A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma.

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting senescent cells, so-called senolytics, constitute a promising avenue for improved cancer treatment in combination with targeted therapies. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCell-based integration of bulk and single-cell RNA-seq data identifies biological processes perturbed during senescence and predicts 90 new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

Systems level analysis of sex-dependent gene expression changes in Parkinson's disease.

Parkinson's disease (PD) is a heterogeneous disorder, and among the factors which influence the symptom profile, biological sex has been reported to play a significant role. While males have a higher age-adjusted disease incidence and are more frequently affected by muscle rigidity, females present more often with disabling tremors. The molecular mechanisms involved in these differences are still largely unknown, and an improved understanding of the relevant factors may open new avenues for pharmacological disease modification. To help address this challenge, we conducted a meta-analysis of disease-associated molecular sex differences in brain transcriptomics data from case/control studies. Both sex-specific (alteration in only one sex) and sex-dimorphic changes (changes in both sexes, but with opposite direction) were identified. Using further systems level pathway and network analyses, coordinated sex-related alterations were studied. These analyses revealed significant disease-associated sex differences in mitochondrial pathways and highlight specific regulatory factors whose activity changes can explain downstream network alterations, propagated through gene regulatory cascades. Single-cell expression data analyses confirmed the main pathway-level changes observed in bulk transcriptomics data. Overall, our analyses revealed significant sex disparities in PD-associated transcriptomic changes, resulting in coordinated modulations of molecular processes. Among the regulatory factors involved, NR4A2 has already been reported to harbor rare mutations in familial PD and its pharmacological activation confers neuroprotective effects in toxin-induced models of Parkinsonism. Our observations suggest that NR4A2 may warrant further research as a potential adjuvant therapeutic target to address a subset of pathological molecular features of PD that display sex-associated profiles.

iPSC-Derived Microglia as a Model to Study Inflammation in Idiopathic Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disease with unknown cause in the majority of patients, who are therefore considered "idiopathic" (IPD). PD predominantly affects dopaminergic neurons in the substantia nigra pars compacta (SNpc), yet the pathology is not limited to this cell type. Advancing age is considered the main risk factor for the development of IPD and greatly influences the function of microglia, the immune cells of the brain. With increasing age, microglia become dysfunctional and release pro-inflammatory factors into the extracellular space, which promote neuronal cell death. Accordingly, neuroinflammation has also been described as a feature of PD. So far, studies exploring inflammatory pathways in IPD patient samples have primarily focused on blood-derived immune cells or brain sections, but rarely investigated patient microglia in vitro. Accordingly, we decided to explore the contribution of microglia to IPD in a comparative manner using, both, iPSC-derived cultures and postmortem tissue. Our meta-analysis of published RNAseq datasets indicated an upregulation of IL10 and IL1B in nigral tissue from IPD patients. We observed increased expression levels of these cytokines in microglia compared to neurons using our single-cell midbrain atlas. Moreover, IL10 and IL1B were upregulated in IPD compared to control microglia. Next, to validate these findings in vitro, we generated IPD patient microglia from iPSCs using an established differentiation protocol. IPD microglia were more readily primed as indicated by elevated IL1B and IL10 gene expression and higher mRNA and protein levels of NLRP3 after LPS treatment. In addition, IPD microglia had higher phagocytic capacity under basal conditions-a phenotype that was further exacerbated upon stimulation with LPS, suggesting an aberrant microglial function. Our results demonstrate the significance of microglia as the key player in the neuroinflammation process in IPD. While our study highlights the importance of microglia-mediated inflammatory signaling in IPD, further investigations will be needed to explore particular disease mechanisms in these cells.

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.

BACKGROUND: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. METHODS: We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. RESULTS: We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. CONCLUSIONS: Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

Interplay between coding and exonic splicing regulatory sequences.

The inclusion of exons during the splicing process depends on the binding of splicing factors to short low-complexity regulatory sequences. The relationship between exonic splicing regulatory sequences and coding sequences is still poorly understood. We demonstrate that exons that are coregulated by any given splicing factor share a similar nucleotide composition bias and preferentially code for amino acids with similar physicochemical properties because of the nonrandomness of the genetic code. Indeed, amino acids sharing similar physicochemical properties correspond to codons that have the same nucleotide composition bias. In particular, we uncover that the TRA2A and TRA2B splicing factors that bind to adenine-rich motifs promote the inclusion of adenine-rich exons coding preferentially for hydrophilic amino acids that correspond to adenine-rich codons. SRSF2 that binds guanine/cytosine-rich motifs promotes the inclusion of GC-rich exons coding preferentially for small amino acids, whereas SRSF3 that binds cytosine-rich motifs promotes the inclusion of exons coding preferentially for uncharged amino acids, like serine and threonine that can be phosphorylated. Finally, coregulated exons encoding amino acids with similar physicochemical properties correspond to specific protein features. In conclusion, the regulation of an exon by a splicing factor that relies on the affinity of this factor for specific nucleotide(s) is tightly interconnected with the exon-encoded physicochemical properties. We therefore uncover an unanticipated bidirectional interplay between the splicing regulatory process and its biological functional outcome.

Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

BACKGROUND: One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. RESULTS: We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. CONCLUSION: We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. REVIEWERS: This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.

CD47 is a direct target of SNAI1 and ZEB1 and its blockade activates the phagocytosis of breast cancer cells undergoing EMT.

We report that CD47 was upregulated in different EMT-activated human breast cancer cells versus epithelial MCF7 cells. Overexpression of SNAI1 or ZEB1 in epithelial MCF7 cells activated EMT and upregulated CD47 while siRNA-mediated targeting of SNAI1 or ZEB1 in mesenchymal MDA-MB-231 cells reversed EMT and strongly decreased CD47. Mechanistically, SNAI1 and ZEB1 upregulated CD47 by binding directly to E-boxes in the human CD47 promoter. TCGA and METABRIC data sets from breast cancer patients revealed that CD47 correlated with SNAI1 and Vimentin. At functional level, different EMT-activated breast cancer cells were less efficiently phagocytosed by macrophages vs. MCF7 cells. The phagocytosis of EMT-activated cells was rescued by using CD47 blocking antibody or by genetic targeting of SNAI1, ZEB1 or CD47. These results provide a rationale for an innovative preclinical combination immunotherapy based on PD-1/PD-L1 and CD47 blockade along with EMT inhibitors in patients with highly aggressive, mesenchymal, and metastatic breast cancer.

Identification of protein features encoded by alternative exons using Exon Ontology.

Transcriptomic genome-wide analyses demonstrate massive variation of alternative splicing in many physiological and pathological situations. One major challenge is now to establish the biological contribution of alternative splicing variation in physiological- or pathological-associated cellular phenotypes. Toward this end, we developed a computational approach, named "Exon Ontology," based on terms corresponding to well-characterized protein features organized in an ontology tree. Exon Ontology is conceptually similar to Gene Ontology-based approaches but focuses on exon-encoded protein features instead of gene level functional annotations. Exon Ontology describes the protein features encoded by a selected list of exons and looks for potential Exon Ontology term enrichment. By applying this strategy to exons that are differentially spliced between epithelial and mesenchymal cells and after extensive experimental validation, we demonstrate that Exon Ontology provides support to discover specific protein features regulated by alternative splicing. We also show that Exon Ontology helps to unravel biological processes that depend on suites of coregulated alternative exons, as we uncovered a role of epithelial cell-enriched splicing factors in the AKT signaling pathway and of mesenchymal cell-enriched splicing factors in driving splicing events impacting on autophagy. Freely available on the web, Exon Ontology is the first computational resource that allows getting a quick insight into the protein features encoded by alternative exons and investigating whether coregulated exons contain the same biological information.

Candidate gene prioritization with Endeavour.

Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using 'gold standard' gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene-phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.

Beegle: from literature mining to disease-gene discovery.

Disease-gene identification is a challenging process that has multiple applications within functional genomics and personalized medicine. Typically, this process involves both finding genes known to be associated with the disease (through literature search) and carrying out preliminary experiments or screens (e.g. linkage or association studies, copy number analyses, expression profiling) to determine a set of promising candidates for experimental validation. This requires extensive time and monetary resources. We describe Beegle, an online search and discovery engine that attempts to simplify this process by automating the typical approaches. It starts by mining the literature to quickly extract a set of genes known to be linked with a given query, then it integrates the learning methodology of Endeavour (a gene prioritization tool) to train a genomic model and rank a set of candidate genes to generate novel hypotheses. In a realistic evaluation setup, Beegle has an average recall of 84% in the top 100 returned genes as a search engine, which improves the discovery engine by 12.6% in the top 5% prioritized genes. Beegle is publicly available at http://beegle.esat.kuleuven.be/.

RNA helicases DDX5 and DDX17 dynamically orchestrate transcription, miRNA, and splicing programs in cell differentiation.

The RNA helicases DDX5 and DDX17 are members of a large family of highly conserved proteins that are involved in gene-expression regulation; however, their in vivo targets and activities in biological processes such as cell differentiation, which requires reprogramming of gene-expression programs at multiple levels, are not well characterized. Here, we uncovered a mechanism by which DDX5 and DDX17 cooperate with heterogeneous nuclear ribonucleoprotein (hnRNP) H/F splicing factors to define epithelial- and myoblast-specific splicing subprograms. We then observed that downregulation of DDX5 and DDX17 protein expression during myogenesis and epithelial-to-mesenchymal transdifferentiation contributes to the switching of splicing programs during these processes. Remarkably, this downregulation is mediated by the production of miRNAs induced upon differentiation in a DDX5/DDX17-dependent manner. Since DDX5 and DDX17 also function as coregulators of master transcriptional regulators of differentiation, we propose to name these proteins "master orchestrators" of differentiation that dynamically orchestrate several layers of gene expression.

The Ddx5 and Ddx17 RNA helicases are cornerstones in the complex regulatory array of steroid hormone-signaling pathways.

Estrogen and androgen receptors (ER and AR) play key roles in breast and prostate cancers, respectively, where they regulate the transcription of large arrays of genes. The activities of ER and AR are controlled by large networks of protein kinases and transcriptional coregulators, including Ddx5 and its highly related paralog Ddx17. The Ddx5 and Ddx17 RNA helicases are also splicing regulators. Here, we report that Ddx5 and Ddx17 are master regulators of the estrogen- and androgen-signaling pathways by controlling transcription and splicing both upstream and downstream of the receptors. First, Ddx5 and Ddx17 are required downstream of ER and AR for the transcriptional and splicing regulation of a large number of steroid hormone target genes. Second, Ddx5 and Ddx17 act upstream of ER and AR by controlling the expression, at the splicing level, of several key regulators of ER and AR activities. Of particular interest, we demonstrate that Ddx5 and Ddx17 control alternative splicing of the GSK3ß kinase, which impacts on both ER and AR protein stability. We also provide a freely available online resource which gives information regarding splicing variants of genes involved in the estrogen- and androgen-signaling pathways.

Endothelial, epithelial, and fibroblast cells exhibit specific splicing programs independently of their tissue of origin.

Alternative splicing is the main mechanism of increasing the proteome diversity coded by a limited number of genes. It is well established that different tissues or organs express different splicing variants. However, organs are composed of common major cell types, including fibroblasts, epithelial, and endothelial cells. By analyzing large-scale data sets generated by The ENCODE Project Consortium and after extensive RT-PCR validation, we demonstrate that each of the three major cell types expresses a specific splicing program independently of its organ origin. Furthermore, by analyzing splicing factor expression across samples, publicly available splicing factor binding site data sets (CLIP-seq), and exon array data sets after splicing factor depletion, we identified several splicing factors, including ESRP1 and 2, MBNL1, NOVA1, PTBP1, and RBFOX2, that contribute to establishing these cell type-specific splicing programs. All of the analyzed data sets are freely available in a user-friendly web interface named FasterDB, which describes all known splicing variants of human and mouse genes and their splicing patterns across several dozens of normal and cancer cells as well as across tissues. Information regarding splicing factors that potentially contribute to individual exon regulation is also provided via a dedicated CLIP-seq and exon array data visualization interface. To the best of our knowledge, FasterDB is the first database integrating such a variety of large-scale data sets to enable functional genomics analyses at exon-level resolution.

eXtasy: variant prioritization by genomic data fusion.

Massively parallel sequencing greatly facilitates the discovery of novel disease genes causing Mendelian and oligogenic disorders. However, many mutations are present in any individual genome, and identifying which ones are disease causing remains a largely open problem. We introduce eXtasy, an approach to prioritize nonsynonymous single-nucleotide variants (nSNVs) that substantially improves prediction of disease-causing variants in exome sequencing data by integrating variant impact prediction, haploinsufficiency prediction and phenotype-specific gene prioritization.

An unbiased evaluation of gene prioritization tools.

MOTIVATION: Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates-so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined, and some of them have been implemented and made available through freely available web tools. In this study, we aim at comparing the predictive performance of eight publicly available prioritization tools on novel data. We have performed an analysis in which 42 recently reported disease-gene associations from literature are used to benchmark these tools before the underlying databases are updated. RESULTS: Cross-validation on retrospective data provides performance estimate likely to be overoptimistic because some of the data sources are contaminated with knowledge from disease-gene association. Our approach mimics a novel discovery more closely and thus provides more realistic performance estimates. There are, however, marked differences, and tools that rely on more advanced data integration schemes appear more powerful. CONTACT: yves.moreau@esat.kuleuven.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Annotate-it: a Swiss-knife approach to annotation, analysis and interpretation of single nucleotide variation in human disease.

The increasing size and complexity of exome/genome sequencing data requires new tools for clinical geneticists to discover disease-causing variants. Bottlenecks in identifying the causative variation include poor cross-sample querying, constantly changing functional annotation and not considering existing knowledge concerning the phenotype. We describe a methodology that facilitates exploration of patient sequencing data towards identification of causal variants under different genetic hypotheses. Annotate-it facilitates handling, analysis and interpretation of high-throughput single nucleotide variant data. We demonstrate our strategy using three case studies. Annotate-it is freely available and test data are accessible to all users at http://www.annotate-it.org.

ReLiance: a machine learning and literature-based prioritization of receptor--ligand pairings.

MOTIVATION: The prediction of receptor-ligand pairings is an important area of research as intercellular communications are mediated by the successful interaction of these key proteins. As the exhaustive assaying of receptor-ligand pairs is impractical, a computational approach to predict pairings is necessary. We propose a workflow to carry out this interaction prediction task, using a text mining approach in conjunction with a state of the art prediction method, as well as a widely accessible and comprehensive dataset. Among several modern classifiers, random forests have been found to be the best at this prediction task. The training of this classifier was carried out using an experimentally validated dataset of Database of Ligand-Receptor Partners (DLRP) receptor-ligand pairs. New examples, co-cited with the training receptors and ligands, are then classified using the trained classifier. After applying our method, we find that we are able to successfully predict receptor-ligand pairs within the GPCR family with a balanced accuracy of 0.96. Upon further inspection, we find several supported interactions that were not present in the Database of Interacting Proteins (DIPdatabase). We have measured the balanced accuracy of our method resulting in high quality predictions stored in the available database ReLiance. AVAILABILITY: http://homes.esat.kuleuven.be/~bioiuser/ReLianceDB/index.php CONTACT: yves.moreau@esat.kuleuven.be; ernesto.iacucci@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational tools for prioritizing candidate genes: boosting disease gene discovery.

At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.

Optimized data fusion for kernel k-means clustering.

This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem. In the proposed algorithm, the problem to optimize the cluster membership and the problem to optimize the kernel coefficients are all based on the same Rayleigh quotient objective; therefore the proposed algorithm converges locally. OKKC has a simpler procedure and lower complexity than other algorithms proposed in the literature. Simulated and real-life data fusion applications are experimentally studied, and the results validate that the proposed algorithm has comparable performance, moreover, it is more efficient on large-scale data sets. (The Matlab implementation of OKKC algorithm is downloadable from http://homes.esat.kuleuven.be/~sistawww/bio/syu/okkc.html.).

Congenital heart defects in a novel recurrent 22q11.2 deletion harboring the genes CRKL and MAPK1.

The proximal region of the long arm of chromosome 22 is rich in low copy repeats (LCR). Non-allelic homologous recombination (NAHR) between these substrates explains the high prevalence of recurrent rearrangements within this region. We have performed array comparative genomic hybridization in a normally developing girl with growth delay, microcephaly, and truncus arteriosus, and have identified a novel recurrent 22q11 deletion that spans LCR22-4 and partially affects the common 22q11.2 deletion syndrome and the distal 22q11 deletion syndrome. This deletion is atypical as it did not occur by NAHR between any of the major LCRs found on 22q11.2. However, the breakpoint containing regions coincide with highly homologous regions. An identical imbalance was reported previously in a patient with striking phenotypic similarity. Computational gene prioritization methods and biological evidence denote the genes CRKL and MAPK1 as the highest ranking candidates for causing congenital heart disease within the deleted region.