System-based proteomic and metabonomic analysis of the Df(16)A+/- mouse identifies potential miR-185 targets and molecular pathway alterations

Deletions on chromosome 22q11.2 are a strong genetic risk factor for development of schizophrenia and cognitive dysfunction. We employed shotgun liquid chromatography-mass spectrometry (LC-MS) proteomic and metabonomic profiling approaches on prefrontal cortex (PFC) and hippocampal (HPC) tissue from Df(16)A +/- mice, a model of the 22q11.2 deletion syndrome. Proteomic results were compared with previous transcriptomic profiling studies of the same brain regions. The aim was to investigate how the combined effect of the 22q11.2 deletion and the corresponding miRNA dysregulation affects the cell biology at the systems level. The proteomic brain profiling analysis revealed PFC and HPC changes in various molecular pathways associated with chromatin remodelling and RNA transcription, indicative of an epigenetic component of the 22q11.2DS. Further, alterations in glycolysis/gluconeogenesis, mitochondrial function and lipid biosynthesis were identified. Metabonomic profiling substantiated the proteomic findings by identifying changes in 22q11.2 deletion syndrome (22q11.2DS)-related pathways, such as changes in ceramide phosphoethanolamines, sphingomyelin, carnitines, tyrosine derivates and panthothenic acid. The proteomic findings were confirmed using selected reaction monitoring mass spectrometry, validating decreased levels of several proteins encoded on 22q11.2, increased levels of the computationally predicted putative miR-185 targets UDP-N-acetylglucosamine-peptide N-acetylglucosaminyltransferase 110 kDa subunit (OGT1) and kinesin heavy chain isoform 5A and alterations in the non-miR-185 targets serine/threonine-protein phosphatase 2B catalytic subunit gamma isoform, neurofilament light chain and vesicular glutamate transporter 1. Furthermore, alterations in the proteins associated with mammalian target of rapamycin signalling were detected in the PFC and with glutamatergic signalling in the hippocampus. Based on the proteomic and metabonomic findings, we were able to develop a schematic model summarizing the most prominent molecular network findings in the Df(16)A +/- mouse. Interestingly, the implicated pathways can be linked to one of the most consistent and strongest proteomic candidates, (OGT1), which is a predicted miR-185 target. Our results provide novel insights into system-biological mechanisms associated with the 22q11DS, which may be linked to cognitive dysfunction and an increased risk to develop schizophrenia. Further investigation of these pathways could help to identify novel drug targets for the treatment of schizophrenia

Multiple Ionization Mass Spectrometry Strategy Used To Reveal the Complexity of Metabolomics

A multiple ionization mass spectrometry strategy is presented based on the analysis of human serum extracts. Chromatographic separation was interfaced inline with the atmospheric pressure ionization techniques electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) in both positive (+) and negative (-) ionization modes. Furthermore, surface-based matrixassisted laser desorption/ionization (MALDI) and desorption ionization on silicon (DIOS) mass spectrometry were also integrated with the separation through fraction collection and offline mass spectrometry. Processing of raw data using the XCMS software resulted in time-aligned ion features, which are defined as a unique m/z at a unique retention time. The ion feature lists obtained through LC-MS with ESI and APCI interfaces in both ( ionization modes were compared, and unique ion tables were generated. Nonredundant, unique ion features, were defined as mass numbers for which no mass numbers corresponding to [M + H] + , [M -H] -, or [M + Na] + were observed in the other ionization methods at the same retention time. Analysis of the extracted serum using ESI for both (+) and (-) ions resulted in >90% additional unique ions being detected in the (-) ESI mode. Complementing the ESI analysis with APCI resulted in an additional ∼20% increase in unique ions. Finally, ESI/ APCI ionization was combined with fraction collection and offline-MALDI and DIOS mass spectrometry. The parts of the total ion current chromatograms in the LC-MS acquired data corresponding to collected fractions were summed, and m/z lists were compiled and compared to the m/z lists obtained from the DIOS/MALDI spectra. It was observed that, for each fraction, DIOS accounted for ∼50% of the unique ions detected. These results suggest that true global metabolomics will require multiple ionization technologies to address the inherent metabolite diversity and therefore the complexity in and of metabolomics studies. Quantitative global analysis of endogenous metabolites from cells, tissues, fluids or whole organismssmetabolomicssis becoming an integral part of functional genomics efforts 1-3 as well as a tool for finding diagnostic biomarkers. 4-7 From a mass spectrometry ionization point of view, the transcriptome and proteome are relatively homogeneous in their respective physicalchemical composition of 4 and 20 chemical building blocks, whereas vast physical-chemical heterogeneity is contained in the metabolome, where the complexity is dictated at the atomic level presenting diversity similar to that of combinatorial libraries. This diversity makes it especially challenging to gain a comprehensive and quantitative measure of the metabolome. For example, simultaneous separation and mass spectrometric detection of the substrate-product pair fructose and fructose 1-phosphate is not trivial. Nuclear magnetic resonance spectroscopy (NMR) 8 and mass spectrometry (MS) 9,10 have become the primary analytical technologies of metabolomics, where they have a great potential to complement each other. 11 Theoretically, 1 H and 13 C NMR are capable of measuring most aspects of the metabolome, yet the low concentrations (<pM) and the extremely large dynamic range (low abundant signaling compounds to central metabolism carbohydrates), typically encountered in biological systems paire

Assessment of metabolic phenotypic variability in children's urine using 1H NMR spectroscopy

The application of metabolic phenotyping in clinical and epidemiological studies is limited by a poor understanding of inter-individual, intra-individual and temporal variability in metabolic phenotypes. Using 1H NMR spectroscopy we characterised short-term variability in urinary metabolites measured from 20 children aged 8-9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%). Pooled samples captured the best inter-individual variability with an ICC of 0.40 (median). Trimethylamine, N-acetyl neuraminic acid, 3-hydroxyisobutyrate, 3-hydroxybutyrate/3-aminoisobutyrate, tyrosine, valine and 3-hydroxyisovalerate exhibited the highest stability with over 50% of variance specific to the child. The pooled sample was shown to capture the most inter-individual variance in the metabolic phenotype, which is of importance for molecular epidemiology study design. A substantial proportion of the variation in the urinary metabolome of children is specific to the individual, underlining the potential of such data to inform clinical and exposome studies conducted early in life

Untargeted UPLC-MS Profiling Pipeline to Expand Tissue Metabolome Coverage: Application to Cardiovascular Disease.

Metabolic profiling studies aim to achieve broad metabolome coverage in specific biological samples. However, wide metabolome coverage has proven difficult to achieve, mostly because of the diverse physicochemical properties of small molecules, obligating analysts to seek multiplatform and multimethod approaches. Challenges are even greater when it comes to applications to tissue samples, where tissue lysis and metabolite extraction can induce significant systematic variation in composition. We have developed a pipeline for obtaining the aqueous and organic compounds from diseased arterial tissue using two consecutive extractions, followed by a different untargeted UPLC-MS analysis method for each extract. Methods were rationally chosen and optimized to address the different physicochemical properties of each extract: hydrophilic interaction liquid chromatography (HILIC) for the aqueous extract and reversed-phase chromatography for the organic. This pipeline can be generic for tissue analysis as demonstrated by applications to different tissue types. The experimental setup and fast turnaround time of the two methods contributed toward obtaining highly reproducible features with exceptional chromatographic performance (CV % < 0.5%), making this pipeline suitable for metabolic profiling applications. We structurally assigned 226 metabolites from a range of chemical classes (e.g., carnitines, α-amino acids, purines, pyrimidines, phospholipids, sphingolipids, free fatty acids, and glycerolipids) which were mapped to their corresponding pathways, biological functions and known disease mechanisms. The combination of the two untargeted UPLC-MS methods showed high metabolite complementarity. We demonstrate the application of this pipeline to cardiovascular disease, where we show that the analyzed diseased groups (<i>n </i>= 120) of arterial tissue could be distinguished based on their metabolic profiles

Interacting mindreaders

Could interacting mindreaders be in a position to know things which they would be unable to know if they were manifestly passive observers? This paper argues that they could. Mindreading is sometimes reciprocal: the mindreader's target reciprocates by taking the mindreader as a target for mindreading. The paper explains how such reciprocity can significantly narrow the range of possible interpretations of behaviour where mindreaders are, or appear to be, in a position to interact. A consequence is that revisions and extensions are needed to standard theories of the evidential basis of mindreading. The view also has consequences for understanding how abilities to interact combined with comparatively simple forms of mindreading may explain the emergence, in evolution or development, of sophisticated forms of social cognition

A Statistically Rigorous Test for the Identification of Parent−Fragment Pairs in LC-MS Datasets

Untargeted global metabolic profiling by liquid chromato-graphy−mass spectrometry generates numerous signals that are due to unknown compounds and whose identification forms an important challenge. The analysis of metabolite fragmentation patterns, following collision-induced dissociation, provides a valuable tool for identification, but can be severely impeded by close chromatographic coelution of distinct metabolites. We propose a new algorithm for identifying related parent−fragment pairs and for distinguishing these from signals due to unrelated compounds. Unlike existing methods, our approach addresses the problem by means of a hypothesis test that is based on the distribution of the recorded ion counts, and thereby provides a statistically rigorous measure of the uncertainty involved in the classification problem. Because of technological constraints, the test is of primary use at low and intermediate ion counts, above which detector saturation causes substantial bias to the recorded ion count. The validity of the test is demonstrated through its application to pairs of coeluting isotopologues and to known parent−fragment pairs, which results in test statistics consistent with the null distribution. The performance of the test is compared with a commonly used Pearson correlation approach and found to be considerably better (e.g., false positive rate of 6.25%, compared with a value of 50% for the correlation for perfectly coeluting ions). Because the algorithm may be used for the analysis of high-mass compounds in addition to metabolic data, we expect it to facilitate the analysis of fragmentation patterns for a wide range of analytical problems

Incorporation of Radio Frequency Identification Tag in Dentures to Facilitate Recognition and Forensic Human Identification

Forensic identification using odontology is based on the comparison of ante-mortem and post mortem dental records. The insertion of a radio frequency identification (RFId) tag into dentures could be used as an aid to identify decomposed bodies, by storing personal identification data in a small transponder that can be radio-transmitted to a reader connected to a computer. A small passive, 12 x 2,1 mm, read-only RFId-tag was incorporated into the manufacture of three trial complete upper dentures and tested for a signal. The aim of this article is to demonstrate the feasibility of manufacturing such a dental prosthesis, the technical protocols for its implantation in the denture resin and its working principles. Future research and tests are required in order to verify human compatibility of the tagged denture and also to evaluate any potential deterioration in strength when subjected to high temperatures, or for damage resulting from everyday wear and tear. It should also be able to withstand the extreme conditions resulting from major accidents or mass disasters and procedures used to perform a forensic identification