EpiFactors: a comprehensive database of human epigenetic factors and complexes

Abstract

Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians.Database URL: http://epifactors.autosome.ru

Enhancer Remodeling and MicroRNA Alterations Are Associated with Acquired Resistance to ALK Inhibitors

“…2B).The mRNA expression of CREBBP and EP300 were decreased in H3122-LR cells, suggesting that the global decrease in H3K27ac levels was at least partially due to the decreased expression of H3K27 acetyltransferase. To explore the expression changes in other epigenetic regulators during acquired resistance, we analyzed the expression levels of 720 genes encoding epigenetic regulators including 101 chromatin remodelers, 158 histone modification writers, 90 histone modification readers, and 66 histone modification erasers (26) in both H3122-LR cells and H3122-derived ceritinib resistant xenograft tumors using RNA-seq and expression microarray, respectively.…”

Can cloud-based tools accelerate Alzheimer’s disease drug discovery?

“…A set of plural information cyber-resources [9,12,13,14,[16][17][18][19] can be useful for the implementation of preclinical and clinical procedures oriented to an efficient drug development. Figure 1 summarizes the most relevant ICRs that might be useful for researchers, clinicians, and the pharmaceutical industry to treat AD from a global perspective, assuming that the three pillars for an effective therapeutic intervention include: (i) to understand pathogenesis for the appropriate definition of suitable targets for treatment, (ii) to establish the biomarkers for an early diagnosis of the disease, and (iii) to personalize the treatment with available drugs or with new drugs which will be introduced in the future.…”

“…[4,5] In the mid-term, the optimization of AD therapeutics requires the establishment of new postulates regarding (i) the costs of medicines (improvement in cost-effectiveness), (ii) the assessment of protocols for a multifactorial treatment (it is unlikely that a single drug will be powerful enough to slow down the disease progression in a complex disorder such as AD), (iii) the implementation of novel therapeutics addressing causative factors (identification of primary targets), (iv) the setting-up of pharmacogenomic strategies for drug development, (v) the incorporation of pharmacogenomics and pharmacoepigenomics into the clinical setting (epigenetic changes are potentially reversible with pharmacologic intervention), [12,13] and (vi) new regulations for the development of vaccines and/or other preventive strategies to treat susceptible patients in presymptomatic conditions. [4, 5,12,13,16] In our opinion, the incorporation of pharmacogenomics and pharmacoepigenomics into drug development in preclinical stages and in clinical trials would provide several benefits: (i) to identify candidate drugs for specific targets on a predictive basis (not relying on serendipity or trial-and-error assays); (ii) to reduce costs and time in preclinical studies; (iii) to identify candidate patients with the ideal genomic profile to receive a particular drug; (iv) to adapt the dose in over 90% of the cases according to the condition of CYP-related extensive, intermediate, poor, or ultra-rapid metabolizer (reducing the occurrence of direct side-effects in 30-50% of the cases); (v) to ensure drug penetration into the brain (drug transporter genotyping); (vi) to reduce drug interactions by 30-50%, avoiding the administration of inhibitors or inducers capable of modifying the normal enzymatic activity on a particular substrate (AD patients may take 6-10 drugs/day); (vii) to enhance efficacy and to reduce toxicity; and (viii) to eliminate the unnecessary costs (>30% of pharmaceutical global costs) derived from the consequences of inappropriate drug (or patient) selection and from the overmedication administered to mitigate adverse drug reactions. [4,5] Many different types of ICRs are available ( Figure 1) to facilitate proper decision-making in drug development for AD; however, promiscuity, redundancy, rigidity, conflict of interest, restricted accessibility, and sometimes, biased opinions, devaluate the potential utility of some cloud-based tools.…”

“…The genetic and epigenetic defects identified in AD can be classified into several categories: Mendelian mutations, susceptibility single-nucleotide polymorphisms, copy number variation, mitochondrial DNA mutations, and epigenetic changes (DNA methylation, chromatin/histone remodeling, and micro-RNA dysregulation). [12] The genomic, epigenomic, transcriptomic, proteomic, and metabolomic components, together with environmental factors, and concomitant pathologies ( Figure 1) are determinant for disease progression and the therapeutic response to drugs.…”

Integrative CAGE and DNA Methylation Profiling Identify Epigenetically Regulated Genes in NSCLC

“…To investigate possible associations between epi-markers and known epigenetic factors, we calculated gene expression correlations between the 22 epi-markers and 683 genes from EpiFactor database (39). Intriguingly, in LUAD, the most positively correlated EpiFactors were strongly enriched for cell-cycle genes (12/14 genes, GO_CELL_CYCLE, FDR q-value ¼ 5.6eÀ14) and were more specifically related to the G 2 -M checkpoint genes (8/14 genes, HALLMARK_G2M_CHECK-POINT, FDR q-value ¼ 4.44eÀ13; Supplementary Fig.…”