The flagellated soil-dwelling bacterium Sinorhizobium meliloti is known for its symbiotic relationship with several leguminous genera. The symbiosis between the bacterium and its host plants facilitates fixation of atmospheric nitrogen and ultimately replenishment of nitrogen to the soil. However, before nitrogen fixation can occur, the bacterial cells must actively travel to the plant's roots and successfully induce formation of a plant organ called a root nodule. To initiate the nodulation process, the bacterium needs to be in direct contact with the root hairs. This requires free living bacterial cells within the soil to sense the presence of their host plant and travel to its roots. S. meliloti is able to do this through a process called chemotaxis. Chemotaxis is the ability to respond to chemical gradients within the environment by directed movement. It is facilitated by Methyl-accepting Chemotaxis Proteins (MCPs) as part of a two-component signal transduction system. These receptor proteins are able to bind ligands and influence the state of the signal transduction system, ultimately controlling flagellar behavior. The chemotaxis system of Escherichia coli has been well characterized and serves as a useful point of comparison to that of S. melilot throughout this work. Within this work we have determined the stoichiometry of all chemotaxis proteins of S. meliloti by means of quantitative immunoblotting. Chapter 2 addresses the stoichiometry of MCPs and the histidine kinase CheA. The eight MCPs were grouped by total abundance within the cell, in high abundance (McpV), low abundance (IcpA, McpU, McpX, and McpW), and very low abundance (McpY, McpZ and McpT). The approximate cellular ratio of these three receptor groups is 300:30:1. The chemoreceptor-to-CheA ratio is 22.3:1, highly similar to the 23:1 ratio known for Bacilius subtiltis. Chapter 3 continues the investigation of the protein stoichiometry, expanding to all chemotaxis proteins. We compare ratios of S. meliloti chemotaxis proteins to those of E. coli and B. subtilis. We address the possible reasons for the high ratio of CheR / CheB to the total amount of receptors. Proteins again can be grouped by abundance: CheD, CheY1, and CheY2 are the most abundant. CheR and CheB appear in lower amounts, CheS and CheT appear to be auxiliary proteins, and finally CheW1 and CheW2, which directly interact with the receptors and CheA. Chapter 4 focuses on altered receptor abundance in S. meliloti due to the fusion of common epitope tags to the C-terminus. The fusion of these tags promotes greater cellular abundance of many receptors including McpU. The fusion of charged residues to the C-terminus promotes a greater increase in McpU abundance thanthe addition of single amino acid residues. Truncations of McpU were made to investigate the presence of a protease recognition site near the C-terminus. These truncations resulted in an increase in abundance similar to those resulting from epitope tag fusions. As epitope tags are widely used in protein studies to help determine protein stoichiometry, this study obviates a potential stumbling block for future experimenters. The function of CheT, a small protein (13.4 kDa) encoded by the last gene in S. meliloti's major chemotaxis operon, is the subject of chapter 5. A cheT deletion strain is chemotactically deficient compared to the S. meliloti wild-type strain. Through two separate experiments (a glutaraldehyde cross-linking assay and co-purification) we demonstrated that CheT interacts with the methyltransferase CheR. We also investigate its possible role in CheR's methylation activity through a series of methylation assays. This work contributes to our understanding of Sinorhizobium meliloti's chemotaxis signal transduction system. We have discovered evidence for new a protein-protein interaction within our system and have revealed the abundance of all chemotaxis proteins within the cell. We also showed that fusions of epitope tags to various chemotaxis proteins can dramatically influence their abundance. We shed light on the possible function of a previously uncharacterized protein, although more work is required to determine its exact role. / Doctor of Philosophy / Sinorhizboium meliloti is a bacterium that lives in the soil and forms a symbiotic relationship with many plants including alfalfa, a commonly grown cover crop. This symbiotic relationship is important because it allows for nitrogen to be replenished into the soil without the use of artificial fertilizer. However, to form this relationship the bacterial cells in the soil must be able to colonize the plant roots. The soil is a complex environment with many different kinds of chemical molecules and sources of nutrients. Like many other types of bacteria, S. meliloti uses flagella (long helical structures that rotate much like a propeller) to move through the soil. Control of the flagella falls to what is known as a chemotaxis signal transduction system, which can be thought of as a navigation system for each bacterial cell. The system has proteins that act as receptors to sense different chemical molecules. The bacterial cells can sense signals for the plant and move towards their host. This work shows the abundance of each type of receptor and other components within the cell. It also examines the function of a previously unknown protein, CheT, within the chemotaxis system.

Chronic myeloid leukemia (CML) is characterized by the Philadelphia chromosome, a minute chromosome that leads to the creation of the fusion gene BCR/ABL and the transcription of the fusion protein BCR/ABL in transformed cells. The constitutively active tyrosine kinase BCR/ABL confers enhanced proliferation and survival on leukemic cells. CML has in only a few decades gone from being a disease with very bad prognosis to being a disease that can be effectively treated with oral tyrosine kinase inhibitors (TKIs). TKIs are drugs inhibiting BCR/ABL as well as other tyrosine kinases. In this thesis, the focus has been on the immune system of CML patients, on immune escape mechanisms present in untreated patients and on how these are affected by TKI therapy. We have found that newly diagnosed, untreated CML patients exert different kinds of immune escape mechanisms. Patients belonging to the Sokal high-risk group had higher levels of myeloid-derived suppressor cells (MDSCs) as well as high levels of the programmed death receptor 1 (PD-1)-expressing cytotoxic T cells compared to control subjects. Moreover, CML patients had higher levels of myeloid cells expressing the ligand for PD-1, PD-L1. CML patients as well as patients with B cell malignacies had high levels of soluble CD25 in blood plasma. In B cell malignacies, sCD25 was found to be released from T regulatory cells (Tregs). Treatment with the TKIs imatinib or dasatinib decreased the levels of MDSCs in peripheral blood. Tregs on the other hand increased during TKI therapy. The immunostimulatory molecule CD40 as well as NK cells increased during therapy, indicating an immunostimulatory effect of TKIs. When evaluating immune responses, multiplex techniques for quantification of proteins such as cytokines and chemokines are becoming increasingly popular. With these techniques a lot of information can be gained from a small sample volume and complex networks can be more easily studied than when using for example the singleplex ELISA. When comparing different multiplex platforms we found that the absolute protein concentration measured by one platform rarely correlated with the absolute concentration measured by another platform. However, relative quantification was better correlated.

The emergence of tandem mass spectrometry (MS/MS) technology has significantly accelerated protein identification and quantification in proteomics. It enables high-throughput analysis of proteins and their quantities in a complex protein mixture. A mass spectrometer can easily and rapidly generate large volumes of mass spectral data for a biological sample. This bulk of data makes manual interpretation impossible and has also brought numerous challenges in automated data analysis. Algorithmic solutions have been proposed and provide indispensable analytical support in current proteomic experiments. However, new algorithms are still needed to either improve result accuracy or provide additional data analysis capabilities for both protein identification and quantification. Accurate identification of proteins in a sample is the preliminary requirement of a proteomic study. In many cases, a mass spectrum cannot provide complete information to identify the peptide without ambiguity because of the inefficiency of the peptide fragmentation technique and the prevalent existence of noise. We propose ADEPTS to this problem using the complementary information provided in different types of mass spectra. Meanwhile, the occurrence of posttranslational modifications (PTMs) on proteins is another major issue that prevents the interpretation of a large portion of spectra. Using current software tools, users have to specify possible PTMs in advance. However, the number of possible PTMs has to be limited since specifying more PTMs to the software leads to a longer running time and lower result accuracy. Thus, we develop DeNovoPTM and PeaksPTM to provide efficient and accurate solutions. Glycosylation is one of the most frequently observed PTMs in proteomics. It plays important roles in many disease processes and thus has attracted growing research interest. However, lack of algorithms that can identify intact glycopeptides has become the major obstacle that hinders glycoprotein studies. We propose a novel algorithm, GlycoMaster DB, to fulfil this urgent requirement. Additional research is presented on protein quantification, which studies the changes of protein quantity by comparing two or more mass spectral datasets. A crucial problem in the quantification is to correct the retention time distortions between different datasets. Heuristic solutions from previous research have been used in practice but none of them has yet claimed a clear optimization goal. To address this issue, we propose a combinatorial model and practical algorithms for this problem.

The emergence of tandem mass spectrometry (MS/MS) technology has significantly accelerated protein identification and quantification in proteomics. It enables high-throughput analysis of proteins and their quantities in a complex protein mixture. A mass spectrometer can easily and rapidly generate large volumes of mass spectral data for a biological sample. This bulk of data makes manual interpretation impossible and has also brought numerous challenges in automated data analysis. Algorithmic solutions have been proposed and provide indispensable analytical support in current proteomic experiments. However, new algorithms are still needed to either improve result accuracy or provide additional data analysis capabilities for both protein identification and quantification. Accurate identification of proteins in a sample is the preliminary requirement of a proteomic study. In many cases, a mass spectrum cannot provide complete information to identify the peptide without ambiguity because of the inefficiency of the peptide fragmentation technique and the prevalent existence of noise. We propose ADEPTS to this problem using the complementary information provided in different types of mass spectra. Meanwhile, the occurrence of posttranslational modifications (PTMs) on proteins is another major issue that prevents the interpretation of a large portion of spectra. Using current software tools, users have to specify possible PTMs in advance. However, the number of possible PTMs has to be limited since specifying more PTMs to the software leads to a longer running time and lower result accuracy. Thus, we develop DeNovoPTM and PeaksPTM to provide efficient and accurate solutions. Glycosylation is one of the most frequently observed PTMs in proteomics. It plays important roles in many disease processes and thus has attracted growing research interest. However, lack of algorithms that can identify intact glycopeptides has become the major obstacle that hinders glycoprotein studies. We propose a novel algorithm, GlycoMaster DB, to fulfil this urgent requirement. Additional research is presented on protein quantification, which studies the changes of protein quantity by comparing two or more mass spectral datasets. A crucial problem in the quantification is to correct the retention time distortions between different datasets. Heuristic solutions from previous research have been used in practice but none of them has yet claimed a clear optimization goal. To address this issue, we propose a combinatorial model and practical algorithms for this problem.

Abstract   Tripeptidyl-peptidase II (TPPII), is present in most eukaryotic cells. It cuts tripeptides from the N-terminus of peptides and is especially important for degrading peptides longer than 15 amino acids. TPPII also tailors long peptides into suitable substrates for the enzymes which transport and produce the peptides that MHC I present. Increased levels of TPPII have also been found in certain cancer cells, thus it is of interest to determine if TPPII could be used as a tumour marker. The aim of this study was to optimise and evaluate 3 different methods for quantifying TPPII. Western blot, enzyme-linked immunosorbent assay (ELISA) and fluorophore-linked immunosorbent assay (FLISA) protocols were optimised regarding incubation times and antibody dilutions. Sensitivity and linearity were the most important parameters when evaluating the results. The coefficient of determination of western blot was R2=0.98-1 within the range of 1.29-250ng TPPII/well and ELISA had a coefficient of determination of R2=0.96 within the range of 0.03-250ng TPPII/well. Presently western blot is the only one of these methods to yield reliable results with impure samples, but ELISA is superior regarding sensitivity and throughput. Thus further optimisation of ELISA is interesting to pursue.

The common concept in this thesis was to adapt and develop quantitative mass spectrometric assays focusing on reagents originating from the Human Protein Atlas project to quantify proteins in human cell lines, tissues and blood. The work is based around stable isotope labeled protein fragment standards that each represent a small part of a human protein-coding gene. This thesis shows how they can be used in various formats to describe the protein landscape and be used to standardize mass spectrometry experiments. The first part of the thesis describes the use of antibodies in combination with heavy stable isotope labeled antigens to establish a semi-automated protocol for protein quantification of complex samples with fast analysis time  (Paper~I). Paper II introduces a semi-automated cloning protocol that can be used to selectively clone variants of recombinant proteins, and highlights the automation process that is necessary for large-scale proteomics endeavors. This paper also describes the technology that was used to clone all protein standards that are used in all of the included papers.                       The second part of the thesis includes papers that focus on the generation and application of antibody-free targeted mass spectrometry methods. Here, absolute protein copy numbers were determined across human cell lines and tissues (Paper III) and the protein data was correlated against transcriptomics data. Proteins were quantified to validate antibodies in a novel method that evaluates antibodies based on differential protein expression across multiple cell lines (Paper IV). Finally, a large-scale study was performed to generate targeted proteomics assays (Paper V) based on protein fragments. Here, assay coordinates were mapped for more than 10,000 human protein-coding genes and a subset of peptides was thereafter used to determine absolute protein levels of 49 proteins in human serum.                       In conclusion, this thesis describes the development of methods for protein quantification by targeted mass spectrometry and the use of recombinant protein fragment standards as the common denominator. / <p>QC 20161013</p>

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Gyros Protein Technologies develops instruments for automated immunoassays. Fluorescent antibodies are added to samples and excited with a laser. This results in a 16-bit image where the intensity is correlated to concentration of bound antibody. Artefacts may appear on the images due to dust, fibers or other problems, which affect the quantification. This project seeks to automatically detect such artifacts by classifying the images as good or bad using Deep Convolutional Neural Networks (DCNNs). To augment the dataset a simulation approach is used and a simulation program is developed that generates images based on developed simulation models. Several classification models are tested as well as different techniques used for training. The highest performing classifier is a VGG16 DCNN, pre-trained on simulated images, which reaches 94.8% accuracy. There are many sub-classes in the bad class, and many of these are very underrepresented in both the training and test datasets. This means that not much can be said of the classification power of these sub-classes. The conclusion is therefore that until more of this rare data can be collected, focus should lie on classifying the other more common examples. Using the approaches from this project, we believe this could result in a high performing product.

No / Cytochrome P450 (CYP) is one of the most important drug-metabolizing enzyme families, which participates in the biotransformation of many endogenous and exogenous compounds. Quantitative analysis of CYP expression levels is important when studying the efficacy of new drug molecules and assessing drug-drug interactions in drug development. At present, chemical probe-based assay is the most widely used approach for the evaluation of CYP activity although there are cross-reactions between the isoforms with high sequence homologies. Therefore, quantification of each isozyme is highly desired in regard to meeting the ever-increasing requirements for carrying out pharmacokinetics and personalized medicine in the academic, pharmaceutical, and clinical setting. Herein, an absolute quantification method was employed for the analysis of the seven isoforms CYP1A2, 2B6, 3A4, 3A5, 2C9, 2C19, and 2E1 using a proteome-derived approach in combination with stable isotope dilution assay. The average absolute amount measured from twelve human liver microsomes samples were 39.3, 4.3, 54.0, 4.6, 10.3, 3.0, and 9.3 (pmol/mg protein) for 1A2, 2B6, 3A4, 3A5, 2C9, 2C19, and 2E1, respectively. Importantly, the expression level of CYP3A4 showed high correlation (r = 0.943, p < 0.0001) with the functional activity, which was measured using bufalin-a highly selective chemical probe we have developed. The combination of MRM identification and analysis of the functional activity, as in the case of CYP3A4, provides a protocol which can be extended to other functional enzyme studies with wide application in pharmaceutical research.