Functionalized Ag nanoparticles with tunable optical properties for selective protein analysis

We present a preparation procedure for small sized biocompatibly coated Ag nanoparticles with tunable surface plasmon resonances. The conditions were optimised with respect to the resonance Raman signal enhancement of heme proteins and to the preservation of the native protein structure....

Biotechnologies for cancer diagnostics : cell sorting, protein analysis and imaging of cellular metabolism

This thesis presents the development of chip-based technology for informative in vitro cancer diagnostics. In the first part of this thesis, I will present my contribution in the development of a technology called “Nucleic Acid Cell Sorting (NACS)”, based on microarrays composed of nucleic acid encoded peptide major histocompatibility complexes (p/MHC), and the experimental and theoretical methods to detect and analyze secreted proteins from single or few cells.

Secondly, a novel portable platform for imaging of cellular metabolism with radio probes is presented. A microfluidic chip, so called “Radiopharmaceutical Imaging Chip” (RIMChip), combined with a beta-particle imaging camera, is developed to visualize the uptake of radio probes in a small number of cells. Due to its sophisticated design, RIMChip allows robust and user-friendly execution of sensitive and quantitative radio assays. The performance of this platform is validated with adherent and suspension cancer cell lines. This platform is then applied to study the metabolic response of cancer cells under the treatment of drugs. Both cases of mouse lymphoma and human glioblastoma cell lines, the metabolic responses to the drug exposures are observed within a short time (~ 1 hour), and are correlated with the arrest of cell-cycle, or with changes in receptor tyrosine kinase signaling.

The last parts of this thesis present summaries of ongoing projects: development of a new agent as an in vivo imaging probe for c-MET, and quantitative monitoring of glycolytic metabolism of primary glioblastoma cells. To develop a new agent for c-MET imaging, the one-bead-one-compound combinatorial library method is used, coupled with iterative screening. The performance of the agent is quantitatively validated with cell-based fluorescent assays. In the case of monitoring the metabolism of primary glioblastoma cell, by RIMChip, cells were sorting according to their expression levels of oncoprotein, or were treated with different kinds of drugs to study the metabolic heterogeneity of cancer cells or metabolic response of glioblastoma cells to drug treatments, respectively.

Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.

The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.

The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.

The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells (~300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.

Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL- 10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Compositional protein analysis of high density lipoproteins in hypercholesterolemia by shotgun LC-MS/MS and probabilistic peptide scoring

A protein of a biological sample is usually quantified by immunological techniques based on antibodies. Mass spectrometry offers alternative approaches that are not dependent on antibody affinity and avidity, protein isoforms, quaternary structures, or steric hindrance of antibody-antigen recognition in case of multiprotein complexes. One approach is the use of stable isotope-labeled internal standards; another is the direct exploitation of mass spectrometric signals recorded by LC-MS/MS analysis of protein digests. Here we assessed the peptide match score summation index based on probabilistic peptide scores calculated by the PHENYX protein identification engine for absolute protein quantification in accordance with the protein abundance index as proposed by Mann and co-workers (Rappsilber, J., Ryder, U., Lamond, A. I., and Mann, M. (2002) Large-scale proteomic analysis of the human spliceosome. Genome Res. 12, 1231-1245). Using synthetic protein mixtures, we demonstrated that this approach works well, although proteins can have different response factors. Applied to high density lipoproteins (HDLs), this new approach compared favorably to alternative protein quantitation methods like UV detection of protein peaks separated by capillary electrophoresis or quantitation of protein spots on SDS-PAGE. We compared the protein composition of a well defined HDL density class isolated from plasma of seven hypercholesterolemia subjects having low or high HDL cholesterol with HDL from nine normolipidemia subjects. The quantitative protein patterns distinguished individuals according to the corresponding concentration and distribution of cholesterol from serum lipid measurements of the same samples and revealed that hypercholesterolemia in unrelated individuals is the result of different deficiencies. The presented approach is complementary to HDL lipid analysis; does not rely on complicated sample treatment, e.g. chemical reactions, or antibodies; and can be used for projective clinical studies of larger patient groups.

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Analysis of acute-phase proteins, AHSG, C3, CLI, HP and SAA, reveals distinctive expression patterns associated with breast, colorectal and lung cancer

Early detection, clinical management and disease recurrence monitoring are critical areas in cancer treatment in which specific biomarker panels are likely to be very important in each of these key areas. We have previously demonstrated that levels of alpha-2-heremans-schmid-glycoprotein (AHSG), complement component C3 (C3), clusterin (CLI), haptoglobin (HP) and serum amyloid A (SAA) are significantly altered in serum from patients with squamous cell carcinoma of the lung. Here, we report the abundance levels for these proteins in serum samples from patients with advanced breast cancer, colorectal cancer (CRC) and lung cancer compared to healthy controls (age and gender matched) using commercially available enzyme-linked immunosorbent assay kits. Logistic regression (LR) models were fitted to the resulting data, and the classification ability of the proteins was evaluated using receiver-operating characteristic curve and leave-one-out cross-validation (LOOCV). The most accurate individual candidate biomarkers were C3 for breast cancer [area under the curve (AUC) = 0.89, LOOCV = 73%], CLI for CRC (AUC = 0.98, LOOCV = 90%), HP for small cell lung carcinoma (AUC = 0.97, LOOCV = 88%), C3 for lung adenocarcinoma (AUC = 0.94, LOOCV = 89%) and HP for squamous cell carcinoma of the lung (AUC = 0.94, LOOCV = 87%). The best dual combination of biomarkers using LR analysis were found to be AHSG + C3 (AUC = 0.91, LOOCV = 83%) for breast cancer, CLI + HP (AUC = 0.98, LOOCV = 92%) for CRC, C3 + SAA (AUC = 0.97, LOOCV = 91%) for small cell lung carcinoma and HP + SAA for both adenocarcinoma (AUC = 0.98, LOOCV = 96%) and squamous cell carcinoma of the lung (AUC = 0.98, LOOCV = 84%). The high AUC values reported here indicated that these candidate biomarkers have the potential to discriminate accurately between control and cancer groups both individually and in combination with other proteins. Copyright © 2011 UICC.

Homeostatic regulation of free retinol-binding protein and free thyroxine pools of plasma by their plasma carrier proteins in chicken

The mechanism underlying homeostatic regulation of the plasma levels of free retinol-binding protein and free thyroxine, the systemic distribution of which is of great importance, has been investigated. A simple method has been developed to determine the rate of dissociation of a ligand from the binding protein. Analysis of the dissociation process of retinol-binding protein from prealbumin-2 reveals that the free retinol-binding protein pool undergoes massive flux, and the prealbumin-2 participates in homeostatic regulation of the free retinol-binding protein pool. Studies on the dissociation process of thyroxine from its plasma carrier proteins show that the various plasma carrier proteins share two roles. Of the two types of protein, the thyroxine-binding globulin (the high affinity binding protein) contributes only 27% of the free thyroxine in a rapid transition process, despite its being the major binding protein. But prealbumin-2, which has lower affinity towards thyroxine, participates mainly in a rapid flux of the free thyroxine pool. Thus thyroxine-binding globulin acts predominantly as a plasma reservoir of thyroxine, and also probably in the �buffering� action on plasma free thyroxine level, in the long term, while prealbumin-2 participates mainly in the maintainance of constancy of free thyroxine levels even in the short term. The existence of these two types of binding protein facilitates compensation for the metabolic flux of the free ligand and maintenance of the thyroxine pool within a very narrow range.

Whole-body amino acid pattern of F-4 human growth hormone gene-transgenic red common carp (Cyprinus carpio) fed diets with different protein levels

F-4 generation of human growth hormone (hGH) gene-transgenic red common carp, and the non-transgenic controls were fed for 8 weeks on purified diets with 20%, 30% or 40% protein. Analysis of whole-body amino acids showed that the proportions of lysine, leucine, phenylalanine, valine and alanine, as percentages of body protein, increased significantly, while those of arginine, glutamic acid and tyrosine decreased, with increases in dietary protein level in at least one strain of fish. Proportions of the other amino acids were unaffected by the diets. The proportions of lysine and arginine were significantly higher, while those of leucine and alanine were lower in the transgenics than in the controls in at least one diet group. Proportions of the other amino acids were unaffected by strain. The results suggest that the whole-body amino acid profile of transgenic carp, when expressed as proportions of body protein, was in general, similar to that of the non-transgenic controls. (C) 2000 Elsevier Science B.V. All rights reserved.

Oxidised, glycated LDL selectively influences tissue inhibitor of metalloproteinase-3 gene expression and protein production in human retinal capillary pericytes

Aims/hypothesis: Matrix metalloproteinases (MMPs) and their natural inhibitors, tissue inhibitor of metalloproteinases (TIMPs), regulate important biological processes including the homeostasis of the extracellular matrix, proteolysis of cell surface proteins, proteinase zymogen activation, angiogenesis and inflammation. Studies have shown that their balance is altered in retinal microvascular tissues in diabetes. Since LDLs modified by oxidation/glycation are implicated in the pathogenesis of diabetic vascular complications, we examined the effects of modified LDL on the gene expression and protein production of MMPs and TIMPs in retinal pericytes. Methods: Quiescent human retinal pericytes were exposed to native LDL (N-LDL), glycated LDL (G-LDL) and heavily oxidised and glycated LDL (HOG-LDL) for 24 h. We studied the expression of the genes encoding MMPs and TIMPs mRNAs by analysis of microarray data and quantitative PCR, and protein levels by immunoblotting and ELISA. Results: Microarray analysis showed that MMP1, MMP2, MMP11, MMP14 and MMP25 and TIMP1, TIMP2, TIMP3 and TIMP4 were expressed in pericytes. Of these, only TIMP3 mRNA showed altered regulation, being expressed at significantly lower levels in response to HOG- vs N-LDL. Quantitative PCR and immunoblotting of cell/matrix proteins confirmed the reduction in TIMP3 mRNA and protein in response to HOG-LDL. In contrast to cellular TIMP3 protein, analysis of secreted TIMP1, TIMP2, MMP1 and collagenase activity indicated no changes in their production in response to modified LDL. Combined treatment with N- and HOG-LDL restored TIMP3 mRNA expression to a level comparable with that after N-LDL alone. Conclusions/interpretation: Among the genes encoding for MMPs and TIMPs expressed in retinal pericytes, TIMP3 is uniquely regulated by HOG-LDL. Reduced TIMP3 expression might contribute to microvascular abnormalities in diabetic retinopathy. © 2007 Springer-Verlag.

Leucogram and serum acute phase protein concentrations in queens submitted to conventional or videolaparoscopic ovariectomy

Trinta gatas, saudáveis, foram submetidas à ovariectomia pela técnica convencional e por videolaparoscopia. Amostras de sangue foram obtidas com o objetivo de verificar a intensidade da resposta inflamatória por meio da análise das concentrações de proteinas de fase aguda e contagem de leucócitos antes e até 144 horas após procedimento cirúrgico. As proteínas que apresentaram aumento significativo 24 horas após a cirurgia foram: ceruloplasmina, hemopexina, haptoglobina e α1-glicoproteína ácida, 69,8%, 103,5%, 117,3% e 199,0%, respectivamente, para ovariectomia convencional, e 22,3%, 46,1%, 79,8% e 74,6%, respectivamente, para ovariectomia por videolaparoscopia. A resposta inflamatória foi mais evidente nas gatas submetidas à ovariectomia convencional. Os resultados mostram aumento e diminuição na concentração de proteínas de fase aguda e na contagem de leucócitos, podendo ser utilizados na avaliação da resposta inflamatória induzida por procedimentos cirúrgicos.