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Showing 3431 to 3434 of 5186 entries
Published in March 2018
Systematic Identification of Lysine 2-hydroxyisobutyrylated Proteins in Proteus mirabilis.
Abstract: Lysine 2-hydroxyisobutyrylation (Khib) is a novel post-translational modification (PTM), which was thought to play a role in active gene transcription and cellular proliferation. Here we report a comprehensive identification of Khib in Proteus mirabilis (P. mirabilis). By combining affinity enrichment with two-dimensional liquid chromatography and high-resolution mass spectrometry, 4735 2-hydroxyisobutyrylation sites were identified on 1051 proteins in P. mirabilis. These proteins bearing modifications were further characterized in abundance, distribution and functions. The interaction networks and domain architectures of these proteins with high confidence were revealed using bioinformatic tools. Our data demonstrate that many 2-hydroxyisobutyrylated proteins are involved in metabolic pathways, such as purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. The extensive distribution of Khib also indicates that the modification may play important influence to bacterial metabolism. The speculation is further supported by the observation that carbon sources can influence the occurrence of Khib Furthermore, we demonstrate that 2-hydroxyisobutyrylation on K343 was a negative regulatory modification on Enolase (ENO) activity, and molecular docking results indicate the regulatory mechanism that Khib may change the binding formation of ENO and its substrate 2-phospho-d-glycerate (2PG) and cause the substrate far from the active sites of enzyme. We hope this first comprehensive analysis of nonhistone Khib in prokaryotes is valuable for further functional investigation of this modification.
Published in March 2018
Osteoarthritis year in review 2017: genetics and epigenetics.
Abstract: OBJECTIVE: The purpose of this review is to describe highlights from original research publications related to osteoarthritis (OA), epigenetics and genomics with the intention of recognising significant advances. DESIGN: To identify relevant papers a Pubmed literature search was conducted for articles published between April 2016 and April 2017 using the search terms 'osteoarthritis' together with 'genetics', 'genomics', 'epigenetics', 'microRNA', 'lncRNA', 'DNA methylation' and 'histone modification'. RESULTS: The search term OA generated almost 4000 references. Publications using the combination of descriptors OA and genetics provided the most references (82 references). However this was reduced compared to the same period in the previous year; 8.1-2.1% (expressed as a percentage of the total publications combining the terms OA and genetics). Publications combining the terms OA with genomics (29 references), epigenetics (16 references), long non-coding RNA (lncRNA) (11 references; including the identification of novel lncRNAs in OA), DNA methylation (21 references), histone modification (3 references) and microRNA (miR) (79 references) were reviewed. Potential OA therapeutics such as histone deacetylase (HDAC) inhibitors have been identified. A number of non-coding RNAs may also provide targets for future treatments. CONCLUSION: There continues to be a year on year increase in publications researching miRs in OA (expressed as a percentage of the total publications), with a doubling over the last 4 years. An overview on the last year's progress within the fields of epigenetics and genomics with respect to OA will be given.
Published on March 31, 2018
Diclofenac Identified as a Kynurenine 3-Monooxygenase Binder and Inhibitor by Molecular Similarity Techniques.
Abstract: In this study, we apply a battery of molecular similarity techniques to known inhibitors of kynurenine 3-monooxygenase (KMO), querying each against a repository of approved, experimental, nutraceutical, and illicit drugs. Four compounds are assayed against KMO. Subsequently, diclofenac (also known by the trade names Voltaren, Voltarol, Aclonac, and Cataflam) has been confirmed as a human KMO protein binder and inhibitor in cell lysate with low micromolar KD and IC50, respectively, and low millimolar cellular IC50. Hit to drug hopping, as exemplified here for one of the most successful anti-inflammatory medicines ever invented, holds great promise for expansion into new disease areas and highlights the not-yet-fully-exploited potential of drug repurposing.
Published in March 2018
Optimising drug dosing in patients receiving extracorporeal membrane oxygenation.
Abstract: Optimal pharmacological management during extracorporeal membrane oxygenation (ECMO) involves more than administering drugs to reverse underlying disease. ECMO is a complex therapy that should be administered in a goal-directed manner to achieve therapeutic endpoints that allow reversal of disease and ECMO wean, minimisation of complications (treatment of complications when they do occur), early interruption of sedation and rehabilitation, maximising patient comfort and minimising risks of delirium. ECMO can alter both the pharmacokinetics (PK) and pharmacodynamics (PD) of administered drugs and our understanding of these alterations is still evolving. Based on available data it appears that modern ECMO circuitry probably has a less significant impact on PK when compared with critical illness itself. However, these findings need further confirmation in clinical population PK studies and such studies are underway. The altered PD associated with ECMO is less understood and more research is indicated. Until robust dosing guidelines become available, clinicians will have to rely on the principles of drug dosing in critically ill and known PK alterations induced by ECMO itself. This article summarises the PK alterations and makes preliminary recommendations on possible dosing approaches.
Published on March 31, 2018
In silico analysis of putative drug and vaccine targets of the metabolic pathways of Actinobacillus pleuropneumoniae using a subtractive/comparative genomics approach.
Abstract: Actinobacillus pleuropneumoniae is a Gram-negative bacterium that resides in the respiratory tract of pigs and causes porcine respiratory disease complex, which leads to significant losses in the pig industry worldwide. The incidence of drug resistance in this bacterium is increasing; thus, identifying new protein/gene targets for drug and vaccine development is critical. In this study, we used an in silico approach, utilizing several databases including the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Database of Essential Genes (DEG), DrugBank, and Swiss-Prot to identify non-homologous essential genes and prioritize these proteins for their druggability. The results showed 20 metabolic pathways that were unique and contained 273 non-homologous proteins, of which 122 were essential. Of the 122 essential proteins, there were 95 cytoplasmic proteins and 11 transmembrane proteins, which are potentially suitable for drug and vaccine targets, respectively. Among these, 25 had at least one hit in DrugBank, and three had similarity to metabolic proteins from Mycoplasma hyopneumoniae, another pathogen causing porcine respiratory disease complex; thus, they could serve as common therapeutic targets. In conclusion, we identified glyoxylate and dicarboxylate pathways as potential targets for antimicrobial therapy and tetra-acyldisaccharide 4'-kinase and 3-deoxy-D-manno-octulosonic-acid transferase as vaccine candidates against A. pleuropneumoniae.
Published in March 2018
Drug Repurposing by Simulating Flow Through Protein-Protein Interaction Networks.
Abstract: As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein-protein interaction networks, and used support vector machine to identify potentially effective drugs in our model disease, psoriasis. Using this method, we screened about 1,500 marketed and investigational substances, identified 51 drugs that were potentially effective, and selected three of them for experimental confirmation. All drugs inhibited tumor necrosis factor alpha-induced nuclear factor kappa B activity in vitro, suggesting they might be effective for treating psoriasis in humans. Additionally, these drugs significantly inhibited imiquimod-induced ear thickening and inflammation in the mouse model of the disease. All results suggest high prediction performance for the algorithm.
Published on March 29, 2018
Extensive impact of non-antibiotic drugs on human gut bacteria.
Abstract: A few commonly used non-antibiotic drugs have recently been associated with changes in gut microbiome composition, but the extent of this phenomenon is unknown. Here, we screened more than 1,000 marketed drugs against 40 representative gut bacterial strains, and found that 24% of the drugs with human targets, including members of all therapeutic classes, inhibited the growth of at least one strain in vitro. Particular classes, such as the chemically diverse antipsychotics, were overrepresented in this group. The effects of human-targeted drugs on gut bacteria are reflected on their antibiotic-like side effects in humans and are concordant with existing human cohort studies. Susceptibility to antibiotics and human-targeted drugs correlates across bacterial species, suggesting common resistance mechanisms, which we verified for some drugs. The potential risk of non-antibiotics promoting antibiotic resistance warrants further exploration. Our results provide a resource for future research on drug-microbiome interactions, opening new paths for side effect control and drug repurposing, and broadening our view of antibiotic resistance.
Published on March 28, 2018
A Landscape of Metabolic Variation across Tumor Types.
Abstract: Tumor metabolism is reorganized to support proliferation in the face of growth-related stress. Unlike the widespread profiling of changes to metabolic enzyme levels in cancer, comparatively less attention has been paid to the substrates/products of enzyme-catalyzed reactions, small-molecule metabolites. We developed an informatic pipeline to concurrently analyze metabolomics data from over 900 tissue samples spanning seven cancer types, revealing extensive heterogeneity in metabolic changes relative to normal tissue across cancers of different tissues of origin. Despite this heterogeneity, a number of metabolites were recurrently differentially abundant across many cancers, such as lactate and acyl-carnitine species. Through joint analysis of metabolomic data alongside clinical features of patient samples, we also identified a small number of metabolites, including several polyamines and kynurenine, which were associated with aggressive tumors across several tumor types. Our findings offer a glimpse onto common patterns of metabolic reprogramming across cancers, and the work serves as a large-scale resource accessible via a web application (http://www.sanderlab.org/pancanmet).
Published on March 26, 2018
Methylation-to-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, and Pathologic Complete Response in Multiple Cohorts.
Abstract: Prognostic biomarkers serve a variety of purposes in cancer treatment and research, such as prediction of cancer progression, and treatment eligibility. Despite growing interest in multi-omic data integration for defining prognostic biomarkers, validated methods have been slow to emerge. Given that breast cancer has been the focus of intense research, it is amenable to studying the benefits of multi-omic prognostic models due to the availability of datasets. Thus, we examined the efficacy of our methylation-to-expression feature model (M2EFM) approach to combining molecular and clinical predictors to create risk scores for overall survival, distant metastasis, and chemosensitivity in breast cancer. Gene expression, DNA methylation, and clinical variables were integrated via M2EFM to build models of overall survival using 1028 breast tumor samples and applied to validation cohorts of 61 and 327 samples. Models of distant recurrence-free survival and pathologic complete response were built using 306 samples and validated on 182 samples. Despite different populations and assays, M2EFM models validated with good accuracy (C-index or AUC >/= 0.7) for all outcomes and had the most consistent performance compared to other methods. Finally, we demonstrated that M2EFM identifies functionally relevant genes, which could be useful in translating an M2EFM biomarker to the clinic.
Published on March 23, 2018
Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes.
Abstract: Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.
Showing 3431 to 3434 of 5186 entries