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Electron transfer (ET) reactions play a crucial role in the metabolic pathways of all organisms. In biotechnological approaches, the redox properties of the protein cytochrome c (cyt c), which acts as an electron shuttle in the respiratory chain, was utilized to engineer ET chains on electrode surfaces. With the help of the biopolymer DNA, the redox protein assembles into electro active multilayer (ML) systems, providing a biocompatible matrix for the entrapment of proteins.
In this study the characteristics of the cyt c and DNA interaction were defined on the molecular level for the first time and the binding sites of DNA on cyt c were identified. Persistent cyt c/DNA complexes were formed in solution under the assembly conditions of ML architectures, i.e. pH 5.0 and low ionic strength. At pH 7.0, no agglomerates were formed, permitting the characterization of the NMR spectroscopy. Using transverse relaxation-optimized spectroscopy (TROSY)-heteronuclear single quantum coherence (HSQC) experiments, DNAs’ binding sites on the protein were identified. In particular, negatively charged AA residues, which are known interaction sites in cyt c/protein binding were identified as the main contact points of cyt c and DNA.
Moreover, the sophisticated task of arranging proteins on electrode surfaces to create functional ET chains was addressed. Therefore, two different enzyme types, the flavin dependent fructose dehydrogenase (FDH) and the pyrroloquinoline quinone dependent glucose dehydrogenase (PQQ-GDH), were tested as reaction partners of freely diffusing cyt c and cyt c immobilized on electrodes in mono- and MLs. The characterisation of the ET processes was performed by means of electrochemistry and the protein deposition was monitored by microgravimetric measurements. FDH and PQQ-GDH were found to be generally suitable for combination with the cyt c/DNA ML system, since both enzymes interact with cyt c in solution and in the immobilized state. The immobilization of FDH and cyt c was achieved with the enzyme on top of a cyt c monolayer electrode without the help of a polyelectrolyte. Combining FDH with the cyt c/DNA ML system did not succeed, yet. However, the basic conditions for this protein-protein interaction were defined. PQQ-GDH was successfully coupled with the ML system, demonstrating that that the cyt c/DNA ML system provides a suitable interface for enzymes and that the creation of signal chains, based on the idea of co-immobilized proteins is feasible.
Future work may be directed to the investigation of cyt c/DNA interaction under the precise conditions of ML assembly. Therefore, solid state NMR or X-ray crystallography may be required. Based on the results of this study, the combination of FDH with the ML system should be addressed. Moreover, alternative types of enzymes may be tested as catalytic component of the ML assembly, aiming on the development of innovative biosensor applications.
Polyadenylation is a decisive 3’ end processing step during the maturation of pre-mRNAs. The length of the poly(A) tail has an impact on mRNA stability, localization and translatability. Accordingly, many eukaryotic organisms encode several copies of canonical poly(A) polymerases (cPAPs). The disruption of cPAPs in mammals results in lethality. In plants, reduced cPAP activity is non-lethal. Arabidopsis encodes three nuclear cPAPs, PAPS1, PAPS2 and PAPS4, which are constitutively expressed throughout the plant. Recently, the detailed analysis of Arabidopsis paps1 mutants revealed a subset of genes that is preferentially polyadenylated by the cPAP isoform PAPS1 (Vi et al. 2013). Thus, the specialization of cPAPs might allow the regulation of different sets of genes in order to optimally face developmental or environmental challenges.
To gain insights into the cPAP-based gene regulation in plants, the phenotypes of Arabidopsis cPAPs mutants under different conditions are characterized in detail in the following work. An involvement of all three cPAPs in flowering time regulation and stress response regulation is shown. While paps1 knockdown mutants flower early, paps4 and paps2 paps4 knockout mutants exhibit a moderate late-flowering phenotype. PAPS1 promotes the expression of the major flowering inhibitor FLC, supposedly by specific polyadenylation of an FLC activator. PAPS2 and PAPS4 exhibit partially overlapping functions and ensure timely flowering by repressing FLC and at least one other unidentified flowering inhibitor. The latter two cPAPs act in a novel regulatory pathway downstream of the autonomous pathway component FCA and act independently from the polyadenylation factors and flowering time regulators CstF64 and FY. Moreover, PAPS1 and PAPS2/PAPS4 are implicated in different stress response pathways in Arabidopsis. Reduced activity of the poly(A) polymerase PAPS1 results in enhanced resistance to osmotic and oxidative stress. Simultaneously, paps1 mutants are cold-sensitive. In contrast, PAPS2/PAPS4 are not involved in the regulation of osmotic or cold stress, but paps2 paps4 loss-of-function mutants exhibit enhanced sensitivity to oxidative stress provoked in the chloroplast. Thus, both PAPS1 and PAPS2/PAPS4 are required to maintain a balanced redox state in plants. PAPS1 seems to fulfil this function in concert with CPSF30, a polyadenylation factor that regulates alternative polyadenylation and tolerance to oxidative stress.
The individual paps mutant phenotypes and the cPAP-specific genetic interactions support the model of cPAP-dependent polyadenylation of selected mRNAs. The high similarity of the polyadenylation machineries in yeast, mammals and plants suggests that similar regulatory mechanisms might be present in other organism groups. The cPAP-dependent developmental and physiological pathways identified in this work allow the design of targeted experiments to better understand the ecological and molecular context underlying cPAP-specialization.
Characterization of drought tolerance in potato cultivars for identification of molecular markers
(2014)
Plant cell walls are complex structures that underpin plant growth and are widely exploited in diverse human activities thus placing them with a central importance in biology. Cell walls have been a prominent area of research for a long time, but the chemical complexity and diversity of cell walls not just between species, but also within plants, between cell-types, and between cell wall micro-domains pose several challenges. Progress accelerated several-fold in cell wall biology owing to advances in sequencing technology, aided soon thereafter by advances in omics and imaging technologies. This development provides additional perspectives of cell walls across a rapidly growing number of species, highlighting a myriad of architectures, compositions, and functions.
Furthermore, rather than the component centric view, integrative analysis of the different cell wall components across system-levels help to gain a more in-depth understanding of the structure and biosynthesis of the cell envelope and its interactions with the environment.
To this end, in this work three case studies are detailed, all pertaining to the integrative analysis of heterogeneous cell wall related data arising from different system-levels and analytical techniques. A detailed account of multiblock methods is provided and in particular canonical correlation and regression methods of data integration are discussed. In the first integrative analysis, by employing canonical correlation analysis - a multivariate statistical technique to study the association between two datasets - novel insight to the relationship between glycans and phenotypic traits is gained. In addition, sparse partial least squares regression approach that adapts Lasso penalization and allows for the selection of a subset of variables was employed. The second case study focuses on an integrative analysis of images obtained from different spectroscopic techniques. By employing yet another multiblock approach - multiple co-inertia analysis, insitu biochemical composition of cell walls from different cell-types is studied thereby highlighting the common and complementary parts of the two hyperspectral imaging techniques. Finally, the third integrative analysis facilitates gene expression analysis of the Arabidopsis root transcriptome and translatome for the identification of cell wall related genes and compare expression patterns of cell wall synthesis genes. The computational analysis considered correlation and variation of expression across cell-types at both system-levels, and also provides insight into the degree of co-regulatory relationships that are preserved between the two processes.
The integrative analysis of glycan data and phenotypic traits in cotton fibers using canonical methods led to the identification of specific polysaccharides which may play a major role during fiber development for the final fiber characteristics. Furthermore, this analysis provides a base for future studies on glycan arrays in case of developing cotton fibers. The integrative analysis of images from infrared and Raman spectroscopic approaches allowed the coupling of different analytical techniques to characterize complex biological material, thereby, representing various facets of their chemical properties. Moreover, the results from the co-inertia analysis demonstrated that the study was well adapted as it is relevant for coupling data tables in a symmetric way. Several indicators are proposed to investigate how the global and block scores are related. In addition, studying the root cells of \textit{Arabidopsis thaliana} allowed positing a novel pipeline to systematically investigate and integrate the different levels of information available at the global and single-cell level. The conducted analysis also confirms that previously identified key transcriptional activators of secondary cell wall development display highly conserved patterns of transcription and translation across the investigated cell-types. Moreover, the biological processes that display conserved and divergent patterns based on the cell-type-specific expression and translation levels are identified.
Monoclonal antibodies (mAbs) are engineered immunoglobulins G (IgG) used for more than 20 years as targeted therapy in oncology, infectious diseases and (auto-)immune disorders. Their protein nature greatly influences their pharmacokinetics (PK), presenting typical linear and non-linear behaviors.
While it is common to use empirical modeling to analyze clinical PK data of mAbs, there is neither clear consensus nor guidance to, on one hand, select the structure of classical compartment models and on the other hand, interpret mechanistically PK parameters. The mechanistic knowledge present in physiologically-based PK (PBPK) models is likely to support rational classical model selection and thus, a methodology to link empirical and PBPK models is desirable. However, published PBPK models for mAbs are quite diverse in respect to the physiology of distribution spaces and the parameterization of the non-specific elimination involving the neonatal Fc receptor (FcRn) and endogenous IgG (IgGendo). The remarkable discrepancy between the simplicity of biodistribution data and the complexity of published PBPK models translates in parameter identifiability issues.
In this thesis, we address this problem with a simplified PBPK model—derived from a hierarchy of more detailed PBPK models and based on simplifications of tissue distribution model. With the novel tissue model, we are breaking new grounds in mechanistic modeling of mAbs disposition: We demonstrate that binding to FcRn is indeed linear and that it is not possible to infer which tissues are involved in the unspecific elimination of wild-type mAbs. We also provide a new approach to predict tissue partition coefficients based on mechanistic insights: We directly link tissue partition coefficients (Ktis) to data-driven and species-independent published antibody biodistribution coefficients (ABCtis) and thus, we ensure the extrapolation from pre-clinical species to human with the simplified PBPK model. We further extend the simplified PBPK model to account for a target, relevant to characterize the non-linear clearance due to mAb-target interaction.
With model reduction techniques, we reduce the dimensionality of the simplified PBPK model to design 2-compartment models, thus guiding classical model development with physiological and mechanistic interpretation of the PK parameters. We finally derive a new scaling approach for anatomical and physiological parameters in PBPK models that translates the inter-individual variability into the design of mechanistic covariate models with direct link to classical compartment models, specially useful for PK population analysis during clinical development.