Abstract: The aim of this study was to review aspects of the pathobiology of the meniscus in health and disease and show how degeneration of the meniscus can contribute to deleterious changes in other knee joint components. The menisci, distinctive semilunar weight bearing fibrocartilages, provide knee joint stability, co-ordinating functional contributions from articular cartilage, ligaments/tendons, synovium, subchondral bone and infra-patellar fat pad during knee joint articulation. The meniscus contains metabolically active cell populations responsive to growth factors, chemokines and inflammatory cytokines such as interleukin-1 and tumour necrosis factor-alpha, resulting in the synthesis of matrix metalloproteases and A Disintegrin and Metalloprotease with ThromboSpondin type 1 repeats (ADAMTS)-4 and 5 which can degrade structural glycoproteins and proteoglycans leading to function-limiting changes in meniscal and other knee joint tissues. Such degradative changes are hall-marks of osteoarthritis (OA). No drugs are currently approved that change the natural course of OA and translate to long-term, clinically relevant benefits. For any pharmaceutical therapeutic intervention in OA to be effective, disease modifying drugs will have to be developed which actively modulate the many different cell types present in the knee to provide a global therapeutic. Many individual and combinatorial approaches are being developed to treat or replace degenerate menisci using 3D printing, bioscaffolds and hydrogel delivery systems for therapeutic drugs, growth factors and replacement progenitor cell populations recognising the central role the menisci play in knee joint health.

Abstract: The 21st century has finally seen the full achievement of targeted therapy to treat cancer with the promise of lower side effects and improved efficacy with respect to cytotoxic chemotherapeutics [...].

Abstract: BACKGROUND: Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology. RESULTS: We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call "drug-protein interaction signatures" from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers. CONCLUSIONS: Our method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions and are expected to contribute to rational drug design.

Abstract: Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict safety. Characterizing the effect of natural variation in the genes encoding drug targets should present a powerful approach to predict side effects arising from drugging particular proteins. In this retrospective analysis, we report a correlation between the organ systems affected by genetic variation in drug targets and the organ systems in which side effects are observed. Across 1819 drugs and 21 phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0 vs 19.2%; OR = 1.80). This result suggests that human genetic data should be used to predict safety issues associated with drug targets.

Abstract: A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.

Abstract: Functional drug actions refer to drug-affected GO terms. They aid in the investigation of drug effects that are therapeutic or adverse. Previous studies have utilized the linkage information between drugs and functions in molecular level biological networks. Since the current knowledge of molecular level mechanisms of biological functions is still limited, such previous studies were incomplete. We expected that the multi-level biological networks would allow us to more completely investigate the functional drug actions. We constructed multi-level biological networks with genes, GO terms, and diseases. Meta-paths were utilized to extract the features of each GO term. We trained 39 SVM models to prioritize the functional drug actions of the various 39 drugs. Through the multi-level networks, more functional drug actions were utilized for the 39 models and inferred by the models. Multi-level based features improved the performance of the models, and the average AUROC value in the cross-validation was 0.86. Moreover, 60% of the candidates were true.

Abstract: SUMMARY: Mechanistic molecular studies in biomedical research often discover important genes that are aberrantly over- or under-expressed in disease. However, manipulating these genes in an attempt to improve the disease state is challenging. Herein, we reveal Drug Gene Budger (DGB), a web-based and mobile application developed to assist investigators in order to prioritize small molecules that are predicted to maximally influence the expression of their target gene of interest. With DGB, users can enter a gene symbol along with the wish to up-regulate or down-regulate its expression. The output of the application is a ranked list of small molecules that have been experimentally determined to produce the desired expression effect. The table includes log-transformed fold change, P-value and q-value for each small molecule, reporting the significance of differential expression as determined by the limma method. Relevant links are provided to further explore knowledge about the target gene, the small molecule and the source of evidence from which the relationship between the small molecule and the target gene was derived. The experimental data contained within DGB is compiled from signatures extracted from the LINCS L1000 dataset, the original Connectivity Map (CMap) dataset and the Gene Expression Omnibus (GEO). DGB also presents a specificity measure for a drug-gene connection based on the number of genes a drug modulates. DGB provides a useful preliminary technique for identifying small molecules that can target the expression of a single gene in human cells and tissues. AVAILABILITY AND IMPLEMENTATION: The application is freely available on the web at http://DGB.cloud and as a mobile phone application on iTunes https://itunes.apple.com/us/app/drug-gene-budger/id1243580241? mt=8 and Google Play https://play.google.com/store/apps/details? id=com.drgenebudger. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: Mesial Temporal Lobe Epilepsy (mTLE) characterized by progressive development of complex partial seizures originating from the hippocampus is the most prevalent and refractory type of epilepsy. One of the remarkable features of mTLE is the rhythmic pattern of occurrence of spontaneous seizures, implying a dependence on the endogenous clock system for seizure threshold. Conversely, circadian rhythms are affected by epilepsy too. Comprehending how the circadian system and seizures interact with each other is essential for understanding the pathophysiology of epilepsy as well as for developing innovative therapies that are efficacious for better seizure control. In this review, we confer how the temporal dysregulation of the circadian clock in the hippocampus combined with multiple uncoupled oscillators could lead to periodic seizure occurrences and comorbidities. Unraveling these associations with additional research would help in developing chronotherapy for mTLE, based on the chronobiology of spontaneous seizures. Notably, differential dosing of antiepileptic drugs over the circadian period and/or strategies that resynchronize biological rhythms may substantially improve the management of seizures in mTLE patients.

Abstract: Immune-mediated diseases affect more than 20% of the population, and many autoimmune diseases affect the skin. Drug repurposing (or repositioning) is a cost-effective approach for finding drugs that can be used to treat diseases for which they are currently not prescribed. We implemented an efficient bioinformatics approach using word embedding to summarize drug information from more than 20 million articles and applied machine learning to model the drug-disease relationship. We trained our drug repurposing approach separately on nine cutaneous diseases (including psoriasis, atopic dermatitis, and alopecia areata) and eight other immune-mediated diseases and obtained a mean area under the receiver operating characteristic of 0.93 in cross-validation. Focusing in particular on psoriasis, a chronic inflammatory condition of skin that affects more than 100 million people worldwide, we were able to confirm drugs that are known to be effective for psoriasis and to identify potential candidates used to treat other diseases. Furthermore, the targets of drug candidates predicted by our approach were significantly enriched among genes differentially expressed in psoriatic lesional skin from a large-scale RNA sequencing cohort. Although our algorithm cannot be used to determine clinical efficacy, our work provides an approach for suggesting drugs for repurposing to immune-mediated cutaneous diseases.

Abstract: Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of EcDHFR.