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La traduction implique l’utilisation d’aminoacyl-ARNt produits par les aminoacyl-ARNt synthétases (aaRS). Il devrait exister 20 aaRS, une spécifique de chaque acide aminé. Or, les données actuelles montrent qu’une grande majorité des organismes ne possèdent pas l’asparaginyl- (AsnRS) et/ou la glutaminyl-ARNt synthétase (GlnRS). Ils ne peuvent synthétiser l’Asn-ARNtAsn et le Gln-ARNtGln que par l’utilisation de voies impliquant la formation préalable d’aspartyl-ARNtAsn et/ou de glutamyl-ARNtGln. Ces précurseurs « mésacylés » sont synthétisés par une aspartyl-ARNt synthétase et/ou une glutamyl-ARNt synthétase non-discriminantes (AspRS-ND ou GluRS-ND). Ils sont ensuite amidés par une amidotransférase (AdT), pour fournir à la cellule l’Asn-ARNtAsn et/ou le Gln-ARNtGln nécessaires à la traduction des codons Asn et Gln.Ce travail de thèse, effectué dans le contexte biologique de deux organismes différents, Thermus thermophilus et Helicobacter pylori, a permis de montrer que les étapes enzymatiques – formation du précurseur, et amidation par l’AdT – sont réalisées au sein de complexes ribonucléoprotéiques, réunissant l’aaRS-ND, l’ARNtAsn ou l’ARNtGln, et l’AdT : l’Asn-transamidosome ou le Gln-transamidosome. Selon leur origine ou la voie à laquelle ils appartiennent (asparaginylation ou glutaminylation), ces complexes possèdent des particularités mécanistiques et structurales très différentes, mais sont tous adaptés pour éviter la libération des intermédiaires mésacylés toxiques par des stratégies spécifiques. Ce travail permet de mieux comprendre les mécanismes évolutifs qui ont conduit à l’incorporation de l’Asn et de la Gln dans le code génétique. / Protein synthesis requires the biosynthesis of aminoacyl-tRNAs by aminoacyl-tRNA synthétases (aaRS). Since 20 amino acids are présent within the genetic code, 20 aaRS should be used by a single organism. However, the vast majority of organisms found today are deprived of asparaginyl- and/or glutaminyl-tRNA synthetases (Asn- or GlnRS). They can only synthesize Asn-tRNAAsn and/or Gln-tRNAGln through biosynthesis pathways involving the preliminary formation of aspartyl-tRNAAsn and /or glutamyl-tRNAGln. Those « misacylated » precursors are synthesized by so called non-discriminating aspartyl- or glutamyl-tRNA synthetases (ND-AspRS or –GluRS). Then, they are transferred to an amidotransferase (AdT) to provide the Asn-tRNAAsn and/or Gln-tRNAGln species (necessary to fuel protein synthesis) through amidation.This work was performed in the context of two organisms – Thermus thermophilus and Helicobacter pylori. It showed that the two enzymatic steps of asparaginylation and glutaminylation – biosynthesis of the misacylated precursor and amidation by AdT – are carried out within a single ribonucleoprotein complex, namely the (Asn- or Gln-) transamidosome, gathering the ND-aaRS necessary for the misacylation, the tRNA substrate (Asn or Gln) and the AdT. According to their origin or the pathway they originate from (asparaginylation or glutaminylation), those complexes display significant mechanistical and structural peculiarities, but they are all adapted to prevent libération of the toxic misacylated species through specific strategies. This work shed new light on the évolutive mechanisms that led to the incorporation of Asn or Gln into the genetic code.

The deposition of β-amyloid peptides and of α-synuclein proteins is a neuropathological hallmark in the brains of Alzheimer’s disease (AD) and Parkinson’s disease (PD) subjects, respectively. However, there is accumulative evidence that both proteins are not exclusive for their clinical entity but instead co-exist and interact with each other. Here, we investigated the presence of a newly identified, pyroglutamate79-modified α-synuclein variant (pGlu79-aSyn)—along with the enzyme matrix metalloproteinase-3 (MMP-3) and glutaminyl cyclase (QC) implicated in its formation—in AD and in the transgenic Tg2576 AD mouse model. In the human brain, pGlu79-aSyn was detected in cortical pyramidal neurons, with more distinct labeling in AD compared to control brain tissue. Using immunohistochemical double and triple labelings and confocal laser scanning microscopy, we demonstrate an association of pGlu79-aSyn, MMP-3 and QC with β-amyloid plaques. In addition, pGlu79-aSyn and QC were present in amyloid plaque-associated reactive astrocytes that were also immunoreactive for the chaperone heat shock protein 27 (HSP27). Our data are consistent for the transgenic mouse model and the human clinical condition. We conclude that pGlu79-aSyn can be generated extracellularly or within reactive astrocytes, accumulates in proximity to β-amyloid plaques and induces an astrocytic protein unfolding mechanism involving HSP27.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is neuropathologically characterized by degeneration of dopaminergic neurons of the substantia nigra (SN) and formation of Lewy bodies and Lewy neurites composed of aggregated α-synuclein. Proteolysis of α-synuclein by matrix metalloproteinases was shown to facilitate its aggregation and to affect cell viability. One of the proteolysed fragments, Gln79-α-synuclein, possesses a glutamine residue at its N-terminus. We argue that glutaminyl cyclase (QC) may catalyze the pyroglutamate (pGlu)79-α-synuclein formation and, thereby, contribute to enhanced aggregation and compromised degradation of α-synuclein in human synucleinopathies. Here, the kinetic characteristics of Gln79-α-synuclein conversion into the pGlu-form by QC are shown using enzymatic assays and mass spectrometry. Thioflavin T assays and electron microscopy demonstrated a decreased potential of pGlu79-α-synuclein to form fibrils. However, size exclusion chromatography and cell viability assays revealed an increased propensity of pGlu79- α-synuclein to form oligomeric aggregates with high neurotoxicity. In brains of wild-type mice, QC and α-synuclein were co-expressed by dopaminergic SN neurons. Using a specific antibody against the pGlu-modified neo-epitope of α-synuclein, pGlu79-α-synuclein aggregates were detected in association with QC in brains of two transgenic mouse lines with human α-synuclein overexpression. In human brain samples of PD and dementia with Lewy body subjects, pGlu79-α-synuclein was shown to be present in SN neurons, in a number of Lewy bodies and in dystrophic neurites. Importantly, there was a spatial co-occurrence of pGlu79-α-synuclein with the enzyme QC in the human SN complex and a defined association of QC with neuropathological structures. We conclude that QC catalyzes the formation of oligomer-prone pGlu79-α-synuclein in human synucleinopathies, which may—in analogy to pGlu-Aβ peptides in Alzheimer’s disease—act as a seed for pathogenic protein aggregation.

Intra- und extrazelluläre ß-Amyloid-Ablagerungen (Abeta) sind ein neuropathologisches Hauptmerkmal der Alzheimerschen Demenz (AD). Aktuelle Studien belegen, dass nicht Abeta-Plaques, sondern Abeta-Oligomere die Schädigung von Synapsen und Nervenzellen verursachen und dass ihre Konzentration gut mit der Schwere der kognitiven Dysfunktion korreliert. Allerdings sind Abeta-Peptide eine heterogene Gruppe schwer wasserlöslicher Peptide mit zahlreichen C- und N-terminalen Modifikationen. Dabei hängt die Tendenz von Abeta-Peptiden Oligomeren zu bilden, ihre proteolytische Resistenz und ihr neurotoxisches Potential maßgeblich von ihrer N-terminalen Struktur ab. Abeta-Peptide, die N-terminal einen Pyroglutamyl-Laktamring (pE-Abeta) aufweisen, machen einen Hauptbestandteil der Abeta-Last in den frühen Stadien der AD aus. Diese modifizierten Abeta-Peptide aggregieren schneller als unmodifiziertes Abeta, sind gegen Proteolyse geschützt und wirken als Aggregationskeim für andere Abeta-Spezies. Das Enzym Glutaminylzyklase (QC) katalysiert die n-terminale pE-Modifikation von Abeta in vitro und in vivo und wird in Neuronenpopulationen gefunden, für die ein starker Verlust von Synapsen und Neuronen im Zusammenhang mit der AD beschrieben wurde. Diese Arbeit stellt die schichtspezifische Verteilung von QC im temporalen Kortex und der hippocampalen Formation von Alzheimerpatienten und Kontrollen vergleichend dar und zeigt einen direkten Zusammenhang zwischen der Überexpression von QC und der Vulnerabilität betreffender Neuronenpopulationen auf. Darüber hinaus bestätigen die vorgestellten Ergebnisse die These, wonach QC und pE-Abeta das Potential haben, nach axonalem Transport eine Kaskade in efferenten Hirnregionen zu initiieren, an deren Ende der Verlust von Nervenzellen steht. Diese Erkenntnisse unterstützen das Interesse an QC als Gegenstand zukünftiger Grundlagenforschung und Wirkstoffentwicklungen für die Therapie der AD.:1. Einleitung 1 1.1. Fallbeispiel 1 1.2. Epidemiologie der Alzheimerschen Erkrankung 1-2 1.3. Derzeitige Pharmakotherapie 2 1.4. Neuropathologie der Alzheimerschen Demenz 3 1.5. Amyloidprozessierung 3-5 1.6. Das Enzym Glutaminylzyklase 7-8 1.7. Fragestellung 8-9 2. Material und Methoden 10 2.1. Humanes Hirngewebe von Alzheimer- und Kontrollfällen 10-11 2.2. Anfertigung von Gefrierschnitten 11 2.3. Kresylviolett-Färbung nach Nissl 12 2.4. Immunhistochemie 12-16 2.5. Vergleich von vier Anti-QC-Antikörpern 17-18 2.6. Zählmethodik 19-22 2.7. Verwendete Hard- und Software 20 3. Ergebnisse 23 3.1. Neuronendichten der untersuchten Hirnregionen in 23-24 Alzheimer- und Kontrollgehirnen 3.2. QC-Immunreaktivitat in Alzheimer- und Kontrollgehirnen 25 3.2.1. QC-Immunreaktivität im temporalen Kortex 25-26 3.2.2. QC-Immunreaktivität im entorhinalen Kortex 27-28 3.2.3. QC-Immunreaktivität im Subikulum und Ammonshorn 29-31 3.3. Stärke der QC-Immunreaktivität in Alzheimer- und Kontrollgehirnen 32 3.3.1. Stärke der QC-Immunreaktivität im temporalen Kortex 32-33 3.3.2. Stärke der QC-Immunreaktivität im entorhinalen Kortex 34-35 3.4. Schichtspezifische Verteilung der QC-Immunreaktivität im temporalen und entorhinalen Kortex in Alzheimer- und Kontrollgehirnen 36-38 3.5. QC-Immunreaktivität der Ammonshornregionen CA1 – CA4 39-40 4. Diskussion 41 4.1. Abeta-Spezies und QC in der AD 41-42 4.2. QC im temporalen Kortex 42-44 4.3. QC im entorhinalen Kortex 44-47 4.4. QC im Hippocampus 47-49 4.5. Regionale, schichtspezifische und neuronale Verteilung von QC 49-51 4.6. Ausblick 51-53 5. Zusammenfassung (Deutsch und Englisch) 54-55 6. Literaturverzeichnis 56-65 7. Anhang 66 7.1. Färbeprotokoll für Immunhistochemie 66 7.2. Erklärung über die eigenständige Abfassung der Arbeit 67 7.3. Lebenslauf 68 7.4. Danksagung 69 / Intra- and extracellular s-amyloid (Abeta) deposits are a major neuropathological hallmark of Alzheimer\''s disease (AD). Recent studies demonstrate that Abeta oligomers rather than Abeta plaques cause severe damage of synapses and nerve cells and in addition the concentration of Abeta oligomers correlates well with the severity of cognitive dysfunction. However, Abeta peptides are a heterogeneous group of poorly water-soluble peptides with various C- and N-terminal modifications. Biophysical properties of these peptides such as their propensity to form oligomers, their proteolytic resistance and their neurotoxic potential particularly depends on their N-terminal structure. Abeta-peptides that contain a pyroglutamyl-a-lactam ring at their N-Terminus (pE-Abeta) constitute a major component of the Abeta load in the early stages of AD. These modified Abeta-peptides aggregate faster than unmodified Abeta, are protected against proteolysis and act as aggregation seed for other Abeta-species. The enzyme glutaminyl cyclase (QC)catalyzes the cyclization of Abeta to pE-Abeta in vitro and in vivo and is found in neuronal populations for which a strong loss of synapses and neurons in the context of AD is described. This thesis presents the layer-specific distribution of QC in the temporal cortex and the hippocampal formation of Alzheimer\''s patients and controls, showing a direct correlation between the overexpression of QC and the vulnerability of respective neuronal populations. Moreover, the presented results confirm the hypothesis that QC and pE-Abeta have the potential to initiate a cascade leading to the loss of nerve cells due to axonal transport and release in efferent brain regions. These findings support the interest in QC as a subject of fundamental research and future drug developments for the treatment of AD.:1. Einleitung 1 1.1. Fallbeispiel 1 1.2. Epidemiologie der Alzheimerschen Erkrankung 1-2 1.3. Derzeitige Pharmakotherapie 2 1.4. Neuropathologie der Alzheimerschen Demenz 3 1.5. Amyloidprozessierung 3-5 1.6. Das Enzym Glutaminylzyklase 7-8 1.7. Fragestellung 8-9 2. Material und Methoden 10 2.1. Humanes Hirngewebe von Alzheimer- und Kontrollfällen 10-11 2.2. Anfertigung von Gefrierschnitten 11 2.3. Kresylviolett-Färbung nach Nissl 12 2.4. Immunhistochemie 12-16 2.5. Vergleich von vier Anti-QC-Antikörpern 17-18 2.6. Zählmethodik 19-22 2.7. Verwendete Hard- und Software 20 3. Ergebnisse 23 3.1. Neuronendichten der untersuchten Hirnregionen in 23-24 Alzheimer- und Kontrollgehirnen 3.2. QC-Immunreaktivitat in Alzheimer- und Kontrollgehirnen 25 3.2.1. QC-Immunreaktivität im temporalen Kortex 25-26 3.2.2. QC-Immunreaktivität im entorhinalen Kortex 27-28 3.2.3. QC-Immunreaktivität im Subikulum und Ammonshorn 29-31 3.3. Stärke der QC-Immunreaktivität in Alzheimer- und Kontrollgehirnen 32 3.3.1. Stärke der QC-Immunreaktivität im temporalen Kortex 32-33 3.3.2. Stärke der QC-Immunreaktivität im entorhinalen Kortex 34-35 3.4. Schichtspezifische Verteilung der QC-Immunreaktivität im temporalen und entorhinalen Kortex in Alzheimer- und Kontrollgehirnen 36-38 3.5. QC-Immunreaktivität der Ammonshornregionen CA1 – CA4 39-40 4. Diskussion 41 4.1. Abeta-Spezies und QC in der AD 41-42 4.2. QC im temporalen Kortex 42-44 4.3. QC im entorhinalen Kortex 44-47 4.4. QC im Hippocampus 47-49 4.5. Regionale, schichtspezifische und neuronale Verteilung von QC 49-51 4.6. Ausblick 51-53 5. Zusammenfassung (Deutsch und Englisch) 54-55 6. Literaturverzeichnis 56-65 7. Anhang 66 7.1. Färbeprotokoll für Immunhistochemie 66 7.2. Erklärung über die eigenständige Abfassung der Arbeit 67 7.3. Lebenslauf 68 7.4. Danksagung 69

The flow of genetic information from genes to proteins is mediated through proteins which interact with the nucleic acids at several stages to successfully transmit the information from the nucleus to the cell cytoplasm. Unlike in the case of protein-protein interactions, the principles behind protein-nucleic acid interactions are still not very (Pabo and Nekludova, 2000) and efforts are still underway to arrive at the basic principles behind the specific recognition of nucleic acids by proteins (Prabakaran et al., 2006). This is mainly due to the innate complexity involved in recognition of nucleotides by proteins, where, even within a given family of DNA binding proteins, different modes of binding and recognition strategies are employed to suit their function (Luscomb et al., 2000). Such difficulties have also not made possible, a thorough classification of DNA/RNA binding proteins based on the mode of interaction as well as the specificity of recognition of the nucleotides. The availability of a large number of structures of protein-nucleic acids complexes (albeit lesser than the number of protein structures present in the PDB) in the past few decades has provided the knowledge-base for understanding the details behind their molecular mechanisms (Berman et al., 1992). Previously, studies have been carried out to characterize these interactions by analyzing specific non-covalent interactions such as hydrogen bonds, van der Walls, and hydrophobic interactions between a given amino acid and the nucleic acid (DNA, RNA) in a pair-wise manner, or through the analysis of interface areas of the protein-nucleic acid complexes (Nadassy et al., 1998; Jones et al., 1999). Though the studies have deciphered the common pairing preferences of a particular amino acid with a given nucleotide of DNA or RNA, there is little room for understanding these specificities in the context of spatial interactions at a global level from the protein-nucleic acid complexes. The representation of the amino acids and the nucleotides as components of graphs, and trying to explore the nature of the interactions at a level higher than exploring the individual pair-wise interactions, could provide greater details about the nature of these interactions and their specificity. This thesis reports the study of protein-nucleic interactions using graph and network based approaches. The evaluation of the parameters for characterizing protein-nucleic acid graphs have been carried out for the first time and these parameters have been successfully employed to capture biologically important non-covalent interactions as clusters of interacting amino acids and nucleotides from different protein-DNA and protein-RNA complexes. Graph and network based approaches are well established in the field of protein structure analysis for analyzing protein structure, stability and function (Kannan and Vishveshwara, 1999; Brinda and Vishveshwara, 2005). However, the use of graph and network principles for analyzing structures of protein-nucleic acid complexes is so far not accomplished and is being reported the first time in this thesis. The matter embodied in the thesis is presented as ten chapters. Chapter 1 lays the foundation for the study, surveying relevant literature from the field. Chapter 2 describes in detail the methods used in constructing graphs and networks from protein-nucleic acid complexes. Initially, only protein structure graphs and networks are constructed from proteins known to interact with specific DNA or RNA, and inferences with regard to nucleic acid binding and recognition were indirectly obtained . Subsequently, parameters were evaluated for representing both the interacting amino acids and the nucleotides as components of graphs and a direct evaluation of protein-DNA and Protein-RNA interactions as graphs has been carried out. Chapter 3 and 4 discuss the graph and network approaches applied to proteins from a dataset of DNA binding proteins complexed with DNA. In chapter 3, the protein structure graphs were constructed on the basis of the non-covalent interactions existing between the side chains of amino acids. Clusters of interacting side chains from the graphs were obtained using the graph spectral method. The clusters from the protein-DNA interface were analyzed in detail for the interaction geometry and biological importance (Sathyapriya and Vishveshwara, 2004). Chapter 4 also uses the same dataset of DNA binding proteins, but a network-based approach is presented. From the analysis of the protein structure networks from these DNA binding proteins, interesting observations relating the presence of highly connected nodes(or hubs) of the network to functionally important amino acids in the structure, emerged. Also, the comparison between the hubs identified from the protein-protein and the protein-DNA interfaces in terms of their amino acid composition and their connectivity are also presented (Sathyapriya and Vishveshwara, 2006) Chapter 5 and 6 deal with the graph and network applications to a specific system of protein-RNA complex (aminoacyl-tRNA synthetases) to gain insights into their interface biology based on amino acid connectivity. Chapter 5 deals with a dataset of aminoacyl-tRNA synthetase (aaRS) complexes obtained with various ligands like ATP, tRNA and L-amino acids. A graph based identification of side chain clusters from these ligand-bound aaRS structures has highlighted important features of ligand-binding at the catalytic sites of the two structurally different classes of aaRS (Class I and Class II). Side chain clusters from other regions of aaRS such as the anticodon binding region and the ligand-activation sites are discussed. A network approach is used in a specific system of aaRS(E.coli Glutaminyl-tRNA synthetase (GlnRS) complexed with its ligands, to specifically understand the effects of different ligand binding., in chapter 6. The structure networks of E.coli GlnRS in the ligand-free and different ligand-bound states are constructed. The ligand-free and the ligand-bound complexes are compared by analyzing their network properties and the presence of hubs to understand the effect of ligand-binding. These properties have elegantly captured the effects of ligand-binding to the GlnRS structure and have also provided an alternate method for comparing three dimensional structures of proteins in different ligand-bound states (Sathyapriya and Vishveshwara, 2007). In contrast to protein structure graphs (PSG), both the interacting amino acids and nucleotides (DNA/RNA) form the components of the protein-nucleic acid graphs (PNG) from protein-nucleic acid complexes. These graphs are constructed based on the non-covalent interactions existing between the side chains of the amino acids and nucleotides. After representing the interacting nucleotides and amino acids as graphs, clusters of the interacting components are identified. These clusters are the strongly interacting amino acids and nucleotides from the protein-nucleic acid complexes. These clusters can be generated at different strengths of interaction between the amino acid side chain and the nucleotide (measured in terms of its atomic connectivity) and can be used for detecting clusters of non-specific as well as specific interactions of amino acids and nucleotides. Though the methodology of graph construction and cluster identification are given in chapter 2, the details of the parameters evaluated for constructing PNG are given in chapter 7. Unlike in the previous chapters, the succeeding chapters deal exclusively with results that are obtained from the analyses of PNG. Two examples of obtaining clusters from a PNG are given, one each for a protein-DNA and a protein-RNA complex. In the first example, a nucleosome core particle is subjected to the graph based analysis and different clusters of amino acids with different regions of the DNA chain such as phosphate, deoxyribose sugar and the base are identified. Another example of aminoacyl-tRNA synthetase complexed with its cognate tRNA is used to illustrate the method with a protein-RNA complex. Further, the method of constructing and analyzing protein-nucleic acid graphs has been applied to the macromolecular machinery of the pre-translocation complex of the T. thermophilus 70S ribosome. Chapter 8 deals exclusively with the results identified from the analysis of this magnificent macromolecular ensemble. The availability of the method that can handle interactions between both amino acids and the nucleotides of the protein-nucleic acid complexes has given us the basis fro evaluating these interactions in a level higher than that of analyzing pair-wise interactions. A study on the evaluation of short hydrogen bonds(SHB) in proteins, which does not fall under the realm of the main objective of the thesis, is discussed in the Chapter 9. The short hydrogen bonds, defined by the geometrical distance and angle parameters, are identified from a non-redundant dataset of proteins. The insights into their occurrence, amino acid composition and secondary structural preferences are discussed. The SHB are present in distinct regions of protein three-dimensional structures, such that they mediate specific geometrical constraints that are necessary for stability of the structure (Sathyapriya and Vishveshwara, 2005). The significant conclusions of various studies carried out are summarized in the last chapter (Chapter 10). In conclusion, this thesis reports the analyses performed with protein-nucleic acid complexes using graph and network based methods. The parameters necessary for representing both amino acids and the nucleotides as components of a graph, are evaluated for the first time and can be used subsequently for other analyses. More importantly, the use of graph-based methods has resulted in considering the interaction between the amino acids and the nucleotides at a global level with respect to their topology of the protein-nucleic acid complexes. Such studies performed on a wide variety of protein-nucleic acid complexes could provide more insights into the details of protein-nucleic acid recognition mechanisms. The results of these studies can be used for rational design of experimental mutations that ascertain the structure-function relationships in proteins and protein-nucleic acid complexes.