Lowering β-Amyloid Levels Rescues Learning and Memory in a Down Syndrome Mouse Model

β-amyloid levels are elevated in Down syndrome (DS) patients throughout life and are believed to cause Alzheimer's disease (AD) in adult members of this population. However, it is not known if β-amyloid contributes to intellectual disability in younger individuals. We used a γ-secretase inhibitor to lower β-amyloid levels in young mice that model DS. This treatment corrected learning deficits characteristic of these mice, suggesting that β-amyloid-lowering therapies might improve cognitive function in young DS patients

Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

BACKGROUND: Metabolic phenotyping has become an important 'bird's-eye-view' technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of 'top-down' chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems. METHODOLOGY/PRINCIPAL FINDINGS: The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 (1)H and (13)C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each (13)C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient (13)C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts. CONCLUSIONS/SIGNIFICANCE: Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of (13)C atoms of given metabolites on development-dependent changes in the 56 identified (13)C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism

Tracking Subtle Stereotypes of Children with Trisomy 21: From Facial-Feature-Based to Implicit Stereotyping

Background: Stigmatization is one of the greatest obstacles to the successful integration of people with Trisomy 21 (T21 or Down syndrome), the most frequent genetic disorder associated with intellectual disability. Research on attitudes and stereotypes toward these people still focuses on explicit measures subjected to social-desirability biases, and neglects how variability in facial stigmata influences attitudes and stereotyping. Methodology/Principal Findings: The participants were 165 adults including 55 young adult students, 55 non-student adults, and 55 professional caregivers working with intellectually disabled persons. They were faced with implicit association tests (IAT), a well-known technique whereby response latency is used to capture the relative strength with which some groups of people—here photographed faces of typically developing children and children with T21—are automatically (without conscious awareness) associated with positive versus negative attributes in memory. Each participant also rated the same photographed faces (consciously accessible evaluations). We provide the first evidence that the positive bias typically found in explicit judgments of children with T21 is smaller for those whose facial features are highly characteristic of this disorder, compared to their counterparts with less distinctive features and to typically developing children. We also show that this bias can coexist with negative evaluations at the implicit level (with large effect sizes), even among professional caregivers

Human Disease-Drug Network Based on Genomic Expression Profiles

BACKGROUND: Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes. METHODOLOGY/PRINCIPAL FINDINGS: We performed a systematic, large-scale analysis of genomic expression profiles of human diseases and drugs to create a disease-drug network. A network of 170,027 significant interactions was extracted from the approximately 24.5 million comparisons between approximately 7,000 publicly available transcriptomic profiles. The network includes 645 disease-disease, 5,008 disease-drug, and 164,374 drug-drug relationships. At least 60% of the disease-disease pairs were in the same disease area as determined by the Medical Subject Headings (MeSH) disease classification tree. The remaining can drive a molecular level nosology by discovering relationships between seemingly unrelated diseases, such as a connection between bipolar disorder and hereditary spastic paraplegia, and a connection between actinic keratosis and cancer. Among the 5,008 disease-drug links, connections with negative scores suggest new indications for existing drugs, such as the use of some antimalaria drugs for Crohn's disease, and a variety of existing drugs for Huntington's disease; while the positive scoring connections can aid in drug side effect identification, such as tamoxifen's undesired carcinogenic property. From the approximately 37K drug-drug relationships, we discover relationships that aid in target and pathway deconvolution, such as 1) KCNMA1 as a potential molecular target of lobeline, and 2) both apoptotic DNA fragmentation and G2/M DNA damage checkpoint regulation as potential pathway targets of daunorubicin. CONCLUSIONS/SIGNIFICANCE: We have automatically generated thousands of disease and drug expression profiles using GEO datasets, and constructed a large scale disease-drug network for effective and efficient drug repositioning as well as drug target/pathway identification

A Structure-Based Approach for Mapping Adverse Drug Reactions to the Perturbation of Underlying Biological Pathways

Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug's ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs. At present, no method exists to discover these ADR-pathway associations. In this paper we introduce a computational framework for identifying a subset of these associations based on the assumption that drugs capable of modulating the same pathway may induce similar ADRs. Our model exploits multiple information resources. First, we utilize a publicly available dataset pairing drugs with their observed ADRs. Second, we identify putative protein targets for each drug using the protein structure database and in-silico virtual docking. Third, we label each protein target with its known involvement in one or more biological pathways. Finally, the relationships among these information sources are mined using multiple stages of logistic-regression while controlling for over-fitting and multiple-hypothesis testing. As proof-of-concept, we examined a dataset of 506 ADRs, 730 drugs, and 830 human protein targets. Our method yielded 185 ADR-pathway associations of which 45 were selected to undergo a manual literature review. We found 32 associations to be supported by the scientific literature

Utility of Survival Motor Neuron ELISA for Spinal Muscular Atrophy Clinical and Preclinical Analyses

Genetic defects leading to the reduction of the survival motor neuron protein (SMN) are a causal factor for Spinal Muscular Atrophy (SMA). While there are a number of therapies under evaluation as potential treatments for SMA, there is a critical lack of a biomarker method for assessing efficacy of therapeutic interventions, particularly those targeting upregulation of SMN protein levels. Towards this end we have engaged in developing an immunoassay capable of accurately measuring SMN protein levels in blood, specifically in peripheral blood mononuclear cells (PBMCs), as a tool for validating SMN protein as a biomarker in SMA.A sandwich enzyme-linked immunosorbent assay (ELISA) was developed and validated for measuring SMN protein in human PBMCs and other cell lysates. Protocols for detection and extraction of SMN from transgenic SMA mouse tissues were also developed.The assay sensitivity for human SMN is 50 pg/mL. Initial analysis reveals that PBMCs yield enough SMN to analyze from blood volumes of less than 1 mL, and SMA Type I patients' PBMCs show ∼90% reduction of SMN protein compared to normal adults. The ELISA can reliably quantify SMN protein in human and mouse PBMCs and muscle, as well as brain, and spinal cord from a mouse model of severe SMA.This SMN ELISA assay enables the reliable, quantitative and rapid measurement of SMN in healthy human and SMA patient PBMCs, muscle and fibroblasts. SMN was also detected in several tissues in a mouse model of SMA, as well as in wildtype mouse tissues. This SMN ELISA has general translational applicability to both preclinical and clinical research efforts

Biochemical Trade-Offs: Evidence for Ecologically Linked Secondary Metabolism of the Sponge Oscarella balibaloi

Secondary metabolite production is assumed to be costly and therefore the resource allocation to their production should be optimized with respect to primary biological functions such as growth or reproduction. Sponges are known to produce a great diversity of secondary metabolites with powerful biological activities that may explain their domination in some hard substrate communities both in terms of diversity and biomass. Oscarella balibaloi (Homoscleromorpha) is a recently described, highly dynamic species, which often overgrows other sessile marine invertebrates. Bioactivity measurements (standardized Microtox assay) and metabolic fingerprints were used as indicators of the baseline variations of the O. balibaloi secondary metabolism, and related to the sponge reproductive effort over two years. The bioactivity showed a significant seasonal variation with the lowest values at the end of spring and in early summer followed by the highest bioactivity in the late summer and autumn. An effect of the seawater temperature was detected, with a significantly higher bioactivity in warm conditions. There was also a tendency of a higher bioactivity when O. balibaloi was found overgrowing other sponge species. Metabolic fingerprints revealed the existence of three principal metabolic phenotypes: phenotype 1 exhibited by a majority of low bioactive, female individuals, whereas phenotypes 2 and 3 correspond to a majority of highly bioactive, non-reproductive individuals. The bioactivity was negatively correlated to the reproductive effort, minimal bioactivities coinciding with the period of embryogenesis and larval development. Our results fit the Optimal Defense Theory with an investment in the reproduction mainly shaping the secondary metabolism variability, and a less pronounced influence of other biotic (species interaction) and abiotic (temperature) factors

The dental calculus metabolome in modern and historic samples.

INTRODUCTION: Dental calculus is a mineralized microbial dental plaque biofilm that forms throughout life by precipitation of salivary calcium salts. Successive cycles of dental plaque growth and calcification make it an unusually well-preserved, long-term record of host-microbial interaction in the archaeological record. Recent studies have confirmed the survival of authentic ancient DNA and proteins within historic and prehistoric dental calculus, making it a promising substrate for investigating oral microbiome evolution via direct measurement and comparison of modern and ancient specimens. OBJECTIVE: We present the first comprehensive characterization of the human dental calculus metabolome using a multi-platform approach. METHODS: Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) quantified 285 metabolites in modern and historic (200 years old) dental calculus, including metabolites of drug and dietary origin. A subset of historic samples was additionally analyzed by high-resolution gas chromatography-MS (GC-MS) and UPLC-MS/MS for further characterization of metabolites and lipids. Metabolite profiles of modern and historic calculus were compared to identify patterns of persistence and loss. RESULTS: Dipeptides, free amino acids, free nucleotides, and carbohydrates substantially decrease in abundance and ubiquity in archaeological samples, with some exceptions. Lipids generally persist, and saturated and mono-unsaturated medium and long chain fatty acids appear to be well-preserved, while metabolic derivatives related to oxidation and chemical degradation are found at higher levels in archaeological dental calculus than fresh samples. CONCLUSIONS: The results of this study indicate that certain metabolite classes have higher potential for recovery over long time scales and may serve as appropriate targets for oral microbiome evolutionary studies

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules