Mutational spectrum by phenotype: panel-based NGS testing of patients with clinical suspicion of RASopathy and children with multiple café-au-lait macules.

Children with neurofibromatosis type 1 (NF1) may exhibit an incomplete clinical presentation, making difficult to reach a clinical diagnosis. A phenotypic overlap may exist in children with other RASopathies or with other genetic conditions if only multiple café-au-lait macules (CALMs) are present. The syndromes that can converge in these inconclusive phenotypes have different clinical courses. In this context, an early genetic testing has been proposed to be clinically useful to manage these patients. We present the validation and implementation into diagnostics of a custom NGS panel (I2HCP, ICO-IMPPC Hereditary Cancer Panel) for testing patients with a clinical suspicion of a RASopathy (n = 48) and children presenting multiple CALMs (n = 102). We describe the mutational spectrum and the detection rates identified in these two groups of individuals. We identified pathogenic variants in 21 out of 48 patients with clinical suspicion of RASopathy, with mutations in NF1 accounting for 10% of cases. Furthermore, we identified pathogenic mutations mainly in the NF1 gene, but also in SPRED1, in more than 50% of children with multiple CALMs, exhibiting an NF1 mutational spectrum different from a group of clinically diagnosed NF1 patients (n = 80). An NGS panel strategy for the genetic testing of these two phenotype-defined groups outperforms previous strategies.

Epigenetic and transcriptional dysregulation of VWA2 associated with a MYC-driven oncogenic program in colorectal cancer.

VWA2 encodes AMACO, a secreted protein up-regulated in most colorectal carcinomas (CRC), constituting a promising biomarker. The mechanism responsible for its aberrant up-regulation has not been previously described. In this work, we analyzed VWA2 DNA methylation in over 400 primary CRCs. No epigenetic alterations were found in its promoter-associated CpG island. However, the region located downstream of the transcriptional start site was hypomethylated in most CRCs. ChIP-Seq revealed increased levels of the active mark H3K4me3 and reduction of the repressive mark H3K27me3. In contrast, several CRC cell lines exhibited hypermethylation of VWA2. 5-AZA-2-deoxycitidine treatment led to transcriptional activation of VWA2, supporting a functional link between DNA methylation and transcription. VWA2 expression in primary CRCs correlated with that of Myc and Myc-target genes. Transcriptional up-regulation of VWA2 is extremely frequent (78%) and strong (average fold change >15) in CRC, but not in other types of cancer. VWA2 undergoes hypomethylation in the majority of CRCs. This alteration could partly underlie the previously reported over-expression of AMACO. Co-expression profiling suggests that VWA2 might be a constituent of a larger oncogenic transcriptional program regulated by c-Myc. Up-regulation of VWA2 is virtually exclusive of CRC, reinforcing its potential as a specific biomarker.

Analysis of Somatic DNA Methylation Alterations of Genes Encoding Cell Surface Metallopeptidases in Colorectal Cancer.

The genome of cancer cells accumulates numerous genetic and epigenetic somatic alterations ultimately conferring capabilities for unrestrained growth, invasion of local tissues, migration, and colonization of distant organs. Many of these new capabilities require the disruption of the cell-to-cell interactions between the cancer cell and its microenvironment. These interactions are mediated, among other factors, by the activity of extracellular enzymes that reshape not only the extracellular compartment of the cancer cells but also that of the neighboring non-cancerous stroma cells. Cell surface metallopeptidases play a crucial role in this process, by cleaving and modifying fundamental components of the extracellular compartment. The transcriptional profile of cell surface metallopeptidases becomes deregulated in several human cancers by genetic and epigenetic alterations, contributing to the tumor phenotype. In this article, we describe two common strategies to analyze somatic epigenetic alterations of cell surface metallopeptidases, i.e., high-resolution single locus analysis and high-throughput multi-loci analysis, presenting several illustrative analyses performed on our CRC collection. These analyses demonstrate that cell surface metallopeptidases, particularly those belonging to the ADAMTS gene family, frequently undergo somatic DNA hypermethylation in CRC suggesting the existence of an underlying mechanism or a strong selection process favoring the transcriptional silencing of these genes.

Helicase Lymphoid-Specific Enzyme Contributes to the Maintenance of Methylation of SST1 Pericentromeric Repeats That Are Frequently Demethylated in Colon Cancer and Associate with Genomic Damage.

DNA hypomethylation at repetitive elements accounts for the genome-wide DNA hypomethylation common in cancer, including colorectal cancer (CRC). We identified a pericentromeric repeat element called SST1 frequently hypomethylated (>5% demethylation compared with matched normal tissue) in several cancers, including 28 of 128 (22%) CRCs. SST1 somatic demethylation associated with genome damage, especially in tumors with wild-type TP53. Seven percent of the 128 CRCs exhibited a higher ("severe") level of demethylation (≥10%) that co-occurred with TP53 mutations. SST1 demethylation correlated with distinct histone marks in CRC cell lines and primary tumors: demethylated SST1 associated with high levels of the repressive histone 3 lysine 27 trimethylation (H3K27me3) mark and lower levels of histone 3 lysine 9 trimethylation (H3K9me3). Furthermore, induced demethylation of SST1 by 5-aza-dC led to increased H3K27me3 and reduced H3K9me3. Thus, in some CRCs, SST1 demethylation reflects an epigenetic reprogramming associated with changes in chromatin structure that may affect chromosomal integrity. The chromatin remodeler factor, the helicase lymphoid-specific (HELLS) enzyme, called the "epigenetic guardian of repetitive elements", interacted with SST1 as shown by chromatin immunoprecipitation, and down-regulation of HELLS by shRNA resulted in demethylation of SST1 in vitro. Altogether these results suggest that HELLS contributes to SST1 methylation maintenance. Alterations in HELLS recruitment and function could contribute to the somatic demethylation of SST1 repeat elements undergone before and/or during CRC pathogenesis.

MYCL is a target of a BET bromodomain inhibitor, JQ1, on growth suppression efficacy in small cell lung cancer cells.

We aimed to elucidate the effect of JQ1, a BET inhibitor, on small cell lung cancers (SCLCs) with MYCL amplification and/or expression. Fourteen SCLC cell lines, including four with MYCL amplification, were examined for the effects of JQ1 on protein and gene expression by Western blot and mRNA microarray analyses. The sensitivity of SCLC cells to JQ1 was assessed by cell growth and apoptosis assays. MYCL was expressed in all the 14 cell lines, whereas MYC/MYCN expression was restricted mostly to cell lines with gene amplification. ASCL1, a transcription factor shown to play a role in SCLC, was also expressed in 11/14 cell lines. All SCLC cell lines were sensitive to JQ1 with GI50 values ≤1.23 µM, with six of them showing GI50 values <0.1 µM. Expression of MYCL as well as MYCN, ASCL1 and other driver oncogenes including CDK6 was reduced by JQ1 treatment, in particular in the cell lines with high expression of the respective genes; however, no association was observed between the sensitivity to JQ1 and the levels of MYCL, MYCN and ASCL1 expression. In contrast, levels of CDK6 expression and its reduction rates by JQ1 were associated with JQ1 sensitivity. Therefore, we concluded that CDK6 is a novel target of JQ1 and predictive marker for JQ1 sensitivity in SCLC cells.

Computational Prediction of HIV-1 Resistance to Protease Inhibitors.

The development of mutations in HIV-1 protease (PR) hinders the activity of antiretroviral drugs, forcing changes in drug prescription. Most resistance assessments used to date rely on expert-based rules on predefined sets of stereotypical mutations; such an information-driven approach cannot capture new polymorphisms or be applied for new drugs. Computational modeling could provide a more general assessment of drug resistance and could be made available to clinicians through the Internet. We have created a protocol involving sequence comparison and all-atom protein-ligand induced fit simulations to predict resistance at the molecular level. We first compared our predictions with the experimentally determined IC50 values of darunavir, amprenavir, ritonavir, and indinavir from reference PR mutants displaying different resistance levels. We then performed analyses on a large set of variants harboring more than 10 mutations. Finally, several sequences from real patients were analyzed for amprenavir and darunavir. Our computational approach detected all of the genotype changes triggering high-level resistance, even those involving a large number of mutations.

Integration of multi-omics data of a genome-reduced bacterium: Prevalence of post-transcriptional regulation and its correlation with protein abundances.

We developed a comprehensive resource for the genome-reduced bacterium Mycoplasma pneumoniae comprising 1748 consistently generated '-omics' data sets, and used it to quantify the power of antisense non-coding RNAs (ncRNAs), lysine acetylation, and protein phosphorylation in predicting protein abundance (11%, 24% and 8%, respectively). These factors taken together are four times more predictive of the proteome abundance than of mRNA abundance. In bacteria, post-translational modifications (PTMs) and ncRNA transcription were both found to increase with decreasing genomic GC-content and genome size. Thus, the evolutionary forces constraining genome size and GC-content modify the relative contributions of the different regulatory layers to proteome homeostasis, and impact more genomic and genetic features than previously appreciated. Indeed, these scaling principles will enable us to develop more informed approaches when engineering minimal synthetic genomes.

MyMpn: a database for the systems biology model organism Mycoplasma pneumoniae.

MyMpn (http://mympn.crg.eu) is an online resource devoted to studying the human pathogen Mycoplasma pneumoniae, a minimal bacterium causing lower respiratory tract infections. Due to its small size, its ability to grow in vitro, and the amount of data produced over the past decades, M. pneumoniae is an interesting model organisms for the development of systems biology approaches for unicellular organisms. Our database hosts a wealth of omics-scale datasets generated by hundreds of experimental and computational analyses. These include data obtained from gene expression profiling experiments, gene essentiality studies, protein abundance profiling, protein complex analysis, metabolic reactions and network modeling, cell growth experiments, comparative genomics and 3D tomography. In addition, the intuitive web interface provides access to several visualization and analysis tools as well as to different data search options. The availability and--even more relevant--the accessibility of properly structured and organized data are of up-most importance when aiming to understand the biology of an organism on a global scale. Therefore, MyMpn constitutes a unique and valuable new resource for the large systems biology and microbiology community.

HumMeth27QCReport: an R package for quality control and primary analysis of Illumina Infinium methylation data.

BACKGROUND: The study of the human DNA methylome has gained particular interest in the last few years. Researchers can nowadays investigate the potential role of DNA methylation in common disorders by taking advantage of new high-throughput technologies. Among these, Illumina Infinium assays can interrogate the methylation levels of hundreds of thousands of CpG sites, offering an ideal solution for genome-wide methylation profiling. However, like for other high-throughput technologies, the main bottleneck remains at the stage of data analysis rather than data production. FINDINGS: We have developed HumMeth27QCReport, an R package devoted to researchers wanting to quickly analyse their Illumina Infinium methylation arrays. This package automates quality control steps by generating a report including sample-independent and sample-dependent quality plots, and performs primary analysis of raw methylation calls by computing data normalization, statistics, and sample similarities. This package is available at CRAN repository, and can be integrated in any Galaxy instance through the implementation of ad-hoc scripts accessible at Galaxy Tool Shed. CONCLUSIONS: Our package provides users of the Illumina Infinium Methylation assays with a simplified, automated, open-source quality control and primary analysis of their methylation data. Moreover, to enhance its use by experimental researchers, the tool is being distributed along with the scripts necessary for its implementation in the Galaxy workbench. Finally, although it was originally developed for HumanMethylation27, we proved its compatibility with data generated with the HumanMethylation450 Bead Chip.

DNA-binding specificity prediction with FoldX.

With the advent of Synthetic Biology, a field between basic science and applied engineering, new computational tools are needed to help scientists reach their goal, their design, optimizing resources. In this chapter, we present a simple and powerful method to either know the DNA specificity of a wild-type protein or design new specificities by using the protein design algorithm FoldX. The only basic requirement is having a good resolution structure of the complex. Protein-DNA interaction design may aid the development of new parts designed to be orthogonal, decoupled, and precise in its target. Further, it could help to fine-tune the systems in terms of specificity, discrimination, and binding constants. In the age of newly developed devices and invented systems, computer-aided engineering promises to be an invaluable tool.

Using a structural and logics systems approach to infer bHLH-DNA binding specificity determinants.

Numerous efforts are underway to determine gene regulatory networks that describe physical relationships between transcription factors (TFs) and their target DNA sequences. Members of paralogous TF families typically recognize similar DNA sequences. Knowledge of the molecular determinants of protein-DNA recognition by paralogous TFs is of central importance for understanding how small differences in DNA specificities can dictate target gene selection. Previously, we determined the in vitro DNA binding specificities of 19 Caenorhabditis elegans basic helix-loop-helix (bHLH) dimers using protein binding microarrays. These TFs bind E-box (CANNTG) and E-box-like sequences. Here, we combine these data with logics, bHLH-DNA co-crystal structures and computational modeling to infer which bHLH monomer can interact with which CAN E-box half-site and we identify a critical residue in the protein that dictates this specificity. Validation experiments using mutant bHLH proteins provide support for our inferences. Our study provides insights into the mechanisms of DNA recognition by bHLH dimers as well as a blueprint for system-level studies of the DNA binding determinants of other TF families in different model organisms and humans.

Molecular basis of engineered meganuclease targeting of the endogenous human RAG1 locus.

Homing endonucleases recognize long target DNA sequences generating an accurate double-strand break that promotes gene targeting through homologous recombination. We have modified the homodimeric I-CreI endonuclease through protein engineering to target a specific DNA sequence within the human RAG1 gene. Mutations in RAG1 produce severe combined immunodeficiency (SCID), a monogenic disease leading to defective immune response in the individuals, leaving them vulnerable to infectious diseases. The structures of two engineered heterodimeric variants and one single-chain variant of I-CreI, in complex with a 24-bp oligonucleotide of the human RAG1 gene sequence, show how the DNA binding is achieved through interactions in the major groove. In addition, the introduction of the G19S mutation in the neighborhood of the catalytic site lowers the reaction energy barrier for DNA cleavage without compromising DNA recognition. Gene-targeting experiments in human cell lines show that the designed single-chain molecule preserves its in vivo activity with higher specificity, further enhanced by the G19S mutation. This is the first time that an engineered meganuclease variant targets the human RAG1 locus by stimulating homologous recombination in human cell lines up to 265 bp away from the cleavage site. Our analysis illustrates the key features for à la carte procedure in protein-DNA recognition design, opening new possibilities for SCID patients whose illness can be treated ex vivo.

Using protein design algorithms to understand the molecular basis of disease caused by protein-DNA interactions: the Pax6 example.

Quite often a single or a combination of protein mutations is linked to specific diseases. However, distinguishing from sequence information which mutations have real effects in the protein's function is not trivial. Protein design tools are commonly used to explain mutations that affect protein stability, or protein-protein interaction, but not for mutations that could affect protein-DNA binding. Here, we used the protein design algorithm FoldX to model all known missense mutations in the paired box domain of Pax6, a highly conserved transcription factor involved in eye development and in several diseases such as aniridia. The validity of FoldX to deal with protein-DNA interactions was demonstrated by showing that high levels of accuracy can be achieved for mutations affecting these interactions. Also we showed that protein-design algorithms can accurately reproduce experimental DNA-binding logos. We conclude that 88% of the Pax6 mutations can be linked to changes in intrinsic stability (77%) and/or to its capabilities to bind DNA (30%). Our study emphasizes the importance of structure-based analysis to understand the molecular basis of diseases and shows that protein-DNA interactions can be analyzed to the same level of accuracy as protein stability, or protein-protein interactions.

Structure-based DNA-binding prediction and design.

Structure-based DNA-binding prediction is a powerful tool to infer protein-binding sites and design new specificities. It can limit experiments in scope and help focus them toward candidates with higher chances of success. The zinc finger domain is an excellent scaffold for design due to its small and robust fold and relatively simple interaction pattern. It presents some degree of modularity, and modeling can be used to guide experiments and help increase zinc finger module libraries. In this chapter we present a fast and simple but still powerful method for predicting and designing DNA-binding specificities applied to C(2)H(2) zinc finger proteins, based on FoldX, a semiautomatic protein design tool. Given a template structure, this method generates candidate mutants for a given target DNA sequence selected by energetic criteria.

Molecular basis of xeroderma pigmentosum group C DNA recognition by engineered meganucleases.

Xeroderma pigmentosum is a monogenic disease characterized by hypersensitivity to ultraviolet light. The cells of xeroderma pigmentosum patients are defective in nucleotide excision repair, limiting their capacity to eliminate ultraviolet-induced DNA damage, and resulting in a strong predisposition to develop skin cancers. The use of rare cutting DNA endonucleases-such as homing endonucleases, also known as meganucleases-constitutes one possible strategy for repairing DNA lesions. Homing endonucleases have emerged as highly specific molecular scalpels that recognize and cleave DNA sites, promoting efficient homologous gene targeting through double-strand-break-induced homologous recombination. Here we describe two engineered heterodimeric derivatives of the homing endonuclease I-CreI, produced by a semi-rational approach. These two molecules-Amel3-Amel4 and Ini3-Ini4-cleave DNA from the human XPC gene (xeroderma pigmentosum group C), in vitro and in vivo. Crystal structures of the I-CreI variants complexed with intact and cleaved XPC target DNA suggest that the mechanism of DNA recognition and cleavage by the engineered homing endonucleases is similar to that of the wild-type I-CreI. Furthermore, these derivatives induced high levels of specific gene targeting in mammalian cells while displaying no obvious genotoxicity. Thus, homing endonucleases can be designed to recognize and cleave the DNA sequences of specific genes, opening up new possibilities for genome engineering and gene therapy in xeroderma pigmentosum patients whose illness can be treated ex vivo.

Crystal structure of I-DmoI in complex with its target DNA provides new insights into meganuclease engineering.

Homing endonucleases, also known as meganucleases, are sequence-specific enzymes with large DNA recognition sites. These enzymes can be used to induce efficient homologous gene targeting in cells and plants, opening perspectives for genome engineering with applications in a wide series of fields, ranging from biotechnology to gene therapy. Here, we report the crystal structures at 2.0 and 2.1 A resolution of the I-DmoI meganuclease in complex with its substrate DNA before and after cleavage, providing snapshots of the catalytic process. Our study suggests that I-DmoI requires only 2 cations instead of 3 for DNA cleavage. The structure sheds light onto the basis of DNA binding, indicating key residues responsible for nonpalindromic target DNA recognition. In silico and in vivo analysis of the I-DmoI DNA cleavage specificity suggests that despite the relatively few protein-base contacts, I-DmoI is highly specific when compared with other meganucleases. Our data open the door toward the generation of custom endonucleases for targeted genome engineering using the monomeric I-DmoI scaffold.

PaLS: filtering common literature, biological terms and pathway information.

Many biological experiments and their subsequent analysis yield lists of genes or proteins that can potentially be important to the prognosis or diagnosis of certain diseases (e.g. cancer). Nowadays, information about the function of those genes or proteins may be already gathered in some databases, but it is essential to understand if some of the members of those lists have a function in common or if they belong to the same metabolic pathway. To help researchers filter those genes or proteins that have such information in common, we have developed PaLS (pathway and literature strainer, http://pals.bioinfo.cnio.es). PaLS takes a list or a set of lists of gene or protein identifiers and shows which ones share certain descriptors. Four publicly available databases have been used for this purpose: PubMed, which links genes with those articles that make reference to them; Gene Ontology, an annotated ontology of terms related to the cellular component, biological process or molecular function where those genes or proteins are involved; KEGG pathways and Reactome pathways. Those descriptors among these four sources of information that are shared by more members of the list (or lists) are highlighted by PaLS.

Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software suite.

Asterias (http://www.asterias.info) is an open-source, web-based, suite for the analysis of gene expression and aCGH data. Asterias implements validated statistical methods, and most of the applications use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. These applications cover from array normalization to imputation and preprocessing, differential gene expression analysis, class and survival prediction and aCGH analysis. The source code is available, allowing for extention and reuse of the software. The links and analysis of additional functional information, parallelization of computation and open-source availability of the code make Asterias a unique suite that can exploit features specific to web-based environments.

IDconverter and IDClight: conversion and annotation of gene and protein IDs.

BACKGROUND: Researchers involved in the annotation of large numbers of gene, clone or protein identifiers are usually required to perform a one-by-one conversion for each identifier. When the field of research is one such as microarray experiments, this number may be around 30,000. RESULTS: To help researchers map accession numbers and identifiers among clones, genes, proteins and chromosomal positions, we have designed and developed IDconverter and IDClight. They are two user-friendly, freely available web server applications that also provide additional functional information by mapping the identifiers on to pathways, Gene Ontology terms, and literature references. Both tools are high-throughput oriented and include identifiers for the most common genomic databases. These tools have been compared to other similar tools, showing that they are among the fastest and the most up-to-date. CONCLUSION: These tools provide a fast and intuitive way of enriching the information coming out of high-throughput experiments like microarrays. They can be valuable both to wet-lab researchers and to bioinformaticians.

Asterias: a parallelized web-based suite for the analysis of expression and aCGH data.

The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including finding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias (http://www.asterias.info) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifiers (IDconverter/IDClight); filtering and imputation (preP); finding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.