Abstract: BACKGROUND: Colorectal cancer arises from the accumulation of genetic mutations that induce dysfunction of intracellular signaling. However, the underlying mechanism of colorectal tumorigenesis driven by genetic mutations remains yet to be elucidated. RESULTS: To investigate colorectal tumorigenesis at a system-level, we have reconstructed a large-scale Boolean network model of the human signaling network by integrating previous experimental results on canonical signaling pathways related to proliferation, metastasis, and apoptosis. Throughout an extensive simulation analysis of the attractor landscape of the signaling network model, we found that the attractor landscape changes its shape by expanding the basin of attractors for abnormal proliferation and metastasis along with the accumulation of driver mutations. A further hypothetical study shows that restoration of a normal phenotype might be possible by reversely controlling the attractor landscape. Interestingly, the targets of approved anti-cancer drugs were highly enriched in the identified molecular targets for the reverse control. CONCLUSIONS: Our results show that the dynamical analysis of a signaling network based on attractor landscape is useful in acquiring a system-level understanding of tumorigenesis and developing a new therapeutic strategy.

Abstract: Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar "moneyball" approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.

Abstract: BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. RESULTS: In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. CONCLUSIONS: To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Abstract: Systemic lupus erythematosus is an autoimmune disease that can affect numerous tissues and is characterized by the production of nuclear antigen-directed autoantibodies (e.g., anti-dsDNA). Using a combination of virtual and ELISA-based screens, we made the intriguing discovery that several HIV-protease inhibitors can function as decoy antigens to specifically inhibit the binding of anti-dsDNA antibodies to target antigens such as dsDNA and pentapeptide DWEYS. Computational modeling revealed that HIV-protease inhibitors comprised structural features present in DWEYS and predicted that analogues containing more flexible backbones would possess preferred binding characteristics. To address this, we reduced the internal amide backbone to improve flexibility, producing new small-molecule decoy antigens, which neutralize anti-dsDNA antibodies in vitro, in situ, and in vivo. Pharmacokinetic and SLE model studies demonstrated that peptidomimetic FISLE-412,1 a reduced HIV protease inhibitor analogue, was well-tolerated, altered serum reactivity to DWEYS, reduced glomeruli IgG deposition, preserved kidney histology, and delayed SLE onset in NZB/W F1 mice.

Abstract: A minimal gene set (MGS) is critical for the assembly of a minimal artificial cell. We have developed a proposal of simplifying bacterial gene set to approximate a bacterial MGS by the following procedure. First, we base our simplified bacterial gene set (SBGS) on experimentally determined essential genes to ensure that the genes included in the SBGS are critical. Second, we introduced a half-retaining strategy to extract persistent essential genes to ensure stability. Third, we constructed a viable metabolic network to supplement SBGS. The proposed SBGS includes 327 genes and required 431 reactions. This report describes an SBGS that preserves both self-replication and self-maintenance systems. In the minimized metabolic network, we identified five novel hub metabolites and confirmed 20 known hubs. Highly essential genes were found to distribute the connecting metabolites into more reactions. Based on our SBGS, we expanded the pool of targets for designing broad-spectrum antibacterial drugs to reduce pathogen resistance. We also suggested a rough semi-de novo strategy to synthesize an artificial cell, with potential applications in industry.

Abstract: More aggressive and therapy-resistant oestrogen receptor (ER)-positive breast cancers remain a great clinical challenge. Here our integrative genomic analysis identifies tousled-like kinase 2 (TLK2) as a candidate kinase target frequently amplified in approximately 10.5% of ER-positive breast tumours. The resulting overexpression of TLK2 is more significant in aggressive and advanced tumours, and correlates with worse clinical outcome regardless of endocrine therapy. Ectopic expression of TLK2 leads to enhanced aggressiveness in breast cancer cells, which may involve the EGFR/SRC/FAK signalling. Conversely, TLK2 inhibition selectively inhibits the growth of TLK2-high breast cancer cells, downregulates ERalpha, BCL2 and SKP2, impairs G1/S cell cycle progression, induces apoptosis and significantly improves progression-free survival in vivo. We identify two potential TLK2 inhibitors that could serve as backbones for future drug development. Together, amplification of the cell cycle kinase TLK2 presents an attractive genomic target for aggressive ER-positive breast cancers.

Abstract: One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development.

Abstract: Peptide:N-glycanase (NGLY1) is an enzyme responsible for cleaving oligosaccharide moieties from misfolded glycoproteins to enable their proper degradation. Deletion and truncation mutations in this gene are responsible for an inherited disorder of the endoplasmic reticulum-associated degradation pathway. However, the literature is unclear whether the disorder is a result of mutations leading to loss-of-function, loss of substrate specificity, loss of protein stability or a combination of these factors. In this communication, without burdening ourselves with the mechanistic underpinning of disease causation because of mutations on the NGLY1 protein, we demonstrate the successful application of virtual ligand screening (VLS) combined with experimental high-throughput validation to the discovery of novel small-molecules that show binding to the transglutaminase domain of NGLY1. Attempts at recombinant expression and purification of six different constructs led to successful expression of five, with three constructs purified to homogeneity. Most mutant variants failed to purify possibly because of misfolding and the resultant exposure of surface hydrophobicity that led to protein aggregation. For the purified constructs, our threading/structure-based VLS algorithm, FINDSITE(comb), was employed to predict ligands that may bind to the protein. Then, the predictions were assessed by high-throughput differential scanning fluorimetry. This led to the identification of nine different ligands that bind to the protein of interest and provide clues to the nature of pharmacophore that facilitates binding. This is the first study that has identified novel ligands that bind to the NGLY1 protein as a possible starting point in the discovery of ligands with potential therapeutic applications in the treatment of the disorder caused by NGLY1 mutants.

Abstract: OBJECTIVE: Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. MATERIALS AND METHODS: Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). RESULTS: We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. DISCUSSION: Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. CONCLUSIONS: We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.

Abstract: Emerging organic contaminants in wastewater are usually analyzed by targeted approaches, and especially estrogens have been the focus of environmental research due to their high hormonal activity. The selection of specific target compounds means, however, that most of the sample components, including transformation products and potential new contaminants, are neglected. In this study, the fate of steroidal compounds in wastewater treatment processes was evaluated by a nontargeted approach based on comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. The potential of the nontargeted approach to generate comprehensive information about sample constituents was demonstrated with use of statistical tools. Transformation pathways of the tentatively identified compounds with steroidal four-ring structure were proposed. The purification efficiency of the wastewater treatment plants was studied, and the distribution of the compounds of interest in the suspended solids, effluent water, and sludge was measured. The results showed that, owing to strong adsorption of hydrophobic compounds onto the solid matter, the steroids were mostly bound to the suspended solids of the effluent water and the sewage sludge at the end of the treatment process. The most abundant steroid class was androstanes in the aqueous phase and cholestanes in the solid phase. 17beta-estradiol was the most abundant estrogen in the aqueous phase, but it was only detected in the influent samples indicating efficient removal during the treatment process. In the sludge samples, however, high concentrations of an oxidation product of 17beta-estradiol, estrone, were measured.