Proteomic Assessment of C57BL/6 Hippocampi after Non-Selective Pharmacological Inhibition of Nitric Oxide Synthase Activity: Implications of Seizure-like Neuronal Hyperexcitability Followed by Tauopathy.

Nitric oxide (NO) is a small gaseous signaling molecule responsible for maintaining homeostasis in a myriad of tissues and molecular pathways in neurology and the cardiovasculature. In recent years, there has been increasing interest in the potential interaction between arterial stiffness (AS), an independent cardiovascular risk factor, and neurodegenerative syndromes given increasingly epidemiological study reports. For this reason, we previously investigated the mechanistic convergence between AS and neurodegeneration via the progressive non-selective inhibition of all nitric oxide synthase (NOS) isoforms with N(G)-nitro-L-arginine methyl ester (L-NAME) in C57BL/6 mice. Our previous results showed progressively increased AS in vivo and impaired visuospatial learning and memory in L-NAME-treated C57BL/6 mice. In the current study, we sought to further investigate the progressive molecular signatures in hippocampal tissue via LC-MS/MS proteomic analysis. Our data implicate mitochondrial dysfunction due to progressive L-NAME treatment. Two weeks of L-NAME treatment implicates altered G-protein-coupled-receptor signaling in the nerve synapse and associated presence of seizures and altered emotional behavior. Furthermore, molecular signatures implicate the cerebral presence of seizure-related hyperexcitability after short-term (8 weeks) treatment followed by ribosomal dysfunction and tauopathy after long-term (16 weeks) treatment.

QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments.

In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on mixed models can use large amounts of (computing) time. MS-based proteomics laboratories are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at qcquan.net ) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

In vitro assessment of hepatotoxicity by metabolomics: a review.

Omics technologies, and in particular metabolomics, have received an increasing attention during the assessment of hepatotoxicity in vitro. However, at present, a consensus on good metabolomics practices has yet to be reached. Therefore, in this review, a range of experimental approaches, applied methodologies, and data processing workflows are compared and critically evaluated. Experimental designs among the studies are similar, reporting the use of primary hepatocytes or hepatic cell lines as the most frequently used cell sources. Experiments are usually conducted in short time-frames (< 48 h) at sub-toxic dosages. Applied sample preparations are protein precipitation or Bligh-and-Dyer extraction. Most analytical platforms rely on chromatographic separations with mass spectrometric detection using high-resolution instruments. Untargeted metabolomics was typically used to allow the simultaneous detection of several classes of the metabolome, including endogenous metabolites that are not initially linked to toxicity. This non-biased detection platform is a valuable tool for generating hypothesis-based mechanistic research. The most frequently reported metabolites that are altered under toxicological impulses are alanine, lactate, and proline, which are often correlated. Other unspecific biomarkers of hepatotoxicity in vitro are the down-regulation of choline, glutathione, and 3-phospho-glycerate. Disruptions on the Krebs cycle are associated with increased glutamate, tryptophan, and valine. Phospholipid alterations are described in steatosis, lipo-apoptosis, and oxidative stress. Although there is a growing trend towards quality control, data analysis procedures do often not follow good contemporary metabolomics practices, which include feature filtering, false-discovery rate correction, and reporting the confidence of metabolite annotation. The currently annotated biomarkers can be used to identify hepatotoxicity in general and provide, to a certain extent, a tool for mechanistic distinction.

Unsupervised Quality Assessment of Mass Spectrometry Proteomics Experiments by Multivariate Quality Control Metrics.

Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/ .