CHEM 109: General Chemistry I—A Peer Review of Teaching Project Benchmark Portfolio

This portfolio has been developed for CHEM 109: General Chemistry I. The course is the first of a freshman level two semester sequence in General Chemistry taken by students with majors in a wide variety of technical and scientific disciplines. CHEM 109 is a high enrollment class, with class sizes for individual sections nearing 200 students. The development of this portfolio was conducted with the following objectives: 1. Clearly identify, justify, and codify the major learning objectives for this course, 2. Describe and rationalize the course structure and learning assessment methods, 3. Analyze and reflect on student achievement in the context of the objectives and assessments. Reflections on the portfolio development outcomes and potential changes for future iterations of the course are also presented

A Comparison of Energy-Resolved Vibrational Activation/Dissociation Characteristics of Protonated and Sodiated High Mannose N-Glycopeptides

Fragmentation of glycopeptides in tandem mass spectrometry (MS/MS) plays a pivotal role in site-specific protein glycosylation profiling by allowing specific oligosaccharide compositions and connectivities to be associated with specific loci on the corresponding protein. Although MS/MS analysis of glycopeptides has been successfully performed using a number of distinction dissociation methods, relatively little is known regarding the fragmentation characteristics of glycopeptide ions with various charge carriers. In this study, energy-resolved vibrational activation/ dissociation was examined via collision-induced dissociation for a group of related high mannose tryptic glycopeptides as their doubly protonated, doubly sodiated, and hybrid protonated sodium adduct ions. The doubly protonated glycopeptide ions with various compositions were found to undergo fragmentation over a relatively low but wide range of collision energies compared with the doubly sodiated and hybrid charged ions, and were found to yield both glycan and peptide fragmentation depending on the applied collision energy. By contrast, the various doubly sodiated glycopeptides were found to dissociate over a significantly higher but narrow range of collision energies, and exhibited only glycan cleavages. Interestingly, the hybrid protonated sodium adduct ions were consistently the most stable of the precursor ions studied, and provided fragmentation information spanning both the glycan and the peptide moieties. Taken together, these findings illustrate the influence of charge carrier over the energyresolved vibrational activation/dissociation characteristics of glycopeptides, and serve to suggest potential strategies that exploit the analytically useful features uniquely afforded by specific charge carriers or combinations thereof

Optimizing Sequence Coverage for a Moderate Mass Protein in Nano-Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry

Sample pretreatment was optimized to obtain high sequence coverage for human serum albumin (HSA, 66.5 kDa) when using nano-electrospray ionization quadrupole time-of-flight mass spectrometry (nESI-Q-TOF-MS). Use of the final method with trypsin, Lys-C and Glu-C digests gave a combined coverage of 98.8%. The addition of peptide fractionation resulted in 99.7% coverage. These results were comparable to those obtained previously with matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The sample pretreatment/nESI-Q-TOF-MS method was also used with collision-induced dissociation to analyze HSA digests and to identify peptides that could be employed as internal mass calibrants in future studies of modifications to HSA

Optimizing Sequence Coverage for a Moderate Mass Protein in Nano-Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry

Sample pretreatment was optimized to obtain high sequence coverage for human serum albumin (HSA, 66.5 kDa) when using nano-electrospray ionization quadrupole time-of-flight mass spectrometry (nESI-Q-TOF-MS). Use of the final method with trypsin, Lys-C and Glu-C digests gave a combined coverage of 98.8%. The addition of peptide fractionation resulted in 99.7% coverage. These results were comparable to those obtained previously with matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The sample pretreatment/nESI-Q-TOF-MS method was also used with collision-induced dissociation to analyze HSA digests and to identify peptides that could be employed as internal mass calibrants in future studies of modifications to HSA

Combining DI-ESI–MS and NMR datasets for metabolic profiling

Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death

Combining DI-ESI–MS and NMR datasets for metabolic profiling

Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death

The potential of urinary metabolites for diagnosing multiple sclerosis

A definitive diagnostic test for multiple sclerosis (MS) does not exist; instead physicians use a combination of medical history, magnetic resonance imaging, and cerebrospinal fluid analysis (CSF). Significant effort has been employed to identify biomarkers from CSF to facilitate MS diagnosis; however none of the proposed biomarkers have been successful to date. Urine is a proven source of metabolite biomarkers and has the potential to be a rapid, non-invasive, inexpensive, and efficient diagnostic tool for various human diseases. Nevertheless, urinary metabolites have not been extensively explored as a source of biomarkers for MS. Instead, we demonstrate that urinary metabolites have significant promise for monitoring disease-progression, and response to treatment in MS patients. NMR analysis of urine permitted the identification of metabolites that differentiate experimental autoimmune encephalomyelitis (EAE)-mice (prototypic disease model for MS) from healthy and MS drug-treated EAE mice

The Cyst Nematode SPRYSEC Protein RBP-1 Elicits Gpa2- and RanGAP2-Dependent Plant Cell Death

Plant NB-LRR proteins confer robust protection against microbes and metazoan parasites by recognizing pathogen-derived avirulence (Avr) proteins that are delivered to the host cytoplasm. Microbial Avr proteins usually function as virulence factors in compatible interactions; however, little is known about the types of metazoan proteins recognized by NB-LRR proteins and their relationship with virulence. In this report, we demonstrate that the secreted protein RBP-1 from the potato cyst nematode Globodera pallida elicits defense responses, including cell death typical of a hypersensitive response (HR), through the NB-LRR protein Gpa2. Gp-Rbp-1 variants from G. pallida populations both virulent and avirulent to Gpa2 demonstrated a high degree of polymorphism, with positive selection detected at numerous sites. All Gp-RBP-1 protein variants from an avirulent population were recognized by Gpa2, whereas virulent populations possessed Gp-RBP-1 protein variants both recognized and non-recognized by Gpa2. Recognition of Gp-RBP-1 by Gpa2 correlated to a single amino acid polymorphism at position 187 in the Gp-RBP-1 SPRY domain. Gp-RBP-1 expressed from Potato virus X elicited Gpa2-mediated defenses that required Ran GTPase-activating protein 2 (RanGAP2), a protein known to interact with the Gpa2 N terminus. Tethering RanGAP2 and Gp-RBP-1 variants via fusion proteins resulted in an enhancement of Gpa2-mediated responses. However, activation of Gpa2 was still dependent on the recognition specificity conferred by amino acid 187 and the Gpa2 LRR domain. These results suggest a two-tiered process wherein RanGAP2 mediates an initial interaction with pathogen-delivered Gp-RBP-1 proteins but where the Gpa2 LRR determines which of these interactions will be productive

The potential of urinary metabolites for diagnosing multiple sclerosis

A definitive diagnostic test for multiple sclerosis (MS) does not exist; instead physicians use a combination of medical history, magnetic resonance imaging, and cerebrospinal fluid analysis (CSF). Significant effort has been employed to identify biomarkers from CSF to facilitate MS diagnosis; however none of the proposed biomarkers have been successful to date. Urine is a proven source of metabolite biomarkers and has the potential to be a rapid, non-invasive, inexpensive, and efficient diagnostic tool for various human diseases. Nevertheless, urinary metabolites have not been extensively explored as a source of biomarkers for MS. Instead, we demonstrate that urinary metabolites have significant promise for monitoring disease-progression, and response to treatment in MS patients. NMR analysis of urine permitted the identification of metabolites that differentiate experimental autoimmune encephalomyelitis (EAE)-mice (prototypic disease model for MS) from healthy and MS drug-treated EAE mice

Cognitive Control Reflects Context Monitoring, Not Motoric Stopping, in Response Inhibition

The inhibition of unwanted behaviors is considered an effortful and controlled ability. However, inhibition also requires the detection of contexts indicating that old behaviors may be inappropriate – in other words, inhibition requires the ability to monitor context in the service of goals, which we refer to as context-monitoring. Using behavioral, neuroimaging, electrophysiological and computational approaches, we tested whether motoric stopping per se is the cognitively-controlled process supporting response inhibition, or whether context-monitoring may fill this role. Our results demonstrate that inhibition does not require control mechanisms beyond those involved in context-monitoring, and that such control mechanisms are the same regardless of stopping demands. These results challenge dominant accounts of inhibitory control, which posit that motoric stopping is the cognitively-controlled process of response inhibition, and clarify emerging debates on the frontal substrates of response inhibition by replacing the centrality of controlled mechanisms for motoric stopping with context-monitoring