LSD1: more than demethylation of histone lysine residues

Lysine-specific histone demethylase 1 (LSD1) represents the first example of an identified nuclear protein with histone demethylase activity. In particular, it plays a special role in the epigenetic regulation of gene expression, as it removes methyl groups from mono- and dimethylated lysine 4 and/or lysine 9 on histone H3 (H3K4me1/2 and H3K9me1/2), behaving as a repressor or activator of gene expression, respectively. Moreover, it has been recently found to demethylate monomethylated and dimethylated lysine 20 in histone H4 and to contribute to the balance of several other methylated lysine residues in histone H3 (i.e., H3K27, H3K36, and H3K79). Furthermore, in recent years, a plethora of nonhistone proteins have been detected as targets of LSD1 activity, suggesting that this demethylase is a fundamental player in the regulation of multiple pathways triggered in several cellular processes, including cancer progression. In this review, we analyze the molecular mechanism by which LSD1 displays its dual effect on gene expression (related to the specific lysine target), placing final emphasis on the use of pharmacological inhibitors of its activity in future clinical studies to fight cancer

Tracing and tracking epiallele families in complex DNA populations

DNA methylation is a stable epigenetic modification, extremely polymorphic and driven by stochastic and deterministic events. Most of the current techniques used to analyse methylated sequences identify methylated cytosines (mCpGs) at a single-nucleotide level and compute the average methylation of CpGs in the population of molecules. Stable epialleles, i.e. CpG strings with the same DNA sequence containing a discrete linear succession of phased methylated/non-methylated CpGs in the same DNA molecule, cannot be identified due to the heterogeneity of the 5′–3′ ends of the molecules. Moreover, these are diluted by random unstable methylated CpGs and escape detection. We present here MethCoresProfiler, an R-based tool that provides a simple method to extract and identify combinations of methylated phased CpGs shared by all components of epiallele families in complex DNA populations. The methylated cores are stable over time, evolve by acquiring or losing new methyl sites and, ultimately, display high information content and low stochasticity. We have validated this method by identifying and tracing rare epialleles and their families in synthetic or in vivo complex cell populations derived from mouse brain areas and cells during postnatal differentiation

RNA stabilizes transcription-Dependent Chromatin Loops Induced By Nuclear Hormones

We show that transcription induced by nuclear receptors for estrogen (e2) or retinoic acid (RA) is associated with formation of chromatin loops that juxtapose the 5’ end (containing the promoter) with the enhancer and the 3′ polyA addition site of the target gene. We nd three loop con gurations which change as a function of time after induction: 1. RA or E2-induced loops which connect the 5′ end, the enhancer and the 3′ end of the gene, and are stabilized by RNA early after induction; 2. E2-independent loops whose stability does not require RNA; 3. Loops detected only by treatment of chromatin with RNAse H1 prior to hormonal induction. RNAse H1 digests RNA that occludes the relevant restriction sites, thus preventing detection of these loops. R-loops at the 5′ and 3′ ends of the RA or e2-target genes were demonstrated by immunoprecipitation with anti-DNA-RNA hybrid antibodies as well as by sensitivity to RNAse H1. The cohesin RAD21 subunit is preferentially recruited to the target sites upon RA or e2 induction of transcription. R21 binding to chromatin is eliminated by RNAse H1. We identi ed e2-induced and RNase H1-sensitive antisense RNAs located at the 5′ and 3′ ends of the e2-induced transcription unit which stabilize the loops and RAD21 binding to chromatin. This is the rst report of chromatin loops that form after gene induction that are maintained by RNA:DNA hybrids

Genome sequences and great expectations

To assess how automatic function assignment will contribute to genome annotation in the next five years, we have performed an analysis of 31 available genome sequences. An emerging pattern is that function can be predicted for almost two-thirds of the 73,500 genes that were analyzed. Despite progress in computational biology, there will always be a great need for large-scale experimental determination of protein function

High-coverage methylation data of a gene model before and after DNA damage and homologous repair

Genome-wide methylation analysis is limited by its low coverage and the inability to detect single variants below 10%. Quantitative analysis provides accurate information on the extent of methylation of single CpG dinucleotide, but it does not measure the actual polymorphism of the methylation profiles of single molecules. To understand the polymorphism of DNA methylation and to decode the methylation signatures before and after DNA damage and repair, we have deep sequenced in bisulfite-treated DNA a reporter gene undergoing site-specific DNA damage and homologous repair. In this paper, we provide information on the data generation, the rationale for the experiments and the type of assays used, such as cytofluorimetry and immunoblot data derived during a previous work published in Scientific Reports, describing the methylation and expression changes of a model gene (GFP) before and after formation of a double-strand break and repair by homologous-recombination or non-homologous-end-joining. These data provide: 1) a reference for the analysis of methylation polymorphism at selected loci in complex cell populations; 2) a platform and the tools to compare transcription and methylation profiles

Targeted DNA oxidation by LSD1–SMAD2/3 primes TGF-β1/ EMT genes for activation or repression

Abstract The epithelial-to-mesenchymal transition (EMT) is a complex transcriptional program induced by transforming growth factor β1 (TGF-β1). Histone lysine-specific demethylase 1 (LSD1) has been recognized as a key mediator of EMT in cancer cells, but the precise mechanism that underlies the activation and repression of EMT genes still remains elusive. Here, we characterized the early events induced by TGF-β1 during EMT initiation and establishment. TGF-β1 triggered, 30–90 min post-treatment, a nuclear oxidative wave throughout the genome, documented by confocal microscopy and mass spectrometry, mediated by LSD1. LSD1 was recruited with phosphorylated SMAD2/3 to the promoters of prototypic genes activated and repressed by TGF-β1. After 90 min, phospho-SMAD2/3 downregulation reduced the complex and LSD1 was then recruited with the newly synthesized SNAI1 and repressors, NCoR1 and HDAC3, to the promoters of TGF-β1-repressed genes such as the Wnt soluble inhibitor factor 1 gene (WIF1), a change that induced a late oxidative burst. However, TGF-β1 early (90 min) repression of transcription also required synchronous signaling by reactive oxygen species and the stress-activated kinase c-Jun N-terminal kinase. These data elucidate the early events elicited by TGF-β1 and the priming role of DNA oxidation that marks TGF-β1-induced and -repressed genes involved in the EMT

Loss of p53 activates thyroid hormone via type 2 deiodinase and enhances DNA damage

: The Thyroid Hormone (TH) activating enzyme, type 2 Deiodinase (D2), is functionally required to elevate the TH concentration during cancer progression to advanced stages. However, the mechanisms regulating D2 expression in cancer still remain poorly understood. Here, we show that the cell stress sensor and tumor suppressor p53 silences D2 expression, thereby lowering the intracellular THs availability. Conversely, even partial loss of p53 elevates D2/TH resulting in stimulation and increased fitness of tumor cells by boosting a significant transcriptional program leading to modulation of genes involved in DNA damage and repair and redox signaling. In vivo genetic deletion of D2 significantly reduces cancer progression and suggests that targeting THs may represent a general tool reducing invasiveness in p53-mutated neoplasms

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio