A Grave as Proscenium in the Poetry of Emily Dickinson

A monograph presented to the faculty of the Graduate School at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Arts by Lois T. Ficarro in June of 1973

Identification of the Plasticity-Relevant Fucose-α(1−2)-Galactose Proteome from the Mouse Olfactory Bulb

Fucose-α(1−2)-galactose [Fucα(1−2)Gal] sugars have been implicated in the molecular mechanisms that underlie neuronal development, learning, and memory. However, an understanding of their precise roles has been hampered by a lack of information regarding Fucα(1−2)Gal glycoproteins. Here, we report the first proteomic studies of this plasticity-relevant epitope. We identify five classes of putative Fucα(1−2)Gal glycoproteins: cell adhesion molecules, ion channels and solute carriers/transporters, ATP-binding proteins, synaptic vesicle-associated proteins, and mitochondrial proteins. In addition, we show that Fucα(1−2)Gal glycoproteins are enriched in the developing mouse olfactory bulb (OB) and exhibit a distinct spatiotemporal expression that is consistent with the presence of a “glycocode” to help direct olfactory sensory neuron (OSN) axonal pathfinding. We find that expression of Fucα(1−2)Gal sugars in the OB is regulated by the α(1−2)fucosyltransferase FUT1. FUT1-deficient mice exhibit developmental defects, including fewer and smaller glomeruli and a thinner olfactory nerve layer, suggesting that fucosylation contributes to OB development. Our findings significantly expand the number of Fucα(1−2)Gal glycoproteins and provide new insights into the molecular mechanisms by which fucosyl sugars contribute to neuronal processes

The Relationship Between Attitudes Towards Exercise and Endorphin Release on Cognitive Performance Following Treadmill Running

Exercise is cost-effective and reliably suggested to rescue cognitive decline in aging populations (Gomez-Pinilla & Hillman, 2013; Karssemeijer et al., 2017). In addition, exercise enhances cognitive functioning across the lifespan (Ellemberg & St. Louis-Deschenes, 2010; Jentsch & Wolf, 2020; Roig et al., 2013), however the specific mechanisms of exercise that enhance cognition are still unclear. Because exercise is linked with the brain’s opioid system (Farrell et al., 1982), the purpose of this project is to determine how the opioid system is activated by exercise to affect cognitive functioning. Additionally, we know that acute exercise enhances cognitive function and releases beta-endorphins, but we do not fully understand the effect that endorphin release after running has on cognitive function, nor what mediates endorphin release on an individual level. Thus, a major emphasis we are also investigating is attitudes towards running. We analyzed a university population (n=51) for cognitive performance and beta-endorphin concentration before and after running on a treadmill. To analyze cognitive performance, we utilized a Trail Making Test to measure executive function and a 2-back Task to measure working memory (Kirchner, 1958; Reitan, 1956). A short survey was used to assess participant’s attitudes towards running and running frequency (Ogden et al., 1997). Treadmill running was enacted to maintain a runner’s ideal running heart rate according to their age. Saliva taken before and after running was analyzed by Peninsula Labs immunoassay kit. To analyze the relationship between working memory, executive function, and endorphin release, a correlation was run. A correlation was also run for relationship between running attitudes, running frequency, and endorphin release. We expect there to be a negative correlation between working memory, executive function, and endorphin release as well as between running attitudes, running frequency, and endorphin release

Multiplierz: An Extensible API Based Desktop Environment for Proteomics Data Analysis

BACKGROUND. Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge. RESULTS. We describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines. CONCLUSION. Collectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research.Dana-Farber Cancer Institute; National Human Genome Research Institute (P50HG004233); National Science Foundation Integrative Graduate Education and Research Traineeship grant (DGE-0654108

Parallel identification of O-GlcNAc-modified proteins from cell lysates

We report a new strategy for the parallel identification of O-GlcNAc-glycosylated proteins from cell lysates. The approach permits specific proteins of interest to be rapidly interrogated for the modification in any tissue or cell type and can be extended to peptides to facilitate the mapping of glycosylation sites. As an illustration of the approach, we identified four new O-GlcNAc-glycosylated proteins of low cellular abundance (c-Fos, c-Jun, ATF-1, and CBP) and two short regions of glycosylation in the enzyme O-GlcNAc transferase (OGT). The ability to target specific proteins across various tissue or cell types complements emerging proteomic technologies and should advance our understanding of this important posttranslational modification

Genome-scale Proteome Quantification by DEEP SEQ Mass Spectrometry

Advances in chemistry and massively parallel detection underlie DNA sequencing platforms that are poised for application in personalized medicine. In stark contrast, systematic generation of protein-level data lags well-behind genomics in virtually every aspect: depth of coverage, throughput, ease of sample preparation, and experimental time. Here, to bridge this gap, we develop an approach based on simple detergent lysis and single-enzyme digest, extreme, orthogonal separation of peptides, and true nanoflow LC-MS/MS that provides high peak capacity and ionization efficiency. This automated, deep efficient peptide sequencing and quantification (DEEP SEQ) mass spectrometry platform provides genome-scale proteome coverage equivalent to RNA-seq ribosomal profiling and accurate quantification for multiplexed isotope labels. In a model of the embryonic to epiblast transition in murine stem cells, we unambiguously quantify 11,352 gene products that span 70% of Swiss-Prot and capture protein regulation across the full detectable range of high-throughput gene expression and protein translation

multiplierz: An Extensible API Based Desktop Environment for Proteomics Data Analysis

Background: Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge. Results: We describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines. Conclusion: Collectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research

Large scale localization of protein phosphorylation by use of electron capture dissociation mass spectrometry.

We used on-line electron capture dissociation (ECD) for the large scale identification and localization of sites of phosphorylation. Each FT-ICR ECD event was paired with a linear ion trap collision-induced dissociation (CID) event, allowing a direct comparison of the relative merits of ECD and CID for phosphopeptide identification and site localization. Linear ion trap CID was shown to be most efficient for phosphopeptide identification, whereas FT-ICR ECD was superior for localization of sites of phosphorylation. The combination of confident CID and ECD identification and confident CID and ECD localization is particularly valuable in cases where a phosphopeptide is identified just once within a phosphoproteomics experiment

Determination of pyridoxal-5′-phosphate (PLP)-bonding sites in proteins: a peptide mass fingerprinting approach based on diagnostic tandem mass spectral features of PLP-modified peptides

Peptides modified by pyridoxal-5′-phosphate (PLP), linked to a lysine residue via reductive amination, exhibit distinct spectral characteristics in the collision-induced dissociation (CID) tandem mass (MS/MS) spectra that are described here. The MS/MS spectra typically display two dominant peaks whose m/z values correspond to neutral losses of [H 3 PO 4 ] (−98 Da) and the PLP moiety as [C 8 H 10 NO 5 P] (−231 Da) from the precursor peptide ion, respectively. Few other peaks are observed. Recognition of this distinct fragmentation behavior is imperative since determining sequences and sites of modifications relies on the formation of amide backbone cleavage products for subsequent interpretation via proteome database searching. Additionally, PLP-modified peptides exhibit suppressed precursor ionization efficiency which diminishes their detection in complex mixtures. Presented here is a protocol which describes an enrichment strategy for PLP-modified peptides combined with neutral loss screening and peptide mass fingerprinting to map the PLP-bonding site in a known PLP-dependent protein. This approach represents an efficient alternative to site-directed mutagenesis which has been the traditional method used for PLP-bonding site localization in proteins. Copyright © 2009 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64342/1/4270_ftp.pd