Identification and drug-induced reversion of molecular signatures of Alzheimer's disease onset and progression in AppNL-G-F, AppNL-F, and 3xTg-AD mouse models.

BACKGROUND: In spite of many years of research, our understanding of the molecular bases of Alzheimer's disease (AD) is still incomplete, and the medical treatments available mainly target the disease symptoms and are hardly effective. Indeed, the modulation of a single target (e.g., ß-secretase) has proven to be insufficient to significantly alter the physiopathology of the disease, and we should therefore move from gene-centric to systemic therapeutic strategies, where AD-related changes are modulated globally. METHODS: Here we present the complete characterization of three murine models of AD at different stages of the disease (i.e., onset, progression and advanced). We combined the cognitive assessment of these mice with histological analyses and full transcriptional and protein quantification profiling of the hippocampus. Additionally, we derived specific Aß-related molecular AD signatures and looked for drugs able to globally revert them. RESULTS: We found that AD models show accelerated aging and that factors specifically associated with Aß pathology are involved. We discovered a few proteins whose abundance increases with AD progression, while the corresponding transcript levels remain stable, and showed that at least two of them (i.e., lfit3 and Syt11) co-localize with Aß plaques in the brain. Finally, we found two NSAIDs (dexketoprofen and etodolac) and two anti-hypertensives (penbutolol and bendroflumethiazide) that overturn the cognitive impairment in AD mice while reducing Aß plaques in the hippocampus and partially restoring the physiological levels of AD signature genes to wild-type levels. CONCLUSIONS: The characterization of three AD mouse models at different disease stages provides an unprecedented view of AD pathology and how this differs from physiological aging. Moreover, our computational strategy to chemically revert AD signatures has shown that NSAID and anti-hypertensive drugs may still have an opportunity as anti-AD agents, challenging previous reports.

Publisher Correction: Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

In silico Characterization of Human Prion-Like Proteins: Beyond Neurological Diseases.

Prion-like behavior has been in the spotlight since it was first associated with the onset of mammalian neurodegenerative diseases. However, a growing body of evidence suggests that this mechanism could be behind the regulation of processes such as transcription and translation in multiple species. Here, we perform a stringent computational survey to identify prion-like proteins in the human proteome. We detected 242 candidate polypeptides and computationally assessed their function, protein-protein interaction networks, tissular expression, and their link to disease. Human prion-like proteins constitute a subset of modular polypeptides broadly expressed across different cell types and tissues, significantly associated with disease, embedded in highly connected interaction networks, and involved in the flow of genetic information in the cell. Our analysis suggests that these proteins might play a relevant role not only in neurological disorders, but also in different types of cancer and viral infections.

Rationalizing Drug Response in Cancer Cell Lines.

Cancer cell lines (CCLs) play an important role in the initial stages of drug discovery allowing, among others, for the screening of drug candidates. As CCL panels continue to grow in size and diversity, many polymorphisms in genes encoding drug-metabolizing enzymes, transporters and drug targets, as well as disease-related genes have been linked to altered drug sensitivity. However, identifying the correlation between this variability and pharmacological responses remains challenging due to the heterogeneity of cancer biology and the intricate interplay between cell lines and drug molecules. Here, we propose a network-based strategy that exploits information on gene expression and somatic mutations of CCLs to group cells according to their molecular similarity. We then identify genes that are characteristic of each cluster and correlate their status with drug response. We find that CCLs with similar characteristic active network regions present specific responses to certain drugs, and identify a limited set of genes that might be directly involved in drug sensitivity or resistance.

IntSide: a web server for the chemical and biological examination of drug side effects.

SUMMARY: Drug side effects are one of the main health threats worldwide, and an important obstacle in drug development. Understanding how adverse reactions occur requires knowledge on drug mechanisms at the molecular level. Despite recent advances, the need for tools and methods that facilitate side effect anticipation still remains. Here, we present IntSide, a web server that integrates chemical and biological information to elucidate the molecular mechanisms underlying drug side effects. IntSide currently catalogs 1175 side effects caused by 996 drugs, associated with drug features divided into eight categories, belonging to either biology or chemistry. On the biological side, IntSide reports drug targets and off-targets, pathways, molecular functions and biological processes. From a chemical viewpoint, it includes molecular fingerprints, scaffolds and chemical entities. Finally, we also integrate additional biological data, such as protein interactions and disease-related genes, to facilitate mechanistic interpretations. AVAILABILITY AND IMPLEMENTATION: Our data and web resource are available online (http://intside.irbbarcelona.org/). CONTACT: patrick.aloy@irbbarcelona.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.