Environmental metabolomics approaches to identify and enhance secondary compounds in medicinal plants for bio-based plant protection

The use of plant extracts such as essential oils (EOs) as botanical pesticides has numerous advantages, including the high effectiveness against a wide range of diseases and pest insects of agricultural and medicinal importance due to multiple mechanisms of action. EOs belong to secondary plant metabolites, which mediate direct and indirect plant defenses against biotic and abiotic stress conditions. The main objective of this thesis was to obtain plant extracts from aromatic plants, which can be used as bio-based plant protection products. The general hypothesis investigated whether a) the content of biologically active substances of two Iranian medicinal plants species (Ferula assa-foetida and Zataria multiflora) originated from a wide range of extreme environments is correlated with certain environmental factors; whether b) certain secondary plant metabolites from F. assa-foetida and Z. multiflora can be used as biologically active compounds against fungal infestations in crops; and whether c) the production of these metabolites can be artificially increased by mimicking extreme environmental conditions during cultivation. a) First of all this study evaluated whether environmental factors can be utilized to predict the presence of specific compounds from medicinal plants by two different methods, near-infrared spectroscopy (NIRS) and gas chromatography coupled mass spectrometry (GC−MS). Using an environmental metabolomics approach (GC−MS), on the basis of EOs from roots, three distinct chemotypes were found among 10 Ferula assa-foetida L. populations. These were characterized by (I) monoterpenes and Z-1-propenyl sec-butyl disulfide; (II) eudesmane sesquiterpenoids and α-agarofuran; (III) Z- and E-1-propenyl sec-butyl disulfide. NIRS measurements indicated a similar, but less distinct pattern. Structural equation models (SEMs) showed that EO compounds and content were directly influenced by edaphic factors and temperature (chapter 2). Essential oil compounds from leaves of 14 populations of Z. multiflora were classified into three main groups, each representing a distinct chemotype with linalool, thymol and carvacrol as the major components. Corresponding to the phytochemical cluster analysis, the hierarchical cluster analysis based on NIR data also recognized the carvacrol, thymol and linalool chemotypes. The SEMs approach revealed direct effects of soil factors and mostly indirect effects of latitude and altitude on EO compounds and content of Z. multiflora populations. Therefore, predicting EO compounds or chemotypes by environmental metabolomics can be used in medicinal plants to select populations with the desired chemical profile (chapter 3). b) Due to increasing demand of natural compounds for food preservatives and plant pathogen control, plant extracts with bioactive secondary metabolites can be used as an effective and ecofriendly plant protection approach. Hydroalcoholic extracts and EOs of Z. multiflora were assessed to identify biologically active compounds and/or chemotypes against the plant pathogens Fusarium culmorum, Fusarium sambucinum, Botrytis cinerea, Alternaria dauci and Colletotrichum lindemuthianum (chapter 4 and 5). On the basis of non-volatiles compounds of Z. multiflora hydroalcoholic extracts analyzed by LC−MS, three major chemical classes were found among the 14 populations. A total of 32 metabolites were annotated including flavonoid conjugates, hydroxycinnamic acid derivatives and phenolic terpenes. Flavonoids were the main compounds in the extracts, considering that two third of the annotated compounds represent flavonoid conjugates. The antifungal activity of extracts from Z. multiflora populations showed high variability from weak (≤ 37 %) to high inhibition rates (up to 65 %). Nine compounds such as dihydroquercetin, dihydrokaempferol, naringenin and eriodictyol strongly positively correlated with antifungal activity (chapter 4). Corresponding to the single volatile compounds, even low concentrations of the carvacrol and thymol, but not of the linalool chemotype EOs inhibited significantly the growth of all fungal pathogens. Bioassays revealed positive correlation between relative amounts of p-cymene, γ-terpinene, thymol and carvacrol and the inhibition of the fungal mycelium growth, whereas myrcene and linalool relative amounts had a strong negative correlation with antifungal activity (chapter 5). c) To enhance the production of biologically active metabolites, carvacrol and linalool chemotypes of Z. multiflora were cultivated under extreme environmental conditions including UV-A radiation, heat and drought stresses. Although no significant differences were observed in extracted volatile compounds in UV-A irradiated plants, the relative content of linalool was slightly reduced in the linalool chemotype, whilst the relative amount of carvacrol was slightly increased. Drought stress alone did not alter the relative contents of volatile compounds in both chemotypes, whilst high temperatures lead to a decrease of the linalool content and an increase of the relative amount of carvacrol in the linalool chemotype. Furthermore, the interaction of drought and heat induced changes in plants of the linalool chemotype resulting in higher relative amounts of carvacrol and lower relative amounts of linalool. Moreover, the main volatile compounds of plants from the carvacrol chemotype did not change in response to abiotic stresses (chapter 5). Understanding the effect of environmental conditions on populations and chemotypes of medicinal plants supports the development of natural and sustainable fungicides or insecticides. Although several hypotheses and questions have been developed and tested or answered in this study, further studies are needed to gain deeper insight into the bioactive metabolite biosynthesis of Z. multiflora. We have to study further, how severe stress conditions affect different chemotypes of F. assa-foetida and other medicinal and aromatic plant species

Spline based controller for nonlinear systems

The objective of this thesis is to apply spline theory to implement controllers for nonlinear systems. Two different systems, forced Duffing oscillators and power systems, are investigated. The spline method is used to mimic the controller which drives a state of the Duffing system toward a desired path. The spline-based nonlinear controller has piecewise polynomial segments with different order of polynomials on each segment. Controller efforts for different order of polynomial interpolants and power spectral densities of the controller signals are compared with the exact feedback linearizaton method.;The first objective for power systems is to design nonlinear excitation controllers for a multi-machine power system using Direct Feedback Linearization. The designed controllers, whose parameters are obtained, require the internal variables of the machines. These variables are verified by using a proposed internal variable identifying algorithm. The objective is to design nonlinear excitation controllers for power system stability enhancement. Spline techniques are used to approximate the nonlinear controllers obtained through feedback linearization by piecewise polynomials while enhancing the stability of the system

Power system damping controllers design using a backstepping control technique

The objective of this dissertation is to design and coordinate controllers that will enhance transient stability of power systems subject to large disturbances. Two specific classes of controllers have been investigated, the first one is a type of supplementary signals added to the excitation systems of the generating units, and the second is a type of damping signal added to a device called a Static Var Compensator that can be placed at any node in the system. To address a wide range of operating conditions, a nonlinear control design technique, called backstepping control, is used. While these two types of controllers improve the dynamic performance significantly, a coordination of these controllers is even more promising. Control coordination is presented in two parts. First part concerns simultaneous optimization of selected control gains of exciter and SVC in coping with the complex nature of power systems. Second part proposes a combination of reinforcement learning and a backstepping control technique for excitation control system. The reinforcement learning progressively learns and adapts the backstepping control gains to handle a wide range of operating conditions. Results show that the proposed control technique provides better damping than conventional power system stabilizers and backstepping fixed gain controllers

Replication characteristics of herpes simplex virus type-1 (HSV-1) recombinants in 3 types of tissue cultures

A complication in the analysis of the role of ICP34.5 gene in the herpes simplex virus type-1 (HSV-1) lifecycle is the presence of overlapping antisense gene, open reading frame P (ORF P), which is also deleted in HSV-1 ICP34.5 negative mutants. A HSV-1 wild type strain (17+) ICP34.5/ORF P deletion mutant (1716) is totally avirulent in animal models and impaired in a number of in vitro functions: replication in 3T6 cells; and replication and shutoff of cellular protein synthesis in SK-N-SH cells. To attribute characteristics of 1716 to each of these two genes (ICP34.5 or ORF P), a number of HSV-1 recombinant viruses that express ICP34.5 and ORF P independently were constructed, purified and characterized. The parent of these recombinants is 1716 and they are (so called): 1622, expressing ICP34.5; 1624/24.5, expressing ORF P; and 1625, expressing both ICP34.5 and ORF P in separate loci. Using homologous recombination in cell culture, the recombinants were constructed and their DNA were analyzed by Southern-blotting. Expression of ICP34.5 and ORF P from the recombinants was checked by Western-blotting. Using cell culture, titration and plaque assay techniques, replication kinetics of the recombinants were compared with 17+ and 1716 in 3 cell lines, BHK, 3T6, and SK-N-SH. The results showed that (i) ICP34.5 restored the ability to replicate and prevent host shutoff similar to wild type in SK-N-SH cells; (ii) ICP34.5 restored the replication phenotype to near wild type levels in 3T6 cells; and (iii) ORF P expression had no effect on the replication of these mutants. The characteristics of 1716 is obviously due to the lack of ICP34.5 and ORF P has no role in the characteristics studied. Thus, the function of ORF P still has to be determined

In vitro interaction and colocalization of HSV-1 ORF P with a cellular splicing factor (SC35) using pulldown assay

Herpes simplex virus type-1 (HSV-1) causes a variety of diseases in human. This virus is a neurotropic pathogen of human that establishes latent infection in the sensory ganglia innervating the site of primary infection. A number of genes including ICP34.5 control HSV-1 pathogenicity and ICP34.5 has been identified as HSV-1 virulence gene. Open reading frame P (ORF P) is also a HSV-1 gene that might have a role in latency. A complication in the analysis of the role of ICP34.5 and ORF P in the HSV-1 life cycle is that these two are overlapping antisense genes. ORF P is also deleted in ICP34.5 negative mutants and to date, no definite function is attributed to it. To attribute characteristics which were originally attributed solely to ICP34.5 to each of these two genes (ORF P or ICP34.5), an approach is to construct a number of HSV-1 recombinant viruses that express ICP34.5 and ORF P independently. An alternative way is to determine if ORF P interacts with any of the cellular and viral proteins both in vitro and in vivo. Using Glutathione-S-transferase (GST) pulldown assay and Western-blotting, we showed that ORF P interacts with a cellular splicing factor (SC35) in vitro. To investigate the colocalization of ORF P and SC35, nuclear and cytoplasmic fractionation of ORF P/SC35 was also carried out. Our results showed that both SC35 and ORF P are located in the nucleus of HSV-1 infected cells. Conclusively, because ORF P interacts and colocalizes with SC35, it might have a role in splicing

Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI

Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion tensor registration to compute biomarker atlases and to estimate atlas reliability maps. We also use nonlinear tensor registration to align the atlas to a subject and to estimate the error of this alignment. We use the biomarker atlas, atlas reliability map, and alignment error map, in addition to the dMRI signal, as inputs to a deep learning model for biomarker estimation. We use our framework to estimate fractional anisotropy and neurite orientation dispersion from down-sampled dMRI data on a test cohort of 70 newborn subjects. Results show that our method significantly outperforms standard estimation methods as well as recent deep learning techniques. Our method is also more robust to stronger measurement down-sampling factors. Our study shows that the advantages of deep learning and atlases can be synergistically combined to achieve unprecedented accuracy in biomarker estimation from dMRI data