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Down syndrome (DS) is caused by an extra copy of human chromosome 21 (Hsa21). Although it is the most common genetic cause of intellectual disability (ID), there are, as yet, no effective pharmacotherapies. The Ts65Dn mouse model of DS is trisomic for orthologs of ∼55% of Hsa21 classical protein coding genes. These mice display many features relevant to those seen in DS, including deficits in learning and memory (L/M) tasks requiring a functional hippocampus. Recently, the N-methyl-D-aspartate (NMDA) receptor antagonist, memantine, was shown to rescue performance of the Ts65Dn in several L/M tasks. These studies, however, have not been accompanied by molecular analyses. In previous work, we described changes in protein expression induced in hippocampus and cortex in control mice after exposure to context fear conditioning (CFC), with and without memantine treatment. Here, we extend this analysis to Ts65Dn mice, measuring levels of 85 proteins/protein modifications, including components of MAP kinase and MTOR pathways, and subunits of NMDA receptors, in cortex and hippocampus of Ts65Dn mice after failed learning in CFC and after learning was rescued by memantine. We show that, compared with wild type littermate controls, (i) of the dynamic responses seen in control mice in normal learning, >40% also occur in Ts65Dn in failed learning or are compensated by baseline abnormalities, and thus are considered necessary but not sufficient for successful learning, and (ii) treatment with memantine does not in general normalize the initial protein levels but instead induces direct and indirect responses in approximately half the proteins measured and results in normalization of the endpoint protein levels. Together, these datasets provide a first view of the complexities associated with pharmacological rescue of learning in the Ts65Dn. Extending such studies to additional drugs and mouse models of DS will aid in identifying pharmacotherapies for effective clinical trials.

Background The apolipoprotein E ( APOE ) ε4 allele confers the strongest risk for late-onset Alzheimer’s disease (AD) besides age itself, but the mechanisms underlying this risk are debated. One hypothesis supported by evidence from multiple labs is that apoE4 binds to the amyloid-β (Aβ) peptide and catalyzes its polymerization into neurotoxic oligomers and fibrils. Inhibiting this early step in the amyloid cascade may thereby reduce or prevent neurodegeneration and AD. Methods Using a design of experiments (DOE) approach, we developed a high-throughput assay to identify inhibitors of apoE4-catalyzed polymerization of Aβ into oligomers and fibrils. We used it to screen the NIH Clinical Collection of small molecule drugs tested previously in human clinical trials. We then evaluated the efficacy and cytotoxicity of the hit compounds in primary neuron models of apoE4-induced Aβ and phosphorylated tau aggregation. Finally, we performed retrospective analyses of the National Alzheimer’s Coordinating Center (NACC) clinical dataset, using Cox regression and Cox proportional hazards models to determine if the use of two FDA-approved hit compounds was associated with better cognitive scores (Mini-Mental State Exam), or improved AD clinical diagnosis, when compared with other medications of the same clinical indication. Results Our high-throughput screen identified eight blood-brain barrier (BBB)-permeable hit compounds that reduced apoE4-catalyzed Aβ oligomer and fibril formation in a dose-dependent manner. Five hit compounds were non-toxic toward cultured neurons and also reduced apoE4-promoted Aβ and tau neuropathology in a dose-dependent manner. Three of the five compounds were determined to be specific inhibitors of apoE4, whereas the other two compounds were Aβ or tau aggregation inhibitors. When prescribed to AD patients for their normal clinical indications, two of the apoE4 inhibitors, imipramine and olanzapine, but not other (non-hit) antipsychotic or antidepressant medications, were associated with improvements in cognition and clinical diagnosis, especially among APOE4 carriers. Conclusions The critical test of any proposed AD mechanism is whether it leads to effective treatments. Our high-throughput screen identified two promising FDA-approved drugs, imipramine and olanzapine, which have no structural, functional, or clinical similarities other than their shared ability to inhibit apoE4-catalyzed Aβ polymerization, thus identifying this mechanism as an essential contribution of apoE4 to AD.

Background While many sex differences in structure and function of the mammalian brain have been described, the molecular correlates of these differences are not broadly known. Also unknown is how sex differences at the protein level are perturbed by mutations that lead to intellectual disability (ID). Down syndrome (DS) is the most common genetic cause of ID and is due to trisomy of human chromosome 21 (Hsa21) and the resulting increased expression of Hsa21-encoded genes. The Dp(10)1Yey mouse model (Dp10) of DS is trisomic for orthologs of 39 Hsa21 protein-coding genes that map to mouse chromosome 10 (Mmu10), including four genes with known sex differences in functional properties. How these genes contribute to the DS cognitive phenotype is not known. Methods Using reverse phase protein arrays, levels of ~100 proteins/protein modifications were measured in the hippocampus, cerebellum, and cortex of female and male controls and their trisomic Dp10 littermates. Proteins were chosen for their known roles in learning/memory and synaptic plasticity and include components of the MAPK, MTOR, and apoptosis pathways, immediate early genes, and subunits of ionotropic glutamate receptors. Protein levels were compared between genotypes, sexes, and brain regions using a three-level mixed effects model and the Benjamini-Hochberg correction for multiple testing. Results In control mice, levels of approximately one half of the proteins differ significantly between females and males in at least one brain region; in the hippocampus alone, levels of 40 % of the proteins are significantly higher in females. Trisomy of the Mmu10 segment differentially affects female and male profiles, perturbing protein levels most in the cerebellum of female Dp10 and most in the hippocampus of male Dp10. Cortex is minimally affected by sex and genotype. Diverse pathways and processes are implicated in both sex and genotype differences. Conclusions The extensive sex differences in control mice in levels of proteins involved in learning/memory illustrate the molecular complexity underlying sex differences in normal neurological processes. The sex-specific abnormalities in the Dp10 suggest the possibility of sex-specific phenotypic features in DS and reinforce the need to use female as well as male mice, in particular in preclinical evaluations of drug responses.

Abstract Study Objectives Slow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan. Methods Coupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6–88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles. Results Two different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep contain a mixture of discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails with increasing age. Conclusions Distinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.

Background Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism’s genome as a database restricts metabolite identification to only those compounds that the organism can produce. Results To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment’s MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Conclusions Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

Background The apolipoprotein E (APOE) [epsilon]4 allele confers the strongest risk for late-onset Alzheimer's disease (AD) besides age itself, but the mechanisms underlying this risk are debated. One hypothesis supported by evidence from multiple labs is that apoE4 binds to the amyloid-[beta] (A[beta]) peptide and catalyzes its polymerization into neurotoxic oligomers and fibrils. Inhibiting this early step in the amyloid cascade may thereby reduce or prevent neurodegeneration and AD. Methods Using a design of experiments (DOE) approach, we developed a high-throughput assay to identify inhibitors of apoE4-catalyzed polymerization of A[beta] into oligomers and fibrils. We used it to screen the NIH Clinical Collection of small molecule drugs tested previously in human clinical trials. We then evaluated the efficacy and cytotoxicity of the hit compounds in primary neuron models of apoE4-induced A[beta] and phosphorylated tau aggregation. Finally, we performed retrospective analyses of the National Alzheimer's Coordinating Center (NACC) clinical dataset, using Cox regression and Cox proportional hazards models to determine if the use of two FDA-approved hit compounds was associated with better cognitive scores (Mini-Mental State Exam), or improved AD clinical diagnosis, when compared with other medications of the same clinical indication. Results Our high-throughput screen identified eight blood-brain barrier (BBB)-permeable hit compounds that reduced apoE4-catalyzed A[beta] oligomer and fibril formation in a dose-dependent manner. Five hit compounds were non-toxic toward cultured neurons and also reduced apoE4-promoted A[beta] and tau neuropathology in a dose-dependent manner. Three of the five compounds were determined to be specific inhibitors of apoE4, whereas the other two compounds were A[beta] or tau aggregation inhibitors. When prescribed to AD patients for their normal clinical indications, two of the apoE4 inhibitors, imipramine and olanzapine, but not other (non-hit) antipsychotic or antidepressant medications, were associated with improvements in cognition and clinical diagnosis, especially among APOE4 carriers. Conclusions The critical test of any proposed AD mechanism is whether it leads to effective treatments. Our high-throughput screen identified two promising FDA-approved drugs, imipramine and olanzapine, which have no structural, functional, or clinical similarities other than their shared ability to inhibit apoE4-catalyzed A[beta] polymerization, thus identifying this mechanism as an essential contribution of apoE4 to AD. Keywords: Amyloid-[beta], Apolipoprotein E, Dementia, High-throughput screening, Antidepressant, Antipsychotic

Down syndrome (DS), trisomy of human chromosome 21 (Hsa21), is challenging to model in mice. Not only is it a contiguous gene syndrome spanning 35 Mb of the long arm of Hsa21, but orthologs of Hsa21 genes map to segments of three mouse chromosomes, Mmu16, Mmu17, and Mmu10. The Ts65Dn was the first viable segmental trisomy mouse model for DS; it is a partial trisomy currently popular in preclinical evaluations of drugs for cognition in DS. Limitations of the Ts65Dn are as follows: (i) it is trisomic for 125 human protein-coding orthologs, but only 90 of these are Hsa21 orthologs and (ii) it lacks trisomy for ~75 Hsa21 orthologs. In recent years, several additional mouse models of DS have been generated, each trisomic for a different subset of Hsa21 genes or their orthologs. To best exploit these models and interpret the results obtained with them, prior to proposing clinical trials, an understanding of their trisomic gene content, relative to full trisomy 21, is necessary. Here we first review the functional information on Hsa21 protein-coding genes and the more recent annotation of a large number of functional RNA genes. We then discuss the conservation and genomic distribution of Hsa21 orthologs in the mouse genome and the distribution of mouse-specific genes. Lastly, we consider the strengths and weaknesses of mouse models of DS based on the number and nature of the Hsa21 orthologs that are, and are not, trisomic in each, and discuss their validity for use in preclinical evaluations of drug responses.

The apolipoprotein E (APOE) ε4 allele confers the strongest risk for late-onset Alzheimer's disease (AD) besides age itself, but the mechanism(s) underlying this risk are debated. The critical test of any proposed AD mechanism is whether it leads to effective treatments. We developed a high-throughput assay to identify inhibitors of apoE4-catalyzed polymerization of the amyloid β (Aβ) peptide into neurotoxic fibrils. Screening a human drug library, we identified five non-toxic, blood-brain-barrier-permeable hit compounds that reduced apoE4-promoted Aβ and tau neuropathology in cultured neurons. Two hit compounds, imipramine and olanzapine, but not other (non-hit) antipsychotics or antidepressants, when prescribed to AD patients for their normal clinical indications, led to improvements in cognition and clinical diagnosis. Imipramine and olanzapine have no structural, functional, or clinical similarities other than their ability to inhibit apoE4-catalyzed Aβ polymerization, thus identifying this mechanism as an essential contribution of apoE4 to AD. Competing Interest Statement The authors have declared no competing interest.

Abstract Study Objectives Slow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan. Methods Coupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles. Results Two different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep are composed of two discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails during old age. Conclusions Distinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease. Statement of Significance Slow waves of nonrapid eye movement sleep couple with sleep spindles in a process hypothesized to support memory functions. This coupling has recently gained interest as a possible biomarker of cognitive aging and onset of Alzheimer’s disease. Most studies have been limited by an assumption that all slow waves (and coupled spindles) are fundamentally the same physiological events. Here we demonstrate that distinct subtypes of slow waves and their coupled spindles can be identified in human sleep. A mixture of different slow wave and spindle subtypes shifts in composition during lighter versus deeper sleep, and aging favors the deep sleep subtypes. These data should inform any future attempts to use slow wave sleep as a biomarker or clinical interventional target. Competing Interest Statement The authors have declared no competing interest. Footnotes * Conflict of interest: The authors declare they have no competing financial interests

In the post-genomic era, there is a dire need for tools to perform metabolic analyses that include the structural, functional, and regulatory analysis of metabolic networks. This need arose because of the lag between the two phases of metabolic engineering, namely, synthesis and analysis. Molecular biological tools for synthesis like recombinant DNA technology and genetic engineering have advanced a lot farther than tools for systemic analysis. Consequently, bioinformatics is poised to play an important role in bridging the gap between the two phases of metabolic engineering, thereby accelerating the improvement of organisms by using predictive simulations that can be done in minutes rather than mutant constructions that require weeks to months. In addition, metabolism occurs at a rapid speed compared to other cellular activities and has two states, dynamic state and steady state. Dynamic state analysis sheds more light on the mechanisms and regulation of metabolism than its steady state counterpart. Currently, several in silico tools exist for steady-state analysis of metabolism, but tools for dynamic analysis are lacking. This research focused on simulating the dynamic state of metabolism for predictive analysis of the metabolic changes in an organism during metabolic engineering. The goals of this research were accomplished by developing two software tools. Metabolome Searcher, a web-based high throughput tool, facilitates putative compound identification and metabolic pathway mapping of mass spectrometry data by applying genome-restriction. The second tool, DynaFlux, uses these compound identifications along with time course data obtained from a mass spectrometer in conjunction with the pathways of interest to simulate and estimate dynamic-state metabolic flux, as well as to analyze the network properties. The features available in DynaFlux are: (1) derivation of the metabolic reconstructions from Pathway Tools software for the simulation; (2) automated building of the mathematical model of the metabolic network; (3) estimation of the kinetic parameters, KR, v, Vmaxf, Vmaxr, and Kdy, using hybrid-mutation random-restart hill climbing search; (4) perturbation studies of enzyme activities; (5) enumeration of feasible routes between two metabolites; (6) determination of the minimal enzyme set and dispensable enzyme set; (7) imputation of missing metabolite data; and (8) visualization of the network.