RESUMEN
SUMMARY: Proteins binding to specific nucleotide sequences, such as transcription factors, play key roles in the regulation of gene expression. Their binding can be indirectly observed via associated changes in transcription, chromatin accessibility, DNA methylation and histone modifications. Identifying candidate factors that are responsible for these observed experimental changes is critical to understand the underlying biological processes. Here, we present monaLisa, an R/Bioconductor package that implements approaches to identify relevant transcription factors from experimental data. The package can be easily integrated with other Bioconductor packages and enables seamless motif analyses without any software dependencies outside of R. AVAILABILITY AND IMPLEMENTATION: monaLisa is implemented in R and available on Bioconductor at https://bioconductor.org/packages/monaLisa with the development version hosted on GitHub at https://github.com/fmicompbio/monaLisa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Asunto(s)
Programas Informáticos , Factores de TranscripciónRESUMEN
Rapid cellular responses to environmental stimuli are fundamental for development and maturation. Immediate early genes can be transcriptionally induced within minutes in response to a variety of signals. How their induction levels are regulated and their untimely activation by spurious signals prevented during development is poorly understood. We found that in developing sensory neurons, before perinatal sensory-activity-dependent induction, immediate early genes are embedded into a unique bipartite Polycomb chromatin signature, carrying active H3K27ac on promoters but repressive Ezh2-dependent H3K27me3 on gene bodies. This bipartite signature is widely present in developing cell types, including embryonic stem cells. Polycomb marking of gene bodies inhibits mRNA elongation, dampening productive transcription, while still allowing for fast stimulus-dependent mark removal and bipartite gene induction. We reveal a developmental epigenetic mechanism regulating the rapidity and amplitude of the transcriptional response to relevant stimuli, while preventing inappropriate activation of stimulus-response genes.
Asunto(s)
Cromatina/genética , Regulación del Desarrollo de la Expresión Génica , Genes Inmediatos-Precoces , Proteínas del Grupo Polycomb/genética , Animales , Cromatina/metabolismo , Células Madre Embrionarias/fisiología , Proteína Potenciadora del Homólogo Zeste 2/genética , Proteína Potenciadora del Homólogo Zeste 2/metabolismo , Epigénesis Genética , Histonas/metabolismo , Ratones Transgénicos , Mutación , Proteínas del Grupo Polycomb/metabolismo , Regiones Promotoras Genéticas , ARN Polimerasa II/genética , ARN Mensajero/genética , ARN Mensajero/metabolismo , Rombencéfalo/efectos de los fármacos , Rombencéfalo/embriología , Células Receptoras Sensoriales/fisiologíaRESUMEN
BACKGROUND: DNA methylation is a highly studied epigenetic signature that is associated with regulation of gene expression, whereby genes with high levels of promoter methylation are generally repressed. Genomic imprinting occurs when one of the parental alleles is methylated, i.e., when there is inherited allele-specific methylation (ASM). A special case of imprinting occurs during X chromosome inactivation in females, where one of the two X chromosomes is silenced, to achieve dosage compensation between the sexes. Another more widespread form of ASM is sequence dependent (SD-ASM), where ASM is linked to a nearby heterozygous single nucleotide polymorphism (SNP). RESULTS: We developed a method to screen for genomic regions that exhibit loss or gain of ASM in samples from two conditions (treatments, diseases, etc.). The method relies on the availability of bisulfite sequencing data from multiple samples of the two conditions. We leverage other established computational methods to screen for these regions within a new R package called DAMEfinder. It calculates an ASM score for all CpG sites or pairs in the genome of each sample, and then quantifies the change in ASM between conditions. It then clusters nearby CpG sites with consistent change into regions. In the absence of SNP information, our method relies only on reads to quantify ASM. This novel ASM score compares favorably to current methods that also screen for ASM. Not only does it easily discern between imprinted and non-imprinted regions, but also females from males based on X chromosome inactivation. We also applied DAMEfinder to a colorectal cancer dataset and observed that colorectal cancer subtypes are distinguishable according to their ASM signature. We also re-discover known cases of loss of imprinting. CONCLUSION: We have designed DAMEfinder to detect regions of differential ASM (DAMEs), which is a more refined definition of differential methylation, and can therefore help in breaking down the complexity of DNA methylation and its influence in development and disease.
