Erika M. Zink

Activity-based protein profiling reveals mitochondrial oxidative enzyme impairment and restoration in diet-induced obese mice.

High-fat diet (HFD) induced obesity and concomitant development of insulin resistance (IR) and type 2 diabetes mellitus have been linked to mitochondrial dysfunction. However, it is not clear whether mitochondrial dysfunction is a direct effect of a HFD, or if mitochondrial function is reduced with increased HFD duration. We hypothesized that the function of mitochondrial oxidative and lipid metabolism functions in skeletal muscle mitochondria for HFD mice are similar, or elevated, relative to standard diet (SD) mice; thereby, IR is neither cause nor consequence of mitochondrial dysfunction. We applied a chemical probe approach to identify functionally reactive ATPases and nucleotide-binding proteins in mitochondria isolated from skeletal muscle of C57Bl/6J mice fed HFD or SD chow for 2-, 8-, or 16-weeks; feeding time points known to induce IR. A total of 293 probe-labeled proteins were identified by mass spectrometry-based proteomics, of which 54 differed in abundance between HFD and SD mice. We found proteins associated with the TCA cycle, oxidative phosphorylation (OXPHOS), and lipid metabolism were altered in function when comparing SD to HFD fed mice at 2-weeks, however by 16-weeks HFD mice had TCA cycle, β-oxidation, and respiratory chain function at levels similar to or higher than SD mice.

Signature-Discovery Approach for Sample Matching of a Nerve-Agent Precursor Using Liquid Chromatography−Mass Spectrometry, XCMS, and Chemometrics

This report demonstrates the use of bioinformatic and chemometric tools on liquid chromatography-mass spectrometry (LC-MS) data for the discovery of trace forensic signatures for sample matching of ten stocks of the nerve-agent precursor known as methylphosphonic dichloride (dichlor). XCMS, a software tool primarily used in bioinformatics, was used to comprehensively search and find candidate LC-MS peaks in a known set of dichlor samples. These candidate peaks were down selected to a group of 34 impurity peaks. Hierarchal cluster analysis and factor analysis demonstrated the potential of these 34 impurities peaks for matching samples based on their stock source. Only one pair of dichlor stocks was not differentiated from one another. An acceptable chemometric approach for sample matching was determined to be variance scaling and signal averaging of normalized duplicate impurity profiles prior to classification by K-nearest neighbors. Using this approach, a test set of seven dichlor samples were all correctly matched to their source stock. The sample preparation and LC-MS method permitted the detection of dichlor impurities quantitatively estimated to be in the parts-per-trillion (w/w). The detection of a common impurity in all dichlor stocks that were synthesized over a 14-year period and by different manufacturers was an unexpected discovery. Our described signature-discovery approach should be useful in the development of a forensic capability to assist investigations following chemical attacks.

Live cell chemical profiling of temporal redox dynamics in a photoautotrophic cyanobacterium

Protein reduction-oxidation (redox) modification is an important mechanism that allows microorganisms to sense environmental changes and initiate cellular responses. We have developed a quantitative chemical probe approach for live cell labeling and imaging of proteins that are sensitive to redox modifications. We utilize this in vivo strategy to identify 176 proteins undergoing ∼5-10-fold dynamic redox change in response to nutrient limitation and subsequent replenishment in the photoautotrophic cyanobacterium Synechococcus sp. PCC 7002. We detect redox changes in as little as 30 s after nutrient perturbation and oscillations in reduction and oxidation for 60 min following the perturbation. Many of the proteins undergoing dynamic redox transformations participate in the major components for the production (photosystems and electron transport chains) or consumption (Calvin-Benson cycle and protein synthesis) of reductant and/or energy in photosynthetic organisms. Thus, our in vivo approach reveals new redox-susceptible proteins and validates those previously identified in vitro.

Integrated Omics Analysis of Pathogenic Host Responses during Pandemic H1N1 Influenza Virus Infection: The Crucial Role of Lipid Metabolism

Pandemic influenza viruses modulate proinflammatory responses that can lead to immunopathogenesis. We present an extensive and systematic profiling of lipids, metabolites, and proteins in respiratory compartments of ferrets infected with either 1918 or 2009 human pandemic H1N1 influenza viruses. Integrative analysis of high-throughput omics data with virologic and histopathologic data uncovered relationships between host responses and phenotypic outcomes of viral infection. Proinflammatory lipid precursors in the trachea following 1918 infection correlated with severe tracheal lesions. Using an algorithm to infer cell quantity changes from gene expression data, we found enrichment of distinct T cell subpopulations in the trachea. There was also a predicted increase in inflammatory monocytes in the lung of 1918 virus-infected animals that was sustained throughout infection. This study presents a unique resource to the influenza research community and demonstrates the utility of an integrative systems approach for characterization of lipid metabolism alterations underlying respiratory responses to viruses.

Characterization of intact N- and O-linked glycopeptides using higher energy collisional dissociation.

Simultaneous elucidation of the glycan structure and the glycosylation site are needed to reveal the biological function of protein glycosylation. In this study, we employed a recent type of fragmentation termed higher energy collisional dissociation (HCD) to examine fragmentation patterns of intact glycopeptides generated from a mixture of standard glycosylated proteins. The normalized collisional energy (NCE) value for HCD was varied from 30 to 60% to evaluate the optimal conditions for the fragmentation of peptide backbones and glycoconjugates. Our results indicated that HCD with lower NCE values preferentially fragmented the sugar chains attached to the peptides to generate a ladder of neutral loss of monosaccharides, thereby enabling the putative glycan structure characterization. In addition, detection of the oxonium ions enabled unambiguous differentiation of glycopeptides from non-glycopeptides. In contrast, HCD with higher NCE values preferentially fragmented the peptide backbone and, thus, provided information needed for confident peptide identification. We evaluated the HCD approach with alternating NCE parameters for confident characterization of intact N- and O-linked glycopeptides in a single liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. In addition, we applied a novel data analysis pipeline, so-called GlycoFinder, to form a basis for automated data analysis. Overall, 38 unique intact glycopeptides corresponding to eight glycosylation sites (six N-linked and two O-linked sites) were confidently identified from a standard protein mixture. This approach provided concurrent characterization of both the peptide and the glycan, thereby enabling comprehensive structural characterization of glycoproteins in a single LC-MS/MS analysis.

Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples

Laser capture microdissection (LCM)-enabled region-specific tissue analyses are critical to better understand complex multicellular processes. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, impacting measurement robustness, quantification and throughput. Here, we coupled LCM with a proteomics workflow that provides fully automated analysis of proteomes from microdissected tissues. Benchmarking against the current state-of-the-art in ultrasensitive global proteomics (FASP workflow), our approach demonstrated significant improvements in quantification (~2-fold lower variance) and throughput (>5 times faster). Using our approach we for the first time characterized, to a depth of >3,400 proteins, the ontogeny of protein changes during normal lung development in microdissected alveolar tissue containing only 4,000 cells. Our analysis revealed seven defined modules of coordinated transcription factor-signaling molecule expression patterns, suggesting a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes.

Silymarin Suppresses Cellular Inflammation by Inducing Reparative Stress Signaling

Silymarin, a characterized extract of the seeds of milk thistle (Silybum marianum), suppresses cellular inflammation. To define how this occurs, transcriptional profiling, metabolomics, and signaling studies were performed in human liver and T cell lines. Cellular stress and metabolic pathways were modulated within 4 h of silymarin treatment: activation of Activating Transcription Factor 4 (ATF-4) and adenosine monophosphate protein kinase (AMPK) and inhibition of mammalian target of rapamycin (mTOR) signaling, the latter being associated with induction of DNA-damage-inducible transcript 4 (DDIT4). Metabolomics analyses revealed silymarin suppression of glycolytic, tricarboxylic acid (TCA) cycle, and amino acid metabolism. Anti-inflammatory effects arose with prolonged (i.e., 24 h) silymarin exposure, with suppression of multiple pro-inflammatory mRNAs and signaling pathways including nuclear factor kappa B (NF-κB) and forkhead box O (FOXO). Studies with murine knock out cells revealed that silymarin inhibition of both mTOR and NF-κB was partially AMPK dependent, whereas silymarin inhibition of mTOR required DDIT4. Other natural products induced similar stress responses, which correlated with their ability to suppress inflammation. Thus, natural products activate stress and repair responses that culminate in an anti-inflammatory cellular phenotype. Natural products like silymarin may be useful as tools to define how metabolic, stress, and repair pathways regulate cellular inflammation.

Lipidomics reveals dramatic lipid compositional changes in the maturing postnatal lung

Lung immaturity is a major cause of morbidity and mortality in premature infants. Understanding the molecular mechanisms driving normal lung development could provide insights on how to ameliorate disrupted development. While transcriptomic and proteomic analyses of normal lung development have been previously reported, characterization of changes in the lipidome is lacking. Lipids play significant roles in the lung, such as dipalmitoylphosphatidylcholine in pulmonary surfactant; however, many of the roles of specific lipid species in normal lung development, as well as in disease states, are not well defined. In this study, we used liquid chromatography-mass spectrometry (LC-MS/MS) to investigate the murine lipidome during normal postnatal lung development. Lipidomics analysis of lungs from post-natal day 7, day 14 and 6–8 week mice (adult) identified 924 unique lipids across 21 lipid subclasses, with dramatic alterations in the lipidome across developmental stages. Our data confirmed previously recognized aspects of post-natal lung development and revealed several insights, including in sphingolipid-mediated apoptosis, inflammation and energy storage/usage. Complementary proteomics, metabolomics and chemical imaging corroborated these observations. This multi-omic view provides a unique resource and deeper insight into normal pulmonary development.

Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome

Although the gut microbiome plays important roles in host physiology, health and disease1, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut. We used the genetically diverse Collaborative Cross mouse system2 to discover that early life history impacts the microbiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes in the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.

Characterization of protein redox dynamics induced during light-to-dark transitions and nutrient limitation in cyanobacteria

Protein redox chemistry constitutes a major void in knowledge pertaining to photoautotrophic system regulation and signaling processes. We have employed a chemical biology approach to analyze redox sensitive proteins in live Synechococcus sp. PCC 7002 cells in both light and dark periods, and to understand how cellular redox balance is disrupted during nutrient perturbation. The present work identified 300 putative redox-sensitive proteins that are involved in the generation of reductant, macromolecule synthesis, and carbon flux through central metabolic pathways, and may be involved in cell signaling and response mechanisms. Furthermore, our research suggests that dynamic redox changes in response to specific nutrient limitations, including carbon and nitrogen limitations, contribute to the regulatory changes driven by a shift from light to dark. Taken together, these results contribute to a high-level understanding of post-translational mechanisms regulating flux distributions and suggest potential metabolic engineering targets for redirecting carbon toward biofuel precursors.