Cell-free circulating tumour DNA (ctDNA) in plasma has been shown to be informative of the genomic alterations present in tumours and has been used to monitor tumour progression and response to treatments. However, patients with brain tumours do not present with or present with low amounts of ctDNA in plasma precluding the genomic characterization of brain cancer through plasma ctDNA. Here we show that ctDNA derived from central nervous system tumours is more abundantly present in the cerebrospinal fluid (CSF) than in plasma. Massively parallel sequencing of CSF ctDNA more comprehensively characterizes the genomic alterations of brain tumours than plasma, allowing the identification of actionable brain tumour somatic mutations. We show that CSF ctDNA levels longitudinally fluctuate in time and follow the changes in brain tumour burden providing biomarkers to monitor brain malignancies. Moreover, CSF ctDNA is shown to facilitate and complement the diagnosis of leptomeningeal carcinomatosis.

SummaryThe existence of different patterns of chemical modifications (acetylation, methylation, phosphorylation, ubiquitination and ADP-ribosylation) of the histone tails led, some years ago, to the histone code hypothesis. According to this hypothesis, these modifications would provide binding sites for proteins that can change the chromatin state to either active or repressed. Interestingly, some protein domains present in histone-modifying enzymes are known to interact with these covalent marks in the histone tails. This was first shown for the bromodomain, which was found to interact selectively with acetylated lysines at the histone tails. More recently, it has been described that the chromodomain can be targeted to methylation marks in histone N-terminal domains. Finally, the interaction between the SANT domain and histones is also well documented. Overall, experimental evidence suggests that these domains could be involved in the recruitment of histone-modifying enzymes to discrete chromosomal locations, and/or in the regulation their enzymatic activity. Within this context, we review the distribution of bromodomains, chromodomains and SANT domains among chromatin-modifying enzymes and discuss how they can contribute to the translation of the histone code. The histone code hypothesis The packing of the eukaryotic genome into chromatin provides the means for compaction of the entire genome inside the nucleus. However, this packing restricts the access to DNA of the many regulatory proteins essential for biological processes like replication, transcription, DNA repair and recombination.(1)There are two mechanisms that can counterbalance the repressive nature of chromatin, allowing access to nucleosomal DNA: (i) covalent modification of histone tails like acetylation, methylation, phosphorylation and ubiquitination; (2)(3)(4)(5) and (ii) altering of the nucleosomal structure by enzymes utilising energy from ATP hydrolysis.In the early nineties, it was proposed that histone covalent modifications can work as recognition signals, directing the binding to chromatin of non-histone proteins that determine chromatin function. (7,8) More recently, it has been hypothesized that specific tail modifications and/or their combinations constitute a code, the histone code, that determines the transcriptional state of the genes. (9)(10)(11) According to this hypothesis, ''multiple histone modifications, acting in a combinatorial or sequential fashion on one or multiple tails, specify unique downstream functions''.In the last years, an increasing amount of experimental data has provided clear support for the different aspects of the histone code hypothesis, contributing to refine and improve it.(For review 12,13) One important point that has been addressed by different authors is the idea that the histone code must use combinations of modifications.(9) For example, H3 methylated at K9 could initiate chromatin condensation and silencing (14,15) but, in the context of methylated H3K4 and H4K20, methyl-K9 H3 helps to maintain ...

The recent drop in genome sequencing costs has created a promising horizon for the development of genomic medicine. Within the biomedical environment, sequencing data are increasingly used for disease diagnosis and prognosis, treatment development, counseling, and so on. Many of these applications rely on the identification of disease causing variants. This is a particularly challenging problem because of the large number and wide variety of sequence variants identified in sequencing projects, and also because we only have a limited understanding of the physicochemical/biochemical properties that differentiate neutral from pathologic variants. Nonetheless, these last years have witnessed important methodological advances for one class of variants, those corresponding to changes in the amino‐acid sequence of proteins. Proteins are a main constituent of living systems. We know that although their biological properties are essentially determined by the amino‐acid sequence, not all the changes in this sequence have the same impact. Some are neutral, but others affect protein function and lead to disease. A large body of evidence shows that whether one or the other is the case that depends on properties such as mutation location in the protein structure, interspecies conservation, and so on. Mutation prediction methods based on these features have good success rates, in the 70–90% range, although representation over time suggests there is a performance plateau that would limit their applicability. In light of the most recent advances in the field, and after reviewing the foundations of prediction methods, we discuss the existence of this performance threshold and how it can be overcomed.This article is categorized under: Computer and Information Science > Databases and Expert Systems

In recent years, high-throughput next-generation sequencing technology has allowed a rapid increase in diagnostic capacity and precision through different bioinformatics processing algorithms, tools, and pipelines. The identification, annotation, and classification of sequence variants within different target regions are now considered a gold standard in clinical genetic diagnosis. However, this procedure lacks the ability to link regulatory events such as differential splicing to diseases. RNA-seq is necessary in clinical routine in order to interpret and detect among others splicing events and splicing variants, as it would increase the diagnostic rate by up to 10–35%. The transcriptome has a very dynamic nature, varying according to tissue type, cellular conditions, and environmental factors that may affect regulatory events such as splicing and the expression of genes or their isoforms. RNA-seq offers a robust technical analysis of this complexity, but it requires a profound knowledge of computational/statistical tools that may need to be adjusted depending on the disease under study. In this article we will cover RNA-seq analyses best practices applied to clinical routine, bioinformatics procedures, and present challenges of this approach.

The progressive restriction of differentiation potential from pluripotent embryonic stem cells (ESCs) to tissue-specific stem cells involves widespread epigenetic reprogramming, including modulation of DNA methylation patterns. Skeletal muscle stem cells are required for the growth, maintenance, and regeneration of skeletal muscle. To investigate the contribution of DNA methylation to the establishment of the myogenic program, we analyzed ESCs, skeletal muscle stem cells in proliferating (myoblasts) and differentiating conditions (myotubes), and mature myofibers. About 1.000 differentially methylated regions were identified during musclelineage determination and terminal differentiation, mainly located in gene bodies and intergenic regions. As a whole, myogenic stem cells showed a gain of DNA methylation, while muscle differentiation was accompanied by loss of DNA methylation in CpG-poor regions. Notably, the hypomethylated regions in myogenic stem cells were neighbored by enhancer-type chromatin, suggesting the involvement of DNA methylation in the regulation of cell-type specific enhancers. Interestingly, we demonstrated the hypomethylation of the muscle cell-identity Myf5 superenhancer only in muscle cells. Furthermore, we observed that upstream stimulatory factor 1 binding to Myf5 super-enhancer occurs upon DNA demethylation in myogenic stem cells. Taken altogether, we characterized the unique DNA methylation signature of skeletal muscle stem cells and highlighted the importance of DNA methylation-mediated regulation of cell identity Myf5 super-enhancer during cellular differentiation.