Structure-based design of novel donepezil-like hybrids for a multi-target approach to the therapy of Alzheimer's disease.

Alzheimer's disease (AD) is a widespread multifactorial aging-related pathology, which includes cholinergic deficit among its main causes. Following a multi-target design strategy, the structure of the approved drug donepezil was taken as the starting point for generating some new potential multi-functional compounds. Therefore, a series of twenty molecular hybrids were synthesized and assayed against three different enzymes, namely the well-established targets acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the innovative one fatty acid amide hydrolase (FAAH). In silico studies confirmed the interaction of benzylpiperidine and the benzylpiperazine isostere with the catalytic anionic site (CAS) of AChE, while the aryloxycarbonyl portion appeared to be important for the interaction with the peripheral site (PAS). A QSAR study was carried out on AChE inhibition data, which revealed that the inhibition potency seems to depend upon the length of the spacer and the number of polar atoms. The docking poses of selected compounds within BChE and FAAH were also calculated. Furthermore, pharmacokinetics and drug-likeness properties were assessed by chemoinformatic tools. Several piperidine derivatives (in particular compound 10) showed interesting profiles as multi-target directed agents, while the lead piperazine derivative 12 (SON38) was found to be a more potent and selective AChE inhibitor (IC50 = 0.8 nM) than donepezil, besides being able to bind bivalent copper cations (pCu = 7.9 at physiological pH). Finally, the selected lead compounds (10 and 12, SON38) did not show significant cytotoxicity on SH-SY5Y and HepG2 cells at the highest tested concentration (100 µM) in a MTT assay.

Correlation Imputation for Single-Cell RNA-seq.

Recent advances in single-cell RNA sequencing (scRNA-seq) technologies have yielded a powerful tool to measure gene expression of individual cells. One major challenge of the scRNA-seq data is that it usually contains a large amount of zero expression values, which often impairs the effectiveness of downstream analyses. Numerous data imputation methods have been proposed to deal with these "dropout" events, but this is a difficult task for such high-dimensional and sparse data. Furthermore, there have been debates on the nature of the sparsity, about whether the zeros are due to technological limitations or represent actual biology. To address these challenges, we propose Single-cell RNA-seq Correlation completion by ENsemble learning and Auxiliary information (SCENA), a novel approach that imputes the correlation matrix of the data of interest instead of the data itself. SCENA obtains a gene-by-gene correlation estimate by ensembling various individual estimates, some of which are based on known auxiliary information about gene expression networks. Our approach is a reliable method that makes no assumptions on the nature of sparsity in scRNA-seq data or the data distribution. By extensive simulation studies and real data applications, we demonstrate that SCENA is not only superior in gene correlation estimation, but also improves the accuracy and reliability of downstream analyses, including cell clustering, dimension reduction, and graphical model estimation to learn the gene expression network.

First Integrative Morphological and Genetic Characterization of Tremoctopus violaceussensu stricto in the Mediterranean Sea.

An integrative approach based on morphological and genetic analyses was undertaken for the first time to confirm the species identification of Mediterranean samples belonging to the genus Tremoctopus. Sequences of two mtDNA genes (cytochrome c oxidase subunit (COI) and 16S) were generated for the first time from Mediterranean samples. Both the similarity-based identifications and tree-based methods indicated that three females can be identified as Tremoctopus violaceus sensu stricto in agreement with their morphological classifications. All Mediterranean sequences clustered with the sequences of Tremoctopus violaceus from the Gulf of Mexico and were clearly differentiated from the sequences attributed to T. gracilis and T. robsoni. The chromatic pattern of the web and some features of gill filaments, arms formula, stylets, radulae, beaks, and stomach contents were given for all the samples; 105,758, 20,140, and 11,237 oocytes were estimated in the mature, immature, and developing samples, respectively. The presence of four spermatangia inside the cavity of the maturing female suggested the ability of this species to mate before reaching full maturity with more partners. Age investigation using beaks, performed for the first time in T. violaceus and within the genus gave results consistent with the different sizes and maturity conditions of the samples.

Correlation Imputation in Single cell RNA-seq using Auxiliary Information and Ensemble Learning.

Single cell RNA sequencing is a powerful technique that measures the gene expression of individual cells in a high throughput fashion. However, due to sequencing inefficiency, the data is unreliable due to dropout events, or technical artifacts where genes erroneously appear to have zero expression. Many data imputation methods have been proposed to alleviate this issue. Yet, effective imputation can be difficult and biased because the data is sparse and high-dimensional, resulting in major distortions in downstream analyses. In this paper, we propose a completely novel approach that imputes the gene-by-gene correlations rather than the data itself. We call this method SCENA: Single cell RNA-seq Correlation completion by ENsemble learning and Auxiliary information. The SCENA gene-by-gene correlation matrix estimate is obtained by model stacking of multiple imputed correlation matrices based on known auxiliary information about gene connections. In an extensive simulation study based on real scRNA-seq data, we demonstrate that SCENA not only accurately imputes gene correlations but also outperforms existing imputation approaches in downstream analyses such as dimension reduction, cell clustering, graphical model estimation.

Adjusted regularization of cortical covariance.

It is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an [Formula: see text] penalty. However, the partial correlations found in neural spike count data are neither large nor sparse, so techniques that perform well in sparse settings will typically perform poorly in the context of neural spike count data. Fortunately, the correlated firing for any pair of cortical neurons depends strongly on both their distance apart and the features for which they are tuned. We introduce a method that takes advantage of these known, strong effects by allowing the penalty to depend on them: thus, for example, the connection between pairs of neurons that are close together will be penalized less than pairs that are far apart. We show through simulations that this physiologically-motivated procedure performs substantially better than off-the-shelf generic tools, and we illustrate by applying the methodology to populations of neurons recorded with multielectrode arrays implanted in macaque visual cortex areas V1 and V4.

ADJUSTED REGULARIZATION IN LATENT GRAPHICAL MODELS: APPLICATION TO MULTIPLE-NEURON SPIKE COUNT DATA.

A major challenge in contemporary neuroscience is to analyze data from large numbers of neurons recorded simultaneously across many experimental replications (trials), where the data are counts of neural firing events, and one of the basic problems is to characterize the dependence structure among such multivariate counts. Methods of estimating high-dimensional covariation based on ℓ 1-regularization are most appropriate when there are a small number of relatively large partial correlations, but in neural data there are often large numbers of relatively small partial correlations. Furthermore, the variation across trials is often confounded by Poisson-like variation within trials. To overcome these problems we introduce a comprehensive methodology that imbeds a Gaussian graphical model into a hierarchical structure: the counts are assumed Poisson, conditionally on latent variables that follow a Gaussian graphical model, and the graphical model parameters, in turn, are assumed to depend on physiologically-motivated covariates, which can greatly improve correct detection of interactions (non-zero partial correlations). We develop a Bayesian approach to fitting this covariate-adjusted generalized graphical model and we demonstrate its success in simulation studies. We then apply it to data from an experiment on visual attention, where we assess functional interactions between neurons recorded from two brain areas.

Separating Spike Count Correlation from Firing Rate Correlation.

Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4 in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC.