistical significance in comparison to carrier (DMSO) control assessed by one-way ANOVA (**,P <0.01; *,P <0.05).

Fig. 1 in Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots

Fig. 1. Establishment and culture maintenance of transformed E. lathyris roots. (a) Stem explants of 3-week-old greenhouse grown plants were used for co-culture with A. rhizogenes. (b) Roots emerged from callus at the site of infection after 2–3 weeks.(c) Adventitious roots displaying the characteristic of the "hairy root" phenotype. (d) Growth characteristics of the isogenic root line used in this study on agar media and in (e) liquid media.

Fig. 2 in Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots

Fig. 2. Base peak chromatograms of transformed E. lathyris roots compared to wild-type plant roots and aerial parts with putatively assigned metabolites that are structurally related to ingenol.

Fig. 4 in Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots

Fig. 4. Bioinformatic analysis and chemical elicitation of early diterpenoid biosynthetic genes expressed in transformed E. lathyris roots. Comparison of the deduced amino acid sequences of (a) ElFPS and (b) ElGGPS highlighting two conserved aspartate-rich domains [DDxx(xx)D]. (c) Comparison of the deduced amino acid sequence of ElCS highlighting a conserved [DDxxD] motif that is essential to the cyclization functionalities of terpene synthases. (d) Time course of E. lathyris diterpenoid biosynthetic gene transcript levels in transformed root cultures treated with 100 μM methyl jasmonate. Asterisks indicate statistical significance in comparison to 0 h control assessed by one-way ANOVA (**,P <0.01; *,P <0.05).

Fig. 3. A in Bio-genetic analysis of resistance in tomato to early blight disease, Alternaria alternata

Fig. 3. A two-dimensional plot of the Principal Component Analysis (PCA) of ISSR data showing the clustering of 35 tomato genotypes. On PCA plot, three groups are formed and two other genotypes, namely 111 Falat American and Roma, scattered on the plot and do not fall under any of the groups. The distribution of genetic variation within, and among, groups, was detected using the molecular variance analysis (AMOVA) in GenAlEx software. Genetic variation indices, including observed number of alleles (Na) and effective numbers of alleles (Ne), Shannon's information index (I), Nei's gene diversity (H), percent of polymorphic loci (PPL), were estimated using POP-GENE software.

Fig. 3 in Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots

Fig. 3. The MS/MS (fragmentation) data of m/z 477.2732 aided in the putative assignment of compound 8 as 15-O-acetyl-3-O-iso-butyryljolkinol-5β,6β-oxide. The fragmentation structures and m/z values correspond to each other (i.e. structures and peaks A-D).

Fig. 1 in Bio-genetic analysis of resistance in tomato to early blight disease, Alternaria alternata

Fig. 1. Ward's clustering dendrogram of 35 tomato genotypes based on resistance to Alternaria alternata. Note: Cluster Ӏ consisted of moderately susceptible and moderately resistant genotypes, Cluster ӀӀ consisted of resistant genotypes and Cluster ӀӀӀ consisted of highly susceptible genotypes. Disease severity was assessed based on infection percent (0–100) against the scoring scales of: 0, 5, 10, 25, 50, 75, or 100%.

Fig. 2 in Bio-genetic analysis of resistance in tomato to early blight disease, Alternaria alternata

Fig. 2. Grouping of 35 tomato genotypes and lines using the UPGMA method based on complete algorithm and Jaccard's similarity coefficient of 11 ISSR molecular markers. G1 consisted of highly susceptible genotype, G2 consisted of susceptible genotype, G3 consisted of moderately susceptible genotype, G4 consisted of moderately resistant genotype and G5 consisted of resistant genotype. Pearson correlation analysis was carried out to analyze the relationship among the tomato genotypes and response variability and cluster analysis of data was performed using SPSS software.

Fig. 6 in Assessing the genetic and chemical diversity of Taraxacum species in the Korean Peninsula

Fig. 6. The chemical dendrogram obtained by HCA (Euclidean distance and Ward's linkage method) on the LC–MS feature table. TCA: T. campylodes; TCO: T. coreanum; TE: T. erythrospermum; TU: T. ussuriense; TP: T. platycarpum.

Fig. 5 in Assessing the genetic and chemical diversity of Taraxacum species in the Korean Peninsula

Fig. 5. Bar plots showing the ion intensities of selected defensive metabolites (3, 5, 7, 9, 14, 16, 17, 18, and 19) in analyzed Taraxacum samples. TCA: T. campylodes; TCO: T. coreanum; TE: T. erythrospermum; TU: T. ussuriense; TP: T. platycarpum.

Fig. 3 in Assessing the genetic and chemical diversity of Taraxacum species in the Korean Peninsula

Fig. 3. LC–MS base peak ion (BPI) chromatograms of 13 Taraxacum extracts. The selected major chromatographic peaks are annotated with peak numbers. T. campylodes, T. coreanum, and T. platycarpum were triplicated, while T. erythrospermum and T. ussuriense were duplicated. Gaps between the chromatograms were added to help visualize the differences, so the y-axis values do not equal the absolute intensities.

Fig. 1 in Assessing the genetic and chemical diversity of Taraxacum species in the Korean Peninsula

Fig. 1. Maps of the complete chloroplast genomes and 45S rDNA units of six Taraxacum species and the locations of polymorphic regions. The number of variable regions for each species is in Table 2 for the chloroplast genomes and Table 3 for the 45S rDNA. (A) The chloroplast genome map of the six Taraxacum species. Polymorphic regions among the species are indicated inside the circle in black and red bars for SNP and InDel, respectively. The innermost graph is Pi value representing nucleotide diversity. (B) The 45S rDNA coding region of the six Taraxacum species. SNPs and IndeLs among the six Taraxacum species are represented in blue and red, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Assessing the genetic and chemical diversity of Taraxacum species in the Korean Peninsula

Fig. 2. The phylogenomic relationship of Taraxacum species based on chloroplast (CP) and 45S rDNA. The phylogenetic position of each species was analyzed using 74 protein coding sequences from the CP genomes and 45S rDNA transcription unit sequences, along with previously reported CP sequences and 45S rDNA (Genbank accession number: MT003979) of Artemisia fukudo (Asteraceae) (Whitehead and Bowers, 2013).

Fig. 4 in Metabolomic fingerprinting and genetic discrimination of four Hypericum taxa from Greece

Fig. 4. UPGMA dendrogram of the Hypericum taxa studied revealed four distinct strongly supported clades. A close phylogenetic relationship was observed between H. triquetrifolium and H. perforatum ssp. veronense. These two taxa were phylogenetically related to H. perfoliatum, while H. empetrifolium ssp. empetrifolium was the most distant. Bootstrap values were calculated from 1000 resamplings of the alignment data.

Fig. 2 in Efficiency of RAPD, ISSR, iPBS, SCoT and phytochemical markers in the genetic relationship study of five native and economical important bamboos of North-East India

Fig. 2. Mantel test showing significant correlation between different markers: (A) ISSR vs RAPD, (B) ISSR vs iPBS, (c) RAPD vs iPBS, (D) ISSR vs SCoT, (E) RAPD vs SCoT, (F) SCoT vs iPBS.

Fig. 5 in Efficiency of RAPD, ISSR, iPBS, SCoT and phytochemical markers in the genetic relationship study of five native and economical important bamboos of North-East India

Fig. 5. Principal coordinate analysis (PCoA) plot of 50 bamboo genotypes belonging to 5 bamboo species based on RAPD, ISSR, SCoT and iPBS combined data.

Fig. 7 in Efficiency of RAPD, ISSR, iPBS, SCoT and phytochemical markers in the genetic relationship study of five native and economical important bamboos of North-East India

Fig. 7. Bi-plot PCA (Principal Component Analysis) of the 20 genotypes of 5 bamboo species generated from 5 biochemical parameters.