Facioscapulohumeral dystrophy weakened sarcomeric contractility is mimicked in induced pluripotent stem cells-derived innervated muscle fibres.

BACKGROUND: Facioscapulohumeral dystrophy (FSHD) is a late-onset autosomal dominant form of muscular dystrophy involving specific groups of muscles with variable weakness that precedes inflammatory response, fat infiltration, and muscle atrophy. As there is currently no cure for this disease, understanding and modelling the typical muscle weakness in FSHD remains a major milestone towards deciphering the disease pathogenesis as it will pave the way to therapeutic strategies aimed at correcting the functional muscular defect in patients. METHODS: To gain further insights into the specificity of the muscle alteration in this disease, we derived induced pluripotent stem cells from patients affected with Types 1 and 2 FSHD but also from patients affected with Bosma arhinia and microphthalmia. We differentiated these cells into contractile innervated muscle fibres and analysed their transcriptome by RNA Seq in comparison with cells derived from healthy donors. To uncover biological pathways altered in the disease, we applied MOGAMUN, a multi-objective genetic algorithm that integrates multiplex complex networks of biological interactions (protein-protein interactions, co-expression, and biological pathways) and RNA Seq expression data to identify active modules. RESULTS: We identified 132 differentially expressed genes that are specific to FSHD cells (false discovery rate < 0.05). In FSHD, the vast majority of active modules retrieved with MOGAMUN converges towards a decreased expression of genes encoding proteins involved in sarcomere organization (P value 2.63e-12 ), actin cytoskeleton (P value 9.4e-5 ), myofibril (P value 2.19e-12 ), actin-myosin sliding, and calcium handling (with P values ranging from 7.9e-35 to 7.9e-21 ). Combined with in vivo validations and functional investigations, our data emphasize a reduction in fibre contraction (P value < 0.0001) indicating that the muscle weakness that is typical of FSHD clinical spectrum might be associated with dysfunction of calcium release (P value < 0.0001), actin-myosin interactions, motor activity, mechano-transduction, and dysfunctional sarcomere contractility. CONCLUSIONS: Identification of biomarkers of FSHD muscle remain critical for understanding the process leading to the pathology but also for the definition of readouts to be used for drug design, outcome measures, and monitoring of therapies. The different pathways identified through a system biology approach have been largely overlooked in the disease. Overall, our work opens new perspectives in the definition of biomarkers able to define the muscle alteration but also in the development of novel strategies to improve muscle function as it provides functional parameters for active molecule screening.

AKT Signaling Modifies the Balance between Cell Proliferation and Migration in Neural Crest Cells from Patients Affected with Bosma Arhinia and Microphthalmia Syndrome.

Over the recent years, the SMCHD1 (Structural Maintenance of Chromosome flexible Hinge Domain Containing 1) chromatin-associated factor has triggered increasing interest after the identification of variants in three rare and unrelated diseases, type 2 Facio Scapulo Humeral Dystrophy (FSHD2), Bosma Arhinia and Microphthalmia Syndrome (BAMS), and the more recently isolated hypogonadotrophic hypogonadism (IHH) combined pituitary hormone deficiency (CPHD) and septo-optic dysplasia (SOD). However, it remains unclear why certain mutations lead to a specific muscle defect in FSHD while other are associated with severe congenital anomalies. To gain further insights into the specificity of SMCHD1 variants and identify pathways associated with the BAMS phenotype and related neural crest defects, we derived induced pluripotent stem cells from patients carrying a mutation in this gene. We differentiated these cells in neural crest stem cells and analyzed their transcriptome by RNA-Seq. Besides classical differential expression analyses, we analyzed our data using MOGAMUN, an algorithm allowing the extraction of active modules by integrating differential expression data with biological networks. We found that in BAMS neural crest cells, all subnetworks that are associated with differentially expressed genes converge toward a predominant role for AKT signaling in the control of the cell proliferation-migration balance. Our findings provide further insights into the distinct mechanism by which defects in neural crest migration might contribute to the craniofacial anomalies in BAMS.

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.

High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook-multi-omics mix (momix)-to foster reproducibility, and support users and future developers.

Integrative proteomic and phosphoproteomic profiling of prostate cell lines.

BACKGROUND: Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells, are nowadays among the leading approaches to tackle the challenges not only of better diagnosis, but also of unraveling mechanistic details related to disease etiology and progression. METHODS: We conducted here a large SILAC-based Mass Spectrometry experiment to map the proteomes and phosphoproteomes of four widely used prostate cell lines, namely PNT1A, LNCaP, DU145 and PC3, representative of different cancerous and hormonal status. RESULTS: We identified more than 3000 proteins and phosphosites, from which we quantified more than 1000 proteins and 500 phosphosites after stringent filtering. Extensive exploration of this proteomics and phosphoproteomics dataset allowed characterizing housekeeping as well as cell-line specific proteins, phosphosites and functional features of each cell line. In addition, by comparing the sensitive and resistant cell lines, we identified protein and phosphosites differentially expressed in the resistance context. Further data integration in a molecular network highlighted the differentially expressed pathways, in particular migration and invasion, RNA splicing, DNA damage repair response and transcription regulation. CONCLUSIONS: Overall, this study proposes a valuable resource toward the characterization of proteome and phosphoproteome of four widely used prostate cell lines and reveals candidates to be involved in prostate cancer progression for further experimental validation.

Molecular Inverse Comorbidity between Alzheimer's Disease and Lung Cancer: New Insights from Matrix Factorization.

Matrix factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology. Here, we challenge MF in depicting the molecular bases of epidemiologically described disease-disease (DD) relationships. As a use case, we focus on the inverse comorbidity association between Alzheimer's disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To this day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities. To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which we confirm the involvement of processes related to the immune system and mitochondrial metabolism. We then distinguish mechanisms specific to LC from those shared with other cancers through a pan-cancer analysis. Additionally, new candidate molecular players, such as estrogen receptor (ER), cadherin 1 (CDH1) and histone deacetylase (HDAC), are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, also suggesting the existence of heterogeneity across patients in the context of inverse comorbidity.

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Background: Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Methods: Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them. Results: Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. Conclusions: Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer.

A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer's Disease, Glioblastoma and Lung cancer.

Epidemiological studies indicate that patients suffering from Alzheimer's disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the molecular scenarios that might underlie direct and inverse co-morbidities between these diseases. Transcriptomic meta-analyses reveal significant numbers of genes with inverse patterns of expression in Alzheimer's disease and lung cancer, and with similar patterns of expression in Alzheimer's disease and glioblastoma. These observations support the existence of molecular substrates that could at least partially account for these direct and inverse co-morbidity relationships. A functional analysis of the sets of deregulated genes points to the immune system, up-regulated in both Alzheimer's disease and glioblastoma, as a potential link between these two diseases. Mitochondrial metabolism is regulated oppositely in Alzheimer's disease and lung cancer, indicating that it may be involved in the inverse co-morbidity between these diseases. Finally, oxidative phosphorylation is a good candidate to play a dual role by decreasing or increasing the risk of lung cancer and glioblastoma in Alzheimer's disease.

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling.

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.

The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms.

BACKGROUND: The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders. RESULTS: We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO ( http://sblab.celldesigner.org:18080/Payao11/bin/). CONCLUSION: We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of cellular processes.

The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets.

Previously, we identified the stress-induced chaperone, Hsp27, as highly overexpressed in castration-resistant prostate cancer and developed an Hsp27 inhibitor (OGX-427) currently tested in phase I/II clinical trials as a chemosensitizing agent in different cancers. To better understand the Hsp27 poorly-defined cytoprotective functions in cancers and increase the OGX-427 pharmacological safety, we established the Hsp27-protein interaction network using a yeast two-hybrid approach and identified 226 interaction partners. As an example, we showed that targeting Hsp27 interaction with TCTP, a partner protein identified in our screen increases therapy sensitivity, opening a new promising field of research for therapeutic approaches that could decrease or abolish toxicity for normal cells. Results of an in-depth bioinformatics network analysis allying the Hsp27 interaction map into the human interactome underlined the multifunctional character of this protein. We identified interactions of Hsp27 with proteins involved in eight well known functions previously related to Hsp27 and uncovered 17 potential new ones, such as DNA repair and RNA splicing. Validation of Hsp27 involvement in both processes in human prostate cancer cells supports our system biology-predicted functions and provides new insights into Hsp27 roles in cancer cells.

Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses.

There is epidemiological evidence that patients with certain Central Nervous System (CNS) disorders have a lower than expected probability of developing some types of Cancer. We tested here the hypothesis that this inverse comorbidity is driven by molecular processes common to CNS disorders and Cancers, and that are deregulated in opposite directions. We conducted transcriptomic meta-analyses of three CNS disorders (Alzheimer's disease, Parkinson's disease and Schizophrenia) and three Cancer types (Lung, Prostate, Colorectal) previously described with inverse comorbidities. A significant overlap was observed between the genes upregulated in CNS disorders and downregulated in Cancers, as well as between the genes downregulated in CNS disorders and upregulated in Cancers. We also observed expression deregulations in opposite directions at the level of pathways. Our analysis points to specific genes and pathways, the upregulation of which could increase the incidence of CNS disorders and simultaneously lower the risk of developing Cancer, while the downregulation of another set of genes and pathways could contribute to a decrease in the incidence of CNS disorders while increasing the Cancer risk. These results reinforce the previously proposed involvement of the PIN1 gene, Wnt and P53 pathways, and reveal potential new candidates, in particular related with protein degradation processes.

EnrichNet: network-based gene set enrichment analysis.

MOTIVATION: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. RESULTS: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. AVAILABILITY: EnrichNet is freely available at http://www.enrichnet.org. CONTACT: Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

No paradox, no progress: inverse cancer comorbidity in people with other complex diseases.

In the past 5 years, several leading groups have attempted to explain why individuals with Down's syndrome have a reduced risk of many solid tumours and an increased risk of leukaemia and testicular cancer. Niels Bohr, the Danish physicist, noted that a paradox could initiate progress. We think that the paradox of a medical disorder protecting against cancer could be formalised in a new model of inverse cancer morbidity in people with other serious diseases. In this Personal View, we review evidence from epidemiological and clinical studies that supports a consistently lower than expected occurrence of cancer in patients with Down's syndrome, Parkinson's disease, schizophrenia, diabetes, Alzheimer's disease, multiple sclerosis, and anorexia nervosa. Intriguingly, most comorbidities are neuropsychiatric or CNS disorders. We provide a brief overview of evidence indicating genetic and molecular connections between cancer and these complex diseases. Inverse comorbidity could be a valuable model to investigate common or related pathways or processes and test new therapies, but, most importantly, to understand why certain people are protected from the malignancy.

Extending pathways and processes using molecular interaction networks to analyse cancer genome data.

BACKGROUND: Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways. RESULTS: We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes. CONCLUSIONS: The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.

Mutated genes, pathways and processes in tumours.

Integration of the many available sources of cancer gene information--such as large-scale tumour-resequencing studies--identifies the 'usual suspect' genes, mutated in many tumour types, as well as different sets of mutated genes according to the specific tumour type. Scaling-up the analysis reveals that this large collection of mutated genes cluster into a smaller number of signalling pathways and processes. From this, we draw a map of the altered processes, and their combinations, in more than 10 tumours types. Literature searches identify pathways and processes that are covered sparsely in the literature, and invite the proposal of new hypotheses to investigate cancer initiation and progression.

Interactome mapping of the phosphatidylinositol 3-kinase-mammalian target of rapamycin pathway identifies deformed epidermal autoregulatory factor-1 as a new glycogen synthase kinase-3 interactor.

The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway, we initiated a large scale yeast two-hybrid screen for 33 components of the PI3K-mTOR pathway. Identification of 67 new interactions was followed by validation by co-affinity purification and exhaustive literature curation of existing information. We provide a nearly complete, functionally annotated interactome of 802 interactions for the PI3K-mTOR pathway. Our screen revealed a predominant place for glycogen synthase kinase-3 (GSK3) A and B and the AMP-activated protein kinase. In particular, we identified the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor as an interactor and in vitro substrate of GSK3A and GSK3B. Moreover, GSK3 inhibitors increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. We propose that DEAF1 may represent a therapeutic target of lithium and other GSK3 inhibitors used in bipolar disease and depression.

TopoGSA: network topological gene set analysis.

UNLABELLED: TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, can be computed and compared with those of known cellular pathways and processes. AVAILABILITY: Freely available at http://www.infobiotics.net/topogsa.

From cancer genomes to cancer models: bridging the gaps.

Cancer genome projects are now being expanded in an attempt to provide complete landscapes of the mutations that exist in tumours. Although the importance of cataloguing genome variations is well recognized, there are obvious difficulties in bridging the gaps between high-throughput resequencing information and the molecular mechanisms of cancer evolution. Here, we describe the current status of the high-throughput genomic technologies, and the current limitations of the associated computational analysis and experimental validation of cancer genetic variants. We emphasize how the current cancer-evolution models will be influenced by the high-throughput approaches, in particular through efforts devoted to monitoring tumour progression, and how, in turn, the integration of data and models will be translated into mechanistic knowledge and clinical applications.