<p>  </p> <p>Aromatic C-N bond formation is critical for synthetic chemistry in pharmaceutical, agrochemical, and natural product synthesis. Due to the prevalence of this bond class, many synthetic routes have been developed over time to meet the demand. The most recent and robust C-N bond formation reaction is the palladium catalyzed Buchwald-Hartwig amination. Considering the importance of the Buchwald-Hartwig amination, a high-throughput experimentation (HTE) campaign was devised to create a library in which chemists can refer to optimal reaction conditions and ligand/catalyst choice based on the nature of their substrates to be coupled. This study showed trends for the appropriate choice of ligand and catalyst, along with what bases, temperatures, stoichiometries, and solvents are appropriate for the selected substrate combination at hand. </p>

A high-throughput low-power Soft-Output Viterbi decoder designed for the convolutional codes used in the ECMA-368 UWB standard is presented in this thesis. The ultra wide band (UWB) wireless communication technology is supposed to be used in physical layer of the wireless personal area network (WPAN) and next generation Blue Tooth. MB-OFDM is a very popular scheme to implement the UWB system and is adopted as the ECMA-368 standard. To make the high speed data transferred over the channel reappear reliably at the receiver, the error correcting codes (ECC) are wildly utilized in modern communication systems. The ECMA-368 standard uses concatenated convolutional codes and Reed-Solomon (RS) codes to encode the PLCP header and only convolutional codes to encode the PPDU Payload. The Viterbi algorithm (VA) is a popular method of decoding convolutional codes for its fairly low hardware implementation complexity and relatively good performance. Soft-Output Viterbi Algorithm (SOVA) proposed by J. Hagenauer in 1989 is a modified Viterbi Algorithm. A SOVA decoder can not only take in soft quantized samples but also provide soft outputs by estimating the reliability of the individual symbol decisions. These reliabilities can be provided to the subsequent decoder to improve the decoding performance of the concatenated decoder. The SOVA decoder is designed to decode the convolutional codes defined in the ECMA-368 standard. Its code rate and constraint length is R=1/3 and K=7 respectively. Additional code rates derived from the "mother" rate R=1/3 codes by employing "puncturing", including 1/2, 3/4, 5/8, can also be decoded. To speed up the add-compare-select unit (ACSU), which is always the speed bottleneck of the decoder, the modified CSA structure proposed by E.Yeo is adopted to replace the conventional ACS structure. Besides, the seven-level quantization instead of the traditional eight-level quantization is proposed to be used is in this decoder to speed up the ACSU in further and reduce its hardware implementation overhead. In the SOVA decoder, the delay line storing the path metric difference of every state contains the major portion of the overall required memory. A novel hybrid survivor path management architecture using the modified trace-forward method is proposed. It can reduce the overall required memory and achieve high throughput without consuming much power. In this thesis, we also give the way to optimize the other modules of the SOVA decoder. For example, the first K-1 necessary stages in the Path Comparison Unit (PCU) and Reliability Measurement Unit (RMU) are IX removed without affecting the decoding results. The attractiveness of SOVA decoder enables us to find a way to deliver its soft output to the RS decoder. We have to convert bit reliability into symbol reliability because the soft output of SOVA decoder is the bit-oriented while the reliability per byte is required by the RS decoder. But no optimum transformation strategy exists because the SOVA output is correlated. This thesis compare two kinds of the sub-optimum transformation strategy and proposes an easy to implement scheme to concatenate the SOVA decoder and RS decoder under various kinds of convolutional code rates. Simulation results show that, using this scheme, the concatenated SOVA-RS decoder can achieve about 0.35dB decoding performance gain compared to the conventional Viterbi-RS decoder.

Indiana University-Purdue University Indianapolis (IUPUI) / Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects over 10 million people. Treatments for PD are limited to symptom mitigation with no means of stopping or slowing disease progression. Mutations within the protein leucine- rich repeat kinase 2 (LRRK2) are the most common cause of familial PD and are indistinguishable from the more common sporadic cases. Identifying molecules capable of modulating LRRK2 GTPase activity may serve as the foundation for future development of novel PD therapeutics. We recently discovered that the G-domain (ROC) of LRRK2 is capable of transitioning between monomer and dimer form in solution upon GTP/GDP binding. R1441C/G/H pathogenic mutations were demonstrated to alter this dynamic shifting toward a monomeric ROC conformation while decreasing GTPase activity. Using our ROC dimeric crystal structure, we strategically introduced disulfide bonds to generate locked monomer and locked dimer states. Monomeric ROC was shown to increase GTPase activity while the dimeric form decreased activity. Solvent mapping performed using the dimeric ROC crystal structure and a homology model of the ROC monomer revealed a binding hotspot at the ROC dimeric interface and adjacent to the R1441 residue in the monomeric model. In this study our goal was to identify more compounds capable of influencing GTPase activity. We performed high throughput screening of ROC against two compound libraries (LOPAC1280 and ChemBridge 50K) in a GTP binding assay. Twenty-three hits were identified and four compounds were further investigated in dose-response experiments. 3,4-Methylenedioxy-beta nitrostyrene (MNS) was demonstrated to decrease GTP binding and inhibit GTPase activity (IC50=23.92μM) while the compound N-phenylanthranilic acid increased GTP binding (EC50=4.969μM) and decreased GTPase activity. Identification of these compounds is the first step in the development of a novel PD therapeutic targeting the G-domain of LRRK2.

Immunotherapy is a promising tool in the fight against cancer and aims to recruit patients own immune systems to seek out and destroy malignant cells. Options such as oncolytic viruses (OVs), autologous tumour vaccines and chimeric antigen receptors have shown clinical success to date, yet there remain significant hurdles to overcome. Here, we demonstrate a novel vaccine combining irrCell priming and infected cell boosting dramatically improves the tumour-specific CTL response against CT26 tumours and can be further enhanced using additional immunogenic factors (armed OVs, adjuvants). We also developed a novel fluorescence-based high-throughput screening platform to identify compounds that sensitize resistant solid tumours to killing by CAR-T cells, which ultimately revealed cardiac glycosides as putative tumour sensitizers. Overall, this thesis identifies several novel enhancers of the anticancer immune response, including a heterologous irr:ICV vaccine regimen and the potential ability to identify molecules to overcome resistance to CAR-T therapy.

In the field of protein research in general and the pharmaceutical industry in particular, it is now necessary to perform measurements of the secondary structure of proteins on many samples simultaneously, for instance to screen for molecules that stabilize proteins or to evaluate the action of multiple environmental conditions. In this context, we have proposed a new approach to evaluate the secondary structure of proteins on a very large scale (approximately 2000 to 4000 samples / cm2), by combining infrared imaging and 2D printing of protein microarrays. In view of the large amount of data, in a first step, methods for automating the extraction of spectra of interest from microarray infrared images and for automating the processing of the spectra were developed. Since the estimation of the secondary structure from infrared spectra is based on the construction of prediction models by chemometric methods, a relevant set of proteins for calibration was mandatory. A protein bank consisting of 92 commercially available proteins, the structure of which was well characterized by X-ray crystallography, was established for this purpose. After the development of predictive models for secondary structure determination and the validation of the protein microarray approach, we tried to optimize the models to improve the secondary structure prediction by different approaches as secondary structure definition, partial deuteration or subtraction of side chain contribution to the spectra. On the other hand, dealing with non-native structures not present in the reference protein library was a challenge. We took the opportunity to analyze the structural modifications of a subset of our protein library subjected to moderate denaturation conditions. Multivariate curve resolution-alternating least squares (MCR-ALS) was used to model a new spectral component appearing in the protein set subjected to denaturing conditions, which could represent a potential spectroscopic marker of aggregation and could allow a semi-quantitative evaluation of the aggregation. While the assessment of secondary structure was well established in the first part of this work, tertiary structure and stability are also critical. Hydrogen / deuterium exchange (HDX) is a potential approach for studying the structure and dynamics of proteins. In the last part of this work, we built a device which allowed to follow the HDX exchange kinetics simultaneously on the entire microarray. In conclusion, protein microarray FTIR imaging opens the door to high throughput analysis of protein secondary structure without any labelling and would allow better understanding of three-dimensional structure and dynamics of proteins through recording HDX curves. / Dans le domaine de la recherche sur les protéines et de l'industrie pharmaceutique, il s’avère désormais nécessaire d'effectuer des mesures de la structure secondaire des protéines sur de nombreux échantillons simultanément, de cribler des molécules qui stabilisent les protéines, ou d'évaluer l'action de multiples conditions environnementales. Dans ce contexte, nous avons proposé une nouvelle approche pour évaluer la structure secondaire des protéines à très grande échelle (environ 2 000 à 4 000 échantillons / cm2), en associant l'imagerie infrarouge et l'impression 2D de damiers de protéines. Dans un premier temps, des méthodes d'automatisation de l'extraction des spectres d'intérêt à partir des images infrarouges des damiers et d'automatisation des spectres ont été développées. L'estimation de la structure secondaire à partir des spectres infrarouges étant basée sur la construction de modèles de prédiction à partir de méthodes chimiométriques, un ensemble pertinent de protéines pour l'étape de calibration était obligatoire. Une banque de protéines constituée de 92 protéines disponibles dans le commerce, dont la structure était bien caractérisée par cristallographie aux rayons X, a été constituée dans ce but. Après élaboration des modèles prédictifs de la structure secondaire et la validation de l'approche des damiers de protéines, nous avons tenté d'optimiser les modèles pour améliorer les prédictions de structure secondaire par différentes approches. D'autre part, traiter des protéines présentant une structure jamais rencontrée dans les structures natives de notre bibliothèque de protéines de référence constituait un défi. Nous avons saisi l'opportunité d'analyser les modifications structurales d'un sous-ensemble de notre bibliothèque de protéines, caractérisé par un contenu structurel secondaire très différent en le soumettant à des conditions de dénaturation modérées La méthode de résolution de courbes multivariées des moindres carrés alternés (MCR-ALS) a été utilisée pour modéliser une nouvelle composante spectrale apparaissant dans l'ensemble protéique soumis à des conditions dénaturantes, et a permis de révéler un marqueur spectroscopique potentiel d'agrégation protéique permettant une évaluation semi-quantitative de son contenu. Alors que l’évaluation de la structure secondaire a été bien établie dans la première partie de ce travail, la structure tertiaire et la stabilité sont également critiques. L'échange hydrogène / deutérium (HDX) est une approche potentielle pour l’étude de la structure et de la dynamique des protéines. Dans la dernière partie de ce travail, nous avons construit un dispositif qui a permis de suivre la cinétique d’échange HDX simultanément sur l'ensemble d’un damier. En conclusion, l'imagerie FTIR de damiers de protéines ouvre la porte à une analyse à haut débit de la structure secondaire des protéines et permettrait de mieux comprendre la structure et la dynamique tridimensionnelles grâce à l'enregistrement des courbes HDX. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

In this thesis the processing mechanisms of short non-coding RNAs (ncRNAs) is investigated by using data generated by the current method of high-throughput sequencing (HTS). The recently adapted short RNA-seq protocol allows the sequencing of RNA fragments of microRNA-like length (∼18-28nt). Thus, after mapping the data back to a reference genome, it is possible to not only measure, but also visualize the expression of all ncRNAs that are processed to fragments of this specific length. Short RNA-seq data was used to show that a highly abundant class of small RNAs, called microRNA-offset-RNAs (moRNAs), which was formerly detected in a basal chordate, is also produced from human microRNA precursors. To simplify the search, the blockbuster tool that automatically recognizes blocks of reads to detect specific expression patterns was developed. By using blockbuster, blocks from moRNAs were detected directly next to the miR or miR* blocks and could thus easily be registered in an automated way. When further investigating the short RNA-seq data it was realized that not only microRNAs give rise to short ∼22nt long RNA pieces, but also almost all other classes of ncRNAs, like tRNAs, snoRNAs, snRNAs, rRNAs, Y-RNAs, or vault RNAs. The formed read patterns that arise after mapping these RNAs back to a reference genome seem to reflect the processing of each class and are thus specific for the RNA transcripts of which they are derived from. The potential of this patterns in classification and identification of non-coding RNAs was explored. Using a random forest classifier which was trained on a set of characteristic features of the individual ncRNA classes, it was possible to distinguish three types of ncRNAs, namely microRNAs, tRNAs, and snoRNAs. To make the classification available to the research community, the free web service ‘DARIO’ that allows to study short read data from small RNA-seq experiments was developed. The classification has shown that read patterns are specific for different classes of ncRNAs. To make use of this feature, the tool deepBlockAlign was developed. deepBlockAlign introduces a two-step approach to align read patterns with the aim of quickly identifying RNAs that share similar processing footprints. In order to find possible exceptions to the well-known microRNA maturation by Dicer and to identify additional substrates for Dicer processing the small RNA sequencing data of a Dicer knockdown experiment in MCF-7 cells was re-evaluated. There were several Dicer-independent microRNAs, among them the important tumor supressor mir-663a. It is known that many aspects of the RNA maturation leave traces in RNA sequencing data in the form of mismatches from the reference genome. It is possible to recover many well- known modified sites in tRNAs, providing evidence that modified nucleotides are a pervasive phenomenon in these data sets.

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal malignancies because it is often diagnosed at a late disease stage and has a poor response rate to currently available treatments. Therefore, it is critical to develop new therapeutic approaches that will enhance the efficacy and reduce the toxicity of currently used therapies. Here we aimed to evaluate the therapeutic potential and mechanisms of action for piperlongumine (PL), an alkaloid from long pepper, in PDAC models. We postulated that PL causes PDAC cell death through oxidative stress and complements the therapeutic efficacy of chemotherapeutic agents in PDAC cells. First, we determined that PL is one of the most abundant alkaloids with antitumor properties in the long pepper plant. We also showed PL in combination with gemcitabine, a chemotherapy agent used to treat advanced pancreatic cancer, reduced tumor weight and volume compared to vehicle-control and individual treatments. Further, biochemical analysis, including RNA sequencing and immunohistochemistry, suggested that the antitumor activity of PL was associated with decreased cell proliferation, induction of cell cycle arrest, and oxidative stress-induced cell death. Moreover, we identified that c-Jun N-terminal kinase (JNK) inhibition blocks PL-induced cell death, translocation of Nrf2, and transcriptional activation of HMOX1 in PDAC. Finally, high-throughput drug and CRISPR screenings identified potential targets that could be used in combination with PL to treat PDAC cells. Collectively, our data suggests that cell cycle regulators in combination with PL might be an effective approach to combat pancreatic cancer. / NIH

Indiana University-Purdue University Indianapolis (IUPUI) / The over-accumulation of glycogen appears as a hallmark in various glycogen storage diseases (GSDs), including Pompe, Cori, Andersen, and Lafora disease. Glycogen synthase (GS) is the rate-limiting enzyme for glycogen synthesis. Recent evidence suggests that suppression of glycogen accumulation represents a potential therapeutic approach for treating these diseases. Herein, we describe the discovery, characterization, and development of small molecule inhibitors of GS through a multicomponent study including biochemical, biophysical, and cellular assays. Adopting an affinity-based fluorescence polarization assay, we identified a substituted imidazole molecule (H23), as a first-in-class inhibitor of yeast glycogen synthase 2 (yGsy2) from the 50,000 ChemBridge DIVERSet library. Structural data derived from X-ray crystallography at 2.85 Å, and enzyme kinetic data, revealed that H23 bound within the uridine diphosphate glucose binding pocket of yGsy2. Medicinal chemistry efforts examining over 500 H23 analogs produced structure-activity relationship (SAR) profiles that led to the identification of potent pyrazole and isoflavone compounds with low micromolar potency against human glycogen synthase 1 (hGYS1). Notably, several of the isoflavones demonstrated cellular efficacy toward suppressing glycogen accumulation. In an alternative effort to screen inhibitors directly against human GS, an activity-based assay was designed using a two-step colorimetric approach. This assay led to the identification of compounds with submicromolar potency to hGYS1 from a chemical library comprised of 10,000 compounds. One of the hit molecules, hexachlorophene, was crystallized bound to the active site of yGsy2. The structure was determined to 3.15 Å. Additional kinetic, mutagenic, and SAR studies validated the binding of hexachlorophene in the catalytic pocket and its non-competitive mode of inhibition. In summary, these two novel assays provided feasible biochemical platforms for large-scale screening of small molecule modulators of GS. The newly-developed, potent analogs possess diverse promising scaffolds for drug development efforts targeting GS activity in GSDs associated with excess glycogen accumulation. / 2021-07-01

Myotonic dystrophy type 1 (DM1) is the most common form of adult muscular dystrophy (1:8000) and is caused by an abnormal expansion of CTG repeats in the 3’ untranslated region of the dystrophia myotonica protein kinase (DMPK) gene. The expanded repeats of the DMPK mRNA forms hairpin structures which sequester RNA-binding proteins (RBP) in intranuclear foci, such as the splicing regulator muscleblind-like 1 (MBNL1), which results in aberrant splicing of several mRNAs and underlie, at least in part, DM1 pathogenesis. It has been previously shown that disaggregating these RNA foci repletes free and thus functional MBNL1, rescuing DM1 spliceopathy and alleviating associated signs and symptoms such as myotonia. Importantly, the direct upregulation of MBNL1 has comparable beneficial outcomes. The focus of this thesis was to develop novel and practical therapeutic avenues for DM1 by employing high-throughput screening technology to identify key pathways and small molecule candidates which reduce CUG foci in patient cells, and ultimately correct DM1 spliceopathy and associated signs in vivo. First, a high-throughput kinome screen using an siRNA library targeting 692 kinase subunits identified PACT, HIPK4, and PKA2β as candidates for reducing CUG foci in patient fibroblasts. Knockdown of each gene resulted in a partial reduction in CUG foci, but ultimately did not correct aberrant splicing of insulin receptor (IR) or sarcoplasmic/endoplasmic reticulum Ca2+-ATPase (SERCA1), two genes which are typically misspliced in DM1. A second set of screens focused on testing small molecules, several of which are FDA-approved for clinical use, in an effort to expedite drug discovery. One approach was to data-mine from a previously completed chemical screen, which used system-wide RNA sequencing to establish drug-gene interactions in mouse neuronal cultures treated with blood brain barrier-penetrant drugs, and specifically look for compounds which downregulate DMPK mRNA or upregulate MBNL mRNA (MBNL1 and MBNL2). No compounds were found to downregulate DMPK mRNA. However, several compounds upregulated MBNL mRNAs; the activity of one of these, nilotinib, was validated in human DM1 fibroblasts and converted myoblasts, mediating a small correction in SERCA1 spliceopathy. Administration of nilotinib to unaffected mice did not result in in vivo MBNL gene upregulation in mouse skeletal muscle, as was seen in vitro. Further testing of nilotinib in DM1 in vivo models is required. A final set of chemical screens in patient myoblasts using an FDA-approved drug library and a chemogenomic drug library identified several HDAC inhibitors which reduced foci and rescued SERCA1 spliceopathy in vitro in DM1 differentiated myoblasts. Of these, vorinostat (SAHA) was further tested in a mouse model of DM1 (HSALR), proving safe and effective in correcting aberrant muscle pathology as well as splicing defects of RYR1, SERCA1, and CLCN1. Functional validation, such as myotonia, remains to be completed, but given the strong evidence for CUG foci reduction and splicing correction, vorinostat has emerged as a promising novel candidate for DM1 therapy.

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