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Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.
ContributorsRai, Samita (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Borges, Chad (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer remains one of the leading killers throughout the world. Death and disability due to lung cancer in particular accounts for one of the largest global economic burdens a disease presents. The burden on third-world countries is especially large due to the unusually large financial stress that comes from late tumor detection and expensive treatment options. Early detection using inexpensive techniques may relieve much of the burden throughout the world, not just in more developed countries. I examined the immune responses of lung cancer patients using immunosignatures – patterns of reactivity between host serum antibodies and random peptides. Immunosignatures reveal disease-specific patterns that are very reproducible. Immunosignaturing is a chip-based method that has the ability to display the antibody diversity from individual sera sample with low cost. Immunosignaturing is a medical diagnostic test that has many applications in current medical research and in diagnosis. From a previous clinical study, patients diagnosed for lung cancer were tested for their immunosignature vs. healthy non-cancer volunteers. The pattern of reactivity against the random peptides (the ‘immunosignature’) revealed common signals in cancer patients, absent from healthy controls. My study involved the search for common amino acid motifs in the cancer-specific peptides. My search through the hundreds of ‘hits’ revealed certain motifs that were repeated more times than expected by random chance. The amino acids that were the most conserved in each set include tryptophan, aspartic acid, glutamic acid, proline, alanine, serine, and lysine. The most overall conserved amino acid observed between each set was D - aspartic acid. The motifs were short (no more than 5-6 amino acids in a row), but the total number of motifs I identified was large enough to assure significance. I utilized Excel to organize the large peptide sequence libraries, then CLUSTALW to cluster similar-sequence peptides, then GLAM2 to find common themes in groups of peptides. In so doing, I found sequences that were also present in translated cancer expression libraries (RNA) that matched my motifs, suggesting that immunosignatures can find cancer-specific antigens that can be both diagnostic and potentially therapeutic.
ContributorsShiehzadegan, Shima (Author) / Johnston, Stephen (Thesis director) / Stafford, Phillip (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The influenza virus, also known as "the flu", is an infectious disease that has constantly affected the health of humanity. There is currently no known cure for Influenza. The Center for Innovations in Medicine at the Biodesign Institute located on campus at Arizona State University has been developing synbodies as a possible Influenza therapeutic. Specifically, at CIM, we have attempted to design these initial synbodies to target the entire Influenza virus and preliminary data leads us to believe that these synbodies target Nucleoprotein (NP). Given that the synbody targets NP, the penetration of cells via synbody should also occur. Then by Western Blot analysis we evaluated for the diminution of NP level in treated cells versus untreated cells. The focus of my honors thesis is to explore how synthetic antibodies can potentially inhibit replication of the Influenza (H1N1) A/Puerto Rico/8/34 strain so that a therapeutic can be developed. A high affinity synbody for Influenza can be utilized to test for inhibition of Influenza as shown by preliminary data. The 5-5-3819 synthetic antibody's internalization in live cells was visualized with Madin-Darby Kidney Cells under a Confocal Microscope. Then by Western Blot analysis we evaluated for the diminution of NP level in treated cells versus untreated cells. Expression of NP over 8 hours time was analyzed via Western Blot Analysis, which showed NP accumulation was retarded in synbody treated cells. The data obtained from my honors thesis and preliminary data provided suggest that the synthetic antibody penetrates live cells and targets NP. The results of my thesis presents valuable information that can be utilized by other researchers so that future experiments can be performed, eventually leading to the creation of a more effective therapeutic for influenza.
ContributorsHayden, Joel James (Author) / Diehnelt, Chris (Thesis director) / Johnston, Stephen (Committee member) / Legutki, Bart (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2014-05
Description
Technologies capable of characterizing the full breadth of cellular systems need to be able to measure millions of proteins, isoforms, and complexes simultaneously. We describe an approach that fulfils this criterion: Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT). ADAPT employs an enriched library of single-stranded oligodeoxynucleotides (ssODNs) to profile complex biological samples, thus achieving an unprecedented coverage of system-wide, native biomolecules. We used ADAPT as a highly specific profiling tool that distinguishes women with or without breast cancer based on circulating exosomes in their blood. To develop ADAPT, we enriched a library of ~10[superscript 11] ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 10[superscript 6] enriched ssODNs were then profiled against plasma from independent groups of healthy and breast cancer-positive women. ssODN-mediated affinity purification and mass spectrometry identified low-abundance exosome-associated proteins and protein complexes, some with known significance in both normal homeostasis and disease. Sequencing of the recovered ssODNs provided quantitative measures that were used to build highly accurate multi-analyte signatures for patient classification. Probing plasma from 500 subjects with a smaller subset of 2000 resynthesized ssODNs stratified healthy, breast biopsy-negative, and -positive women. An AUC of 0.73 was obtained when comparing healthy donors with biopsy-positive patients.
ContributorsDomenyuk, Valeriy (Author) / Zhong, Zhenyu (Author) / Stark, Adam (Author) / Xiao, Nianqing (Author) / O'Neill, Heather A. (Author) / Wei, Xixi (Author) / Wang, Jie (Author) / Tinder, Teresa T. (Author) / Tonapi, Sonal (Author) / Duncan, Janet (Author) / Hornung, Tassilo (Author) / Hunter, Andrew (Author) / Miglarese, Mark R. (Author) / Schorr, Joachim (Author) / Halbert, David D. (Author) / Quackenbush, John (Author) / Poste, George (Author) / Berry, Donald A. (Author) / Mayer, Gunter (Author) / Famulok, Michael (Author) / Spetzler, David (Author) / Consortium for Biosocial Complex Systems (Contributor) / Complex Adaptive Systems Initiative (Contributor)
Created2017-02-20
Description
In this study, we demonstrate the effectiveness of a cancer type specific FrAmeShifT (FAST) vaccine. A murine breast cancer (mBC) FAST vaccine and a murine pancreatic cancer (mPC) FAST vaccine were tested in the 4T1 breast cancer syngeneic mouse model. The mBC FAST vaccine, both with and without check point inhibitors (CPI), significantly slowed tumor growth, reduced pulmonary metastasis and increased the cell-mediated immune response. In terms of tumor volumes, the mPC FAST vaccine was comparable to the untreated controls. However, a significant difference in tumor volume did emerge when the mPC vaccine was used with CPI. The collective data indicated that the immune checkpoint blockade therapy was only beneficial with suboptimal neoantigens. More importantly, the FAST vaccine, though requiring notably less resources, performed similarly to the personalized version of the frameshift breast cancer vaccine in the same mouse model. Furthermore, because the frameshift peptide (FSP) array provided a strong rationale for a focused vaccine, the FAST vaccine can theoretically be expanded and translated to any human cancer type. Overall, the FAST vaccine is a promising treatment that would provide the most benefit to patients while eliminating most of the challenges associated with current personal cancer vaccines.
ContributorsMurphy, Sierra Nicole (Author) / Johnston, Stephen (Thesis director) / Peterson, Milene (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The goal of this paper is to discuss the most efficient method to achieve early detection in lung cancers by reducing the occurrences of false-positive readings. Imaging techniques (computed tomography screenings) have greater impact than non-imaging techniques in early detection for lung cancer. On the other hand, positron emission tomography and non-imaging techniques, such as liquid biopsy, are better at distinguishing cancer stages. Therefore, these techniques are not suitable early detection methods for lung cancer. Based on literature reviews, the combination that is most capable of early lung cancer detection incorporate low-dose computed tomography screenings, thin-section computed tomography screenings, and computer-aided diagnosis. Low-dose computed tomography screenings has lower radiation-associated risks compared to the standard-dose computed tomography. This technique can be used as both at the first examination and the follow-up examinations. Thin-section computed tomography screenings can be used as a supplement to check if there is any nodules that have not yet been discovered. Computer-aided diagnosis is an add-on method to make sure the computed tomography screenings images are being correctly labeled. Identifying other contributing factors to the effectiveness of the early lung cancer detection, such as the amount of forced expiratory volume, forced vital capacity, and the presence of emphysema, could also decrease the percentage of false positive outcomes.
ContributorsChuang, Hao-Yun (Author) / Johnston, Stephen (Thesis director) / Peterson, Milene (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The objective of this thesis was to determine whether Zika Virus (ZIKV) can be effectively inactivated by Selective Photonic Disinfection (SEPHODIS) and determine whether key proteins involved in the infection process are preserved, making SEPHODIS a possible source for vaccine development. As of January 2018, there have been 3,720 confirmed cases of Congenital Zika Syndrome in infants, making a Zika Vaccine a high priority (Mitchell, 2018). SEPHODIS is a process that involves prolonged exposure of an object to a pulsing laser which can render it ineffective. Initially, ZIKV was subjected to laser inactivation for 6 hours, then a plaque assay was performed on both laser-treated and control samples. ZIKV was inactivated two-fold? after laser treatment, when compared with control, as indicated by the plaque assay results. Additionally, both samples were submitted to ELISA to evaluate antigenicity with a panel of monoclonal and human sera. As a second control, virus inactivated by formaldehyde (2%) was used. ELISA results showed that antigenicity of some proteins were preserved while others were probably disturbed. However, ELISA results show that ZIKV envelope protein (E-protein), the protein responsible for viral entry into cells, was effectively preserved after laser-treatment, implying that if laser parameters were tweaked to obtain more complete inactivation, then SEPHODIS may be an appropriate source for the development of a vaccine.
ContributorsViafora, Ataiyo Blue (Author) / Johnston, Stephen (Thesis director) / Tsen, Kong-Thon (Committee member) / School of Life Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
PD-L1 blockade has shown recent success in cancer therapy and cancer vaccine regimens. One approach for anti-PD-L1 antibodies has been their application as adjuvants for cancer vaccines. Given the disadvantages of such antibodies, including long half-life and adverse events related to their use, a novel strategy using synbodies in place of antibodies can be tested. Synbodies offer a variety of advantages, including shorter half-life, smaller size, and cheaper cost. Peptides that could bind PD-L1 were identified via peptide arrays and used to construct synbodies. These synbodies were tested with inhibition ELISA assays, SPR, and pull down assays. Additional flow cytometry analysis was done to determine the binding specificity of the synbodies to PD-L1 and the ability of those synbodies to inhibit the PD-L1/PD-1 interaction. Although analysis of permeabilized cells expressing PD-L1 indicated that the synbodies could successfully bind PD-L1, those results were not replicated in non-permeabilized cells. Further assays suggested that the binding of the synbodies was non-specific. Other tests were done to see if the synbodies could inhibit the PD-1/PD-L1 interaction. This assay did not yield any conclusive results and further experimentation is needed to determine the efficacy of the synbodies in inhibiting this interaction.
ContributorsMujahed, Tala (Author) / Johnston, Stephen (Thesis director) / Blattman, Joseph (Committee member) / Diehnelt, Chris (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12