Improvement of STEAP 1 Biosynthesis from Pichia pastoris X33 cells under an optimized feeding strategy

Prostate cancer (PCa) is the most common type of cancer in aged men. Actually, the main problem arises from the fact that both PCa diagnosis and therapy are still invasive and limited in advanced stages of this disease. Thus, it is necessary to identify, study and characterize specific proteins whose expression correlates with these pathologies. Concerning this, it has been suggested that the Six-transmembrane epithelial antigen of the prostate 1 (STEAP1) protein as a good biomarker and/or immunotherapeutic target for PCa. It is located in the plasma membrane of epithelial cells, in both tight and gap junctions. STEAP1 is composed of six transmembrane domains, connected by three extracellular and two intracellular loops. Therefore, it has been suggested that this protein plays an important role in intracellular communication between cancer cells, contributing to the cancer process and tumor invasiveness. The characterization of STEAP1 structure and function might allow the development of specific inhibitors, envisaging a decrease of its oncogenic role. However, the techniques used for protein structural and functional characterization demand for high quantities of the target protein, which may be achieved through the recombinant DNA technology. Therefore, the aim of this work was to improve STEAP1 biosynthesis from mini-bioreactor Pichia pastoris X33 methanol induced cultures. This was achieved through the study of different glycerol and methanol feeding profiles during the fed-batch phases. Briefly, the medium supplementation with Proline 1M in a gradient glycerol and constant methanol feed, leads to high quantities of STEAP1 (increase for the double). An exponential glycerol and constant methanol feed produces fewer amounts of the protein but in the correct molecular weight (~35kDa). The influence of the fermentation conditions on STEAP1 molecular weight and N-glycosylation was studied using the enzyme PNGase F. The results showed that a constant glycerol feed seems to produce STEAP1 with N-glycosylation. However, the dimers produced in the gradient glycerol feed are not due N-glycosylation process. Two-dimensional electrophoresis proves this, and it was demonstrated that they correspond to different N-glycosylation patterns. Overall, it was successfully optimized a new strategy for recombinant STEAP1 biosynthesis, through the study of different feeding profiles. Future work encompassing will be developed an alternative strategy to perform the purification on the target protein.O cancro da próstata é uma das patologias com maior incidência em todo o mundo. Atualmente, os meios de diagnóstico e terapêuticos existentes são invasivos e apresentam uma eficácia limitada sobretudo em estágios mais avançados da doença. Desta forma, torna-se necessário o estudo de proteínas específicas cuja expressão esteja relacionada com o seu desenvolvimento e progressão. Diversos estudos têm sugerido a proteína Six-transmembrane Epithelial Antigen of the Prostate 1 (STEAP1) como um possível biomarcador e/ou alvo imunoterapêutico para o cancro da próstata. A STEAP1 é constituída por 6 domínios transmembranares e encontra-se presente na membrana plasmática das células epiteliais, nomeadamente nas junções que promovem a comunicação célula-célula. Alguns estudos suportam a hipótese que a STEAP 1 assume um papel preponderante na comunicação entre células tumorais, estando desta forma envolvida na progressão do cancro. No entanto, estudos complementares são ainda necessários para resolver a sua estrutura tridimensional de forma a melhor compreender as suas funções na carcinogénese assim como delinear novas estratégias terapêuticas. Deste modo, elevadas quantidades de STEAP1 são requeridas a partir de tecnologias emergentes de DNA recombinante. Nestes domínios, a levedura Pichia pastoris tem-se revelado um hospedeiro adequado na expressão de proteínas recombinantes. Em particular, a sua capacidade para realizar modificações pós-tradução torna-a num sistema microbiano ideal para a produção recombinante de proteínas membranares. Assim, os principais objetivos do presente trabalho são: 1) Aumentar a escala de produção da proteína STEAP1 para bioreator em culturas de Pichia pastoris X33 testando diferentes feeds de glicerol e metanol; 2) Avaliar a adição de diferentes chaperones químicos que contribuam para a estabilização conformacional da STEAP1; 3) Estudar a influência dos diferentes feeds de glicerol em eventuais processos de N-glicosilação. Os resultados obtidos demonstraram que - através um feed por gradiente de glicerol e constante de metanol - houve um aumento da produção de STEAP1, para cerca do dobro, aquando a suplementação do meio com 1M de Prolina. Observa-se que na aplicação de um feed exponencial de glicerol e constante de metanol, a quantidade de STEAP1 produzida encontra-se no peso molecular correto (~35kDa), embora se tivesse verificado níveis de produção reduzidos. Adicionalmente, denotou-se através da digestão dos lisados com a enzima PNGase F que um feed constante de glicerol e metanol parece produzir STEAP1 com N-glicosilação. Como trabalho futuro serão desenvolvidas estratégias de purificação da proteína STEAP1

Sensing approaches for the discrimination of small molecules and multivalent analytes

Differential arrays, composed of receptors that are capable of generating unique patterns of responses, have been shown to be useful for discrimination of molecular analytes. Herein, differential arrays have been developed and utilized for the discrimination of small molecule and multivalent biological analytes using cross-reactive receptors. A variety of carboxylate-binding guanidinium-based receptors were tested for their ability to discriminate carboxylate enantiomers. Lanthanide complexes showed the most promising enantiodifferentiation. A dynamic receptor for multivalent biological analytes was developed using self-assembling components designed to target cancer cell lines in a cross-reactive manner. Using this differential array, cancer cell lines of different tissue origin were classified using principal component analysis. The receptors in the array responded to targets as hypothesized but also behaved in a cross-reactive manner that allowed for analyte differentiation. The classification response of the array was reproducible. Boronic acid receptors and receptor arrays were also developed for discrimination of cell surface glycans. In this work, the success of cross-reactive receptors with designed components in differential sensing for small molecules as well as complex multivalent analytes is demonstrated.Chemistr

Systems Biology of Protein Secretion in Human Cells: Multi-omics Analysis and Modeling of the Protein Secretion Process in Human Cells and its Application.

Since the emergence of modern biotechnology, the production of recombinant pharmaceutical proteins has been an expanding field with high demand from industry. Pharmaceutical proteins have constituted the majority of top-selling drugs in the pharma industry during recent years. Many of these proteins require post-translational modifications and are therefore produced using mammalian cells such as Chinese Hamster Ovary cells. Despite frequent improvements in developing efficient cell factories for producing recombinant proteins, the natural complexity of the protein secretion process still poses serious challenges for the production of some proteins at the desired quantity and accepted quality. These challenges have been intensified by the growing demands of the pharma industry to produce novel products with greater structural complexity,\ua0\ua0as well as increasing expectations from regulatory authorities in the form of new quality control criteria to guarantee product safety.This thesis focuses on different aspects of the protein secretion process, including its engineering for cell factory development and analysis in diseases associated with its deregulation. A major part of this thesis involved the use of HEK293 cells as a human model cell-line for investigating the protein secretion process by generating different types of omics data and developing a computational model of the human protein secretion pathway. We compared the transcriptomic profile of cell lines producing erythropoietin (EPO; as a model secretory protein) at different rates to identify key genes that potentially contributed to higher rates of protein secretion. Moreover, by performing a transcriptomic comparison of cells producing green fluorescent protein (GFP; as a model non-secretory protein) with EPO producers, we captured differences that specifically relate to secretory protein production. We sought to further investigate the factors contributing to increased recombinant protein production by analyzing additional omic layers such as proteomics and metabolomics in cells that exhibited different rates of EPO production. Moreover, we developed a toolbox (HumanSec) to extend the reference human genome-scale metabolic model (Human1) to encompass protein-specific reactions for each secretory protein detected in our proteomics dataset. By generating cell-line specific protein secretion models and constraining the models using metabolomics data, we could predict the top host cell proteins (HCPs) that compete with EPO for metabolic and energetic resources.\ua0Finally,\ua0based on the detected patterns of changes in our multi-omics investigations combined with a protein secretion sensitivity analysis using the metabolic model, we identified a list of genes and pathways that potentially play a key role in recombinant protein production and could serve as promising candidates for targeted cell factory design.In another part of the thesis, we studied the link between the expression profiles of genes involved in the protein secretory pathway (PSP) and various hallmarks of cancer. By\ua0implementing a dual approach involving differential expression analysis and eight different machine learning algorithms, we investigated the expression changes in secretory pathway components across different cancer types to identify PSP genes whose expression was associated with tumor characteristics. We demonstrated that a combined machine learning and differential expression approach have a complementary nature and could highlight key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets

Molecular Characterization of Metastatic Endometrial Cancer by Mass Spectrometry

One of the most reliable prognostic factors in endometrial cancer is the presence of lymph node metastasis. Clinicians presently face the challenge that radiological imaging and conventional surgical-pathological variables such as tumour size, depth of invasion and grade of disease are unreliable in determining if the endometrial cancer has metastasized. Although only 10% of endometrial cancer patients suffer from lymph node metastasis, the majority of them undergo lymphadenectomy, which can be associated with significant complications including lower extremity lymphedema. Based on the assumption that metastasis is mainly determined by the properties of the primary tumour and its interaction with the surrounding tissues, a tissue based proteomic approach combining two complementary methods, peptide matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI) and liquid chromatographytandem mass spectrometry (LC-MS/MS) was undertaken to identify molecular discriminators in primary endometrial cancers which correlate with lymph node metastasis. In a discovery approach, MALDI MSI was carried out on two tissue micro arrays (TMA), containing a total of 43 patients. Upon data acquisition, a canonical correlation analysis (CCA) based method was applied to rank the acquired m/z values based on their power to discriminate the primary carcinomas with and without metastatic potential. The highly ranked m/z values were able to classify 38 out of 43 patients (88.4%) correctly. The top discriminative m/z values were identified using a combination of in situ sequencing and LC-MS/MS from digested tumour samples. The differential abundance of the two identified proteins, plectin and α-Actin- 2 was further validated using data independent acquisition LC-MS/MS and immunohistochemistry. In a targeted approach, we aimed to improve the prediction model for endometrial cancer metastasis preoperatively. From publically available data and published research, we compiled a list of 60 target proteins with the potential to display differential abundance between primary endometrial cancers with lymph node metastasis versus those without. Using data dependent acquisition LC-MS/MS, we were able to detect 23 of these proteins in an independent cohort of endometrial cancer patients. Using data independent acquisition LC-MS/MS, the differential abundance of 5 of those proteins was observed (p < 0.05). Upon validation by immunohistochemistry, our data indicates that annexin A2 is upregulated while annexin A1 and alpha actinin 4 were downregulated in primary endometrial cancers with lymph node metastasis versus those without. The results of this immunohistochemistry analysis were used to generate a predictive model of endometrial cancer metastasis. Additionally the predictive model using highly ranked m/z values identified by MALDI MSI was generated and compared with other models containing the histopathological variables. However, when compared the MALDI MSI model showed significantly higher predictive accuracy than the model using immunohistochemistry data. Our results showed that the highly ranked m/z values identified from MALDI MSI data serve as new independent prognostic information beyond the established risk factors. The developed molecular classification tool has the potential to predict which tumours have metastasized and which patients would therefore benefit from radical surgery while avoiding those who will not benefit from it and consequently decreasing the risk of post-surgical morbidity. In conclusion, these findings demonstrate a successful application of MALDI MSI for the identification of protein biomarkers of endometrial cancer metastasis.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 201

Examining the suitability of molecular and metabolomic-based techniques as tools for assessing the effects of pharmaceuticals in the aquatic environment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Pharmaceuticals represent important and indispensable elements in modern society and their usage is considerable. Post consumption and body-elimination, pharmaceuticals are not completely removed in sewage treatment works (STWs) and as such, have been detected at low levels in STW effluents, surface waters, seawaters, ground waters and some drinking waters. Accordingly, pharmaceutical toxicity has been detected in several aquatic organisms. To date, environmental risk assessments (ERA) examine for toxicity using a series of chronic toxicity assays that examine for standard physiological responses in algae, Daphnia and fish and do not address pharmaceutical mode of action. Therefore, using the fathead minnow (Pimephales promelas) and the β-blocking pharmaceutical propranolol as the test-species and test-drug, respectively, the aim of this study was to establish an intelligent targeted 4-phased ERA using molecular, in vivo exposure, metabolomic and quantitative expression analytical techniques. The first phase established that the fathead minnow expressed the β3bi-adrenergic receptor (AR), which is a target receptor for propranolol in humans. The in vivo pair-breeding assay suggested that at 1mgL-1 and 10mgL-1, propranolol levels in fish blood plasma exceeded the human therapeutic concentration and caused 80% and 100% mortality, respectively. The most likely causes of mortality were liver failure and central nervous system toxicity. It was not possible to identify a robust biomarker of propranolol exposure using proton nuclear magnetic resonance (1H NMR) as there was considerable metabolic variation between male liver tissues within the same treatment groups. β3bi¬¬-AR expression was significantly lower at 1mgL-1 in the brain and liver, which was most likely the result of desensitisation in response to elevated levels of epinephrine and cortisol. β3bi¬¬-AR expression was significantly increased in the heart at the environmentally relevant concentration of 0.001mgL 1, however it was not possible to link β3bi¬¬-AR expression to a toxic response. Propranolol is unlikely to pose a threat to the aquatic environment as the concentrations measured in the environment are approximately 1000-fold lower than those that induced a toxic response. The proposed ERA represents a marked improvement over the existing ERA as it addresses pharmaceutical mode of action and both subtle and physiological toxicity responses, however it still requires further validation studies to address both metabolomic and gene expression variation

New Developments in 1H NMR-linked Metabolomics: Identification of New Biomarkers for the Metabolomic Classification of Niemann-Pick Disease, Type C1, and its Response to Treatment

NMR-linked metabolomics analysis was employed to investigate urinary and human plasma profiles collected from Niemann Pick type C1 disease patients (NP-C1), in addition to aqueous extracts of liver samples of an NP-C1 mouse model. NP-C1 is a lysosomal storage disorder caused by mutations in the lysosomal proteins NPC1 and NPC2, which are involved in lysosomal cholesterol trafficking. NP-C1 disease is a fatal genetic disorder, characterised by neurodegeneration and hepatic damage. Miglustat (MGS) is the only approved drug for this disease, and consequently, plasma and urine samples collected from MGS-treated patients were also investigated. The ability of 1H NMR analysis to detect a wide range of metabolites simultaneously served to characterize the metabolic profiles of urine, plasma and hepatic tissue samples investigated in order to perform linked multivariate analysis (MVA). Additionally, MGS was identified in urine samples collected from NP-C1 treated patients. MVA employing both parametric and machine learning-based techniques was conducted to classify samples according to their disease status, and also to seek biomarkers that could aid in the diagnosis and/or prognosis of the disease. Moreover, a new technique was introduced in a metabolomics context, Correlated Component Regression (CCR), and the suitability of Random Forests (RFs) for variable selection was also explored. We were able to differentiate urine samples collected from NP-C1 patients from those collected from heterozygous controls, and also propose several metabolites as NP-C1 urinary biomarkers such as bile acids, 2-hydroxy-3-methylbutyrate, 3-aminoisobutyrate, 5-aminovalerate, trimethylamine, methanol, creatine and quinolinate. The 1H NMR linked metabolomics study of plasma samples revealed major distinctions among the groups investigated, metabolic alterations ascribable to the disease pathology were mainly observed as changes in the lipoprotein profiles of NP-C1 patients. Hepatic tissue extracts analysed revealed major disturbances in amino acid metabolism, along with impairments in the NAD+/NADH production and redox status. Gut microbiota and bile acid metabolism were also highlighted as features altered in NP-C1 disease. CCR linked to Linear Discriminant Analysis was evaluated as a new tool for metabolomics analysis, giving accurate results when compared to alternative techniques tested. Additionally, the suitability of Random Forests and associated recursive feature elimination for variable selection in metabolomics studies was contrasted, suggesting that those strategies relying on a variable ranking to select the top features for discrimination are more suitable for metabolomics investigations than those that iteratively remove a percentage of the least effective features until the classification performance decays.Hope Against Cance

Predictive QSAR tools to aid in early process development of monoclonal antibodies

Ph. D. Thesis.Monoclonal antibodies (mAbs) have become one of the fastest growing markets for diagnostic and therapeutic treatments over the last 30 years with a global sales revenue around $89 billion reported in 2017. A popular framework widely used in pharmaceutical industries for designing manufacturing processes for mAbs is Quality by Design (QbD) due to providing a structured and systematic approach in investigation and screening process parameters that might influence the product quality. However, due to the large number of product quality attributes (CQAs) and process parameters that exist in an mAb process platform, extensive investigation is needed to characterise their impact on the product quality which makes the process development costly and time consuming. There is thus an urgent need for methods and tools that can be used for early risk-based selection of critical product properties and process factors to reduce the number of potential factors that have to be investigated, thereby aiding in speeding up the process development and reduce costs. In this study, a framework for predictive model development based on Quantitative Structure-Activity Relationship (QSAR) modelling was developed to link structural features and properties of mAbs to Hydrophobic Interaction Chromatography (HIC) retention times and expressed mAb yield from HEK cells. Model development was based on a structured approach for incremental model refinement and evaluation that aided in increasing model performance until becoming acceptable in accordance to the OECD guidelines for QSAR models. The resulting models showed that it was possible to predict HIC retention times of mAbs based on their inherent structure. Further improvements of the models are suggested due to performance being adequate but not sufficient for implementation as a risk assessment tool in QbD. However, the described methodology and workflow has been proven to work for retention time prediction in a HIC column and is therefore likely to be applicable to other purification columns.European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No 643056 (Biorapid project)

MALDI-ToF mass spectrometry biomarker profiling via multivariate data analysis application in the biopharmaceutical bioprocessing industry

PhD ThesisMatrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF MS) is a technique by which protein profiles can be rapidly produced from biological samples. Proteomic profiling and biomarker identification using MALDI-ToF MS have been utilised widely in microbiology for bacteria identification and in clinical proteomics for disease-related biomarker discovery. To date, the benefits of MALDI-ToF MS have not been realised in the area of mammalian cell culture during bioprocessing. This thesis explores the approach of ‘intact-cell’ MALDI-ToF MS (ICM-MS) combined with projection to latent structures – discriminant analysis (PLS-DA), to discriminate between mammalian cell lines during bioprocessing. Specifically, the industrial collaborator, Lonza Biologics is interested in adopting this approach to discriminate between IgG monoclonal antibody producing Chinese hamster ovaries (CHO) cell lines based on their productivities and identify protein biomarkers which are associated with the cell line productivities. After classifying cell lines into two categories (high/low producers; Hs/Ls), it is hypothesised that Hs and Ls CHO cells exhibit different metabolic profiles and hence differences in phenotypic expression patterns will be observed. The protein expression patterns correlate to the productivities of the cell lines, and introduce between-class variability. The chemometric method of PLS-DA can use this variability to classify the cell lines as Hs or Ls. A number of differentially expressed proteins were matched and identified as biomarkers after a SwissProt/TrEMBL protein database search. The identified proteins revealed that proteins involved in biological processes such as protein biosynthesis, protein folding, glycolysis and cytoskeleton architecture were upregulated in Hs. This study demonstrates that ICM-MS combined with PLS-DA and a protein database search can be a rapid and valuable tool for biomarker discovery in the bioprocessing industry. It may help in providing clues to potential cell genetic engineering targets as well as a tool in process development in the bioprocessing industry. With the completion of the sequencing of the CHO genome, this study provides a foundation for rapid biomarker profiling of CHO cell lines in culture during recombinant protein manufacturing.Lonza Biologics

Characterization of NOTCH signalling pathway in PTEN-deficient prostate tumorigenesis

RésuméProstate cancer is a commonly diagnosed non-cutaneous malignancy and one of the leading causes of cancer-related deaths in men. Particularly, advanced prostate cancer is considered of high-risk with poor prognosis and survival. Several studies have identified that activation NOTCH pathway is associated with advanced stage of the disease and therapy-resistance in patients. However, the mechanism by which NOTCH pathway is activated in prostate cancer still remains unknown. Moreover, preclinical studies determining the therapeutic efficacy of NOTCH pathway inhibitors in prostate cancer is lacking. Here, in this study we show that loss of PTEN, a frequently altered tumour suppressor gene in prostate cancer, upregulates the expression of ADAM17, thereby activating NOTCH signalling in prostate tumours. Mechanistically, loss of PTEN triggers the accumulation of an oncogenic isoform of the transcription factor CUX1 that favours ADAM17 transcription. Notably, over-expression of the oncogenic isoform of CUX1 (p110 CUX1) both in vitro and in vivo resulted in up-regulation of ADAM17 and activation of NOTCH signalling. Using prostate conditional inactivation of both Pten and Notch1 along with preclinical trials carried out in Pten-null prostate conditional mouse models, we demonstrate that Pten-deficient prostate tumours are addicted to the Notch signalling. Importantly, we demonstrate that pharmacological inhibition of Notch pathway using g-secretase inhibitor promotes growth arrest and restricts tumourinvasiveness in both Pten-null and Pten/Trp53-null prostate tumours by triggering cellular senescence. Taken together, our study describes a novel pro-tumorigenic network that links PTEN-loss to ADAM17 and NOTCH signalling in a PI3K-independent manner, thus providing the rationale for the use of NOTCH pathway inhibitors in advance prostate cancer patients