Proteolytic fragments of laminin promote excitotoxic neurodegeneration by up-regulation of the KA1 subunit of the kainate receptor

Degradation of the extracellular matrix (ECM) protein laminin contributes to excitotoxic cell death in the hippocampus, but the mechanism of this effect is unknown. To study this process, we disrupted laminin γ1 (lamγ1) expression in the hippocampus. Lamγ1 knockout (KO) and control mice had similar basal expression of kainate (KA) receptors, but the lamγ1 KO mice were resistant to KA-induced neuronal death. After KA injection, KA1 subunit levels increased in control mice but were unchanged in lamγ1 KO mice. KA1 levels in tissue plasminogen activator (tPA)–KO mice were also unchanged after KA, indicating that both tPA and laminin were necessary for KA1 up-regulation after KA injection. Infusion of plasmin-digested laminin-1 into the hippocampus of lamγ1 or tPA KO mice restored KA1 up-regulation and KA-induced neuronal degeneration. Interfering with KA1 function with a specific anti-KA1 antibody protected against KA-induced neuronal death both in vitro and in vivo. These results demonstrate a novel pathway for neurodegeneration involving proteolysis of the ECM and KA1 KA receptor subunit up-regulation

Development of analytical workflows and bioinformatic programs for mass spectrometry-based metabolomics

Quantitative determination of metabolite concentrations in biological samples is fundamental to biological and clinical research. Metabolomics analyzes the entire set of metabolites in a given biological system. It is an emerging technology in the post-genomic era to interrogate cellular biochemistry, perform diagnostic testing, stratify patient populations, and characterize biochemical mechanisms of disease. Recent successes in metabolomics demonstrate the central role of mass spectrometry (MS) in small molecule quantification, owing to its high sensitivity, high throughput, and broad metabolic coverage. Even though diverse MS instruments have been developed for metabolite quantification, it is still challenging to quantify the entire metabolome accurately and precisely. Besides MS hardware advances, quantitative metabolomics also requires extensive efforts in other analytical and bioinformatic methodology development. For a given MS platform, analytical method development focuses on laboratory practice, including sample handling, metabolome extraction, and data acquisition. In comparison, bioinformatic method development emphasizes computational data processing, such as data calibration, data curation, and statistical analysis. The subsequent chapters detail the development of analytical and bioinformatic solutions for quantitative metabolomics from improving metabolic coverage, analytical accuracy, analytical precision, and statistical analysis. Lastly, this thesis describes a metabolomics study of mouse brain regional differences in metabolism between males and females. Collectively my studies of quantitative metabolomics improve quantitative performance, deepen our knowledge of the MS-based quantification process, and facilitate the generation of confident biological conclusions.Science, Faculty ofChemistry, Department ofGraduat

Numerical model of A.C. glow discharge plasma anemometer via the coupling of gas flow and plasma model

A new approach to build the numerical modeling of AC (alternating current) plasma anemometer is proposed. Firstly, the plasma model and gas flow model utilized in the proposed method are introduced. The plasma model (xpdp2) is built by PIC/MCC modeling method, while gas flow field model is the fluid model. By combining the flow field model and plasma model, the proposed anemometer model could be obtained. Then the effects of flow velocity on the ion density distribution, electron density distribution and electric potential distribution are studied from micro perspective, and the results show that charged particles move towards the direction of flow velocity. Another facts can also be observed, the movement of electron is not obvious, and flow velocity has no effect on the electronic potential. Finally, the effects of supply voltage, discharge frequency and electrode spacing on the discharge characteristics are investigated from macro perspective, and the results show that there is a nearly linear relationship between flow velocity and gap voltage, which indicate that the plasma anemometer could be applied for flow velocity measurement. The simulation result shows that linear relationships are pretty good when the frequencies are 2 MHz and 3.65 MHz. In addition, the result also shows that, within our chosen distance, small spacing is more suitable for high frequency plasma anemometer

Research on the Plasma Anemometer Based on AC Glow Discharge

A new plasma anemometer based on AC glow discharge is designed in this article. Firstly, theoretical analysis of plasma anemometer working principle is introduced to prove the feasibility of the experimental measurement method. Then the experiments are carried out to study the effects of different parameters on the static discharge characteristics of the plasma anemometer system, by which the system optimization methods are obtained. Finally, several groups of appropriate parameters are selected to build the plasma anemometer system based on resistance capacitance coupling negative feedback AC glow discharge, and different airflow speeds are applied to obtain the achievable velocity measurement range. The results show that there is a linear relationship between airflow velocity and discharge current in an allowable error range, which can be applied for airflow velocity measurement. Negative feedback coupling module, which is composed of the coupling resistance and the coupling capacitance, has good effects on improving the system stability. The measurement range of the airflow velocity is significantly increased when the electrode gap is 3 mm, coupling resistance is 470 Ω, and coupling capacitance is 220 pF

Quantitative challenges and their bioinformatic solutions in mass spectrometry-based metabolomics

Improving the quantitative performance of mass spectrometry (MS)-based metabolomics is the key to its successful application in a broad range of research questions. Like other analytical pipelines, there are quantitative challenges in metabolomics. In particular, due to the large amount of data generated from MS, metabolomics data present unique quantitative challenges that conventional wet-lab approaches cannot address. Complementary bioinformatic methods exhibit unique advantages in tackling these problems. However, analytical chemists often underestimate the importance of bioinformatic solutions in the era of omics. This review summarizes the critical quantitative challenges in MS-based metabolomics. It highlights the existing bioinformatic solutions and discusses ongoing issues as future directions for method development. A specific focus is given to liquid chromatography-mass spectrometry (LC-MS)-based metabolomics because of its wide usage. Through this review, we hope to encourage awareness of the existing quantitative biases and their bioinformatic solutions. We also hope to motivate the development of bioinformatic methods for accurate, precise, and robust quantitative metabolomics

An important role for triglyceride in regulating spermatogenesis

Drosophila is a powerful model to study how lipids affect spermatogenesis. Yet, the contribution of neutral lipids, a major lipid group which resides in organelles called lipid droplets (LD), to sperm development is largely unknown. Emerging evidence suggests LD are present in the testis and that loss of neutral lipid- and LD-associated genes causes subfertility; however, key regulators of testis neutral lipids and LD remain unclear. Here, we show LD are present in early-stage somatic and germline cells within the Drosophila testis. We identified a role for triglyceride lipase brummer (bmm) in regulating testis LD, and found that whole-body loss of bmm leads to defects in sperm development. Importantly, these represent cell-autonomous roles for bmm in regulating testis LD and spermatogenesis. Because lipidomic analysis of bmm mutants revealed excess triglyceride accumulation, and spermatogenic defects in bmm mutants were rescued by genetically blocking triglyceride synthesis, our data suggest that bmm-mediated regulation of triglyceride influences sperm development. This identifies triglyceride as an important neutral lipid that contributes to Drosophila sperm development, and reveals a key role for bmm in regulating testis triglyceride levels during spermatogenesis

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(−) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.Non UBCChemistry, Department ofReviewedFacult

Experimental study on the effects of light intensity on energy conversion efficiency of photo-thermo chemical synergetic catalytic water splitting

Hydrogen production from water using a catalyst and solar energy was an ideal future fuel source. In this study, an elaborate experimental test rig of hydrogen production from solar water splitting was designed and established with self- controlled temperature system. The effects of light intensity on the reaction rate of hydrogen production from solar water splitting were experimentally investigated with the consideration of optical losses, reaction temperature, and photocatalysts powder cluster. Besides, a revised expression of full-spectrum solar-to-hydrogen energy conversion efficiency with the consideration of optical losses was also put forward, which can be more accurate to evaluate the full-spectrum solar-to-hydrogen energy of photo-catalysts powders. The results indicated that optical losses of solar water splitting reactor increased with the increase of the incoming light intensity, and the hydrogen production rate increased linearly with the increase of effective light intensity even at higher light intensity region when the optical losses of solar water splitting reactor were considered