Simultaneous Nbs1 and p53 inactivation in neural progenitors triggers high-grade gliomas.

AIMS: Nijmegen breakage syndrome (NBS) is a rare autosomal recessive disorder caused by hypomorphic mutations of NBS1. NBS1 is a member of the MRE11-RAD50-NBS1 (MRN) complex that binds to DNA double-strand breaks and activates the DNA damage response (DDR). Nbs1 inactivation in neural progenitor cells leads to microcephaly and premature death. Interestingly, p53 homozygous deletion rescues the NBS1-deficient phenotype allowing long-term survival. The objective of this work was to determine whether simultaneous inactivation of Nbs1 and p53 in neural progenitors triggered brain tumorigenesis and if so in which category this tumour could be classified. METHODS: We generated a mouse model with simultaneous genetic inactivation of Nbs1 and p53 in embryonic neural stem cells and analysed the arising tumours with in-depth molecular analyses including immunohistochemistry, array comparative genomic hybridisation (aCGH), whole exome-sequencing and RNA-sequencing. RESULTS: NBS1/P53-deficient mice develop high-grade gliomas (HGG) arising in the olfactory bulbs and in the cortex along the rostral migratory stream. In-depth molecular analyses using immunohistochemistry, aCGH, whole exome-sequencing and RNA-sequencing revealed striking similarities to paediatric human HGG with shared features with radiation-induced gliomas (RIGs). CONCLUSIONS: Our findings show that concomitant inactivation of Nbs1 and p53 in mice promotes HGG with RIG features. This model could be useful for preclinical studies to improve the prognosis of these deadly tumours, but it also highlights the singularity of NBS1 among the other DNA damage response proteins in the aetiology of brain tumours.

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency.

BACKGROUND: Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. RESULTS: Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns' overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. CONCLUSIONS: Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q.

decompTumor2Sig: identification of mutational signatures active in individual tumors.

BACKGROUND: The somatic mutations found in a tumor have in most cases been caused by multiple mutational processes such as those related to extrinsic carcinogens like cigarette smoke, and those related to intrinsic processes like age-related spontaneous deamination of 5-methylcytosine. The effect of such mutational processes can be modeled by mutational signatures, of which two different conceptualizations exist: the model introduced by Alexandrov et al., Nature 500:415-421, 2013, and the model introduced by Shiraishi et al., PLoS Genetics 11(12):e1005657, 2015. The initial identification and definition of mutational signatures requires large sets of tumor samples. RESULTS: Here, we present decompTumor2Sig, an easy to use R package that can decompose an individual tumor genome into a given set of Alexandrov-type or Shiraishi-type signatures, thus quantifying the contribution of the corresponding mutational processes to the somatic mutations identified in the tumor. Until now, such tools were available only for Alexandrov signatures. We demonstrate the correctness and usefulness of our approach with three test cases, using somatic mutations from 21 breast cancer genomes, from 435 tumor genomes of ten different tumor entities, and from simulated tumor genomes, respectively. CONCLUSIONS: The decompTumor2Sig package is freely available and has been accepted for inclusion in Bioconductor.

No correlation between NF1 mutation position and risk of optic pathway glioma in 77 unrelated NF1 patients.

Neurofibromatosis type 1 (NF1) is a common monogenic disorder whereby affected individuals are predisposed to developing CNS tumors, including optic pathway gliomas (OPGs, occurring in ~15 to 20 % of cases). So far, no definite genotype-phenotype correlation determining NF1 patients at risk for tumor formation has been described, although enrichment for mutations in the 5' region of the NF1 gene in OPG patients has been suggested. We used whole exome sequencing, targeted sequencing, and copy number analysis to screen 77 unrelated NF1 patients with (n = 41) or without (n = 36; age ≥10 years) optic pathway glioma for germline NF1 alterations. We identified germline NF1 mutations in 69 of 77 patients (90 %), but no genotype-phenotype correlation was observed. Our data using a larger patient cohort did not confirm the previously reported clustering of mutations in the 5' region of the NF1 gene in patients with OPG. Thus, NF1 mutation location should not currently be used as a clinical criterion to assess the risk of developing OPGs.

Disease-gene discovery by integration of 3D gene expression and transcription factor binding affinities.

MOTIVATION: The computational evaluation of candidate genes for hereditary disorders is a non-trivial task. Several excellent methods for disease-gene prediction have been developed in the past 2 decades, exploiting widely differing data sources to infer disease-relevant functional relationships between candidate genes and disorders. We have shown recently that spatially mapped, i.e. 3D, gene expression data from the mouse brain can be successfully used to prioritize candidate genes for human Mendelian disorders of the central nervous system. RESULTS: We improved our previous work 2-fold: (i) we demonstrate that condition-independent transcription factor binding affinities of the candidate genes' promoters are relevant for disease-gene prediction and can be integrated with our previous approach to significantly enhance its predictive power; and (ii) we define a novel similarity measure-termed Relative Intensity Overlap-for both 3D gene expression patterns and binding affinity profiles that better exploits their disease-relevant information content. Finally, we present novel disease-gene predictions for eight loci associated with different syndromes of unknown molecular basis that are characterized by mental retardation.

Meningeal hemangiopericytoma and solitary fibrous tumors carry the NAB2-STAT6 fusion and can be diagnosed by nuclear expression of STAT6 protein.

Non-central nervous system hemangiopericytoma (HPC) and solitary fibrous tumor (SFT) are considered by pathologists as two variants of a single tumor entity now subsumed under the entity SFT. Recent detection of frequent NAB2-STAT6 fusions in both, HPC and SFT, provided additional support for this view. On the other hand, current neuropathological practice still distinguishes between HPC and SFT. The present study set out to identify genes involved in the formation of meningeal HPC. We performed exome sequencing and detected the NAB2-STAT6 fusion in DNA of 8/10 meningeal HPC thereby providing evidence of close relationship of these tumors with peripheral SFT. Due to the considerable effort required for exome sequencing, we sought to explore surrogate markers for the NAB2-STAT6 fusion protein. We adopted the Duolink proximity ligation assay and demonstrated the presence of NAB2-STAT6 fusion protein in 17/17 HPC and the absence in 15/15 meningiomas. More practical, presence of the NAB2-STAT6 fusion protein resulted in a strong nuclear signal in STAT6 immunohistochemistry. The nuclear reallocation of STAT6 was detected in 35/37 meningeal HPC and 25/25 meningeal SFT but not in 87 meningiomas representing the most important differential diagnosis. Tissues not harboring the NAB2-STAT6 fusion protein presented with nuclear expression of NAB2 and cytoplasmic expression of STAT6 proteins. In conclusion, we provide strong evidence for meningeal HPC and SFT to constitute variants of a single entity which is defined by NAB2-STAT6 fusion. In addition, we demonstrate that this fusion can be rapidly detected by STAT6 immunohistochemistry which shows a consistent nuclear reallocation. This immunohistochemical assay may prove valuable for the differentiation of HPC and SFT from other mesenchymal neoplasms.

Secretory meningiomas are defined by combined KLF4 K409Q and TRAF7 mutations.

Meningiomas are among the most frequent intracranial tumors. The secretory variant of meningioma is characterized by glandular differentiation, formation of intracellular lumina and pseudopsammoma bodies, expression of a distinct pattern of cytokeratins and clinically by pronounced perifocal brain edema. Here we describe whole-exome sequencing analysis of DNA from 16 secretory meningiomas and corresponding constitutional tissues. All secretory meningiomas invariably harbored a mutation in both KLF4 and TRAF7. Validation in an independent cohort of 14 secretory meningiomas by Sanger sequencing or derived cleaved amplified polymorphic sequence (dCAPS) assay detected the same pattern, with KLF4 mutations observed in a total of 30/30 and TRAF7 mutations in 29/30 of these tumors. All KLF4 mutations were identical, affected codon 409 and resulted in a lysine to glutamine exchange (K409Q). KLF4 mutations were not found in 89 non-secretory meningiomas, 267 other intracranial tumors including gliomas, glioneuronal tumors, pituitary adenomas and metastases, 59 peripheral nerve sheath tumors and 52 pancreatic tumors. TRAF7 mutations were restricted to the WD40 domains. While KLF4 mutations were exclusively seen in secretory meningiomas, TRAF7 mutations were also observed in 7/89 (8 %) of non-secretory meningiomas. KLF4 and TRAF7 mutations were mutually exclusive with NF2 mutations. In conclusion, our findings suggest an essential contribution of combined KLF4 K409Q and TRAF7 mutations in the genesis of secretory meningioma and demonstrate a role for TRAF7 alterations in other non-NF2 meningiomas.

Network medicine: linking disorders.

The molecular events underlying many human hereditary disorders remain to be discovered despite the significant advances made in molecular biology and genetics in the past years. Given the complexity of cellular systems and the interplay between different functional modules, it is becoming increasingly evident that profound insights into human disease cannot be derived by analyzing single genetic defects. The generation of different types of disease interaction networks has recently emerged as a unifying approach that holds the promise of shedding some light on common pathological mechanisms by placing the single disorders into a larger context. In this review, I summarize the rationale behind these disease networks and different ways of constructing them. Finally, I highlight some of the first results that have been obtained by systematically analyzing the intertwined relationships between human disorders because they suggest that the current disease classification does not always sufficiently reflect biologically and medically relevant disease relationships.

Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR.

MOTIVATION: The identification of genes involved in specific phenotypes, such as human hereditary diseases, often requires the time-consuming and expensive examination of a large number of positional candidates selected by genome-wide techniques such as linkage analysis and association studies. Even considering the positive impact of next-generation sequencing technologies, the prioritization of these positional candidates may be an important step for disease-gene identification. RESULTS: Here, we report a large-scale analysis of spatial, i.e. 3D, gene-expression data from an entire organ (the mouse brain) for the purpose of evaluating and ranking positional candidate genes, showing that the spatial gene-expression patterns can be successfully exploited for the prediction of gene-phenotype associations not only for mouse phenotypes, but also for human central nervous system-related Mendelian disorders. We apply our method to the case of X-linked mental retardation, compare the predictions to the results obtained from a previous large-scale resequencing study of chromosome X and discuss some promising novel candidates.

MutViz 2.0: visual analysis of somatic mutations and the impact of mutational signatures on selected genomic regions.

Patterns of somatic single nucleotide variants observed in human cancers vary widely between different tumor types. They depend not only on the activity of diverse mutational processes, such as exposure to ultraviolet light and the deamination of methylated cytosines, but largely also on the sequence content of different genomic regions on which these processes act. With MutViz (http://gmql.eu/mutviz/), we have presented a user-friendly web tool for the identification of mutation enrichments that offers preloaded mutations from public datasets for a variety of cancer types, well organized within an effective database architecture. Somatic mutation patterns can be visually and statistically analyzed within arbitrary sets of small, user-provided genomic regions, such as promoters or collections of transcription factor binding sites. Here, we present MutViz 2.0, a largely extended and consolidated version of the tool: we took into account the immediate (trinucleotide) sequence context of mutations, improved the representation of clinical annotation of tumor samples and devised a method for signature refitting on limited genomic regions to infer the contribution of individual mutational processes to the mutation patterns observed in these regions. We described both the features of MutViz 2.0, concentrating on the novelties, and the substantial re-engineering of the cloud-based architecture.

Genome-wide insights into population structure and host specificity of Campylobacter jejuni.

The zoonotic pathogen Campylobacter jejuni is among the leading causes of foodborne diseases worldwide. While C. jejuni colonises many wild animals and livestock, persistence mechanisms enabling the bacterium to adapt to host species' guts are not fully understood. In order to identify putative determinants influencing host preferences of distinct lineages, bootstrapping based on stratified random sampling combined with a k-mer-based genome-wide association was conducted on 490 genomes from diverse origins in Germany and Canada. We show a strong association of both the core and the accessory genome characteristics with distinct host animal species, indicating multiple adaptive trajectories defining the evolution of C. jejuni lifestyle preferences in different ecosystems. Here, we demonstrate that adaptation towards a specific host niche ecology is most likely a long evolutionary and multifactorial process, expressed by gene absence or presence and allele variations of core genes. Several host-specific allelic variants from different phylogenetic backgrounds, including dnaE, rpoB, ftsX or pycB play important roles for genome maintenance and metabolic pathways. Thus, variants of genes important for C. jejuni to cope with specific ecological niches or hosts may be useful markers for both surveillance and future pathogen intervention strategies.

Impact of mutational signatures on microRNA and their response elements.

MicroRNAs are a class of small non-coding RNA molecules with great importance for regulating a large number of diverse biological processes in health and disease, mostly by binding to complementary microRNA response elements (MREs) on protein-coding messenger RNAs and other non-coding RNAs and subsequently inducing their degradation. A growing body of evidence indicates that the dysregulation of certain microRNAs may either drive or suppress oncogenesis.The seed region of a microRNA is of crucial importance for its target recognition. Mutations in these seed regions may disrupt the binding of microRNAs to their target genes. In this study, we investigate the theoretical impact of cancer-associated mutagenic processes and their mutational signatures on microRNA seeds and their MREs. To our knowledge, this is the first study which provides a probabilistic framework for microRNA and MRE sequence alteration analysis based on mutational signatures and computationally assessing the disruptive impact of mutational signatures on human microRNA-target interactions.

PERK-mediated expression of peptidylglycine α-amidating monooxygenase supports angiogenesis in glioblastoma.

PKR-like kinase (PERK) plays a significant role in inducing angiogenesis in various cancer types including glioblastoma. By proteomics analysis of the conditioned medium from a glioblastoma cell line treated with a PERK inhibitor, we showed that peptidylglycine α-amidating monooxygenase (PAM) expression is regulated by PERK under hypoxic conditions. Moreover, PERK activation via CCT020312 (a PERK selective activator) increased the cleavage and thus the generation of PAM cleaved cytosolic domain (PAM sfCD) that acts as a signaling molecule from the cytoplasm to the nuclei. PERK was also found to interact with PAM, suggesting a possible involvement in the generation of PAM sfCD. Knockdown of PERK or PAM reduced the formation of tubes by HUVECs in vitro. Furthermore, in vivo data highlighted the importance of PAM in the growth of glioblastoma with reduction of PAM expression in engrafted tumor significantly increasing the survival in mice. In summary, our data revealed PAM as a potential target for antiangiogenic therapy in glioblastoma.

Molecular Transition of an Adult Low-Grade Brain Tumor to an Atypical Teratoid/Rhabdoid Tumor Over a Time-Course of 14 Years.

Atypical teratoid/rhabdoid tumor (AT/RT) of the central nervous system is a highly malignant, pediatric brain tumor typically arising de novo. Inactivation of SMARCB1 is a defining molecular event. We present here a rare case of an adult (35 years) low-grade SMARCB1-deleted brain tumor with transition into prototypical AT/RT over 14 years. Molecular analysis was performed for 3 tumor presentations including copy number analysis, DNA methylation analysis (450k), and whole exome sequencing. We detected the identical somatic SMARCB1 deletion at all 3 time-points. In an unsupervised hierarchical clustering of methylation data together with 127 reference cases comprising 9 brain tumor classes all 3 manifestations clustered with AT/RT. Exome sequencing revealed an increase of mutational burden over time. The acquired mutations and additional copy number changes did not affect known cancer genes. In conclusion, we demonstrate molecular changes associated with histological and clinical transition of a low-grade brain tumor to an adult AT/RT. Our observation of a stable disease course for nearly 10 years in a tumor with SMARCB1 loss and an AT/RT-like DNA methylation profile indicates that caution may be required in the diagnostic interpretation of such findings in adult patients.

Network topology-based detection of differential gene regulation and regulatory switches in cell metabolism and signaling.

BACKGROUND: Common approaches to pathway analysis treat pathways merely as lists of genes disregarding their topological structures, that is, ignoring the genes' interactions on which a pathway's cellular function depends. In contrast, PathWave has been developed for the analysis of high-throughput gene expression data that explicitly takes the topology of networks into account to identify both global dysregulation of and localized (switch-like) regulatory shifts within metabolic and signaling pathways. For this purpose, it applies adjusted wavelet transforms on optimized 2D grid representations of curated pathway maps. RESULTS: Here, we present the new version of PathWave with several substantial improvements including a new method for optimally mapping pathway networks unto compact 2D lattice grids, a more flexible and user-friendly interface, and pre-arranged 2D grid representations. These pathway representations are assembled for several species now comprising H. sapiens, M. musculus, D. melanogaster, D. rerio, C. elegans, and E. coli. We show that PathWave is more sensitive than common approaches and apply it to RNA-seq expression data, identifying crucial metabolic pathways in lung adenocarcinoma, as well as microarray expression data, identifying pathways involved in longevity of Drosophila. CONCLUSIONS: PathWave is a generic method for pathway analysis complementing established tools like GSEA, and the update comprises efficient new features. In contrast to the tested commonly applied approaches which do not take network topology into account, PathWave enables identifying pathways that are either known be involved in or very likely associated with such diverse conditions as human lung cancer or aging of D. melanogaster. The PathWave R package is freely available at http://www.ichip.de/software/pathwave.html.

Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition.

Smoothened (SMO) inhibitors recently entered clinical trials for sonic-hedgehog-driven medulloblastoma (SHH-MB). Clinical response is highly variable. To understand the mechanism(s) of primary resistance and identify pathways cooperating with aberrant SHH signaling, we sequenced and profiled a large cohort of SHH-MBs (n = 133). SHH pathway mutations involved PTCH1 (across all age groups), SUFU (infants, including germline), and SMO (adults). Children >3 years old harbored an excess of downstream MYCN and GLI2 amplifications and frequent TP53 mutations, often in the germline, all of which were rare in infants and adults. Functional assays in different SHH-MB xenograft models demonstrated that SHH-MBs harboring a PTCH1 mutation were responsive to SMO inhibition, whereas tumors harboring an SUFU mutation or MYCN amplification were primarily resistant.

BCAT1 promotes cell proliferation through amino acid catabolism in gliomas carrying wild-type IDH1.

Here we show that glioblastoma express high levels of branched-chain amino acid transaminase 1 (BCAT1), the enzyme that initiates the catabolism of branched-chain amino acids (BCAAs). Expression of BCAT1 was exclusive to tumors carrying wild-type isocitrate dehydrogenase 1 (IDH1) and IDH2 genes and was highly correlated with methylation patterns in the BCAT1 promoter region. BCAT1 expression was dependent on the concentration of α-ketoglutarate substrate in glioma cell lines and could be suppressed by ectopic overexpression of mutant IDH1 in immortalized human astrocytes, providing a link between IDH1 function and BCAT1 expression. Suppression of BCAT1 in glioma cell lines blocked the excretion of glutamate and led to reduced proliferation and invasiveness in vitro, as well as significant decreases in tumor growth in a glioblastoma xenograft model. These findings suggest a central role for BCAT1 in glioma pathogenesis, making BCAT1 and BCAA metabolism attractive targets for the development of targeted therapeutic approaches to treat patients with glioblastoma.

Computational approaches to disease-gene prediction: rationale, classification and successes.

The identification of genes involved in human hereditary diseases often requires the time-consuming and expensive examination of a great number of possible candidate genes, since genome-wide techniques such as linkage analysis and association studies frequently select many hundreds of 'positional' candidates. Even considering the positive impact of next-generation sequencing technologies, the prioritization of candidate genes may be an important step for disease-gene identification. In this paper we develop a basic classification scheme for computational approaches to disease-gene prediction and apply it to exhaustively review bioinformatics tools that have been developed for this purpose, focusing on conceptual aspects rather than technical detail and performance. Finally, we discuss some past successes obtained by computational approaches to illustrate their beneficial contribution to medical research.

Evaluation of candidate genes from orphan FEB and GEFS+ loci by analysis of human brain gene expression atlases.

Febrile seizures, or febrile convulsions (FEB), represent the most common form of childhood seizures and are believed to be influenced by variations in several susceptibility genes. Most of the associated loci, however, remain 'orphan', i.e. the susceptibility genes they contain still remain to be identified. Further orphan loci have been mapped for a related disorder, genetic (generalized) epilepsy with febrile seizures plus (GEFS+).We show that both spatially mapped and 'traditional' gene expression data from the human brain can be successfully employed to predict the most promising candidate genes for FEB and GEFS+, apply our prediction method to the remaining orphan loci and discuss the validity of the predictions. For several of the orphan FEB/GEFS+ loci we propose excellent, and not always obvious, candidates for mutation screening in order to aid in gaining a better understanding of the genetic origin of the susceptibility to seizures.